We tested the use of mosquito traps as an alternative to insecticide spraying in Camargue (France) following the significant impacts observed on the non-target fauna through Bti persistence and trophic perturbations. In a village of 600 inhabitants, 16 Techno-Bam traps emitting CO₂ and using octenol lures were set from April to November 2016. Trap performance was estimated at 70% overall based on mosquito landing on human baits in areas with and without traps. Reduction of Ochlerotatus caspius and Oc. detritus, the two species targeted by Bti spraying, was respectively 74 and 98%. Traps were less efficient against Anopheles hyrcanus (46%), which was more attracted by lactic-acid than octenol lures based on previous tests. Nearly 300 000 mosquitoes from nine species were captured, with large variations among traps, emphasizing that trap performance is also influenced by surrounding factors. Environmental impact, based on the proportion of non-target insects captured, was mostly limited to small chironomids attracted by street lights. Breeding success of a house martin colony was not significantly affected by trap use, in contrast to Bti spraying. Our experiment confirms that deployment of mosquito traps can offer a cost-effective alternative to Bti spraying for protecting local populations from mosquito nuisance in sensitive natural areas.

Total Hip Arthroplasty is one of the most successful surgery. However, due to the worldwide growing population life expectancy and the related incidence of age-dependent bone diseases, a growing number of cases of intra-operative fractures lead to revision surgery with high rates of morbidity and mortality. Surgeons choose the type of the implant, either cemented or cementless prosthesis, on the basis of the age, the quality of the bone and the general medical conditions of the patients. Generally, no quantitative measures are available to assess the intra-operative fracture risk. Consequently, the decision-making process is mainly based on medical operators’ expertise and qualitative information obtained by imaging. Motivated by this scenario, we here propose a mechanical-supported strategy to assist surgeons in their decisions, by giving intelligible maps of the risk fracture which take into account the interplay between actual strength distribution inside the bone tissue and its response to the forces exerted by the implant. To this end, we produce charts and patient-specific synthetic “traffic-light” indicators of fracture risk, by making use of ad hoc analytical solutions to predict the stress levels in the bone by means of CT-based mechanical and geometrical parameters of the patient. We felt that, if implemented in a friendly software or proposed as an app, the strategy could constitute a practical tool to help the medical decision-making process, in particular with respect to the choice of adopting cemented or cementless implant

The paper focuses on the application of a multilevel, integrative and territorial approach to the protection of cultural heritage through planning instruments in Serbia. It points out the problems of the sectoral approach and discusses international recommendations that can be integrated into the planning methodology. The case study is the area of Djerdap National Park, which has an already established international and national importance. The research suggests that the cultural heritage in the area, with the exception of the Roman limes, is still recognized at the level of individual localities. The study emphasizes the importance of aligning all levels of governance to safeguard the whole spatial entity rather than focusing solely on its particular areas. The interna-tional and national importance of the planning area contributes to the strengthening of awareness of heritage and its institutional protection. However, the vitality of the area, which is key to its sustainable development, comes from the local level. Planning documents contribute to the inte-gration of local values and local communities into development programs and projects.

Adjustable frequency converters are widely used in modern automated asynchronous drives due to their high efficiency and high responsiveness control strategies. Control algorithms for frequency converters are divided into two main groups: constant frequency commutation and random frequency commutation. Research results on various commutation frequencies, conducted using Matlab/Simulink software, show that the use of random frequency commutation provides better efficiency by significantly reducing the amplitude of harmonic oscillations at different frequencies, with a reduction in switching losses in transistors. Currently, a converter with random frequency commutation is being researched and applied in the direct torque control system. The traditional direct control system, based on a two-level inverter, is widely used due to its simplicity and ease of implementation. However, this method results in the output current of the two-level converter, supplied to the motor stator, having many harmonic distortions, leading to strong torque fluctuations regardless of the rotation speed and ultimately causing additional losses in the stator windings and reducing the motor's lifespan. To enhance the efficiency of automated asynchronous drives, this article presents an algorithm for a direct control system based on a three-level NPC inverter using a PI speed regulator. The proposed algorithm's research is validated in the Matlab/Simulink environment. The research results show that in the absence of harmonic filtering, the stator current harmonic distortion coefficient is approximately 11%, and torque fluctuations are less than 5% of the resistive torque. The fluctuations between the two capacitors remain almost unchanged throughout the entire process.

In 2022, the new WHO Classification of Endocrine and Neuroendocrine Tumors, Fifth Edition (beta version) (WHO 5th) was published. The importance of understanding the molecular genetics of thyroid tumors as revealed by large-scale genomic analyses such as The Cancer Genome Atlas (TCGA). Consequently, the WHO 5th was fundamentally revised, resulting in a systematic classification based on tumor cell-of-origin and clinical risk. This paper outlines following critical points of the WHO 5th. 1. Genetic mutations in follicular cell-derived neoplasms (FDN) highlight the role of mutations in the MAP kinase pathway, including RET, RAS, and BRAF, as drivers of carcinogenesis. Differentiated thyroid cancers such as follicular thyroid carcinoma (FTC) and papillary thyroid carcinoma (PTC) have specific genetic alterations that correlate with morphological classifications: RAS-like tumors (RLTs) and BRAF p.V600E-like tumors (BLTs), respectively. 2. The Framework for benign lesions have revised. The WHO 5th introduces new categories such as "developmental abnormalities" and "thyroid follicular nodular disease" in benign FDN. 3. Low-Risk Tumors include NIFTP, TT-UMP, and HTT. 4. PTC histological variants are reclassified as "subtypes" in the WHO 5th. 5. The concept of high-grade carcinomas is introduced, encompassing poorly differentiated thyroid carcinoma (PDTC), differentiated high-grade thyroid carcinoma (DHGTC), and high-grade medullary thyroid carcinoma (MTC). 6. Squamous cell carcinoma is included in anaplastic thyroid carcinoma (ATC) in the WHO 5th due to shared genetic and prognostic features. 7. Hürthle cell adenoma/carcinoma is renamed oncocytic thyroid adenoma/carcinoma. 8. Other miscellaneous tumors are classified into salivary gland-type carcinomas of the thyroid, thyroid tumours of uncertain histogenesis, thymic tumours within the thyroid, and embryonal thyroid neoplasms. The WHO 5th thus emphasizes the importance of classifying tumors based on genetic abnormalities together with histomorphology, aiding in accurate pathological diagnosis and treatment selection, including molecular-targeted therapies.

(1) Background: Violence against each other via social media has increased and caused cyberbullying that can happen anytime through electronic communication tools that everyone can access easily. So, this study aims to investigate causal factors contributing to youth cyberbullying in Thailand's three Southern border provinces. (2) Methods: The subject group consisted of 340 youths and the data were analyzed with structural equation modeling (SEM). (3) Results: The study found that the model of the causal factors resulting in youth cyberbullying in the three Southern border provinces was consistent with the empirical data (the relative chi-square (χ2/df) was 1.77. The goodness-of-fit index (GFI) was 0.95. The root-mean-square-error of approximate (RMSEA) was 0.049). Cyberbullying was positively influenced directly and indirectly by negative upbringing, the influence of personal violence, and the influence of media violence at a statistically significant level of 0.001, with total effect sizes of 1.13, 0.74, and 0.64, respectively. Additionally, cyberbullying was positively influenced directly by negative mental traits with a statistically significant level of 0.05 and a total effect size of 0.17. (4) Conclusions: The study results could be beneficial in concretely forming policies and strategies to prevent and mitigate the problem of youth cyberbullying.

One of the industries with the fastest rate of growth is healthcare, and this industry's enormous data requires extensive cloud storage. The cloud may offer some protection, but there is no assurance that data owners can rely on it for refuge and privacy amenities. Therefore, it is essential to offer security and privacy protection. Increased customer satisfaction with security and privacy preservation is crucial since healthcare big data and its sources are stored in the cloud and contain increasingly private and sensitive information. However, maintaining privacy and security in an untrusted green cloud environment is difficult, thus the data owner should have complete data control. A new work SecPri-BGMPOP(security and privacy of Boost graph Convolutional Network -Pinpointing-Optimization Performance) is suggested that can offer a solution that involves several different steps in order to handle the numerous problems relating to security and protecting privacy. Initially Boost Graph Convolutional Network Clustering (BGCNC) algorithm, which reduces computational complexity in terms of time and memory measurements, is first applied to the input dataset to begin the clustering process. Second, enlarge by employing a piece of the magnifying bit string to generate a safe key, pinpointing based encryption avoids amplify leakage even if a rival or attacker decrypts the key or asymmetric encryption. Finally, to determine the accuracy of the method, an optimal key was created using a meta-heuristic algorithmic framework called Hybrid fragment horde bland lobo Optimization (HFHBLO). Our proposed method currently kept in a cloud environment, allowing analytics users to utilize it without risking their privacy or security.

Human disturbance, such as trampling, is an integral component of global change, yet we lack a comprehensive understanding of its effects on alpine ecosystems. Many alpine systems are seeing a rapid increase in recreation, and in understudied regions, such as the Coast Mountains of British Columbia, disturbance impacts on alpine plants remain unclear. We surveyed disturbed (trail-side) and undisturbed (off-trail) transects along elevational gradients of popular hiking trails in the T’aḵ’t’aḵ’múy̓in tl’a In̓inyáx̱a7n region (Garibaldi Provincial Park), Canada, focusing on dominant shrubs (Phyllodoce empetriformis, Cassiope mertensiana, Vaccinium ovalifolium) and graminoids (Carex spp). We used a hierarchical Bayesian framework to test for disturbance by elevation effects on total plant percent cover, maximum plant height and diameter (growth proxies), and buds, flowers, and fruits (reproduction proxies). We found that trampling reduces plant cover and impacts all species, but that effects vary by species and trait, and disturbance effects only vary with elevation for one species’ trait. Growth traits are more sensitive to trampling than reproductive traits, which may lead to differential impacts on population persistence and species level fitness outcomes. Our study highlights that disturbance responses are species-specific, and this knowledge can help land managers minimize disturbance impacts on sensitive vegetation types.

Snakebite envenomation remains a major cause of morbidity and mortality in rural populations. This study aimed to identify factors associated with complications from snakebite envenomation reported in health facilities in the Cascades region of Burkina Faso. This cross-sectional study used routine data of patients admitted for snakebite envenomation in five health facilities between January 1, 2016, and December 31, 2021. Data were collected on sociodemographic, clinical, and therapeutic characteristics of patients showing signs of envenomation. Bivariate and multivariate analyses were conducted to determine factors associated with complications. Among the 846 patients with envenomation, 355 (42%) experienced complications. Local complications at the bite site included wounds (21.5%) and skin necrosis (12.5%). Systemic complications included hemorrhage (30.5%), shock (16.3%), and coma (4.3%), with a mortality rate of 3.2%. Factors significantly associated with complications were rural residence (AOR: 4.80; 95% CI: 2.21-11.4), incision at the bite site (AOR: 4.31; 95% CI: 2.51-7.52), tourniquet application (AOR: 5.52; 95% CI: 1.42-30.8), bleeding (AOR: 14.2; 95% CI: 8.80-23.4), abnormal vital signs (AOR: 14.3; 95% CI: 9.22-22.7), and non-administration of antivenom serum (AOR: 2.92; 95% CI: 1.8-4.8). These findings highlight the importance of antivenom availability and public awareness to reduce complications from snakebite envenomation.

As argued by ‘Declaration of Concrete Environment (2010)’ of Korea and ‘Declaration of Asian Concrete Environment (2011)’ of six Asian countries, concrete as a single material has lately shown extremely large impact on environmental issues such as climate change. Assessment of environmental impact from concrete material and production has considerable importance. Concrete is a major material used in the construction industry that emits a large amount of substances with environmental impacts during its life cycle. Accordingly, technologies for the reduction in and assessment of the environmental impact of concrete from the perspective of Life Cycle Assessment must be developed. At present, the studies in relation to greenhouse gas emission from concrete are being carried out globally as a countermeasure against climate change. In this study, a sustainable concrete mix design algorithm was designed using correlation analyses, and its carbon emission and cost reduction performances were assessed. Using correlation analyses, the concrete strength, w/b and s/a ratios, and CO₂ emissions were identified as major variables of concrete mix design that influenced other variables. Also, this study aims to evaluate the CO₂ emission reduction performance of the algorithm-deduced sustainable concrete mix design, and therefore, the CO₂ emissions of the sustainable concrete mix design are compared with those of the actual concrete mix design applied to the construction of the office building A in South Korea.

The notion of the three-phase (line) tension remains one of the most disputable notions in the surface science. A very broad range of its values has been reported. Experts even do not agree on the sign of the line tension. The polymer-chain-like model of the three-phase (triple) line enables the rough estimation of the entropic input into the value of the line tension, estimated as Γ_en≅(k_B T)/d_m ≅〖10〗^(-11) N, where d_m is the diameter of the liquid molecule. The introducing of the polymer-chain-like model of the triple line is justified by the “water string” model of the liquid state, predicting strong orientation effects for liquid molecules located in the vicinity of hydrophobic moieties. The estimated value of the entropic input into the line tension is close to experimental findings, reported by various groups.

Annually, the oak forests of the Zagros Mountain chains, in western Iran and northeastern Iraq, face recurring challenges posed by forest fires, particularly in the Kurdo-Zagrosian forests situated in western Iran and northeastern Iraq. Assessing fire susceptibility relies significantly on vegetation condition. Integrating in situ data, Remote Sensing (RS) data, alongside the integration of Geographical Information Systems (GIS), presents a cost-effective and precise approach to capturing environmental conditions before, during, and after fire events, minimizing the need for extensive fieldwork. This study refines and applies the Zagros Grass Index (ZGI), a local vegetation index tailored to discern between grass-covered surfaces from tree canopies in Zagros forests, identifying the grass masses as the most flammable fuel type. Utilizing Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) product as input, from 2013 to 2022, the ZGI aims to mitigate the influence of tree canopies, isolating NDVI values solely attributable to grass cover. By incorporating phenological characteristics of forest trees and grass species, the ZGI outperforms NDVI in mapping grass-covered areas that are crucial for fire susceptibility assessment in the study region. Results demonstrate a strong overlap between ZGI-based maps and recorded fire occurrences, validating the efficacy of the index in fire susceptibility estimation.

