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Abstract Tyrosinase plays a crucial role as an enzyme in the production of melanin, which is the pigment accountable for determining the color of the hair, eyes, and skin. Tyrosinase inhibitory peptides (TIPs), mainly designed to regulate the activity of the enzyme tyrosinase, are of interest in various domains, including cosmetics, dermatology, and pharmaceuticals, due to their potential applications in controlling skin pigmentation. To date, a few machine learning-based models have been proposed for predicting TIPs, but their predictive performance remains unsatisfactory. In this study, we propose an innovative computational approach, named TIPred-MVFF, to accurately predict TIPs using only sequence information. Firstly, we established an up-to-date and high-quality dataset by collecting samples from various sources. Secondly, we applied a multi-view feature fusion (MVFF) strategy to extract and explore probability and category information embedded in TIPs, employing several machine learning (ML) algorithms coupled with different commonly used sequence-based feature encodings. Then, we employed resampling approaches to address the class imbalance issue. Finally, to maximize the utility of each feature, we fused probability-based and sequence-based features, generating more informative feature that were used to develop the final prediction model. Based on the independent test, experimental results showed that TIPred-MVFF outperformed several conventional ML classifiers and existing methods in terms of prediction accuracy and robustness, achieving an accuracy of 0.937 and a Matthew’s correlation coefficient of 0.847. This new computational approach is anticipated to aid community-wide efforts in rapidly and cost-effectively discovering novel peptides with strong tyrosinase inhibitory activities.

Translesion DNA synthesis (TLS) is typically performed by inherently error-prone Y-family DNA polymerases. Extensively studied Escherichia coli Pol V mutasome, composed of UmuC, an UmuD′ dimer and RecA is an example of a multimeric Y-family TLS polymerase. Less commonly TLS is performed by DNA polymerases of other families. One of the most intriguing such cases in B-family is represented by archaeal PolB2 and its bacterial homologs. Previously thought to be catalytically inactive, PolB2 was recently shown to be absolutely required for targeted mutagenesis in Sulfolobus islandicus. However, the composition and structure of the PolB2 holoenzyme remain unknown. We used highly accurate AlphaFold structural models, coupled with protein sequence and genome context analysis to comprehensively characterize PolB2 and its associated proteins, PPB2, a small helical protein, and iRadA, a catalytically inactive Rad51 homolog. We showed that these three proteins can form a heteropentameric PolB2 complex featuring high confidence modeling scores. Unexpectedly, we found that PolB2 binds iRadA through a structural motif reminiscent of RadA/Rad51 oligomerization motif. In some mutasomes we identified clamp binding motifs, present in either iRadA or PolB2, but rarely in both. We also used AlphaFold to derive a three-dimensional structure of Pol V, for which the experimental structure remains unsolved thus precluding comprehensive understanding of its molecular mechanism. Our analysis showed that the structural features of Pol V explain many of the puzzling previous experimental results. Even though models of PolB2 and Pol V mutasomes are structurally different, we found striking similarities in their architectural organization and interactions.

OBJECTIVES: Low-quality and precarious employment have been associated with adverse mental health and wellbeing. More evidence is needed on how the quality of employment trajectories – including transitions in and out of unemployment, inactivity, and employment of varying quality – are associated with individuals’ mental health over time. This paper aimed to derive a typology of multidimensional employment trajectories and assess associations with mental health in the UK. METHODS: Data from waves 1–9 of the UK Household Longitudinal Study were used (2009–2019). Individuals aged 30–40 at baseline were included (N=1603). Using multichannel sequence and clustering analyses, we derived a typology of employment trajectories across employment statuses and four employment quality indicators. We assessed associations with subsequent psychological distress, accounting for baseline mental health. Changes in average General Health Questionnaire scores are described. RESULTS: A typology of five trajectory clusters highlighted stable and secure and precarious/low-quality trajectories for both men and women. Women who reported being economically inactive at most waves had higher odds of experiencing psychological distress than did women in ‘standard’ trajectories, regardless of baseline mental health. Women’s scores of psychological distress in the ‘precarious’ group on average increased along their trajectories characterized by instability and transitions in/out of unemployment, before a move into employment. Men who likely moved in and out of unemployment and economic inactivity, with low probability of paid employment, reported increased psychological distress at the end of follow-up. This may partly be due to pre-existing mental ill-health. CONCLUSION: This paper shows the importance of high-quality employment for individuals’ mental health over time. Researchers need to consider dynamic associations between employment quality and mental health across the life-course.

Abstract Background In standard Sequence Analysis, similar trajectories are clustered together to create a typology of trajectories, which is then often used to evaluate the association between sequence patterns and covariates inside regression models. The sampling uncertainty, which affects both the derivation of the typology and the associated regressions, is typically ignored in this analysis, an oversight that may lead to wrong statistical conclusions. We propose utilising sampling variation to derive new estimates that further inform on the association of interest. Methods We introduce a novel procedure to assess the robustness of regression results obtained from the standard analysis. Bootstrap samples are drawn from the data, and for each bootstrap, a new typology replicating the original one is constructed, followed by the estimation of the corresponding regression models. The bootstrap estimates are then combined using a multilevel modelling framework that mimics a meta-analysis. The fitted values from this multilevel model allow to account for the sampling uncertainty in the inferential analysis. We illustrate the methodology by applying it to the study of healthcare utilisation trajectories in a Swiss cohort of diabetic patients. Results The procedure provides robust estimates for an association of interest, along with 95% prediction intervals, representing the range of expected values if the clustering and associated regressions were performed on a new sample from the same underlying distribution. It also identifies central and borderline trajectories within each cluster. Regarding the illustrative application, while there was evidence of an association between regular lipid testing and subsequent healthcare utilisation patterns in the original analysis, this is not supported in the robustness assessment. Conclusions Investigating the relationship between trajectory patterns and covariates is of interest in many situations. However, it is a challenging task with potential pitfalls. Our Robustness Assessment of Regression using Cluster Analysis Typologies (RARCAT) may assist in ensuring the robustness of such association studies. The method is applicable wherever clustering is combined with regression analysis, so its relevance goes beyond State Sequence Analysis.

The global tuberculosis (TB) epidemic is becoming progressively more complex due to the increasing prevalence of multidrug-resistant TB (MDR-TB), particularly with resistance to fluoroquinolones (FQs). This study focuses on identifying genetic mutations in the gyrA and gyrB genes of Mycobacterium tuberculosis that drive FQ resistance. Sputum samples from suspected pulmonary TB patients were analyzed using PCR and sequencing to detect mutations within the quinolone resistance-determining regions (QRDR). The analysis revealed that mutations in gyrA, especially S95T, are prevalent and play a key role in FQ resistance. Additionally, less frequent mutations in gyrB, such as E501D and A533P, were also detected. These findings shed light on the molecular mechanisms contributing to FQ resistance in MDR-TB strains and underscore the need for enhanced diagnostic methods to identify resistance patterns more accurately. The insights gained from this research offer a foundation for improving TB treatment approaches and addressing the growing challenge of drug-resistant TB worldwide.

