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Microbiome perturbations can have long-term effects on health. The dynamics of the gut microbiome and virome in women living with HIV (WLHIV) and their newborn infants is poorly understood. Here, we performed metagenomic sequencing analyses on longitudinal stool samples including 23 mothers (13 WLHIV, 10 HIV-negative) and 12 infants that experienced SARS-CoV-2 infection with mild disease, as well as 40 mothers (18 WLHIV, 22 HIV-negative) and 60 infants that remained SARS-CoV-2 seronegative throughout the study follow-up. Regardless of HIV or SARS-CoV-2 status, maternal bacterial and viral profiles were distinct from infants. Using linear mixed effects models, we showed that the microbiome alpha diversity trajectory was not significantly different between SARS-CoV-2 seropositive and seronegative women. However, seropositive women’s positive trajectory while uninfected was abruptly reversed after SARS-CoV-2 infection (p = 0.015). Gut virome signatures of women were not associated with SARS-CoV-2. Alterations in infant microbiome and virome diversities were generally not impacted by SARS-CoV-2 but were rather driven by development. We did not find statistically significant interactions between HIV and SARS-CoV-2 on the gut microbiome and virome. Overall, our study provides insights into the complex interplay between maternal and infant bacterial microbiome, virome, and the influence of SARS-CoV-2 and HIV status.

ABSTRACT Little is known about the urinary virome and how it interacts with the host, particularly in renal transplant diseases. Using metagenomic sequencing, we characterized the urinary virome of 23 kidney transplant recipients longitudinally (11 BKV+ patients and 12 BKV− patients). We applied linear mixed effects models, PERMANOVA, k-means clustering, and MaAsLin2 algorithms to determine virome signatures associated with post-transplant time, BK viremia status, and patient sex. We found that the richness and alpha diversity of urinary virome were significantly different in renal transplant recipients with BKV+ over time in comparison to BKV− (richness P = 0.012, alpha P < 0.0001). Female BKV− patients had significantly higher virome richness than males (P = 0.0063). Virome beta diversity was significantly different between patients by BKV status (P < 0.001). Additionally, we identified underlying interactions between patient sex and BKV status, in terms of virome beta diversity (P = 0.008). BK polyomavirus infections were primarily of subtypes IA, IB1, and IB2. The non-BK dominant samples clustered into six urinary virome community states. BKV− samples had more anelloviruses than BKV+ samples though this difference was not statistically significant. Lastly, we identified specific viruses, associated with BKV+ and time in our samples. Our results indicate that dynamic alterations in the urinary virome over the post-transplant period in kidney transplant recipients can be shaped by BK viremia and patient sex. These findings advance our fundamental understanding of the urinary virome and support a new line of investigation in renal disease and transplantation.IMPORTANCEThe urinary microbiome is increasingly implicated in renal health and disease. While most research focuses on bacteria communities of the microbiome, factors that influence the urinary virome are not understood. Here, we investigated the urinary virome of 23 adult kidney transplant recipients longitudinally over 14 weeks post-transplant. We show that alterations in the urinary virome are associated with kidney transplant recipients with BK polyomavirus viremia that can lead to BK nephropathy and allograft rejection. By modeling the temporal dynamics post-transplant, we delineated specific profiles of the urinary virome associated with patient sex and urinary community states. These findings reveal fundamental aspects of the urinary virome that can be leveraged to better manage kidney diseases.

ABSTRACT Wastewater surveillance can reveal population-level infectious disease burden and emergent public health threats can be reliably assessed through wastewater surveillance. While molecular methods for wastewater monitoring of microorganisms have traditionally relied on PCR-based approaches, next-generation sequencing (NGS) can provide deeper insights via genomic analyses of multiple diverse pathogens. We conducted a year-long sequencing surveillance of 1,408 composite wastewater samples collected from 12 neighborhood-level access points in the greater Tempe area, Arizona, USA, and show that variation in wastewater viruses is driven by seasonal time and location. The temporal dynamics of viruses in wastewater were influenced cyclically, with the most dissimilarity between samples 23 weeks apart (i.e., winter vs summer, spring vs fall). We identified diverse urinary and enteric viruses including polyomaviruses, astroviruses, and noroviruses, and showed that their genotypes/subtypes shifted across seasons. We show that while wastewater data of certain respiratory viruses like severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) strongly correlate with clinical case rates, laboratory-reported case incidences were discordant with surges of high viral load in wastewater for other viruses like human coronavirus 229E. These results demonstrate the utility of wastewater sequencing for informing decision-making in public health.IMPORTANCEWastewater surveillance can provide insights into the spread of pathogens in communities. Advances in next-generation sequencing (NGS) methodologies allow for more precise detection of viruses in wastewater. Long-term wastewater surveillance of viruses is an important tool for public health preparedness. This system can act as a public health observatory that gives real-time early warning for infectious disease outbreaks and improved response times.

