Preclinical animal studies provide valuable opportunities to better understand human diseases and contribute to major advances in medicine. This review provides a comprehensive overview of ocular parameters in humans and selected animals, with a focus on the ocular surface, detailing species differences in ocular surface anatomy, physiology, tear film dynamics and tear film composition. We describe major pitfalls that tremendously limit the translational potential of traditional laboratory animals (ie., rabbits, mice and rats) in ophthalmic research, and highlight the benefits of integrating companion dogs with clinical analogues to human diseases into preclinical pharmacology studies. This One Health approach can help accelerate and improve the framework in which ophthalmic research is translated to the human clinic. Studies can be conducted in canine subjects with naturally occurring or non-invasively induced ocular surface disorders (eg., dry eye disease, conjunctivitis), reviewed herein, and tear fluid can be easily retrieved from canine eyes for various bioanalytical purposes. In this review, we discuss common tear collection methods, including capillary tubes and Schirmer tear strips, and provide guidelines for tear sampling and extraction to improve the reliability of analyte quantification (drugs, proteins, others).

Non-typhoid salmonellosis is a common and problematic foodborne zoonotic disease in which pork and pork products can be an important potential source of infection. In order to prevent this disease important efforts to monitor the situation in the main source, livestock, are conducted in most developed countries. In the European Union EFSA and ECDC compile information at the member state level, even though important differences in production systems and surveillance systems exist. Here, Salmonella surveillance systems in one of the main sources of foodborne salmonellosis, swine, and humans in Spain were reviewed to identify potential gaps and discuss potential ways of integration under a One Health approach. Despite the extensive information generated through the surveillance activities source attribution can be only routinely performed through ad-hoc outbreak investigations, and national reports on human outbreaks do not provide sufficiently detailed information to gain a better understanding of the epidemiology of the pathogen. Human and animal monitoring of Salmonella would benefit from a better exchange of information and collaboration. Analysis of spatio-temporal trends in livestock and humans could help to identify likely sources of infection and to target surveillance efforts in areas with higher prevalence or where specific strains are found.

Different researchers analyzed effective computational methods that maintain the precision of Allen-Chan (AC) equations and their constant security. This article presents a method known as reduced order model technique by utilizing kernel principle component analysis (KPCA), a nonlinear variation of traditional principal component analysis (PCA). KPCA is utilized on the data matrix created using discrete solution vectors of the AC equation. In order to achieve discrete solutions, small variations are applied for dividing up extraterrestrial elements, while Kahan’s method is used for temporal calculations. Handling the process of backmapping from small-scale space involves utilizing a non-iterative formula rooted in the concept of the multi-dimensional scaling (MDS) method. Using KPCA, we show that simplified sorting methods preserve dissipation of energy structure. The effectiveness of simplified solutions from linear PCA and KPCA, the retention of invariants, and computational speeds are shown through one-, two-, and three-dimensional AC equations.

The susceptibility of seven species of chestnuts to the orchid leafminer was studied in the arboretum of the Main Botanical Garden of the Russian Academy of Sciences (MBG RAS), taking into account their interspecific characteristics. Using pheromone delta traps, the highest number of moths was shown for A. hippocstanuam and the lowest for A. chinensis. A number of anatomical parameters of leaves were investigated, such as the thickness of the epidermal cell wall and the thickness of the palisade and spongy parenchyma layers. As a result, it was shown that the most infected chestnut species had a greater thickness of the nutritious parenchyma tissue. No de-pendence was found between the degree of susceptibility to the Ohrid leaf miner and such indi-cators as the content of chlorophyll a + b and carotenoids in the leaves of seven species of chestnuts. Nevertheless, resistance of different species of the genus Aesculus to C. ohridella under increased tannin content in leaves has been shown. Evaluation of phenolic compounds and flavonoids has not established their reliable role as repellents. The high levels of carbohydrates found during the study contributed to increased susceptibility to the Ohrid leaf miner.

The fixed and random effects models are popular methods for identifying the causal effects of treatment. First, I introduce the two-way fixed effects model and show a class of the estimators for the random effects models. Misidentification may occur in these models due to the treatment effect heterogeneity. Then, I propose the differential error component models to address such problems while the models exclude the fixed effects of units and time periods and consider the random effects of units and time periods. Meanwhile, I present a simulation of a staggered design and revisit the application in (De Chaisemartin and D'Haultfoeuille, 2020). The results demonstrate that one additional newspaper increases the 0.31% average effect of the presidential turnout and the 1.05% average effect when the newspapers’ changes are non-negative, where the proposed estimators are robust and efficient.

The increasing concern regarding climate change continues to motivate research in the Arctic. Within the delicate cycle of the cryosphere, leads are an important kinematic feature that regulate heat balances in the Arctic. Therefore, it is necessary to quantify the genesis of leads over time to identify when and where changes are occurring. The use of a principal component analysis (PCA) is one such tool that is used to identify the characteristics of seasonal ice variability. The PCA not only identifies “normal” or “dominant” conditions or features, but also extract anomalous spatial features from an archive of long-term sequence of images.

This study explores novel techniques to improve the detection accuracy of skarn iron deposits using advanced image processing methodologies. Leveraging the capabilities of Aster image, Band Ratio (BR) images, and Principal Component Analysis (PCA), alongside the power of 3D Convolutional Neural Networks (3D-CNN), the research aims to enhance the precision and efficiency of ore detection in complex geological environments. The proposed method employs a specific 3D-CNN architecture, accepting input as a 7×7×C image patch, where C represents the combined number of selected Aster image bands, Principal Component (PC) bands, and computed BR images. To evaluate the accuracy of the proposed method, five distinct image bands combinations, including the proposed bands combination, were tested and evaluated based on the overall accuracy (OA), average accuracy (AA), and kappa coefficient. The results demonstrated that while the incorporation of BR images alongside Aster bands initially seemed promising, it introduced significant confusion in certain classifications, leading to unexpected mis-classification rates. Surprisingly, utilizing solely Aster bands as input parameters yielded higher accuracy rates (OA= 93.13%, AA = 91.96%, Kappa = 90.91%) compared to scenarios involving the integration with band ratios (OA = 87.02%, AA = 79.15, and Kappa = 82.60%) or the integration of BR images to PC bands (OA= 87.78%, AA = 82.39%, Kappa = 83.81%). However, the amalgamation of Aster bands with selected PC bands showed slight improvements in accuracy (OA= 94.65%, AA = 92.93%, Kappa = 93.45%), although challenges in accurately classifying certain features persisted. Ultimately, the proposed combination of Aster bands, PC bands, and BR images (proposed bands combination) presented the most visually appealing and statistically accurate results (OA = 96.95%, AA= 94.87%, and Kappa = 95.93%), effectively addressing misclassifications observed in the other combinations. These findings underscore the synergistic contributions of each of the Aster bands, PC bands, and BR images, with Aster bands proving pivotal for optimal skarn classification, PC bands enhancing intrusions classification accuracy, and BR images strengthening wall rock classification accuracy. In conclusion, the proposed combination of input image bands emerges as a robust and comprehensive methodology, demonstrating unparalleled accuracy in the remote sensing detection of skarn iron minerals.

Use of probiotic lactobacilli has been proposed as a strategy to mitigate damage associated to exposure to toxic metals. Their protective effect against cationic metal ions, such as those of mercury or lead, is believed to stem from their chelating and accumulating potential. However, their retention of anionic toxic metalloids, such as inorganic arsenic, is generally lower. Through the construction of mutants in phosphate transporter genes (pst) in Lactiplantibacillus plantarum and Lacticaseibacillus paracasei strains, coupled with arsenate [As(V)] uptake and toxicity assays, we determined that the incorporation of As(V), which structurally resembles phosphate, is likely facilitated by phosphate transporters. Surprisingly, inactivation in Lc. paracasei of PhoP, the tran-scriptional regulator of the two-component system PhoPR, a signal transducer involved in phosphate sensing, led to an increased resistance to arsenite [As(III)]. In comparison to the wild type, the phoP strain exhibited no differences in the ability to retain As(III), and there were no observed changes in the oxidation of As(III) to the less toxic As(V). These results reinforce the idea that specific transport, and not unspecific cell retention plays a role in As(V) biosorption by lactobacilli, while they reveal an unexpected phenotype for the lack of the pleiotropic regulator PhoP.

This study employs Principal Component Analysis to examine cranial measurements obtained from both extant and fossil specimens spanning 38 species and comprising over 300 individuals within the subfamily Cervinae. Our findings elucidate craniometric distinctions among cervids characterized by varying body sizes and certain evolutionary adaptations. While our results generally corroborate previous assertions regarding the limited craniometric variability among plesiometacarpal deer, they also unveil specific cranial adaptations within certain genera and species. The Principal Component Analysis of craniometric data revealed that giant and large-sized deer display significantly broader ecomorphological diversity in cranial shape compared to small-sized deer. Secondly, small-sized deer exhibit greater uniformity in their cranial shape, appearing densely clustered on the factorial map. Thus, we conclude that body size imposes ecological constraints, limiting the available niches due to eco-physiological factors. The study demonstrates that endemic insular deer do not evolve consistent craniometric features attributable to insular isolation, while the cranial proportions of medium-to-small sized deer delineate a ubiquitous morphological archetype characteristic of numerous cervid taxa spanning diverse phylogenetic lineages and sharing comparable body sizes. This group of "Dama-like" deer, characterized by similar body size, metabolic rates, ecological needs, and cranial morphometry, is referred to here as the fundamental eco-physiological type, typical of warm regions within the Palearctic and Oriental biogeographic realms..

This paper performs a comprehensive analysis of the wind energy potential of onshore regions in Greece with emphasis on quantifying the volume risk and the spatial covariance structure. Optimization techniques are employed to derive efficient wind capacity allocation plans (also known as generation portfolios) incorporating different yield aspirations. The generation profile of minimum variance and other optimal portfolios along the efficient frontier are subject to rigorous evaluation using a fusion of descriptive and statistical methods. In particular, principal component analysis is employed to estimate factor models and investigate the spatiotemporal properties of wind power generation, providing valuable insights into the persistence of volume risk. The overarching goal of the study is to employ a set of statistical and mathematical programming tools guiding investors, aggregators and policy makers in their selection of wind energy generating assets. The findings of this research challenge the effectiveness of current policies and industry practices, offering a new perspective on wind energy harvesting with a focus on the management of volume risk.

Since the outbreak of the COVID-19 pandemic, patients infected with COVID-19 may experience abnormal heart rates, posing potential health risks. This study proposes a non-contact method for measuring heart rate (HR) and heart rate variability (HRV) to effectively reduce the risk of infection and assist healthcare professionals in more accurate diagnosis and treatment. In this study, except for the different image acquisition method, other research processes and methods are similar to the research methods in [15]. The image acquisition method is changed from forehead image to palm image. The new non-contact measurement performance of the proposed method can effectively not only avoid infection concerns but also obtain heart rate and HRV quickly and conveniently, providing higher accuracy for physiological parameters, root mean square error (RMSE), and mean absolute percentage error (MAPE) compared to those in recently published papers.

To mitigate the interference of waves on offshore operation vessels, heave compensation systems find widespread application. The performance of heave compensation systems significantly influences the efficiency and safety of maritime operations. This paper establishes a mathematical model for winch-based active heave compensation system. It introduces a three-loop active disturbance rejection control (ADRC) strategy encompassing piston position control, winch speed control, and load displacement control, enabling real-time estimation and compensation of system disturbances, thereby enhancing the performance of the heave compensation system. To assess the effectiveness of this control strategy, the study employs Matlab/Simulink and AME Sim to construct a co-simulation model and conducts a comparative analysis with traditional PID control systems. The research findings indicate that the three-loop ADRC position control strategy consistently delivers superior compensation performance across various operational scenarios.

Background The Delta Neutrophil Index (DNI), a prodiagnostic marker for sepsis, utilizes leukocyte count. Platelet activation, similar to leukocytes, plays a crucial role in host defence against pathogens and may serve as a predictor of sepsis outcome. However, the combined evaluation of Mean Platelet Component (MPC) and DNI in assessing sepsis has been rarely used. Methods To assess the prodiagnostic validity of simultaneous utilization of DNI and MPC in cases of human febrile sepsis, we conducted measurements of cellular indices, including DNI and MPC, as well as molecular biomarkers such as procalcitonin (PCT) and C-reactive protein (CRP). This study was carried out on patients admitted to the emergency department with suspected sepsis. Results Using a cutoff value of 2.65%, the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of DNI in sepsis were found to be 69%, 73.9%, 77.9%, and 64.1%, respectively. Furthermore, significant differences in DNI and MPC levels were observed between the sepsis and non-sepsis groups (6.7±7.8% versus 2.1±2.2%, P=0.000 and 26.0±1.9 g/dl versus 26.8±1.4 g/dl, P=0.002), respectively. Notably, there was a negative correlation between DNI and MPC, with the strength of the correlation varying based on the cause of sepsis. By setting the cutoff value for DNI at 6.2%, the sensitivity, specificity, and NPV improved to 100%, 80.3%, and 100%, respectively, although the PPV remained at 10.6%. Importantly, the DNI≥6.2 group exhibited a higher incidence of death (10.9%, 5/46) and multiorgan failure (19.6%, 9/46) compared to the DNI<6.2 group (0.0%, 0/46 and 0.0%, 0/167, respectively) (P=0.000 and P=0.000, respectively). Conclusions In our study, DNI demonstrates superior effectiveness compared to other molecular biomarkers such as CRP and procalcitonin in distinguishing febrile septic patients from febrile nonseptic patients and predicting prognosis. Additionally, there exists a negative correlation between MPC and DNI, making MPC a valuable marker for differentiating the etiology of sepsis. These findings hold significant clinical implications as DNI/MPC is a cost-effective and readily applicable approach in various impending sepsis scenarios. Notably, this study represents the first examination of the prodiagnostic validity of simultaneously employing DNI and MPC in human cases of febrile sepsis.