The virus species Protoparvovirus carnivoran 1 encompasses pathogens that infect both domestic and wild carnivores, including feline panleukopenia virus. We identified and characterized feline panleukopenia virus strains in a Marsican brown bear (Ursus arctos marsicanus) and a crested porcupine (Hystrix cristata) in Italy, extending the known host range of this virus.

Sheeppox and goatpox are transboundary viral diseases of sheep and goats that cause significant economic losses to small and marginal farmers worldwide, including India. Members of the genus Capripoxvirus (CaPV), namely Sheeppox virus (SPPV), Goatpox virus (GTPV), and Lumpy skin disease virus (LSDV), are antigenically similar, and species differentiation can only be accomplished using molecular approaches. The present study aimed to understand the molecular epidemiology and host specificity of SPPV and GTPV circulating in India through sequencing and structural analysis of the RNA polymerase subunit-30 kDa (RPO30) gene. A total of 29 field isolates from sheep (n = 19) and goats (n = 10) belonging to different geographical regions of India during the period: Year 2015 to 2023, were analyzed based on the sequence and structure of the full-length RPO30 gene/protein. Phylogenetically, all the CaPV isolates were separated into three major clusters: SPPV, GTPV, and LSDV. Multiple sequence alignment revealed a highly conserved RPO30 gene, with a stretch of 21 nucleotide deletion in all SPPV isolates. Additionally, the RPO30 gene of the Indian SPPV and GTPV isolates possessed several species-specific conserved signature residues/motifs that could act as genotyping markers. Secondary structure analysis of the RPO30 protein showed four α-helices, two loops, and three turns, similar to that of the E4L protein of vaccinia virus (VACV). All the isolates in the present study exhibited host preferences across different states of India. Therefore, in order to protect vulnerable small ruminants from poxviral infections, it is recommended to take into consideration a homologous vaccination strategy.

The rapid progression of genomics and proteomics has been driven by the advent of advanced sequencing technologies, large, diverse, and readily available omics datasets, and the evolution of computational data processing capabilities. The vast amount of data generated by these advancements necessitates efficient algorithms to extract meaningful information. K-mers serve as a valuable tool when working with large sequencing datasets, offering several advantages in computational speed and memory efficiency and carrying the potential for intrinsic biological functionality. This review provides an overview of the methods, applications, and significance of k-mers in genomic and proteomic data analyses, as well as the utility of absent sequences, including nullomers and nullpeptides, in disease detection, vaccine development, therapeutics, and forensic science. Therefore, the review highlights the pivotal role of k-mers in addressing current genomic and proteomic problems and underscores their potential for future breakthroughs in research.

Background and aims: Familial hypercholesterolemia (FH) and other disorders with similar features are common genetic disorders that remain underdiagnosed and undertreated, due in part to the cost of screening. The aim of this study was to design and implement a whole gene targeted NGS panel for the molecular diagnosis of FH and statin intolerance with an emphasis on high quality variant calling, including copy number analysis. Methods: A whole gene panel for hybridisation-based short read NGS was designed for the dominant FH-genes low density lipoprotein receptor (LDLR), apolipoprotein B (APOB), proproteinconvertas subtilisin/kexin type 9 (PCSK9), apolipoprotein E (APOE) and the recessive FH-genes low density lipoprotein receptor adaptor protein 1 (LDLRAP1), ATP binding cassette subfamily member 5/8 (ABCG5/8) and lipase A, lysosomal acid type (LIPA), as well as solute carrier organic anion transporter family member 1B1 (SLCO1B1), not an FH gene but linked to statin intolerance. Polygenetic risk score markers were also included. The panel was used for screening of a Swedish FH-study population (n = 133). Results: The panel sequencing resulted in high coverage and confident variant calling of included genes. Known causal variants were found in common dominant FH-genes in 43 % of the cohort. Copy number variants were found in LDLR in 10 individuals and a whole gene deletion of SLCO1B1 in one individual. In addition, coding variants in recessive genes and rare non-coding intronic and untranslated region variants were found in a large proportion of the study individuals highlighting the need for extended gene panels. Conclusions: This new tool can be used for a comprehensive high-quality molecular genetic analysis according to guidelines for the diagnosis and treatment of FH.

Abstract Background The HIV epidemic in Kenya remains a significant public health concern, particularly among gay, bisexual, and other men who have sex with men (GBMSM), who continue to bear a disproportionate burden of the epidemic. This study’s objective is to describe HIV phylogenetic clusters among different subgroups of Kenyan GBMSM, including those who use physical hotspots, virtual spaces, or a combination of both to find male sexual partners. Methods Dried blood spots (DBS) were collected from GBMSM in Kisumu, Mombasa, and Kiambu counties, Kenya, in 2019 (baseline) and 2020 (endline). HIV pol sequencing was attempted on all seropositive DBS. HIV phylogenetic clusters were inferred using a patristic distance cutoff of ≤ 0.02 nucleotide substitutions per site. We used descriptive statistics to analyze sociodemographic characteristics and risk behaviors stratified by clustering status. Results Of the 2,450 participants (baseline and endline), 453 (18.5%) were living with HIV. Only a small proportion of seropositive DBS specimens were successfully sequenced (n = 36/453; 7.9%), likely due to most study participants being virally suppressed (87.4%). Among these sequences, 13 (36.1%) formed eight distinct clusters comprised of seven dyads and one triad. The clusters mainly consisted of GBMSM seeking partners online (n = 10/13; 76.9%) and who tested less frequently than recommended by Kenyan guidelines (n = 11/13; 84.6%). Conclusions Our study identified HIV phylogenetic clusters among Kenyan GBMSM who predominantly seek sexual partners online and test infrequently. These findings highlight potential unmet HIV prevention, testing, and treatment needs within this population. Furthermore, these results underscore the importance of tailoring HIV programs to address the diverse needs of GBMSM in Kenya across different venues, including both physical hotspots and online platforms, to ensure comprehensive prevention and care strategies.

Understanding protein-protein interactions (PPIs) is essential because knowledge regarding PPIs helps in determining the biochemical functions of organisms. Advances made in machine learning techniques, for example, DeepFE-PPI, GcForest-PPI, and DeepPPI, have been applied to enhance PPI prediction performance. However, most methods use either handcrafted or learned features. Improving protein representation quality can significantly impact PPI model performance. This study aims to propose a deep neural network-based model, in which both handcrafted and learned features are fused to improve PPI prediction. In our method, the handcrafted features are computed by sequence descriptors, including Amino Acid Composition (AAC), Local Descriptors, Pseudo-AAC, Amphiphilic Pseudo-AAC, and Quasi-Sequence-Order. The learned features are generated by Word2vec after being trained on a large database of protein sequences. Standard evaluation methods and benchmark datasets are used to assess the proposed model. The results show that our model achieves 95.8% and 99.3% accuracy on the Saccharomyces cerevisiae and Human datasets, respectively. Additionally, the generalizability of our model is evaluated through independent tests, and our findings indicate that the proposed feature combination method correctly classifies experimentally validated interactions in eight datasets, including cross-species and PPI networks. The source code and data for this work are included at https://github.com/npxquynhdhsp/ComFeatPPI.git.