Abstract The heterogeneous data acquired by educational institutes about students’ careers (e.g., performance scores, course preferences, attendance record, demographics, etc.) has been a source of investigation for Educational Data Mining (EDM) researchers for over two decades. EDM researchers have primarily focused on course-specific data analyses of students’ performances, and rare attempts are made at the domain level that may benefit the educational institutes at large to gauge and improve their institutional effectiveness. Our work aims to fill this gap by examining students’ transcripts data for identifying similar groups of students and patterns that might associate with these different cohorts of students based on: (a) difficulty level of a course category, (b) formation of course trajectories, and, (c) transitioning of students between different performance groups. We have exploited descriptive data mining and visualization methods to analyze transcript data of 1398 undergraduate Computer Science students of a private university in Pakistan. The dataset includes students’ transcript data of 124 courses from nine distinct course categories. In the end, we have discussed our findings in detail, challenges, and, future work directions.

Influenza viruses are constantly evolving and are therefore monitored worldwide in the hope to reduce the burden of disease by annual updates to vaccine recommendations. We conducted genomic sequencing of 110 influenza A and 30 influenza B viruses from specimens collected between October 2023 and February 2024 in Arizona, USA. We identified mutations in the hemagglutinin (HA) antigenic sites as well as the neuraminidase (NA) gene in our samples. We also found no unique HA and NA mutations in vaccinated yet influenza-infected individuals. Real-time genomic sequencing surveillance is important to ensure influenza vaccine effectiveness.

BackgroundHIV may increase SARS-CoV-2 infection risk and COVID-19 severity generally, but data are limited about its impact on postpartum women and their infants. As such, we characterized SARS-CoV-2 infection among mother-infant pairs in Nairobi, Kenya.MethodsWe conducted a nested study of 62 HIV-uninfected and 64 healthy women living with HIV, as well as their HIV-exposed uninfected (N = 61) and HIV-unexposed (N = 64) infants, participating in a prospective cohort. SARS-CoV-2 serology was performed on plasma collected between May 1, 2020-February 1, 2022 to determine the incidence, risk factors, and symptoms of infection. SARS-CoV-2 RNA PCR and sequencing was also performed on available stool samples from seropositive participants.ResultsSARS-CoV-2 seropositivity was found in 66% of the 126 mothers and in 44% of the 125 infants. There was no significant association between SARS-CoV-2 infection and maternal HIV (Hazard Ratio [HR] = 0.810, 95% CI: 0.517-1.27) or infant HIV exposure (HR = 1.47, 95% CI: 0.859-2.53). Maternal SARS-CoV-2 was associated with a two-fold increased risk of infant infection (HR = 2.31, 95% CI: 1.08-4.94). Few participants (13% mothers, 33% infants) had symptoms; no participant experienced severe COVID-19 or death. Seroreversion occurred in about half of mothers and infants. SARS-CoV-2 sequences obtained from stool were related to contemporaneously circulating variants.ConclusionsThese data indicate that postpartum Kenyan women and their infants were at high risk for SARS-CoV-2 infection and that antibody responses waned over an average of 8-10 months. However, most cases were asymptomatic and healthy women living with HIV did not have a substantially increased risk of infection or severe COVID-19.

Genomic surveillance can provide early insights into new circulating severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants. While conducting genomic surveillance (1,663 cases) from December 2020–April 2021 in Arizona, USA, we detected an emergent E484K-harboring variant, B.1.243.1. This finding demonstrates the importance of real-time SARS-CoV-2 surveillance to better inform public health responses.