This study aims to develop an auditory experience evaluation questionnaire to improve the presence of metaverse environments, and to derive evaluation components considering auditory presence and auditory user experience (AUX) through a survey. After conducting a survey with a total of 232 participants, five evaluation components were extracted from auditory presence and AUX evaluation factors through principal component analysis (PCA) and reliability analysis (RA): 'realistic auditory background', 'acoustic aesthetics', 'consideration of acoustic control and accessibility', 'auditory utility and minimalist design', and 'auditory consistency'. In particular, although AUX evaluation factors such as 'ease of access to sound control' have limitations in improving the sense of presence, negative factors of presence such as 'distraction due to sound' can be improved by utilizing AUX evaluation factors, so it is judged that the sense of presence in the metaverse environments can be improved by enhancing the auditory sense of presence and AUX evaluation factors according to the composition of the five evaluation components derived in this study. This study can be used as a basis for developing an auditory experience evaluation questionnaire for the metaverse platform, creating sound design guidelines, and identifying sound development priorities.

This study aims to develop novel automated computer vision algorithms and systems for component replacement inspection for Printed Circuit Boards (PCBs). The proposed algorithms are able to identify locations as well as sizes of different components. They are object detection algorithms based on keypoints of the target components. The algorithms can be implemented as neural networks consisting of two portions: frontend networks and backend networks. The frontend networks are used for the feature extractions of input images. The backend networks are adopted for producing component inspection results. Each component class can has its own frontend and backend networks. In this way, the neural model for the component class can be effectively reused for different PCBs. To reduce the computation time for the inference of the networks, different component classes can share the same frontend networks. A two-stage training process is proposed for effectively exploring features of different components for accurate component inspection. The proposed algorithm has the advantages of the simplicity for training data collection, high accuracy for defect detection, and high reusability and flexibility for online inspection. The algorithm is an effective alternative for the automated inspection in smart factory with growing demand for product quality and diversification.

Soil quality is related to food security and human survival and development. In recent years, due to the acceleration of urbanization and the increase of abandoned land, land degradation occurs, poor topsoil quality. In this study, the minimum data set (MDS) was constructed through principal component analysis (PCA) to determine the indicator data set for evaluating topsoil quality in Tieling County, China. In addition, the soil quality index (SQI) was calculated to analyze the spatial distribution characteristics and influencing reasons of topsoil quality in Tieling County. The re-sults showed that MDS included total potassium (TK), Clay, zinc (Zn), soil organic matter (SOM), soil water content (SWC), cation exchange capacity (CEC), pH, and copper (Cu). The MDS indicators can well replace all indicators to evaluate the topsoil quality in the study area. The overall soil quality of Tieling County showed a trend of low in the east and high in the west, and gradually increased from the hilly area to the plain area. The evaluation results are consistent with field research, which can provide reference for other topsoil quality evaluation, and it also provide a basis for the formulation of soil quality improvement measures.

The scientific work posted on the Internet, which its author constantly keeps up to date, will be called an alive publication. The genre of alive publishing has many attractive features. However, it requires a certain expansion of the composition of the meta-attributes of the publication: along with the traditional attributes, the date of the appearance of the new, fresh revision is brought to the fore here. Such date is placed in a prominent place in the text of the publication. Along with this, it becomes highly desirable to include such a dynamically ("on the fly") generated date in a bibliographic reference to an alive publication. The currently used methods of dynamic extraction of this date are considered for a simple online publication, for a publication that has received a DOI through Crossref, for a publication posted in arXiv and in Preprints. Thanks to adding this meta-attribute, references to alive publications will beautify any bibliographic list.

Drought and high temperature often occur simultaneously will eventually affect rice (Oryza sativa L.) yield and seed quality. The objective of the study was to compare the effects of high temperature (HT) and water limitation (WL) during seed development and maturation on yield component, seed quality, and amylose content in aerobic rice cv. MRIA. Two experiments were conducted concurrently in greenhouse with ⁓35/26℃ subjected to elevated HT and/or WL; 5 days 38/28°C at 10, 15, 29 DAA and/or HT; 7 days at 40/28℃ at 8 and 21 DAA with 12h photoperiod in control environment growth chamber. Both experiments were respectively harvested at physiological maturity of 31 and 35 DAA. Grain yielded from the irrigated plant declined from over 27% to below 18% between 15 and 29 DAA. Combination stresses resulted in greater reduction of grain yield than single stress. Late seed development (29 DAA) had no effect either on yield component or seed quality as well as produce a high concentration in amylase content (16.7%) with greater germination capacity (91%). The non-stress plant harvested at 21 DAA resulted the highest percentage of germinated seeds (97%) whereas the vigour index (VI) was reduced to 70.3% and 58.9% at 8 and 21 DAA respectively. Extended plant stress to 7 consecutive days incurred the highest VI with low concentration of amylose content. There was a linear relationship incurred between seed amylase content with germination capacity and VI, however, there was an inverse relationship (R2 = 0.19) between amylase content with VI from plant treated with (HT and/or WL) for 7 days. The present results confirmed that the avoidance HT and WL during early (10 DAA) and mid (15 DAA) seed development with short duration of stress (5 days) obtained better in yield component, enhanced the seed quality and amylose concentration. The combined HT and WL damaged seeds’ quality were more than each stress alone. Therefore, avoiding HT and WL can be practically applied by farmers by irrigating throughout the rice plant during histodifferentiation to save water usage during seed maturation.

Immunoglobulin G (IgG) adopts a modular multidomain structure that mediates antigen recognition and effector functions, such as complement-dependent cytotoxicity. IgG molecules are self-assembled into a hexameric ring on antigen-containing membranes, recruiting the complement component, C1q. To provide deeper insights into the initial step of the complement pathway, we report a high-speed atomic force microscopy study for quantitative visualization of the interaction between IgG and the C1 complex composed of C1q, C1r, and C1s. Results showed that C1q in the C1 complex is restricted regarding internal motion and has a stronger binding affinity for on-membrane IgG assemblages than C1q alone, presumably because of smaller conformational entropy loss upon binding. Furthermore, we visualized a 1:1 stoichiometric interaction between C1/C1q and an IgG variant that lacks the entire CH1 domain in the absence of antigen. In addition to the canonical C1q-binding site on Fc, their interactions are mediated through a secondary site on the CL domain that is cryptic in the presence of the CH1 domain. Our findings offer clues for novel-modality therapeutic antibodies.

The COVID-19 pandemic starting in the first half of 2020 has changed the lives of everyone across the world. Reduced mobility was essential due to it being the largest impact possible against the spread of the little understood SARS-CoV-2 virus. To understand the spread, a comprehension of human mobility patterns is needed. The use of mobility data in modelling is thus essential to capture the intrinsic spread through the population. It is necessary to determine to what extent mobility data convey the same message of mobility within a region. This paper compares different mobility data sources by constructing spatial weight matrices and further compares the results through hierarchical clustering. This provides insight for the user into which data provides what type of information and in what situations a particular source is most useful.

Alnus glutinosa is important woody plant in Lithuanian forest ecosystems. Knowledge of fluorescence properties of black alder pollen is necessary for scientific and practical purposes. By the results of the study we aimed to evaluate possibilities of identifying Alnus glutinosa pollen fluorescence properties by modeling ozone effect and applying two different fluorescence-based devices. To implement experiments, black alder pollen was collected in a typical habitat during the annual flowering period in 2018-2019. There were three groups of experimental variants, which differed in the duration of exposure to ozone, conditions of pollen storage before the start of the experiment, and the experiment start time. Data for pollen fluorescence analysis were collected using two methods. The microscopy method was used in order to evaluate the possibility of employing image analysis systems for investigation of pollen fluorescence. The second data collection method is related to the automatic device identifying pollen in real-time, which uses the fluorescence method in the pollen recognition process. Data were assessed employing image analysis and principal component analysis (PCA) methods. Digital images of ozone-exposed pollen observed under the fluorescence microscope showed the change of the dominant green colour towards the blue spectrum. Meanwhile, the automatic detector detects more pollen whose fluorescence is at the blue light spectrum. It must be noted that assessing pollen fluorescence several months after exposure to ozone, no effect of ozone on fluorescence remains.

The mantle mass flow interconnected with the process of Glacial Isostatic Adjustment (GIA) and the reformation of the Earth’s crust constantly perturbs the observed gravity field towards a hypothetic isostatic state. We analyse the temporal changes of the gravity field from the GRACE data, using different mathematical and/or statistical methods to detect the GIA amidst other gravity signals. A number of gravimetric post-glacial land uplift rate (LUR) modelling methods are investigated and compared with the data from a total number of 515 GPS stations and preferred GIA forward models in Fennoscandia and North America. We investigate three mathematical methods, namely regression, principal component, and independent component analysis (ICA) to extracting the GIA signal from the GRACE monthly geoid heights. We use some regularization techniques to exploit the GRACE monthly data to their maximum spatial resolution and to increase the Signal to Noise Ratio of their short wavelengths. Near the centres of the study areas the gravimetric LUR model using the fast-ICA algorithm of Hyvärinen and Oja (2000) is shown to be in a complete agreement with the GPS data and the predictions of the GIA forward models, and for the whole areas, subject to epeirogeny movement of the two regions, their discrepancies reach to the extrema at -1.8 and +3.3, and -4.5 and +7.5 mm/a, respectively. We show that the largest discrepancies between the gravimetric model using the ICA method and the GIA forward model, occur for the sub-regions likely collocated with strong ice mass change signals.

Does the choice of proxy for stock market development matter? This paper suggests that the growth effect of stock market development is sensitive to the choice of proxy and using alternative financial development indicators have practically no influence on the results. We found that using either the stock market capitalization to GDP ratio or the stock market returns; have a positive and significant effect on growth. However, we cannot make same conclusion when one uses either the ratio of total value of trades on the major stock exchanges to GDP or stock market turnover ratio to proxy for stock market development as the coefficient on these variables were found to be statistically insignificant. The indexes extracted from principal component analysis confirm the sensitivity of the effect to the choice of proxy. This finding suggest that stock market development is a conceptual terms, thus, representing it with single indicators make it impossible to identify which stock market development indicators have a significant positive growth effects.

This article presents a self balanced multistage DC-DC step-up converter for photovoltaic applications. Proposed converter topology is designed for unidirectional power transfer and provides a doable solution for photovoltaic applications where voltage is required to be stepped up without magnetic components (Transformer-less and Inductor-less). The output voltage obtained from renewable sources will be low and must be stepped up by using a DC-DC converter for photovoltaic applications. K diodes and K capacitors along with two semiconductor control switch are used in the K-stage proposed converter to obtain an output voltage which is (K+1) times the input voltage. The conspicuous features of proposed topology are i) Magnetic components free (Transformer-less and Inductor-less). ii) Continuous input current iii) Low voltage rating semiconductor devices and capacitors iv) Modularity v) Easy to add a higher number of levels to increase voltage gain vi) Only two control switches with alternating operation and simple control. The proposed converter is compared with recent existing transformer-less and Inductor-less power converter in term of voltage gain, number of devices and cost. The application of proposed circuit is discussed in detail. The proposed converter has been designed with rated power of 60W, input voltage is 24V, output voltage is 100V and switching frequency is 100 kHz. The performance of the converter is verified through experimental and simulation results.

Background: One challenge in implant dentistry is to reduce marginal bone resorption to avoid long-term esthetic and biological complications. Sub-gingival implant placement has been suggested as a suitable strategy. Methods: 38 healthy patients were enrolled to receive bone level (BL – Control group) or 2 mm sub-crestal (SC - Test group) conical connection, platform-switched implants. An immediate tissue level abutment was used in the Test group by following a One Time Abutment (OTA) restorative procedure. Marginal Bone Modification (MBM) was calcu-latied on standardized radiographs at surgery (T0), loading (T1), and 6 months (T2) and 12 months after loading (T3), and cathegorised in Bone Loss (BL) if the resorption occurred below the implant neck and Bone Remodeling (BR) if above. Pocket probing depth (PPD), Bleeding on probing (BoP) and Plaque Index (PI) were also collected. Results: The mean MBM in the test group after 12 months was 0.61mm, compared to 0.52mm in the control group. However, no BL was observed in the test group, as the MBM completely occurred above the implant neck as BR, whilst in the control group MBM corresponded in total to BL. The average PPD was 2.38mm in the test group and 3.04mm in the control group after 12 months, BoP 50% and 43%, PI 33% and 19.44% respectively. Conclusions: Sub-crestal conical connection, platform-switched implants could be a suitable clinical choice to avoid BL with exposure of the treated implants surfaces.