Abstract This manuscript is concerned with obtaining results for fixed points that arise from new contractive mappings on controlled metric spaces. These mappings are a mixture of Wardowski’s contractions with both multivalued, nonlinear mappings and auxiliary functions. It is also proved that the obtained fixed-point outcomes are well-posed. Additionally, a data-dependence result for fixed points is given. To aid with understanding, several illustrative examples are also provided. Numerous findings that are currently in the literature are specific instances of the findings that were made.

Abstract Background Fabry disease is an X-linked lysosomal storage disease caused by the impairment of α-galactosidase A. The complex intronic haplotype (CIH) variants, located in promoter and intronic regulatory lesions, has been found in patients with classical forms of Fabry disease. We present a case of Fabry disease in female monozygotic twins exhibiting the CIH mutation and classical manifestations. Case presentation A 61-year-old woman with a history of stroke, carotid artery occlusion, hypertrophic cardiomyopathy, and chronic kidney disease was referred to the nephrology clinic for management of her chronic kidney disease. Her monozygotic twin sister also presented with hypertrophic cardiomyopathy, atrial flutter, carotid stenosis, and proteinuria. Clinical symptoms and a comprehensive family history strongly suggested the presence of Fabry disease. Genetic analysis revealed the presence of 5 variants within a complex intronic haplotype (CIH): c.−10 C > T, c.369 + 990 C > A, c.370 − 81_370–77delCAGCC, c.640–16 A > G, and c.1000–22 C > T. We conducted a review of the patient’s previous kidney biopsy findings, which demonstrated the presence of lamellated inclusion bodies in electron microscopy. Remarkably, both the monozygotic twin sister and her son exhibited the same genetic mutation. Enzyme replacement therapy was initiated for the patient. Her kidney function decreased throughout a thorough 2-year follow-up period, while there was a slight decrease in the left ventricular mass index. Conclusions This is the first reported case of female monozygotic twins with the CIH variants representing cardiac, cerebrovascular, and renal manifestations suggestive of Fabry disease.

Abstract Background Novel protein-coding genes were considered to be born by re-organization of pre-existing genes, such as gene duplication and gene fusion. However, recent progress of genome research revealed that more protein-coding genes than expected were born de novo, that is, gene origination by accumulating mutations in non-genic DNA sequences. Nonetheless, the in-depth process (scenario) for de novo origination is not well understood. Results We have conceived bioinformatic analysis for sketching a scenario for de novo origination of protein-coding genes. For each de novo protein-coding gene, we firstly identified an edge of a given phylogenetic tree where the gene was born based on parsimony. Then, from a multiple sequence alignment of the de novo gene and its orthologous regions, we constructed ancestral DNA sequences of the gene corresponding to both end nodes of the edge. We finally revealed statistical features observed in evolution between the two ancestral sequences. In the analysis of the Saccharomyces cerevisiae lineage, we have successfully sketched a putative scenario for de novo origination of protein-coding genes. (1) In the beginning was GC-rich genome regions. (2) Neutral mutations were accumulated in the regions. (3) ORFs were extended/combined, and then (4) translation signature (Kozak consensus sequence) was recruited. Interestingly, as the scenario progresses from (2) to (4), the specificity of mutations increases. Conclusion To the best of our knowledge, this is the first report outlining a scenario of de novo origination of protein-coding genes. Our bioinformatic analysis can capture events that occur during a short evolutionary time by directly observing the evolution of the ancestral sequences from non-genic to genic. This property is suitable for the analysis of fast evolving de novo genes.

Abstract This study investigates the relationship between students' self-efficacy beliefs, goal-setting, and learning tactics in an online business course. Using sequence analysis and process mining techniques, we analyzed log data from 209 students to identify distinct learning tactics and their association with self-efficacy beliefs, inferred from initial goal-setting and final grades. Four learning tactics were identified, with significant differences observed between students with high and low self-efficacy beliefs. High self-efficacy students demonstrated more varied and adaptive tactics, including greater use of quizzes and project-focused activities. In contrast, low self-efficacy students and those who didn't set goals showed less engagement and fewer monitoring activities. The project-focused tactic had the strongest correlation with final grades and goal achievement. Goal-setting at the course's start was linked to more effective learning behaviors and better outcomes. The study reveals how students' online learning behavior changes based on initial goal-setting and subsequent reflection. These findings contribute to research on self-regulated learning in online environments and offer practical implications for designing online courses and learning analytics interventions. Understanding these differences enables the development of targeted interventions to improve learning tactics and self-efficacy beliefs among students, ultimately enhancing their ability to achieve learning goals in online educational settings.

ObjectiveTo analyze the whole-genome characteristics of four coxsackievirus B5 (CVB5) strains isolated in Yunnan province, China in 2022. MethodsPrimers were designed to amplify and sequence the whole genome of CVB5 isolates. The whole genome was amplified in segments, and sequenced using the "primer walking" method. Sequence splicing and assembly were performed. Genetic characteristics of the CVB5 strains were analyzed using software such as MEGA 7.0, Simplot 3.5.1, and DNAStar 7.1. ResultsBased on VP1 and whole-genome phylogenetic analysis, all four CVB5 isolates belonged to genotype C. Compared with the prototype strain, multiple amino acid mutations occurred in the VP1 region of the four strains. Phylogenetic analysis of P1, P2, and P3 regions and Simplot recombination analysis showed that the CVB5 isolates in this study may have undergone recombination with multiple enterovirus B strains. ConclusionThe four CVB5 strains isolated from clinical samples of hand, foot and mouth disease (HFMD) patients in Kunming, Yunnan province in 2022 all belonged to genotype C.