ABSTRACT Shared bacteria between maternal breast milk and infant stool, infers that transfer of maternal breast milk microbiota through breastfeeding seeds the establishment of the infant gut microbiome. Whether combination antiretroviral therapy (cART) impacts the breast milk microbiota in women living with HIV is unknown. Since current standard of care for people living with HIV includes cART, it has been difficult to evaluate the impact of cART on the microbiome. Here, we performed a next-generation sequencing retrospective study from pre-ART era clinical trials in Nairobi, Kenya (between 2003–2006 before cART was standard of care) that tested the effects of ART regimens to prevent mother-to-child HIV transmission. Kenyan women living with HIV were randomized to receive either no ART during breastfeeding (n = 24) or cART (zidovudine, nevirapine, lamivudine; n = 25) postpartum. Using linear mixed-effects models, we found that alpha diversity and beta diversity of the breast milk bacterial microbiome changed significantly over time during the first 4 weeks postpartum (alpha diversity P

Digital technology is so broad today as to encompass almost everything. It eases the difficulties of day-to day life with internet. E-commerce plays an important role in digital age of technology. Instead of many benefits of online system, it faces many types of threats. Sometime, choosing the best e-commerce security has a lot of uncertainties and ambiguities to reduce the effect of threats. To reduce these uncertainties, this article developed a new concept of complex cubic picture fuzzy set (CoCPFS) of fuzzy algebra that explains the positive effect, neutral effect, and negative effect of any object and additionally discussed the Cartesian product between two CoCPFSs, CoCPF relations, and their types. The relationship among the e-commerce securities and threats is investigated for the first time in the history of fuzzy algebra. These relations show the ways of disabling the effects of a threat by an effective security technique. Furthermore, the advantages and benefits of CoCPFS are explained through comparison tests with preexisting frameworks of fuzzy sets.

Many industries are developing robust models, capable of analyzing huge and complex data by using machine learning (ML) while delivering faster and more accurate results on vast scales. ML is a subfield of artificial intelligence, which is broadly defined as the capability of a machine to imitate intelligent human behavior. ML tools enable organizations to swiftly identify profitable opportunities and potential risks. Besides these uses, ML also has a wide range of applications in our daily lives. So, the development in ML is most important in this age of digital system to solve more complex problems. In order to further develop ML and diminish the uncertainties to improve accuracy, an innovative concept of complex bipolar intuitionistic fuzzy sets (CBIFSs) is introduced in this article. Further, the Cartesian product of two CBIFSs is defined. Moreover, the complex bipolar intuitionistic fuzzy relations (CBIFRs) and their types with suitable examples are defined. In addition, some important results and properties are also presented. The proposed modeling techniques are used to study different ML factors and their interrelationship, so that the functionality of ML might be enhanced. Furthermore, the advantages and benefits of proposed methods are described by their side to side comparison with preexisting frameworks in the literature.

In the current study, methanol (ADAM) extracts and their fractions, including chloroform (ADAC), ethyl acetate (ADAE), n-hexane (ADAH), and aqueous (ADAA) fractions, were prepared from aerial parts of Anogeissus dhofarica and evaluated for phytochemical assessment, high-resolution electrospray ionization mass spectrometry (HR-ESI-MS) analysis, and in vitro bioassays. The qualitative analysis determined that, except alkaloids, all the representative groups were found to be present in the analyzed samples. Samples under quantitative study displayed the highest amount of total phenolic contents in the ADAE fraction, while total flavonoid contents were highest in the ADAM extract. The ADAM extract was subjected to HR-ESI-MS to identify the chemical constituents that presented twenty-two bioactive ingredients, outlined for the first time from A. dhofarica, mainly contributed by sub-class flavanones. In the case of antimicrobial activity, the ADAE extract revealed an effective zone of inhibition (ZOI) against the Gram-positive bacterial strain (Staphylococcus aureus) with an MIC value of 0.78 ± 0.3 mg/mL, while the ADAA extract exhibited higher ZOI (34 ± 0.12 mm) against the fungal strain Candida kruzei with an MIC of 0.78 mg/mL. In the DPPH (2,2-diphenyl-1-picrylhydrazyl) analysis, the ADAE extract exhibited a maximum scavenging potential with an IC50 of 9.8 ± 1.2 μg/mL, succeeded by the ADAM extract with an IC50 of 17.4 ± 0.4 μg/mL free radical scavenging capability. In the antidiabetic assessment, the ADAE extract was the most effective, with an IC50 of 6.40 ± 0.1 μg/mL, while the same extract demonstrated prominent activity with 30.8% viability and an IC50 of 6.2 ± 0.3 μg/mL against breast cancer cell lines. The brine shrimp lethality assay demonstrated a correlation with the in vitro cytotoxicity assay, showing the ADAE extract as the most active, with a 70% mortality rate and an LC50 of 300.1 μg/mL. In conclusion, all the tested samples, especially the ADAE and ADAM extracts, have significant capabilities for the investigated activities that could be due to the presence of the bioactive compounds.