This comprehensive review elucidates the profound relationship between the human microbiome and breast cancer managrement. Recent findings highlight the significance of microbial alterations in tissues, such as the gut and the breast, and their role in influencing breast cancer risk, development, progression, and treatment outcomes. We delve into how the gut microbiome can modulate systemic inflammatory responses and estrogen levels, thereby impacting cancer initiation and therapeutic drug efficacy. Furthermore, we explore the unique microbial diversity within breast tissue, indicating potential imbalances brought about by cancer and highlighting specific microbes as promising therapeutic targets. Emphasizing a holistic One Health approach, this review underscores the importance of integrating insights from human, animal, and environmental health to gain a deeper understanding of the complex microbe-cancer interplay. As the field advances, strategic manipulation of the microbiome and its metabolites presents innovative prospects for enhancing cancer diagnostics and therapeutics. However, rigorous clinical trials remain essential to confirm the potential of microbiota-based interventions in breast cancer management.

In a rapidly evolving global landscape characterized by increased international travel, migration, and ecological shifts, this study sheds light on the emergence of protozoal and helminthic infections targeting the central nervous system (CNS) within Europe. Despite being traditionally associated with tropical regions, these infections are progressively becoming more prevalent in non-endemic areas. By scrutinizing the inherent risks, potential outcomes, and attendant challenges, this study underscores the intricate interplay between diagnostic limitations, susceptibility of specific population subsets, and the profound influence of climate fluctuations. The contemporary interconnectedness of societies serves as a conduit for the introduction and establishment of these infections, warranting comprehensive assessment. This study emphasizes the pivotal role of heightened clinician vigilance, judicious public health interventions, and synergistic research collaborations to mitigate the potential consequences of these infections. Though rare, their profound impact on morbidity and mortality underscores the collective urgency required to safeguard the neurological well-being of the European populace. Through this multifaceted approach, Europe can effectively navigate the complex terrain posed by these emergent infections.

The entropy and charge distributions have been calculated for a simple model of polyelectrolytes attached to the surface of DNA using a field-theoretic method that includes fluctuations to the lowest one-loop order beyond mean-field theory. Experiments have revealed correlation-driven behavior of DNA in charged solutions, including charge inversion and condensation. In our model, the condensed polyelectrolytes are taken to be doubly charged dimers of length comparable to the distance between sites along the phosphate chains. Within this lattice-gas model, each adsorption site is assumed to have either a vacancy or a positively-charged dimer attached with the dimer oriented either parallel or perpendicular to the double helix DNA chain. We find that the inclusion of the fluctuation terms decreases the entropy by ∼50% in the weak binding regime. There, the bound dimer concentration is low because the dimers are repelled from the DNA molecule, which competes with the chemical potential driving them from the solution to the DNA surface. Surprisingly, this decrease in entropy due to correlations is so significant that it overcompensates for the entropy increase at the mean field level, so that the total entropy is even lower than in the absence of interactions between lattice sites. As a bonus, we present a transparent exposition of the methods used that could be useful to students and others wishing to use this formulation to extend this calculation to more realistic models.

Human pose estimation is a complex detection task in which the network needs to capture the rich information contained in the images. In this paper, we propose MSTPose (Multi-Scale Transformer for human Pose estimation). Specifically, MSTPose leverages a high-resolution Convolution neural network (CNN) to extract texture information from images. For the feature maps from three different scales produced by the backbone network, each branch performs the coordinate attention operations. The feature maps are then spatially and channel-wise flattened, combined with keypoint tokens generated through random initialization, and fed into a parallel Transformer structure to learn spatial dependencies between features. As the Transformer outputs one-dimensional sequential features, the mainstream two-dimensional heatmap method is abandoned in favor of one-dimensional coordinate vector regression. The experiments show that MSTPose outperforms other CNN-based pose estimation models and demonstrates clear advantages over CNN + Transformer networks of similar types.

3,4-Dihydroquinolin-2(1H)-ones represent a class of valuable bioactive compounds with six-membered nitrogen-containing heterocyclic structures. The development of simple, mild and efficient synthetic methods has been widely concerned by synthetic chemists. In this review, we have summarized a series of different synthetic strategies for the synthesis of dihydroquinoline-2(1H)-ones via catalytic annulation of α,β-unsaturated N-arylamides in the past decade, including covering electrophilic cyclization, radical initiated cyclization and photochemical cyclization reactions. Besides, the substrate scope and mechanistic details are also discussed. This paper will provide a useful reference for the development of diverse synthesis methodologies of 3,4-dihydroquinolin-2(1H)-ones.

Industries of the food sector have made a great effort to control SARS-CoV-2 indirect transmission, through objects or surfaces, by updating cleaning and disinfection protocols previously focused on inactivating other pathogens, as well as food spoilage microorganisms. The information, although scarce at the beginning of the COVID-19 pandemic, has started to be sufficiently reliable to avoid over-conservative disinfection procedures. This work reviews the literature to propose a holistic view of the disinfection process where the decision variables, such as type and concentration of active substance, are optimised to guarantee the inactivation of SARS-CoV-2 and other usual pathogens and spoilage microorganisms while minimising possible side-effects on the environment and animal and human health.

. In today’s world, cyber attack is one of the major issues concerning the organizations that deal with technologies like cloud computing, big data, IoT etc. In the area of cyber security, intrusion detection system (IDS) plays a crucial role to identify suspicious activities in the network traffic. Over the past few years, a lot of research has been done in this area but in the current scenario, network attacks are diversifying in both volume and variety. In this regard, this research article proposes a novel IDS where a combination of information gain and decision tree algorithm has been used for the purpose of dimension reduction and classification. For experimental purpose the NSL-KDD dataset has been used. Initially out of 41 features present in the dataset only 5 high information gain valued features are selected for classification purpose. The applicability of the selected features are evaluated through various machine learning based algorithms. The experimental result shows that the decision tree based algorithm records highest recognition accuracy among all the classifiers. Based on the initial classification result a novel methodology based on decision tree has been further developed which is capable of identifying multiple attacks by analyzing the packets of various transactions in real time.

In this paper, we consider a manufacturer who produces and sells a kind of innovative product in the monopoly market environment. Because the life cycle of innovative product is usually shorter than its procurement lead time, one unique demand quantity (scenario) will occur in the selling season, thus there is only one chance for the manufacturer to determine both optimal production quantity and optimal sale price. Considering this one-time feature of such a decision problem, a price-setting newsvendor model for innovative products is proposed. Different to the existing price-setting newsvendor models, the proposed models determine the optimal production quantity and sale price based on some specific state (scenario) which is most applicable for the manufacturer. The theoretical analysis provides managerial insights into the manufacturers’ behaviors in a monopoly market of an innovative product and several phenomena in the luxury goods market are well explained.

Chitosan decorated copper nanoparticles (CS/CuNPs) catalysts were synthesized via reduction methods utilizing green protocol. The CS/CuNPs hybrid catalysts were tested for the synthesis of quinoline derivatives utilizing one-pot multicomponent reaction (MCR) under ultrasonic irradiation. The best catalyst (CS/CuNPs) that provided good conversion reaction yield and high turnover frequency (TOF) was characterized using Fourier transform infrared (FTIR), Thermogravimetric analyses (TGA), X-ray diffraction (XRD), , scanning electron microscopy (SEM), transmission electron microscope (TEM) and X-ray photoelectron spectroscopy (XPS) techniques. Generalization of the scope of the proposed catalytic process was studied using different aldehydes. Excellent products yield and high TOF in even shorter reaction time (~5 min) was attained. Recyclability performance of the catalyst over five times re-use without detectable loss in product yield was recorded. The current method is green process utilizing environmentally benign catalyst and considered to be promising sustainable protocol for the synthesis of fine chemicals.

In recent times, the increasing concern for climate control has led to the widespread application of grid-connected inverter (GIC) based renewable energy systems. In addition, the increased usage of non-linear loads and electrification of the transport sector cause ineffective grid-frequency management and the introduction of harmonics. These grid conditions affect power quality and result in uncertainty and inaccuracy in monitoring and measurement. Incorrect measurement leads to overbilling/underbilling, ineffective demand and supply forecasts for the power system, and inefficient performance analysis. To address the outlined problem, a novel three-phase frequency component extraction and power measurement method based on Digital Lock-in Amplifier (DLIA) and Digital Lock-in Amplifier Frequency-Locked Loop (DLIA-FLL) is proposed to provide accurate measurement under the conditions of harmonics and frequency offset. A combined filter with a lowpass filter and notch filter for DLIA is employed to improve computation speed. A comparative study is performed to validate the performance of the proposed power measurement method, by comparing the proposed method to the windowed interpolated fast Fourier transform (WIFFT). The ZERA COM 3003 (a commercial high-accuracy power measurement instrument) is used as the reference instrument in the experiment

The possibility of applying Mo and Cr coatings by CVD method to the inner surfaces of tubular products Ø ˂ 60 mm has been studied.The process of applying Mo and Cr coatings by the method of gas-phase deposition was developed.The areas of parameters for obtaining coatings with a uniform structure, speed, hardness, as well as patterns of change in these characteristics when changing the main parameters of the process of obtaining such coatings are determined.The proven technological processes of applying Mo and Cr CVD coatings can be the basis for the development of pilot technologies for applying functional coatings to precision parts with small-diameter inner tubular surfaces in the mechanical engineering and aviation industries.A controlled process of applying two-component chromium-aluminum coatings by gas-phase deposition using organic and organometallic substances was developed. Optimization of the processes of applying high-quality two-component chromium-aluminum coatings on prototype models of the internal cavities of cooled turbine blades was carried out.The studies revealed the characteristics of two-component chromium-aluminum coatings comparable to those of coatings used in production, while ensuring good quality coatings in terms of adhesion strength to the turbine blade material. The reproducibility of the coatings obtained makes it possible to develop pilot technologies for production in the future.The lower toxicity of the precursors used makes it possible to create an environmentally friendly technology and significantly reduce environmental problems in production when applying two-component chromium-aluminum coatings to the internal cavities of cooled turbine blades.

This study innovatively assesses the Mun River flow components in Thailand, integrating Artificial Neural Networks (ANN) and Isotopic (18O) End-Member Mixing Analysis (IEMMA). It quantifies the contributions of the Upper Mun River (UMR) and Chi River (CR) to the overall flow, revealing a discrepancy in their estimated contributions. ANN predicts the UMR and CR contribute approximately 70.1% and 29.5% respectively, while IEMMA indicates a more pronounced disparity with 84% from UMR and 16% from CR. This divergence highlights the distinct perspectives of ANN, focusing on hydrological data patterns, and IEMMA, emphasizing isotopic signatures. Despite discrepancies, both methods validate UMR as a significant contributor to the overall flow, highlighting their utility in hydrological research. The findings underscore the complexity of river systems and advocate for an integrated approach of river flow analysis for a comprehensive understanding, crucial for effective water resource management and planning.

To accurately predict the matching relationships between the various components and the engine performance in the whole aero-engine environment, this study introduces a two-dimensional throughflow simulation method for the whole aero-engine. This method is based on individual throughflow solvers for the turbo-machinery and the combustor. It establishes a throughflow simulation model for the whole engine by integrating with the compressor-turbine co-operating equations and boundary conditions. The turbo-machinery throughflow solver employs a circumferentially averaged form of the time-dependent Navier-Stokes equations (N-S) as the governing equation. The combustor solver uses the Reynolds Average Navier-Stokes (RANS) method to solve flow and chemical reaction processes by constructing turbulence, combustion, and radiation models. The accuracy of the component solver is validated using Pratt &amp; Whitney’s three-stage axial compressor (P&amp;W3S1) and General Electric’s high-pressure turbine (GE-EEE HPT), and the predicted results are consistent with the experimental data. Finally, the developed throughflow method is applied to simulate the throttling characteristics of the WZ-X turboshaft engine. The results predicted by the throughflow program are consistent with the GasTurb calculations, including the trends of shaft power delivered, specific fuel consumption (SFC), inlet airflow, and total pressure ratio of the compressor. The developed method to perform throughflow simulation of the whole aero-engine eliminates the dependence on a general component map. It can quickly obtain the meridian flow field parameters and overall engine characteristics, which is expected to guide the design and modification of the engine in the future.

The Internet of Things technology opens the horizon for a broader scope of intelligent applications in smart cities. However, the massive amount of traffic exchanged among devices may cause security risks, significantly when devices are compromised or vulnerable to cyber-attack. An intrusion detection system is the most powerful tool to detect unauthorized attempts to access smart systems. It identifies malicious and benign traffic by analyzing network traffic. In most cases, only a fraction of network traffic can be considered malicious. As a result, it is difficult for an intrusion detection system to detect attacks at high detection rates while maintaining a low false alarm rate. This work proposes an integrated framework to detect suspicious traffic to address secure data communication in smart cities. This paper presents an approach to developing an intrusion detection system to detect various attack types. It can be done by implementing a Principal Component Analysis method that eliminates redundancy and reduces system dimensionality. Furthermore, the proposed model shows how to improve intrusion detection system performance by implementing an ensemble model.

The splitting method developed in a previous study for linear, multi – component, convection-diffusion mass transfer equations is extended to non-linear, multi-component, convection-diffusion mass transfer equations. The diffusion term is non-linear (the diffusion coefficients depend on chemical species concentrations). Theoretical results about the splitting error are presented, as well as numerical simulations for mixtures with n ≤ 5. The numerical experiments show second-order accuracy and stability for the present splitting algorithm.

