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In this investigation, the study focused on the RNAseq data generated in response to Fusarium oxysporum f.sp. cubense (Foc) race1 (Cavendish infecting strain VCG 0124), targeting both resistant (cv. Rose, AA) and susceptible cultivars (Namarai, AA), and Tropical Race 4 (TR4, strain VCG 01213/16), involving resistant (cv. Rose, AA) and susceptible cultivars (Matti, AA). The respective contrasting cultivars were independently challenged with Foc race1 and TR4, and the root and corm samples were collected in two replications at varying time intervals [0th (control), 2nd, 4th, 6th, and 8th days] in duplicates. The RNA samples underwent stringent quality checks, with all 80 samples meeting the primary parameters, including a satisfactory RNA integrity number (>7). Subsequent library preparation and secondary quality control steps were executed successfully for all samples, paving the way for the sequencing phase. Sequencing generated an extensive amount of data, yielding a range of 10 to 31 million paired-end raw reads per sample, resulting in a cumulative raw data size of 11–50 GB. These raw reads were aligned against the reference genome of Musa acuminata ssp. malaccensis version 2 (DH Pahang), as well as the pathogen genomes of Foc race 1 and Foc TR4, using the HISAT2 alignment tool. The focal point of this study was the investigation of differential gene expression patterns of Musa spp. upon Foc infection. In Foc race1 resistant and susceptible root samples across the designated day intervals, a significant number of genes displayed up-regulation (ranging from 1 to 228) and down-regulation (ranging from 1 to 274). In corm samples, the up-regulated genes ranged from 1 to 149, while down-regulated genes spanned from 3 to 845. For Foc TR4 resistant and susceptible root samples, the expression profiles exhibited a notable up-regulation of genes (ranging from 31 to 964), along with a down-regulation range of 316–1315. In corm samples, up-regulated genes ranged from 57 to 929, while down-regulated genes were observed in the range of 40–936. In addition to the primary analysis, a comprehensive secondary analysis was conducted, including Gene Ontology (GO), euKaryotic Orthologous Groups (KOG) classification, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, and investigations into Simple Sequence Repeats (SSRs), Single Nucleotide Polymorphisms (SNPs), and microRNA (miRNA). The complete dataset was carefully curated and housed at ICAR-NRCB, Trichy, ensuring its accuracy and accessibility for the duration of the study. Further, the raw transcriptome read datasets have been successfully submitted to the National Center for Biotechnology Information - Sequence Read Archive (NCBI-SRA) database, ensuring the accessibility and reproducibility of this valuable dataset for further research endeavors

This study explored the effects of bacterial and fungal biocontrol agents (consortia) on the microbiome of Fusarium wilt (Foc TR4)-infected Cavandish banana soils in terms of alteration of prevalence and abundance. The results showed a significant shift in microbial diversity, dominance, abundance, evenness, richness and composition core and indicator microbiome in response to soil applied consortia and untreated controls. A total of 2857 bacterial OTUs from 331 families across 40 phyla dominated with Bacillaceae (40.2%), Acidobacteriaceae (14.2%), Haloarculaceae (12.6%), and Paenibacillaceae (9.4%). There were 4,868 fungal OTUs from 520 families across 18 phyla dominant with Mortierellaceae (20.9%), Cortinariaceae (7.6%), Aspergillaceae (6.2%), Pandeidae (5.6%), and Pyronemataceae (5.0%). Alpha diversity analysis indicated that bacterial diversity varied across treatments where T2 has the highest OTUs, while fungal diversity remained relatively stable across the treatments. Beta diversity and PCoA analysis revealed the differences in community compositions across treatments in both bacterial and fungal microbiome. Bacterial communities in T3 and T5 were highly similar, whereas T4 had a notable difference in fungal communities. This study identified a total of 192 bacterial core OTUs dominated with Firmicutes, Proteobacteria, and Acidobacteriia. In the case of fungi, 59 core OTUs from Ascomycota, Basidiomycota, and Mucoromycota are the most abundant ones within the treatments. Venn diagram revealed unique, common and shared OTUs suggesting antagonistic interactions of the soil applied consortia. DESeq2 analysis revealed a significant shift of core microbiome, where positive fold changes in Betaproteobacteria for bacterial, and Fusarium sp. for fungi were noticeable. Heatmap analysis revealed the treatment-dependent differences in community composition where T2 has higher bacterial abundance and T4 has higher fungal abundance suggesting that the biocontrol treatments affect the soil microbiome differently depending on the combinations and the origins of the consortia. The indicator species analysis identified 37 bacterial and 34 fungal OTUs that were specific and indicative of particular treatments that suggest microbial consortia might be selectively enhancing the growth of functionally beneficial microbial populations of the soil that promote soil health and disease suppressiveness. This study recommends that the use of biocontrol agents in the form of consortia would not only expand the diversity of the soil microbiome but also improve the effectiveness and the sustainability of Fusarium wilt management., Supplementary Information: The online version contains supplementary material available at 10.1007/s13205-025-04223-7., Competing Interests: Conflicts of interestThe authors declare no conflict of interest., (© King Abdulaziz City for Science and Technology 2025. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.)

