PanAmerican Bioinformatics Institute

В современном мире онлайн-транзакции являются неотъемлемой частью жизни большинства людей, поэтому финансовое мошенничество становится серьёзной угрозой для компаний, которые занимаются онлайн-платежами. Ежегодно потери от мошенничества только увеличиваются и достигают миллиардов долларов, при этом мошенники постоянно совершенствуют свои методы. В данной статье освещаются ключевые проблемы, с которыми могут столкнуться компании при борьбе с разными методами мошенничества, такими как фишинг, кража личных данных, инсайдерские атаки, метод фальшивых заказов или возврата средств. Также подробно рассматриваются эффективные способы создания антифрод-систем, с помощью которых осуществляется защита финансовых транзакций и снижение рисков. Особое внимание уделено использованию современных технологий, таких как искусственный интеллект, аналитика больших данных и блокчейн. С помощью данных технологий осуществляется поиск аномальных активностей при анализе данных в реальном времени и предпринимаются действия по борьбе с мошенническими операциями. Важной частью антифрод-систем является сбор и обработка данных, мониторинг операций и транзакций, а также создание инструментов отчётности и реагирования на подозрительные операции. Кроме технических вопросов, в статье произведён акцент на необходимости обеспечения масштабируемости и безопасности антифрод-систем. Под этим подразумевается защита данных с использованием современных протоколов шифрования и нормативных требований по безопасности. Данная статья предлагает пошаговое руководство по созданию, внедрению в проект систем для борьбы с мошенничеством, которое будет полезно как для крупных компаний, так и для малого и среднего бизнеса.

The class Crinoidea is recorded for the first time in the Colombian Pacific Ocean based on 407 specimens collected on soft bottoms in eight stations at depths ranging from 350 to 934 m. Sampling was carried out using trawls with an epibenthic net (9 × 1 m opening, 2.5 knots for 10 min) during the Tumaco Offshore project (2012–2013). The specimens represent three comatulid crinoids, two of which were identified as Florometra tanneri (Hartlaub, 1895) and Psathyrometra fragilis (A.H. Clark, 1907); and one to the family level (Antedonidae). Taxonomic descriptions with images of the specimens, distribution, and comments of the two species collected in the Colombian Pacific are presented and discussed.

<div>Abstract<p>While overall cancer mortality has steadily decreased in recent decades, cancer health disparities among racial and ethnic population groups persist. Here we studied the relationship between cancer survival disparities (CSD), genetic ancestry (GA), and tumor molecular signatures across 33 cancers in a cohort of 9,818 patients. GA correlated with race and ethnicity but showed observable differences in effects on CSD, with significant associations identified in four cancer types: breast invasive carcinoma (BRCA), head and neck squamous cell carcinoma (HNSCC), kidney renal clear cell carcinoma (KIRC), and skin cutaneous carcinoma (SKCM). Differential gene expression and methylation between ancestry groups associated cancer-related genes with CSD, of which, seven protein-coding genes [progestin and adipoQ receptor family member 6 (<i>PAQR6</i>), Lck-interacting transmembrane adaptor 1 (<i>LIME1</i>), Sin3A-associated protein 25 (<i>SAP25</i>), MAX dimerization protein 3 (<i>MXD3</i>), coiled-coil glutamate rich protein 2 (<i>CCER2</i>), refilin A (<i>RFLNA</i>), and cathepsin W (<i>CTSW</i>)] significantly interacted with GA and exacerbated observed survival disparities. These findings indicated that regulatory changes mediated by epigenetic mechanisms have a greater contribution to CSD than population-specific mutations. Overall, we uncovered various molecular mechanisms through which GA might impact CSD, revealing potential population-specific therapeutic targets for groups disproportionately burdened by cancer.</p>Significance:<p>This large-cohort, multicancer study identifies four cancer types with cancer survival disparities and seven cancer-related genes that interact with genetic ancestry and contribute to disparities.</p></div>

Foodborne pathogens are a major public health burden in the United States, leading to 9.4 million illnesses annually. Since 1996, a national laboratory-based surveillance program, PulseNet, has used molecular subtyping and serotyping methods with the aim to reduce the burden of foodborne illness through early detection of emerging outbreaks. PulseNet affiliated laboratories have used pulsed-field gel electrophoresis (PFGE) and immunoassays to subtype and serotype bacterial isolates. Widespread use of serotyping and PFGE for foodborne illness surveillance over the years has resulted in the accumulation of a wealth of routine surveillance and outbreak epidemiological data. This valuable source of data has been used to understand seasonal frequency, geographic distribution, demographic information, exposure information, disease severity, and source of foodborne isolates. In 2019, PulseNet adopted whole genome sequencing (WGS) at a national scale to replace PFGE with higher-resolution methods such as the core genome multilocus sequence typing. Consequently, PulseNet's recent shift to genome-based subtyping methods has rendered the vast collection of historic surveillance data associated with serogroups and PFGE patterns potentially unusable. The goal of this study was to develop a bioinformatics method to associate the WGS data that are currently used by PulseNet for bacterial pathogen subtyping to previously characterized serogroup and PFGE patterns. Previous efforts to associate WGS to PFGE patterns relied on predicting DNA molecular weight based on restriction site analysis. However, these approaches failed owing to the non-uniform usage of genomic restriction sites by PFGE restriction enzymes. We developed a machine learning approach to classify isolates to their most probable serogroup and PFGE pattern, based on comparisons of genomic k-mer signatures. We applied our WGS classification method to 5,970 Shiga toxin-producing Escherichia coli (STEC) isolates collected as part of PulseNet's routine foodborne surveillance activities between 2003 and 2018. Our machine learning classifier is able to associate STEC WGS to higher-level serogroups with very high accuracy and lower-level PFGE patterns with somewhat lower accuracy. Taken together, these classifications support the ability of public health investigators to associate currently generated WGS data with historical epidemiological knowledge linked to serogroups and PFGE patterns in support of outbreak surveillance for food safety and public health.