Using morphological characteristics and SSR markers, we evaluated the morphological and genetic variation of 200 Perilla accessions collected from the five regions of South Korea and other region. In morphological characteristics analysis, particularly, leaf color, stem color, degree of pubescence, leaf size were found to be useful for distinguishing the characteristics of native Perilla accessions cultivated in South Korea. A total of 137 alleles were identified in the 20 simple sequence repeat (SSR) markers, and the number of alleles per locus ranged from 3 to 13, and the average number of alleles per locus was 6.85. The average gene diversity (GD) was 0.649, with a range of 0.290-0.828. From analysis of SSR markers, accessions from the Jeolla-do and Gyeongsang-do regions showed comparatively high genetic diversity values compared with those from other regions in South Korea. In the unweighted pair group method with arithmetic mean (UPGMA) analysis, the 200 Perilla accessions were found to cluster into three major groups and an outgroup with a genetic similarity of 42%, and did not showed a clear geographic structure from the five regions of South Korea. Therefore, it is believed that landrace Perilla seeds are frequently exchanged by farmers through various routes between the five regions of South Korea. The results of this study are expected to provide useful information for conservation of these genetic resources and selection of useful resources for the development of varieties for seeds and leafy vegetables of cultivated var. frutescens of Perilla crop in South Korea.

Anomaly occurrences in hydraulic machinery may lead to massive systems shut down, jeopardizing the safety of the machinery and its surrounding human operator(s) and environment, and the severe economic implications succeeding the faults and their associated damage. Hydraulics are mostly placed in ruthless environments, where they are consistently vulnerable to many faults. Hence, not only the machines and their components are prone to anomalies, but also the sensors attached to them, which monitor and report their health and behavioral changes. In this work, a comprehensive applicational analysis of anomalies in hydraulic systems extracted from a hydraulic test rig is thoroughly achieved. Firstly, we provided a combination of a new architecture of LSTM autoencoders and supervised machine and deep learning methodologies to perform two separate stages of fault detection and diagnosis. The two phases are condensed by: the detection phase using the LSTM autoencoder. Followed by the fault diagnosis phase represented by the classification schema. The previously mentioned framework is applied to both component and sensor faults in hydraulic systems, deployed in the form of two in-depth applicational experiments. Moreover, a thorough literature review of the past decade related work for the two stages separately is successfully conducted in this paper.

The bulk backscattering ratio ($\tilde{b_{bp}}$) is commonly used as a descriptor of the bulk real refractive index of the particulate assemblage in natural waters. Based on numerical simulations, we analyze the impact of heterogeneity of phytoplankton cells on $\tilde{b_{bp}}$. $\tilde{b_{bp}}$ is modeled considering viruses, heterotrophic bacteria, phytoplankton, detritus, and minerals. Three study cases are defined according to the relative abundance of these different components. Two study cases represent typical situations in open ocean, outside (No-B/No-M) and inside bloom (B/No-M). The third study case is typical of coastal waters with the presence of minerals. Phytoplankton cells are modeled by a two-layered spherical geometry representing a chloroplast surrounding the cytoplasm. The $\tilde{b_{bp}}$ values are higher when heterogeneity is considered because the contribution of coated spheres to backscattering is higher than homogeneous spheres. The impact of heterogeneity is however strongly conditioned by the hyperbolic slope $\xi$ of the particle size distribution. Even if the relative concentration of phytoplankton is small (<1%), $\tilde{b_{bp}}$ increases by about 60% (for $\xi=4.3$ and for the No-B/No-M water body), when the heterogeneity is taken into account, in comparison with a particulate population only composed by homogeneous spheres. As expected, heterogeneity has a much smaller impact (about 5$\%$ for $\xi=4.3$) on $\tilde{b_{bp}}$ when minerals are added.

Bonfire night is a worldwide phenomenon given to numerous annual celebrations characterised by bonfires and fireworks. Since Thailand has no national ambient air quality standards for metal particulates, it is important to investigate the impacts of particulate injections on elevations of air pollutants and ecological health impacts resulting from firework displays. In this investigation, Pb and Ba were considered potential firework tracers because their concentrations were significantly higher during the episode and lower than/comparable with minimum detection limits during other periods, indicating that their elevated concentrations were principally due to pyrotechnic displays. Pb/Ca, Pb/Al, Pb/Mg, and Pb/Cu can be used to pin-point emissions from firework displays. Air mass backward trajectories (72 h) from the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model indicated that areas east and north-east of the study site were the main sources for the air transportation. Although the combined risk associated with levels of Pb, Cr, Co, Ni, Zn, As, Cd, V, and Mn was far below the standards mentioned in international guidelines, the lifetime cancer risks associated with As and Cr levels exceeded US-EPA guidelines, and may expose inhabitants of surrounding areas of Bangkok to elevated cancer risk.

Specific foods and nutrients help prevent the progression from persistent high-risk human papillomavirus (hrHPV) infection to cervical cancer (CC). We aimed to focus on dietary patterns which may be associated with hrHPV status and risk of high-grade cervical intraepithelial neoplasia (CIN2+). Overall, 539 eligible women, including 127 CIN2+, were enrolled in a cross-sectional study, and tested for hrHPV infection. Food intakes were estimated using a food frequency questionnaire. Logistic regression models were applied. Using the Mediterranean Diet Score, we demonstrated that, among 252 women with normal cervical epithelium, medium adherence to Mediterranean diet decreased odds of hrHPV infection when compared to low adherence (adjOR=0.40, 95%CI=0.22-0.73). Using principle component analysis, we also identified two dietary patterns which explained 14.31% of variance. Women in the 3^rd and 4^th quartiles of the “western pattern” had higher odds of hrHPV infection when compared with 1^st quartile (adjOR=1.77, 95%CI=1.04-3.54 and adjOR=1.97, 95%CI=1.14-4.18, respectively). Adjusting for hrHPV status and age, women in the 3^rd quartile of the “prudent pattern” had lower odds of CIN2+ when compared with 1^st quartile (OR=0.50, 95%CI=0.26-0.98). Our study is the first to demonstrate the association of dietary patterns with hrHPV infection and CC, discouraging unhealthy habits in favour of Mediterranean-like diet.

With the increasing demand for precise condition monitoring and diagnosis of gas-insulated switchgears (GIS), it has become a challenge to improve the detection sensitivity of partial discharge (PD) induced in the GIS spacer. This paper deals with the elimination of the capacitive component from the phase resolved partial discharge (PRPD) signal generated in GIS spacer based on the discrete wavelet transform. Three types of typical insulation defects were simulated using PD cells. The single PD pulses were detected and were further used to determine the optimal mother wavelet. As a result, the bior6.8 was selected to decompose the PD signal into 8 levels and the signal energy at each level was calculated. The decomposed components related with capacitive disturbance were discarded whereas those associated with PD were de-noised by a threshold and a thresholding function. Finally, the PRPD signals were reconstructed using the de-noised components.

Antibiotic-loaded bone cement has been traditionally advocated as a key step for the success of one-stage hip revision surgery, while cementless techniques have been re-cently proposed with and without the use of local antibacterials as an equally suc-cessful alternative. Aim of this review is to investigate the effective role of local anti-microbial protection for one-stage cemented and cementless hip revision surgery. Twelve studies reporting the results of cemented single-stage procedure at a minimum two years follow-up were reviewed. When pooling together the data, no infection re-currence was observed on average in 83.3% of the patients (range 75.0% to 100%). Only two papers, both from the same French group, included patients treated without the use of antibiotic-loaded bone cement, with an average infection control of 95.9% in a total of 195 patients. This figure appears better that the 80.7% infection control ob-tained by pooling together all the remaining studies. Concerning cementless one-stage revision, a total of 17 studies, reporting on 521 patients, showed an average 90.0% (range 56.8% to 100%) no infection recurrence, at a minimum two years follow-up. Eight papers reported the outcomes of cementless implants without any local antibac-terial protection and nine more described four different techniques for local antimi-crobial implant protection. No comparative study investigated cementless revision with or without local antibacterial protection. Pooled data showed an average infection control of 86.7%, without the application of local antibacterials, compared to 90.1% to 100% with local antimicrobial protection, depending on the technology used. Howev-er, due to the relatively low number of patients treated with each local antimicrobial protection, no statistical difference could be found, either considering local antibacte-rial strategies alone or pooled together. No side effect had been reported by any local antibacterial technique. This review points out that local antibacterial protection for one-stage hip revision surgery, although safe and largely performed in the clinical set-ting, appears still to rely mainly on experts’ opinion and on observational series with no prospective or comparative trial, hence no definitive conclusion can be drawn con-cerning its effective role in one-stage hip revision surgery.

Chlamydia trachomatis (Ct) infections are the most common sexually-transmitted infection (STI). Despite effective antibiotics for Ct, undetected infections or delayed treatment can lead to infertility, ectopic pregnancies, and chronic pelvic pain. Besides humans, chlamydia poses similar health challenges in animals such as C. suis (Cs) in pigs. Based on the similarities between humans and pigs as well as their chlamydia species, we use pigs as a large biomedical animal model for chlamydia research. In this study, we use the pig model to develop a vaccine candidate against Ct. The vaccine candidate consists of TriAdj-adjuvanted chlamydial protease-like activity factor (CPAF) protein. We tested two weekly administration options – twice intranasal (IN) followed by twice intramuscular (IM), or twice IM followed by twice IN. We assessed the humoral immune response not only in serum by CPAF-specific IgG including antibody avidity determination but also in cervical and rectal swabs by CPAF-specific IgG and IgA ELISAs. The systemic T-cell response was analyzed upon in vitro CPAF-restimulation via IFN-γ and IL-17 ELISpots as well as intracellular cytokine staining flow cytometry. Our data demonstrate that while the IN/IM vaccination mainly led to non-significant systemic immune responses, the vaccine candidate is highly immunogenic if administered IM/IN. This vaccination strategy induced high serum anti-CPAF IgG levels with strong avidity as well as high IgA and IgG levels in vaginal and rectal swabs and in uterine horn flushes. In addition, this vaccination strategy prompted a pronounced cellular immune response: Besides inducing IL-17 production, the vaccine candidate induced a strong IFN-γ response by CD4 T cells. In the IM/IN vaccinated pigs, these cells also significantly downregulated their CCR7 expression – a sign for differentiation into peripheral tissue homing effector/ memory cells. Conclusively, this study demonstrates strong immunogenicity of the IM/IN administered TriAdj-adjuvanted Ct CPAF vaccine candidate. Future studies will test vaccine efficacy of this promising Ct vaccine candidate. In addition, this project demonstrates suitability of the Cs pre-exposed outbred pig model for Ct vaccine development. Thereby, we aim to open the bottleneck of large animal models to facilitate the progression of Ct vaccine candidates into clinical trials.

In the context of globalization One Health enables and concurs to the achievement of Global Health. One Health approach involves wide knowledge and understanding of its critical factors, next to an adequate implementation, also in farm animal sector. Moreover, animal farming and breeding shows particularities depending on country. Therefore, well-defined and specific strategies are still needed to be developed and adapted to the current conditions found in various countries for ensuring a well implementation, thus driving to better and healthier global outcomes and security. This paper will explore the One Health status in Romania, highlighting the current progress registered until now and the future perspectives based on its potential, in the context of the livestock farming framework and challenges. Our findings may help to disclose the challenges of putting in practice One Health in Romania, alongside to the pathways showing potential to enhance sustainability in livestock farming.

Animal hoarding is a complex issue that, when discovered, frequently necessitates opening shelter doors to many animals. This is due to hoarders' inability to provide even the most basic welfare standards for their animals, resulting in poor welfare conditions that frequently border on mis-treatment. These people are frequently unaware of their failure to care for their animals, as well as of the harm that they cause to people around them and the environment. They usually don't care for themselves either. The majority of hoarders have difficult histories, and they all need help get-ting back on track. Meanwhile, when the agencies discover the status quo, the animals are usually seized and taken to shelters, where they face a variety of welfare consequences, beginning with confinement in an unknown environment that is associated with additional risks (e.g., infectious diseases, behavioral deterioration, and distress). Furthermore, the targeted shelters are frequently overcrowded and cannot adequately accommodate the large number of animals found in hoard-ers' environments. The One Welfare approach, which is increasingly being used alongside One Health to work at the intersection of human and animal health and welfare, could be adopted to benefit animals while also addressing the poor states of humans. This concept’s depiction of the interconnections between animal welfare, human wellbeing, and the environment can fit with all the components of the animal hoarding phenomenon, including the peculiarities of the hoarding environment as well as those of shelters where animals are often moved. The purpose of this paper is to offer insights into how the One Welfare concept may be critical in tackling all of the interests concerned in these cases and offering solutions.

Peripheral nerve injuries (PNI) can have several etiologies, such as trauma and iatrogenic interventions that can lead to the loss of structure and/or function impairment. These changes can cause a partial or complete loss of motor and sensory functions, physical disability, and neuropathic pain, what in turn can affect the quality of life. For those reasons, PNI is a major public health concern. This review aims to revisit the concepts associated with the PNI. First, the anatomy of the peripheral nerve is detailed to explain the different types of injury. Then, some of the available therapeutic strategies are explained, including surgical methods, pharmacological therapies, and the use of cell-based therapies alone or in combination with biomaterials in the form of tube guides. Nevertheless, even with the various available treatments, it is difficult to achieve a perfect outcome with complete functional recovery. This review aims to explain the urge for new approaches and to understand the methods to evaluate nerve regeneration in a One Health perspective. In vitro models followed by in vivo models are very important to be able to translate the achievements to human medicine.

