In situ aircraft observations in typhoons have been scarce. This paper documents and analyses the aircraft and dropsonde data collected in Super Typhoon Saola (2023) over the northern part of the South China Sea. The wind and turbulence structures of the typhoon are investigated. The turbulence intensities are quantified in terms of turbulent kinetic energy (TKE) and eddy dissipate rate (EDR), and these data are compared with other available estimates of turbulence intensities, such as those based on weather radars, meteorological satellites, and numerical weather predic-tion (NWP) models. It is found that the TKE and EDR are closely correlated, and they are con-sistent with the weather radar/satellite observations as well as NWP-based outputs. Furthermore, the boundary layer inflow, vertical wind profiles, and atmospheric stability are analysed based on the dropsonde observations. The analysed results would advance the understanding of typhoon structures and offer references for the validation of remote sensing observations and NWP models.

This article presents a method for extracting neural signal features to identify the imagination of left and right hand grasping movements. A functional magnetic resonance imaging (fMRI) experiment is employed to identify four brain regions with significant activations during motor imagery(MI) and the effective connections between these regions of interest (ROIs) were calculated using Dynamic Window-level Granger Causality (DWGC). Then, a real-time fMRI(rt-fMRI) classification system for left and right hand MI is developed using the Open-NFT platform. The experimental results show that incorporating effective connections can enhance the average accuracy of real-time three-class classification (rest, left hand and right hand) by 3% in comparison to traditional multivoxel pattern classification analysis(MVPA). Moreover, it significantly improves classification accuracy during the initial stage of MI tasks while reducing the latency effects in real-time decoding. The study suggests that the effective connections obtained through the DWGC method serve as valuable features for real-time decoding of MI using fMRI. Moreover, they exhibit higher sensitivity to changes in brain states. This research offers theoretical support and technical guidance for extracting neural signal features in the context of fMRI-based studies.

This article presents a comprehensive study on enhancing grid resilience through advanced forecasting and optimization techniques in the context of power outages. Power outages pose significant challenges to modern societies, affecting various sectors such as industries, households, and critical infrastructures. The research combines statistical analysis, machine learning algorithms, and optimization methods to address this issue to develop a holistic approach for predicting and mitigating power outage events. The proposed methodology involves the use of Monte Carlo simulations in MATLAB for future outage prediction, Long Short-Term Memory (LSTM) networks for forecasting solar irradiance and load profiles, and a hybrid LSTM-Particle Swarm Optimization (PSO) model to improve accuracy. Furthermore, the role of Battery State of Charge (SoC) in enhancing system resilience is explored. The study also assesses the techno-economic advantages of a grid-tied microgrid integrated with solar panels and batteries over conventional grid systems. The results highlight the potential of the proposed approach in strengthening grid resilience, reducing downtime, and fostering sustainable energy utilization.

Groundwater modeling is a useful tool for assessing sustainability in water resources planning. However, groundwater models are difficult to construct in regions with limited data availability. We illustrated how remote sensing data can be used leverage limited in situ data to build and calibrate a regional groundwater model in the Goulbi Maradi alluvial aquifer in Southern Niger in Western Africa. We used data from the NASA Gravity Recovery and Climate Experiment (GRACE) satellite mission to estimate recharge rates, the primary source of water to the aquifer. Additionally, we incorporated groundwater storage changes obtained from GRACE data from 2009 to 2021 to establish an overall water budget from which we could back-calculate groundwater withdrawals from pumping in the region. This approach allowed us to calibrate the model and then convert it to a predictive tool to analyze the impact of various assumptions about future recharge and groundwater extraction patterns associated with the development of groundwater infrastruction in the region. The results indicate that the Goulbi Maradi alluvial aquifer is sustainable, even an increase of groundwater extraction up to 28%.

The internal wave recognition algorithm in an ocean data buoy system can be used to realize the real-time and flexible observation of internal waves, but there is no accurate automatic recognition method. To meet the need for automatic, real-time, and reliable internal wave recognition, an automatic internal wave recognition algorithm has been proposed for a tightly profiled intelligent buoy system. The sea profile temperature data collected by the Bailong buoy system in the Andaman Sea in 2018 were used to train and test the internal wave recognition neural network model, which consists of two parts: feature extraction and feature classification. The experiment compares the long short-term memory network (LSTM), convolutional neural network (CNN) with different layers, and deep neural network (DNN) without a feature extraction network and adjusts the number of convolutional nuclei and convolutional strides to improve the feature extraction efficiency. Experiments show that the best results can be obtained when a CNN layer is used as the feature extraction network, the convolutional step length is 4, the number of convolutional kernels is 5. The recall reaches 95.31% and the precision is 97.53%. The internal wave identification delay of the algorithm is 5.0862 minutes, the number of parameters is 1593, and the number of calculations is 3024. The algorithm can be directly deployed to the ocean data buoy system to realize the demand for automatic, real-time and reliable internal wave identification at the buoy end.

An approach to the implementation of neural network for real-time cryptographic data protection with symmetric keys oriented on embedded systems is presented. This approach is valuable especially for onboard communication systems in unmanned aerial vehicles (UAV) because of suitability to hardware implementation. In this study we evaluate the possibility to build such a system in hardware implementation at FPGA. The proposed integrated neural network approach for real-time cryptographic data protection was based on theoretical foundations of neural network cryptographic data protection, new algorithms and structures of neural network data encryption and decryption, modern hardware components with programmable structure. The development and implementation of the on-board neural network system for cryptographic data protection in real-time are based on the following principles: variable composition of equipment; modularity; conveyorization and spatial parallelism; hardware and software modification and making them suitable for data encryption and decryption. The tabular-algorithmic method for calculation of the scalar product has been improved that makes it possible to perform fast calculation of the scalar product of fixed-point and floating-point input data by bringing them to the largest common order of weights and forming tables of macro-partial products. Components of neural network cryptographic data encryption and decryption have been developed on the processor core supplemented by specialized scalar product calculation modules. The specialized hardware for neural network cryptographic data encryption was developed using VHDL for equipment programming in the Quartus II development environment ver. 13.1 and the appropriate libraries, and implemented on the basis of the FPGA EP3C16F484C6 Cyclone III family.

Background: Ceftobiprole is a fifth-generation cephalosporin that has been approved in Europe solely for the treatment of community-acquired and nosocomial pneumonia. The objective was to analyze the use of ceftobiprole medocaril (Cefto-M) in Spanish clinical practice in patients with infection in hospital or outpatient parenteral antimicrobial therapy (OPAT). Methods: This retrospective, observational, multicenter study included patients treated from September 1, 2021 to December 31, 2022. Results: 249 individuals were enrolled, aged 66.6±15.4 years, 59.4% male, with Charlson index of 4 (IQR 2-6); 13.7% had COVID-19, and 4.8% were in intensive care unit (ICU). The most frequent type of infection was respiratory (55.8%), followed by skin and soft tissue infection (21.7%). Cefto-M was administered to 67.9% as empirical treatment, being in monotherapy for 7 days (5-10) in 53.8% of cases. The infection-related mortality was 11.2%.The highest mortality rates were for ventilator-associated pneumonia [40%], and infections due to methicillin-resistant S. aureus (20.8%) and Pseudomonas aeruginosa (16.1%). Mortality-related factors were age (OR: 1.1, 95%CI [1.04-1.16]), ICU admission (OR:42.02, 95%CI[4.49-393.4]), and sepsis/septic shock (OR:2.94, 95%CI [1.01-8.54]).Conclusions: In real life, Cefto-M is a safe antibiotic, with only half of prescriptions in respiratory infections, mainly administered as rescue therapy in pluripathological patients with severe infectious diseases.

The rising global population amidst the growing concerns of climate change will have a dire consequence on global food security and socio-economic activities. Wheat is one of the most important staple foods consumed by more than four billion people in the world, but climate change impacts account for a decline of 5.5% in wheat yield and predictions indicate that the production could further dwindle by nearly 30% in 2050, due to trends in temperature, precipitation, and carbon dioxide. An effective annual crop estimate is necessary not only to inform government the status of national food security, but also is used to determine the benchmark on which agricultural commodities are priced in the market. Thus, annual crop monitoring and yield estimate is paramount to determine the amount of wheat imports required to make up for the shortfalls in the national wheat production in South Africa, which has been a net importer of wheat since 1998. A joint project between South Africa and Poland investigated satellite based-crop growth monitoring using Sentinel 2 and determined the most distinguishable crop phenology for an accurate winter wheat classification during the growing season from August – December with Random Forest (RF) algorithm. The winter wheat crop was more accurately identified during the crop ‘heading’ stage in October yielding the highest user’s (75.56%) and producer’s (92.52%) accuracies, despite the relatively lower overall accuracy (78.14%) compared to that of December with OA of 83.58% obtained during the maturity stage. This study, therefore, confirms the suitability of sentinel 2 for an effective phenology-based winter wheat crop classification during the heading stage, reducing the ambiguity of spectral confusion created with surrounding grass and maize crops.

According to data from the U.S FDA, the likelihood of a new drug progressing from Phase I clinical trials to market approval is only 8% (DiMasi et al., 2016). Furthermore, a significant attrition rate of 35% occurs during Phase II studies due to the failure to demonstrate clinical efficacy (Arrowsmith, 2011). These statistics underscore the urgent need to reassess the current research and development (R&D) paradigm. To address this challenge, a promising approach called reverse translational pharmacology (RTP) has emerged, aiming to enhance the predictive value of preclinical models used in biomedical research by leveraging animal models that spontaneously develop analogous diseases to humans (Schneider et al., 2018). The rationale behind RTP is that knowledge gained from clinical trials in canines can subsequently inform and guide human clinical trials, leading to improved outcomes for both species. Additionally, this approach capitalizes on the opportunity to stimulate veterinary drug development by leveraging pharmacokinetic (PK), efficacy, and safety data obtained from human clinical studies (Schneider et al., 2018). The application of reverse translational pharmacology holds immense potential for expediting drug development and advancing medical knowledge. By employing animal models that naturally develop diseases akin to those found in humans, researchers can enhance the predictive capacity of preclinical models, thereby mitigating the high attrition rates encountered in later stages of clinical trials. Moreover, the exchange of knowledge and data between human and veterinary medicine engenders a synergistic relationship, fostering progress in both domains (Schneider et al., 2018). As this innovative approach gains momentum, fostering collaborations between basic and clinician scientists, pharmaceutical companies, and veterinary professionals becomes increasingly imperative. By sharing resources, expertise, and findings, we can pave the way for groundbreaking discoveries and the development of safe and efficacious treatments for diverse diseases. Embracing reverse translational pharmacology offers an avenue to redefine the R&D paradigm, bringing us closer to a future where a higher proportion of drugs successfully traverse the arduous path from the laboratory to market approval, ultimately benefiting patients across species. In this brief commentary, we highlight four examples of diseases where reverse translational modeling has been used to support pharmaceutical research, including some applications developed in our research laboratory (SMART Pharmacology) at the Iowa State University College of Veterinary Medicine. In doing so, this report also seeks to provide a brief overview of the heuristic value of computational approaches in comparative research. To further illustrate the value of these in silico approaches, we deliberately chose to employ some features of ChatGPT to produce the present summary (OpenAI. (2023). GPT-3.5 [ChatGPT]. Retrieved from https://openai.com).

Streptococcus pneumoniae is the major cause of invasive pneumococcal disease (IPD). Since 1998, multilocus sequence typing (MLST) has been used for identifying the genotypes of strains of S. pneumoniae and helped reveal a diversity of local and regional epidemiological patterns for IPD, resulting in an archived MLST dataset of over 74,000 isolates. However, the global patterns of MLST sequence type (ST) and allele type (AT) distributions remain largely unexplored. In this study, we investigated the spatial and temporal patterns of AT and ST distributions of S. pneumoniae. We extracted S. pneumoniae MLST data from PubMLST.org and conducted various population genetic and phylogenetic analyses. Our analyses demonstrated both shared and unique distributions of STs and ATs among continental and national/regional populations. Among the 17915 STs in the dataset, 36 STs representing 15263 isolates were broadly shared among all six continents, consistent with recent gene flow and clonal dispersal of this pathogen. The analysis of molecular variance revealed that >96% genetic variations were found within individual continental and national/regional populations. However, though low (<4%), statistically significant genetic differentiation among continental and national populations were observed. Comparisons between non-clone-corrected and clone-corrected datasets showed that localized clonal expansion contributed significantly to the observed genetic differentiations among continents and countries/regions. Temporal analyses of the isolates showed that implementation of pneumococcal conjugate vaccine impacted the distributions of STs. Linkage disequilibrium analyses identified evidence for non-random recombination in all continental populations of this species. We discussed the implications of our analyses to the global epidemiology and future vaccine developments for S. pneumoniae.

Semantic interoperability facilitates exchange and access of health data that is being documented in EHRs with various semantic features. In the EU, the regulation proposal of European health data space requires development of semantic interoperability. To achieve a fully integrated EHDS ecosystem leveraging the value of health data, stakeholders need to overcome challenges of implementing common standards and other semantic interoperability features. We aimed to research what scientific evidence is available on developing semantic interoperability. Our research questions focused specifically on key features and approaches for semantic interoperability and on possible benefits of these choices. For that purpose, we performed a systematic literature review by defining our study framework based on previous research. Our results consisted of 10 studies where data models, ontologies, terminologies, classifications, and standards were applied for building interoperability. Through increased access to interoperable patient information, a better quality and outcomes in care can be achieved. Better communication based on easily accessible data is facilitated between health professionals and between clinicians and the patients. When heading towards semantic harmonization as outlined in the EHDS proposal, more experiences and analyses are needed to assess how applicable the chosen solutions are for semantic interoperability of European health care data.

The quality of underground space information has become a major problem that endangers the safety of underground spaces. Currently, the main methods for the high-precision and long-distance transmission of detection information are radar and optical methods. However, in practical applications, we found that the radar method has the shortcomings of large energy loss and poor anti-jamming ability, which limit the accuracy of information data transmission and distance. The optical method has the shortcomings that the weather has a great impact on its accuracy and can only be applied to static objects above ground; therefore, it has the limitation of application objects and use environment. More importantly, the current high-precision information remote detection methods are limited to the detection of overground space objects and are not applicable to the detection of various information data in underground space. In this study, we analyze the spectral properties of the fractional differential operator and find that it is suitable for studying non-linear, non-causal, and non-stationary signals. The theory of fractional calculus is applied to the field of data processing, and a mathematical model of remote transmission and high-precision detection of information based on fractional difference is established, which realizes the functions of high-precision and remote detection of information. By fusing the information data to detect the mathematical model over a long distance and with high accuracy, a mathematical model for stratum data processing used to provide long-distance and high-accuracy data was established. Through application in engineering practice, the effectiveness of this method for underground space information data detection was verified.

Precision agriculture and open-source data repositories provide a plethora of field-specific ecological data about agroecosystems, but few mechanisms have been developed to turn that information into management recommendations for crop production. The On-Farm Precision Experiments (OFPE) framework is an agroecological model-based methodology to improve crop manager’s abilities to make field-scale agronomic input decisions. This work evaluates the use of field-specific experiments that employ open-source data and the data emanating from precision agriculture technologies to gain local knowledge of the spatial and temporal variability in agroeconomic performance at the sub-field scale. Quantification of the temporal variability in crop response to inputs (e.g., crop seeding rates, crop rotations, fertilizers, other soil amendments, pesticides, etc.) allows for estimation of the probability that a future management scenario will outcompete another, in terms of crop yield, crop quality, farmer net return, or environmental quality. The challenge is to integrate OFPE into applied management with minimal disruption of stakeholder practices while drawing on historic knowledge about the field and economic constraints. OFPE is the basis of a decision support system that includes a six-step cyclical process that harnesses precision agriculture technology to apply experiments and gather field-specific data, incorporates modern data management and analytical approaches, and generates management recommendations as probabilities of outcomes. The OFPE framework allows field managers to assess the tradeoffs in agronomic input management between the maximization of crop production, quality and profits from production while considering environmental effects.