Bananas (Musa spp.) are a critical global food crop, providing a primary source of nutrition for millions of people. Traditional methods for disease monitoring and detection are often time-consuming, labor-intensive, and prone to inaccuracies. This study introduces an AI-powered multiplatform georeferenced surveillance system designed to enhance the detection and management of banana wilt diseases. We developed and evaluated several deep learning foundation models, including YOLO-NAS, YOLOv8, YOLOv9, and Faster-RCNN to perform accurate disease detection on both platforms. Our results demonstrate the superior performance of YOLOv9 in detecting healthy, Fusarium Wilt and Xanthomonas Wilt diseased plants in aerial images, achieving high mAP@50, precision and recall metrics ranging from 55 to 86%. In terms of ground level images, we organized the dataset based on disease occurrence in Africa, Latin America, India, Asia and Australia. For this platform, YOLOv8 outperforms the rest and achieves mAP@50, precision and recall between 65 and 99% depending on the plant part and region. Additionally, we incorporated Explainable AI techniques, such as Gradient-weighted Class Activation Mapping, to enhance model transparency and trustworthiness. Human in the Loop Artificial Intelligence was also utilized to enhance the ground level model's predictions., Competing Interests: Declarations. Competing interests: The authors declare no competing interests. Consent for publication: All authors agreed to publish this manuscript. Research involving plant statement: The authors affirm that all requisite permissions and licenses for the collection of plant and all plant parts and their accompanying images, utilized in this study, have been duly obtained in adherence to relevant regulations and guidelines. Additionally, the authors confirm that the species utilized in this study are not endangered., (© 2025. The Author(s).)

Banana streak disease was identified as a new threat to banana cultivar Virupakshi (syn: Hill banana, AAB), registered under Geographical Indications (G.I. 124) in India. PCR and rolling circle amplification (RCA) of infected leaf samples revealed the presence of banana streak GF virus (BSGFV) as the causative agent of streak symptoms. RCA and sequence analysis identified an episomal BSGFV variant (BSGFV-IN; MW389538) along with the full-length BSGFV genome (BSGFV-IN1; PP134844). The shorter BSGFV-IN genome measured 6590 bp with a deletion of 673 bp, while the full-length BSGFV-IN1 genome was 7263 bp. Sequencing of the cloned fragments using MinION nanopore sequencer further confirmed these findings, with both isolates showing > 99% sequence similarity to the BSGFV (AY493509) from Ecuador. Interestingly, BSGFV-infected tissue-cultured (TC) plants exhibited variation in streak symptom expression under different climatic conditions. At lower temperatures (below 25 °C), typical streak symptoms were prevalent, whereas at higher temperatures, the symptoms completely remitted. This is the first documented report of the natural co-existence of a novel episomal BSGFV variant with a shorter genome, along with the full-length genome, associated with streak symptoms in cv. Virupakshi from India., Supplementary Information: The online version contains supplementary material available at 10.1007/s12298-025-01546-w., Competing Interests: Conflict of interestThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to infuence the work reported in this paper. On behalf of all authors, the corresponding author states that there is no conflict of interest., (© Prof. H.S. Srivastava Foundation for Science and Society 2025. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.)

Fusarium wilt of banana is a major production constraint in India, prompting banana growers to replace bananas with less remunerative crops. Effective disease management practices thus need to be developed and implemented to prevent further spread and damage caused by Fusarium oxysporum f. sp. cubense ( Foc ), the cause of Fusarium wilt. Currently, knowledge of disease incidence, affected varieties, and the geographical spread of Foc races in India are only scantily available. An extensive field survey was conducted in 53 districts of 16 major banana-growing states of and one union territory of India that covered both tropical and subtropical regions. Disease incidence ranged from 0 to 95% on farms, with Cavendish bananas (AAA) most affected. No Fusarium wilt symptoms due to Foc R1 were observed in Nendran (AAB) or Red Banana (AAA) in South India. During the survey, 293 Foc isolates were collected from Cavendish, Pisang Awak (ABB), Silk (AAB), Monthan (ABB), Neypoovan (AB), and Mysore (AAB) bananas. Isolate diversity was assessed through Vegetative Compatibility Group (VCG) analyses, sequencing of EF1α gene sequences, phylogenetic analyses, and characterisation by SIX gene composition. Thirteen VCGs were identified, of which VCGs 0124, 0125, 01220, and 01213/16 were dominant and infected Cavendish bananas. Phylogenetic analysis divided the Indian Foc isolates into race 1 (R1), subtropical race 4 (STR4), and tropical race 4 (TR4). Secreted in Xylem ( SIX) gene analyses indicated that the effector genes SIX 4 and SIX 6 were present in the VCGs 0124, 0124/5, 0125, and 01220 of race 1, SIX 7 was present only in Foc STR4, and SIX 8 was found only in Foc R4 (TR4 and STR4) isolates. Insights into the geographical distribution of Foc races, and their interactions with banana varieties, can guide integrated disease management intervention strategies across India.