The implementation of One Health/EcoHealth/Planetary Health approaches has been identified as key (i) to address the strong interconnections between risk for pandemics, climate change and biodiversity loss, and (ii) to develop and implement solutions to these interlinked crises. As a response to the multiple calls of scientists in that direction, we have put forward seven long term research questions regarding COVID-19 and emerging infectious diseases (EIDs) that are based on an effective integration of environmental, ecological, evolutionary, and social sciences to better anticipate and mitigate EIDs. Research needs cover the social-ecology of infectious disease agents, their evolution, the determinants of susceptibility of humans and animals to infections, and the human and ecological factors accelerating infectious disease emergence. For comprehensive investigation, they include the development of nature-based solutions to interlinked global planetary crises, addressing ethical and philosophical questions regarding the relationship of humans to nature and regarding transformative changes to safeguard the environment and human health. In support of this research, we propose the implementation of innovative multidisciplinary facilities embedded in social-ecosystems locally: the “ecological health observatories” and the “living laboratories”. This work has been carried out in the frame of the EC project HERA (www.HERAresearchEU.eu) that aims to set the priorities for an environment, climate and health research agenda in the EU by adopting a systemic approach in the face of global environmental change.

Cyber security plays an important role to protect our computer, network, program and data from unauthorized access. Intrusion detection system (IDS) and intrusion prevention system (IPS) are two main categories of cyber security, designed to identify any suspicious activities present in inbound and outbound network packets and restrict the suspicious incident. Deep neural network plays a significant role in the construction of IDS and IPS. This paper highlights a novel IDS using optimized convolution neural network (CNN-IDS). An optimized CNNIDS model is an improvement over CNN which selects the best weighted model by considering the loss in every epoch. All the experiments have been conducted on the well known NSL-KDD dataset. Information gain has been used for dimensionality reduction. The accuracy of the proposed model is evaluated through optimized CNN for both binary and multiclass categories. Finally, a critical comparison has been performed with other general classifiers like J48, Naive Bayes, NB tree, Random forest, Multilayer Perceptron (MLP), Support Vector Machine (SVM), Recurrent Neural Network (RNN) and Convolution Neural Network(CNN). All the experimental results demonstrate that the optimized CNN-IDS model records the best recognition rate with minimum model construction time.

In the past decades, several new diseases have emerged in new geographical areas, with pathogens including Ebola, Zika, Nipah, and coronaviruses (CoVs). Recently, a new type of viral infection has emerged in Wuhan City, China, and initial genomic sequencing data of this virus does not match with previously sequenced CoVs, suggesting a novel CoV strain (2019-nCoV), which has now been termed as severe acute respiratory syndrome CoV-2 (SARS-CoV-2). Although Coronavirus disease 2019 (COVID-19) is suspected to originate from an animal host (zoonotic origin) followed by human-to-human transmission, the possibility of other routes such as food-borne transmission should not be ruled out. Compared to diseases caused by previously known human CoVs, COVID-19 shows less severe pathogenesis but higher transmission competence, as is evident from the continuously increasing number of confirmed cases globally. Compared to other emerging viruses such as Ebola virus, avian H7N9, SARS-CoV, or MERS-CoV, SARS-CoV-2 has shown relatively low pathogenicity and moderate transmissibility. Codon usage studies suggest that this novel virus may have been transferred from an animal source such as bats. Early diagnosis by real-time PCR and next-generation sequencing has facilitated the identification of the pathogen at an early stage. Since, no antiviral drug or vaccine exists to treat or prevent SARS-CoV-2, potential therapeutic strategies that are currently being evaluated predominantly stem from previous experience with treating SARS-CoV, MERS-CoV, and other emerging viral diseases. In this review, we address epidemiological, diagnostic, clinical, and therapeutic aspects, including perspectives of vaccines and preventive measures that have already been globally recommended.

Most of the research of mental fatigue evaluation mainly concentrated on some indexes that require sophisticate and large instruments which make the detection of mental fatigue cumbersome, time-consuming, and difficult to apply on a large scale. A quick and sensitive mental fatigue detection index is necessary so that mental workers can be alerted in time and take corresponding countermeasures. But to date, no studies have compared the sensitivity of common objective evaluation indexes. To solve these problems this study recruited 56 human subjects. These subjects were evaluated using six fatigue indexes: the Stanford sleepiness scale, digital span, digital decoding, short-term memory, critical flicker fusion frequency (CFF), and speed perception deviation. The results of fatigue tests before and after mental fatigue were compared, and a one-way analysis of variance (ANOVA) was performed on the speed perception deviation. The result indicated the significance of this index. Considering individual differences, the relative fatigue index (RFI) was proposed to compare the sensitivity of the indexes. The results showed that when the self-rated fatigue grade changed from non-fatigue to mild fatigue, the ranges of RFI values for digital span, digital decoding, short-term memory and CFF were 0.175–0.258, 0.194–0.316, 0.068–0.139, and 0.055–0.075, respectively. Correspondingly, when the self-rated fatigue grade changed from non-fatigue to severe fatigue, the ranges of RFI values for the above indexes were 0.175–0.258, 0.194–0.316, 0.068–0.139, and 0.055–0.075, respectively. These results suggest that the sensitivity of the digital decoding, digital span, short-term memory, and CFF decreased sequentially when the self-evaluated fatigue grade changed from no fatigue to mild or severe fatigue. The RFI individuality of the speed perception deviation is highly variable and is not suitable as an evaluation index. In mental fatigue testing, digital decoding testing can provide faster, more convenient, and more accurate results.

The class of A-stable symmetric one-step Hermite-Obrechkoff (HO) methods introduced in [1] for dealing with Initial Value Problems is analyzed. Such schemes have the peculiarity of admitting a multiple knot spline extension collocating the differential equation at the mesh points. As a new result, it is shown that these maximal order schemes are conjugate symplectic which is a benefit when the methods have to be applied to Hamiltonian problems. Furthermore a new efficient approach for the computation of the spline extension is introduced, adopting the same strategy developed in [2] for the BS linear multistep methods. The performances of the schemes are tested in particular on some Hamiltonian benchmarks and compared with those of the Gauss Runge-Kutta schemes and Euler-Maclaurin formulas of the same order.

Various short-range radars, such as impulse-radio ultra-wideband (IR-UWB) and frequency-modulated continuous-wave (FMCW) radars, are currently employed to monitor vital signs, including respiratory and cardiac rates (RR and CR). However, these methods do not consider the motion of an individual, which can distort the phase of the reflected signal, leading to inaccurate estimation of RR and CR because of a smeared spectrum. Therefore, motion compensation (MOCOM) is crucial for accurately estimating these vital rates. This paper proposes an efficient method incorporating MOCOM to estimate RR and CR with super-resolution accuracy. The proposed method effectively models the radar signal phase and compensates for motion. Additionally, applying the super-resolution technique to RR and CR separately further increases the estimation accuracy. Experimental results from the IR-UWB and FMCW radars demonstrate that the proposed method successfully estimates RR and CR even in the presence of body movement.

In this study, our primary objective was to analyze the tradeoff between accuracy and complexity in machine learning models, with a specific focus on the impact of reducing complexity and entropy on the production of landslide susceptibility maps. We aimed to investigate how simplifying the model and reducing entropy can affect the capture of complex patterns in the susceptibility maps. To achieve this, we conducted a comprehensive evaluation of various machine-learning algorithms for classification tasks. We compared the performance of these algorithms in terms of accuracy and complexity, considering both "before" and "after" scenarios of dimensionality reduction using Principal Component Analysis (PCA). Our findings revealed that reducing complexity and lowering entropy can lead to an increase in model accuracy. However, we also observed that this reduction in complexity comes at the cost of losing important complex patterns in the produced landslide susceptibility maps. By simplifying the model and reducing entropy, certain intricate relationships and uncertain patterns may be overlooked, resulting in a loss of information and potentially compromising the accuracy of the susceptibility maps. The analysis encompassed a diverse range of machine learning algorithms, including Random Forest (RF), Extra Trees (EXT), XGboost, LightGBM, Catboost, Naive Bayes (NB), K-Nearest Neighbors (KNN), Gradient Boosting Machine (GBM), and Decision Trees (DT). Each algorithm was evaluated for its strengths and limitations, considering the tradeoff between accuracy and complexity. Before dimensionality reduction, the algorithms demonstrated promising results, with RF exhibiting excellent AUC/ROC scores and average accuracy. However, computational costs were noted as a potential drawback for RF, especially when dealing with large datasets. EXT showcased robust performance and good accuracy, while XGboost demonstrated its ability to handle complex relationships within large datasets, albeit requiring careful hyperparameter tuning. The efficiency and scalability of LightGBM made it a suitable choice for large datasets, although it displayed sensitivity to class imbalance. Catboost excelled in handling categorical features, but longer training times were observed for larger datasets. NB showcased simplicity and computational efficiency but assumed independence among features. KNN, known for its capability to capture local patterns and spatial relationships, was found to be sensitive to the choice of distance metric. GBM, while capturing complex relationships effectively, was prone to overfitting without proper regularization. DT, with its interpretability and ease of understanding, faced limitations in terms of overfitting and limited generalization. After dimensionality reduction, certain algorithms exhibited improvements in their AUC/ROC scores and average accuracy, including RF, EXT, XGboost, and LightGBM. However, for a few algorithms, such as NB and DT, a decrease in performance was observed. This study provides valuable insights into the performance characteristics, strengths, and limitations of various machine learning algorithms in classification tasks. Researchers and practitioners can utilize these findings to make informed decisions when selecting algorithms for their specific datasets and requirements. We also aim to identify the potential factors contributing to the high accuracy rates obtained from these ensembled algorithms and explore possible shortcomings of non-ensembled algorithms that may result in lower accuracy rates. By conducting a comprehensive analysis of these algorithms, we seek to provide valuable insights into the benefits and limitations of ensembled approaches for landslide susceptibility mapping. Our study sheds light on the challenges faced when balancing accuracy and complexity in machine learning models for landslide susceptibility mapping. It emphasizes the importance of carefully considering the level of complexity and entropy reduction in relation to the specific patterns and uncertainties present in the data. By providing insights into this tradeoff, our research aims to assist researchers and practitioners in making informed decisions regarding model complexity and entropy reduction, ultimately improving the quality and interpretability of landslide susceptibility maps.

This article presents the development of the theoretical background and the design of an electronic device for monitoring the condition of a gapless Metal Oxide Surge Arrester (MOSA). The device is intended to be used online. Due to the inaccessibility and possible remote location of most surge arresters, it is equipped with a communication system, allowing the device to convey the measurement of the surge arrester characteristics under any conditions. By gathering measurements of the surge arrester’s resistive component of leakage current, it is possible to determine the condition of the MOSA. After interpreting the results, these can be sent via a data transfer unit to a server, which, in turn, sends it onward to the authorised personnel through the surge arrester control centre.

Diet influences, and is influenced by, a wide range of socioeconomic, cultural, geographic, and genetic variables. Here we survey a matrix of such interactions as well as their connection to a variety of health outcomes, in a cohort of 689 diverse adults employed at Emory University and enrolled in the Center for Health Discovery and Well-Being (CHDWB) study. Principal component analysis (PCA) of the Block Food Frequency Questionnaire revealed seven PC cumulatively explaining 25.8% and each individually at least 2% of the proportional consumption of 110 food items. PC1 is strongly correlated with the Healthy Eating Index-2015 measure, and accordingly healthier scores associate with multiple measures of physical and mental health. It, as well as PC2 (likely a measure of food expense) and PC3 (carbohydrate versus protein consumption) show significant geographic structure across the Atlanta metropolitan area, correlating with race and ethnicity, income level, age and sex. Notably, a polygenic score for body mass index (BMI) consisting of 281 SNPs explains 2.8% of the variance in PC5, which is as strong as its association with BMI itself. PC5 appears to differentiate participants with respect to conscious eating behavior related to the choice of diet or comfort foods. Our analysis adds to the growing literature on factor analysis of socio-demographic influences on nutrition and health.

Hyperspectral imaging provides vital information about the objects and elements present inside the image. That’s why they are very useful in satellite imagery as well as image forensics. Hyperspectral document analysis (HSDI) can be used for document authentication using ink analysis which can provide sufficient information about the composition and type of ink. In this project, we have implemented HSDI based ink classification technique using Principle Component Analysis for dimensionality reduction and K-means clustering for ink classification. This is unsupervised learning approach and it is very simple and efficient in order to classify limited number of bands. We have used this technique to classify 33 different bands of ink.

NMR spectral datasets, especially in systems with limited samples, can be difficult to interpret if they contain multiple chemical components (phases, polymorphs, molecules, crystals, glasses, etc…) and the possibility of overlapping resonances. In this paper, we benchmark several blind source separation techniques for analysis of NMR spectral datasets containing negative intensity. For benchmarking purposes, we generated a large synthetic datasbase of quadrupolar solid-state NMR-like spectra that model spin-lattice T1 relaxation or nutation tip/flip angle experiments. Our benchmarking approach focused exclusively on the ability of blind source separation techniques to reproduce the spectra of the underlying pure components. In general, we find that FastICA (Fast Independent Component Analysis), SIMPLISMA (SIMPLe-to-use-Interactive Self-modeling Mixture Analysis), and NNMF (Non-Negative Matrix Factorization) are top-performing techniques. We demonstrate that dataset normalization approaches prior to blind source separation do not considerably improve outcomes. Within the range of noise levels studied, we did not find drastic changes to the ranking of techniques. The accuracy of FastICA and SIMPLISMA degrades quickly if excess (unreal) pure components are predicted. Our results indicate poor performance of SVD (Singular Value Decomposition) methods, and we propose alternative techniques for matrix initialization. The benchmarked techniques are also applied to real solid state NMR datasets. In general, the recommendations from the synthetic datasets agree with the recommendations and results from the real data analysis. The discussion provides some additional recommendations for spectroscopists applying blind source separation to NMR datasets, and for future benchmark studies. Applications of blind source separation to NMR datasets containing negative intensity may be especially useful for understanding complex and disordered systems with limited samples and mixtures of chemical components.