We live in the information age and, ironically, meeting the core function of journalism – i.e., to provide people access to unbiased information – has never been more difficult. This paper explores deep journalism, our data-driven Artificial Intelligence (AI) based journalism approach to study how the LinkedIn media could be useful for journalism. Specifically, we apply our deep journalism approach to LinkedIn to automatically extract and analyse big data to provide the public with information about labour markets, people’s skills and education, and businesses and industries from multi-generational perspectives. The Great Resignation and Quiet Quitting phenomena coupled with rapidly changing generational attitudes are bringing unprecedented and uncertain changes to labour markets and our economies and societies, and hence the need for journalistic investigations into these topics is highly significant. We combine big data and machine learning to create a whole machine learning pipeline and a software tool for journalism that allows discovering parameters for age dynamics in labour markets using LinkedIn data. We collect a total of 57,000 posts from LinkedIn and use it to discover 15 parameters by Latent Dirichlet Allocation algorithm (LDA) and group them into five macro-parameters, namely Generations-Specific Issues, Skills & Qualifications, Employment Sectors, Consumer Industries, and Employment Issues. The journalism approach used in this paper can automatically discover and make objective, cross-sectional, and multi-perspective information available to all. It can bring rigour to journalism by making it easy to generate information using machine learning and can make tools and information available so that anyone can uncover information about matters of public importance. This work is novel since none of the earlier works have reported such an approach and tool and leveraged it to use LinkedIn media for journalism and to discover multigenerational perspectives (parameters) for age dynamics in labour markets. The approach could be extended with additional AI tools and other media.

Using the OCT platform, we can generate high-resolution data on retinal nerve fiber layer (RNFL) thickness in a given eye at a large number of angular-points defined on a circular coordinate system. However, insightful characteristics of OCT RNFL data such as its circularity and granularity generally remain unutilized in its common downstream statistical analyses. We present a new circular statistical framework that defines an Angular Decay function and thereby provides a directional representation of an eye in terms of its focused RNFL loss. By applying to a clinical cohort of Asian Indian eyes, the generated circular data was modeled with a finite mixture of von Mises distributions, which led to unsupervised identification in different age-groups of 2 recurrent clusters of glaucomatous eyes with distinct directional signatures of RNFL decay. New indices of global and local RNFL loss were computed for comparing the structural differences between these glaucoma clusters across the age-groups.

Gas chromatography-coupled mass spectrometry (GC-MS) has been used in biomedical research to analyze volatile, non-polar, and polar metabolites in a wide array of sample types. Despite advances in technology, missing values are still common in metabolomics datasets and must be properly handled. We evaluated the performance of ten commonly used missing value imputation methods with metabolites analyzed on an HR GC-MS instrument. By introducing missing values into the complete (i.e., data without any missing values) NIST plasma dataset we demonstrate that Random Forest (RF), Glmnet Ridge Regression (GRR), and Bayesian Principal Component Analysis (BPCA) shared the lowest Root Mean Squared Error (RMSE) in technical replicate data. Further examination of these three methods in data from baboon plasma and liver samples demonstrated they all maintained high accuracy. Overall, our analysis suggests that any of the three imputation methods can be applied effectively to untargeted metabolomics datasets with high accuracy. However, it is important to note that imputation will alter the correlation structure of the dataset, and bias downstream regression coefficients and p-values.

Internet of Things (IoT) technologies can greatly benefit from machine learning techniques and Artificial Neural Networks for data mining and vice versa. In the agricultural field, this convergence could result in the development of smart farming systems suitable for use as decision support systems by peasant farmers. This work presents the design of a smart farming system for crop production, which is based on low-cost IoT sensors and popular data storage services and data analytics services on the Cloud. Moreover, a new data mining method exploiting climate data along with crop production data is proposed for the prediction of production volume from heterogeneous data sources. This method was initially validated using traditional machine learning techniques and open historical data of the northeast region of the state of Puebla, Mexico, which were collected from data sources from the National Water Commission and the Agri-food Information Service of the Mexican Government.

This study investigated and classified typical structures in rural village and analyzed the vulnerability of various typical types of structures. Based on the statistics of earthquake damages with magnitudes above 5 from 1996 to 2013 in China, the damage matrixes of different types of structures in rural village are obtained. And The vulnerability index and the vulnerability equation of structure are crucial to assess the earthquake losses of typical structures under different magnitudes earthquakes. According to the seismic loss of different types of structures under different earthquake magnitudes, there are possible to improve the seismic resilience of the buildings in rural village. Moreover, the regional vulnerability is analyzed by β probability distribution function, and the comprehensive seismic performance index of different types of agricultural buildings in the region is obtained. The main research is to predict the loss of different types of structures under different earthquake magnitudes in the future, and to provide technical support for different types of building in rural village reinforcement.

Automated extraction of buildings from earth observation (EO) data is important for various applications, including updating of maps, risk assessment, urban planning, policy making. Combining data from different sensors such as high-resolution multispectral (HRI) and light detection and ranging (LiDAR) has shown great potential in building extraction. Deep learning (DL) is increasingly used in multimodal data fusion and urban object extraction. However, DL-based multimodal fusion networks may underperform due to insufficient learning of “joint features” from multiple sources and oversimplified approaches to fusing multimodal features. Recently, an hybrid attention-aware fusion network (HAFNet) has been proposed for building extraction from a dataset including co-located Very-High-Resolution (VHR) optical images and Light Detection And Ranging (LiDAR) joint data. The system reported good performances thanks to the adaptivity of the attention mechanism to the features of the information content of the three streams but suffered from model overparametrization, which inevitably leads to long training times and heavy computational load. In this paper the authors propose a restructuring of the scheme, which involved replacing VGG-16-like encoders with the recently proposed EfficientNet, whose advantages counteract exactly the issues found with the HAFNet scheme. The novel configuration was tested on multiple benchmark datasets, reporting great improvements in terms of processing times, and also in terms of accuracy. The new scheme, called HAFNetE (HAFNet with EfficientNet integration), appears indeed capable of achieving good results with less parameters, translating into better computational efficiency. Based on these findings, we can conclude that, given the current advancements in single-thread schemes, the classical multi-thread HAFNet scheme could be effectively transformed by the HAFNetE scheme by replacing VGG-16 with EfficientNet blocks on each single thread. The remarkable reduction achieved in computational requirements moves the system one step closer to on-board implementation in a possible, future “urban mapping” satellite constellation.

Access to reliable, clean, modern cooking enhances life chances. One option is photovoltaic cooking systems. Accurate solar data is needed to ascertain to what extent these can satisfy the needs of local people. This paper investigates how to choose the most accurate satellite derived solar irradiance database for use in Africa. This is necessary because there is a general shortage of ground measurements for Africa. The solar data is needed to model the output of solar cooking systems, for instance, a solar panel, battery and electric pressure cooker. Four easily accessible satellite databases are validated against ground measurements using a range of statistical tests. Results demonstrate the impact of the mathematical measure used and the phenomenon of balancing errors. Fitting of the satellite model to appropriate climate zone and/or nearby measurements improves accuracy, as does higher spatial and temporal resolution of input parameters. That said, all the four databases reviewed were found to be suitable for simulating PV yield in East Africa.

Prostate cancer (Pca) is a highly heterogeneous disease and the second more common tumor in males. Molecular and genetic profiles have been used to identify subtypes and guide therapeutic intervention. However, roughly 26% of primary Pca are driven by unknown molecular lesions. We use Principal Component Analysis (PCA) and custom RNAseq-data normalization to identify a gene expression signature which segregates primary PRAD from normal tissues. This Core-Expression Signature (PRAD-CES) includes 33 genes and accounts for 39% of data complexity along the PC1-cancer axis. The PRAD-CES is populated by protein-coding (AMACR, TP63, HPN) and RNA-genes (PCA3, ARLN1) sparsely found in previous studies, validated/predicted biomarkers (HOXC6, TDRD1, DLX1), and/or cancer drivers (PCA3, ARLN1, PCAT-14). Of note, the PRAD-CES also comprises six over-expressed LncRNAs without previous Pca association, four of them potentially modulating driver’s genes TMPRSS2, PRUNE2 and AMACR. Overall, our PCA capture 57% of data complexity within PC1-3. GO enrichment and correlation analysis involving major clinical features (i.e., Gleason Score, AR Score, TMPRSS2-ERG fusion and Tumor Cellularity) suggest that PC2 and PC3 gene signatures might describe more aggressive and inflammation-prone transitional forms of PRAD. Of note, surfaced genes may entail novel prognostic biomarkers and molecular alterations to intervene. Particularly, our work uncovered RNA genes with appealing implications on Pca biology and progression.

Transposable elements (TEs) are mobile genetic elements capable of rapidly altering the genome through their movements. The importance of TE activity has been documented in many biological processes, such as introducing genetic instability, altering patterns of gene expression, and accelerating genome evolution. Increasing appreciation of TEs results in the growing number of bioinformatics software to identify insertion events. However, the application of existing TE finding tools is limited by either narrow-focused design of the package, too many dependencies on other tools, or prior knowledge required as input files that may not be readily available to all users. Here, we report a simple pipeline, TEfinder, developed for the detection of new TE insertions with minimal software dependencies using four inputs that can be easily generated with popular variant calling pipelines. The external software requirements are BEDTools, SAMtools, and Picard. Necessary inputs include TEs present in the reference genome, binary paired-end alignment, reference genome index, and a list of TE names. We tested TEfinder pipeline among several evolving populations of Fusarium oxysporum generated through a short-term adaptation study. Our results demonstrate that this easy-to-use tool can effectively detect new TE insertion events, making it accessible and practical for TE analysis.

Cosmic Rays (CR) are a powerful tool for the investigation of the structure of the magnetic fields in the galactic halo and the property of the Inter-Stellar Medium. Two parameters of the CR propagation models: the galactic halo thickness, H, and the diffusion coefficient, D, are loosely constrained by current CR flux measurements, in particular a large degeneracy exist being only H/D well measured. The 10Be/9Be isotopic flux ratio (thanks to the 2 My lifetime of 10Be) can be used as a radioactive clock providing the measurement of CR residence time in the galaxy. This is an important tool to solve the H/D degeneracy. Past measurements of 10Be/9Be isotopic flux ratio in CR are scarce, limited to low energy and affected by large uncertainties. Here a new technique to measure 10Be/9Be isotopic flux ratio, with a Data-Driven approach, in magnetic spectrometers is presented. As an example by applying the method to Beryllium events published by PAMELA experiment it is now possible to determine the important 10Be/9Be measurement avoiding the prohibitive uncertainties coming from the Monte Carlo simulation. It is shown how the accuracy of PAMELA data permits to infer a value of the halo thickness H within 25% precision.

Total cloud cover (TCC) retrieval from ground-based optical imagery is a problem being tackled by a few generations of researchers. The number of human-designed algorithms for the estimation of TCC grows every year. However, there is not very much progress in terms of quality, mostly due to the lack of systematic approach to the design of the algorithms, to the assessment of their generalization ability, and to the assessment of the TCC retrieval quality. In this study, we discuss the optimization nature of data-driven schemes for TCC retrieval. In order to compare the algorithms, we propose the framework for the assessment of the algorithms characteristics. We present several new algorithms that are based on deep learning techniques: a model for outliers filtering, and a few models for TCC retrieval from all-sky imagery. For training and assessment of data-driven algorithms of this study, we present the Dataset of All-Sky Imagery over the Ocean (DASIO) containing over one million of all-sky optical images of visible sky dome taken in various regions of the World Ocean. The research campaigns contributed to DASIO collection took place in the Atlantic ocean, the Indian ocean the Red and Mediterranean seas, and also in the Arctic ocean. Optical imagery collected during these missions are accompanied by standard meteorological observations of cloudiness characteristics made by experienced observers. We assess the generalization ability of the presented models in several scenarios that differ in terms of the regions selected for the train and validation subsets. As a result, we demonstrate that our models based on convolutional neural networks deliver superior quality compared to all previously published schemes. As a key result, we demonstrate considerable drop of the ability to generalize the training data in case of strong covariate shift between training and validation subsets of imagery which may take place in case of region-aware subsampling.

Artificial intelligence methods may help in unveliling information hidden in high-dimensional oncological data. Flow cytometry studies of haematological malignancies provide quantitative data with the potential to be used for the construction of response biomarkers. Many computational methods from the bioinformatics toolbox can be applied to these data but have not been exploited in their full potential in leukaemias, specifically for the case of childhood B-cell acute lymphoblastic leukemia. In this paper we analysed flow cytometry data obtained on diagnosis from 54 paediatric B-cell acute lymphoblastic leukemia patients from two local institutions. We constructed classifiers based on the Fisher’s Ratio to quantify differences in expression levels of immunophenotypical markers between patients with relapsing and non-relapsing disease. The distribution of the marker CD38 was found and validated to have a strong discriminating power between both patient cohorts, thus providing a classifier.

Three typhoons (Rammasun, Kalmaegi, and Sarika) travelled through the deployed stations in the northern South China Sea from 2014–2016. During the passage of typhoons, strong winds and vigorous currents resulted in horizontal displacement of buoy over 2000 m, vertical displacement of ropes on buoys as much as 200 m. The rectification can correct the warm anomaly to cool anomaly of temperature. These movements lead to biases of raw data, with temperature bias as much as 4°C, salinity as much as 0.05 psu, velocity bias as much as 0.4 m/s. The crosscheck of current velocity from different instruments shows that the bias of overlapping velocity and correlation coefficient after depth rectification obviously enhances. The observation shows that temperature cools 1.5 °C, and 0.1 psu saltier in maximum, the near-inertial current increases to 0.4 m/s in the upper layer. The inertial kinetic energy propagates downward with the upward phase, and the maximum depth can reach over 2000 m.

The main objective of this study is to develop a Hapke photometric model that is suited for Chang’E-1 (CE-1) Interference Imaging Spectrometer (IIM) data. We first divided the moon into three areas including ‘maria’, ‘new highland’ and old ‘highland’ with similar photometry characteristic based on the Hapke parameters of the moon derived from Lunar Reconnaissance Orbiter Camera (LROC) Wide Angle Camera (WAC) multispectral data. Then, we selected the sample data in the ‘maria’ area and obtained a new set of Hapke model’s parameters that can best fit these data. Result shows that photometric correction using Hapke model with these new derived parameters can eliminate the effect of variations in viewing and luminating geometry, especially ‘opposition surge’, more efficiently than the empirical model. The corrected mosaic shows no significant artifacts along the tile boundaries, and more detailed information of the image can be exhibited due to a better correction of ‘opposition surge’ at small phase angle (g <15°).

The boundary layer height (BLH) distinguishes the interface between the lower and free atmosphere, which is a key variable in numerical simulation, aerosol, and environmental pollution studies. Thus, the goal of this work is to propose a novel method conjuncting with numerical regularization to analyze climate characteristics of the marine boundary layer height (MBLH) using 2007-2011 GPS-RO data from the COSMIC mission. While traditionally, the difference method has been used to achieve this aim, herein, we propose an innovative method, in which the bending angle profile gradient was calculated using the numerical regulation method where the regulation parameters are determined by the double-parameter model function method. Then, the MBLH was determined by employing the maximum gradient method to ascertain the height corresponding to the smallest gradient. The results show a correlation between currents and the MBLH—a relationship that has not been previously demonstrated. A low MBLH is associated with seasons and regions where cold ocean currents are prevailing; whereas a high MBLH is observed in the seasons and places where warm currents are prevailing. This correlation was validated by comparing the obtained results with different occultation data—i.e., atmprf and echprf—which also established that atmprf is more sensitive to convective cloud top capture. In seas with active convection, the armprf calculated bending angle is higher than that from echprf. Subsequently, the standard deviation was used to express the MBLH confidence level. The results show that the MBLH standard deviation is highest in low latitudes and lowest in the middle and high latitudes. Furthermore, we analyzed the interannual MBLH variation trend, which displayed a seasonal variation and spatial distribution corresponding with the current and subsolar point. Finally, we conducted a case study in the South China Sea, and identified a distinctive seasonal change and downward trend.