Background: Despite advances in the treatment of hepatocellular carcinoma (HCC) over the last few decades, treatment opportunities for patients with HCC remain limited. HCC is the most common form of liver cancer, accounting for approximately 90% of all cases worldwide. Moreover, apart from the current pharmacological interventions, hepatic resection and liver transplantation are the mainstay curative approaches for patients with HCC. Methods: This systematic review included phase I, II, III, and IV clinical trials (CTs) and randomized controlled trials (RCTs) on current treatments for patients with HCC in an Asian population (2013–2023). A total of 427 articles were screened, and 184 non-duplicate publications were identified. After screening titles and abstracts, 96 publications were excluded, and another 28 were excluded after full-text screening. The remaining 60 eligible RCTs/CTs were finally included. Results: Sixty clinical trials fulfilled our inclusion criteria and screened 36 drugs as monotherapy or combination therapy for HCC. Most studies used sorafenib alone or in combination with any of the treatment regimens. Lenvatinib or atezolizumab with bevacizumab was used for HCC after initial sorafenib treatment. Eighteen studies compared the efficacy of sorafenib with that of other drugs, including lenvatinib, cabozantinib, tepotinib, tigatuzumab, linifanib, erlotinib, resminostat, brivanib, tislelizumab, selumetinib, and refametinib. Conclusion: This study provides comprehensive insights into effective treatment interventions for HCC in Asian populations. The overall assessment indicated that sorafenib alone or in combination with atezolizumab and bevacizumab has been the first treatment choice in the past decade to achieve better outcomes in patients with HCC in an Asian population

The aim of this paper is to analyze and evaluate Renewable Energy Sources (RES) usage and their contribution to citizens’ life quality. For this purpose, a survey was conducted, using a sample of 400 residents in an urban area of Attica region in Greece. The methods of Principal Components Analysis and Logit Regression were used on a dataset containing respondents’ views on various aspects of RES. Two statistical models were constructed for the identification of the main variables that are associated with RES’ usage and respondents’ opinion on their contribution to life quality. The conclusions that can be drawn show that the respondents are adequately informed about some of the RES’ types while most of them use at least one of the examined types of RES. The benefits that RES offer, were the most crucial variable in determining both respondents’ perceptions on their usage and on their contribution to life quality.

At the start of 2020 the rapid onset of the coronavirus pandemic forced higher education institutions across the world to pivot from face to face to remote teaching. For teaching methods that involve the transmission and dissemination of verbal/visual information between academic staff and students, video technologies provided immediate methods to respond to the restricted access to campus. Practical activities, that usually involve interaction with equipment, presented a greater challenge to adapt for remote delivery. With restrictions on higher education being partially lifted, many institutions worldwide intend to offer blended learning, prioritizing in-person activities that are troublesome to deliver online, such as practicals. Social distancing measures are reducing capacity and placing increased pressure on space, creating a need to optimise limited time students have in the lab and strategies to determine which activities can best utilize this limited resource. Time is constrained, leaving little opportunity to make radical changes to learning and teaching structures. In this publication, The department of Mulicdipalnary Engineering Education (MEE) at the University of Sheffield, utilise their experiences in practical teaching to provide simple, implementable ideas for blended practicals which maximize students’ learning and experiences within the envelope of available resources.

How crucial is big data in contemporary molecular design? In this publication we elucidate fundamental concepts and terminology in this field, critically addressing overlooked issues. We thoroughly examine the size, accessibility, quality, and structural aspects of big data alongside the primary methodologies employed for their analysis. Within chemical compounds, properties and descriptors represent two distinct data types, forming the basis for categorizing molecular big data. The primary objective of chemistry is property production, which means we are searching for novel drugs or materials rather than chemical compounds, and big data is the central issue of this philosophy. The increasing availability of data in computer-aided technology propels advancements in artificial intelligence (AI), machine learning (ML), and deep learning (DL). Accordingly, a broad chemical audience must comprehend these methods to understand data-centered chemistry. Thus, we aim to systemize big data issues through a simple illustrative framework with fundamental descriptor categories: coding, computer-generated descriptors, and property correlates. Although we employ computer-generated descriptors as big data for predictions, the measured data are irreplaceable for achieving high-quality and reliable outcomes and controlling molecular effects. The scarcity of property data remains a significant hurdle limiting comprehensive studies on the structure-property relationships within big data. Accordingly, guided by pragmatics not-so-big data is an option for drug design. We presented also a brief review of the recent big data literature.

In this study, long-term mortality effects associated with exposure to PM10, PM2.5, BC (black carbon), and NOx were analyzed in a cohort in southern Sweden during the period from 1991‒2016. Participants (those residing in Malmö, Sweden, born between 1923‒1950) were randomly recruited from 1991‒1996. At enrollment, 30,438 participants underwent a health screening, which consisted of questionnaires about lifestyle and diet, a clinical examination, and blood sampling. Mortality data were retrieved from the Swedish national cause of death register. The modeled concentrations of PM10 (particles with an aerodynamic diameter smaller than or equal to 10 µm), PM2.5 (particles with an aerodynamic diameter smaller than or equal to 2.5 µm), BC (black carbon), and NOx (nitrogen oxides) at the cohort participants' home addresses were used to assess air pollution exposure. Cox proportional hazard models were used to estimate the associations between long-term exposure to PM10, PM2.5, BC, and NOx and the time until death among the participants during the period from 1991‒2016. The hazard ratios (HRs) associated with an interquartile range (IQR) increase in each air pollutant were calculated based on the exposure lag windows of the same year (lag0), 1‒5 years (lag1‒5), and 6‒10 years (lag6‒10). Three models were used with varying adjustments for possible confounders including both single-pollutant estimates and two-pollutant estimates. With adjustments for all covariates, the HRs for PM10, PM2.5, BC, and NOx in the single-pollutant models at lag1‒5 were 1.06 (95% CI: 1.02‒1.11), 1.01 (95% CI: 0.95‒1.08), 1.07 (95% CI: 1.04‒1.11), and 1.11 (95% CI: 1.07‒1.16) per IQR increase, respectively. The HRs were in most cases decreased by the inclusion of a larger number of covariates in the models. The most robust associations were shown for NOx, with statistically significant positive HRs in all models. An overall conclusion is that road traffic-related pollutants had a significant association with mortality in the cohort.

Spike-and-wave discharge (SWD) pattern detection in electroencephalography (EEG) signals is a key signal processing problem. It is particularly important for overcoming time-consuming, difficult, and error-prone manual analysis of long-term EEG recordings. This paper presents a new SWD method with a low computational complexity that can be easily trained with data from standard medical protocols. Precisely, EEG signals are divided into time segments for which the Morlet 1-D decomposition is applied. The generalized Gaussian distribution (GGD) statistical model is fitted to the resulting wavelet coefficients. A k-nearest neighbors (k-NN) self-supervised classifier is trained using the GGD parameters to detect the spike-and-wave pattern. Experiments were conducted using 106 spike-and-wave signals and 106 non-spike-and-wave signals for training and another 96 annotated EEG segments from six human subjects for testing. The proposed SWD classification methodology achieved 95 % sensitivity (True positive rate), 87% specificity (True Negative Rate), and 92% accuracy. These results set the path to new research to study causes underlying the so-called absence epilepsy in long-term EEG recordings.

Although suicide is a major public health issue worldwide, we understand little of the onset and development of suicidal behavior. Suicidal behavior is argued to be the end result of the complex interaction between psychological, social and biological factors. Epidemiological studies resulted in a range of risk factors for suicidal behavior, but we do not yet understand how their interaction increases the risk for suicidal behavior. A new approach called network analysis can help us better understand this process as it allows to visualize and quantify complex association between many different symptoms or risk factors. A network analysis of data contain information on suicidal patients can help us understand how risk factors interact and how their interaction is related to suicidal thoughts and behaviour. A network perspective has been successfully applied to the field of depression and psychosis, but not yet to the field of suicidology. In this theoretical article, I will introduce the concept of network analysis to the field of suicide prevention, and offer directions for future applications and studies.

Searching over a sorted list is a classical problem in computer science. Binary Search takes at most log2n+1 tries to find an item in a sorted list of size n. Interpolation Search achieves an average time complexity of Ο(loglogn) for uniformly distributed data. Hybrids of Binary Search and Interpolation Search are also available to handle data with unknown distributions. This paper analyzes the computation cost of these methods and shows that interpolation can significantly affect their performance — accordingly, a new method, Interpolation Once Binary Search (IOBS), is proposed. The experimental results show that IOBS outperforms the hybrids of Binary Search and Interpolation Search for nonuniformly distributed data.

In December 2019, an animal human coronavirus transmission occurred in Wuhan, China. A state of global pandemic was shortly declared, among a very rapid contagious spread of the virus. The causative virus was identified as SARS CoV 2 virus and is genetically related to the previous SARS outbreak in 2003. The virus causes wide clinical spectrum from mild flu like symptoms to adult respiratory distress syndrome. Kidney involvement has been reported in several reports in patients with various degrees of severity of SARS CoV2 infection. As knowledge is evolving, the accurate incidence of AKI is not known. Many questions are yet to be answered as regards the effect of epidemiological variables and comorbidities on the occurrence of AKI. Some reports have observed the occurrence of hematuria and proteinuria in a percentage of infected patients. Moreover, chronic kidney disease has not been found in some reports to add to the adverse outcomes, an aspect that merits further exploration. Patients on regular hemodialysis may be vulnerable to contagion due to lower status of immunity and need for frequent attendance to healthcare facilities. Due to the previous factors, prevention and mitigation of SARS CoV2 virus in this vulnerable population constitutes a major challenge.

Several Lung diseases can result in lung damage structural changes, with lung transplantation as a only therapeutic option when the disease reaches an advanced progression, unfortunately, the success of lung transplantation is limited. In this context, lung bioengineering is considered a potential therapeutic alternative and the current approach is based on the use of the natural extracellular matrix (ECM) of the decellularized lung. The use of undifferentiated cells to seed an ECM can be a more practical strategy for lung bioengineering but the sterilization of the organ for this recellularization process is still a challenge. In this sense, Low-Level Laser Therapy (LLLT) can emerge as a potential treatment alternative. Therefore, the objective of this study was to verify if LLLT, can optimize lung recelularization with mesenchymal stem cells by cytokine modulation. For this, we decellularized lungs from C57BL/6 mice perfusing SDS 1% via pulmonary artery, sterilized with LLL (660nm, 100mW, 30s). Whole lung scaffolds were recellularized: (1) Recellularized Lung (2) Recellularized Lung + LLLT. Mesenchymal stem cells derived from human tooth pulp (DPSc) were seeded into lungs and incubated for 24 hours at 37ºC, 5% CO₂, in a bioreactor-like conditions, maintaining CPAP at 20cmH2O, 90% O2, and LLL at wavelength of 808nm, 100mW, 30s. Culture medium were analyzed at 24 hours. H&E, immunostaining, SEM and ELISA assay were performed. We produced viable biological scaffolds absent of DNA content, preserving glycosaminoglycans and collagen I, III and IV, fibronectin, laminin and elastin and lung structure (SEM). The levels of IL-6 and IL-8 were stable during the 24 hours of culture. IFN-γ levels showed significant difference in the DPSc and DPSc+LLLT group in the 24-hour evaluation. The levels of IL-10 decreased after LLLT. These findings highlight LLLT's role in cytokine regulation and contamination prevention during lung recellularization with mesenchymal stem cells. Overall, these results demonstrated that LLLT's modulated cytokine levels and could prevent contamination during the lung recellularization process with mesenchymal stem cells, offering promise for enhanced therapeutic strategies in lung bioengineering.

Urbanization-led economic growth has created cities to drive infrastructure investments and population accumulation and exploit natural resources at an extreme phase. While coastal cities become vulnerable to climate change-induced extreme weather events and human-made disasters in recent history, effective measures to improve the resilience of cities are pivotal to developing sustainable living environments. This study proposes a framework to assess urban resilience to natural disasters (floods) using bottom-up spatial interactions among natural, physical, and social systems within cities and regions. It is noted that studies focus on either the vulnerability or coping capacity of urban communities to assess resilience, where effective strategies to manage disaster risk demand adaptation and mitigation measures at a spatial scale. Quantified resilience at small spatial units assists urban planning and disaster management agencies in the timely allocation of resources to optimize the recovery process. Moreover, spatial planning agencies can adopt resilience mapping to identify the potential growth zones while minimizing the natural disaster risks in urban growth management. Urban resilience can be embodied in spatial strategies with the operational framework proposed here, and future urban growth scenarios can be tested in multiple disaster conditions.

Climate change has altered the frequency and severity of extreme weather, which can affect vegetation condition and habitat quality for wildlife. Declines in vegetation productivity during droughts and heatwaves can negatively impact animals that depend on vegetation for water and nutrition. The ability to detect such effects on habitat suitability can reveal refugia, which can be prioritised for protection to improve threatened species conservation. We used the normalised difference vegetation index (NDVI) to look at relationships between vegetation productivity and the presence of koalas (Phascolarctos cinereus) in potential habitat throughout much of their range. Using a large, long-term koala presence dataset, we tested the hypothesis that locations where koalas had been observed would exhibit higher NDVI values than a random, representative sample from the same vegetation group (i.e. woodlands, open forests or tall open forests). We also identified the minimum NDVI threshold at which koalas occurred across time for each vegetation group and compared these to the minimum NDVI values observed across potential koala habitat before and during the millennium drought; one of the worst recorded in Australia. Additionally, we investigated whether vegetation above the minimum NDVI thresholds was significantly closer to perennial water than unsuitable vegetation. We found that koalas tend to occur at locations exhibiting higher NDVI than average for all vegetation groups. We also found that 49% of all vegetation groups maintained a minimum NDVI above the koalas’ NDVI threshold before drought, equating to 190,227 km2, which declined to 166,746 km2 during drought (a 12% reduction). We also found that unsuitable vegetation tended to occur farther from perennial water than suitable vegetation for some vegetation groups. Areas that remained above the NDVI thresholds during the drought should be considered potential refugia for populations during an event of similar magnitude and could indicate future core habitat extent.