Background and Objectives: Septic arthritis (SA) is an orthopedic emergency mainly caused by bacteria. SA due to Escherichia coli (E. coli) is rare with a poor prognosis. This study aimed to assess the occurrence and antibiotic resistance patterns of E. coli in SA patients in Quetta, Balochistan, Pakistan. Materials and Methods: A cross-sectional study was conducted from March 2021 to December 2023. 220 samples were collected from SA patients from tertiary care hospitals. Joint aspirates (2ml) and blood (5ml) were analyzed for microbial and hematological examination. Results: There were 5.45% samples positive, and 94.5% negative for E. coli. SA due to E. coli was more common in male (6.2%) than female (4.6%) patients with the knee being the most affected joint (6.3%). E. coli was more common in patients aged 41-60 years (7.7%), lower socioeconomic (6.9%), and illiterate (8.6%) patients. Suspected patients showed a significant increase in the levels of white blood cells (WBC), C-reactive protein (CRP), and erythrocyte sedimentation rate (ESR), notably, these levels were further elevated in E. coli-positive patients. The polymerase chain reaction (PCR) based identification of E. coli showed clear bands of 204bp of the 16S rRNA gene. Sequence analysis using the Basic Local Alignment Search Tool found high similarity with pathogenic E. coli from Egypt and China. The identified E. coli strain showed significant resistance to common antibiotics: amoxicillin, amoxicillin-clavulanate, ceftriaxone, sulfamethoxazole/trimethoprim, gentamicin, tetracycline, and erythromycin. Conclusion: Antibiotic resistance in E. coli from SA patients suggests the need for accurate antibiotic selection to ensure prompt treatment.

Mycoplasma bovis (M. bovis) is the most common Mycoplasma species, which has a growing impact on the dairy industry. The purpose of this study was to investigate the molecular characterization, virulence, and antibiotic susceptibility of M. bovis isolates from dairy cows with chronic mastitis in Egypt. Eighty-four composite milk samples from mastitic cows were aseptically collected from dairy farms in Egypt. Based on microbiological examination and the polymerase chain reaction (PCR) technique targeting the 16S rRNA, 14 (16.7%) of all milk samples were positive for Mycoplasma spp. PCR targeting M. bovis-specific gene identified six (7.14%) of the 14 mycoplasma isolates as M. bovis. PCR assays for different virulence genes showed that all M. bovis isolates exhibited the presence of vspA gene, while other virulence genes (uvrC, gap, and p40 pseudogenes) were determined in only two M. bovis isolates (2/6). The 16S rRNA gene phylogenetic analysis demonstrated 100% homology with reference strains of M. bovis isolated from different species and locations. When compared to other isolates in the GenBank, the amino acid sequence alignment of our isolate vspA-like protein indicated a distinct mutation. The in vitro antimicrobial susceptibility testing of the six M. bovis isolates in this study to seven antimicrobial drugs revealed that tilmycosin and tylosin had the lowest minimum inhibitory concentrations (MIC) values (≤1 μg/mL), while danofloxacin, streptomycin and florfenicol had the highest MIC values (

This study examined the genetic relationships among six species—chicken (Gallus gallus), quail (Coturnix coturnix), duck (Anas platyrhynchos), goose (Anseranser domestica), and turkey (Meleagris gallopavo) and rabbit (Lepus capensis), using partial mitochondrial cytochrome b (Cyt-b) gene sequences. PCR amplification of the mitochondrial Cyt-b gene resulted in an approximately 358 bp amplicon in size for all species, and sequences alignment and phylogenetic analyses were performed with Bio Edit and MEGA X software’s to determine genetic similarities and distances. Results revealed high nucleotide similarity between chicken and quail (89.1%) and chicken and turkey (83.6%). The closest genetic relationship was between duck and domestic goose, with 87.4% identity and the smallest genetic distance of 0.126. Chicken (Gallus gallus) demonstrated a relatively close relationship with both duck and domestic goose, with identities (83.6%). While, Turkey (Meleagris gallopavo) exhibited a slightly more distant relationship with avian species, with the lowest similarity at 81.6% with domestic goose. The rabbit, positioned at the root of the phylogenetic tree, exhibited the greatest genetic distance from avian species, with a 28.3% distance and 71.7% identity with chicken. This study provides solid evidence of the effectiveness of using Cyt-b gene sequences as a reliable tool for species identification across various applications, underscoring the effectiveness of partial Cyt-b gene sequences for avian species identification and genetic analysis, which is valuable for breeding and diversity studies.

This study examined the genetic relationships among six species—chicken (Gallus gallus), quail (Coturnix coturnix), duck (Anas platyrhynchos), goose (Anseranser domestica), and turkey (Meleagris gallopavo) and rabbit (Lepus capensis), using partial mitochondrial cytochrome b (Cyt-b) gene sequences. PCR amplification of the mitochondrial Cyt-b gene resulted in an approximately 358 bp amplicon in size for all species, and sequences alignment and phylogenetic analyses were performed with Bio Edit and MEGA X software’s to determine genetic similarities and distances. Results revealed high nucleotide similarity between chicken and quail (89.1%) and chicken and turkey (83.6%). The closest genetic relationship was between duck and domestic goose, with 87.4% identity and the smallest genetic distance of 0.126. Chicken (Gallus gallus) demonstrated a relatively close relationship with both duck and domestic goose, with identities (83.6%). While, Turkey (Meleagris gallopavo) exhibited a slightly more distant relationship with avian species, with the lowest similarity at 81.6% with domestic goose. The rabbit, positioned at the root of the phylogenetic tree, exhibited the greatest genetic distance from avian species, with a 28.3% distance and 71.7% identity with chicken. This study provides solid evidence of the effectiveness of using Cyt-b gene sequences as a reliable tool for species identification across various applications, underscoring the effectiveness of partial Cyt-b gene sequences for avian species identification and genetic analysis, which is valuable for breeding and diversity studies.

Chlorophyll is an important nutrient in celery and one of the main indexes of quality evaluation. RNA editing in chloroplasts is an important factor affecting chloroplast development and chlorophyll biosynthesis. Multisite organelle RNA editing factor (MORF) protein is a necessary regulator of chloroplast RNA editing. In this study, a total of 8 MORF genes in celery were identified, which were named AgMORF1a, AgMORF1b, AgMORF2a, AgMORF2b, AgMORF3, AgMORF7, AgMORF8 and AgMORF9 according to their subfamily classification. The physicochemical property, conserved motifs, cis-acting elements and protein interaction were predicted according to the sequences. The phylogenetic relationships and evolutionary selective pressure between MORF genes in celery and other Apiaceae plants were further analyzed. The results showed that AgMORF1b, AgMORF2a, AgMORF2b and AgMORF9 were predicted to be localized in chloroplasts. The evolution of MORF genes in 4 Apiaceae plants including celery, carrot, coriander and water dropwort was influenced by purify selection. Transcriptome data showed that the transcriptional levels of AgMORF2a, AgMORF2b, AgMORF8 and AgMORF9 were relatively higher among all MORF genes in petioles of celery, indicating their major role. RT-qPCR data showed that the expression levels of the above 4 genes were significantly higher in petioles of green celery than those of white celery. This study provided a basis for analyzing the effects of MORF proteins on chloroplast development of celery with different chlorophyll accumulation.

Summary: Disclosure of the key events in tumor progress will help us deepen the understanding of tumorigenesis. Here, we present a protocol for multi-regional tissue capture of malignant continuum. We describe steps for preparing tissue sections, laser capture microdissection, and whole-genome library preparation, which enable the concurrent analysis of potential driver events in precancer initiation. This protocol overcomes challenges posed by limited DNA quantity and preserves the spatial information of the target regions.For complete details on the use and execution of this protocol, please refer to Li et al.,1 Chang et al.,2 and Chen et al.3 : Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.