We model the COVID-19 coronavirus epidemic in China. We use early reported case data to predict the cumulative number of reported cases to a final size. The key features of our model are the timing of implementation of major public policies restricting social movement, the identification and isolation of unreported cases, and the impact of asymptomatic infectious cases.

In the electric distribution systems, the “Smart Grid” concept is implemented to encourage energy savings and integration of the innovative technologies, helping the Distribution Network Operators (DNOs) in choosing the investment plans which to lead the optimal operation of the networks and increasing the energy efficiency. In this context, a new phase load balancing algorithm was proposed to be implemented in the low voltage distribution networks with hybrid structures of the consumption points (switchable and non-switchable consumers). It can work in both operation modes (on-line and off-line), uploading information from different databases of the DNO which contain: the consumers’ characteristics, the real loads of the consumers integrated into the Smart Metering System (SMS), and the typical load profiles for the consumers non-integrated in the SMS. The algorithm was tested in a real network, having a hybrid structure of the consumption points, on a time interval by 24 hours. The obtained results were analyzed and compared with other algorithms from the heuristic (Minimum Count of Loads Adjustment algorithm) and the metaheuristic (Particle Swarm Optimization and Genetic Algorithms) categories. The best performances were provided by the proposed algorithm, such that the unbalance coefficient resulted in the smallest value (1.0017). The phase load balancing led to the following technical effects: decreasing the average current in the neutral conductor with 94% and for the energy losses with 61.75 %, and increasing the minimum value of the phase voltage at the farthest pillar with the 7.14 %, compared to the unbalanced case.

Knowledge of the spatio-temporal occurrence of avalanche activity is critical for avalanche forecasting and hazard mapping. We present a near-real time automatic avalanche monitoring system that outputs detected avalanche polygons within roughly 10 min after Sentinel- 1 SAR data download. Our avalanche detection algorithm has an average probability of detection of 67.2 % with a false alarm rate averaging 45.9, with maximum POD's over 85 % and minimum FAR's of 24.9 % compared to manual detection of avalanches. The high variability in performance stems from the dynamic nature of snow in the Sentinel-1 data. After tuning parameters of the detection algorithm, we processed five years of Sentinel-1 images acquired over a 150 x 100 km large area in Northern Norway, with the best setup. Compared to a dataset of field-observed avalanches, 77.3 % were manually detectable. Using these manual detections as benchmark, the avalanche detection algorithm achieved an accuracy of 79 % with high POD's in cases of medium to large wet snow avalanches. For the first time, we can present a dataset of spatiotemporal avalanche activity over several winters from a large region. This unique dataset allows for research into the relationship between avalanche activity and triggering meteorological factors, mapping of avalanche prone areas and near-real time avalanche activity monitoring to assist public avalanche forecasting. Currently, the Norwegian Avalanche Warning Service is using our processing system for pre-operational use in three regions in Norway.

Motivation: 16S rRNA gene amplicon based sequencing has significantly expanded the scope of metagenomics research by enabling microbial community analyses in a cost-effective manner. The possibility to infer functional potential of a microbiome through amplicon sequencing derived taxonomic abundance profiles has further strengthened the utility of 16S sequencing. In fact, a surge in 'inferred function metagenomic analysis' has recently taken place, wherein most 16S microbiome studies include inferred functional insights in addition to taxonomic characterization. Tools like PICRUSt, Tax4Fun, Vikodak and iVikodak have significantly eased the process of inferring function potential of a microbiome using the taxonomic abundance profile. A platform that can enable hosting of inferred function 'metagenomic studies' with comprehensive metadata driven search utilities (of a typical database), coupled with on-the-fly comparative analytics between studies of interest, can be a major improvement to the state of art. ReFDash represents an effort in the proposed direction. Methods: This work introduces ReFDash - a Repository of Functional Dashboards. ReFDash, developed as a significant extension of iVikodak (function inference tool), provides three broad unique offerings in inferred function space - (i) a platform that hosts a database of inferred function data being continously updated using public 16S metagenomic studies (ii) a tool to search studies of interest and compare upto three metagenomic environments on the fly (iii) a community initiative wherein users can contribute their own inferred function data to the platform. ReFDash therefore provides a first-of-its-kind community-driven frame-work for scientific collaboration, data analytics, and sharing in this area of microbiome research. Results: Overall, the ReFDash database is aimed at compiling together a global ensemble of 16S-derived Functional Metagenomics projects. ReFDash currently hosts close to 50 ready-to-use, re-analyzable functional dashboards representing data from approximately 18,000 microbiome samples sourced from various published studies. Each entry also provides direct downloadable links to associated taxonomic files and metadata employed for analysis. Conclusion: The vision behind ReFDash is creation of a framework, wherein users can not only analyze their microbiome datasets in functional terms, but also contribute towards building an information base by submitting their functional analyses to ReFDash database. ReFDash web-server may be freely accessed at https://web.rniapps.net/iVikodak/refdash/

The role of the submission was to find out what changes people think they need to make in their home because of getting older. At advanced age, the likelihood of different limitations such as vision impairment, hearing impairment or physical inability is increased. Currently, tenants are often forced to leave their long-term living space, as these spaces cannot serve “new” individual needs. This transition from the privacy of their home to the new environment often appears to be a painful change. They will not have a well-known environment because their homes cannot be adapted to their new needs. The aim is a comprehensive approach to the design of such an exterior and interior space which could serve people at all stages of their life, including the terms of mobility. This means that even if there is an unexpected situation and changes in movement abilities and physiological limitations of man, not only by natural aging, but according to accidents or disabilities we can adapt the living space to the given conditions. The survey results are presented in Germany and Slovakia. In the survey, respondents expressed their opinion on what they considered important in creating an adaptive environment considering various life changes. Results are processed graphically with explanation. The results could be of an interest to architects and designers of the environment. Based on the results of the questionnaire survey, studies of possible modifications of flats and houses were developed. The contribution brought these results to three age groups of respondents; people aged 35, 50 and over 50.

New approaches to collect in-situ data are needed to complement the high spatial (10~m) and temporal (5-day) resolution of Copernicus Sentinel satellite observations. Making sense of Sentinel observations requires high quality and timely in-situ data for training and validation. Classical ground truth collection is expensive, lacks scale, fails to exploit opportunities for automation, and is prone to sampling error. Here we evaluate the potential contribution of opportunistically exploiting crowd-sourced street-level imagery to collect massive high-quality in-situ data in the context of crop monitoring. This study assesses this potential by answering two questions: 1) what is the spatial availability of these images across the European Union (EU)? and 2) can these images be transformed to useful data? To answer the first question, we evaluated the EU availability of street-level images on Mapillary - the largest open-access platform for such images - against the Land Use and land Cover Area frame Survey (LUCAS) 2018, a systematic surveyed sampling of 337031 points. For 37.78% of the LUCAS points a crowd-sourced image is available within a 2-km buffer, with a mean distance of 816.11 m. We estimate that 9.44% of the EU territory has a crowd-sourced image within 300-m from a LUCAS point, illustrating the huge potential of crowd-sourcing as a complementary sampling tool. After artificial and built up (63.14%), and inland water (43.67%) land cover classes, arable land has the highest availability at 40.78%. To answer the second question, we focus on identifying crops at parcel level using all 13.6 million Mapillary images collected in the Netherlands. Only 1.9% of the contributors generated 75.15% of the images. A procedure was developed to select and harvest the pictures potentially best suited to identify crops using the geometries of 785710 Dutch parcels and the pictures' meta-data such as camera orientation and focal length. Availability of crowd-sourced imagery looking at parcels was assessed for 8 different crop groups with the 2017 parcel level declarations. Parcel revisits during the growing season allowed to track crop growth. Examples illustrate the capacity to recognize crops and their phenological development on crowd-sourced street-level imagery. Consecutive images taken during the same capture track allow selecting the image with the best unobstructed view. In the future, dedicated crop capture tasks can improve image quality and expand coverage in rural areas.

Secondary forests (SF) are important carbon sinks, removing CO₂ from the atmosphere through the photosynthesis process and storing photosynthates in their aboveground live biomass (AGB). This process occurring at large-scales partially counteracts C emissions from land-use change, playing, hence, an important role in the global carbon cycle. The absorption rates of carbon in these forests depend on forest physiology, controlled by environmental and climatic conditions as well as on the past land use, which is rarely considered for retrieving AGB from remotely sensed data. In this context, the main goal of this study is to evaluate the potential of full polarimetric ALOS-2 PALSAR-2 data for estimating AGB by taking into account the past-land use of SF areas in the Brazilian Amazon. We surveyed a chronosequence of 42 SF plots (20 ha) near the Tapajós National Forest in Pará state to quantifying AGB growth rates. We explored the full polarimetric data testing three regression models including non-linear (NL), multiple linear regressions models (MLR), and the semi-empirical extended water cloud model (EWCM). The results showed that the intensity of previous use has affected the structure of SF by reducing the AGB accumulation and being noticeable by several polarimetric attributes. The combination of multiple prediction variables with MLR improved the AGB estimation by 70% comparing amongst other models (R² adj. = 0.51; RMSE = 13.2 Mg ha^-1) bias = 2.1 ± 37.9 Mg ha^-1. The error propagation of the MLR model was estimated to be 15%.

Although our emotional connection with the physical urban setting is often valued, it is rarely recognised or used as a resource to understand future actions in city planning. Yet, despite the importance of emotion, citizens’ emotions are typically seen as difficult to quantify and individualistic, even though knowledge about people’s response to space could help planners understand people’s behaviours and learn about how citizens use and live in the city. The study explores the relationship between the physical space and emotions through identifying the links between stress levels, and specific features of the urban environment. This study aims to show the potential of integrating the use of galvanic skin response (GSR) within urban spatial analysis and city planning, in order to address the relationship between emotions and urban spaces. This method involved participants using a (GSR) device linked to location data to measure participant’s emotional responses along a walking route in a city centre environment. Findings show correlations between characteristics of environment and stress levels, as well as how specific features of the city spaces such as road crossing create stress ‘hotspots’. We suggest that the data obtained could contribute to citizens creating their own information layer - an emotional layer- that could inform urban planning decision-making. The implications of this application of this method as an approach to public participation in urban planning are also discussed.

The Volturno Plain is one of the largest alluvial plain of peninsular Italy. This area is characterized by both natural and human induced subsidence, and is and most susceptible to coastal hazards. The present study is based on post-processing, analysis and mapping of the available Persistent Scatterer interferometry datasets, derived from combination of both ascending and descending orbits of three different SAR satellite systems, during an observation period of almost two decades (June 1992 - September 2010). The main output of the research work is a map of the vertical deformation that provides new insights into the areal variability of ground deformation processes (subsidence/uplift) of Volturno plain over the last decades. Vertical displacement values derived by interferometric data post-processing show that the Volturno river plain is characterized by significant subsidence in the central axial sectors and in the river mouth area, whereas moderate uplift is detected in the eastern part of the plain. Other sectors of the study area are characterized by moderate subsidence and/or stability. We infer that the subsidence recorded in the Volturno plain is mainly a consequence of a natural process related to the compaction of the fluvial deposits that fill up the alluvial plain. Anthropic influence (e.g. water exploitation, urbanization) can be substantially regarded as an additional factor that only locally may enhance subsidence. The uplift imaged in the eastern sector of the plain can be related to tectonic activity. The study of subsidence in the Volturno plain is a valuable tool relevant for river flood analyses and coastal inundation hazard assessment addressed to risk mitigation.

Configurable process models are frequently used to represent business workflows and other discrete event systems among different branches of large organizations: they unify commonalities shared by all branches and describe their differences, at the same time. The configuration of such models is usually done manually, which is challenging. On the one hand, when the number of configurable nodes in the configurable process model grows, the size of the search space increases exponentially. On the other hand, the person performing the configuration may lack the holistic perspective to make the right choice for all configurable nodes at the same time, since choices influence each other. Nowadays, information systems that support the execution of business processes create event data reflecting how processes are performed. In this article, we propose three strategies (based on exhaustive search, genetic algorithms, and greedy heuristic) that use event data to automatically derive a process model from a configurable process model that better represents the characteristics of the process in a specific branch. These strategies have been implemented in our proposed framework, and tested in both business-like event logs as recorded in a higher educational ERP system, and a real case scenario involving a set of Dutch municipalities.

Human microbiome data from genomic sequencing technologies is fast accumulating, giving us insights into bacterial taxa that contribute to health and disease. Predictive modeling of such microbiota count data for classification of human infection from parasitic worms such as helminths can help in detection and management across global populations. Real-world datasets of microbiome experiments are typically sparse containing hundreds of measurements for bacterial species, of which only a few are detected in the bio-specimens that are analyzed. This feature of microbiome data produces the challenge of needing more observations for accurate predictive modeling and has been dealt with previously using different methods of feature reduction. To our knowledge, integrative methods such as transfer learning has not been explored in the microbiome domain yet, as a way to deal with data sparsity by incorporating knowledge of different but related datasets. One way of incorporating this knowledge is by using a meaningful mapping among features of these datasets. In this paper, we claim that this mapping would exist among members of each individual cluster grouped based on phylogenetic dependency among taxa and their association to the phenotype. We validate our claim by showing that models incorporating associations in such a grouped feature space result in no performance deterioration for the given classification task. In this paper, we test our hypothesis using classification models that detect helminth infection in microbiota of human fecal samples obtained from Indonesia and Liberia countries. In our experiments, we first learn binary classifiers for helminth infection detection by using Naive Bayes, Support Vector Machines, Multilayer Perceptrons, and Random Forest methods. In the next step, we add taxonomic modeling using the SMART-scan module to group the data, and learn classifiers using the same four methods, to test the validity of the achieved groupings. We observed a 6% to 23% and 7% to 26% performance improvement based on the Area Under ROC Curve (AUC) and Balanced Accuracy(Bacc) measures, respectively, over 10 runs of 10-fold cross-validation. These results show that using phylogenetic dependency for grouping our microbiota data actually results in a noticeable improvement in classification performance for helminth infection detection. These promising results from this feasibility study demonstrate that methods such as SMART-scan can be utilized in the future for knowledge transfer from different but related microbiome datasets by phylogenetically-related functional mapping, to enable novel integrative biomarker discovery.

Georgia is ranked fifth highest among states for rates of HIV diagnosis. About 4% of persons living with HIV infection in the United States reside in Georgia, and almost 19% of these people do not know their HIV status. The present study examined the trends and associated factors of HIV testing among adults in Georgia between 2010 and 2014 by analyzing data of the Behavioral Risk Factor Surveillance System (BRFSS). A total of 30,791 persons aged ≥18 years were identified who responded to the question “Have you ever been tested for HIV?” Overall, there were 11,543 respondents who had been tested for HIV, with a decrease in percentage from 49.4% in 2010 to 43.7% in 2014 (p<0.001). Factors associated with HIV testing were being black (p<0.001), being younger than 55 years (p<0.001), single (p=0.02), attaining education level above high school (P<0.001), engaging in HIV high-risk behaviors (p<0.001), and not having healthcare coverage (p=0.03). Overall in Georgia, there has been a decline in the temporal trend of HIV testing, and more than half of adults have never been tested for HIV. For reducing HIV transmission in Georgia, enhancing access and utilization of HIV testing should be a public health priority.