Healthcare researchers are increasingly utilizing smartphone sensor data as a scalable and cost-effective approach to studying individualized health-related behaviors in real-world settings. However, to develop reliable and robust digital behavioral signatures that may help in the early prediction of the individualized disease trajectory and future prognosis, there is a critical need to quantify the potential variability that may be present in the underlying sensor data due to variations in the smartphone hardware and software used by large population. Using sensor data collected in real-world settings from 3000 participants' smartphones for up to 84 days, we compared differences in the completeness, correctness, and consistency of the three most common smartphone sensors-the accelerometer, gyroscope, and GPS- within and across Android and iOS devices. Our findings show considerable variation in sensor data quality within and across Android and iOS devices. Sensor data from iOS devices showed significantly lower levels of anomalous point density (APD) compared to Android across all sensors ( p   <  1 × 10 -4 ). iOS devices showed a considerably lower missing data ratio (MDR) for the accelerometer compared to the GPS data ( p   <  1 × 10 -4 ). Notably, the quality features derived from raw sensor data across devices alone could predict the device type (Android vs. iOS) with an up to 0.98 accuracy 95% CI [0.977, 0.982]. Such significant differences in sensor data quantity and quality gathered from iOS and Android platforms could lead to considerable variation in health-related inference derived from heterogenous consumer-owned smartphones. Our research highlights the importance of assessing, measuring, and adjusting for such critical differences in smartphone sensor-based assessments. Understanding the factors contributing to the variation in sensor data based on daily device usage will help develop reliable, standardized, inclusive, and practically applicable digital behavioral patterns that may be linked to health outcomes in real-world settings.

Escalating oil consumption has resulted in an increase in accidental spills of petroleum hydrocarbons, causing severe environmental degradation, notably in vulnerable regions like the Niger Delta. Complex mixture of these hydrocarbons particularly long-chain alkanes presents unique challenges in restoration of polluted environment due to their chemical properties. This study aimed to investigate the long-chain hydrocarbon-degrading bacterial communities within long-term chronically polluted soil in Ogoniland, by utilizing both traditional cultivation methods and modern culture-independent techniques. Results revealed that surface-polluted soil (SPS) and subsurface soil (SPSS) exhibit significantly higher total organic carbon (TOC) ranging from 5.64 to 5.06% and total petroleum hydrocarbons (TPH) levels ranging from 36,775 ppm to 14,087 ppm, compared to unpolluted soil (UPS) with 1.97% TOC and 479 ppm TPH, respectively. Analysis of carbon chain lengths reveals the prevalence of longer-chain alkanes (C20-28) in the surface soil. Culture-dependent methods, utilizing crude oil enrichment (COE) and paraffin wax enrichment (PWE), yield 47 bacterial isolates subjected to a long-chain alkane degradation assay. Twelve bacterial strains demonstrate significant degradation abilities across all enriched media. Three bacterial members, namely Pseudomonas sp. (almA), Marinomonas sp. (almA), and Alteromonas (ladA), exhibit genes responsible for long-chain alkane degradation, demonstrating efficiency between 50 and 80%. Culture-independent analysis reveals that surface SPS samples exhibit greater species richness and diversity compared to subsurface SPSS samples. Proteobacteria dominates as the phylum in both soil sample types, ranging from 22.23 to 82.61%, with Firmicutes (0.2-2.22%), Actinobacteria (0.4-3.02%), and Acidobacteria (0.1-3.53%) also prevalent. Bacterial profiles at genus level revealed that distinct variations among bacterial populations between SPS and SPSS samples comprising number of hydrocarbon degraders and the functional predictions also highlight the presence of potential catabolic genes (nahAa, adh2, and cpnA) in the polluted soil. However, culture-dependent analysis only captured a few of the dominant members found in culture-independent analysis, implying that more specialized media or environments are needed to isolate more bacterial members. The findings from this study contribute valuable information to ecological and biotechnological aspects, aiding in the development of more effective bioremediation applications for restoring oil-contaminated environments., (© 2024. The Author(s).)