Abstract Background The earliest microbial colonizers of the human gut can have life-long consequences for their hosts. Precisely how the neonatal gut bacterial microbiome and virome are initially populated is not well understood. To better understand how the maternal gut microbiome influences acquisition of the infant gut microbiome, we studied the early life bacterial microbiomes and viromes of 28 infant twin pairs and their mothers. Results Infant bacterial and viral communities more closely resemble those of their related co-twin than unrelated infants. We found that 63% of an infant’s bacterial microbiome can be traced to their mother’s gut microbiota. In contrast, only 15% of their viral communities are acquired from their mother. Delivery route did not determine how much of the bacterial microbiome or virome was shared from mother to infant. However, bacteria-bacteriophage interactions were altered by delivery route. Conclusions The maternal gut microbiome significantly influences infant gut microbiome acquisition. Vertical transmission of the bacterial microbiome is substantially higher compared to vertical transmission of the virome. However, the degree of similarity between the maternal and infant gut bacterial microbiome and virome did not vary by delivery route. The greater similarity of the bacterial microbiome and virome between twin pairs than unrelated twins may reflect a shared environmental exposure. Thus, differences of the inter-generation transmissibility at birth between the major kingdoms of microbes indicate that the foundation of these microbial communities are shaped by different rules. Video Abstract

Quality education is necessary as it provides the basis for equality in society. It is also significantly important that educational institutes be focused on tracking and improving the academic performance of each student. Thus, it is important to identify the key factors (i.e., diverse backgrounds, behaviors, etc.) that help students perform well. However, the increasing number of students makes it challenging and leaves a negative impact on credibility and resources due to the high dropout rates. Researchers tend to work on a variety of statistical and machine learning techniques for predicting student performance without giving much importance to their spatial and behavioral factors. Therefore, there is a need to develop a method that considers weighted key factors which have an impact on their performance. To achieve this, we first surveyed by considering experts’ opinions in selecting weighted key factors using the Fuzzy Delphi Method (FDM). Secondly, a geospatial-based machine learning technique was developed which integrated the relationship between students’ location-based features, semester-wise behavioral features, and academic features. Three different experiments were conducted to prove the superiority and predict student performance. The experimental results reveal that Long Short-Term Memory (LSTM) achieved higher accuracy of 90.9% as compared to other machine learning methods, for instance, Support Vector Machine (SVM), Random Forest (RF), Naive Bayes (NB), Multilayer Perceptron (MLP), and Decision Tree (DT). Scientific analysis techniques (i.e., Fuzzy Delphi Method (FDM)) and machine learning feature engineering techniques (i.e., Variance Threshold (VT)) were used in two different experiments for selecting features where scientific analysis techniques had achieved better accuracy. The finding of this research is that, along with the past performance and social status key factors, the semester behavior factors have a lot of impact on students’ performance. We performed spatial statistical analysis on our dataset in the context of Pakistan, which provided us with the spatial areas of students’ performance; furthermore, their results are described in the data analysis section.

ABSTRACT Breast milk is nutritionally and immunologically beneficial in early life but is also a potential source of infection. Little is known about breast milk microbiota of women living with HIV (WLHIV), the impact of severe immunosuppression, and the contribution to mortality of HIV-exposed infants. Here, we performed metagenomic sequencing to characterize the bacterial microbiome and DNA virome of breast milk samples at 1 month postpartum from Kenyan WLHIV who were not receiving combination antiretroviral therapy (cART), 23 women with CD4 counts of 500; and additionally, 19 WLHIV with infants that lived and 26 WLHIV with infants that died during the first 2 years of life were included. We found that breast milk bacterial microbiomes in this study population were highly diverse but shared a core community composed of the Streptococcaceae, Staphylococcaceae, Moraxellaceae, and Eubacteriaceae families. The breast milk virome was dominated by human cytomegalovirus (CMV) and included the bacteriophage families Myoviridae, Siphoviridae, and Podoviridae. Bacterial microbiome and virome profiles and diversity were not significantly altered by HIV immunosuppression, as defined by a CD4 of

