Miracle-dz/UniFAR-FTUs huggingface dataset:
Proteome Analysis of Bevacizumab Intervention in Experimental Central Retinal Vein Occlusion

Bevacizumab is a frequently used inhibitor of vascular endothelial growth factor (VEGF) in the management of macular edema in central retinal vein occlusion (CRVO). Studying retinal protein changes in bevacizumab intervention may provide insights into mechanisms of action. In nine Danish Landrace pigs, experimental CRVO was induced in both eyes with argon laser. The right eyes received an intravitreal injection of 0.05 mL bevacizumab (n = 9), while the left control eyes received 0.05 mL saline water (NaCl). Retinal samples were collected 15 days after induced CRVO. Label-free quantification nano-liquid chromatography–tandem mass spectrometry identified 59 proteins that were regulated following bevacizumab treatment. Following bevacizumab intervention, altered levels of bevacizumab components, including the Ig gamma-1 chain C region and the Ig kappa chain C region, were observed. Changes in other significantly regulated proteins ranged between 0.58–1.73, including for the NADH-ubiquinone oxidoreductase chain (fold change = 1.73), protein-transport protein Sec24B (fold change = 1.71), glycerol kinase (fold change = 1.61), guanine-nucleotide-binding protein G(T) subunit-gamma-T1 (fold change = 0.67), and prefoldin subunit 6 (fold change = 0.58). A high retinal concentration of bevacizumab was achieved within 15 days. Changes in the additional proteins were limited, suggesting a narrow mechanism of action.
Host ecology drives frog skin microbiome diversity across ecotone in South-Central North America

Anurans (frogs and toads) are an ecologically diverse group of vertebrate organisms that display a myriad of reproductive modes and life history traits. To persist in such an expansive array of habitats, these organisms have evolved specialized skin that is used for respiration while also protecting against moisture loss, pathogens, and environmental contaminants. Anuran skin is also colonized by communities of symbiotic microorganisms, and these skin microbiota serve critical roles in numerous processes associated with anuran host health and persistence such as pathogen resistance and immunity. However, gaps remain in our understanding of the environmental and evolutionary processes that shape frog skin microbial communities. Here, we combined existing anuran disease data with 16S rRNA skin microbial inventories to elucidate the roles that geographic location, host evolutionary history, host ecology, and pathogen presence play in the microbial community assemblage of five co-distributed frog host species in Oklahoma. These focal species possess distinct ecological preferences: aquatic, semi-aquatic, and arboreal, and our results indicate that host ecology is the primary driver of frog skin microbial community structure. Additionally, compositional differences were observed among select host species based on geographic location, but this was not consistent among all five frog species. We did not find evidence of phylogenetic signal among our samples and results from the Classification and Regression Tree Analysis revealed that the presence of the amphibian pathogen Batrachochytrium dendrobatidis and the severity of infection were not drivers of skin microbiome differences among our focal host species. Results from this comparative study contribute to our growing understanding of the environmental and host-associated drivers of skin microbial community assemblage and represents one of the first studies on landscape-level variation in skin microbial communities among North American frogs.
AC Electromagnetic Field Controls the Biofilms on the Glass Surface by Escherichia coli & Staphylococcus epidermidis Inhibition Effect

Biofilms, mainly comprised of bacteria, form on materials’ surfaces due to bacterial activity. They are generally composed of water, extracellular polymeric substances (polysaccharides, proteins, nucleic acids, and lipids), and bacteria. Some bacteria that form biofilms cause periodontal disease, corrosion of the metal materials that make up drains, and slippage. Inside of a biofilm is an environment conducive to the growth and propagation of bacteria. Problems with biofilms include the inability of disinfectants and antibiotics to act on them. Therefore, we have investigated the potential application of alternating electromagnetic fields for biofilm control. We obtained exciting results using various materials’ specimens and frequency conditions. Through these studies, we gradually understood that the combination of the type of bacteria, the kind of material, and the application of an electromagnetic field with various low frequencies (4 kHz–12 kHz) changes the circumstances of the onset of the biofilm suppression effect. In this study, relatively high frequencies (20 and 30 kHz) were applied to biofilms caused by Escherichia coli (E. coli) and Staphylococcus epidermidis (S. epidermidis), and quantitative evaluation was performed using staining methods. The sample surfaces were analyzed by Raman spectroscopy using a Laser Raman spectrometer to confirm the presence of biofilms on the surface.
ECAE: Edge-Aware Class Activation Enhancement for Semisupervised Remote Sensing Image Semantic Segmentation