This paper focuses on CEOs, who are considered the strategic leaders of corporations because of their crucial role in making important decisions. This research paper examines how female CEOs influence green innovation (GI) in Asian industrial sector. Its main objective is to answer the following research questions: Are there differences in GI endeavors between Asian industrial firms with female CEOs and those with male CEOs? Does the size of the industrial firms have a positive moderating effect on the relationship between CEO gender and GI? Based on our research questions, the size of firms is likely a determining factor in the GI of female CEOs. This research employs rigorous econometric modeling to analyze a substantial dataset of Asian industrial listed companies spanning from 2013 to 2022. It has been proven that industrial firms led by females CEOs are more inclined to have environmentally friendly practices. Furthermore, the size of the industrial firms amplifies the beneficial influence of female CEOs on the firm's chances of engaging in GI initiatives. Our outcomes stay robust to endogeneity issues using two-step GMM estimators. This study proposes that stakeholders, particularly in Asian countries, should promote the increased representation of females in CEO roles, particularly within large corporations. This is because women-led companies demonstrate superior performance in GI endeavors. Thus, regulators need to implement policies that promote the inclusion of women in CEO positions within large-scale enterprises. These policies may potentially strengthen the private sector’s capacity to foster sustainable innovation.

Landraces are considered a crucial component of biodiversity conservation, serving as a reservoir of genetic diversity. Consequently, the collection, cultivation and detailed characterization of such landraces constitute an inherent aspect of the world’s natural resource heritage. This effort holds promise for the development of elite varieties capable of thriving amidst continuous global climate fluctuations. In this context, we conducted a comprehensive assessment of the main agronomic attributes, physico-chemical properties, and functional quality traits of the major hot pepper landraces adapted to diverse climatic conditions in Tunisia. These landraces include ‘Dhirat’, ‘Semmane’, ‘Beldi’, ‘Nabeul’, ‘Jerid’, ‘Mahdia’, ‘Cayenne’, ‘Kairouan’ and ‘Baklouti’. Most of the pepper landraces exhibited satisfactory yields, ranging from 1163.25 to 1841.67 g plant-1 in ‘Jerid’ and ‘Kairouan’, respectively, indicating robust productivity, especially under prevailing climatic changes and high temperatures. Landraces such as ‘Baklouti’, ‘Beldi’, ‘Nabeul’, ‘Semmane’, ‘Kairouan’ and ‘Mahdia’ appear suitable for both fresh consumption and processing, owing to their favorable average fruit weight and soluble solids content. Functional quality and the levels of antioxidants, as well as radical scavenging activity, emerged as key discriminating factors among pepper landraces. Irrespective of genotype, capsaicin and dihydrocapsaicin constituted the major capsaicinoids, accounting for at least 87% of the total. Total capsaicinoids ranged from 1.81 µg g−1 to 193.71 µg g−1, with ‘Baklouti’ and ‘Jerid’ identified as the most pungent landraces. Total carotenoids ranged from 45.94 µg g−1 to 174.52 µg g−1 with ‘Semmane and ‘Jerid’ exhibiting the highest levels. Considerable variation was observed in β-carotene content, spanning from 3% to 24% of the total carotenoids. α-Tocopherol content ranged from 19.03 µg g-1 fw in ‘Kair-ouan’ to 30.93 µg g-1 fw in ‘Beldi’, exerting a notable influence on the overall tocopherol content. Conversely, the other isomers were detected at very low concentrations. The total vitamin C content ranged from 132 mg 100g-1 fw in ‘Mahdia’ to 200 mg 100 g-1 fw in ‘Nabeul’, indicating relatively low genetic variability. However, large variability was detected in total phenolics content, ranging from 168.58 mg GAE kg-1 fw in ‘Beldi ‘to 302.98 mg GAE kg-1 fw in ‘Cayenne’. Among the pepper landraces tested, ‘Cayenne’ achieved the highest value of radical scavenging activity in both hydrophilic and lipophilic fractions (RSAHF and RSALF), with variations rang-ing from 59% to 120% for RSAHF and from 4% to 64% for RSALF. This study aims to: 1) preserve and enhance the value of local genetic resources, 2) identify desiderable traits for incorporation into breeding programs, and 3) develop high-quality, high-yielding cultivars capable of with-standing climate variations.

The present exploratory study tested the hypothesis that computerized cognitive training (CCT) in home telemonitoring may beneficially affect eyes-closed resting-state electroencephalographic (rsEEG) rhythms in Parkinson's disease patients with cognitive deficits (PDCD). A Eurasian database provided clinical-demographic-rsEEG datasets in 40 PDCD patients, 29 PD patients without cognitive deficits (PDNCD), 40 Alzheimer’s disease patients with cognitive deficits (ADCD), and 40 cognitively normal older adults (Healthy). Sixteen of the 40 PDCD patients completed a cross-over unsupervised CCT program of simple, serious video games (versus a sham program) consisting of 14 daily sessions of approximately 20 minutes each in the patients' homes. Compared to the Healthy, PDNCD, and ADCD groups, the PDCD group was characterized by greater diffuse rsEEG delta (about 2-4 Hz) and theta (about 4-7 Hz) source activity. The PDCD patients who underwent the CCT program showed an improvement in video game performance and a reduction in these delta-theta source activities after this program compared to the control condition. In conclusion, these results suggest that the 2-week CCT program in home telemonitoring may mitigate the abnormal “slowing” of rsEEG rhythms in PDCD patients, possibly enhancing the regulation of brain arousal and quiet vigilance.

Spray penetration into citrus canopies is critical for adequate coverage and deposition to ensure effective pest control. However, mismatch of air assistance to target canopy characteristics can lead to unintended spray loss through over penetration. To evaluate the effect of air assistance on on-target deposition, two sprayers (surrogates for airflow rates) were used to apply a fluorescent tracer dye solution @ target concentration of 300 ppm to 16 medium foliage density tree blocks in a commercial mandarin orchard. The complete factorial experiment in three replications comprised two ground speeds (1.6 and 4.8 km/h), two disc-core nozzles (TeeJet® D3-25 and D6-45), and either one or two nozzle rows to obtain a wide range of application rates (496 to 9,719 L/ha). Dye deposition significantly decreased with canopy depth (p=&lt;0.001) by nearly 7 times across the 3.4-m-wide canopies but was not significant over 1.2 to 2.2 m sampling height (p=0.867). Deposition obtained with the low airflow rate sprayer was significantly greater (p=&lt;0.001) than that obtained with the high airflow rate sprayer over the dose range likely due to too much air pushing out spray droplets. This study underscores the importance of matching the air assistance of orchard sprayers to the target canopy.

The glaciers of the Himalayas are essential for water resources in South Asia and the Qinghai-Tibet Plateau, but they are undergoing accelerated mass loss, posing risks to water security and increasing glacial hazards. This study examines long-term changes in the geometry and flow speeds of both land- and lake-terminating glaciers at the headwaters of the Yarlung Zangbo River, using field measurements, remote sensing, and numerical ice flow modeling. We observed significant heterogeneity in glacier behavior across the region, with notable differences between glacier terminus types and even among neighboring glaciers of the same type. Between 1974 and 2020, glacier thinning and mass loss rates doubled in the early 21st century (−0.57±0.05 m w.e. a−1) compared to 1974–2000 (−0.24±0.11 m w.e. a−1). While lake-terminating glaciers generally experienced more rapid retreat and mass loss, the land-terminating N241 Glacier displayed comparable mass loss rates. Lake-terminating glaciers retreated by over 1000 m between 1990 and 2019, while land-terminating glaciers retreated by less than 750 m. The ITS_LIVE velocity dataset showed higher and more variable flow speeds in lake-terminating glaciers. Numerical modeling from 2000 to 2018 revealed divergent flow regime changes, with some lake-terminating glaciers accelerating and others slowing down. Our findings underscore the significant role of lake-terminating glaciers in contributing to ice mass loss, emphasizing the need for advanced glacier models that incorporate dynamic processes such as frontal calving and longitudinal coupling.

Background: Front-of-package labelling (FoPL) is shown to be effective in assisting consumers make healthier food choices. Several countries are implementing various types of FoPL systems. Objective: This study explored consumers’ perceptions and understanding of three different FoPLs and their insights into features that could influence the effectiveness of the symbols in Kenya Methods: A qualitative study was conducted in four Counties in Kenya; Nairobi, Mombasa, Garissa, and Kisumu. Data was collected through twelve focus group discussions with 137 adults drawn from diverse socio-demographic and socio-economic backgrounds. Three FoPLs were assessed: Red and Green (RG) Octagon, Red and Green Octagon with icons and text (RGI), and the Black Octagon Warning label (WL). The FGDs explored participants’ perceptions of the symbols’ visibility and memorability, their understanding, potential effectiveness, and cultural appropriateness. Printed images displaying four different commonly consumed products bearing specific FoPLs were used during data collection. Data was analyzed thematically and organized using NVivo version 14.0. Results: Results showed that Kenyan consumers had varied perceptions about the proposed FoPL symbols. Majority of the participants found the black Octagon WL to be most visible and memorable due to its good blend of black and white colours. Although the RG octagon symbol and RGI octagon symbols were also found to be eye-catching due to their bright colours, it emerged that the different colours often confused some participants on their meaning. Most participants understood the WL compared to the RG and RGI because they found the additional text more educative. Generally, the WL was preferred as the symbol most likely to influence consumer purchase behaviours and food consumption. All the three FoPLs tested were found to be culturally appropriate. Conclusions: The Black Octagon Warning Label is the most easily visible and understood among the three FoPLs tested and has the potential to influence purchase and consumption behaviours among Kenyan consumers. Although the RG and RGI symbols are visually appealing to many, confusion related to different colours could affect their effectiveness. Consumers need to be educated on the use of FoPLs to ensure they discourage the purchase and consumption of unhealthy foods.

Microalgae and cyanobacteria are photosynthetic microorganisms with significant biotechnological potential for the production of bioactive compounds, making them a promising resource for diverse industrial applications. This study presents the development and validation of a modular, droplet-based microfluidic photobioreactor (µ-PBR) designed for high-throughput screening and cultivation under controlled light conditions. The µPBR, based on PTFE tubing and a 4-channel LED illumination system, enables precise modulation of light intensity, wavelength, and photoperiod, facilitating dose-response experiments. Synechococcus elongatus UTEX 2973 and Chlorella vulgaris were used to demonstrate the system's capacity to support photosynthetic growth under various conditions. The results indicate that continuous illumination, particularly under blue and mixed blue-red light, promotes higher autofluorescence and chlorophyll-a content in cyanobacteria Synechococcus elongatus UTEX2973, while Chlorella vulgaris achieved optimal growth under a 16:8 light-dark cycle with moderate light intensity. This µ-PBR offers not only a flexible, scalable platform for optimizing growth parameters but also investigating highly resolved dose response screenings of environmental stressors such as salinity. The findings highlight its potential for advancing microalgal biotechnology research and applications.

Pluripotent stem cells (PSCs) hold many potential research and clinical benefits due to their ability to differentiate into nearly every cell type in the body. They are often used as model systems for early stages of ontogenesis to better understand key developmental pathways, and for drug screening. However, in order to fully realise the potential of PSCs and their translational applications, a deeper understanding of developmental pathways, especially in humans, is required. Several signalling molecules play important roles during development and are required for proper differentiation of PSCs. The concentration and timing of signal activation is important, with perturbations resulting in improper development and/or pathology. Bone morphogenetic protein (BMP) is one such key signalling molecule involved in the specification and differentiation of various cell types and tissues in the human body including tooth and otic development. In this review, we describe the role of BMP signalling and its regulation, the consequences of BMP dysregulation in disease and differentiation, and how PSCs can be used to investigate the effects of BMP modulation during development, mainly focusing on otic development. Finally, we emphasise the unique role of BMP4 in otic specification and how refined knowledge in controlling its regulation could lead to the generation of more robust and reproducible human PSC-derived otic organoids for research and translational applications.

Cytologic diagnosis of extra‐adrenal paraganglioma presenting as a peripancreatic mass is challenging with a high rate of diagnostic error. We present a case of a peripancreatic mass identified by radiology as a gastrointestinal stromal tumor. Endoscopic ultrasound‐guided fine needle aspiration (FNA) of the mass showed a moderately cellular tumor composed of small to medium sized neoplastic cells with round to oval nuclei arranged singly and in loose clusters. The cells were positive for neuroendocrine markers (synaptophysin and chromogranin) and negative for CD117. A diagnosis of a neoplasm with a neuroendocrine tumor (NET) was made on FNA cytology. The subsequent surgical resection of the tumor revealed peripancreatic paraganglioma with immunohistochemistry positive for synaptophysin, chromogranin, and S100. The latter delineates the sustentacular cells. Although paraganglioma is a well recognized tumor, a detailed comparison of peripancreatic paraganglioma versus pancreatic/gastrointestinal NET is still lacking.

Research into lightweight structural concrete using volcanic tuff is of great importance to the construction industry. These materials have excellent thermal insulation properties, which helps improve the energy efficiency of buildings. A three-factor experimental design was used to build the statistical model. The test methods used were methods for determining the crushability of volcanic tuff, determining the average density, compressive strength and thermal conductivity of lightweight structural concrete. The influence of basalt fiber on the properties of lightweight structural concrete has been determined. The optimal compositions of lightweight structural concrete using tuff have been selected. The compressive strength of lightweight structural concrete reached 32.0 MPa. The average density range is 1754.6-2114.0 kg/m³. Good thermal conductivity values ​​were obtained in the range of 0.653-0.818 W/m·K. The article obtained the optimal compositions of lightweight structural concrete using volcanic tuff as a filler.

Cytokine and AMH profiling were done in prepubertal Murrah buffalo heifers post-progesterone supplementation and co-synchronization protocol. Cytokines: IFNγ, IL6, IL1, IL13, TNFα, and TGFβ, and, AMH, Progesterone and Estrogen hormone estimations were done. Ovarian follicular pattern and fertility outcome were recorded. Eighteen prepubertal heifers, 15-17 months of age, 250-300 kg BWt were randomly divided into 3 groups, Group1: n=6, supplemented with altrenogest 0.044mg/kg/day/heifer orally for 14 days along with Co-synch programme; Group2: n=6, administered CIDR for 14 days and co-synch programme, Group3: n=6, received co-synch programme. Ultrasonography was done to ovarian follicle status on respective days of sampling. Pregnancy diagnosis was done around 45 and 60 post-AI. Estradiol 17-β level remained constant in altrenogest group, CIDR implanted and Control groups during the Progesterone treatment and Co-synch treatment. Estradiol levels during Co-synch were significantly less on day 9 of Co-synch (14.41 ± 1.97 pg/mL) than day 0 and on day7 in pre pubertal buffalo heifers in altrenogest group. However, no significance was observed in other groups. Progesterone levels in buffalo heifers subjected to synchronisation protocols varied significantly (P< 0.05) on day 7 of P4 treatment between altrenogest supplemented and CIDR implanted groups, as well as, Co-synch groups. Supplementation of altrenogest showed a significant (P= 0.043) increase in Progesterone levels by day 14 of altrenogest treatment. Progesterone varied significantly in all Groups on day 9 of co synch protocol [ Group 1 (P= 0.02), Group 2 (P= 0.041) and Group 3 (P= 0.007)]. Cytokine IFN-γ showed high correlation with progesterone, indicating the role of IFN-γ in puberty in buffalo heifers (r= 0.626, P< 0.01). AMH had a significant positive correlation supplemented with altrenogest with IFN-γ (r= 0.673, P< 0.01) and TGF-β (r= 0.463, P< 0.01), whereas, with TNF-α it was a negatively correlated (r= -0.34, P< 0.05). Based on Karl Pearson correlation coefficients, IL-13 and TGF-β could be considered as markers for puberty in buffalo heifers.