The loss of integrity and adverse effect on mechanical properties can be concluded as attributing miro/macro-mechanics damage in structures, especially in composite structures. Damage as a progressive degradation of material continuity in engineering predictions for any aspects of initiation and propagation requires to be identified by a trustworthy mechanism to guarantee the safety of structures. Besides the materials design, structural integrity and health are usually prone to be monitored clearly. One of the most powerful methods for the detection of damage is machine learning (ML). This paper presents the state of the art of ML methods and their applications in structural damage and prediction. Popular ML methods are identified and the performance and future trends are discussed.

Dayaoshan, as an important metal ore producing area in China, is faced with the dilemma of resource depletion due to long-term exploitation. In this paper, remote sensing method is used to circle the favorable metallogenic areas and find new ore points for Gulong. Firstly, vegetation interference bas been removed by using mixed pixel decomposition method with hyperplane and genetic algorithm (GA) optimization; then, altered mineral distribution information has been extracted based on principal component analysis (PCA) and support vector machine (SVM) method; Thirdly, the favorable areas of gold mining in Gulong has been delineated by using ant colony algorithm (ACA) optimization SVM model to remove false altered minerals; Lastly, field survey verified that the extracted alteration mineralization information is correct and effective. The results show that the mineral alteration extraction method proposed in this paper has certain guiding significance for metallogenic prediction by remote sensing.

As a renewable industrial crop, Miscanthus offers numerous advantages, namely high photosynthesis activity (as a C4 plant) and exceptional CO2 fixation rate. These properties make Miscanthus very attractive for industrial exploitation, such as lignin generation. Here, we present a systematic study analyzing the correlation of the lignin structure with Miscanthus genotype and plant portion (stem versus leaf). Specifically, the ratio of the three monolignols and corresponding building blocks as well as the linkages formed between the units have been studied. Depending on the Miscanthus genotype and plant component (leaf versus stem), correlations between chemical structure and properties of the lignins have been determined, i.e. correlations in molecular weight, polydispersity and decomposition temperature. Lignin isolation was performed using non-catalyzed organosolv pulping and the structure analysis includes NREL, FTIR, UV-Vis, HSQC-NMR, TGA, pyrolysis GC/MS. Structural differences were found for stem and leaf-derived lignins. Compared to beech wood lignins, Miscanthus lignins possess lower molecular weight and narrow polydispersities (< 1.5 Miscanthus vs. > 2.5 beech) corresponding to improved homogeneity. In addition to conventional univariate analysis of FTIR spectra, multivariate chemometrics revealed distinct differences for aromatic in-plane deformations of stem versus leaf-derived lignins. These results emphasize the potential of Miscanthus as low-input resource and Miscanthus-derived lignin as promising agricultural feedstock.

Associating the most influential parameters with the product distribution is of uttermost importance in complex catalytic processes such as fluid catalytic cracking (FCC). These correlations can lead to the information-driven catalyst screening, kinetic modeling and reactor design. In this work, a dataset of 104 uncorrelated experiments, with 64 variables, has been obtained in an FCC simulator using 6 types of feedstock (vacuum gasoil, polyethylene pyrolysis waxes, scrap tire pyrolysis oil, dissolved polyethylene and blends of the previous), 36 possible sets of conditions (varying contact time, temperature and catalyst/oil ratio) and 3 industrial catalysts. Principal component analysis (PCA) has been applied over the dataset, showing that the main components are associated with feed composition (27.41% variance); operational conditions (19.09%) and catalyst properties (12.72%). The variables of each component have been correlated with the indexes and yields of the products: conversion, octane number, aromatics, olefins (propylene) or coke, among others.

VpStyA1 and VpStyA2B of Variovorax paradoxus EPS is annotated and characterized as the first representative of an E2-type styrene monooxygenase of proteobacteria. It comprises a single epoxidase (VpStyA1) and a fusion protein (VpStyA2B) which serves mainly as NADH:FAD-oxidoreductase. VpStyA2B had a Km of 33.6 ± 4.0 µM for FAD and a kcat of 22.3 ± 1.1 s-1. VpStyA2B and VpStyA1 showed monooxygenase activity on styrene of 0.14 U mg-1 and 0.46 U mg-1 as well as on benzyl methyl sulfide of 1.62 U mg-1 and of 3.11 U mg-1. A putative fusion region at position 408 (AREAV) was mutated to provide insights on VpStyA2B-function. The best mutant (408-AAAAA) obtained showed a 6.6-times higher affinity for FAD while keeping the NADH-affinity and -oxidation activity. Corresponding epoxidase activity increased (1.6-times). But, other mutants showed still NADH:FAD-oxidoreductase activity, but lost mostly their epoxidase activity indicating effects on the monooxygenase-part as well. Thus, this monooxygenase system represents an interesting candidate for biocatalyst development.

In this work, we explore Conjecture 1 proposed in the newly released pioneering paper “Volatility transformers: an optimal transport-inspired approach to arbitrage-free shaping of implied volatility surfaces” by Zetocha Valer, which posits that the “One-X” property is both a necessary and sufficient condition for convex order between non-negative continuous strictly increasing distributions with the same mean. We provide a counterexample demonstrating that the conjecture, as originally stated, does not hold. By examining stochastic orders, particularly the increasing convex order, and the “One-X” property, we propose an enhanced version of the conjecture which is shown to be valid. Furthermore, we discuss the implications of these findings in the context of equity implied volatility fitting and the construction of implied volatility surfaces, highlighting the challenges posed by real-market conditions. In the end, we propose several applications in implied volatility surface construction or simulation.

The One Health approach often reaches classrooms through Environmental Education (EE), which aims to guide society's response to current crises (environmental, health, economic, values). However, traditional EE teaching has focused on isolated ecological concepts and standardized solutions, ignoring the multidimensional nature of EE and failing to integrate the interdependence between environmental, animal, and human health. Moreover, teacher training often neglects didactic content knowledge, preventing students from acquiring the systemic vision needed to address eco-social problems and create sustainable solutions aligned with the Sustainable Development Goals (SDGs). This limits teachers’ ability to influence students' concerns and behaviours. In this context, this study aims to reflect on the current state of the issue and propose strategies informed by Science Education research to improve EE teaching, enabling the integration of One Health dimensions through effective didactics to achieve Transformative Environmental Education (TEE). For this purpose, we begin by addressing the limitations identified in recent systematic reviews, shifting the paradigm towards a symbiosis of EE and Science Education through scientific practices. We then present practical examples showcasing successful EE initiatives that foster a deeper understanding of socio-environmental issues, encourage innovative solutions, and nurture engaged citizens from early education onwards. These proposals can support classroom practice and ongoing teacher self-development. Pedagogical strategies include tackling issues that require systemic and critical thinking by developing scientific and epistemic practices while raising awareness of environmental justice. Thus, this study advocates for a new vision of EE, integrating the One Health approach, which could be applied to develop new educational programs, including teacher training. This would lead to a new learning evaluation model and help identify key determinants that trigger pro-environmental behaviours.

Fungal infections (FIs) are spreading globally causing a significant public health concern. However, its documentation remains sparse in Africa, particularly in Rwanda. This literature review, aims to assess the risk and document current gaps in evidence and policy. It summarizes the published data about FIs in Rwanda, including ten reports published between 1972-and-2022. Blastomyces, Candida, Cryptococcus, Histoplasma, Microsporum, Pneumocystis, Rhinosporidium, and Trichophyton caused human infections. These infections primarily affected the brain, respiratory, urinary and reproductive organs, and they were diagnosed using culturing, histopathology, immunology, and/or microscopic techniques. Our findings provide an overview of the diversity and distribution of FIs, highlighting significant limitations in the country’s diagnostic capacity and surveillance system for FIs. Also, indicating severe gaps in evidence to inform policymaking, guide strategic planning and intervention, underscoring the urgent need to build national capacity in fungal diagnosis, surveillance, and research. Raising awareness among the public, scientific community, healthcare providers, and policymakers remains crucial. Furthermore, this report reveals the threats of FIs on public health and food insecurity in Rwanda. A Multisectoral One Health Strategy is essential in research and intervention to determine and reduce the health and safety impacts of fungal pathogens on human, animal, and the environment.

Deep learning has increasingly been employed to identify minerals. However, deep learning can only be used to identify minerals in the distribution of the training set, while any mineral outside the spectrum of the training set is inevitably categorized erroneously within a predetermined class from the training set. To solve this problem, the study introduces the approach that amalgamates One-Class Support Vector Machines (OCSVM) with the ResNet architecture for the out-of-distribution mineral detection. Initially, ResNet undergoes training using a training set comprising well-defined minerals. Subsequently, the earlier layers of the trained ResNet are employed to extract the discriminative features of the mineral under consideration. These extracted mineral features then become the input for OCSVM. When OCSVM discerns the mineral in the training set's distribution, it triggers the subsequent layers within the trained ResNet, facilitating the accurate classification of the mineral into one of the predefined categories encompassing the known minerals. In the event OCSVM identifies the mineral out of the training set's distribution, it is unequivocally categorized as an unclassified or 'unknown' mineral. Empirical results substantiate the method's capability to identify out-of-distribution minerals while concurrently maintaining a commendably high accuracy rate for the classification of the 36 in-distribution minerals.

The climate crisis is developing to a life-changing event on a global level. Health promotion with the aim to increase the health status of individuals, independent from the present health status has been developed on scientific basis at least for the last eight decades and follows. There are some basic principles, which are prerequisites for both, health promotion and climate protection. Those principles include (1) sustainability, (2) orientation on determinants, and (3) requirement of in-dividual as well as community approaches. People are generally aiming to protect their lifestyle habits (e.g. traveling, consumer habits) and personal properties (e.g. car, house) by easy solution with as little effort as possible, and this can affect both, health and climate. To reduce the emission of greenhouse gases and to protect our environment, changes towards a sustainable lifestyle have to be embed into everybody’s mind. Examples for domains, that need to be addressed in health promotion as well as in climate protection include education/ knowledge/ (health) literacy, physical activity, nutrition and dietary habits, mental health, and social capital. If health promo-tion fails to tackle those domains, this will continue to drive climate crisis. And climate change, in turn, will affect health. On the other hand, developing and promoting health resources in the domains mentioned could again help to mitigate the health-damaging effects of climate change.

The introduction of stool as a readily obtainable sample and the recently developed simple-one-step (SOS) stool processing method offer an opportunity for TB diagnosis in children. We conducted this study at secondary health facilities in Ethiopia which are the first level referral facilities for childhood TB diagnosis and treatment with the aim to determine if stool-based TB diagnosis can be performed with reasonable level of concordance with sputum tests in using Xpert MTB/RIF Ultra. Eligible children 0-14 years with presumptive pulmonary TB were asked to provide stools in addition to routinely requested sputum samples. We computed Cohen’s kappa statistic in SPSS to determine the level of agreement between stool and sputum test results. Of the 373 children included in the study, 61% were &lt;5 years of age and 56% were male. Thirty-six children (9.7%) were diagnosed with TB and all started treatment. The rate of concordance between stool and sputum was high with kappa value of 0.83 (P&lt;0.001). There were more Xpert ultra positive test results on stool (n=27 (7.2%)) than on sputum/NGA (n=23 (6.2%)). Laboratories in secondary hospitals can perform stool-based TB diagnosis in children with high concordance between stool and sputum test results reaffirming the applicability of the SOS method.

World Health Organization (WHO) has announced the monkeypox (MPX) epidemic a global public health emergency due to its re-emergence, remarkable increase in the number of MPX cases worldwide, and its potential spread. This paper introduces the symptoms, complications, and features of MPX; its transmission, diagnosis and testing, vaccines and treatment; MPX and sexually transmitted diseases, especially the human immunodeficiency virus (HIV); possible natural hosts or reservoirs of the monkeypox virus (MPXV). A useful tool for MPX and surgical safety recommendations are presented. The challenges in fighting the MPX epidemic, One Health strategy, and future research are discussed.

Starting from acid chlorides, alkynes, tryptamines, and acroyl chloride, 21 densely substituted tetrahydro-beta-carbolines are prepared in a four-component one-pot reaction. In this study, the aza-Michael addition step to generate intermediate enaminones is optimized in the presence of ytterbium triflate. Moreover, apart from acroyl chloride, all reactants can be deployed in almost equimolar ratios, which increases the atom economy of the sequence. For mechanistic rationali-zation, the concluding aza-anellation was investigated by DFT calculations on potential inter-mediates and corresponding activation energies, revealing that the aza-anellation rather pro-ceeds via ene-reaction than via electrocyclization.

The thoracic surgery has increased drastically in recent years, especially in the light of the severe outbreak of 2019 novel coronavirus disease (COVID-19). Routine “passive” chest computed tomography (CT) screening of inpatients detects some pulmonary diseases requiring thoracic surgeries timely. As an essential device for thoracic anesthesia, the double-lumen tube (DLT) is particularly important for anesthesia and surgery. With the continuous upgrading of the DLTs and the widespread use of the fiberoptic bronchoscopy (FOB), the position of DLT in thoracic surgery is gradually becoming more stable and easier to observe or adjust. However, the DLT malposition still occurs during transferring patients from supine to lateral position in thoracic surgery, which leads to lung isolation failure and hypoxemia during one-lung ventilation (OLV). Recently some innovative DLTs or improved intervention methods have shown good results in reducing the incidence of DLT malposition. This review aims to summarize the recent studies of the incidence of left-sided DLT malposition, the reasons and effects of malposition, and summarize current methods for reducing DLT malposition and prospects for possible approaches. Meanwhile, we use bibliometric analysis to summarize the research trends and hot spots of the DLT research.