Human viral pathogens, including SARS-CoV-2 continue to attract public and research attention due to their disruption of society, global health, and the economy. Several earlier reviews have investigated the occurrence and fate of SARS-CoV-2 in wastewater, and the potential to use such data in wastewater-based epidemiology. However, comprehensive reviews tracking SARS-CoV-2 and other viral pathogens in the wastewater-water-drinking water continuum and the associated risk assessment are still lacking. Therefore, to address this gap, the present paper makes the following contributions: (1) critically examines the early empirical results to highlight the occurrence and stability of SARS-CoV-2 in the wastewater-source water-drinking water continuum, (2) discusses the anthropogenic and hydro(geo)logical processes controlling the circulation of SARS-CoV-2 in the wastewater-source water-drinking water continuum, (3) discusses the risky behaviour, drivers and high-risk settings in the wastewater-source water-drinking water continuum, (4) uses the available empirical data on SARS-CoV-2 occurrence in the wastewater-source water-drinking water continuum to discuss human health risks from multiple exposure pathways, gendered aspects of SARS-CoV-2 transmission via shared on-site sanitation systems, and (5) develops and risk mitigation strategy based on the available empirical evidence and quantitative human risk assessment data. Finally, it presents a comprehensive research agenda on SARS-CoV-2/COVID-19 to guide the mitigation of future similar outbreaks in low-income settings., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Background: Pathogen-related proteins (PR) are pivotal in plant defense, combating diverse biotic and abiotic stresses. While multiple gene families contribute to banana resistance against Fusarium oxysporum f sp. cubense (Foc), Pseudocercospora eumusae, and Pratylenchus coffeae, the significance of PR-1 genes in defense is paramount., Methods: Three PR-1 genes, up-regulated under diverse biotic stresses, were cloned from both resistant and susceptible cultivars of Foc, P. eumusae, and P. coffeae. Molecular characterization, phylogenetic analysis, and docking studies with the Foc TR4 CP gene were conducted., Results: Through transcriptomic and real-time studies, three PR-1 genes (Ma02&#95;g15050, Ma02&#95;g15060, and Ma04&#95;g34800) from Musa spp. were identified. These genes exhibited significant up-regulation in resistant cultivars when exposed to Foc, P. eumusae, and P. coffeae. Cloning of these genes was successfully performed from both resistant and susceptible cultivars of Foc race 1 and TR4, P. eumusae, and P. coffeae. Distinct characteristics were observed among the PR-1 genes, with groups 1 and 2 being acidic with signal peptides, and group 3 being basic without signal peptides. All cloned PR-1 proteins belonged to the CAP superfamily (PF00188). Phylogenetic analysis revealed clustering patterns for acidic PR-1 proteins, and KEGG orthology showed associations with vital pathways, including MAPK signaling, plant hormone signal transduction, and plant-pathogen interaction. Secondary and tertiary structure analyses confirmed sequence conservation across studied species. Docking studies explored interactions between the cerato-platanin (CP) gene from Foc TR4 and Ma02&#95;g15060 from banana, suggesting the potential hindrance of PR-1 antifungal activity through direct interaction., Conclusions: The findings underscore the crucial role of cloned PR-1 genes in banana plant defense mechanisms against a broad spectrum of biotic stresses. These genes, especially those in groups 1 and 2, hold promise as candidates for developing stress-tolerant banana cultivars. The study provides valuable insights into the molecular aspects of banana defense strategies, emphasizing the potential applications of PR-1 genes in enhancing banana resilience., (© 2024. The Author(s), under exclusive licence to Springer Nature B.V.)

Emphasis is always placed on bacterial but not fungal pathogens in marine environments. We analysed the fungal diversity, functional predictions, and toxic metals and metalloids contamination in beach sand from different South African locations. Results revealed a diverse fungal community, with Ascomycota, Rozellomycota, and Basidiomycota being the dominant phyla. Functional predictions highlighted fungal metabolic pathways related to of carbohydrates, amino acids, and lipids, in different beach samples. Elevated concentrations of toxic metals and metalloids were detected in Central and Harbour beach sands, likely due to anthropogenic activities. Correlations among different elements were observed, suggesting complex interactions in the coastal environment. Fungal pathogens like Cladosporium, Fusarium, Aspergillus, and Candida in beach sands raise potential public health risk concerns. Therefore, monitoring fungal diversity (including pathogens) alongside bacterial contamination in beach environments is imperative. The results contribute to understanding fungal community dynamics, functional potential, toxic metal and metalloid contamination, and potential risks associated with beach sand ecosystems., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved.)

The deep ocean is a rich reservoir of unique organisms with great potential for bioprospecting, ecosystem services, and the discovery of novel materials. These organisms thrive in harsh environments characterized by high hydrostatic pressure, low temperature, and limited nutrients. Hydrothermal vents and cold seeps, prominent features of the deep ocean, provide a habitat for microorganisms involved in the production and filtration of methane, a potent greenhouse gas. Methanotrophs, comprising archaea and bacteria, play a crucial role in these processes. This review examines the intricate relationship between the roles, responses, and niche specialization of methanotrophs in the deep ocean ecosystem. Our findings reveal that different types of methanotrophs dominate specific zones depending on prevailing conditions. Type I methanotrophs thrive in oxygen-rich zones, while Type II methanotrophs display adaptability to diverse conditions. Verrumicrobiota and NC10 flourish in hypoxic and extreme environments. In addition to their essential role in methane regulation, methanotrophs contribute to various ecosystem functions. They participate in the degradation of foreign compounds and play a crucial role in cycling biogeochemical elements like metals, sulfur, and nitrogen. Methanotrophs also serve as a significant energy source for the oceanic food chain and drive chemosynthesis in the deep ocean. Moreover, their presence offers promising prospects for biotechnological applications, including the production of valuable compounds such as polyhydroxyalkanoates, methanobactin, exopolysaccharides, ecotines, methanol, putrescine, and biofuels. In conclusion, this review highlights the multifaceted roles of methanotrophs in the deep ocean ecosystem, underscoring their ecological significance and their potential for advancements in biotechnology. A comprehensive understanding of their niche specialization and responses will contribute to harnessing their full potential in various domains., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Authors. Published by Elsevier B.V. All rights reserved.)

