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The increasing convenience of e-commerce and online payment systems has contributed to a rise in financial fraud incidents. This development has prompted significant research aimed at identifying effective techniques for detecting and preventing such fraud. Conventional approaches, such as rule-based systems or statistical models, face challenges due to imbalances in datasets and the constantly evolving tactics of fraudsters, which they struggle to manage. In contrast, sophisticated AI models, particularly deep learning methods, offer practical solutions to these issues. This paper compares various leading AI models for detecting financial fraud, assessing their advantages, disadvantages, and performance on standard datasets. The evaluation emphasizes critical factors such as accuracy, efficiency, and scalability, demonstrating the potential of these models to significantly impact the field of financial fraud detection. Additionally, this paper addresses the evolving dynamics of fraud and the need for models that can adapt in real time, highlighting future research directions for further advancements. [ABSTRACT FROM AUTHOR]

Groundwater is a crucial resource to support surface water bodies via groundwater discharge. In this study, we applied two methods of estimating global environmentally critical groundwater discharge, defined as the flux of groundwater to streamflow necessary to maintain a healthy environment, from 1960 to 2010: the Presumptive Standard stipulates that a standard proportion of groundwater discharge should be maintained at all timesteps, while the Q* is a low‐flow index that focuses on critical periods. We calculated these critical flow thresholds using simulated natural groundwater discharge, and estimated violations of the thresholds when human‐impacted groundwater discharge dropped too low. Our global assessment of the frequency and severity of violations over all timesteps in our study period showed that the Presumptive Standard estimated more frequent and severe violations than the Q*, but that the spatial patterns were similar for both methods. During low‐flow periods, when the relative importance of groundwater to support streamflow is greatest, both methods estimated similar magnitudes of violation frequency and severity. We further compared our results to a method of estimating environmentally critical streamflow, Variable Monthly Flow, which does not explicitly consider groundwater. From the differences in violation frequency between these groundwater‐centric and surface water‐centric methods, we evaluated the influence of including groundwater contributions to streamflow in environmental flow assessments. Our results show that including groundwater in such assessments is particularly important for regions with high groundwater demands in the drier climates of the world, while it is less important for regions with low groundwater demands and more humid climates. Plain Language Summary: We used two global methods of estimating the necessary flow of groundwater to surface water to protect environmental health. One method (Presumptive Standard) is designed to maintain environmental flows over the whole year, while the other method (Q*) focuses on low‐flow periods when groundwater plays a larger role in supporting surface water. We estimated historic violations of these environmentally critical flow thresholds and found that they estimated similar spatial patterns (although at different magnitudes). We then compared the violations to a method of estimating environmentally critical streamflow (Variable Monthly Flow), which does not directly consider groundwater. Here, we found that considering groundwater contributions to surface water affects the estimated environmental impacts of water use, particularly in river basins that are dry, have high amounts of agriculture, and are densely populated. From this study, we conclude that including groundwater in environmental flow assessments is important in regions with significant groundwater use, and that the choice of method should depend on the period of focus. Key Points: First global comparison of methods to calculate the environmentally critical contributions of groundwater to streamflowThe methods identified similar hotspots of historic violations of environmentally critical groundwater dischargeThe utility of the methods depends on whether an environmental flow assessment is important for all flow seasons or only low‐flow periods [ABSTRACT FROM AUTHOR]

Previous studies have established the relationship between the gut microbiota and ulcerative colitis (UC); however, there is a scarcity of data on the fecal microbiome profile of patients with UC in the Indian population. This study aimed to examine the fecal microbiome profile of north Indian patients with UC (n=105), including with active disease (n=64) and in remission (n=41), compared to healthy controls (n=36), using 16S rRNA gene sequencing. Both relative abundance analysis and linear discriminant analysis effect size (LEfSe) revealed a significant enrichment of lactic acid-producing facultative anaerobic pathobionts, namely, Bifidobacterium, Lactobacillus, and Streptococcus, in patients with UC (both with active disease and those in remission). Additionally, a significant decrease was observed in anaerobic genera responsible for the synthesis of short-chain fatty acids (SCFAs), especially butyrate, such as Blautia, Roseburia, Lachnospiraceae, and Ruminococcaceae. Differential metabolic pathway analysis using PICRUSt2 confirmed a loss of SCFAs production and increased lactose and nitrate metabolism in UC patients. Biochemical analysis of fecal samples confirmed the increased colonization of nitrate-reducing microbes in UC patients, suggesting inflammation-driven dysbiosis. LEfSe and PICRUSt2 analyses revealed an enrichment of Prevotella and Blautia microbes and the upregulation of two specific metabolic pathways, sulfolactate degradation and reductive acetyl coenzyme A pathway-I, which can distinguish UC patients in remission from those with active disease. Our findings caution against the use of lactic acid-producing bacteria (LABs) and recommend the exploration of butyrate-producing microbes as probiotics to restore SCFAs levels in UC patients.

Cohesion plays a critical role in chromosome segregation in both eukaryotes and E. coli, with defects leading to aneuploidy and in eukaryotes, contributing to cancer. In E. coli, SeqA regulates genomic cohesion and its loss results in segregation defect and the loss of genomic integrity. The impact of growth rate on cell phenotype and the differential proteome profile under varying growth conditions in cohesion mutants of E. coli remains unknown. In this study, we demonstrate that in nutrient-rich media, ∆seqA strain exhibits significantly increased doubling time and lower cell densities compared to wild-type strain. However, no significant differences in cell doubling time or cell density were observed in nutrient-poor media. Differential whole-proteome analysis of fast-growing ∆seqA cells revealed upregulation of genes involved in translation and chemotaxis, while stress-response genes, including those related to envelope stress and DNA damage repair, were downregulated. In contrast, during the stationary phase, differential expression was observed for genes related to protein and nucleotide metabolism. Our data shows that, during faster growth, ∆seqA mutant strains experience increased defects in chromosome segregation and DNA damage. Additionally, the reduced expression of stress-response genes results in a delayed SOS response, which leads to slower growth and increased doubling time. These findings provide valuable insights into the relationship between cohesion, segregation, growth rate and stress responses. [ABSTRACT FROM AUTHOR]