Effectively preventing and controlling zoonotic diseases requires a One Health approach that involves collaboration across sectors responsible for human health, animal health (both domestic and wildlife), and the environment, as well as other partners. Here we describe the Generalizable One Health Framework (GOHF), a five-step framework that provides structure for using a One Health approach in zoonotic disease programs being implemented at the local, sub-national, national, regional, or international level. Part of the framework is a toolkit that compiles existing resources and presents them following a stepwise schematic, allowing users to identify relevant resources as they are required. Coupled with recommendations for implementing a One Health approach for zoonotic disease prevention and control in technical domains including laboratory, surveillance, preparedness and response, this framework can mobilize One Health and thereby enhance and guide capacity building to combat zoonotic disease threats at the human-animal-environment interface.

The objective of this study was to evaluate the efficacy of one-step nucleic acid amplification (OSNA) for the detection of sentinel lymph node (SLN) metastasis compared to standard pathological ultrastaging in patients with early-stage endometrial cancer (EC). A total of 526 SLNs from 191 patients with EC were included in the study. 379 SLNs (147 patients) were evaluated by both methods, OSNA and standard pathological ultrastaging. The central 1-mm portion of each lymph node was subjected to semi-serial sectioning at 200-μm intervals and examined by hematoxylin-eosin and immunohistochemistry with CK19; the remaining tissue was analysed by OSNA for CK19 mRNA. The OSNA assay detected metastases in 19.7% of patients (14.9% micrometastasis and 4.8% macrometastasis), whereas pathological ultrastaging detected metastasis in 8.8% of patients (3.4% micrometastasis and 5.4% macrometastasis). Using the established cut-off value for detecting SLN metastasis by OSNA in EC (250 copies/μl), the sensitivity of the OSNA assay was 92%; specificity was 82%; diagnostic accuracy was 83%, and the negative predictive value was 99%. Discordant results between both methods were recorded in 20 patients (13.6%). OSNA resulted in an upstaging in 12 patients (8.2%). OSNA could aid in the identification of patients requiring adjuvant treatment at the time of diagnosis.

The global burden of antimicrobial resistance is on the rise, resulting in higher morbidity and mortality in our communities. The spread of antimicrobial resistance in the environment and development of resistant microbes is a challenge to the control of antimicrobial resistance. Approaches, such as antimicrobial stewardship programmes, and enhanced surveillance, have been devised to curb its spread. However, particularly in lower- and middle-income countries, the overall extent of antimicrobial resistance, and knowledge on on-going surveillance, stewardship or investigation efforts, re often poorly understood. This study aimed to look at the efforts that have been undertaken to combat antimicrobial resistance in Uganda as a means of establishing an overview of the situation, to help inform future decisions. We conducted a systematic literature review of the PubMed database to assess the efforts that have been done in Uganda to investigate and combat antimicrobial resistance. A search combining keywords associated with antimicrobial resistance were used to look up relevant studies between 1995 and 2020 on surveillance of antimicrobial resistance in Uganda, and susceptibility of microbes to different drugs. The search yielded 430 records, 163 of which met the inclusion criteria for analysis. The studies were categorized according to country and region, the type of antimicrobial resistance, context of the study, study design and outcome of the study. Antibacterial resistance and antimalarial resistance had the most published studies while antiviral and antifungal resistance each were represented by very few studies. Most studies were conducted in humans and hospital settings, with very few in veterinary and One Health contexts. The results from our work can inform public health policy on antimicrobial stewardship as it contributes to understanding the status of antimicrobial resistance surveillance in Uganda, and can also help to guide future research efforts. Notably, a One Health approach needs to be followed with re-spect to surveillance of antimicrobial resistance to better understand the mechanisms of resistance transfer across the human-animal–environment interface, including additional investigation in antiviral and antifungal resistance.

The bilateral tempered fractional derivatives are introduced generalising previous works on the one-sided tempered fractional derivatives and the two-sided fractional derivatives. An analysis of the tempered Riesz potential is done and showed that it cannot be considered as a derivative.

The holistic approach of One Health, which sees human, animal, plant, and environmental health as a unit, rather than discrete parts, requires not only interdisciplinary cooperation, but standardized methods for communicating and archiving data, enabling participants to easily share what they have learned and allow others to build upon their findings.Ongoing work by NCBI and the GenomeTrakr project illustrates how open data platforms can help meet the needs of federal and state regulators, public health laboratories, departments of agriculture, and universities. Here we describe how microbial pathogen surveillance can be transformed by having an open access database along with Best Practices for contributors to follow. First, we describe the open pathogen surveillance framework, hosted on the NCBI platform. We cover the current community standards for WGS quality, provide an SOP for assessing your own sequence quality and recommend QC thresholds for all submitters to follow. We then provide an overview of NCBI data submission along with step by step details. And finally, we provide curation guidance and an SOP for keeping your public data current within the database. These Best Practices can be models for other open data projects, thereby advancing the One Health goals of Findable, Accessible, Interoperable and Re-usable (FAIR) data.

This study constructs a comprehensive line loss management evaluation knowledge graph, considering the impact of various factors such as power grid structure characteristics, equipment's physical parameters, power grid operation characteristics, electricity consumption features, and natural conditions. It establishes the association between line loss characteristics and line loss retrieval and configures the data source for line loss management evaluation indicators. Based on Principal Component Analysis (PCA), this study selects comprehensive evaluation indicators for line loss management, verifies the rationality of the indicators, and proposes an integrated evaluation method for municipal power grid line loss management. A full-process composite evaluation method based on DEMATEL-Entropy Weight-TOPSIS is constructed, and the accuracy of the evaluation method is verified through ablation experiments. The TOPSIS analysis method is applied to empirical research on cross-sectional data from a city in Guangxi from 2012 to 2022 to validate the evaluation system's effectiveness, scientific nature, and objectivity. The case analysis shows that the line loss evaluation index system established in this paper effectively reduces the redundancy of line loss data and can objectively and rationally reflect the actual status of line loss management work, serving as a basis for decision-making in future line loss management evaluations.

We examine the impact of temperature (T) on the Seebeck coefficient S, i.e., the T dependence of S for the single-component molecular conductor Pd(dddt)_2] (dddt = 5,6-dihydro-1,4-dithiin-2,3-dithiolate) with a half-filled band, where the coefficient is obtained from a ratio of the thermal conductivity to the electrical conductivity. The conductor exhibits Dirac electrons with a nodal line, which shows the energy variation around the chemical potential and the density of states (DOS) with a minimum. Using a three-dimensional tight-binding (TB) model in the presence of both impurity and electron--phonon (e--p) scatterings, we study the Seebeck coefficient S_y for the molecular stacking and the most conducting direction. The impact of T on S_y exhibits a sign change, where S_y > 0 with a maximum at high temperatures and S_y < 0 with a minimum at low temperatures. The T dependence of S_y suggests that the contribution from the conduction (valence) band is dominant at low (high) temperatures.The result is examined using a spectral conductivity \sigma_y(\epsilon,T) as a function of the energy $\epsilon$ close to the chemical potential \mu. Further, the Seebeck coefficients for perpendicular directions (x and z) are examined, to show both S_x and S_z being positive and no sign change in contrast to S_y.

Cervical cancer is one of the ailments that endangers the health of women all over the world and causes infertility. Fortunately, this disease can be avoided. Both the results and participation rates of the current preventative strategy remain poor. Hence, preventative or early detection methods remain challenging and open. In gynecology and computer science fields, few studies on identifying cervical cancer are based on behavior risk factor and machine learning. Moreover, various social and behavioral aspects might often make it more difficult to detect cervical cancer especially in low‑ and middle‑income countries (LMICs). Accordingly, focus on predicting the presence of cervical cancer through behavioral risk factors is the basic structure of this research. We proposed an approach to classify cervical cancer from a social-behavioral perspective using logistic regression to predict cervical cancer. We got significantly improved outcomes than the existing methods. Moreover, we used feature engineering to formulate less dimensionality as a pre-processing step and optimized parameters to be prepared for classification phase. The results proved that using the logistic regression with regularization type L1 had a promising performance. The accuracy of Logistic Regression (LR) L1 was 97.2%. Such results support the proposed work as a reliable classification predictive tool.

Seeds can maintain their quality for a limited time; after that, they will lose their germination ability and vigor. Some physiological and physicochemical changes in the structure of the seeds during storage can decrease the quality of the seeds which is known as aging. Therefore, detection of the strong young seeds from the old ones is a vital issue in the modern agriculture. Conventional methods of detection of the seed viability and germination are destructive, time-consuming and costly. In this research, two peanut cultivars, namely North Carolina 2 (NC-2) and Florispan were selected and three artificial aging levels were induced to them. Hyperspectral images (HSI) of the samples were acquired and the seed viability was evaluated using two pre-trained convolutional neural network (CNN) image processing models, AlexNet and VGGNet. The noise of the reflection spectra of the samples was relatively resolved and modified by combining Preprocessing techniques of moving average (MA) and standard normal variate (SNV). Using principal component analysis (PCA), the dimensions were declined and three principal components (PC) were extracted. These PCs were then used as variables in the classification of support vector machine (SVM) and linear discriminant analysis (LDA). The results showed the high capability of CNN architectures such as AlexNet and VGGNet in detection of the seed viability based on the HIS with no pre-processing and feature extraction. The mentioned architectures reached the accuracy of 0.985 and 0.986, respectively. The combination of feature extraction method of PCA with LDA and SVM classifiers showed that the use of a limited number of PCs instead of all wavelengths can decrease the complexity of modeling, while enhancing the efficiency of the models such that LDA and SVM classifiers achieved the accuracy of 0.983 and 0.986 in classification of peanut sees, respectively.

Mints emit diverse scents that exert specific biological functions and are relevance for applications. The current work strives to develop electronic noses that can electronically discriminate the scents emitted by different species of Mint as alternative to conventional profiling by gas chromatography. Here, 12 different sensing materials including 4 different metal oxide nanoparticle dispersions (AZO, ZnO, SnO2, ITO), one Metal-Organic Frame as Cu(BPDC), and 7 different polymer films including PVA, PEDOT: PSS, PFO, SB, SW, SG, PB were used for functionalizing of QCM sensors. The purpose was to discriminate six economically relevant Mint species (Mentha x piperita, Mentha spicata, Mentha spicata ssp. crispa, Mentha longifolia, Agastache rugosa, and Nepeta cataria). The adsorption and desorption datasets obtained from each modified QCM sensor were processed by three different classification models including Principal Component Analy-sis (PCA), Linear Discriminant Analysis (LDA), and k-Nearest Neighbor Analysis (k-NN). This allowed discriminating the different Mints with classification accuracies of 97.2% (PCA), 100% (LDA), and 99.9% (k-NN), respectively. Prediction accuracies with a repeating test measurement reached up to 90.6% for LDA, and 85.6% for k-NN. These data demonstrate that this electronic nose can discriminate different Mint scents in a reliable and efficient manner.

Asphalt cement will not last for a long time as the world encounters a diminishment in the crude oil. For sustainable, flexible pavement development, new resources can provide a contribution to replace it partially or entirely. In this study, asphalt was partially and entirely replaced by guayule resin as a bioresource by-product, extracted during the guayule natural rubber production. Crumb rubber modifier (CRM) was used as an asphalt enhancer. The Superpave grading system was followed at high, intermediate, and low temperatures to evaluate such innovative binder for rutting, fatigue, and thermal cracking, respectively, in addition to viscosity. Therefore, the original, short-term aging and long-term aging were simulated using tank, rolling thin film oven, and pressure aging vessel materials. Additionally, component analysis using Fourier-transform infrared spectroscopy was provided to link the rheological properties with the chemical changes. Outcomes showed a relatively much lower viscosity of guayule in the same high-temperature asphalt grade indicating savings in plant energy consumption and reduced environmental emissions. CRM enhanced guayule, but not as much as asphalt, proven by polymeric component migration through liquid binder. This enhancement was reflected in the rheological performance besides other factors. As-received guayule seems to have high oxygen content proven by strong absorption peak intensities of oxidative bonds (e.g., Carbonyl and sulfoxide). Such pre-oxidation was negatively reflected in the intermediate- and low-temperature performance of guayule and guayule-based binders. However, the investigated guayule had potential to compensate for asphalt replacement in the presence of CRM by 23–42% by weight of blend.