Remote sensing image semantic segmentation (RSISS) remains challenging due to the scarcity of labeled data. Semisupervised learning can leverage pseudolabels to enhance the model’s ability to learn from unlabeled data. However, accurately generating pseudolabels for RSISS remains a significant challenge that severely affects the model’s performance, especially for the edges of different classes. To overcome these issues, we propose a semisupervised semantic segmentation framework for remote sensing images (RSIs) based on edge-aware class activation enhancement (ECAE). First, the baseline network is constructed based on the average teacher model, which separates the training of labeled and unlabeled data using student and teacher networks. Second, considering local continuity and global discreteness of object distribution in RSIs, the class activation mapping enhancement (CAME) network is designed to predict local areas more remarkably. Finally, the edge-aware network (EAN) is proposed to improve the performance of edge segmentation in RSIs. The combination of the CAME with the EAN further heightens the generation of high-confidence pseudolabels. Experiments were performed on two publicly available remote sensing semantic segmentation datasets, Potsdam and ISPRS Vaihingen, which verify the superiorities of the proposed ECAE model.
The (Mis)use of Psychotropic Drugs and Addiction to Anxiolytics among Older Adults Living at Home or in Retirement Homes: Implications for Quality of Life

Nowadays, the growing number of people aged 65+ has become a global phenomenon. At that age, the most common medical problems are multimorbidity and inappropriate polypharmacy, which have a negative impact on the quality of life in older adults. The aim of this cross-sectional study was to examine comorbidity, the use of psychopharmaceuticals, and symptoms of addiction to anxiolytics among older adults living at home or in retirement homes, and to examine the differences in quality of life in relation to the use and misuse of psychotropic drugs. The research included 383 people aged 65+ living in the Republic of Croatia (EU). A standardized questionnaire CAGE was used to collect data about the use of psychotropic drugs. Quality of life was examined using the WHOQOL-BREF scale. The average age of respondents was 83 years. There is a significantly higher prevalence of anxiety disorders (p = 0.001) in respondents who live at home. Psychopharmaceuticals were used by 218 (56.9%) respondents, equally in both groups of respondents. A total of 77 (20.1%) respondents had been using anxiolytics for more than five years, while 26 (6.8%) of them had significant clinical symptoms of addiction to anxiolytics. All domains and the overall quality of life scale were significantly lower (p < 0.001) in respondents who have clinical symptoms of anxiolytic addiction. The results indicate that the use of psychotropic drugs by respondents is inappropriate. Respondents who inappropriately and excessively use psychotropic drugs have a significantly worse quality of life.
Assessing landscape suitability and connectivity for effective conservation of two semi‐desert ungulates in Iran