In water-stressed regions, the use of treated acid mine drainage (AMD) water for irrigated agriculture has been suggested as an alternative to address the shortage of fresh water sources. However, the short and long-term impact of using such (un)treated AMD water on soil health, particularly the microbiome structure and functional capacity, is not known. We present high-throughput amplicon sequence (HTS) datasets of purified microbial metacommunity DNA of soils under Irish potato production irrigated by quicklime and fly ash treated AMD water. The irrigation treatments included quicklime treated AMD water (A1Q and A2Q; n = 16), and quicklime and fly ash-treated AMD water (AFQ; n = 5), untreated AMD water (uAMD; n = 7) and control group using tap water (n = 5). The V1-V3 hypervariable region of the 16S rRNA gene from each sample were sequenced on an Illumina MiSeq to generate these HTS datasets. The raw sequences underwent quality-checking, demultiplexing into FASTQ files, and processing using MOTHUR pipeline (v1.40.0). Th quality reads classified into taxonomic ids (phylum, class, order, family, and genus) using the Naïve Bayesian classifier algorithm against the SILVA database (v132) and were assigned to operational taxonomic units (OTUs) based on the pairwise distance matrix (Euclidean distance matrix). The applicability of the HTS datasets was confirmed by microbial taxa at the phylum level. All HTS datasets are available through the BioSample Submission Portal under the BioProject ID PRJNA974836 (https://www.ncbi.nlm.nih.gov/bioproject/974836)., (© 2023 The Authors. Published by Elsevier Inc.)

In 2021, the National Guideline Alliance for the Royal College of Obstetricians and Gynaecologists reviewed the body of evidence, including two meta-analyses, implicating supine sleeping position as a risk factor for growth restriction and stillbirth. While they concluded that pregnant people should be advised to avoid going to sleep on their back after 28 weeks' gestation, their main critique of the evidence was that, to date, all studies were retrospective and sleeping position was not objectively measured. As such, the Alliance noted that it would not be possible to prospectively study the associations between sleeping position and adverse pregnancy outcomes. Our aim was to demonstrate the feasibility of building a vision-based model for automated and accurate detection and quantification of sleeping position throughout the third trimester-a model with the eventual goal to be developed further and used by researchers as a tool to enable them to either confirm or disprove the aforementioned associations. We completed a Canada-wide, cross-sectional study in 24 participants in the third trimester. Infrared videos of eleven simulated sleeping positions unique to pregnancy and a sitting position both with and without bed sheets covering the body were prospectively collected. We extracted 152,618 images from 48 videos, semi-randomly down-sampled and annotated 5,970 of them, and fed them into a deep learning algorithm, which trained and validated six models via six-fold cross-validation. The performance of the models was evaluated using an unseen testing set. The models detected the twelve positions, with and without bed sheets covering the body, achieving an average precision of 0.72 and 0.83, respectively, and an average recall ("sensitivity") of 0.67 and 0.76, respectively. For the supine class with and without bed sheets covering the body, the models achieved an average precision of 0.61 and 0.75, respectively, and an average recall of 0.74 and 0.81, respectively., Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: AJK is the volunteer (unpaid) Chief Executive Officer and President of one of the study funders, Shiphrah Biomedical Inc. (SBI). AJK did not receive financial or material payment for his involvement in this study. EP is a volunteer at SBI and a shareholder in SBI. EP received a financial payment for her involvement in this study from the study funds. HH was involved in this work via an internship as part of his graduate studies at the University of Toronto. HH received a financial payment for his internship from the study funds. HH has no role in ownership, management, or control of SBI. RS, HZ, FR, SA, BT, SRH, and ED have no competing interests that could be perceived to bias this work., (Copyright: © 2023 Kember et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

The bioaccumulation of heavy metals (HMs) in marine fish is a growing global concern due to potential human health risks. The study analyzed HM in the muscle tissue, gills, and gut of adult male and female cape horse mackerel and slinger seabream caught in the polluted Durban Basin and pristine Cape Vidal from April 2018 to February 2019. Results revealed interspecific, spatial, and organ-specific variability in HM levels. In the Durban Basin, slinger seabream had bioaccumulation (in mg/kg) of As (2.3 ± 0.2), Cr (2.6 ± 0.2), Ni (2.0 ± 0.1), and Pb (4.1 ± 0.3) while cape horse mackerel had Ni (1.6 ± 0.2), Pb (4.7 ± 0.6), and Zn (52 ± 3.01) exceeding World Health Organization (WHO) regulatory limits. Metal pollution index (MPI) values were also higher in Durban Basin (> 5.13) than Cape Vidal (< 3.32) for both species' muscles. Liver and gills of slinger seabream and gut of cape horse mackerel exhibited higher HM accumulation patterns proportionate to the environmental concentrations, indicating the bioindicative potential of HM pollution by the two species. Risk assessment indicated that both fish species had target hazard quotient > 1 for Cr, and target cancer risk < 10 -4 for Pb, implying significant potential non-carcinogenic and carcinogenic health risks associated with fish consumption from the Durban Basin. The study recommends daily consumption limits of 16 g/day for slinger seabream and 14 g/day for cape horse mackerel to ensure health safety. The findings contribute to the understanding of HM pollution in the Durban Basin and provide important information for decision-makers and policymakers in developing effective strategies to mitigate and manage HM contamination in fish populations., (© 2023. The Author(s).)