The aim of our study was a comprehensive comparative assessment of the effect of consumption of 1 and 2% cholesterol as part of a standard diet with and without regular moderate physical activity on the main biomarkers of lipid and cholesterol metabolism, as well as the intestinal microbiota of male Wistar rats. The animals were randomly divided into 5 groups according to body weight and Open Field test results: Control (n=10), 1% Chol (n=10), 2% Chol (n=10), 1% Chol + RUN (n=10) and 2% Chol + RUN. The cholesterol was additionally added in the amount of 1g or 2g/100g, respectively, to the diet of animals. The rats of experimental groups 1% Chol + RUN and 2% Chol + RUN were subjected to regular (2 days a week) physical activity on a Treadmill. In rats of four experimental groups, when compared to the Control, a response of 39 indicators was revealed. The largest number of changes in biomarkers - 29 (statistically significant, p

Lyme disease, the leading vector-borne ailment in the U.S., annually affects an estimated 476,000 individuals, predominantly in the Northeast and Upper Midwest. Despite its increasing incidence, the evaluation of risk within U.S. cities, including natural public lands, remains inadequate. This study focuses on blacklegged tick occurrences and Borrelia burgdorferi infection prevalence in 24 Staten Island parks, aiming to assess Lyme disease exposure risk. Monthly acarological risk index (ARI) calculations from 2019 to 2022 revealed elevated values (0.16-0.53) in specific parks, notably Wolfe’s Pond Park, High Rock Park, Clay Pit Pond Park, Clove Lake Park, and Fair View Park. June (0.36) and November (0.21) consistently exhibited heightened ARIs, aligning with peak tick collection months. Despite stable yearly infection rates at 28.97%, tick densities varied significantly between parks and years. Identifying a high transmission risk in specific parks in Staten Island, a highly urbanized part of New York City, emphasizes the continuous necessity for Lyme disease risk management, even within the greenspaces of large cities.

The oral microbiome, studied by sampling the saliva or oral rinse, has been long thought to have diagnostic capacity for head and neck cancers (HNC). However, previous reports on the HNC oral microbiome provide inconsistent results. The aim of this study is to consolidate these datasets and determine the oral microbial composition between HNC patients to healthy and premalignant individuals. We analyzed 16 published head and neck cancer (HNC) short-read 16S rRNA sequencing datasets, specifically targeting the V3V4, V4 and V4V5 regions. These datasets included saliva and oral rinse samples from donors with HNC, as well as from healthy and premalignant donors. Differences in diversities and microbial abundance were determined. HNC saliva displayed lower alpha diversity than healthy donors. In contrast, the opposite trend was observed for oral rinse samples. Beta diversity were largely similar across different patient types. Similar oral phyla were detected for all samples, but proportions were largely dependent on sample type (i.e. saliva or oral rinse) and primer set utilised for 16S rRNA sequencing. Neisseria, Leptotrichia and Megasphaera were elevated in healthy saliva, while Mycoplasma was elevated in HNC saliva. Oral rinse and saliva displayed similar enrichment for Fusobacterium, while Veillonella, Alloprevotella, and Campylobacter had conflicting results. The sparse partial least squares discriminant analysis model performed effectively in discriminating HNC from healthy or premalignant patients using V3V4 saliva (AUC = 0.888), and V3V4 oral rinse (AUC = 0.928), while poor discriminative capacity was observed for V4 saliva (AUC = 0.688). In conclusion, our meta-analysis highlighted the limitations of 16S rRNA sequencing, particularly due to variations across study batches, primer sets (i.e. V3V4, V4), and sample types. Hence, caution should be exercised when interpreting 16S rRNA sequencing results across studies, especially when different primer sets and sample types are used.

Malocclusions have a continuously increasing prevalence from a generation to another as a result of climate changes, soil, atmosphere and water pollution. All of these aspects have unfavorable consequences on the nutritional scheme. Thus, nutrition together with other etiopathogenic factors contribute to complex alterations in the somatic development of the entire organism and, implicitly, of the cephalic extremity. The study group included 4147 children from randomly selected schools from Vâlcea County, Romania. The aim of this study is to determine the prevalence of malocclusions in schoolchildren from Vâlcea County, Romania, according to the three main classes of malocclusions (according to Angle’s classification), age groups (from 6 to 10 years old and from 11 to 14 years old), gender (male and female) and place of origin (rural and urban). For Angle class I malocclusions we recorded the highest prevalence (48,78% from the total number of schoolchildren with malocclusions), followed by Angle class II malocclusions (45,85% from the total number of schoolchildren with malocclusions) and for Angle class III malocclusions we found the lowest prevalence (5,37% from the total number of schoolchildren with malocclusions).According to gender, we found the highest prevalence in female gender (29,90% from the total number of female subjects), while in male gender we recorded a prevalence of 27,70% from the total number of male subjects. Regarding the place of origin, there is a higher prevalence of malocclusions in the urban areas (29,16%). The study subgroup included 140 children randomly selected from the total number of subjects from the study group. They were included in a more advanced study. The aim is to find potential associations between the presence of malocclusions and various oral variables. Categorical variables were expressed as numerical values and percentages, and their association was evaluated with either the Chi-square test of association or homogeneity, or the Fisher Exact test. The acquired data were incorporated into a binomial logistic regression model to assess the likelihood of developing malocclusions in relation to the following variables: defective phonation, bruxism, frequency of teeth brushing, onychophagia, oral respiration, infantile deglutition, placing objects between the maxillaries, thumb sucking and salivary aspects. It is also aimed to compare the results obtained with similar ones from the specialized literature.

Glioblastoma (GB) is the most aggressive and prevalent glioma within the Central Nervous System. GB exhibit a dismal 5-year survival rate (~6%) due to unfavorable prognosis and lack of viable treatment options. Therefore, novel therapies with plant-derived compounds emerge as very promising. This study aims to investigate the antitumor activity of 7α-acetoxy-6β-hydroxyroyleanone (Roy) in GB cell lines. Roy was isolated from Plectranthus hadiensis Schweinf. and its antitumor mechanism was assessed in a panel of five GB cell lines to mimic tumor heterogeneity. Metabolic activity was evaluated by Alamar Blue® assay, while cell death, cell cycle regulation, mitochondrial membrane potential, and activated caspase-3 were evaluated by flow cytometry. mRNA levels of target genes were measured by qPCR and protein expression was assessed by Western blotting. Cell uptake and impact on mitochondrial morphology were evaluated by confocal microscopy. Roy induced G2/M cell cycle arrest, mitochondrial fragmentation, and apoptosis by inhibiting the expression of anti-apoptotic proteins and increasing activated caspase-3 levels with concentration 6-9 fold lower than those of temozolomide, the first-line treatment. Roy demonstrated a robust antitumor activity against GB cells, without affecting non tumoral cells (microglia and astrocytes), being a promising natural lead compound for the development of novel chemotherapeutic approaches.

This scoping review aims to systematically examine and analyse the available literature on pure total talar dislocation, focusing on its epidemiology, clinical presentation, imaging techniques, surgical options, rehabilitation protocols, and complications. Following the PRISMA-ScR guidelines, a comprehensive search was conducted across PubMed, Web of Science, and Scopus databases. The search yielded 185 articles, from which 30 met the inclusion criteria, focusing on pure total talar dislocation without fractures. Data from each study were extracted, including patient demographics, injury characteristics, treatment methods, and outcomes. A total of 30 articles were included in this review after screening and quality assessment by five independent reviewers. The studies included case reports, case series, and reviews. Despite the heterogeneity of available studies, key findings suggested that early reduction, careful wound management, and soft tissue preservation are crucial in minimising complications such as avascular necrosis (AVN), post-traumatic arthritis, and infection. Long-term outcomes varied, and the risk of AVN remained high, particularly in cases with compromised blood supply to the talus. Pure total talar dislocation is a rare and challenging condition with no established management protocol. While talar reimplantation and joint fixation offer promising outcomes in preserving function, the risk of complications, particularly AVN, remains significant. Further research is needed to standardise treatment protocols and improve clinical outcomes for this rare but severe injury.

Breast cancer in patients with prior breast augmentation poses unique challenges for detection, diagnosis, and management. Mastectomy rates are increasing, and patients with prior augmen-tation often have a lower body mass index, making autologous techniques unsuitable. This study aims to assess the best reconstructive option in patients with a history of subglandular or dual plane breast augmentation. Materials and methods: a prospective analysis was conducted on pa-tients who underwent breast reconstruction after mastectomy. Patients with subglandular or dual plane breast augmentation were included. Patients were divided into submuscular breast recon-struction (group 2) or prepectoral breast reconstruction (group 1) groups. Demographic and sur-gical data were collected. Results: A total of 47 patients were included, with 23 in Group 1 and 24 in the Group 2. Complications occurred in 11 patients (23.4%), with significant differences be-tween groups. The most common complication was seroma formation. Implant loss occurred in 4.3% of cases in the Group 1, while no implant loss was observed in the Group 2. Patient-reported satisfaction scores were similar between groups at 12 months postoperatively. Conclusions: sub-pectoral breast reconstruction with a tissue expander seems a safer and effective technique for pa-tients with prior breast augmentation. It resulted in fewer complications. This approach should be considered as an option for breast reconstruction after mastectomy in this cohort of patients.

The aging mechanism of 10% and 30% nickel substituted manganese hexacyanoferrate cathode material in aqueous zinc-ion batteries has been explored through the advanced synchrotron-based two-dimensional x-ray fluorescence technique. Thanks to the two-dimension modality, not only the metal concentration dynamics throughout the entire electrodes was followed during the aging process, but their spatial distribution was also revealed, suggesting the route of the material transformation. The dissolution of Mn and Ni, and the penetration of Zn inside the framework, alongside the Mn aggregations outside the hexacyanoferrate framework were detected. Additionally, the possibility of conducting x-ray absorption spectroscopy measurements on the regions of interest made it possible to explore the chemical state of each metal, and furthermore, synchrotron-based powder x-ray diffraction demonstrated the gradual structural modification in 30% Ni-containing sample series, in terms of the different phase formation.

The remote monitoring of clinical parameters plays a fundamental role in different situations, like pandemic health emergencies and post-surgery conditions. In these situations, the patients might be impeded in their movements and it could be difficult to have a specific health monitoring. In recent years, technological advancements in smartphones have opened up new possibilities in this landscape. The present work aims to propose a new method for respiratory kinematics monitoring via smartphone sensors. In particular, a specific application was developed to register Inertial Movement Unit (IMU) sensors data from the smartphone for respiratory kinematics measurement and to guide the user through a specific acquisition session. The session was defined to allow for the monitoring of the respiratory movement in five prescribed positions. The application and the sequence were successfully tested on a given population of 77 healthy volunteers. The resulting accelerometers and gyroscope signals were processed to evaluate the significance of differences according to participants sex, vector components and smartphone positioning and, finally, to estimate the respiratory rate. The emerged statistical differences revealed the significance of information in the different acquisition positions.

The dispersal potential of crown-of-thorns starfish (CoTS) larvae is important in understanding both the initiation and spread of population outbreaks, and is fundamentally dependent upon how long larvae can persist while still retaining the capacity to settle. This study quantified variation in larval survivorship and settlement rates for CoTS maintained at three different chlorophyll concentrations (0.1, 1.0 or 10.0 µg.L^-1), achieved by varying densities of single-celled flagellate phytoplankton, Proteomonas sulcata. Based on the larval starvation hypothesis we expected that low to moderate algal concentrations would significantly constrain both survival and settlement. CoTS larvae were successfully maintained for up to 50 days post-fertilization, but larval survival differed significantly between treatments. Survival was greatest at intermediate (1.0 µg.L^-1) chlorophyll concentrations, and lowest at highest (10.0 µg.L^-1) chlorophyll concentrations. Rates of settlement were also highest at intermediate (1.0 µg.L^-1) chlorophyll concentrations and peaked at 22 days post-fertilization. Peak settlement was delayed at low chlorophyll concentrations, probably reflective of delayed development, but there was no evidence of accelerated development at high chlorophyll concentrations. CoTS larvae were capable of settling 17-43 days post-fertilization, but under optimum conditions with intermediate chlorophyll concentrations, peak settlement occurred at 22 days post-fertilization. Moderate increases in nutrient concentrations and algal densities may increase the number of CoTS that effectively settle, but are unlikely to influence dispersal dynamics.

Research question/problem: This comprehensive review and comparative analysis aims to investigate the significance of enhancing oxygen provision in the context of COVID-19, focusing on the implications for healthcare policies and services.Background/context: The study addresses the shift in the oxygen dissociation curve of blood in COVID-19 patients, emphasizing the impact on oxygen delivery to tissues and the challenges it poses for clinical management. It also explores the disruption of healthcare systems due to the pandemic and the integration of social determinants of health in clinical environments.Methodology: The research incorporates a thorough literature review, comparative analysis of diverse approaches to oxygen delivery, and examination of successful interventions through case studies and best practices. Data collection techniques include the review of scholarly articles, case studies, and policy documents.Results/Key findings: The study highlights the imperative for augmented oxygen supply and the significance of maintaining appropriate oxygen saturation levels, especially in the context of respiratory illnesses like COVID-19. It also emphasizes the positive correlation between increased oxygen saturation levels and life expectancy, indicating potential health benefits of enhanced oxygen availability.Conclusions/Implications: The findings underscore the need for a structured approach to oxygen therapy and the importance of equitable distribution of oxygen to underserved regions. The implications of the research extend to enhancing healthcare policies and services regarding oxygen provision during the COVID-19 pandemic, especially in rural and remote areas.

Gallium-68 prostate-specific membrane antigen positron emission tomography/computed tomography (Ga-68 PSMA-PET/CT) is a diagnostic tool used in urology to detect oligometastatic disease in patients with prostate cancer (PC). Although increasingly popular and extensively studied, it is not considered a standard diagnostic tool for primary PC staging, because it lacks validation when compared to mpMRI.Numerous studies have suggested its promising role in PC diagnostics. Our objective was to comprehensively review and summarize the existing literature on the potential and feasibility of utilizing Ga-68 PSMA-PET/CT as the sole method for primary PC staging and to compare it with currently employed diagnostic tools (mainly mpMRI). The main goal of the study was to consolidate the data on the sensitivity and specificity of PSMA-PET for PCa staging when compared with other diagnostic tools. The use of PSMA-PET/CT as the only diagnostic tool of primary PC in the future might potentially reduce diagnostic time and costs. In addition, we examined the limitations of its use. Ga-68 PSMA-PET/CT demonstrates encouraging outcomes during the initial assessment of PC, potentially leading to a transformative leap in PC diagnosis and treatment, due to the utilization of Ga-68 PSMA, with high affinity to prostate cells.

This work studies how the Elo rating system can be applied to score-based sports, where it’s gaining popularity, and in particular for predicting the result at any point of a game, extending its statistical basis to stochastic processes. We derive some new theoretical results for this model and use them to implement Elo ratings for basketball and soccer leagues, where the assumptions of our model are tested and found to be mostly accurate. We showcase several metrics for comparing the performance of different ratings systems and determine whether adding a feature has a statistically significant impact. Finally, we propose an Elo model based in a discrete process for the score that allows us to obtain draw probabilities for soccer matches, and has a performance competitive with alternatives like SPI ratings.