Conservation outside conservation areas (CAs) is more challenging due to increased exposure to human‐induced disturbances. Therefore, it is important to identify and designate new CAs that can protect core wildlife habitat patches, as well as key linkages to promote connectivity. We performed this study to identify and prioritize core habitat patches and corridors for two semi‐desert sympatric gazelles in Iran (i.e., goitered gazelle Gazella subgutturosa and jebeer gazelle G. bennettii) in order to propose the expansion of existing CAs. We used an ensemble distribution modeling approach based on three algorithms (random forest, maximum entropy, and generalized boosting models) for habitat suitability assessment and a combination of resistant kernel and factorial least‐cost path analysis for connectivity modeling. Our results revealed that distance to CAs, elevation, and annual mean temperature were the most influential variables for predicting the distribution of the two gazelle species. We identified 12 and six core habitat patches for goitered gazelle and jebeer gazelle, respectively with a minimum area of 210 km2. Core habitat patches were mainly occurred in the north of the central basin of Iran with a high priority for the conservation of both species. CAs protected up to half of core habitat patches for goitered gazelle and one‐third of core habitat patches for jebeer gazelle. Due to human‐induced disturbances (e.g., vehicle collisions and poaching) outside CAs, it is necessary to increase regular monitoring and establish new CAs based on the identified core habitat patches for the two gazelles. Our findings highlight opportunities of designating new CAs for wildlife habitat and corridors conservation and for promoting connectivity by limiting road impacts.
Chi-square and Adjusted Standardised Residual Analysis

In hypothesis testing, inference is made by comparing the computed test statistic and the critical value which rely on a specified level of significance and degrees of freedom. This paper examines various study variables to assess whether there exists an interdependency between relationship intimacy and these variables. The chi-square test, likelihood ratio test, the adjusted standardised residual, and the proposed benchmark methods are applied to determine the acceptance or rejection of the null hypothesis and the individual category contribution that enhanced the inference. The comparative analysis involves data collected from a survey conducted before the onset of the Covid-19 pandemic, during the Covid-19 lockdown period, and following the relaxation of post-Covid-19 lockdown measures spanning a duration of six weeks. The findings showed that the null hypothesis cannot be rejected implying that the study variables are independent of relationship intimacy for the periods under study. The adjusted standardised residual and the benchmark methods revealed that sexual intimacy and quarrel are the highest variable contributors to the acceptance of the null hypothesis.
UAVPal: A New Dataset for Semantic Segmentation in Complex Urban Landscape With Efficient Multiscale Segmentation

Semantic segmentation has recently emerged as a prominent area of interest in Earth observation. Several semantic segmentation datasets already exist, facilitating comparisons among different methods in complex urban scenes. However, most open high-resolution urban datasets are geographically skewed toward Europe and North America, while coverage of Southeast Asia is very limited. The considerable variation in city designs worldwide presents an obstacle to the applicability of computer vision models, especially when the training dataset lacks significant diversity. On the other hand, naively applying computationally expensive models leads to inefficacies and sometimes poor performance. To tackle the lack of data diversity, we introduce a new UAVPal dataset of complex urban scenes from the city of Bhopal, India. We complement this by introducing a novel dense predictor head and demonstrate that a well-designed head can efficiently take advantage of the multiscale features to enhance the benefits of a strong feature extractor backbone. We design our segmentation head to learn the importance of features at various scales for each individual class and refine the final dense prediction accordingly. We tested our proposed head with a state-of-the-art backbone on multiple UAV datasets and a high-resolution satellite image dataset for LULC classification. We observed improved intersection over union (IoU) in various classes and up to 2$\%$ better mean IoU. Apart from the performance improvements, we also observed nearly 50$\%$ reduction in computing operations required when using the proposed head compared to the traditional segmentation head.
Harnessing the Power of Cloud-Based Big Data Analytics for E-Government Advancement in Morocco: A Catalyst for Development

In response to economic, political, and technological stimuli, governments across the globe are progressively embracing digital transformation to devise innovative digital solutions. Despite these advancements, challenges persist in the integration of information resources, including deficiencies in government information systems and threats to network and information security. This paper investigates a novel algorithm for the filling and classification of big data within E-government systems, which comprises data management and governance, cultural and industrial shifts tied to human resource development, and data exchange protocols. A cloud computing environment serves as the infrastructure for constructing an E-government big data intelligence system. The system enables parallel data processing and classification via decision trees, thereby promoting the efficacious and sustainable employment of big data analytics in policy formulation and digital innovation. Additionally, the paper delineates the hurdles and issues that confront these agencies, and proposes potential solutions to augment citizen satisfaction and to deliver value within and beyond governmental sectors. The findings suggest that the integration of big data technologies in E-government presents an effective strategy for the provision of interactive services, thereby addressing citizens' demands for enhanced services.
In Search of Alignment between Learning Analytics and Learning Design: A Multiple Case Study in a Higher Education Institution