ABSTRACT In December of 2019, a novel coronavirus, SARS-CoV-2, emerged in the city of Wuhan, China, causing severe morbidity and mortality. Since then, the virus has swept across the globe, causing millions of confirmed infections and hundreds of thousands of deaths. To better understand the nature of the pandemic and the introduction and spread of the virus in Arizona, we sequenced viral genomes from clinical samples tested at the TGen North Clinical Laboratory, the Arizona Department of Health Services, and those collected as part of community surveillance projects at Arizona State University and the University of Arizona. Phylogenetic analysis of 84 genomes from across Arizona revealed a minimum of 11 distinct introductions inferred to have occurred during February and March. We show that >80% of our sequences descend from strains that were initially circulating widely in Europe but have since dominated the outbreak in the United States. In addition, we show that the first reported case of community transmission in Arizona descended from the Washington state outbreak that was discovered in late February. Notably, none of the observed transmission clusters are epidemiologically linked to the original travel-related case in the state, suggesting successful early isolation and quarantine. Finally, we use molecular clock analyses to demonstrate a lack of identifiable, widespread cryptic transmission in Arizona prior to the middle of February 2020. IMPORTANCE As the COVID-19 pandemic swept across the United States, there was great differential impact on local and regional communities. One of the earliest and hardest hit regions was in New York, while at the same time Arizona (for example) had low incidence. That situation has changed dramatically, with Arizona now having the highest rate of disease increase in the country. Understanding the roots of the pandemic during the initial months is essential as the pandemic continues and reaches new heights. Genomic analysis and phylogenetic modeling of SARS-COV-2 in Arizona can help to reconstruct population composition and predict the earliest undetected introductions. This foundational work represents the basis for future analysis and understanding as the pandemic continues.

Studies of viruses that coevolved with lemurs provide an opportunity to understand the basal traits of primate viruses and provide an evolutionary context for host-virus interactions. Germline integration of endogenous retroviruses (ERVs) are fossil evidence of past infections. Hence, characterization of novel ERVs provides insight into the ancient precursors of extant viruses and the evolutionary history of their hosts. Here, we report the discovery of a novel endogenous retrovirus present in the genome of a lemur, Coquerel’s sifaka (Propithecus coquereli). Using next-generation sequencing, we identified and characterized the complete genome sequence of a retrovirus, named prosimian retrovirus 1 (PSRV1). Phylogenetic analyses indicate that PSRV1 is a gamma-type betaretrovirus basal to the other primate betaretroviruses and most closely related to simian retroviruses. Molecular clock analysis of PSRV1 long terminal repeat (LTR) sequences estimated the time of endogenization within 4.56 MYA (±2.4 MYA), placing it after the divergence of Propithecus species. These results indicate that PSRV1 is an important milestone of lemur evolution during the radiation of the Propithecus genus. These findings may have implications for both human and animal health in that the acquisition of a gamma-type env gene within an endogenized betaretrovirus could facilitate a cross-species jump between vertebrate class hosts.

Gulf War illness (GWI) is characterized by the persistence of inflammatory bowel disease, chronic fatigue, neuroinflammation, headache, cognitive impairment, and other medically unexplained conditions. Results using a murine model show that enteric viral populations especially bacteriophages were altered in GWI. The increased viral richness and alpha diversity correlated positively with gut bacterial dysbiosis and proinflammatory cytokines. Altered virome signature in GWI mice also had a concomitant weakening of intestinal epithelial tight junctions with a significant increase in Claudin-2 protein expression and decrease in ZO1 and Occludin mRNA expression. The altered virome signature in GWI, decreased tight junction protein level was followed by the presence an activation of innate immune responses such as increased Toll-like receptor (TLR) signaling pathways. The altered virome diversity had a positive correlation with serum IL-6, IL-1β, and IFN-γ, intestinal inflammation (IFN-γ), and decreased Brain-Derived Neurotrophic Factor (BDNF), a neurogenesis marker. The co-exposure of Gulf War chemical and antibiotic (for gut sterility) or Gulf War chemical and Ribavirin, an antiviral compound to suppress virus alteration in the gut showed significant improvement in epithelial tight junction protein, decreased intestinal-, systemic-, and neuroinflammation. These results showed that the observed enteric viral dysbiosis could activate enteric viral particle-induced innate immune response in GWI and could be a novel therapeutic target in GWI.