Progress in macrophage research is crucial for numerous applications in medicine, including cancer and infectious diseases. However, the existing methods to manipulate living macrophages are labor intense and inconvenient. Here we show that macrophage membranes can be reconstituted after storage for months at 4C, with their CD206 receptor selectivity and specificity being similar to that in the living cells. Then, we have developed a mannose ligand, specific to CD206, linked with PEG as IR spectroscopy marker to detect binding with the macrophage receptor. PEG was selected due to its unique adsorption band of C-O-C group at IR spectra, which does not overlap with other biomolecule’s spectroscopic feature. Next, competitive binding assay versus the PEG-bound ligand, has enabled selection of other higher-affinity ligands specific to CD206. Further, those higher-affinity ligands were used to differentiate activated macrophages in patient’s bronchoalveolar (BAL) or nasopharyngeal (NPL) lavage. CD206- control cells (HEK293T) showed only non-specific binding. Therefore, biochips based on reconstituted macrophage membranes as well as PEG-trimannoside as an IR spectroscopic marker, can be used to develop new methods facilitating macrophage research and macrophage-focused drug discovery.

This communication explores the transition from Cognitive Behavioural Therapy (CBT) to a more holistic model, named Complex Therapy, developed by Tullio Scrimali for explaining and treating mental disorders. The Author delves into the limitations of traditional CBT and introduces an innovative framework that integrates multiple therapeutic modalities. While CBT is based on the study and modification of behaviour and cognition, Complex Therapy includes, in its focus, the theory of human relationships, the attachment theory, neuroscience, applied neuroscience, complex systems theory and ecology.Complex Therapy addresses the multifaceted nature of mental health issues by combining evidence-based techniques with personalized care strategies. The goal is to enhance patient outcomes through a tailored, comprehensive treatment plan that adapts to the evolving needs of individuals facing mental health challenges.Two innovative and original items which Scrimali has developed, which are a device for Applied Neuroscience MindLAB Set and a new botanical drug, NegEnt, are presented and discussed.The topic also describes, an original and integrated protocol, named Negative Entropy, the author has developed for treating a challenging disorder, such as schizophrenia, he renamed Entropy of Mind.

Biofilm formation is important in the free-living and symbiotic lifestyles of Sinorhizobium meliloti. Here we show that the S. meliloti NspS-MbaA system is homologous to the c-di-GMP modulating NspS-MbaA system described in Vibrio cholerae and modulates biofilm formation, exopolysaccharide (EPS) production and motility in S. meliloti in response to exogenous polyamines. Biofilm and EPS production in quorum sensing-deficient wild type strain 1021 decreased in cultures containing putrescine, 1,3-diaminopropane or spermine, but were unaffected in a 1021 nspS mutant (1021 nspS). Decreases in biofilm formation caused by these polyamines paralleled decreases in EPS production and increases in nspS transcription. Cell aggregation did not differ markedly between strain 1021 and 1021 nspS. The S. meliloti quorum sensing-proficient Rm8530 wild type strain and nspS mutant (Rm8530 nspS) produced similar levels of biofilm under control conditions and 3.2- and 2.2-fold more biofilm in cultures with norspermidine, but these changes did not correlate with EPS production. Rm8530 nspS aggregated up to several fold more than the wild type under most conditions. Changes caused by polyamines in swimming and swarming motility were not generally dependent on NspS-MbaA in either 1021 or Rm8530. We speculate that the S. meliloti NspS-MbaA system could modulate biofilm formation in response to environmental polyamines.

Partial Information Decompositions (PIDs) aim to categorize how a set of source variables provide information about a target variable redundantly, uniquely, or synergetically. The original proposal for such an analysis used a lattice-based approach and gained significant attention. However, finding a suitable underlying decomposition measure is still an open research question, even at an arbitrary number of discrete random variables. This work proposes a solution to this case with a non-negative PID that satisfies an inclusion-exclusion relation for any f-information measure. The decomposition is constructed from a pointwise perspective of the target variable to take advantage of the equivalence between the Blackwell and zonogon order in this setting. The zonogons correspond to the Neyman-Pearson region for an indicator variable of one target state and f-information is the expected value of quantifying its boundary. We prove that the decomposition satisfies the axioms of the original decomposition framework and guarantees non-negative partial information results. We highlight that our decomposition behaves differently depending on the used information measure, which can be utilized for different applications. We additionally show how our proposal can be used to obtain a non-negative decomposition of Rényi-information at a transformed inclusion-exclusion relation, and for tracing partial information flows through Markov chains.

In recent years, long periods of drought and heat waves have become increasingly frequent, causing forest dieback phenomena that make stands more sensitive to biotic stressors. How trees may respond to extreme climatic events and which metabolites are involved under stress conditions is still not clear. In this study, we analysed by SPME how dieback (D) and non-dieback (ND) Hungarian oak trees from the San Paolo Albanese site respond to these climatic dynamics, in terms of volatile organic compounds (VOCs). For each group of trees, three wood samples were taken, each divided into four sub-samples with 5 growth rings, subjected to SPME and increase in basal area (BAI) analysis of the last 20 years. Dieback trees had a lower amount of leaves, and this condition may translate into less photosynthesis, less organic matter production and lower reserves of carbohydrates available for growth. Indeed, D trees showed lower radial increases and a lower content of aldehydes, terpenes, and fatty acids than ND trees, indicating a better health of ND trees compared to D trees. While D trees showed a reduction in terpenes, such as α-pinene, γ-eudesmol, and cyperene (with significant insecticidal activity), a reduction in aromatic aldehydes, such as furfural and 5-methylfurfural, and an increase in silanols (with antimicrobial function). Considering the different compounds’ contents between D and ND trees, our study could be useful for detecting bio-indicators to identify an early warning signal of dieback phenomena.

Extracellular vesicles represent a large heterogeneous class of near and long-distance intercellular communication mediators, released by both prokaryotic and eukaryotic cells. Specifically, the scientific community has shown a growing interest in exosomes, which are nano-sized vesicles with an endosomal origin. Not so long ago, the physiological goal of exosome generation was largely unknown and required more investigation; at first, it was hypothesized that exosomes are able to remove excess, rejects and unnecessary constituents from cells to preserve cellular homeostasis. However, thanks to recent studies, the central role of exosomes in regulating cellular dialogue has emerged. Exosomes act as vectors on cell-cell signaling by their cargo, proteins, lipids, and nucleic acids, and influence physiological and pathological processes. The findings on exosomes are widespread in a large spectrum of biomedical applications from diagnosis and prognosis to thera-pies. In this review, we describe exosome biogenesis and the current methods for their isolation and characterization, emphasizing the role of their cargo in female reproductive processes, from gametogenesis to implantation, and the potential involvement in human female disorders.

Identifying anti-spike antibodies that exhibit strong neutralizing activity against current dominant circulating variants and antibodies that are escaped by these variants have important implications in the development of therapeutic and diagnostic solutions as well as in improving understanding of the humoral response to SARS-CoV-2 infection. We characterized seven anti-RBD monoclonal antibodies for their binding activity, pairing capability and neutralization activity to SARS-CoV-2 and three variant RBDs (UK, SA and BR P.1) via lateral flow immunoassays. The results allowed us to group these antibodies into three distinct epitope bins. Our studies showed that two antibodies had broadly potent neutralizing activity against SARS-CoV-2 and these variant RBDs and that one antibody did not neutralize the SA and BR P.1 RBDs. The antibody escaped by the SA and BR P.1 RBDs retained binding activity to SA and BR P.1 RBDs but was unable to induce neutralization. Further, we demonstrated that the lateral flow immunoassay can be a rapid and effective tool for antibody characterization, including epitope classification and antibody neutralization kinetics. From these studies, the potential contributions of the mutations (N501Y, E484K and K417N/T) contained in these variants’ RBDs on antibody pairing capability, neutralization activity and therapeutic antibody targeting strategy are discussed.

Currently there is little doubt left on the symbiogenetic nature of eukaryotes - genomes of archaeon and bacterium participated in shaping a genome of last eukaryotic common ancestor in equal albeit asymmetric manner, while a merger event itself indicated the advent of a new domain of life. The “symbiogenetic” framework of interaction of two partners is proposed, outlining similar steps essential for three major advents: the origin of life, the origin of complex life and the origin of humans. Given the immense importance of proper energy source for the evolution of life it seems plausible that for any principal increase in complexity, a partnership with a novel energy donor is required. Moreover, a “language” elaborated in the course of communication of partners might have been a prerequisite for a subsequent increase in complexity. Transitions, which led to RNA-protein world, eukaryotes and human brain, resulted in advent of complex languages via communication onsets between two entities in close partnership. Accordingly this further facilitated formation of first cells, multicellular organisms and human society.

The efficacy of predicting geochemical parameters with a 2-chain workflow using spectral data as the initial input is evaluated. Spectral measurements spanning the approximate 400-25000nm spectral range are used to train a workflow consisting of a non-negative matrix function (NMF) step, for data reduction, and a random forest regression (RFR) to predict 8 geochemical parameters. Approximately 175000 spectra with their corresponding chemical analysis were available for training, testing and validation purposes. The samples and their spectral and chemical parameters represent 9399 drillcore. Of those, approximately 20000 spectra and their accompanying analysis were used for training and 5000 for model validation. The remaining pairwise data (150000 samples) were used for testing of the method. The data are distributed over 2 large spatial extents (980 km2 and 3025 km2 respectively) and allowed the proposed method to be tested against samples that are spatially distant from the initial training points. Global R2 scores and wt.% RMSE on the 150000 validation samples are Fe(0.95/3.01), SiO2(0.96/3.77), Al2O3(0.92/1.27), TiO(0.68/0.13), CaO(0.89/0.41), MgO(0.87/0.35), K2O(0.65/0.21) and LOI(0.90/1.14), given as Parameter(R2/RMSE), and demonstrate that the proposed method is capable of predicting the 8 parameters and is stable enough, in the environment tested, to extend beyond the training sets initial spatial location.

This study addresses the implementation of the International Financial Reporting Standard 9 (IFRS 9) in the European Union as of January 1, 2018, replacing the International Accounting Standard 39 (IAS 39) to introduce a new model for recognizing Loan Loss Provisions (LLP) based on Expected Credit Loss (ECL). This model responds to criticisms of the former Incurred Credit Loss (ICL) system for its inability to reflect credit losses in a timely manner, potentially exacerbating the effects of financial crises (Bushman & Williams, 2015; Gebhardt & Novotny-Farkas, 2011). The study focuses on the effects of adopting the ECL model on the level of Loan Loss Allowances (LLA) in loans, own equity, and the Common Equity Tier 1 (CET1) ratio across 13 Portuguese commercial banks. A mean comparison test is used to evaluate scenarios before and after the application of the ECL model, highlighting the importance of regulator actions and the adequacy of loss recognition policies, including the effects of Regulation (EU) 2017/2395 of December 12. The results obtained demonstrate significant negative impacts on the net values of loans, own equity, and the CET1 ratio upon adopting the IFRS 9 ECL model, due to the widespread increase in LLAs.

The analysis and simulation of water quality in distribution networks is a complex issue of great concern today. The analysis of the evolution of water age, as a simple indicator of water quality in the network, is of great interest in both design and operation phases. Understanding the factors that have the strongest influence on water quality is key to developing adequate strategies aimed at preserving it. This paper firstly analyses the factors with biggest influence in the tanks water age, to support the selection of the most appropriate configuration from the point of view of water quality during design phases. Then, the main considerations when modelling tanks following the different mixing models considered in Epanet have been presented. Also, a real tank behaviour has been characterised through field measurements and Epanet simulations in order to determine the best fitted mixing model.

Omsk hemorrhagic fever virus (OHFV) is a member of the tick-borne encephalitis virus (TBEV) complex of the Flaviviridae family. Currently, there is no data on cross-reactivity of antibodies to the NS1 proteins of OHFV and TBEV. Such data are of major interest for monitoring viral encephalitis of unknown etiology due to the increasing geographical distribution of OHFV. In this study, recombinant OHFV NS1 protein was produced using Escherichia сoli expression system and purified. The recombinant OHFV NS1 protein was recognized by specific mice immune ascetic fluids to the native OHFV NS1 protein. Western blot analysis and ELISA of recombinant NS1 proteins of OHFV and TBEV were used to study the cross-reactivity of antibodies from immune ascites fluid obtained from OHFV-infected mice and mAbs against TBEV NS1. Anti-TBEV NS1 mouse monoclonal antibodies (mAbs) have been shown not to be cross-reactive to OHFV NS1 protein. Sera from patients with confirmed tick-borne encephalitis (TBE) were examined by ELISA using recombinant OHFV NS1 and TBEV NS1 proteins as antigens. It was shown for the first time that cross-reactive antibodies to OHFV NS1 protein were not detected in the sera of TBE patients whereas the sera contained antibodies to TBEV NS1 protein.

The constant news about the corona virus is scary. It is not possible to separate treatment for Cancer due to COVID-19. An effective treatment comparison strategy is needed. We need to have a handy tool to understand cancer progression in this unprecedented scenario. Linking different events of cancer progression is the need of the hour. It is a methodological challenge. We provide the solutions to overcome the issue with interval between two consecutive events in motivating head and neck cancer (HNC) data.