Summary: Stimulation of CD4 T cells with anti-CD3/CD28 is a commonly used model to study T cell activation. Here, we present a protocol for investigating T cell activation based on anti-CD3/CD28 bead stimulation and single-cell RNA sequencing (scRNA-seq). We describe the workflow from the isolation of human peripheral blood mononuclear cells (PMBCs) and CD4 T cell enrichment to anti-CD3/CD28 bead stimulation, scRNA-seq, and data analysis.For complete details on the use and execution of this protocol, please refer to Li et al.1 : Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.

Summary: Circulating cell-free DNA (cfDNA) fragment end motif profiles are a promising biomarker in precision oncology. Here, we present a protocol for analyzing plasma cfDNA fragment end motifs from ultra-low-pass whole-genome sequencing (WGS) data. We detail a pipeline composed of sequential bash scripts for processing post-alignment BAM files. Subsequently, we outline the procedure for downstream analysis and visualization of 4-mer as well as other n-mer cfDNA end motifs in R.For complete details on the use and execution of this protocol, please refer to Liu et al.1 : Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.

Aptamers are short single-stranded DNA or RNA molecules with high affinity and specificity for targets and are generated using the iterative systematic evolution of ligands by exponential enrichment (SELEX) process. Next-generation sequencing (NGS) revolutionized aptamer selections by allowing a more comprehensive analysis of SELEX-enriched aptamers as compared to Sanger sequencing. The current challenge with aptamer NGS datasets is identifying a diverse cohort of candidate aptamers with the highest likelihood of successful experimental validation. Here we present AptamerRunner, an aptamer sequence and/or structure clustering algorithm that synergistically integrates computational analysis with visualization and expertise-directed decision making. The visual integration of networked aptamers with ranking data, such as fold enrichment or scoring algorithm results, represents a significant advancement over existing clustering tools by providing a natural context to depict groups of aptamers from which ranked or scored candidates can be chosen for experimental validation. The inherent flexibility, user-friendly design, and prospects for future enhancements with AptamerRunner have broad-reaching implications for aptamer researchers across a wide range of disciplines.

Summary: We present a protocol to achieve a higher depth of long-read sequencing of region(s) of interest in potato cyst nematodes without amplification using a Cas9-based Nanopore enrichment approach. We describe steps for designing high-fidelity guide RNAs to be used with Cas9 nuclease, extracting high-molecular-weight DNA from the nematodes, and dephosphorylating genomic DNA ends. We then detail procedures for using Cas9-guide RNA complex to make targeted cleavage of the region of interest followed by a Nanopore library preparation.For complete details on the use and execution of this protocol, please refer to Sonawala et al.1 : Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.

Summary: Lipid accumulation has recently emerged as a key feature underlying the pro-tumorigenic role of macrophages. Here, we present a workflow to study macrophage lipid crosstalk with tumor cells. We describe steps for the identification, purification, and multi-omics characterization of lipid-laden macrophages (LLMs) from murine tumors and outline protocols to assess the functional significance of LLMs in cancer malignancy. This approach has the potential to uncover the source of lipids that drives LLM formation and its pro-tumorigenic potential in multiple cancer types.For complete details on the use and execution of this protocol, please refer to Kloosterman, Erbani, et al.1 : Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.

Abstract Working life is associated with lifestyle, screening uptake, and occupational health risks that may explain differences in cancer onset. To better understand the association between working life and cancer risk, we need to account for the entire employment history. We investigated whether lifetime employment trajectories are associated with cancer risk. We used data from 6809 women and 5716 men, average age 70 years, from the Survey of Health, Ageing, and Retirement in Europe. Employment history from age 16 to 65 was collected retrospectively using a life calendar and trajectories were constructed using sequence analysis. Associations between employment trajectories and self-reported cancer were assessed using logistic regression. We identified eight employment trajectories for women and two for men. Among women, the risk of cancer was higher in the trajectories “Mainly full-time to home/family”, “Full-time or home/family to part-time”, “Mainly full-time”, and “Other” compared with the “Mainly home/family” trajectory. Among men, the risk of cancer was lower in the “Mainly self-employment” trajectory compared with “Mainly full-time”. We could show how employment trajectories were associated with cancer risk, underlining the potential of sequence analysis for life course epidemiology. More research is needed to understand these associations and determine if causal relationships exist.

When inferring the evolution of a gene/gene family, it is advisable to use all available coding sequences (CDS) from as many species genomes as possible in order to infer and date all gene duplications and losses. Nowadays, this means using hundreds or even thousands of CDSs, which makes the inferred phylogenetic trees difficult to visualize and interpret. Therefore, it is useful to have an automated way of collapsing large phylogenetic trees according to a taxonomic term decided by the user (family, class, or order, for instance), in order to highlight the minimal set of sequences that should be used to recapitulate the full history of the gene/gene family being studied at that taxonomic level, that can be refined using additional software. Here we present the Phylogenetic Tree Collapser (PTC) program (https://github.com/pegi3s/phylogenetic-tree-collapser), a flexible tool for automated tree collapsing using taxonomic information, that can be easily used by researchers without a background in informatics, since it only requires the installation of Docker, Podman or Singularity. The utility of PTC is demonstrated by addressing the evolution of the ascorbic acid synthesis pathway in insects. A Docker image is available at Docker Hub (https://hub.docker.com/r/pegi3s/phylogenetic-tree-collapser) with PTC installed and ready-to-run.

Abstract Background With the surge in genomic data driven by advancements in sequencing technologies, the demand for efficient bioinformatics tools for sequence analysis has become paramount. BLAST-like alignment tool (BLAT), a sequence alignment tool, faces limitations in performance efficiency and integration with modern programming environments, particularly Python. This study introduces PxBLAT, a Python-based framework designed to enhance the capabilities of BLAT, focusing on usability, computational efficiency, and seamless integration within the Python ecosystem. Results PxBLAT demonstrates significant improvements over BLAT in execution speed and data handling, as evidenced by comprehensive benchmarks conducted across various sample groups ranging from 50 to 600 samples. These experiments highlight a notable speedup, reducing execution time compared to BLAT. The framework also introduces user-friendly features such as improved server management, data conversion utilities, and shell completion, enhancing the overall user experience. Additionally, the provision of extensive documentation and comprehensive testing supports community engagement and facilitates the adoption of PxBLAT. Conclusions PxBLAT stands out as a robust alternative to BLAT, offering performance and user interaction enhancements. Its development underscores the potential for modern programming languages to improve bioinformatics tools, aligning with the needs of contemporary genomic research. By providing a more efficient, user-friendly tool, PxBLAT has the potential to impact genomic data analysis workflows, supporting faster and more accurate sequence analysis in a Python environment.