Learning design (LD) has increasingly been recognized as a significant contextual element for the interpretation and adoption of learning analytics (LA). Yet, few studies have explored how instructors integrate LA feedback into their learning designs, especially within open automated feedback (AF) systems. This research presents a multiple-case study at one higher education institution to unveil instructors’ pilot efforts in using an open AF system to align LA and LD within their unique contexts, with the goal of delivering personalized feedback and tailored support. A notable finding from these cases is that instructors successfully aligned LA with LD for personalized feedback through checkpoint analytics in highly structured courses. Moreover, they relied on checkpoint analytics as an evaluation mechanism for evaluating impact. Importantly, students perceived a stronger sense of instructors’ support, reinforcing previous findings on the effectiveness of personalized feedback. This study contributes essential empirical insights to the intersection of learning analytics and learning design, shedding light on practical ways educators align LA and LD for personalized feedback and support.
The Role of CD4 T Cell Help in CD8 T Cell Differentiation and Function During Chronic Infection and Cancer

CD4 and CD8 T cells are key players in the immune response against both pathogenic infections and cancer. CD4 T cells provide help to CD8 T cells via multiple mechanisms, including licensing dendritic cells (DCs), co-stimulation, and cytokine production. During acute infection and vaccination, CD4 T cell help is important for the development of CD8 T cell memory. However, during chronic viral infection and cancer, CD4 helper T cells are critical for the sustained effector CD8 T cell response, through a variety of mechanisms. In this review, we focus on T cell responses in conditions of chronic Ag stimulation, such as chronic viral infection and cancer. In particular, we address the significant role of CD4 T cell help in promoting effector CD8 T cell responses, emerging techniques that can be utilized to further our understanding of how these interactions may take place in the context of tertiary lymphoid structures, and how this key information can be harnessed for therapeutic utility against cancer.
Understanding the Thymic Microenvironment: the Cellular and Molecular Basis of T Cell Development

Objectives: The thymus is a primary lymphoid organ that provides specialized microenvironment for T cell development. A variety of thymic stromal cells form the thymic tissue architecture and critically regulate the development and repertoire selection of T cells. Methods: We reviewed historical and recent studies on thymic stromal cells, especially focusing on the well-characterized functions of thymic epithelial cells (TECs) and the significance of as yet less characterized non-TEC thymic stromal cells and hematopoietic antigen-presenting cells in the regulation of T cell development. Results: Cortical TECs (cTECs) induce positive selection of diverse and functional T cells, while medullary TECs (mTECs) establish T cell tolerance via the negative selection of auto-reactive T cells and their conversion into regulatory T cells. These modes of T cell tolerance induction are also mediated by hematopoietic antigen-presenting cells such as dendritic cells and thymic B cells. Thymic mesenchymal cells, a prominent component of non-TEC thymic stromal cells, support the development and maintenance of TECs and thereby T cell production. Conclusion: Understanding the cellular and molecular basis for thymic stromal subsets will provide invaluable information toward in vivo reconstitution of the thymic microenvironment for future therapeutic applications.
A Comparative Analysis of VirLock and Bacteriophage ϕ6 through the Lens of Game Theory