Hydrocarbon footprints in the environment, via biosynthesis, natural seepage, anthropogenic activities and accidents, affect the ecosystem and induce a shift in the healthy biogeochemical equilibrium that drives needed ecological services. In addition, these imbalances cause human diseases and reduce animal and microorganism diversity. Microbial bioremediation, which capitalizes on functional genes, is a sustainable mitigation option for cleaning hydrocarbon-impacted environments. This review focuses on the bacterial alkB functional gene, which codes for a non-heme di‑iron monooxygenase (AlkB) with a di‑iron active site that catalyzes C 8 -C 16 medium-chain alkane metabolism. These enzymes are ubiquitous and share common attributes such as being controlled by global transcriptional regulators, being a component of most super hydrocarbon degraders, and their distributions linked to horizontal gene transfer (HGT) events. The phylogenetic approach used in the HGT detection suggests that AlkB tree topology clusters bacteria functionally and that a preferential gradient dictates gene distribution. The alkB gene also acts as a biomarker for bioremediation, although it is found in pristine environments and absent in some hydrocarbon degraders. For instance, a quantitative molecular method has failed to link alkB copy number to contamination concentration levels. This limitation may be due to AlkB homologues, which have other functions besides n-alkane assimilation. Thus, this review, which focuses on Pseudomonas putida GPo1 alkB, shows that AlkB proteins are diverse but have some unifying trends around hydrocarbon-degrading bacteria; it is erroneous to rely on alkB detection alone as a monitoring parameter for hydrocarbon degradation, alkB gene distribution are preferentially distributed among bacteria, and the plausible explanation for AlkB affiliation to broad-spectrum metabolism of hydrocarbons in super-degraders hitherto reported. Overall, this review provides a broad perspective of the ecology of alkB-carrying bacteria and their directed biodegradation pathways., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

T2DM (type 2 diabetes mellitus) is a chronic and progressive illness with high morbidity and death rates. Oral semaglutide (Rybelsus®) is a combination of semaglutide, a glucagon-like peptide-1 receptor agonist (GLP-1 RA), and sodium N- (8- [2-hydroxybenzoyl] amino) caprylate (SNAC), an absorption enhancer that facilitates semaglutide absorption across the gastric epithelium in a concentration-dependent manner. This family of drugs apart from glucose lowering effects causes significant weight loss with lower risk of hypoglycemia, and some of them have been linked to a significant reduced major adverse cardiovascular events. GLP-1 RAs may assist persons with T2DM and chronic kidney disease (CKD), a major microvascular consequence of T2DM, in ways other than lowering blood sugar. Several large clinical studies, the bulk of which are cardiovascular outcome trials, show that GLP-1 RA treatment is safe and tolerated for persons with T2DM and impaired renal function and that it may potentially have renoprotective characteristics. This article focuses on the advances of oral GLP1-RA and describes the key milestones and predicted advantages., Competing Interests: The authors declare that they have no conflict of interest in this study., (© 2023 Selvarajan and Subramanian.)

The Hamiltonian of a quantum system governs the dynamics of the system via the Schrodinger equation. In this paper, the Hamiltonian is reconstructed in the Pauli basis using measurables on random states forming a time series data set. The time propagation is implemented through Trotterization and optimized variationally with gradients computed on the quantum circuit. We validate our output by reproducing the dynamics of unseen observables on a randomly chosen state not used for the optimization. Unlike existing techniques that try and exploit the structure/properties of the Hamiltonian, our scheme is general and provides freedom with regard to what observables or initial states can be used while still remaining efficient with regard to implementation. We extend our protocol to doing quantum state learning where we solve the reverse problem of doing state learning given time series data of observables generated against several Hamiltonian dynamics. We show results on Hamiltonians involving XX , ZZ couplings along with transverse field Ising Hamiltonians and propose an analytical method for the learning of Hamiltonians consisting of generators of the SU (3) group. This paper is likely to pave the way toward using Hamiltonian learning for time series prediction within the context of quantum machine learning algorithms.

New and emerging plant diseases are caused by different pathogens including viruses that often cause significant crop losses. Badnaviruses are pararetroviruses that contain a single molecule of ds DNA genome of 7 to 9 kb in size and infect a large number of economically important crops such as banana and plantains, black pepper, cacao, citrus, grapevine, pineapple, sugarcane, sweet potato, taro, and yam, causing significant yield losses. Many of the species in the genus have a restricted host range and several of them are known to infect a single crop. Combined infections of different virus species and strains offer conditions that favor the development of new strains via recombination, especially in vegetatively propagated crops. The primary spread of badnaviruses is through vegetative propagating materials while for the secondary spread, they depend on insects such as mealybugs and aphids. Disease emerges as a consequence of the interactions between host and pathogens under favorable environmental conditions. The viral genome of the pararetroviruses is known to be integrated into the chromosome of the host and a few plants with integrants when subjected to different kinds of abiotic stress will give rise to episomal forms of the virus and cause disease. Attempts have been made to develop management strategies for badnaviruses both conventionally and using precision breeding techniques such as genome editing. Until 2016 only 32 badnavirus species infecting different crops were known, but in a span of six years, this number has gone up to 68. The current review highlights the emerging disease problems and management options for badnaviruses infecting economically important crops.