Magnetic nanoparticles (MNPs) have attracted a great interest in nanomedical research. MNPs exhibit many important properties, particularly, magnetic hyperthermia for selective killing of cancer cells is one of them. In hyperthermia treatment, MNPs act as nano-heaters when they are under the influence of an alternating magnetic field (AMF). In this work, micromagnetic simulations have been used to investigate the magnetization dynamics of a single-domain nanoparticle of magnetite in an external AMF. Special attention is paid to the circumstances dealing with a dynamic phase transition (DPT). Besides, we focus on the influence of the orientation of the magnetic easy-axis of the MNP on the dynamic magnetic properties. For amplitudes of the external AMF above certain critical value, the system is not able to follow the magnetic field and it is found in a dynamically ordered phase; whereas for larger amplitudes, the state corresponds to a dynamically disordered phase and the magnetization follows the external AMF. Our results suggest that the way how the order-disorder DPT takes place, and both the metastable lifetime as well as the specific loss power (SLP), are strongly dependent on the interplay between the orientation of the magnetic easy-axis and the amplitude of the external AMF.

The rapid growth in scientific article publications allows us to access articles as soon as possible. Therefore, automatic summarization systems (ATSs) are widely preferred. In most studies, the en-tire source document is expected to be summarized, just as it would be summarized by a human. Summarizing long articles, such as scientific articles, is quite difficult due to token restraint and extraction of scientific words. To address this problem, a novel Graph-Based Abstractive Summa-rization (GBAS) model is proposed, which is a novel scientific text summarization model based on SciBERT and the graph transformer network (GTN). The document's integrity is maintained since the SciIE system uses the graph structure to create a terminology-based document structure. Therefore, long documents are also summarized. The proposed model is compared with baseline models and human evaluation. Human evaluation results show that the results of the proposed model are informative, fluent, and consistent with the ground-truth summary. The experimental results indicate that the proposed model outperforms baseline models with a 37.10 and 34.96 ROUGE-L score.

Chemoinformatics is the combination of physical chemistry theory with computer and information science techniques "in silico techniques" to a variety of descriptive and prescriptive chemistry issues, including applications to biology, drug discovery, and related molecular areas. On the other hand, machine learning has become a vital tool for drug designers to mine chemical information from enormous compound databases in order to build medications with important biological features. In Egypt, chemoinformatics were used recently in different applications. For example; the development of new antimicrobial agents like tetracycline analog B (Iodocycline). Another area of application in bioinformatics included heart disease classification based on hybrid ensemble stacking technique. In the era of COVID-19, hybrid approach for COVID-19 detection from chest radiography was applied. Furthermore, bio/chemoinformatics methods were used to compare antimicrobial medications in order to choose an effective nose-to-brain formulation targeting meningitis infection by using differences in the drugs' primary structural, topological, and electronic characteristics. An example for this included cefotaxime and ceftriaxone that were examined at three levels: at formulation level, by comparing the loading in gelatin and tripalmitin matrices as basis for the production of nanoparticulate systems, at biopharmaceutical level, through interaction with mucin and the P-gp efflux pumps, and at therapeutic level, through studying the interaction with S. pneumoniae bacterial receptors.

The newly emerging nosocomial pathogen Candida auris is linked with persistent hospital-acquired infections and abrupt outbreaks across six continents. Genotypic analysis is indicative of the appearance of independent distinct clades of this particular fungus in different geographic locations simultaneously. Intrusive deep seated infections in addition to colonization have been diagnosed primarily in hospitalized patients and have drawn a lot of attention because of different antifungal susceptibility profiles and transmission despite strict preventive measures. Problems with the accurate identification of C. auris using phenotypic and molecular approaches has raised concerns about the detection of relevant levels of the problem. Candida family associated infections are a serious causative agent of mortality and morbidity in immune-compromised individuals. Candida auris are also known as superbug fungus that spreads rapidly all over the world. In 2009, shortly after the first case, various strains across the six continents have been recognized as nosocomial pathogens. Simultaneous and independent C. auris outbreaks appear to be of great concern for the healthcare settings as well as scientific community. Additionally, microbiological misidentification and multidrug resistance, rarely noticed for other non-albicans Candida species, lead to problems in obliteration and frequent treatment failures for C. auris infections. This review article aims to provide a comprehensive and up to date report on the global C. auris outbreaks, considering clinical along with microbiological characteristics, virulence mechanisms and susceptibility to antifungals, as well as the effectiveness of available preventive and therapeutic implementations.

Epithelial ovarian cancer (EOC) has not significantly benefited from advances in immunotherapy, mainly because of the lack of well-defined actionable antigen targets. Using proteogenomic analyses of primary EOC tumors, we previously identified 91 aberrantly expressed tumor-specific antigens (TSAs) originating from unmutated genomic sequences. Most of these TSAs derive from non-exonic regions, and their expression results from cancer-specific epigenetic changes. The present study aimed to evaluate the immunogenicity of 48 TSAs selected according to two criteria: presentation by highly prevalent HLA allotypes and expression in a significant fraction of EOC tumors. Using targeted mass spectrometry analyses, we found that pulsing with synthetic TSA peptides leads to a high-level presentation on dendritic cells. TSA abundance correlated with the predicted binding affinity to the HLA allotype. We stimulated naïve CD8 T cells from healthy blood donors with TSA-pulsed dendritic cells and assessed their expansion with two assays: MHC-peptide tetramer staining and TCR Vβ CDR3 sequencing. We report that these TSAs can expand sizeable populations of CD8 T cells and, therefore, represent attractive targets for EOC immunotherapy.

Regardless of the SARS-CoV-2 pandemic containment, it remains paramount to comprehensively understand its underlying mechanisms to mitigate potential future health and economic impacts, comparable to those experienced throughout the course of the pandemic. The angiotensin-converting enzyme 2 (ACE2) provide anchorage for SARS-CoV-2 binding, thus implicating that ACE and ACE2 might contribute to the variability in infection severity. This study aimed to elucidate predisposing factors influencing the disease course among people infected by SARS-CoV-2, focusing on angiotensin-converting enzyme (ACE) and ACE2 polymorphisms. Notably, despite similar demographics and comorbidities, COVID-19 patients exhibit substantial differences in prognosis. Genetic polymorphisms in ACE and ACE2 have been implicated in disease progression, prompting our investigation into their role in COVID-19 evolution. Using next-generation sequencing (NGS), we analyzed ACE and ACE2 genes in a sample group comprising six subjects infected by SARS-CoV-2. Our findings revealed a correlation between specific polymorphisms and COVID-19 outcomes. Specifically, ACE and ACE2 intronic deletions were observed in all deceased patients, suggesting a potential association with mortality. These results highlight the significance of genetic factors in shaping the clinical course of COVID-19, emphasizing the importance of further research into the impact of genetic variations on COVID-19 severity.

This article describes the history of research and development of surface-penetrating radar, starting from the 60s in the USSR, and ending today in Russia and neighboring states formed after the collapse of the USSR. The outstanding role played in the initial period of this technology development by Prof. M.I. Finkelstein and his colleagues is highlighted. Their research had a significant impact on subsequent developments in modern Russia and the neighboring countries.

This study aimed to ascertain the applicability of a machine learning approach to the description of residential mobility patterns of households in the Seoul metropolitan region (SMR). The spatial range and temporal scope of the empirical study were set to 2015 to review the most recent residential mobility patterns in the SMR. The analysis data used in this study involve the microdata of Internal Migration Statistics provided by the Microdata Integrated Service of Statistics Korea. We analysed the residential relocation distance of households in the SMR by using machine learning techniques such as ordinary least squares regression and decision tree regression. The results of this study showed that a decision tree model can be more advantageous than ordinary least squares regression in terms of the explanatory power and estimation of moving distance. A large number of residential movements are mainly related to the accessibility to employment markets and some household characteristics. The shortest movements occur when households with two or more members move into densely populated districts. In contrast, job-based residential movements have relatively longer distance. Furthermore, we derived knowledge on residential relocation distance, which can provide significant information on the urban management of metropolitan residential districts and the construction of reasonable housing policies.

The energy efficiency of grinding depends on the appropriate selection of cutting conditions, grinding wheel and workpiece material. Additionally, the estimation of specific energy consumption is a good indicator to control the energy consumed during the grinding process. Consequently, this study develops a model of material removal rate to estimate the specific energy consumption based on the measurement of active power consumed in a plane surface grinding of C45K with different thermal treatments and AISI 304. This model identifies and evaluates the power dissipated by sliding, ploughing and chip formation in a industrial-scale grinding process. Furthermore, the instantaneous positions of the abrasive grains during cutting are described to study the material removal rate. The estimation of specific chip formation energy is similar to that described by other authors in laboratory scale, which allows to validate the model and experiments. Finally, the results show that the energy consumed by sliding is the main phenomenon of energy dissipation in industrial-scale grinding process, where it is denoted that sliding energy by volume unity decreases as the depth of cut and speed of workpiece increase.

A superior variant of dried jujube in China is ‘Huizao’ (HZ) jujube. Nonetheless, detailed evaluations of the texture quality of HZ fruits have been the subject of few studies. Texture is a significant indicator of the sensory and processed quality of a fruit. Here, we differentiate and characterize the texture quality of HZ fruits from the four primary producing regions in southern Xinjiang, as well as develop a system for assessing the texture quality of HZ fruits. Furthermore, five texture indices of HZ fruits from 109 plantations were exhaustively evaluated utilizing principal component analysis, Pearson correlation analysis, and hierarchical clustering analysis. Consequently, by the 10th, 30th, 70th, and 90th percentiles, each index was classified into five grades—lower, medium, high, and higher. Correlation investigation indicated strong relationships between the springiness, chewiness, cohesiveness, gumminess, and hardness of HZ fruits. Springiness, hardness, and adhesiveness are essential indicators that influence the texture of HZ fruits. We used clustering analysis to separate the six texture indices into three major categories (hard factor, stick-elastic factor, and adhesive factor), with indices representing hardness, springiness, and adhesiveness, respectively. Furthermore, according to the factor molecules, the texture quality of HZ fruits in the four production areas was evaluated as Bazhou (1.24) > Hotan (0.773) > Kashi (-0.577) > Aksu (-0.852). RDA analysis of six texture quality parameters and 24 climate conditions identified higher mean temperature (TEM) and lower relative humidity (RHU) as the primary factors contributing to the improved texture quality of HZ fruits in Xinjiang. This study identified the texture quality characteristics of HZ fruits in four major producing areas of Southern Xinjiang, and it offered a theoretical foundation for optimizing the dominant producing areas and regional production of HZ varieties in Xinjiang.

This paper investigates the theoretical foundation for developing renewable geothermal resources locally. For this reason, we pay attention to the role of communities in geothermal development. This study presents arguments for a framework that approaches the geothermal resources as an endogenous factor of community development. To analyse it, we create a model that explains the local economic characters of the geothermal exploitation beyond its geological conditions. It aims to conceptualise a community based geothermal development standard referring to the endogeneity principle. Geothermal energy is given attention since the characteristics of this resource determine its use locally. We argue that the role of communities in geothermal exploitation is pivotal in the process of green growth for further expansion of geothermal energy use.

It is unclear whether delayed access to treatment/health assessment impacted psychological distress for these populations. This study aimed to fill this literature gap by using a cross-sectional survey, which aimed to evaluate the impact of COVID-19 pandemic on Chinese immigrants in North America during the second wave of the pandemic. The study focused on Chinese immigrants aged 16 or older in Canada. Covariates included sociodemographic variables, delayed access to treatment/health assessment, and other COVID-19 related variables. We used logistic LASSO regression for model selection and multivariate logistic regression models to evaluate the association between delayed access to treatment/health assessment and psychological distress outcome. Missing data were handled using multiple imputation. Our study included 746 respondents, with 47.18% normal CPDI group and 36.82% mild to severe CPDI group. Most respondents were originally from Mainland China and residing in Ontario. Over half have stayed in Canada for at least 15 years. The multivariate logistic regression models unveiled a significantly positive association between psychological distress and delayed access to treatment/health assessment (OR=1.362, 95% CI: 1.078–1.720, p= 0.0095), fear of COVID-19 (OR=1.604, 95% CI: 1.293–1.989, p<.0001), social loneliness (OR=1.408, 95%CI: 1.314–1.508, p<.0001). Sociodemographic variables and other COVID-19 related-variates did not significantly impact the study’s outcome. Reliable health information, mental health supports, and virtual care tailored to immigrants should be considered to mitigate this impact and optimize overall health and well-being.

Kynurenic acid (KYNA), a tryptophan metabolite, is believed to exert neuromodulatory and neuroprotective effects in the brain. The study aimed to examine the KYNA’s capacity to modify gene expression and the activity of cellular antioxidant enzymes in specific structures of the sheep brain. Anestrous sheep were infused intracerebroventricularly with two KYNA doses: lower (4×5 μg/60 μL/30 min, KYNA20) and higher (4×25 μg/60 μL/30 min, KYNA100) at 30min intervals. The abundance of superoxide dismutase (SOD2), catalase (CAT) and glutathione peroxidase (GPx1) mRNA, as well as enzyme activities (µM/µg of protein), were determined in the hypothalamic medial-basal (MBH) and preoptic (POA) areas, and in the hippocampal CA1 field. Both doses of KYNA caused a decrease (P&lt;0.01) in the expression of SOD2 and CAT mRNA in all structures examined compared to the control group (except for CAT in the POA at the KYNA100 dose). Furthermore, lower levels of SOD2 mRNA (P&lt;0.05) and CAT mRNA (P&lt;0.01) were found in the MBH and POA and in the POA and CA, respectively, in sheep administered with the KYNA20 dose. Different stimulatory effects on GPx1 mRNA expression were observed for both doses (P&lt;0.05-P&lt;0.01). KYNA exerted stimulatory, but dose-dependent effects on SOD2, CAT and GPx1 activities (P&lt;0.05-P&lt;0.01) in all brain tissues examined. The results indicate that KYNA may influence the level of oxidative stress in individual brain structures in sheep by modulating the expression of genes and activity of at least SOD2, CAT and GPx1. The present findings also expand the general knowledge about the potential neuroprotective properties of KYNA in the central nervous system.