The paper presents the results of the research concerning an evaluation of information 1 technology of gene expression profiles processing stability with the use of gene expression profiles 2 with different levels of noise component. The information technology is presented as a structural 3 block-chart, which contains all stages of the studied data processing. The hybrid model of objective 4 clustering based on SOTA algorithm and the technology of gene regulatory networks reconstruction 5 have been studied to evaluate the stability to the level of the noise component. The results of the 6 simulation have shown that the hybrid model of objective clustering has high level of stability 7 to noise component and vice versa, the technology of gene regulatory networks reconstruction is 8 very sensitivity to level of noise component. The obtained results indicate the importance of gene 9 expression profiles preprocessing at early stage of gene regulatory network reconstruction in order to 10 remove background noise and non-informative genes in terms of used criteria

N-shot learning has emerge in recent year as potential learning approach to solve the problem of data scarcity by learning underlying pattern from a few training sample. Despite recent state-of-the-art research on model-agnostic metal learning, transfer learning, and optimization strategy to rapidly learn valid information from few sample, there remains a big challenge on an actual out-of-distribution zero-day without any similarity to previously known malware family or new variant of an existing malware family. This ultimately questions the effectiveness of current state-of-the-art few-shot learning approach. In this research, we did an in-depth investigation into the performance of state-of-the-art Zero-shot, Single-shot, and few-shot learning approaches on zero-day out-of-distribution malware attack detection based on their static properties using Malimg and Malevis malware dataset. We ensure our model was aware of an out-of-distribution class during training while varying the number of samples in the out-of-distribution class accordingly zero-shot(no sample), single-shot (1 sample), few-shot(5 samples) while using confusion matrix to get the actual number of correct prediction on out-of-distribution malware validation samples. we assert that the model should be smart enough to detect and classify previously unseen data into an empty family as an out-of-distribution considering that the model was made to be aware of the existence of such distribution during training. Result shows 0, 0, and 3 correct out-of-distribution predictions on Zero-shot, single-shot, and few-shot experiments respectively, thereby showing limitation of the current state-of-the-art N-shot approaches on out-of-distribution attack.

Vibrio vulnificus (Vv) is a bacterial pathogen native to warm and brackish water ecosystems that can cause fatal septicemia (Vv-vibriosis) in humans and various farmed fish species. From a One Health perspective, controlling Vv-vibriosis outbreaks on farms is essential not only for animal but also for human health, as it reduces the risk of Vv transmission to humans. Electrolyzed water (EW) is a sustainable control method, exhibiting transient disinfectant properties due to the formation of hypochlorous acid (HOCl). We hypothesized that EW could effectively reduce Vv populations in aquaculture facilities, preventing outbreak emergence. To test this hypothesis, survival assays in EW were conducted under varying conditions of salinity, pH, and free available chlorine (FAC). The results indicated that an intermediate concentration of FAC had a significant bactericidal effect on Vv populations regardless of the condition and tested strain. Consequently, the strategic use of EW could serve as an eco-friendly preventive and control measure against Vv-vibriosis by significantly decreasing the bacterial load in farm water.

The mosquito-borne zoonotic nematode parasites, Dirofilaria immitis and Dirofilaria repens, primarily affect dogs. In recent years, their distribution has expanded due to various factors influencing vector-borne pathogens. This study aimed to investigate the comparative prevalence of infection in dogs and humans within a hyperenzootic region of Europe and to estimate the proportional relationship between infection prevalence in dogs and humans, in the concept of “One Health”. To this end, 604 blood samples from dogs and 625 serum samples from humans living in the Thrace region of northeastern Greece were collected. Dog samples were examined using Kott’s test for Dirofilaria spp. microfilariae and a commercial serological test for D. immitis antigen. Human sera were analysed by western blot for both parasites. The overall prevalence of infection in dogs was 177 (29.3%), with 173 (28.6%) testing positive for D. immitis and 7 (1.3%) for D. repens, including 6 (1%) cases of mixed infection. Specific IgG antibodies were detected in 42 (6.7%) human samples, with 24 (3.8%) positive for D. immitis and 18 (2.9%) for D. repens. The infection proportion in humans was 23.4% of the corresponding canine infection, indicating a high risk of human infection in this hyperenzootic region.

This investigation is part of a topical situation where wireless equipment is gradually being used for energy transfer, particularly for autonomous systems and the use of decarbonized en-ergies. A characteristic example of decarbonized autonomous use is linked to the substitution of thermal engine vehicles by electric vehicles (EVs) equipped with energy storage batteries. This response was considered in an ecological context of reducing air pollution and defending plane-tary biodiversity, which are currently vital. These EVs ultimately operate thanks to the wireless charging of their batteries when stationary or running. By changing long-established means of transport that have become a threat to biodiversity, it is necessary to ensure that innovative re-placement solutions protect this biodiversity. In addition, the construction of wireless power transfer (WPT) battery chargers for these EVs must offer an optimal ecology of clean energy saving. In such a context, the two concepts of One Health (OH) and Responsible Attitude (RA) will find their place in the design and control of WPT tools in EVs. This contribution aims to il-lustrate and analyze the roles of the green and non-wasteful OH and RA approaches in the de-sign and control of WPT embedded in EVs for the smart city (SC) environment. In the paper, WPT tools are first introduced. The design and control of EV battery charging tools are then examined. The biological effects on living tissues due to the electromagnetic field (EMF) radia-tion of WPT are analyzed. The phenomena and equations governing the design of WPT and the effects of EMF radiation are then exposed. The OH and RA approaches in the SC context are af-terward analyzed. The protection against the unsafe effects of WPT tools in the SC environment is consequently explored. The analyses followed in the paper are supported by examples of lit-erature.

This review delves into the intricate relationship between folate (Vitamin B9) intake, especially its synthetic form, folic acid, and its implications on health and disease. While folate plays a pivotal role in the one-carbon cycle, essential for DNA synthesis, repair, and methylation, concerns arise from its excessive intake. The literature underscores potential deleterious effects, such as an increased risk of carcinogenesis, disturbances in DNA methylation, and impacts on embryogenesis, pregnancy outcomes, neurodevelopment, and disease risk. Notably, these consequences stretch beyond the immediate effects, potentially influencing future generations through epigenetic reprogramming. We probe into the molecular mechanisms underlying these effects, including accumulation of unmetabolized folic acid, Vitamin B12 dependent mechanisms, altered one carbon metabolism, altered methylation patterns, and interactions with critical receptors and signaling pathways. Furthermore, we emphasize differences in the effects and mechanisms mediated by folic acid compared to natural folate. Given the widespread folic acid supplementation, it is imperative to further research its optimal intake levels, and the molecular pathways impacted by its excessive intake, ensuring the health and well-being of the global population.

Bistatic scattering coefficient have important application of ocean active microwave remote sensing. In this paper, a general expression of the composite scattering coefficient for multiple dielectric targets of one-dimensional fractal sea surface is derived by MOM method. Secondly, the bistatic scattering coefficient is computed, there are two class missile targets and one class ship target of one-dimensional fractal sea surface, and the influence of the water dielectric constant, fractal dimension, wind speed, the target dielectric constant and spatial position is discussed, and the corresponding conclusions are given. The proposed algorithm is able to solve some composite scattering problems, such as “dielectric or conductive object + rough surface”, “dielectric or conductive floating object +rough ”and it has certain generalization and reference.

BackgroundIt is unclear what underpins the large global variations in the prevalence of fluoroquinolone resistance in gram-negative bacteria. We tested the hypothesis that different intensities in the use of quinolones for food-animals plays a role. MethodsWe used Spearman’s correlation to assess if the country-level prevalence of fluoroquinolone resistance in human infections with Acinetobacter baumannii, Escherichia coli, Klebsiella pneumoniae and Pseudomonas aeruginosa was correlated with the use of quinolones for food producing animals. Linear regression was used to assess the relative contributions of country-level quinolone consumption for food-animals and humans on fluoroquinolone resistance in these 4 species. ResultsThe prevalence of fluoroquinolone resistance in each species was positively associated with quinolone use for food-producing animals (E. coli [ρ=0.55; P<0.001], K. pneumoniae [ρ=0.58; P<0.001]; A. baumanii [ρ=0.54; P=0.004]; P. aeruginosa [ρ=0.48; P=0.008]). Linear regression revealed that both quinolone consumption in humans and food animals were independently associated with fluoroquinolone resistance in E. coli and A. baumanii. ConclusionsReducing quinolone use in food-producing animals may help retard the spread of fluoroquinolone resistance in various gram negative bacterial species.

Abstract Unlike accelerated motion or curvalinear motion,a nonlinear motion state(non-inertial system) can be described by differential equations or the other algebric equations in axiom 2, the accelerated motion in axiom 3 can be considered to be described by using the equation of the linear and curvilinear process of the continuum extending to infinitely distance.Further, this linear and curvilinear process of the continuum is a quantitative continuum in essence as a unity of infinitely quantities and infinitely dimensions at infinite distance (infinity) relative to all orientations in which we exist. These indicates that an accelerated motion accumulates continuously started from a finite quantities, leaping to transit from finite to infinite quantities by an infinitely great accelerating force.

Transport Layer Security (TLS) is a cryptographic protocol that provides communications security between two peers and it is widely used in many applications. To reduce the latency in TLS handshake session resumption using pre-shared key (PSK) had been used. But current methods in PSK mode handshake uses a fixed session key multiple times for the lifetime of session ticket. Reuse of fixed session key should be very careful in the point of communications security. It is vulnerable to replay attacks and there is a possibility of tracking users. Paired token (PT) is a new secondary credential scheme that provides pre-shared key in stateless way in client-server environment. Server issues paired token (public token and secret token) to authenticated client. Public token represents signed identity of client and secret token is a kind of shared secret between client and server. Once client is equipped with PT, it can be used for many symmetric key based cryptographic applications such as authentication, authorization, key establishment, etc. It was also shown that it can be used for one-time authenticated key establishment using the time-based one-time password (TOTP) approach. In this paper we apply the PT and TOTP approach to TLS to achieve stateless one-time authenticated session resumption. Server executes full handshake of TLS 1.3 and issues PT to authenticated client. Then client and server can execute one-time authenticated session resumption using PT in stateless way in server side. In every runs of session resumption distinct session keys are established that the same PT can be used safely for longer lifetime. If anonymous PT is used with renewal issuing, user privacy, untraceability and forward security can be achieved easily. It will provide a huge performance gain in large-scale distributed services.

Sideroflexins (SLC56 family) are highly conserved multi-spanning transmembrane proteins inserted in the inner mitochondrial membrane in eukaryotes. Few data are available on their molecular function but, since their first description, they were thought to be metabolite transporters probably required for iron utilization inside the mitochondrion. Such as numerous mitochondrial transporters, sideroflexins remain poorly characterized. The prototypic member SFXN1 has been recently identified as the previously unknown mitochondrial transporter of serine. Nevertheless, pending questions on the molecular function of sideroflexins remain unsolved, especially their link with iron metabolism. Here, we review the current knowledge on sideroflexins, their presumed mitochondrial functions and the sparse - but growing - evidence linking sideroflexins to iron homeostasis and iron-sulfur cluster biogenesis. Since an imbalance in iron homeostasis can be detrimental at the cellular and organismal levels, we also investigate the relationship between sideroflexins, iron and physiological disorders. Investigating Sideroflexins’ functions constitutes an emerging research field of great interest and will certainly lead to main discoveries on mitochondrial physiopathology.

Directed evolution methods are becoming increasingly popular, as they are extremely powerful toward developing new biomolecules with altered/novel activities, e.g., proteins with new catalytic functions or substrate specificities, and nucleic acids that recognize an intended target. Especially useful are systems that have incorporated continuous evolution, where the protein to be evolved undergoes continuous mutagenesis to evolve a desired trait with little to no input from the researcher once the system is started. However, continuous evolution methods can be challenging to implement in the lab and daunting for researchers to invest time and resources. Our intent is to provide basic information and helpful suggestions that we have gained from our experience with bacterial phage-assisted continuous evolution (PACE). Specifically, we review factors to consider before adopting PACE for a given evolution scheme, different types of selection circuits that can be utilized with particular focus on the PACE-B1H selection system, what optimization of a PACE selection circuit may look like using directed evolution of ME47 as a case study, and additional techniques that may be incorporated into a PACE experiment. With this information, researchers will be better equipped to determine if PACE is a valid strategy to use to evolve their proteins and how to set up a valid selection circuit.

Developing particles of different anisotropic shapes are the hot topic since decades as they guarantee some special features of properties not possible through other means. Again, controlling atoms to develop certain size and shape particle is a quite challenging job. In this study, gold particles of different shapes are developed via pulse-based electronphoton-solution interface process. Gold atoms of certain transition state develop monolayer assembly at solution surface around the light glow (known in argon plasma) being generated at bottom of copper capillary (known in cathode). The rate of uplifting gold atoms to solution surface is being controlled by forcing energy (travelling photons) pursuing electrons and high energy photons (in high density) entering to solution. Gold atoms dissociated from the precursor under dissipating heat energy into the solution supplied under propagating photons characteristic current through immersed graphite rod (known in anode). Placing packets of nano shape energy of tuned pulse protocol over compact monolayer assembly comprising transition state atoms develop tiny-sized particles of formed shape. On separation of joint tiny particles into two equilateral triangular-shaped tiny particles, exerting forces of surface format elongate atoms of one-dimensional arrays converting them into structures of smooth elements. Due to immersing level of force, such tiny-shaped particles pack from different zones at centre of light glow where they assembled structures of smooth elements for developing mono-layers of different shapes of particles. Developing one-dimensional particles deal assembling of structures of smooth elements of packing tiny-shaped particles from nearly rearward zones of reflection of north-south poles, whereas, developing multi-dimensional particles deal assembling of structures of smooth elements of packing tiny-shaped particles from the east-west poles and near regions. Depending on the number of assembled structures of smooth elements at point of nucleation, packing of tiny-shaped particles from different zones develop different shapes of the anisotropic particles. At fixed precursor concentration, increasing the process time results into develop particles of low aspect ratio. Under tuned parameters, developing mechanisms of particles exhibiting unprecedented features are discussed.