Despite progress in breast cancer treatment, the survival rate for patients with metastatic breast cancer remains low due to chemotherapeutic agent resistance and the lack of specificity of the current generation of cancer drugs. Our previous findings indicated that the antimicrobial peptide SKACP003 exhibited anticancer properties, particularly against the MCF-7, MDA-MB-231, and MDA-MB-453 breast cancer cell lines. However, the mechanism of SKACP003-induced cancer cell death is unknown. Here, we investigated the molecular mechanism by which SKACP003 inhibits the cell cycle, cell proliferation, and angiogenesis in breast cancer cell lines. The results revealed that all the breast cancer cell lines treated at their IC 50 values significantly inhibited the replicative phase of the cell cycle. The SKACP003-induced growth inhibition induced apoptosis, as evidenced by a decrease in BCL-2 and an increase in BAX and caspase gene (Cas-3, Cas-8, and Cas-9) expression. Reduced expression of the β-Catenin signaling pathway was associated with the SKACP003-induced apoptosis. SKACP003-treated breast cancer cells showed decreased expression of Wnt/β-Catenin targeting genes such as C-Myc, P 68 , and COX-2 and significant downregulation of CDK-4 and CDK-6 genes. Furthermore, cytoplasmic β-catenin protein levels in SKACP003-treated cell lines were significantly lower than in control cell lines. The results of the current study suggest that the newly identified antimicrobial peptide SKACP003 has great potential as a candidate for specifically targeting the β-catenin and thus significantly reducing the progression and prognosis of breast cancer cell lines.

Antibiotics were once considered the magic bullet for all human infections. However, their success was short-lived, and today, microorganisms have become resistant to almost all known antimicrobials. The most recent decade of the 20th and the beginning of the 21st century have witnessed the emergence and spread of antibiotic resistance (ABR) in different pathogenic microorganisms worldwide. Therefore, this narrative review examined the history of antibiotics and the ecological roles of antibiotics, and their resistance. The evolution of bacterial antibiotic resistance in different environments, including aquatic and terrestrial ecosystems, and modern tools used for the identification were addressed. Finally, the review addressed the ecotoxicological impact of antibiotic-resistant bacteria and public health concerns and concluded with possible strategies for addressing the ABR challenge. The information provided in this review will enhance our understanding of ABR and its implications for human, animal, and environmental health. Understanding the environmental dimension will also strengthen the need to prevent pollution as the factors influencing ABR in this setting are more than just antibiotics but involve others like heavy metals and biocides, usually not considered when studying ABR.

Background: Smartphones are increasingly used in health research. They provide a continuous connection between participants and researchers to monitor long-term health trajectories of large populations at a fraction of the cost of traditional research studies. However, despite the potential of using smartphones in remote research, there is an urgent need to develop effective strategies to reach, recruit, and retain the target populations in a representative and equitable manner., Objective: We aimed to investigate the impact of combining different recruitment and incentive distribution approaches used in remote research on cohort characteristics and long-term retention. The real-world factors significantly impacting active and passive data collection were also evaluated., Methods: We conducted a secondary data analysis of participant recruitment and retention using data from a large remote observation study aimed at understanding real-world factors linked to cold, influenza, and the impact of traumatic brain injury on daily functioning. We conducted recruitment in 2 phases between March 15, 2020, and January 4, 2022. Over 10,000 smartphone owners in the United States were recruited to provide 12 weeks of daily surveys and smartphone-based passive-sensing data. Using multivariate statistics, we investigated the potential impact of different recruitment and incentive distribution approaches on cohort characteristics. Survival analysis was used to assess the effects of sociodemographic characteristics on participant retention across the 2 recruitment phases. Associations between passive data-sharing patterns and demographic characteristics of the cohort were evaluated using logistic regression., Results: We analyzed over 330,000 days of engagement data collected from 10,000 participants. Our key findings are as follows: first, the overall characteristics of participants recruited using digital advertisements on social media and news media differed significantly from those of participants recruited using crowdsourcing platforms (Prolific and Amazon Mechanical Turk; P<.001). Second, participant retention in the study varied significantly across study phases, recruitment sources, and socioeconomic and demographic factors (P<.001). Third, notable differences in passive data collection were associated with device type (Android vs iOS) and participants' sociodemographic characteristics. Black or African American participants were significantly less likely to share passive sensor data streams than non-Hispanic White participants (odds ratio 0.44-0.49, 95% CI 0.35-0.61; P<.001). Fourth, participants were more likely to adhere to baseline surveys if the surveys were administered immediately after enrollment. Fifth, technical glitches could significantly impact real-world data collection in remote settings, which can severely impact generation of reliable evidence., Conclusions: Our findings highlight several factors, such as recruitment platforms, incentive distribution frequency, the timing of baseline surveys, device heterogeneity, and technical glitches in data collection infrastructure, that could impact remote long-term data collection. Combined together, these empirical findings could help inform best practices for monitoring anomalies during real-world data collection and for recruiting and retaining target populations in a representative and equitable manner., (©Sophia Xueying Li, Ramzi Halabi, Rahavi Selvarajan, Molly Woerner, Isabell Griffith Fillipo, Sreya Banerjee, Brittany Mosser, Felipe Jain, Patricia Areán, Abhishek Pratap. Originally published in JMIR Formative Research (https://formative.jmir.org), 14.11.2022.)