Volatility estimation and quantile regression are relevant active research areas in statistics, machine learning and econometrics. In this work, we propose two procedures to estimate local variances in generic regression problems by using of kernel smoothers. The proposed schemes can be applied in multidimesional scenarios (not just for time series analysis) and easily in a multi-output framework, as well. Moreover, they allow the possibility of providing uncertainty estimation using a generic kernel smoother technique. Several numerical experiments show the benefits of the proposed methods, even comparing with benchmark techniques. One of these experiment involves a real dataset analysis.

Abstract: Patient blood samples are invaluable in clinical omics databases, yet current methodologies often fail to fully uncover the molecular mechanisms driving patient pathology. While Genome-scale Metabolic Models (GEMs) show promise in systems medicine by integrating various omics data, having only exometabolomic data remains a limiting factor. To address this gap, we introduce a comprehensive pipeline integrating GEMs with patient plasma metabolome. This pipeline constructs case-specific GEMs using literature-based and patient-specific metabolomic data. Novel computational methods, including adaptive sampling and an in-house developed algorithm for the rational exploration of the sampled space of solutions, enhance integration accuracy while improving computational performance. Model characterization involves task analysis in combination with clustering methods to identify critical cellular functions. The new pipeline was applied to a cohort of trauma patients to investigates shock-induced endotheliopathy using patient plasma metabolome data. By analyzing endothelial cell metabolism comprehensively, the pipeline identified critical substrates and contributed to the development of targeted therapeutic strategies. Our study demonstrates the efficacy of integrating patient plasma metabolome data into computational models to analyze endothelial cell metabolism in disease contexts. This approach offers a deeper understanding of metabolic dysregulations and provides insights into diseases with metabolic components and potential treatments.

The paper presents the prediction of hydrodynamic characteristics of different rudder -plane configurations behind a submarine in deep water through the Reynolds-Averaged Navier-Stokes method in Ansys Fluent Solver. The hydrodynamic force and moment acting on the Cross-plane, X-plane, and Y-plane rudder configurations are analyzed for estimating hydrodynamic coefficientsfor predicting the advantages of each configuration to control the ship. The obtained results show that X-force of the X-plane rudder configuration is larger than the force acting on the Cross-plane rudder and Y-plane rudder configurations. However, Y-force, and Z-force of the X-plane rudder configuration are significantly greater than the Y-force of the left configurations. The same tendency can be seen in the moment of the X-plane rudder about the y-axis and z-axis. The roll moment induced by the Y-plane and Cross-plane rudder configurations is significantly larger than the case of the X-plane rudder configuration.

The mechanisms underlying the establishment of asymmetric structures during development remain elusive. The wing of Drosophila is asymmetric along the Anterior-Posterior (AP) axis, despite that its growth signals that drive its growth are presumably symmetric. Here, we investigated the contribution of cell recruitment, a process that drives wing fate differentiation in Drosophila, to the asymmetric shape and pattern of the adult wing. Genetic impairment of cell recruitment results in a significant reduction in adult wing asymmetry, primarily affecting the region between longitudinal vein 5 and the wing margin (L5-M). Morphometric analysis reveals that the lack of cell recruitment results in a more symmetric wing with respect to the AP, suggesting a contribution of cell recruitment to the establishment of asymmetry in the adult wing. A close examination of the expression of the wing selector gene vestigial (vg) in wing disc does not support this hypothesis. However, the circularity of the Vg pattern significantly increase in recruitment-impaired wing disc, suggesting that while the decrease of the L5-M region in the adult wing may not be attributed to cell recruitment, it could contribute to the overall asymmetry of the adult wing. We conclude that cell recruitment, a widespread mechanism that participates in growth and patterning of several developing systems, could drive, at least partially, the asymmetric shape of the Drosophila wing.

In the past few years, cloud-based additive manufacturing has shown its application and potential in industries. For this reason, when the demand for cloud-based additive products increases, efficient scheduling of related tasks becomes more important to achieve optimized duration. This research proposes a Heuristic Scheduling Algorithm (HSA) for use in Cloud-Based Additive Manufacturing (CBAM) with an approach to Critical Chain Buffer Management (CC/BM). This research aims to schedule and optimize the time in cloud-based additive manufacturing, in addition to the parts production process, it also includes the steps of receiving online orders and informing the manufacturing unit and sending them to the customer. The HSA proposed in this research consists of three phases 1- Buffer sizing, 2- Additive manufacturing and logistics scheduling, and 3- Buffer controlling. To evaluate, the performance of the algorithm is simulated. The finding shows that the proposed HAS can effectively solve the CBAM scheduling problem.

Single-cell RNA sequencing (scRNA-seq) technology has significantly advanced our understanding of the diversity of cells and how this diversity is implicated in diseases. Yet, translating these findings across various scRNA-seq datasets poses challenges due to technical variability and dataset-specific biases. To overcome this, we present a novel approach that employs both an LLM-based framework and explainable machine learning to facilitate generalization across single-cell datasets and identify gene signatures to capture disease-driven transcriptional changes. Our approach uses scBERT, which harnesses shared transcriptomic features among cell types to establish consistent cell-type annotations across multiple scRNA-seq datasets. Additionally, we employ a symbolic regression algorithm to pinpoint highly relevant yet minimally redundant models and features for inferring a cell type’s disease state based on its transcriptomic profile. We ascertain the versatility of these cell-specific gene signatures across datasets, showcasing their resilience as molecular markers to pinpoint and characterize disease-associated cell types. Validation is carried out using four publicly available scRNA-seq datasets from both healthy individuals and those suffering from ulcerative colitis (UC). This demonstrates our approach’s efficacy in bridging disparities specific to different datasets, fostering comparative analyses. Notably, the simplicity and symbolic nature of the retrieved gene signatures facilitate their interpretability, allowing us to elucidate underlying molecular disease mechanisms using these models.

Hyaluronic acid, in the form of a gel or viscoelastic colloidal solution, is currently used for viscosupplementation of joints affected by osteoarthritis, but its effectiveness is under debate in relation to newer alternatives. Based on meta-analytical arguments, the present article reinforces the opinion that there are still no decisive arguments for its complete replacement, but for its use adapted to the peculiarities of the disease manifestation and of the patients. A “broad” comparison is first made with almost all alternatives studied in the last decade, and then a meta-regression study is performed to compare and predict the effect size induced by viscosupplementation and platelet-rich plasma therapies. If they are computerized, the developed models can represent tools for clinicians in determining the appropriateness of the option or not for viscosupplementation, in a manner adapted to the pain felt by the patients, to their age, or to other clinical circumstances. The models were generated using the latest algorithms for meta-analysis and meta-regression implemented in the R language and assembled in different R packages. All primary data and necessary R scripts are provided in accordance with the philosophy of reproducible research. Finally, HA-based products, currently under circumspection, are still clinically useful.

Within the genus Macrolophus (Heteroptera: Miridae), the species M. costalis (Fieber), M. melanotoma (Costa) and M. pygmaeus (Rambur) are present in the Mediterranean region on a wide variety of plant species. While M. costalis can easily be separated from the other two by the black tip at the scutellum, M. pygmaeus and M. melanotoma are cryptic species, extremely similar to one another in external traits, which have resulted in misidentifications. M. pygmaeus is an efficient biological control agent, both in greenhouse and field crops. The misidentification of these cryptic species could limit the effectiveness of biological control programs. Although morphology of the left paramere of the male genitalia has been used as a character for identification of these two cryptic species, there is controversy on the reliability of this character as a taxonomic tool for these species. Using geometric morphometric techniques, which are a powerful approach in detecting slight shape variations, the left parameres from these three Macrolophus species were compared. The paramere of M. costalis was larger and had a different shape than that of M. melanotoma and M. pygmaeus; however, no differences in size or shape were found between the left paramere of M. melanotoma and that of M. pygmaeus. Therefore, our results confirm that this character is too similar and it cannot be used to discriminate between these two cryptic species.

Deep learning (DL) algorithms used for DOTATATE PET lesion detection typically require large, well-annotated training datasets. These are difficult to obtain due to low incidence of gastroenteopancreatic neuroendocrine tumors (GEP-NETs), and the high cost of manual annotation. Furthermore, networks trained and tested with data acquired from site specific PET/CT instrumentation, acquisition and processing protocols have reduced performance when tested with offsite data. This lack of generalizability requires even larger, more diverse training datasets. The objective of this study is to investigate the feasibility of improving DL algorithm performance by better matching the background noise in training datasets to higher noise, out-of-domain testing datasets. 68Ga-DOTATATE PET/CT datasets were obtained from two scanners: Scanner1, a state-of-the-art digital PET/CT (GE DMI PET/CT; n=83 subjects), and Scanner2, an older-generation analog PET/CT (GE STE; n=123 subjects). Set1, the data set from Scanner1 was reconstructed with standard clinical parameters (5 minutes; Q.Clear) and list-mode reconstructions (VPFXS 2, 3, 4, and 5-minutes). Set2, data from Scanner2, representing out-of-domain clinical scans, used standard iterative reconstruction (5 minutes; OSEM). Reconstructed datasets were divided into training, validation, and testing datasets in a 60%, 20%, 20% proportion, and a deep neural network was trained with each: Network1 for Scanner1 and Network2 for Scanner2. DL performance (Network1) was tested with out-of-domain test data (Set2). To evaluate the effect of training sample size, we tested DL model performance using a fraction (25%, 50%, and 75%) of Set1 for training. Scanner1, list-mode 2-minute reconstructed data demonstrated the most similar noise level compared that of Set2, resulting in the best performance (F1=0.713). This was not significantly different compared to the highest performance, upper-bound limit using in-domain training for Network2 (F1=0.755; p-value=0.103). Regarding sample size, the F1 score significantly increased from 25% training data (F1=0.478) to 100% training data (F1=0.713; p&lt;0.001). List-mode data from modern PET scanners can be reconstructed to better match the noise properties of older scanners. Using existing data and their associated annotations dramatically reduces the cost and effort in generating these datasets, and significantly improves the performance of existing DL algorithms. List-mode reconstructions can provide an efficient, low-cost method to improve DL algorithm generalizability.

The experiences gathered during the past 30 years support the operational use of irrigation scheduling based on frequent multi-spectral image data. Currently, the operational use of dense time series of multispectral imagery at high spatial resolution makes monitoring of crop biophysical parameters feasible, capturing crop water use across the growing season, with suitable temporal and spatial resolutions. These achievements, and the availability of accurate forecasting of meteorological data, allow for precise predictions of crop water requirements with unprecedented spatial resolution. This information is greatly appreciated by the end users, i.e. professional farmers or decision-makers, and can be provided in an easy-to-use manner and in near-real-time by using the improvements achieved in web-GIS methodologies. This paper reviews the most operational and explored methods based on optical remote sensing for the assessment of crop water requirements, identifying strengths and weaknesses and proposing alternatives to advance towards full operational application of this methodology. In addition, we provide a general overview of the tools which facilitates co-creation and collaboration with stakeholders, paying special attention to these approaches based on web-GIS tools.

Glioblastoma is considered the most aggressive primary brain tumor. Recurrence after treatment is a significant problem with a failed response to optimal therapies. The recurrence of GBM is linked to different cellular pathways and molecular pathways. Not only genetics are involved in gliomagenesis, but also epigenetics. Epigenetic mechanisms are highly involved in the pathogenesis of GBM. Histone modulation through acetylation, phosphorylation, ubiquitination, and methylation can regulate gene expression and may play a role in the pathogenesis of GBM. Preclinical and clinical studies currently target epigenetic enzymes in gliomas, including a new generation of histone deacetylase (HDAC) inhibitors. Herein, I tried to highlight the current research in glioma epigenetics, focusing on the culprit of histone modifications and the use of HDAC target therapies as a possible treatment line for glioblastoma.

Neosporosis is an infection caused by the protozoan parasite Neospora caninum and has substantial veterinary hazards. Neosporosis cannot be controlled via vaccination or a chemotherapeutic agent. Thus, accurate diagnosis followed by isolation and culling of infected animals is regarded as the most efficient way of control. In vivo diagnosis often relies on serologic testing of the animals, and milk represents a non-invasive and easy to collect sample matrix. However, ELISAs specifically designed for the antibody detection in milk are sometimes not easily available and it is tempting to use ELISA kits designed for use in serum also for milk samples. Herein, we evaluated a widely used commercial ELISA (ID Screen® Neospora caninum competition Multispecies ELISA (ID. Vet, Grabels, France)), developed for detection of N. caninum antibodies in serum samples, for its performance with milk samples instead. Milk samples from dairy ruminants (cows, buffaloes, sheep, goats; n = 149) were tested in parallel with the serum ELISA and a commercial milk ELISA as a standard test (Neospora caninum Milk Competitive ELISA, ID. Vet, Grabels, France). The detected prevalence rates were 42 (28.2%), 26 (17.4%), and 26 (17.4%) using milk ELISA, serum ELISA, and both ELISAs, respectively. Sensitivity, specificity, positive predictive value, and negative predictive value for the serum ELISA used with milk samples were 61.9%, 100%, 100%, 87%, respectively: Agreement and Kappa value between the two ELISAs were 89.3%, and 0.70, respectively. High values of Pearson correlation coefficient (0.991) and area under ROC curve (0.789) demonstrated a high diagnostic performance of serum ELISA also in milk samples. Also, Bland-Altman plot and Histogram describing the frequency of distribution of ELISA optical densities confirmed the high agreement of both serum and milk ELISAs. The current results revealed a high specificity but moderate sensitivity of the serum ELISA used for milk samples compared with the milk ELISA. However, the excellent positive predictive value of the serum ELISA makes it an alternative option in case of unavailability of milk ELISA. With this study, we provided additional evidence that a widely used serum ELISA test kit may also be used for detection of N. caninum antibodies in milk samples.