The novelty of this paper lies in its perspective, which underscores the fruitful correlation between biological and computer viruses. In the realm of computer science, the study of theoretical concepts often intersects with practical applications. Computer viruses have many common traits with their biological counterparts. Studying their correlation may enhance our perspective and, ultimately, augment our ability to successfully protect our computer systems and data against viruses. Game theory may be an appropriate tool for establishing the link between biological and computer viruses. In this work, we establish correlations between a well-known computer virus, VirLock, with an equally well-studied biological virus, the bacteriophage ϕ6. VirLock is a formidable ransomware that encrypts user files and demands a ransom for data restoration. Drawing a parallel with the biological virus bacteriophage ϕ6, we uncover conceptual links like shared attributes and behaviors, as well as useful insights. Following this line of thought, we suggest efficient strategies based on a game theory perspective, which have the potential to address the infections caused by VirLock, and other viruses with analogous behavior. Moreover, we propose mathematical formulations that integrate real-world variables, providing a means to gauge virus severity and design robust defensive strategies and analytics. This interdisciplinary inquiry, fusing game theory, biology, and computer science, advances our understanding of virus behavior, paving the way for the development of effective countermeasures while presenting an alternative viewpoint. Throughout this theoretical exploration, we contribute to the ongoing discourse on computer virus behavior and stimulate new avenues for addressing digital threats. In particular, the formulas and framework developed in this work can facilitate better risk analysis and assessment, and become useful tools in penetration testing analysis, helping companies and organizations enhance their security.
Proportions of four distinct classes of sensory neurons are retained even when axon regeneration is enhanced following peripheral nerve injury

Introduction Recovery from peripheral nerve injuries is poor because axon regeneration is slow and inefficient. Experimental therapies that increase signaling of neuronal brain-derived neurotrophic factor (BDNF) through its TrkB receptor or through its downstream effectors enhance axon regeneration, increasing the number of motor and sensory neurons whose axons successfully regenerate and reinnervate muscle targets. The goal of this study was to compare the proportions of four different classes of sensory (dorsal root ganglion, DRG) neurons that successfully reinnervate two different muscle targets in control mice and mice treated pharmacologically to enhance axon regeneration. Methods Following sciatic nerve transection and repair, C57BL/6 J mice were treated for 2 weeks, either with R13, a prodrug that releases the small molecule TrkB ligand, 7,8-dihydroxyflavone, with compound 11 (CP11), an inhibitor of asparaginyl endopeptidase (δ-secretase), or with a control vehicle. Four weeks after injury, different fluorescent retrograde tracers were injected into the gastrocnemius and tibialis anterior muscles to mark DRG neurons that had successfully reinnervated these muscles. Using immunofluorescence, retrogradely labeled DRG neurons also expressing markers of four different sensory neuronal classes were counted. Results and discussion Treatments with R13 or CP11 resulted in muscle reinnervation by many more DRG neurons than vehicletreated controls, but neurons expressing proteins associated with the different classes of DRG neurons studied were largely in the same proportions found in intact mice.
Current Trends and Future Progress in the Banking Risk and Capital Management

During last three decades banking risk and capital management is intensively, constantly changing and developing. The major reasons are following. Starting from 80s of the last century, world economy and banking business had significant development and changing. Basel Committee on Banking Supervision (BCBS), central banks, regulators and business banks were searching for the adequate solutions for the risk and capital management. Regulation related to capital adequacy, measuring, methods and management had one of the central places in the attention of the mentioned institutions. These methods had achieved significant improvement and they are today very comprehensive and sophisticated. On the other hand, during the previous years, mentioned institutions were constantly coming to conclusions, that something in the risk and capital management, measuring, methods, should be improved, so that it would be adequate and efficient. During last period we saw the needs for the new Basel III improvement (in some institutions called Basel IV). Since Basel III improvement publications are available, we can perform further analysis. We have also seen the other recommendations for the improvements related to the risk and capital management. In this paper, we will perform comprehensive analysis of the risk and capital management progress, achieved in the previous period. Additionally, we will try to define recommendations for the improvement of the banking risk and capital management. For this purpose we will perform analysis of the actual risk and capital management and all major factors which are dominantly influencing and shaping risk and capital management in the banks.