Despite medical advances, sepsis and septic shock remain one of the leading causes of mortality worldwide, with a high inter-individual variability in prognosis, clinical manifestations and response to treatment. Given the heterogeneity of sepsis and recent technological progress amongst machine learning analysis technique, a new, personalized approach in sepsis is being intensively studied. Despite the difficulties when tailoring a targeted approach, with the use of artificial intelligence-based pattern recognition, more and more publications are becoming available, highlighting novel factors that may intervene in the high heterogenicity of sepsis. This has led to the devise of a phenotypical approach in sepsis, further dividing patients based on host and trigger-related factors, clinical manifestations and progression towards organ deficiencies, dynamic prognosis algorithms and patient trajectory in the Intensive Care Unit (ICU). Host and trigger-related factors refer to patients’ comorbidities, body-mass index, age, temperature, immune response, type of bacteria and infection site. The progression to organ deficiencies refers to individual particularities of sepsis-related multiorgan failure. Finally, the patient’s trajectory in the ICU points out the need of a better understanding of interindividual response to various supportive therapies. This review aims to identify the main sources of variability in clustering septic patients in various clinical phenotypes, as a useful clinical tool for precision-based approach in sepsis and septic shock.

Background: Current management of COPD is predominantly focused on respiratory aspects. A multidimensional assessment including nutritional assessment, quality of life and disability provides a more reliable perspective of the true complexity of COPD patients. Methods: Prospective observational study of 120 elderly COPD patients at high risk of acute exacerbations. The Mini-Nutritional Assessment (MNA) was administered in addition to the usual respiratory assessment. The primary outcome was a composite of moderate or severe acute exacerbations during 52 weeks of follow-up. Results: The median MNA Short Form (SF) score was 11 (8-12), 39 participants (32.50%) had normal nutritional status, 57 (47.5%) were at risk of malnutrition and 24 (20%) were malnourished. Our multivariate linear logistic models showed that the MNA score was associated with dyspnea and respiratory symptoms severity assessed by the Modified British Medical Research Council (mMRC) and the COPD Assessment Test (CAT) score, with spirometric variables, in particular with the severity of airflow limitation based on the value of FEV1 and with poorer QoL as assessed by the EQ-5D-3 questionnaire. Cox regression analysis according to nutritional status based on the MNA score showed that COPD participants "at risk of malnutrition" and "malnourished" had a higher risk of moderate to severe acute exacerbations with a hazard ratio (HR) of 3.54 (p=0.002) and 8.20 (p=0.0001), respectively. Conclusion: Our study confirms the importance of assessing nutritional status in elderly COPD patients and its prognostic value.

Background: Tibial shaft fractures are the most common of long bone fractures. The gold standard for management is locked intramedullary nailing. The freehand method under fluoroscopic monitoring is the most often utilized method for distal screw locking. Electromagnetic guidance-based distal screw locking methods have gained popularity recently. The study's objective is to evaluate the system using electromagnetic tracking data against the free-hand technique. Methods: Sixty-three patients who received intramedullary nailing for tibial shaft fractures at our institute between January 2018 and May 2022, were included in this study. Two groups of patients were distinguished: Group FH underwent freehand distal locking and intramedullary nailing, while Group SS underwent electromagnetically guided locking. Radiation exposure period, as well as dose area product, were noted and statistically compared. Results: In terms of dose area product, a mean value of 43.57±31.57 cGy/cm2 in group FH and a mean value of 27.62±11.20 cGy/cm2 in group SS (p &lt; 0.05) was assessed. Concerning the duration of surgery a mean value of 128.83±26.51 minutes was reported in group FH and a mean value of 126.42±48.31 minutes in group SS (p &gt; 0.05). Conclusions: This study confirms the use of electromagnetic-guided locking distal screws reduces the total time of exposure to ionizing radiation, with no difference in operating time.

Background/aims: Prior research identified a significant association between heart disease and metabolic dysfunction-associated liver steatohepatitis (MASLD), but the mechanisms are unclear. This study aims to search predictive biomarkers for grade I left ventricular diastolic dysfunction (LVDD), an early stage of heart failure (HF), in type 2 diabetes mellitus (T2DM) patients. Methods: This single-center, prospective observational study screened 73 T2DM patients for grade 1 LVDD and MASLD using 2D echocardiography, tissue analysis, spectral color Doppler, and Fibromax. Results: The study analyzed 50 patients (mean age 58±11.3 years) with a median diabetes duration of 7 years, abdominal obesity (mean BMI 34.4±5.9 kg/m2), and a mean HbA1c of 7.9±1.5%. The prevalence of grade I LVDD, fibrosis, mild steatosis, moderate to severe liver steatosis, mild MASLD, and moderate MASLD was 54%, 44%, 14%, 80%, 43%, and 34%, respectively. Regression analysis revealed that grade 1 LVDD was positively associated with age, Fibrotest, ɑ2-macroglobulin, epicardiac adipose tissue (EAT), and negatively associated with lateral s`, E wave, E/e`, E/A, medium E`, and septal e` (p&lt; 0.05 for all). ɑ2-macroglobulin &gt; 1.92 g/L (AUROC= 0.782, sensitivity 70.4%, specificity 81.2%) and fibrotest score &gt; 0.11 (AUROC 0.766, sensitivity 92.6%, specificity 56.2%) were significant predictors of grade I LVDD. Conclusion: HF is a multifaceted disorder associated with multisystem organ damage, including nonalcoholic MASLD. Although the underlying mechanisms remain unclear, innovative non-invasive biomarkers, such as ɑ2-macroglobulin or fibrotest, could concurrently indicate liver stiffness and the likelihood of grade I LVDD, an early, asymptomatic HF stage in T2DM patients.

A prospective clinical-epidemiological study was conducted to evaluate the dynamics of biomarkers in hospitalized patients with moderate to severe COVID-19 (according to the National Institute of Health severity scale) treated with Anakinra, compared to those receiving standard treatment. The study included 65 patients with a mean age of 75.51±9.54 years, ranging from 44 to 99 years. Among the study participants, 38 (58.5%) were male and 27 (41.5%) were female. The patients were divided into two groups: control group (standard treatment): 24 patients (36.9%) and target group (Anakinra treatment): 41 patients (63.1%). The results showed a significant relationship between the dynamics of leukocytes, ASAT, LDH, D-dimer, ferritin, and IL-6 and the administration of Anakinra. In the control group, no significant dynamics were observed in leukocyte count, D-dimer, ferritin, or IL-6. A statistically significant decrease in ASAT and LDH values in the control group was noted only on Day 7. In the Anakinra group, significantly (or with borderline statistical significance, p&lt;0.1) lower mean values were observed for leukocytes, NLR, D-dimer, ferritin, and IL-6 on Days 3 and 7 compared to the control group. Additionally, significantly lower CRP values were detected in the Anakinra group on Day 7 compared to the control group.

Objectives. To compare the audiological benefits of a non-implantable wearing option of a bone conduction (BC) implant mounted on an arch (SoundArc) to traditional BC hearing aids (HA) mounted on eyeglasses (BCHA) in patients with moderate to severe conductive or mixed hearing loss. Methods: A preliminary cross-sectional observational prospective cohort study was conducted at a Tertiary Audiological Department, University Hospital. Fourteen adults with conductive or mixed hearing loss (PTA at 0.5-1-2-4 KHz = 67 ± 15 dB HL) had been wearing conventional BCHA on eyeglasses for at least 3 years, and refused the surgical implantation of a BC hearing implant (BCHI). Unaided and aided pure-tone AC and BC thresholds and speech test in quiet and in noise were recorded at baseline and in two different settings: with a BCHI mounted on SoundArc®, and with their own BCHA with two couplers, mounted on eyeglasses. International Inventory for Hearing Aids [IOI – HA], the Hearing Handicap Inventory [HHIA/E], Speech, Spatial, Qualities of Hearing Scale [SSQ], a 10-scores visuo-analogic scale [VAS], and Fatigue impact scale [FIS] score were asked in both conditions. Results: a statistical significative functional gain was observed for both settings. (p=.0001) Better speech perception performances in quiet and in noise were observed with SoundArc compared to their conventional BCHA on eyeglasses (word repetition score in noise improvement of +19.3 at SNR +10 dB, p=.002; + 12.1 at SNR 0 dB, p=.006; and +11.4 at SNR -10 dB, p=.002) No significant differences were found in IOI–HA, FIS, and HHIA/E scores, while significative better SSQ scores in all domains were reported for SoundArc. (p=.0038) Conclusions: Although patients were accustomed to their BCHA on eyeglasses, the non-implanted wearable option of the BCHI proved to be a viable alternative in adult patients with conductive or mixed hearing loss unable to receive or refusing BCHI surgery.

The Axillary Seabream Pagellus acarne is of economic importance for recreational and commercial fisheries in the Atlantic and the Mediterranean, but a decline in total landings has been observed and raised some concern. Although biological information on the species has been provided for the Atlantic and Mediterranean, in the Portuguese continental slope data is restricted to the southern part of the country, which points to the relevance of the present work. A total of 520 individuals, between 7.8 cm and 39.9 cm TL, were monthly collected from September 2020 to August 2022 from commercial landings of fishing vessels operating off mainland Portugal, and used to study age and growth, the reproductive strategy and the annual sexual cycle. Age varied between 0 years (7.8 cm TL) and 15 years (34.2 cm TL) and the von Bertalanffy growth parameters were estimated from whole-otolith readings (L∞ = 35.2 cm, k = 0.133 yr–1 and t 0 = –2.78 yr). Pagellus acarne proved to be a protandric hermaphrodite species with males prevailing until 28.0 cm TL and females between 28.0 cm and 34.0 cm TL with a brief sex change phase. Females presented asynchronous oocyte development with a batch spawning pattern. The spawning season occurred from April to October with a spawning peak between June and August aas shown by histological analysis of the gonads and high levels of GSI during the period.

Various symptoms have been reported to persist beyond acute phase of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, which is referred as long coronavirus disease 19 (long COVID-19). Over 65 million individuals suffer from long COVID-19. However, the causes of long COVID-19 have been largely unknown. Since long COVID-19 symptoms are observed throughout the body, vascular endothelial dysfunction may be a strong candidate to induce long COVID-19. The angioten-sin-converting enzyme 2 (ACE2), the entry receptor of SARS-CoV-2, is ubiquitously expressed in endothelial cells. We previously found that the risk factors for athero-sclerotic cardiovascular disease (ASCVD) and the history of ASCVD can be the risk for severe COVID-19, suggesting a contribution of pre-existing endothelial dysfunction to severe COVID-19. Here, we show a significant association of endothelial dysfunction with development of long COVID-19 and show that biomarkers for endothelial dysfunction in patients with long COVID-19 are also crucial players in development of ASCVD. We consider the influence of long COVID-19 on development of chronic kidney disease (CKD) and ASCVD. Furthermore, we suggest the therapeutic interventions for long COVID-19 by considering endothelial dysfunction as the treatment tar-gets for long COVID-19. Such interventions may prevent pandemic of CKD and ASCVD in post COVID-19 era.

Chronic lymphocytic leukemia (CLL) is a distinct category of lymphoproliferative disorder characterized by the clonal expansion of mature B cells, followed by their accumulation in primary and secondary lymphoid organs. Cluster of differentiation (CD) markers such as CD20, CD22, CD23, CD5, and CD81 serve as reliable prognostic indicators and disease progression factors in CLL. The human leukocyte antigen (HLA) system is a critical genetic variable in many diseases, with well-documented associations between HLA and various cancers. This study aims to investigate, for the first time, potential connections between HLA typing and CD marker expression in CLL. Despite being one of the most prevalent neoplasms in developed regions, there is a need for biomarkers that can improve survival predictions and identify possible genetic determinants. Methods: This study included 66 CLL patients and 100 controls, with all samples analyzed using biochemical methods, flow cytometry, and cytomorphology. Next-generation sequencing (NGS) was performed for HLA typing. Results: The results indicate that several CD markers are statistically associated with different HLA alleles. Specifically, CD20 was associated with HLA-DRB1*11:04:01; CD45 with HLA-C*07:01:01; CD79b with HLA-DPA1*02:01:02; CD23 with HLA-B*39:01:01; CD43 with HLA-DRB1*15:01:01; CD22 with HLA-B*49:01:01, HLA-C*07:01:01, HLA-DPB1*02:01:02, and HLA-DRB1*07:01:01; and CD81 with HLA-DPB1*04:02:01, HLA-DQA1*01:04:01, and HLA-DQB1*05:03:01.Conclusion: In conclusion, this research demonstrates significant statistical links between HLA genes and immunophenotypic markers in CLL patients, shedding new light on the immunological context of CLL.

The ongoing SARS-CoV-2 pandemic has raised concerns surrounding immunological protection against the virus, particularly for people with inborn errors of immunity (IEI). While COVID-19 vaccination induces robust antibody, memory B-cell, and T-cell responses in healthy individuals, how well vaccination protects people with IEI against infection and severe disease remains unclear – especially in the context of new viral variants and vaccine formulations – leading to anxiety, ongoing self-isolation, and repeated vaccination with limited evidence of increased efficacy. Whilst most people with IEI generate some level of cellular and/or humoral immunity to COVID-19 vaccination, the level of protection and durability of the response are unclear. Alongside vaccination, antibody-based therapeutics are aimed at limiting infection and severe disease in this cohort. Immunoglobulin replacement therapy (IgRT) provides passive immunity in antibody-deficient individuals. However, to successfully prevent SARS-CoV-2 infection, a sufficient number of neutralizing antibodies must be present in product. While antibodies against circulating variants are eventually present in IgRT products (both from natural infection and vaccination of the donors), evidence of their capacity to recognize new variants is limited. Furthermore, while antibody-based pre- and post-exposure prophylaxis can be effective, they are susceptible to decreased efficacy in the context of variant evolution. This review provides an in-depth overview of current knowledge about COVID-19 vaccine efficacy in IEIs, the efficacy of SARS-CoV-2-specific antibody products, and knowledge and technological advances required for continued protection of people with IEI.

Hemostasis describes the process of blood clotting homeostasis. Hemostasis reflects a balance of procoagulant and anticoagulant mechanisms that aim to prevent both bleeding and thrombosis. If hemostasis is disrupted, and bleeding or thrombosis occur, then laboratory testing may ensue to either diagnose the reason for bleeding or thrombosis, or to manage patients under therapy or treatment for bleeding or thrombosis. A wide range of tests of hemostasis are available to laboratories and to clinicians from routine coagulation assays like prothrombin time (PT) and activated partial thromboplastin time (APTT) to specialized assays for von Willebrand factor (VWF) and platelet function. In the current narrative review, we highlight some of the history of innovative diagnostic solutions such as the integration of chemiluminescence and flow cytometry in the hemostasis diagnostic armamentarium, as well as providing a glimpse to the possible future of diagnostic hemostasis testing. This review also emphasizes the importance of harmonization and standardization in hemostasis testing to reduce variability and improve diagnostic accuracy. Future directions include the potential for artificial intelligence in diagnostics, the development of more global test systems that can assess both primary and secondary hemostasis, and the innovations required to follow continuous evolution of therapies requiring precise monitoring. This review underscores the ongoing need for innovation to enhance the diagnostic landscape of hemostasis, ensuring better patient outcomes through more accurate and efficient diagnostic methods.

Coastal zones, although vital for human livelihoods and environmental services, are increasingly vulnerable to climate change, especially communities dependent on marine ecosystems. These areas face significant threats from urbanization, population growth, and climate impacts, leading to biodiversity loss and ecosystem degradation. Recognizing these challenges, the United Nations declared 2021--2030 the Decade for Ecosystem Restoration, led by the UNEP and FAO, with the aim of restoring degraded ecosystems to achieve sustainable development goals (SDGs). The link between healthy ecosystems and disaster risk reduction (DRR) has gained global prominence, especially through the Sendai Framework for Disaster Risk Reduction (SFDRR), which emphasizes the importance of ecosystems in mitigating risks. Despite this, many tools for assessing risks in different ecosystem scenarios remain inaccessible to key stakeholders in disaster management. Advances in spatial modelling have supported ecosystem management, policy-making, and ecosystem health assessment, particularly following the Millennium Ecosystem Assessment. These models have been applied in regions such as the Great Bahamas, Haiti, and the Republic of Congo, helping shape ecosystem-based approaches to development. Improving access to spatial tools and models is crucial to enabling effective ecosystem-based DRR and climate adaptation strategies.