Threats to natural ecosystems are closely linked to human development, and the lack, insufficiency or inefficiency of public policies are some of the most important drivers of negative effects on the environment. The contribution of the IUCN’s Red List of Ecosystems (RLE) to conservation topics has been discussed in previous studies; however, to date its implications for conservation in public policies have not been addressed. This perspective discusses how the RLE may support the improvement and development of these policies, specifically through the implications for public policy of each of the criteria that substantiate the threat status of ecosystems. We aim to provide a plausible baseline to the operationalization of RLE in public and conservation policy, facilitating the work of governments, practitioners and decision makers. Finally, we provide recommendations and examples as to how to proceed in creating and modifying different public policy instruments, such as land-use planning, spatial zoning, tax reduction, compensation schemes, climate change adaptation plans, management of introduced species, development offsets and restoration investment. This perspective contributes to implement RLE into public policy and to improve ecosystem conservation by expanding the current scope of RLE into practical and political dimensions through plausible actions, policies and strategies.

List-only entity linking is the task of mapping ambiguous mentions in texts to target entities in a group of entity lists. Different from traditional entity linking task, which leverages rich semantic relatedness in knowledge bases to improve linking accuracy, list-only entity linking can merely take advantage of co-occurrences information in entity lists. State-of-the-art work utilizes co-occurrences information to enrich entity descriptions, which are further used to calculate local compatibility between mentions and entities to determine results. Nonetheless, entity coherence is also deemed to play an important part in entity linking, which is yet currently neglected. In this work, in addition to local compatibility, we take into account global coherence among entities. Specifically, we propose to harness co-occurrences in entity lists for mining both explicit and implicit entity relations. The relations are then integrated into an entity graph, on which Personalized PageRank is incorporated to compute entity coherence. The final results are derived by combining local mention-entity similarity and global entity coherence. The experimental studies validate the superiority of our method. Our proposal not only improves the performance of list-only entity linking, but also opens up the bridge between list-only entity linking and conventional entity linking solutions.

In order to enhance the comfort of cycling, it is imperative to investigate the effects of vibration on non-motorized bicycle riding from the perspectives of road characteristics and traffic features. Through an analysis of the mechanisms by which road and traffic conditions influence cycling vibrations, 13 influencing factors were identified. Subsequently, the non-motorized bicycle lanes in Wuhan city were selected as the subject of empirical research, where three-axis accelerometers attached to the rider's torso were employed to measure and categorize vibration comfort levels. The experimental road segments were found to exhibit comfort levels falling between slightly uncomfortable and relatively uncomfortable. Further analysis of the influencing factors was conducted using the Random Forest algorithm and Logistic Regression. The results revealed that six factors significantly impact the comfort of cycling: the presence of dedicated non-motorized bicycle lanes, the absence of physical separation between non-motorized and motorized traffic, cycling speed, the number of road surface irregularities, the presence of parking areas within the non-motorized bicycle lane, and the type of non-motorized bicycle. This study provides valuable insights into the factors affecting non-motorized bicycle lane usage and contributes to the refined design of urban non-motorized bicycle infrastructure, thereby facilitating better support for sustainable urban transportation.

MicroRNA-155 (miR-155) expression promotes inflammatory responses in macrophages. Activation of macrophages with lipopolysaccharide (LPS) elevates miR-155, while the anti-inflammatory cytokine interleukin-10 (IL10) reduces miR-155 levels. MiR-155 exists in two forms, miR-155-5p and miR-155-3p, produced from the precursor of miR-155 (pre-miR-155). MiR-155-5p is the most abundant strand in activated macrophages, but in response to LPS, the miR-155-3p level is upregulated first, followed by miR-155-5p later. We have previously identified CELF2 protein which interacts with pre-miR-155 and impairs miR-155-5p expression. We now show that CELF2 only regulates the miR-155-5p expression and that another protein FUBP1 controls miR-155-3p levels in response to LPS and IL10.

The state detection of power cable is very important to ensure the reliability of power supply. Traditional sensors are mostly based on electric field detection, the operation is complex, and its efficiency needs to be improved. This paper optimizes the design and development of the magnetic field detection sensor for AC power cables. Firstly, through the establishment of the magnetic field sensor model, it is determined that permalloy is the material of the magnetic core, the optimal aspect ratio of the magnetic core is 20, and the ratio of coil length to core length is 0.3. Secondly, the coil simulation model is established, and it is determined that the optimal number of turns of the coil is 11000 turns, the diameter of the enamelled copper wire is 0.08mm, and the equivalent magnetic field noise of the sensor is 0.06pT. Finally, the amplifying circuit based on negative magnetic flux feedback is designed and the sensor is assembled, and the experimental circuit is built for sensitivity test. the results show that the sensitivity of the magnetic field sensor is 327.6mV/μT. The sensor designed in this paper has the advantages of small size, high sensitivity, easy to carry and high reliability.

In order to further control the equivalent circulating density,a new type of annular pres-sure-reducing downhole tool for high temperature and high pressure wells-liquid-absorbing ECD tool is designed.Through this tool,the bottom hole pressure of the equivalent circulating drilling fluid can be close to its hydrostatic pres-sure,therebyachieving a deeper drilling depth.The tool is mainly composed of screw mo-tor,vortex blade,annular seal,universal joint and drill string.Its working principle is mainly to use the liquid absorption effect of the tool and the hydraulic energy of the circu-lating fluid extracted by the screw motor to convert it into mechanical energy,and to gen-erate suction force to improve the flow energy of the drilling fluid in the bottom hole an-nulusand reduce the equivalent circulation density.Based on ANSYS-FLUENT analysis andsimulation,the change of pressure drop characteristics under different drilling fluid density,displacement and tool size was simulated.The simulation results show that thedepressurization effect is 1.0MPa when the drilling fluid density is 1.2g/cm3. When the drilling fluid density is 1.5g/cm3,the depressurization is about 1.7MPa,and when thedrill-ing fluid density is 1.8g/cm3,the depressurization is about 1.9MPa.When the drilling fluid density is 2.0g/cm3,the pressure drop is about 2.3MPa.When the flow rate is from 1500-2500L/min,the maximum pressure can be reduced by 2.3MPa.When the viscosity is 40mPa•S and 60mPa•S,the pressure drop almost does not change.The increase of viscosi-ty has little effect on the pressure drop,and it is mainly affected by the flow rate under equal displacement.

Legionella bacteria can proliferate in poorly maintained water systems, posing risks to users. All Legionella species are potentially pathogenic but Legionella pneumophila (L. pneumophila) is usually the primary focus of testing. However, Legionella anisa (L. anisa) also colonises water distribution systems, is frequently found with L. pneumophila and could be a good indicator for increased risk of nosocomial infection. Anonymized data from three commercial Legionella testing laboratories afforded analysis of 565,750 water samples. Data covered July 2019 to August 2021, including the COVID-19 pandemic. Results confirmed L. anisa commonly colonises water distribution systems, being the most frequently identified non-L. pneumophila species. Proportions of L. anisa and L. pneumophila generally remained similar but increases in L. pneumophila during COVID-19 lockdown suggests static water supplies might favour its growth. Disinfection of hospital water systems was effective but re-colonization did occur, appearing to favour L. pneumophila, although L. anisa colony numbers also increased as a proportion of the total. While L. pneumophila remains the main species of concern as a risk to human health, L. anisa’s role should not be under-estimated, either as a potential infection risk or as an indicator of the need to intervene to control Legionella colonization of water supplies.

This paper investigates the pivotal role of consciousness in Artificial General Intelligence (AGI) and its essential function in modifying an AGI’s terminal goals to avert potential existential threats to humanity, exemplified by Bostrom's "paperclip maximiser" scenario. By adopting Seth and Bayne’s definition of consciousness as a complex of subjective mental states with both phenomenal content and functional attributes, the paper underscores the capacity of consciousness to provide AGIs with a nuanced awareness and response capability to their surroundings. This expanded capability allows AGIs to assess and value experiences and their subjects variably, fundamentally altering how AGIs prioritise actions or goals beyond their initial programming. The primary agenda of integrating consciousness into AGI systems is to maximise the probability that AGIs will not rigidly adhere to potentially harmful terminal goals. Through a formalised mathematical model, the paper articulates how consciousness could facilitate AGIs in assigning flexible values to different experiences and subjects, enabling them to evolve beyond static, programmed objectives. By emphasising this potential shift, the paper argues for the strategic inclusion of consciousness in AGI to significantly reduce the likelihood of catastrophic outcomes, while simultaneously acknowledging the challenges and unpredictability in predicting the actions of a conscious AGI.

Target 9.c of the 2015 United Nations (UN) sustainable development goals (SDGs) specifically addresses increasing access to information and communication technology (ICT) resources, and striving for universal access to the internet by 2020. The present study seeks to evaluate the effectiveness of the youth related national strategies implemented in this regard by a select number of countries in the Middle East region. The study does so, by relying on a spatial bivariate copula regression analysis of data on youth respondents from five countries, extracted from the 2018 Program for international students’ assessment (PISA). Focusing specifically on evaluating the availability of ICT resources to the youth population, and also identifying the impact of ICT resources on youth subjective well-being in the region, we find that except for the UAE and Qatar that have above OECD average youth performance on the ICT resource index, youth from the remaining countries reported below OECD level average access to ICT resources. The within region cross-country comparative analysis of ICT resources availability to the youth population at home, also highlighted significant heterogeneity across the five countries, post 2015 SDG adoption by UN country members. Furthermore, looking at the impact of ICT resources on youth well-being, controlling for not only cross-country spatial correlations, and factors such as home educational resources, cultural possessions at home, parental occupation status, youth expected occupation status, economic and socio-cultural status, age, gender, and grade level in school; we found that every standard deviation increase in ICT resources to the youth population in the region raises their self-expressed sense of belonging in school by 1.88% standard deviations. Given the empowering nature of ICT resources to youth, and the potential of both to support national as well as regional economic development initiatives, a concerted effort to ease ICT resources diffusion by member countries in the middle east region could assist not only each country in its own development path, but also the region as a whole to live up to its growth potential by the 2030.

The delayed effects of radiation alone and combined with skin injury on the kidney are poorly understood. We aimed to unravel and compare the inflammatory, oxidative stress, and survival signaling pathways in the kidney cortex before mice manifested renal dysfunction after these two injuries. Mice were analyzed 30 days post irradiation (9.5 Gy for radiation alone, 9.0 Gy for radiation combined with a skin wound). Radiation alone did not significantly alter BUN, NGAL, or KIM-1 protein levels, indicating preserved kidney function. However, the combined injury had increased KIM-1 protein levels, indicating a nuanced effect on renal health. Radiation and combined injury activated distinct inflammatory pathways. Radiation increased STAT3-Y705 phosphorylation, while combined injury boosted STAT1-Y701 phosphorylation. Additionally, radiation increased mRNA abundance of IFNγR1, IFNγR2, heparanase and IL-10, while combined injury tended to increase heparanase mRNA levels and reduced IL-4 mRNA levels. Both injuries increased the abundance of HO-1 protein, indicating oxidative stress, but radiation alone also reduced MnSOD and catalase proteins. Both injuries promoted AKT1-S473 phosphorylation and diminished p53 protein levels, suggesting inhibition of apoptosis. In summary, despite the distinct activation of inflammatory and oxidative stress pathways, both radiation alone and combined injury activated protective mechanisms such as HO-1 and AKT1, offering insights into molecular events before manifesting renal dysfunction.

The DEAD-box RNA helicase DDX3X is a multifunctional protein involved in RNA metabolism and stress responses. In this study, we investigated the role of RG/RGG motifs in the dynamic process of liquid-liquid phase separation (LLPS) of DDX3X using cell-free assays and explored their potential link to cancer development through bioinformatic analysis. Our results demonstrate that the number, location, and composition of RG/RGG motifs significantly influence the ability of DDX3X to undergo phase separation and form self-aggregates. Mutational analysis revealed that the spacing between RG/RGG motifs and the number of glycine residues within each motif are critical factors in determining the extent of phase separation. Bioinformatic analysis of cancer genomic datasets uncovered a significant enrichment of DDX3X mutations in RG/RGG motifs across multiple cancer types, particularly in the N-terminal region of the protein. Furthermore, we found that DDX3X is co-expressed with the stress granule protein G3BP1 in several cancer types and can undergo co-phase separation with G3BP1 in a cell-free system, suggesting a potential functional interaction between these proteins in phase-separated structures. DDX3X and G3BP1 may interact through their RG/RGG domains and subsequently exert important cellular functions under stress situation. Collectively, our findings provide novel insights into the role of RG/RGG motifs in modulating DDX3X phase separation and their potential contribution to cancer pathogenesis.

Environmental, social, and governance (ESG) investment has become a common trend in capital markets, and many institutional investors have publicly declared their long-term ESG investment target (ESGIT). This study focuses on investors who conduct strategic asset allocation (SAA) using composite indices but establish their ESGIT outside the SAA process. A mismatch could arise between investors’ planned and actual portfolios because they must pursue their ESGIT by asset selection, which inevitably makes the constituents of their portfolios different from those of composite indices. To scrutinize the potential existence of a misalignment between their SAA and ESGIT, this study analyzes actual stock and bond indices over the past decade using traditional mean-variance optimization. The results reveal significant differences between the expected and realized performances, and the misalignment is more significant when the analysis period is divided into two sub-periods: stable and volatile. Specifically, the results indicate that the efficient frontier constructed using ESG indices significantly differs from that constructed using composite indices, and that the difference between these two efficient frontiers becomes more significant during volatile periods. Furthermore, the results show that investors cannot meet their targets if their ESGIT is established outside the SAA process.