Machine learning (ML) has emerged as a formidable force for identifying hidden but pertinent patterns within a given data set with the objective of subsequent generation of automated predictive behavior. In recent years, it is safe to conclude that ML and its close cousin, deep learning (DL), have ushered in unprecedented developments in all areas of physical sciences, especially chemistry. Not only classical variants of ML, even those trainable on near-term quantum hardwares have been developed with promising outcomes. Such algorithms have revolutionized materials design and performance of photovoltaics, electronic structure calculations of ground and excited states of correlated matter, computation of force-fields and potential energy surfaces informing chemical reaction dynamics, reactivity inspired rational strategies of drug designing and even classification of phases of matter with accurate identification of emergent criticality. In this review we shall explicate a subset of such topics and delineate the contributions made by both classical and quantum computing enhanced machine learning algorithms over the past few years. We shall not only present a brief overview of the well-known techniques but also highlight their learning strategies using statistical physical insight. The objective of the review is not only to foster exposition of the aforesaid techniques but also to empower and promote cross-pollination among future research in all areas of chemistry which can benefit from ML and in turn can potentially accelerate the growth of such algorithms.

Banana is the major staple food crop for approximately 400 million people. Bunchy top disease of banana is one of the most devastating diseases caused by banana bunchy top virus (BBTV), which results in stunting of plants, bunchy appearance of leaves and a significant loss of yield. While many isolates of BBTV from various regions of India have been characterized by different groups, no structural study exists for this important virus. To bridge this gap, the pET28a clone of the coat protein (CP) gene from BBTV isolate of Hill banana grown in lower Pulney Hills (Virupakshi) of Tamilnadu was expressed in BL21 (DE3) pLysS. Purification of the CP was achieved by Ni-NTA affinity chromatography. In vitro capsid assembly studied using sucrose density gradient centrifugation suggested that the CP did not assemble as a virus-like particle (VLP), but remained as smaller oligomers. Studies using dynamic light scattering (DLS) indicate that the purified protein is poly-dispersed, represented majorly as pentamers. Homology modeling studies provided useful insights into the probable fold of the CP suggesting that it is a β-sandwich, similar to that seen in the majority of plant viruses. In silico capsid reconstruction aided the understanding of the quaternary organization of subunits in the capsid and their molecular interactions. The location of the aphid-binding EAG motif was identified on the surface loops close to the pentameric axis indicating its role in vector-mediated transmission. Comparison with the CP and capsid structure of geminiviruses provided useful insights into the mode of nucleic acid binding and the role of genome during capsid assembly., Supplementary Information: The online version contains supplementary material available at 10.1007/s13205-022-03204-4., Competing Interests: Conflict of interestOn behalf of all authors, the corresponding author states that there is no conflict of interest., (© King Abdulaziz City for Science and Technology 2022.)

Mungbean yellow mosaic India virus (MYMIV) is one of the most serious commonly occurring yellow mosaic virus (YMV's) group in majority of the pulses especially black gram and green gram in southern India compared to previously reported mungbean yellow mosaic virus. In January 2020 Desmodium laxiflorum and Abelmoscus moschatus showing mosaic symptoms and vein yellowing were collected from Guntur and Prakasam districts respectively in Andhra Pradesh. PCR analysis using MYMIV and betasatellite specific primers gave desired expected amplification from the infected samples of A. moschatus (YMV-ABEL) whereas only MYMIV specific amplification was obtained in D. laxiflorum (YMV-DES). However, no PCR amplification was obtained in respective healthy leaf samples of both plants. Sequence analysis showed that the CP sequence of YMV-ABEL and YMV-DES showed a similarity of 99.19% with MYMIV (KP677496) and 99.75% with MYMIV (JN181003) respectively. The full-length betasatellite (1356 bp) showed highest identity of 90% with bhendi yellow vein mosaic betasatellite (BYVMB) (GU111977). Phylogenetic analysis clustered the test isolates with south Indian isolates of MYMIV whereas the betasatellite sequence clustered with various isolates of BYVMB, tomato leaf curl New Delhi virus betasatellite and okra leaf curl betasatellite reported from India and Pakistan. To the best of our knowledge, this is the first report of a MYMIV in D. laxiflorum and A. moschatus and MYMIV betasatellite complex in A. moschatus., Competing Interests: Conflict of interestThe authors have no conflict of interest., (© The Author(s), under exclusive licence to Indian Virological Society 2022.)