The present study investigates the impact of electrode surface roughness on the electrofreezing of water. This research focuses on how the electrode roughness induced by a laser treatment affects the nucleation and growth of ice crystals under controlled electric fields. To achieve this, a series of experiments were conducted using deionized water samples placed between electrodes with varying surface roughness. The electrodes, fabricated with the Al6061 T6 alloy were structured via the Laser Shock Processing (LSP) method. For this purpose, the pulsed densities during the LSP process were varied from 900 to 2500 pulses/cm2. The increase of the pulse density was correlated to the surface structuring and to the microstructural features and average roughness of the LSP-treated Al6061 alloy. A wavy-like microstructure was induced upon the LSP treatment with average roughnesses between ~ 3.5 and 6 µm at the selected pulse densities. The results indicate that electrode roughness significantly influences the electrofreezing process. Rougher electrodes were found to lower the nucleation temperature compared to smoother ones, suggesting enhanced ice nucleation activity. The findings are attributed to the increased electric field concentration at the asperities of the rough surfaces, which may facilitate the alignment of water molecules and the formation of critical ice nuclei.

Convolutional Neural Networks (CNNs) present drawbacks for modeling geometric transformations, such as scaling and rotation, caused by the convolution operation’s locality. Deformable convolution (DCON), a mechanism that substitutes standard convolution, increasing the receptive field to capture relevant features, is a promising approach to solve this drawback and improve the robustness of CNNs. However, the optimal way to replace the standard convolution with its deformable counterpart in a CNN model is unclear. In this study, we clarify this aseveration by conducting several experiments using deformable convolutions applied in the layers that conform a small four-layer CNN model. We also use deformable convolutions on the four-layers of several ResNet CNNs with depths 18, 34, 50, and 101. The models were tested in binary balanced classes with 2D data for image classification: Cats &amp; Dogs, EyesPACS, Spyders &amp; Chickens, and Shapes. After this testing, we evaluated DCON in 3D data for action recognition: UCF101 and Human2 (a dataset we compiled to control movement, clothing and background). The contribution of this research lies in a guideline to use DCON. It can be summarized as follows: if DCON is used on the first layers of the proposal of model (with simple features), the computational resources expressed as the quantity of Flops will tend to increase and produce bigger misclassification than the standard CNN. However, if the DCON is used at the end layers, the quantity of Flops used in the training and testing will decrease, and the classification accuracy will improve by up to 20% about the base model. Moreover, it gains robustness when using deformable convolutions because it can adapt to the region of interest. Also, the best kernel size of the DCON is three. It showed better results than size five. In the last case, the quantity of Flops increase quadratically, but their performance does not increase significantly. With these results, we propose a guideline to use the DCON and contribute to understanding the impact of DCON on the robustness of CNNs.

Brown rice is a familiar whole grain in Japan. We examined national trends in brown rice consumption among Japanese adults aged ≥ 20 years old, using individual-level data from the National Health and Nutrition Surveys conducted between 2012 and 2019. We employed multivariable logistic regression to identify factors associated with brown rice consumption. The 95th percentile of daily brown rice intake remained at 0.0 grams throughout the study period. The percentage of brown rice consumers increased from 1.8% (95% confidence interval: 1.6–2.1) in 2012 to 2.3% (95% confidence interval: 1.8–3.0) in 2019. Compared with individuals who consumed only white rice, brown rice consumers had significantly higher mean intake levels of macronutrients, legumes, vegetables, fruits, and nuts. Brown rice consumption was positively associated with certain sociodemographic characteristics (being female, older age, residing in a major city, living without very young children, and having higher education levels) and health behaviors (lower body mass index, engaging in regular exercise, and being a former or never smoker). Despite its potential nutritional benefits in balanced diets, only a small fraction of adults in Japan consume brown rice, indicating a need for further promotion, particularly among individuals with characteristics associated with brown rice consumption.

Antisense oligonucleotides (ASOs) are short oligodeoxynucleotides designed to bind to specific regions of target mRNA. ASOs can modulate pre-mRNA splicing, increase levels of functional proteins and decrease levels of toxic proteins. ASOs are being developed for the treatment of several diseases, including motor neuron diseases (MNDs), a group of neurodegenerative disorders characterised by the loss of motor neurons, including spinal muscular atrophy (SMA), amyotrophic lateral sclerosis (ALS) and spinal and bulbar muscular atrophy (SBMA). The biggest success has been the ASO known as nusinersen, the first effective therapy for SMA, able to improve symptoms and slow disease progression. Another success is tofersen, an ASO designed to treat ALS patients with SOD1 gene mutations. Both ASOs have been approved by the FDA and EMA. On the other hand, ASO treatment in ALS patients with the C9orf72 gene mutation did not show any improvement in disease progression. The aim of this review is to provide an up-to-date overview of ASO research in MND, from preclinical studies to clinical trials and, where available, regulatory approval. We highlight the successes and failures, underline the strengths and limitations of the current ASO research, and suggest possible approaches that could lead to more effective treatments.

Background: Metabolic flux analysis (MFA) is an indispensable tool in metabolic engineering. The simplest variant of MFA relies on an overdetermined stoichiometric model of the cell’s metabolism under the pseudo-steady state assumption, to evaluate the intracellular flux distribution. Despite its long history, the issue of model error in the overdetermined MFA, particularly misspecifications of the stoichiometric matrix, has not received much attention. Method: We evaluated the performance of statistical tests from linear least square regressions, namely Ramsey RESET test, F-test and Lagrange multiplier test, in detecting model misspecifications in the overdetermined MFA, particularly missing reactions. We further proposed an iterative procedure using the F-test to correct such an issue. Result: Using Chinese hamster ovary and random metabolic networks, we demonstrated that: (1) a statistically significant regression does not guarantee high accuracy of the flux estimates, (2) the removal of a reaction with a low flux magnitude can cause disproportionately large biases in the flux estimates, (3) the F-test could efficiently detect missing reactions, and (4) the proposed iterative procedure could robustly resolve the omission of reactions. Conclusion: Our work demonstrated that statistical analysis and tests could be used to systematically assess, detect and resolve model misspecifications in the overdetermined MFA.

Various environmental, food security and population health problems seem to be related to intensive agriculture production worldwide. This type of food production system impacted the loss of biodiversity and natural habitats; high usage rates of agrochemicals and natural resources had affected soil composition, human health, and nutritional plant quality in rural areas. Intercropping agroecological systems that respect agrobiodiversity could significantly benefit ecosystems, human health, and food security by modifying the nutritional profile and the content of some health-promoting bioactive compounds of the species cultivated in this system. Research on intercropping strategies focuses on the benefits they can offer to ecosystems and less on nutrient plant composition, leaving information scattered when this theme should be more studied due to the critical impact it could provide for human nutrition. Therefore, this review aims to collect viable details on the investigation status of nutrients and bioactive compounds profile in intercropping systems by verifying different regions of the world with unique mixed crops using plant species, along with the criteria for combining them, as well as the nutrients and bioactive compounds analyzed to show what intercropping systems can contribute to food availability and quality.

Chronic obstructive pulmonary disease (COPD) is a progressive lung disease that is commonly considered to be a potent driver of non-small cell lung cancer (NSCLC) development and related mortality. A growing body of evidence supports the role of the immune system, mainly played by alveolar macrophages (AMs), as key axes regulating the development of COPD or NSCLC phenotypes in response to harmful agents. MicroRNAs (miRNAs) are small non-coding RNAs that influence most biological processes and interfere with several regulatory pathways. The purpose of this study was to assess miRNA expression patterns in patients with COPD, NSCLC, and ever- or never-smoker controls to explore theirinvolvment in smoking-related diseases. Bronchoalveolar lavage (BAL) specimens were collected from a prospective cohort of 43 sex-matched subjects to determine expression of hsa-miR-223-5p, 16-5p, 20a-5p, -17-5p, 34a-5p and 106a-5p by RT-PCR. In addition, bioinformatic analysis of miRNA target genes linked to cancer was performed. Distinct and common miRNA expression levels were identified in each pathological group suggesting their possible role as an index of NSCLC or COPD microenvironment. Moreover, we identified miRNA targets linked to carcinogenesis using in silico analysis. In conclusion, this study identified miRNA signatures in AMs, allowing us to understand the molecular mechanisms underlying smoking-related conditions and potentially providing new insight for diagnosis or pharmacological treatment.

Dysregulation of cartilage homeostasis and the changes in the density and the architecture of the subchondral bone were postulated as a potent mechanically pathological activity contributing to osteoarthritis (OA) pathogenesis. Extracorporeal shockwave therapy (ESWT) is a new, none invasive and effective method in the treatment of animal OA model. In the current study, we demonstrated that shockwave induced the expression of protein-disulfide isomerase-associated 3 (Pdia-3) which is a multifunctional protein hypothesized to be a significant mediator for 1α,25-Dihydroxyvitamin D3 (1α,25(OH)₂D₃) signaling pathway using two-dimensional electrophoresis. Histological analysis and quantitative polymerase chain reaction (qPCR) were verified and observed that the expression of Pdia-3 at 2 weeks was significantly higher than that of any other group at 4 weeks, 8 weeks, and 12 weeks post-shockwave treatment in early OA knee of rat. The other factors of the 1α,25(OH)₂D₃ rapid membrane signaling pathway including extracellular signal-regulated protein kinases 1 (ERK1), osteopontin (OPG), alkaline phosphatase (ALP), and matrix metallopeptidase 13 (MMP13) were measured and significantly increased by qPCR at 2 weeks post-shockwave treatment in early OA knee. Our proteomic data revealed significant Pdia-3 expression in microenvironments of joint tissue that could be actively responded to ESWT, which may potentially regulate biological function of chondrocytes and osteoblasts in the treatment of OA knee.

Electric mobility begins to enter East-African markets. This paper aims to investigate what policy level solutions and stakeholder constellations are established in the context of e-mobility in Dar es Salaam, Kigali, Kisumu and Nairobi and in which ways they attempt to tackle implementation of electric mobility solutions. The study employs two key methods including content analysis of policy and programmatic documents as well as interviews based on purposive sampling ap-proach with stakeholders involved in mobility transitions. The study findings point out that transport operators and their representative associations are less recognized as major players in the transition, far behind new e-mobility players (start-ups) and public authorities. The study further indicates that a set of financial and technical barriers persist such as high upfront invest-ment costs in vehicles and infrastructure, or anxieties regarding competitiveness with fossil fuel vehicles, that constrain the uptake of such private e-mobility initiatives. This study concludes by identifying current gaps that need to be tackled by policy makers and stakeholders in order to implement inclusive electric mobility in East-African cities, considering modalities that include transport providers and address their financial constraints.

The canine distemper virus (CDV) is a morbillivirus implicated in several signs in the Canidae family, in dogs, conjunctivitis, digital hyperkeratosis and neuropathologies are common signs of its infection. Even with the existing vaccination, the canine distemper disease persists worldwide so the molecular pathways implicated in the infection processes has been an interesting and promisor area in the new therapeutic drugs research over recent years. It is known that in the process of virus infection the endoplasmic reticulum organelle lost its homeostasis inducing stress and the subsequence unfolded protein response or UPR in which three ER-trans-membrane proteins are implicated: PERK, IRE1 and ATF6. Moreover, in a prolonged ER stress the apoptosis dead are induced through CHOP protein, as a final step of virus infections. Molecular and cell culture techniques as RT-qPCR and RT-PCR were used in the present study. We demonstrate the activation in vitro of the three UPR pathways after infection with an attenuated strain of CDV. Also, the implication of a MAPK pathway through p38 protein and the apoptotic protein CHOP was funded to participate in all times of infection. Even more, our study suggested that CDV replication occur in a PERK-dependent manner.

How hematopoietic stem and progenitor cell (HSPC) fate decisions are affected by genetic alterations acquired during AML leukemogenesis is poorly understood and mainly explored in animal models. Here, we studied isocitrate dehydrogenases (IDH) genes mutations in the human model of HSPC and provide a review of the literature on this topic. IDH1/2 mutations occur in ~20% of AML, are recognized among the mutations earliest acquired during leukemogenesis, and are target of specific inhibitors (ivosidenib and enasidenib, respectively). In order to investigate the direct effects of these mutations on the HSPC we expressed by lentiviral transduction either IDH1-R132H or IDH2-R140Q mutants into human CD34+ healthy donor cells and analyzed the colony-forming unit (CFU) ability. CFU ability was dramatically compromised with a complete trilineage block of differentiation. Strikingly, the block was reversed by the specific inhibitors, confirming it was a specific effect induced by the mutants. In line with this observation, the CD34+ leukemic precursors isolated from a patient with IDH2-mutated AML at baseline and during enasidenib treatment showed over time a progressive and marked improvement of their fitness in terms of CFU ability and propensity to differentiate, attaining clonal trilinear reconstitution of hematopoiesis and complete hematological remission.

Thromboembolic conditions, such as stroke and myocardial infarction [MI], are the primary causes of mortality globally. High efficiency was demonstrated by fibrinolytic enzymes isolated from microbial sources in dissolving various blood clots. The study aimed to investigate and produce bacterial fibrinolytic enzymes from various Egyptian soil conditions using recombinant DNA technology. Fifty soil samples were examined to determine the production of fibrinolytic enzymes. The plasmin plate technique assessed the thrombolytic activities of the strong fibrinolytic enzyme-producing bacterial isolates. In addition to determining the molecular mass using a mass spectrometer and the western blot method, the activity and thermostability of the enzymes were characterized. An affinity chromatography approach was used to purify the fibrinolytic enzymes. However, bacterial recombinant DNA technology produced the pure bacterial fibrinolytic enzyme. Several dosage formulations for the formulation of fibrinolytic enzymes were tested. By using affinity chromatography and the SDS-PAGE test, the isolated bacterial fibrinolytic enzyme in this investigation was almost 83% pure. Bacillus cereus ATCC 4342 was identified as the predominant bacterial isolate secreting fibrinolytic enzymes. It was discovered that the fibrinolytic enzyme activity of fifty soil samples ranged from 0.167 U/ml to 1.853 U/ml. The extracellular enzyme extracted from Bacillus cereus culture supernatant demonstrated a relatively stable fibrinolytic activity over 18 hours at a pH range of 7.4–10, as well as stability with maximum productivity at 43℃ for over 60 minutes. In this investigation, the enzyme was referred to as Fibrin-proteinase[ FP]. The fibrin clots were directly thrombolyzed by the FP. The calculated values for the specific activity and molecular mass of the FP were approximately 40.71 units/mg of protein and approximately 30 KDa, respectively. It was observed that the presence of Fe+3 marginally boosted protease activity, but the presence of EDTA decreased it. On the other hand, adding monovalent cations like K+1 and Na+1 significantly increased the fibrinolytic activity of FP. The enzyme exhibited activity throughout the temperature range of 20 to 65℃, peaking at 55℃. The ideal range for the generation of protease was 35–43℃ and 8.0–10. The thermostable fibrinolytic enzyme FP, which was isolated from Bacillus cereus ATCC 4342 in various soil environments in Egypt during the current study, was a promising fibrinolytic enzyme because it demonstrated a more extensive and effective fibrinolytic activity and fewer side effects than the fibrinolytic enzymes that are currently available worldwide. It is advised that further research be done on optimizing the right formulation of bacterial fibrinolytic enzymes in the future to help dissolve different types of blood clots and, as a result, lower the high death rate that exists worldwide.