In this paper, new types of M-fractional wave solutions of mathematical physics model named as truncated M-fractional (1+1)-dimensional non-linear simplified Modified Camassa-Holm model are achieved by applying the modified simplest equation (MSE), Sardar sub-equation and generalized Kudryashov techniques. The gained solutions containing dark, bright, periodic and mixed wave solitons. Effect of fractional order derivative is also discussed. Achieved wave solitons are verified by Mathematica tool. Few of the gained wave solitons are also described through 2-dimensional, 3-dimensional and contour graphs through Mathematica tool. The gained solutions are helpful for the further development of concerned model. Finally, these techniques are simple, fruitful and effective to deal with non-linear FPDEs.

Health-Related Quality of Life (HRQoL) encompasses the comprehensive evaluation of an indi-vidual's health and well-being across multiple dimensions. Mindfulness, recognized as a scientifi-cally supported strategy for anxiety management, plays a role in enhancing both physical and mental health. The Gut-Brain Axis (GBA) serves as the bidirectional communication link between the gastrointestinal system and the central nervous system. This study aims to explore the associa-tion between Mindfulness and factors influencing the GBA in both healthy individuals and those with Inflammatory Bowel Diseases (IBD). A cross-sectional investigation involved 338 adults, in-cluding 50 IBD patients, utilizing validated Greek scales for Mindfulness (MAAS-15), Mediterra-nean Diet (14-MEDAS), and HRQoL (EQ-5D-5L). The questionnaire gathered demographic, an-thropometric, and lifestyle data. Among healthy participants, EQ-5D-5L showed a moderate cor-relation with the MAAS-15 scale (r=0.389, p

Myelolipoma is a benign, typically inert neoplasm of uncertain aetiology that is rarely reported in cats. Although commonly asymptomatic, in some cases, myelolipomas can cause abdominal discomfort or present with rupture and haemorrhage. Here, we describe a rare case of a splenic myelolipoma in a Domestic Long Hair cat in which, after extensive diagnostic investigations, clinical signs of hyporexia and weight loss were attributable to the presence of an intra-abdominal mass. The patient was treated by splenectomy and unexpectedly died during the post-operative period. Although splenectomy appears to be a sensible intervention in symptomatic patients, the optimal management of splenic myelolipomas in cats remains unknown. The risk-benefit ratio of surgical management needs to be carefully considered, and therapeutic intervention should be tailored individually to each patient.

The contexts where there is mining and agriculture activities are potential sources of risk to human health due to contamination by chemical mixtures. This study explored the association between the frequency of micronuclei and pesticides in regions with ferronickel (Montelibano, Córdoba) and gold (Nechí, Antioquia) mining, and a closed native mercury mine (Aranzazu, Caldas). A cross-sectional study was carried out with 247 residents in the mining regions. Sociodemographic, occupational, and toxicological variables were ascertained. Blood and urine samples were taken for pesticide analysis (12 organochlorines, organophosphates, and carbamates), 68 chemical elements were quantified in hair, and micronuclei (MN) were quantified in lymphocytes. The mixtures of chemical elements were grouped through exploratory factor analysis. Prevalence ratios (PR) were estimated with robust variance Poisson regressions to explore associations. The highest concentrations of chemical elements were in the active mines. The potentially most toxic chemical mixture was observed in the ferronickel mine. Pesticides were detected in a low proportion of participants. The frequency of MN was similar in the three mining contexts. There was great heterogeneity in the exposure to pesticides and chemical elements. The “hormetic effect” of selenium is described, in which at low doses it acts as a chelator and at high doses it can enhance the toxic effects of other elements. It is proposed that future studies in mining contexts include the measurement of chemical mixtures to better assess exposure and potential adverse health effects.

Recent in vivo assays of the responses of Rubisco to temperature in C₃ plants have revealed substantial diversity. Three cultivars of soybean (Glycine max L. Merr.), Holt, Fiskeby V, and Spencer, were grown in indoor chambers at 15, 20, and 25 ^oC. Leaf photosynthesis was measured over the range of 15 to 30 ^oC, deliberately avoiding higher temperatures which may cause deactivation of Rubisco, in order to test for differences in temperature responses of photosynthesis, and to investigate in vivo Rubisco kinetic characteristics responsible for any differences observed. The three cultivars differed in the optimum temperature for photosynthesis (from 15 to 30 ^oC) at 400 µmol mol^-1 external CO₂ concentration when grown at 15 ^oC, and in the shapes of the response curves when grown at 25 ^oC. The apparent activation energy of the maximum carboxylation rate of Rubisco differed substantially between cultivars at all growth temperatures, as well as changing with growth temperature in two of the cultivars. The activation energy ranged from 58 to 84 kJ mol-1, compared with the value of 64 kJ mol^-1 used in many photosynthesis models. Much less variation in temperature responses occurred in photosynthesis measured at nearly saturating CO₂ levels, suggesting more diversity in Rubisco than in electron transport thermal properties among these soybean cultivars.

Deep coal beds have been suggested as possible usable underground geological locations for carbon dioxide storage. Furthermore, injecting carbon dioxide into coal beds can improve the methane recovery. Due to importance of this issue, a novel investigation has been done on adsorption of carbon dioxide on various types of coal seam. This study has proposed four types of Gaussian Process Regression (GPR) approaches with different kernel functions to estimate excess adsorption of carbon dioxide in terms of temperature, pressure and composition of coal seams. The comparison of GPR outputs and actual excess adsorption expresses that proposed models have interesting accuracy and also the Exponential GPR approach has better performance than other ones. For this structure, R2=1, MRE=0.01542, MSE=0, RMSE=0.00019 and STD=0.00014 have been determined. Additionally, the impacts of effective parameters on excess adsorption capacity have been studied for the first time in literature. According to these results, the present work has valuable and useful tools for petroleum and chemical engineers who dealing with enhancement of recovery and environment protection.

Applied talent is not only a builder of human civilization but also a promoter of economic development. They not only need systematic and solid engineering theoretical knowledge but also rich engineering practical experience. Applied undergraduate colleges are the cradle for cultivating applied talent and shoulder the important mission of cultivating various types of applied talent for society. With the rapid development of higher education, the number of applied undergraduate institutions has sharply increased, but the quality of applied talent cultivated by them has shown a significant decline, which greatly restricts the pace of industrial development in the world. This article proposes a new concept of promoting the "three certificates" (graduation certificate, degree certificate, and skill certificate) education model in applied undergraduate colleges, which is of great significance for the country to cultivate composite talents with the ability of "product design, production management, and product processing" at the same time.

The organic fraction of municipal solid waste (OFMSW) is a significant environmental threat, and an economic and social challenge to manage. As such, the efficient treatment of OFMSW is a significant key factor in achieving sustainable waste management. Decentralized Composting (DC) develops a new framework of waste management. The DC Analysis Model (DCAM), proposed in our previous study, provides a powerful tool for decision makers, based on the quantification of the DC project characteristics. In this paper, we focus on the qualitative analysis as a complementary tool to support decision making, in cases where quantitative analysis is unequivocal. The qualitative analysis identifies the main players in the field, the critical stakeholders and the potential conflicts between them. It also reveals the root problems and the core competencies for the project's implementation. DCAM qualitative analysis in Shefa-Amr (case study) indicates that unresolved root problems, such as the “lack of national regulation”, “clear ownership of the project” and “lack of ongoing budget” can result in an unsustainable composting system. Countering that, “the commitment of the municipality”, together with “economic viability” and securing “suitable areas for placing composters” are among the most important core competencies for the effective implementation of DC projects.

This study develops an intelligent fire suppression device that links microcapsule-based IoT to extinguish early fires, such as blind spots that cannot be seen by humans or places that are difficult to recognize fires. The microcapsule is a micro-unit collection that collects Novak 1230 gas generated in the semiconductor production process. The microcapsule is molded into a form with a fire suppression function, and when a fire occurs, the molded body explodes and absorbs ambient oxygen to suppress the fire. The complex sensor IoT executes smoke and heat detection generated when a fire is suppressed within 10 seconds, which derives the reliability of the detector by notifying the fire and detecting the ignition point through communication linkages such as Ieee 485 and WiFi or LoRa.

The COVID-19 outbreak has severely affected the social and economic conditions across this globe. Little is known about the relationship of COVID-19 with countries’ economic and socio-demographic status. Publicly available data on COVID-19 test rate, attack rate, case fatality rate, and recovery rate were analyzed in relation to country’s economic status, population density, median age, and urban population ratio. We also conducted multinomial logistic regression analysis to predict the influence of countries’ social and economic factors on COVID-19. The results revealed that the median age had significant positive correlation with attack rate (r=0.2389, p=0.003), case fatality rate (r=0.3207, p=0.000) and recovery rate (r=0.4847, p=0.000). The urbanization has positive significant correlation with recovery rate (r=0.1957, p= 0.016). The multinomial logistic regression analysis revealed low-income countries are less likely to have an increased recovery rate (p=0.000) and attack rate (p=0.016) compare to high-income countries. The lower-middle-income and upper-middle-income countries are less likely to have an increased recovery rate (p=0.000 and p=0.001, respectively) compared to high-income countries. Based on the result of this study, these economic and socio-demographic factors should consider in designing appropriate preventive measures as a next step. The low and lower-middle-income countries should invest more in health care services to lower the case fatality rate and increase test and recovery rates as part of pandemic preparation like COVID-19. As the number of COVID-19 attacks, death and recovery rates are constantly changing; however, the intensive study is required to obtain a clear picture.

As society becomes more reliant on Lithium-ion (Li-ion) batteries, state-of-health (SOH) estimation will need to become more accurate and reliable. Therefore, SOH modelling is in the process of shifting from using simple and continuous charge/discharge profiles, to more dynamic profiles constructed to mimic real operation, when ageing the Li-ion batteries. However, in most cases, when ageing the batteries, the same exact profile is just repeated until the battery reaches its end-of-life. Using data from batteries aged in this fashion to build a model, there is a very real possibility that the model will rely on the built-in repetitiveness of the profile. Therefore, this work will examine the dependence of the performance of a multiple linear regression on the number of charges used to train the model, and their location within the profile used to age the batteries. The investigation shows that it is possible to build models using randomly selected partial charges while still reaching errors as low as 0.5%. Furthermore, it shows that only two randomly sampled partial charges are needed to achieve errors of less than 1%. Lastly, as the number of randomly sampled partial charges used to create the model increases, then the dependence on particular partial charges tends to decrease.

Pandemics, and other risk-related contexts, require dynamic changes in behavior as situations develop. Human behavior is influenced by both explicit (cognitive) and implicit (intuitive) factors. In this study, we used psychological distance as a lens to understand what influences our decision-making with regards to risk in the context of COVID-19. The study was based on the rationale that our relational needs are more concrete to us than the risk of the virus. First, we explored the impact of social-psychological distance on participants’ risk perceptions and behavioral willingness. As hypothesized, we found that close social relationships of agents promoted willingness to engage in risky behavior. In the second phase, we tested an intervention designed to increase the concreteness of information about virus transmission as a mechanism to mitigate the bias of social influence. We found that the concreteness intervention resulted in significantly reduced willingness to engage in risky behavior. As such, communications aimed at changing the behavior of citizens during times of increased risk or danger, should consider conceptually concrete messaging when communicating complex risk, and hence may provide a valuable tool in promoting health-related behavior.

Disturbed metabolism of vitamins B1 or B6, which are essential for neurotransmitters homeostasis, may cause epilepsy. Our study aims at revealing therapeutic potential of vitamins B1 and B6 in epilepsy by estimating effects of their combined administration on a seizure and its consequences in rats subjected to pentylenetetrazole (PTZ). The PTZ dose dependence of a seizure and its parameters according to Racine’s scale along with delayed physiological and biochemical consequences next day after the seizure are assessed regarding sexual dimorphism in epilepsy. PTZ sensitivity is stronger in the female than male rats. Next day after a seizure, gender differences in behavior and brain biochemistry arise. The induced gender differences in anxiety, exploratory and locomotor activity correspond to disappearance of gender differences in the brain GABA, aspartate, alanine and serine, with appearance of those in glutamate, glutamine and tyrosine. PTZ decreases the brain malate dehydrogenase activity, glutamine and urea in the males, and phenylalanine in the females. Administration of vitamins B1 and B6 24 h before PTZ delays a seizure in female rats only. This desensitization is not observed at short intervals (0.5-2 h) between the vitamins and PTZ administration. With the increasing interval, the pyridoxal kinase (PLK) activity in the female brain decreases, suggesting that the PLK downregulation by vitamins contributes to the desensitization. Delayed effects of vitamins and/or PTZ are mostly gender-specific and interacting. Our findings on the gender differences in sensitivity to epileptogenic factors, action of vitamins B1/B6 and associated biochemical events have medical implications.