Summary: Host response to environmental exposures such as pathogens and chemicals can include modifications to the epigenome and transcriptome. Improved signature discovery, including the identification of the agent and timing of exposure, has been enabled by advancements in assaying techniques to detect RNA expression, DNA base modifications, histone modifications, and chromatin accessibility. The interrogation of the epigenome and transcriptome cascade requires analyzing disparate datasets from multiple assay types, often at single-cell resolution, derived from the same biospecimen. However, there remains a paucity of rigorous quality control standards of those datasets that reflect quality assurance of the underlying assay. This guide outlines a comprehensive suite of metrics that can be used to ensure quality from 11 different epigenetics and transcriptomics assays. Recommended mitigative actions to address failed metrics are provided. The workflow presented aims to improve benchwork protocols and dataset quality to enable accurate discovery of exposure signatures.

Summary: Non-invasive prenatal testing (NIPT) not only enables the detection of chromosomal anomalies in fetuses but also generates vast amounts of ultra-low-depth sequencing data, which can be leveraged for population genomic studies. Here, we present a protocol designed for massive ultra-low-depth sequencing datasets. We detail the steps for data processing, quality control, and genotype imputation, followed by genome-wide association study (GWAS) and post-GWAS analyses. This protocol applies to a wide range of ultra-low-depth sequencing studies, extending beyond data from NIPT.For complete details on the use and execution of this profile, please refer to Xiao et al.1 : Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.

Summary: The study of somatic mutations in single cells provides insights into aging and carcinogenesis, which is complicated by the dependency on whole-genome amplification (WGA). Here, we describe a detailed workflow starting from single-cell isolation to WGA by primary template-directed amplification (PTA), sequencing, quality control, and downstream analyses. A machine learning approach, the PTA Analysis Toolkit (PTATO), is used to filter the hundreds to thousands of artificial variants induced by WGA from true mutations at high sensitivity and accuracy.For complete details on the use and execution of this protocol, please refer to Middelkamp et al.1 : Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.

Summary: Recently, studies have emerged exploring the potential application of fecal microbiota transplantation (FMT) in pre-clinical settings. Here, we present a protocol for FMT for mice housed in a specific pathogen-free (SPF) facility. We describe steps for sample collection, microaerophilic processing of freshly collected fecal pellets, and administration through oral gavage. We then detail procedures for the engraftment of the bacterial community. This protocol focuses on age- and gender-matched, healthy donor mice using a mobile and cost-effective alternative to an anoxic cabinet. : Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.

Summary: Angiogenesis begins as endothelial cells migrate, forming a sprouting tip and subsequent growth-rich stalk cells. Here, we present a protocol for transcriptomic and epigenomic analyses of tip-like cells in cultured endothelial cells. We describe steps for stimulating human umbilical vein endothelial cells (HUVECs) with vascular endothelial cell growth factor (VEGF) to generate tip-like cells. We then detail procedures for library preparation for single-cell RNA sequencing (RNA-seq) and chromatin immunoprecipitation sequencing (ChIP-seq), and data analysis. This scalable protocol is also applicable to diverse omics studies, including proteomics and metabolomics.For complete details on the use and execution of this protocol, please refer to Miyamura et al.1 : Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.

Although air pollution has been classified as a risk factor for heart disease, the underlying mechanisms remain nebulous. Therefore, this study investigated the effect of diesel particulate matter (DPM) exposure on cardiomyocytes and identified differentially expressed genes (DEGs) induced by DPM. DPM treatment decreased H9C2 cell viability and increased cytotoxicity. Ten genes showed statistically significant differential expression following treatment with DPM at 25 and 100 μg/ml for 3 h. A total of 273 genes showed statistically significant differential expression following treatment with DPM at 25 and 100 μg/ml for 24 h. Signaling pathway analysis revealed that the DEGs were related to the ‘reactive oxygens species,’ ‘IL-17,’ and ‘fluid shear stress and atherosclerosis’ signaling pathways. Hmox1, Fos, and Fosb genes were significantly upregulated among the selected DEGs. This study identified DPM-induced DEGs and verified the selected genes using qRT-PCR and western blotting. The findings provide insights into the molecular events in cardiomyocytes following exposure to DPM.

Summary: Constructing metagenome-assembled genomes (MAGs) from complex metagenomic samples involves a series of bioinformatics operations, each requiring deep bioinformatics knowledge. Here, we present a protocol for constructing MAGs and conducting functional profiling to address biological questions. We describe steps for system configuration, data downloads, read processing, removal of human DNA contamination, metagenomic assembly, and statistical quality assessment of the final assembly. Additionally, we detail procedures for the construction and refinement of MAGs, as well as the functional profiling of MAGs. : Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.

Summary: Seqtometry (sequencing-to-measurement) is an analytical platform for single-cell analysis based on direct profiling of gene expression and accessibility achieved by advanced scoring with gene signatures. Here, we present a protocol for single-cell RNA sequencing (scRNA-seq) and single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq) analysis using Seqtometry. We describe steps for preprocessing, imputation, scoring, and plotting, with extensions to large datasets and integration of multiple datasets. This protocol yields results in the form of biologically interpretable dimensions for direct identification and comprehensive characterization of specific cells.For complete details on the use and execution of this protocol, please refer to Kousnetsov et al.1 : Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.

IntroductionCanine mesotheliomas are uncommon malignant tumors typically detected late. Minimally invasive diagnostic biomarkers would facilitate diagnosis at earlier stages, thereby improving clinical outcomes. We hypothesized that mesothelin could be used as a reliable diagnostic biomarker for canine mesotheliomas since it has been used as a cancer biomarker for human mesothelioma. We aimed to explore and characterize mesothelin gene expression in dogs and assess its use as a diagnostic biomarker for canine mesotheliomas.Materials and methodsWe quantified expressed canine mesothelin transcripts via reverse transcription polymerase chain reaction (RT-PCR) and sequenced them using ribonucleic acid (RNA) extracted from a canine mesothelioma cell line. After confirming mesothelin expression, we assessed its levels in major organ tissues and compared them with those in the mesothelioma tissues using quantitative PCR (qPCR). Mesothelin overexpression in mesotheliomas was detected, and we further compared its levels using qPCR between mesotheliomas and non-mesotheliomas using tumor tissues and clinical sample effusions, confirming its significance as a diagnostic biomarker for canine mesothelioma.ResultsMesothelin complementary deoxyribonucleic acid (cDNA) was amplified via RT-PCR, yielding a single band of expected upon DNA electrophoresis. Sequence analyses confirmed it as a predicted canine mesothelin transcript from the genome sequence database. Comparative sequence analysis of the deduced amino acid sequence of the expressed canine mesothelin demonstrated molecular signature similarities with the human mesothelin. However, the pre-sequence of canine mesothelin lacks the mature megakaryocyte potentiating factor (MPF) portion, which is typically cleaved post-translationally with furin. Mesothelin expression was quantified via qPCR revealing low levels in the mesothelial and lung tissues, with negligible expression in the other major organs. Canine mesothelin exhibited significantly higher expression in the canine mesotheliomas than in the noncancerous tissues. Moreover, analysis of clinical samples using qPCR demonstrated markedly elevated mesothelin expression in canine mesotheliomas compared to non-mesothelioma cases.Discussion and conclusionCanine mesothelin exhibits molecular and biological characteristics akin to human mesothelin. It could serve as a vital biomarker for diagnosing canine mesotheliomas, applicable to both tissue- and effusion-based samples.