Colistin is an important last-line antibiotic to treat highly resistant Enterobacter infections. Resistance to colistin has emerged among clinical isolates but has been associated with a significant growth defect. Here, we describe a clinical Enterobacter isolate with a deletion of mgrB, a regulator of colistin resistance, leading to high-level resistance in the absence of a growth defect. The identification of a path to resistance unrestrained by growth defects suggests colistin resistance could become more common in Enterobacter.

Abstract Pharmacogenomic (PGx) variants mediate how individuals respond to medication, and response differences among racial/ethnic groups have been attributed to patterns of PGx diversity. We hypothesized that genetic ancestry (GA) would provide higher resolution for stratifying PGx risk, since it serves as a more reliable surrogate for genetic diversity than self-identified race/ethnicity (SIRE), which includes a substantial social component. We analyzed a cohort of 8,628 individuals from the United States (US), for whom we had both SIRE information and whole genome genotypes, with a focus on the three largest SIRE groups in the US: White, Black, and Hispanic. Whole genome genotypes were used to characterize individuals’ continental ancestry fractions – European, African, and Native American – and individuals were grouped according to their GA profiles. SIRE and GA groups were found to be highly concordant. Continental ancestry predicts individuals’ SIRE with >96% accuracy, and accordingly GA provides only a marginal increase in resolution for PGx risk stratification. PGx variants are highly diverged compared to the genomic background; 82 variants show significant frequency differences among SIRE groups, and genome-wide patterns of PGx variation are almost entirely concordant with SIRE. Nevertheless, 97% of PGx variation is found within rather than between groups. Examples of highly differentiated PGx variants illustrate how SIRE partitions PGx variation based on group-specific ancestry patterns and contains valuable information for risk stratification. Finally, we show that individuals who identify as Black or Hispanic benefit more when SIRE is considered for treatment decisions than individuals from the majority White population.

While the traditional model for human evolution is often visualized as the canonical bifurcating tree with several serial founder effects involved in the peopling of the world, it is becoming widely accepted that admixture events were common between different ancestral populations. To understand this convoluted evolutionary history, network-based approaches are being employed to gauge population structure and demography using the large amount of genomic information that is now readily available.

Vancomycin is the mainstay of treatment for patients with Staphylococcus aureus infections, and reduced susceptibility to vancomycin is becoming increasingly common. Accordingly, the development of rapid and accurate assays for the diagnosis of vancomycin-intermediate S. aureus (VISA) will be critical. We developed and applied a genome-based machine-learning approach for discrimination between VISA and vancomycin-susceptible S. aureus (VSSA) using 25 whole-genome sequences. The resulting machine-learning model, based on 14 gene parameters, including 3 molecular typing markers and 11 genes implicated in reduced vancomycin susceptibility, is able to unambiguously distinguish between the VISA and VSSA isolates analyzed here despite the fact that they do not form evolutionarily distinct groups. As such, the model is able to discriminate based on specific genomic markers of antibiotic susceptibility rather than overall sequence relatedness. Subsequent evaluation of the model using leave-one-out validation yielded a classification accuracy of 84%. The machine-learning approach described here provides a generalized framework for the application of genome sequence analysis to the classification of bacteria that differ with respect to clinically relevant phenotypes and should be particularly useful in defining the genomic features that underlie antibiotic resistance.

Although some histone modification chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) signals show abrupt peaks across narrow and specific genomic locations, others have diffuse distributions along chromosomes, and their large contiguous enrichment landscapes are better modeled as broad peaks. Here, we present BroadPeak, an algorithm for the identification of such broad peaks from diffuse ChIP-seq datasets. We show that BroadPeak is a linear time algorithm that requires only two parameters, and we validate its performance on real and simulated histone modification ChIP-seq datasets. BroadPeak calls peaks that are highly coincident with both the underlying ChIP-seq tag count distributions and relevant biological features, such as the gene bodies of actively transcribed genes, and it shows superior overall recall and precision of known broad peaks from simulated datasets. The source code and documentations are available at http://jordan.biology.gatech.edu/page/software/broadpeak/.

Mammalian genomes encode numerous cis-natural antisense transcripts (cis-NATs). The extent to which these cis-NATs are actively regulated and ultimately functionally relevant, as opposed to transcriptional noise, remains a matter of debate. To address this issue, we analyzed the chromatin environment and RNA Pol II binding properties of human cis-NAT promoters genome-wide. Cap analysis of gene expression data were used to identify thousands of cis-NAT promoters, and profiles of nine histone modifications and RNA Pol II binding for these promoters in ENCODE cell types were analyzed using chromatin immunoprecipitation followed by sequencing (ChIP-seq) data. Active cis-NAT promoters are enriched with activating histone modifications and occupied by RNA Pol II, whereas weak cis-NAT promoters are depleted for both activating modifications and RNA Pol II. The enrichment levels of activating histone modifications and RNA Pol II binding show peaks centered around cis-NAT transcriptional start sites, and the levels of activating histone modifications at cis-NAT promoters are positively correlated with cis-NAT expression levels. Cis-NAT promoters also show highly tissue-specific patterns of expression. These results suggest that human cis-NATs are actively transcribed by the RNA Pol II and that their expression is epigenetically regulated, prerequisites for a functional potential for many of these non-coding RNAs.