Alzheimer's disease is a progressive neurodegenerative disorder that disrupts the brain regions involved in memory and cognitive functions. This impairment ultimately results in severe deficits in memory, learning, reasoning, communication, and the ability to perform daily activities. Our research reveals a statistically significant decrease in the density of neural progenitor cells within the dentate gyrus of the hippocampus and the subventricular zone of the lateral ventricles in patients diagnosed with Alzheimer's disease. This reduction correlates with the disease's progression. As Alzheimer’s disease advances, cognitive functions decline progressively, leading to the identification of five distinct stages of β-amyloid distribution across various brain structures. Early symptoms include short-term memory deficits and learning difficulties, along with cognitive, psychiatric, and neurological disturbances. Given the hippocampus's essential role in these functions, one would expect to see substantial changes in this brain region. Thus, understanding adult neurogenesis is vital in Alzheimer’s disease, particularly since the hippocampus, a primary neurogenic area, is notably impacted in affected individuals.

Background: Rabies, a zoonotic disease caused by the Lyssavirus genus, is widely distributed and a significant cause of human death in Africa and Asia. This study aimed to investigate human rabies exposure and associated risk factors in Northwest Ethiopia. Methods: The study encompassed four zones, seven districts, and four kebeles from each district. The research team developed a questionnaire for data collection. Results: A total of 642 households were interviewed. The present study highlighted an exposure to rabies of 10.47%.”. Over 98% of residents displayed a strong understanding of rabies, with 99.69% correctly identifying rabies as a zoonotic disease. However, 80.22% of the interviewees believed that traditional medicine can treat the disease. Only 21.93% of victims were subjected to post-exposure prophylaxis. More than 87% of exposed residents were affected by the dogs. Of that, 48.70% were bitten by their own dogs. Metema district had the highest risk of exposure (p < 0.001) compared to the other six that were investigated. Dog ownership (p<0.05) and a higher number of households (p<0.01) were also identified as risk factors for rabies exposure. Conclusion: Residence, low education levels, high household density, free-roaming dogs, and dog ownership are key risk factors for rabies exposure. Public health awareness campaigns are crucial to emphasize the risks associated with animal bites.

Epidermal Growth Factor Receptor (EGFR) expression is an important aspect in the non-small cell lung cancer (NSCLC) diagnosis and treatment. Therefore, it becomes important to identify factors that may influence EGFR level. There are reports suggesting that one of the positive EGFR gene transcription regulators may be Special AT-rich Binding Protein 1 (SATB1), but so far these ob-servations have not been confirmed in NSCLC. The main aim of the present study was to investigate the possible links between the EGFR and SATB1 expression on both protein and mRNA levels in NSCLC clinical samples, and to correlate the obtained results with the clinical-pathological data of the patients. Additionally, we analysed the relationships between the expression levels of EGFR and the known tumour promoters, in-cluding Ki67 proliferative antigen and EMT-promoting transcription factors - SLUG, SNAIL, and Twist1. We demonstrated that EGFR expression in NSCLC was positively correlated both with the SATB1 level and with the expression of EMT-promoting proteins. Moreover, we were the first to analyse EGFR expression exclusively in NSCLC cancer cells without the interference caused by respiratory epithelium and tumour stroma. Our analysis revealed that the prognostic significance of EGFR expression was dependent on tumour histology and differed significantly between the AC and LSCC samples.

Using desi-cow waste products like Jeevamrit, natural farming is widespread among farmers for improving soil biology and productivity. Jeevamrit enhances soil chemical and microbiological properties without needing large quantities of farmyard manure (FYM), as a sustainable farming practice with a reduced carbon footprint. Despite its traditional use, Jeevamrit faces criticism due to a lack of scientific evidence. This study investigated the comparative effect of Jeevamrit and chemical fertilizers on the growth and yield of marigold cv. Siracole. In this study mother block of marigold was raised for both summer and winter seasons. The cuttings were harvested from the mother block and then kept for rooting. The rooted cuttings were planted at monthly intervals and evaluated for flowering parameters and compared to those treated with RDF (30:20:20 N, P, and K g/m2). Soil supplied with Jeevamrit enhanced bacteria (26.33%), fungi (18.92%), and actinomycetes (31.21%) populations compared over the recommended dose of fertilizers (RDF) (i.e. N:P: K @ 30:20:20 g m-2). Jeevamrit-treated plants have marketable flower yield per square meter (3.98 %) and shelf life (9.93 %) compared to RDF. The study concludes that Jeevamrit @ 2 liter m-2 is a sustainable and effective alternative to traditional fertilizers for enhancing marigold production in the mid-hills of the Himalayan region, where natural farming is already accepted.

Efficient gene transfer and expression tool is lacking for shrimps and shrimp cells. To solve this, this study has developed a shrimp DNA virus-mediated gene transfer and expression system, consisting of insect Sf9 cells for viral packaging, shrimp viral vector of pUC19-IHHNV-PH-GUS and baculoviral vector of Bacmid or Bacmid-VP28 encoding shrimp WSSV envelope protein VP28. The pUC19-IHHNV-PH-GUS vector was constructed by assembling the genomic DNA of shrimp infectious hypodermal and hematopoietic necrosis virus (IHHNV), which had shortened inverted terminal repeats, into pUC19 backbone, and then an expression cassette of baculoviral polyhedron (PH) promoter-driven GUS reporter gene was inserted immediately downstream of IHHNV for proof-of-concept. It was found that the viral vector of pUC19-IHHNV-PH-GUS could be successfully packaged into IHHNV-like infective virions in the Sf9 cells, and gene transfer efficiency of this system had been evaluated and verified in three systems of Sf9 cells, shrimp hemolymph cells and tissues of infected shrimps, but GUS expression could be detected only in the cases when the viral vector was co-transfected or co-infected with a baculovirus of Bacmid or Bacmid-VP28 due to the Bacmid-dependence of PH promoter. Moreover, the packaging and infection efficiencies could be significantly improved when Bacmid-VP28 was used instead of Bacmid.

Apple replant disease (ARD) is a complex issue caused by various fungal, oomycetes pathogens and parasites which can differ in their abundance between sites within a geographic region. Knowledge of the specific etiology of ARD at a particular site is required in order to develop commercially viable soil management strategies to combat specific/individual components of the disease. In this study we analyzed the soil from six ARD sites for the presence of fungal, bacterial and oomycetes communities and plant parasitic nematodes. Five fungal, and 17 bacterial classes were differentially represented in the microbiomes from different locations. Mortierellomycetes was the most abundant fungal taxa represented followed by Sordariomycetes. Mortierella exigua a fungal endophyte was the most abundant fungal amplicon sequence variant (ASV) in the core microbiome. Proteobacteria was the most prevalent phylum identified in orchard soils. Several potential phytopathogenic fungi involved in ARD and endophytes including Fusarium oxysporum, F. solani, Nectria ramulariae, Ilyonectria robusta and Nectriaceae were identfied in the orchard soil. Pythium attrantheridium (Globisporangium attrantheridium), P. monospermum and P. ultimum (Globisporangium ultimum) were the most abundant oomycete taxa identified in these soil samples. Six different groups of plant-parasitic nematodes were found across the six orchards soil. Root-lesion nematodes, Pratylenchus spp., which are commonly associated with ARD, were identified in all orchards soil at population densities ranging from 12 to 33/100 cm3 soil. This research contributes valuable understanding of the ARD complex as the apple industry needs alternative approaches to combat the disease.

As Li-ion batteries use is spreading, incidents in large energy storage systems (stationary storage containers, …) or in large-scale cell and battery storages (warehouse, recyclers, …), often leading to fire, are occurring on regular basis. Water remains one of the most efficient fire extinguishing agents for tackling those battery incidents and large quantities are usually necessary. Since bat-teries contains various potentially harmful components (metals and their oxides or salts, sol-vents…) and that thermal runway induced battery incidents are accompanied with complex and potentially multistage fume emissions (containing both gas and particles) the potential impact of fire run-off water on the environment should be considered and assessed carefully. The tests presented in this paper focus on the analysis of the composition of run-off waters used to spray NMC Li-ion modules under thermal runaway. It highlights that waters used for fire-fighting are susceptible to contain many metals, including Ni, Mn, Co, Li and Al mixed with other carbonaceous species (soot, tarballs) and sometimes undecomposed solvents used in the elec-trolyte. Extra-polation of pollutant concentrations compared with PNEC values showed that, for large scale incidents, run-off water could be potentially hazardous to the environment.

Lidocaine, a commonly used local anesthetic, has demonstrated modulatory effects in various cells. This study investigates the impact of lidocaine on cytokine production in peripheral blood mononuclear cells (PBMCs) from healthy donors and tumor-infiltrating immune cells (TIICs) from gastric cancer patients. Flow cytometry and cytokine assays revealed that lidocaine inhibited IFN-γ and IL-12 production in CD8+ and CD14+ PBMCs, respectively, while enhancing anti-inflammatory cytokines IL-10, TGF-β, and IL-35 in CD4+CD25+ and CD14+ PBMCs. In contrast, lidocaine promoted the secretion of pro-inflammatory IFN-γ and IL-12 in CD8+ and CD14+ TIICs, while reducing IL-10, TGF-β, and IL-35 levels, thus shifting CD14+ macrophages toward an M1-like phenotype. Additionally, lidocaine decreased PD-1 expression and increased IFN-γ production in CD8+ TIICs via NF-κB activation, enhancing their cytotoxicity against primary gastric cancer cells (PGCCs). Importantly, lidocaine did not affect the viability of normal PBMCs, TIICs, or PGCCs at concentrations up to 1.5 mM. These findings suggest that lidocaine reprograms the tumor immune microenvironment, supporting its potential as an adjunct therapy in gastric cancer by enhancing anti-tumor immunity.

This study investigates the impact of plasma gradient modification and gradient illumination on the optoelectronic properties of buckypaper (BP), a flexible and large-scale material composed of multi-walled carbon nanotubes (MWCNTs). The BP samples were subjected to argon ion plasma treatment at varying power levels and durations, thereby creating different carrier concentration gradients on the surface. The photovoltage and photocurrent responses of the samples were then measured under uniform full illumination and gradient illumination conditions. The findings revealed that both plasma gradient modification and gradient illumination significantly enhanced the optoelectronic performance of BP. Notably, the combined application of these two methods yielded superior results compared to the application of either method alone. Specifically, the optimal plasma power for improving BP was found to be 20W. Under conditions of plasma gradient modification and gradient illumination, a photovoltage of 267.76 μV was generated, which represents a 21.44 times increase, and a photocurrent of 15.69 μA, reflecting a 32.69 times enhancement. The mechanism underlying this optoelectronic effect can be attributed to the presence of π-bonds in the carbon atoms. These π-bonds are excited by photons, resulting in the generation of small voltages and currents. This study underscores the potential of BP as an optoelectronic material and introduces a novel approach to enhance its optoelectronic properties through plasma gradient modification and gradient illumination.

The use of tuning forks to measure fluid density and viscosity is widely employed in fields such as food, medicine, textiles, automobiles, petrochemicals, and deep drilling. The explicit analytical model based on Euler-Bernoulli cantilever beam theory for the relationship between tuning fork resonance characteristics and the density and viscosity of fluid is only applicable to the situation where the fluid viscous effect is very small. In this paper, the finite element method is used to simulate the influence of large variations in fluid density and viscosity on the resonance characteristic parameters (resonant frequency and quality factor) of the tuning fork. The numerical simulation results are compared with the analytical analysis results and experimental measurement results. Then, the sensitivity of tuning fork resonance characteristic parameters to fluid density and viscosity is studied. The results show that compared with the analytical results, the numerical simulation results have a higher degree of agreement with the experimental measurement results. The relative difference in resonant frequency is less than 2%, and the relative difference in quality factor is less than 4%. This indicates that the finite element method includes the influence of fluid viscosity on tuning fork resonance parameters, which is more in line with the actual conditions than the analytical model. Simulating and analyzing the sensitivity of the tuning fork to fluid density and viscosity by the finite element method, it is possible to consider the situation where fluid density and viscosity vary over a large range. Compared with experimental measurements, this method has higher efficiency and can significantly save time and economic costs. This study can overcome the limitation of existing explicit analytical models, which are only applicable when the viscous effects of the fluid are very small. It enables a more accurate simulation of the coupling vibration between tuning forks and fluids, thereby providing theoretical references for further optimizing tuning fork structural parameters to enhance the accuracy of measuring fluid characteristic parameters.

The mortality rate of women during the gestation period or after delivery is very high worldwide. ToRCH pathogens [Toxoplasma gondii (T.gondii), Rubella Virus (RV), Cytomegalovirus (CMV), and Herpes Simplex Virus (HSV)] are transmitted vertically and are associated with mortality and complications both in the mother and the fetus or children. Therefore, this study is designed to investigate the ToRCH pathogen-induced histopathological changes in placental tissues and associated post-obstetric complications in Pakistani women. Women (N = 68) included in this study were divided into Group A (women with current abortion and bad obstetric history-BOH) and Group B (women with normal delivery and BOH). The venous blood collected was analyzed for ToRCH antibodies through ELISA. A small piece of placenta tissue taken after delivery or abortion was subjected to nucleic acid (NA) detection through PCR using ToRCH pathogen-specific primers. The placental tissue was analyzed for histopathological changes under a microscope. The data obtained were analyzed using percentages and the Chi square test. Overall ToRCH antibody positivity was 64.70% (44/68). Of these, IgM (Immunoglobulin M) was found in 17.65% (12/68), IgG (Immunoglobulin G) in 45.59% (31/68), and one case (1.47%) was seropositive for both IgM and IgG. The NA was detected in 66.18% (45/68) of the placental tissues of the study population. The higher prevalence of antibodies for T.gondii and RV (23.53% each) and NA for RV, CMV, and HSV (17.99% each) was observed in the study population. The ToRCH infection (antibodies and NA) with BOH complications was statistically found to be non-significant (p> 0.05). Overall discrepancies in ToRCH antibodies and NA positivity were found in 86.76% (59/68) of cases, especially for RV (26.47%). Chorionic villitis (CV) in placental tissues was observed in 20.59% (14/68), i.e., 13.23% of Group A and 7.35% of Group B. Of the total, four women with T.gondii, six with RV, and three each with CMV and HSV infection were found with CV. The most common BOH complications observed were recurrent spontaneous abortions (RSAs) (47.05%, 32/68), stillbirth (22.05%, 15/68), congenital abnormalities (14.70%, 10/68), etc. It was observed that RSAs were associated with CV (10.29%) in the study population. The BOH complication and CV were found to be statistically non-significant (p> 0.05). It is concluded that ToRCH pathogens are prevalent in the study population, and CV was the only histopathological change found and associated with BOH complications.

Microsporidia are intracellular parasites infecting a wide range of vertebrates and invertebrates. In bees, they cause a diarrheal disease called nosematosis, which represents a growing problem contributing to the decline of bee colonies. This parasitic disease infects the gastrointestinal tract of bees, disrupts their immune system, weakens them, and shortens their lifespan. There are two known species of this microsporidial parasite with a global distribution: Vairimorpha apis and Vairimorpha ceranae, with current research indicating that the species V. ceranae is gradually replacing V. apis. In this study, we focused on the detection and occurrence of Vairimorpha spp. in bee colonies kept in different environments, as well as on comparing the impact of hive location on its occurrence. The methods used included sampling from winter bee carcasses, microscopic examination of bee gut contents, and subsequent molecular duplex PCR analysis. Altogether, we examined 82 hives located at 4 different sites and focused on the impact of transportation as an abiotic factor disturbing bees leading to the development of Vairimorpha spp. disease. The results indicate an increased occurrence of this parasite in hives located in urban sites and hives situated in intravillage locations, which may be attributed to transportation and associated vibrations as an abiotic stress factor. Detection revealed that all samples were positive for Vairimorpha ceranae.