BackgroundHigh altitude pulmonary edema (HAPE) is an idiopathic, noncardiogenic form of pulmonary edema that occurs at high altitudes. It is characterized by a severe clinical course and carries a significant mortality risk. Despite its clinical relevance, the molecular mechanisms underlying HAPE are not well understood.MethodsWe conducted whole-transcriptome RNA sequencing on blood samples from 6 pairs of HAPE patients and healthy controls to identify differentially expressed (DE) mRNAs, miRNAs, circRNAs, lncRNAs, along with alternative splicing (AS) events, gene fusions, and novel transcripts. To explore the regulatory dynamics, we constructed ceRNA networks and analyzed immune cell infiltration patterns, further annotating the biological functions of these transcripts. For empirical validation, we selected five circRNAs from the ceRNA network and conducted RT-qPCR on 50 paired samples. Additionally, we assessed the correlations between circRNA expression levels and clinical data to evaluate their diagnostic potential.ResultsWe observed 2,023 differentially expressed mRNAs (DEmRNAs), 84 DEmiRNAs, 200 DEcircRNAs, and 3,573 DElncRNAs. A total of 139 ‘A3SS’ events, 103 ‘A5SS’ events, 545 ‘MXE’ events, 14 ‘RI’ events, and 1,482 ‘SE’ events were identified in the AS events analysis between the two groups. Two ceRNA networks were constructed. T cells, follicular helper, and Macrophages M1 cells exhibited the strongest positive correlation (R=0.82), while naive B cells and memory B cells demonstrated the strongest negative correlation (R=-0.62). In total, the expression of three circRNAs was significantly different in a larger cohort. Hsa_circ_0058497, hsa_circ_0081006, and hsa_circ_0083220 demonstrated consistent with the RNA sequencing results. These three circRNAs strongly correlate with clinical indicators and exhibit potential as diagnostic biomarkers. Finally, we verified five genes (CXCR4, HSD17B2, ANGPTL4, TIMP3, N4BP3) that were differentially expressed in endothelial cells under normoxia and hypoxia through bioinformatics and RT-qPCR analyses.ConclusionThis study elucidates the differential expression of coding and non-coding RNAs (ncRNAs) in HAPE, identifies new transcripts and genes, and enhances our understanding of the transcriptional characteristics of HAPE. Moreover, it highlights the potential role of circRNAs in advancing the diagnosis and treatment of HAPE.

We investigate the trajectories of people’s subjective well‐being, measured as their overall life satisfaction at five points in time before, during, and after Covid‐19 in Switzerland. Using sequence analysis and hierarchical clustering, we identify three groups of typical trajectories. About half of all respondents experienced a decline in well‐being right after the first lockdown and subsequent recovery to high, pre‐pandemic levels. A quarter consistently reports very high satisfaction throughout all five waves, and another quarter experienced declining well‐being since the outbreak of the pandemic. As a second contribution, we then demonstrate how improving relations with neighbors increases the likelihood of recovering from the negative impact of the pandemic on subjective well‐being. This effect is largely constant across social groups. Conceptualizing vulnerability as the extent to which social groups with different endowments (e.g., financial situation or individual social networks) cope differently with (exogenous) stressors, we further find slightly more pronounced positive effects of improving neighborly relations during the pandemic for more vulnerable people in terms of household finances and education. Moreover, being able to count on emotional support from neighbors and friends prior to the pandemic generally guarded against experiencing declining well‐being. Meanwhile, people with less financial means, poorer health, and less support from friends and neighbors are also more likely to be in the trajectory cluster of declining well‐being.

Abstract Motivation Drug repurposing speeds up the development of new treatments, being less costly, risky, and time consuming than de novo drug discovery. There are numerous biological elements that contribute to the development of diseases and, as a result, to the repurposing of drugs. Methods In this article, we analysed the potential role of protein sequences in drug repurposing scenarios. For this purpose, we embedded the protein sequences by performing four state of the art methods and validated their capacity to encapsulate essential biological information through visualization. Then, we compared the differences in sequence distance between protein-drug target pairs of drug repurposing and non - drug repurposing data. Thus, we were able to uncover patterns that define protein sequences in repurposing cases. Results We found statistically significant sequence distance differences between protein pairs in the repurposing data and the rest of protein pairs in non-repurposing data. In this manner, we verified the potential of using numerical representations of sequences to generate repurposing hypotheses in the future.

Abstract Background The selection of primer pairs in sequencing-based research can greatly influence the results, highlighting the need for a tool capable of analysing their performance in-silico prior to the sequencing process. We therefore propose PrimerEvalPy, a Python-based package designed to test the performance of any primer or primer pair against any sequencing database. The package calculates a coverage metric and returns the amplicon sequences found, along with information such as their average start and end positions. It also allows the analysis of coverage for different taxonomic levels. Results As a case study, PrimerEvalPy was used to test the most commonly used primers in the literature against two oral 16S rRNA gene databases containing bacteria and archaea. The results showed that the most commonly used primer pairs in the oral cavity did not match those with the highest coverage. The best performing primer pairs were found for the detection of oral bacteria and archaea. Conclusions This demonstrates the importance of a coverage analysis tool such as PrimerEvalPy to find the best primer pairs for specific niches. The software is available under the MIT licence at https://gitlab.citius.usc.es/lara.vazquez/PrimerEvalPy .

Abstract Background Sparganosis is a rare zoonotic disease caused by plerocercoid larvae of the genera Spirometra or Sparganum (Cestoda: Diphyllobothriidae). The larvae of Spirometra generally do not undergo asexual reproduction, whereas those of Sparganum can induce proliferative lesions in infected tissues. This paper presents an unusual case of proliferative sparganosis due to infection with Spirometra mansoni in a cat, normally considered a definitive host of the species. Case presentation A 9-year-old male domestic cat was presented with a mass on the right side of the face that underwent progressive enlargement for 1 month. The morphological and histopathological examinations revealed multiple asexual proliferative cestode larvae in the lesions, suggestive of proliferative sparganosis. Next-generation sequencing analysis of formalin-fixed and paraffin-embedded specimens of surgically excised tissue indicated that the worm was Spirometra mansoni. Conclusion Although S. mansoni a common tapeworm species found in the small intestine of domestic cats and dogs in Japan, proliferative sparganosis is extremely rare. This is the first confirmed case of proliferative sparganosis due to infection with S. mansoni in cat.