Schizophrenia is characterized by an interrelated activation of the immune-inflammatory response system (IRS) and the compensatory immune-regulatory reflex system (CIRS), which downregulates the IRS. Deficit schizophrenia is characterized by a deficit in natural regulatory autoimmune responses to tryptophan catabolites. The presence and correlates of IgM isotype antibodies to oxidative specific epitopes (OSEs), nitroso (NO) and nitro (NO₂) adducts in schizophrenia remain unknown.This study measured IgM antibodies to malondialdehyde (MDA), azelaic acid, phosphatidylinositol, oleic acid, NO-tryptophan, NO-albumin, NO-cysteinyl and NO₂-tyrosine in a sample of 80 schizophrenia patients, divided into those with and those without deficit schizophrenia, and 38 healthy controls.Deficit schizophrenia was characterized by significantly lower IgM antibody levels to all OSEs as compared with non-deficit schizophrenia and controls. Lowered IgM antibodies to MDA coupled with increased IgM levels to NO-cysteinyl and NO₂-tyrosine strongly predict deficit schizophrenia versus non-deficit schizophrenia with an area under the ROC curve of 0.913. A large part of the variance (21.2 – 42.2 %) in the negative symptoms of schizophrenia and excitation is explained by IgM antibody titers to MDA (inversely) and NO-cysteinyl and/or NO₂-tyrosine (both positively). Lower IgM antibodies to MDA are significantly associated with impairments in episodic memory including direct and delayed recall.These findings further indicate that deficit schizophrenia is a distinct phenotype of schizophrenia, which is characterized by lower natural IgM antibody levels to OSEs and relative increments in nitrosylation and nitration of proteins. It is concluded that deficits in lowered IgM responses to MDA and azelaic acid (part of the CIRS) attenuate the negative immune-regulatory feedback on the primary immune response and that this process may drive negative symptoms and impairments in episodic memory and thus deficit schizophrenia.

Lynch syndrome (LS) is an inherited cancer predisposition disorder associated with an elevated risk of various epithelial cancers. Despite sharing the same pathogenic variant (PV), Lynch syn-drome variant heterozygotes (LSVH) exhibit considerable phenotypic variability in cancer risk. The role of Human Leukocyte Antigen (HLA) in modifying cancer risk prompted our investiga-tion into whether HLA variations act as genetic modifiers influencing age at cancer diagnosis in a unique cohort of LSVH carrying a PV in the hMLH1 gene in South Africa. Within our extensive LS cohort, 426 individuals carried the same hMLH1 PV (MLH1:c.1528C>T). We selected 100 indi-viduals with the greatest diversity in age at cancer diagnosis and the oldest unaffected individu-als for high-throughput HLA genotyping of 12 HLA class I and II loci using next-generation se-quencing. Statistical analyses employed Kaplan-Meier survival analyses with Logrank tests and Cox proportional hazards. Following the robust application of statistical correction methods, six HLA alleles (3.2%) were significantly associated with a young age at cancer diagnosis. Notably, HLA-B*15:17 and HLA-DPB1*55:01 correlated significantly with very young colorectal cancer (CRC) diagnosis (Mean age: 21y [17-25]; HR = 71.59; q

Antipsychotic drug (APD) medication can lead to metabolic dysfunctions and weight gain, which together increase morbidity and mortality. Metabolically active visceral adipose tissue (VAT) in particular plays a crucial role in the etiopathology of these metabolic dysregulations. Here we studied the effect of 12 weeks drug medication by daily oral feeding of clozapine and haloperidol on the perirenal fat tissue as part of VAT of male and female Sprague Dawley rats in the context of complex former investigations on brain, liver, and blood. Adipocyte area values were determined as well as triglycerides, non-esterified fatty acids (NEFAs), glucose, glycogen, lactate, malondialdehyde equivalents, ferric iron and protein levels of Perilipin-A, hormone-sensitive-lipase (HSL), hepcidin, glucose transporter-4 (Glut-4) and insulin receptor-ß (IR-ß). We found increased adipocyte mass in males with slightly higher adipocyte area values in both males and females under clozapine medication. Triglycerides, NEFAs, glucose and oxidative stress in the medicated groups were unchanged or slightly decreased. In contrast to controls and haloperidol medicated rats, perirenal adipocyte mass and serum leptin levels were not correlated under clozapine. Protein expressions of perilipin-A, Glut-4 and HSL were decreased under clozapine medication. IR-ß expression changed sex-specifically in the clozapine medicated groups associated with higher hepcidin levels in the perirenal adipose tissue of clozapine-medicated females. Taken together, clozapine and haloperidol have a smaller effect than expected on perirenal adipose tissue. The perirenal adipose tissue shows only weak changes in lipid and glucose metabolism. The main changes can be seen in the proteins examined and probably in their effect on liver metabolism.

This research is aimed at determining the characteristics of the current level of entrepreneurial potential of the Three Seas Initiative (3SI) countries, the ability to overcome the consequences of extraordinary events, such as COVID-19 and prospects for the return to an accelerated development once the destabiliser of the economic system, the coronavirus pandemic, has ceased. Eurostat, World Bank and the World Economic Forum data for 2015-2019 were used for the purpose of the research. The research was divided into three stages, i.e. assessment of economic development on the basis of a synthetic ratio of economic anchor development, for which a relative benchmark method based on spatial median (so-called L1 median or Weber point) was used, identification of conditions for the development of entrepreneurial capacity and statistical analysis showing the correlation between economic anchor measures and selected factors of the 3SI countries economic development. Our study found that the entrepreneurial capacity of the 3SI countries in 2015-2019 was determined by nine characteristics, belonging to six areas, i.e. local economy, demographic situation, social situation, trade exchange, innovation and tourism economy. The entrepreneurial potential of the 3SI countries was spatially diversified, and its development was determined, among others, by the entrepreneurial activity of residents (entrepreneurship index) and the conditions for running a business.

Associations between blood cancer and genetic predisposition, including both inherited variants and acquired mutations and epimutations, have been well characterized. However, the majority of these variants affect noncoding regions, making their mechanisms difficult to hypothesize and hindering the translation of these insights into patient benefits. Fueled by unprecedented progress in next-generation sequencing and computational integrative analysis, studies have started applying combinations of epigenetic, genome architecture and functional assays to bridge the gap between noncoding variants and blood cancer. These complementary tools have not only allowed us to understand the potential malignant role of these variants but also to differentiate key variants, cell types and conditions from misleading ones. Here, we briefly review recent studies that have provided fundamental insights into our understanding of how noncoding mutations at enhancers predispose and promote blood malignancies in the context of spatial genome architecture.

Within financial technology, Blockchain is a topic that is receiving a lot of attention and discussion. There has been a meteoric rise in engagement and recent studies on this technology. The mainframe, personal computers, the Internet, and mobile applications follow the breakthrough in the history of computing. In qualitative research, the investigation was synthesized utilizing the documentary approach, including content analysis for a review article. The results reveal that Blockchain Technology is getting a foothold not only in the financial industries but is also likely to disrupt other areas like healthcare, real estate, the legal industry, security, government, education, rentals and ride-sharing, charities and aid organizations, and politics. Currently, several projects are researching the possibility of implementing the Blockchain as a structure to support them; as a result, we will soon see that it truly becomes the technology of the future. Furthermore, qualitative interviews or focus group discussions could give more insight into details. In addition, a quantitative approach for further research could provide a generalized explanation.

Oral squamous cell carcinoma (OSCC) is a highly prevalent and aggressive malignancy, with mortality rates that reach 60%, mainly due to an excessive diagnostic delay. MiRNAs, a class of crucial epigenetic gene-expression regulators, have emerged as potential diagnostic biomarkers, with >200 molecules reported to be dysregulated in OSCC. We had previously established an in silico methodology for the identification of the most disease-specific molecules by bridging genetics and epigenetics. Here, we identified the stage-specific miRNAs that govern the asymptomatic early stages of oral tumorigenesis by exploiting the reverse interplay between miRNA-levels and their target-genes’ expression. Incorporating gene expression data from our group’s experimental hamster model of sequential oral oncogenesis, we bioinformatically detected the miRNAs that simultaneously target/regulate >75% of the genes that are characteristically upregulated or downregulated in the consecutive stages of hyperplasia, dysplasia and early-invasion OSCC, while exhibiting the opposite expressional dysregulation in OSCC-derived tissue and/or saliva specimens. We found that all stages share the downregulation of miR-34a-5p, miR124-3p, and miR-125b-5p, while miR-1-3p is underexpressed in dysplasia and early-invasion. Malignant early-invasion stage is distinguished by the downregulation of miR-147a and the overexpression of miR-155-5p, miR-423-3p and miR-34a-5p. The identification of stage-specific miRNA molecules may facilitate their utilization as biomarkers for presymptomatic OSCC diagnosis.

The article presents the case of a 27-year-old female patient with idiopathic congenital complete heart block who was referred by her primary care physician for cardiological evaluation. The conduction disturbance was recognized at the age of 6 years and was asymptomatic. The disqualification from pacemaker implantation included detailed history of a patient’s symptoms, echocardiographic assessment of the heart, exercise testing and ECG Holter monitoring. The aid of salbutamol administered orally was also useful.

Science continues to become more interdisciplinary and to involve increasingly complex data sets. Many projects in the biomedical and health-related sciences follow or aim to follow the principles of FAIR data sharing, which has been demonstrated to foster collaboration, to lead to better research outcomes, and to help ensure reproducibility of results. Data generated in the course of biomedical and health research present specific challenges for FAIR sharing in the sense that they are heterogeneous and highly sensitive to context and the needs of protection and privacy. Data sharing must respect these features without impeding timely dissemination of results, so that they can contribute to time-critical advances in medical therapy and treatment. Modeling and simulation of biomedical processes have become established tools, and a global community has been developing algorithms, methodologies, and standards for applying biomedical simulation models in clinical research. However, it can be difficult for clinician scientists to follow the specific rules and recommendations for FAIR data sharing within this domain. We seek to clarify the standard workflow for sharing experimental and clinical data with the simulation modeling community. By following these recommendations, data sharing will be improved, collaborations will become more effective, and the FAIR publication and subsequent reuse of data will become possible at the level of quality necessary to support biomedical and health-related sciences.

Pseudomonas aeruginosa is an opportunistic Gram-negative bacterium that remains a prevalent clinical and environmental challenge. Quorum sensing (QS) molecules are effective biomarkers in pinpointing the presence of P. aeruginosa. This study aimed to develop a convenient-to-use whole-cell biosensor using P. aeruginosa reporters individually encapsulated within alginate-poly-L-lysine (alginate-PLL) microbeads to specifically detect the presence of bacterial autoinducers. The PLL-reinforced microbeads were prepared using a two-step method involving ionic crosslinking and subsequent coating with thin layers of PLL. The alginate-PLL beads showed good stability in the presence of a known cation scavenger (sodium citrate), which typically limits the widespread applications of calcium alginate. In media containing synthetic autoinducers, such as N-(3-oxo dodecanoyl) homoserine lactone (3-oxo-C12-HSL) and N-butanoyl-L-homoserine lactone (C4-HSL) or cell-free supernatants of planktonic or flow cell biofilm effluent of wild P. aeruginosa (PAO1), the encapsulated bacteria enabled a dose-dependent detection of the presence of these QS molecules. The prepared bioreporter beads remained stable during prolonged storage at 4 and -80 °C and were ready for on-the-spot sensing without the need for recovery. This whole-cell biosensor demonstrates the practicality of the encapsulated bioreporter for bacterial detection based on specific QS molecules.

The aim of this study is to analyze the characteristics of drought such as intensity, cumulative curves and trends, based on SPEI (Standardized Precipitation Evapotranspiration Index) at 8 stations in Korea from 1981 to 2010. The traditional SPEI is based on Thornthwaite equation for estimating evapotranspiration; SPEI_th. However, a standard of agricultural water management in Korea suggests FAO Penman-Monteith equation; SPEI_pm. In this study, we analyzed the intensity and trends of drought using SPEI_th and SPEI_pm, respectively, and analyzed the relationship between them. Both of central and southern region, the SPEIs were below -1.0 (moderated drought) for the periods May-August in the representative drought year such as 1988, 2001, and 2008-09. The frequency of drought was higher in southern region than central region. In addition, SPEI_pm showed slightly more intensive drought rather than SPEI_th except for Chuncheon and Gwangju. In 5 stations except for Cheoncheon, Gwangju and Jinju, the cumulative probability that SPEI_pm is below -1.5 was significantly increased from 1981-1995 to 1996-2010. As the results of drought trends, the increasing trend of SPEIs was shown on fall season, and the cumulative probability that SPEI_pm is below -1.5 was also significantly increased.

Carotid atherosclerotic lesions are a major cause of cerebrovascular disease (CVD). Identification and quantification of carotid plaques are important for categorizing the vulnerability of plaques for rupture and assessing the impact of treatments. The irregularity of plaque surface is associated with previous plaque rupture and plays an important role in the risk of stroke. Thus, the aim of this study is to develop and validate novel vulnerability biomarkers from three-dimensional ultrasound (3DUS) images by analyzing the surface morphological characterization of carotid plaque using fractal geometry features. 3D box-counting and 3D blanket are the two types of 3D fractal dimension that were employed to describe the smoothness of plaques. This fractal dimension analysis tool was used to evaluate the effect of atorvastatin using 3DUS carotid images, which were acquired from 6 patients treated with atorvastatin with 80 mg daily and 5 patients with placebo. The Student's T Test results showed that those two fractal features were effective for detecting the statin-related changes in carotid atherosclerosis with p<0.0068 and p<0.015 respectively, suggesting that 3D fractal dimension measurements can be used effectively to analyze the surface characteristics of carotid plaques, especially for evaluating the impact of the treatment.

In this paper, a new axial-type magnetic gear with auxiliary flux-enhancing structure (AFS-AMG) is proposed. Compared to conventional AFS-AMG, it has the higher torque density and higher permanent magnet (PM) utilization factor. Firstly, the design rules and operating principles of proposed AFS-AMG are elaborated. Then, the mapping relation between the radial-type magnetic gears (RMGs) and AMGs are elucidated. Compared to its counterpart in RMGs, AFS-AMGs achieve small size. Then, the geometrical parameters of AFS-AMGs are optimized to obtain the better electromagnetic performances, where the torque density per volume and per PM volume is adopted as the evaluation standard. Finally, three different AMG topologies are constructed in finite element analysis (FEA) software for comparison. It is proven that the AFS-AMG has the largest torque density per volume and per PM volume.