This paper presents the generalized Pell $ p- $numbers and provides some related results. A new sequence is defined using the characteristic polynomial of the Pell $ p- $numbers and generalized Mersenne numbers. Two algorithms for Diffie-Hellman key exchange are given as an application of these sequences. They are illustrated via numerical examples and shown to be secure against attacks. Thus, these new sequences are practical for encryption and constructing private keys.

This paper presents a neutrosophic inference model for bioinformatics. The model is used to develop a system for accurate comparisons of human nucleic acids, where the new nucleic acid is compared to a database of old nucleic acids. The comparisons are analyzed in terms of accuracy, certainty, uncertainty, neutrality, andbias. The proposed system achieves good results and provides a reliable standard for future comparisons. It highlights the potential of neutrosophic inference models in bioinformatics applications. Data mining and bioinformatics play a crucial role in computational biology, with applications in scientific research and industrial development. Biological analysts rely on specialized tools and algorithms to collect, store, categorize, and analyze large volumes of unstructured data. Data mining techniques are used to extract valuable information from this data, aiding in the development of new therapies and understanding genetic relationships between organisms. Recent advancements in bioinformatics include gene expression tools, Bio sequencing, and Bio databases, which facilitate the extraction and analysis of vital biological information. These technologies contribute to the analysis of big data, identification of key bioinformatics insights, and generation of new biological knowledge. Data collection, analysis, and interpretation in this field involvesthe use of modern technologies such as cloud computing, machine learning, and artificial intelligence, enabling more efficient and accurate results. Ultimately, data mining and bioinformatics enhance our understanding of genetic relationships, aid in developing new therapies, and improve healthcare outcomes.

Introduction. The discovery of two EBV types (EBV-1 and EBV-2) has stimulated the study of their prevalence in populations and association with malignancies. Objective. To study the prevalence of EBV-1 and EBV-2 types among ethnic groups in Russia, to analyze PCR products of the LMP1 gene in virus isolates, and to evaluate the contribution of EBV types to the incidence of malignant neoplasms. Materials and methods. EBV isolates from oral lavages of the Republics Adygea, Kalmykia, Tatarstan and the Moscow Region (MR) representatives were studied by nested PCR for the belonging to EBV-1 and EBV-2 types. LMP1 amplicons obtained by real-time PCR from viral isolates DNA were classified and sequenced on an automatic DNA sequencer ABI PRISM 3100-Avant (USA). The sequencing results were analyzed using Chromas 230 and Vector NT programs (Invitrogen, USA). The reliability of the obtained data was assessed using statistical packages Statistica for Windows, 10.0. Results. The prevalence rates of EBV-1 and EBV-2 in representatives of four ethnic groups were compared with the incidence rates of some tumors in the population of three Republics and MR. The dominant persistence of the transforming in vitro EBV-1 type in representatives of the Republic of Tatarstan and MR correlated with a high incidence of gastric cancer and lymphomas in the population of these territories. On the contrary, predominant infection of the non-transforming in vitro EBV-2 type and both types of the virus in approximately the same percentage of representatives of Adygea and Kalmykia, respectively, correlated with a lower level incidence of above tumors in populations of these Republics. The differences between the incidence rates of neoplasms in the compared ethnic populations were statistically insignificant (p 0.05). LMP1 variants of viral isolates did not reflect either the level of EBV persistence types or the incidence of tumors. Conclusion. Infection of ethnic groups with EBV-1 and EBV-2 may vary significantly under the influence of various factors. The predominance of the in vitro transforming EBV-1 type in the population did not increase the incidence of tumors due to cases associated with the dominant virus type.

The MS6.8 earthquake occurred in Luding County, Ganzi Prefecture, Sichuan Province, China at 12:52 p.m. on Sept. 5, 2022, is preliminarily analyzed in terms of regional tectonics, historical earthquakes, sequence characteristics and focal mechanism solutions. The results show that: 1) The magnitude difference ΔM between the main shock and the maximum aftershock, the energy ratio ER of the main shock to the aftershocks, the p-value and the estimated Mmax-value indicate that the MS6.8 Luding earthquake sequence can be divided into the main shock-aftershock type (MAT). 2) The spatial distribution of the aftershocks, their corresponding b-value and expected maximum magnitude are obviously segmented, which reflect the complexity of the seismogenic tectonics. 3) According to the focal mechanisms obtained from the HASH program, the geometry distribution of the sequence, and the relationship between the sequence and the nearby faults, it can be inferred that the Moxi fault segment of the Xianshuihe fault zone is the seismogenic tectonics of the MS6.8 Luding earthquake sequence.

This study explores the potential of large language models as interfaces for conducting sequence analysis on log data from interactive E-Books. As studies show, qualitative methods are not sufficient to comprehensively study the process of interaction with interactive E-Books. The quantitative method of educational data mining (EDM) has been considered as one of the most promising approaches for studying learner interactions with E-Books. Recently, sequence analysis showed potential in identifying typical patterns of interaction from log data collected from the Estonian Interactive E-Book Platform Opiq, allowing one to see the types of sessions from students in different grades, clusters of students based on the amount of the content they studied, and the interaction type they preferred. The main goal of the present study is to understand how teachers can utilize insights from CustomGPT to enhance their understanding of students’ interaction strategies with digital learning environments (DLEs) such as Opiq, and what the potential areas for further development of such tools are. We specified the process for developing a chatbot for transferring teachers’ queries into sequence analysis results and gathered feedback from teachers, allowing us both to estimate current design solutions to make sequence analysis results available and to find potential vectors of its development. Participants provided explicit feedback on CustomGPT, appreciating its potential for group and individual analysis, while suggesting improvements in visualization clarity, legend design, descriptive explanations, and personalized tips to better meet their needs. Potential areas of development, such as integrating personalized learning statistics, enhancing visualizations and reports for individual progress and mitigating AI hallucinations by expanding training data, are described.

Understanding activity-travel patterns and their determinants with regard to multi-purpose commuters is essential for enhancing commuting efficiency and ensuring equal participation in activities. This study applies sequence analysis and hierarchical clustering to identify distinct activity-travel patterns of Kunming commuters using 2016 Household Travel Survey data. Subsequently, a multinomial logistic regression model (MNL) examines the factors influencing these patterns. The results reveal significant heterogeneity across four activity-travel patterns: the fixed commuter pattern (FCP), characterized by pronounced morning and evening peaks with minimal non-commuting activities; the balanced commuter pattern (BCP), where commuters participate in non-commuting activities after afternoon work; the restricted commuter pattern (RCP), with non-commuting activities occurring after midday work; and the flexible commuter pattern (FLCP), featuring a late-start work pattern where some commuters go to work after 5 pm. Additionally, the study finds that female commuters and those with longer commuting and working hours tend to have simpler time allocation. Conversely, male commuters, those from complex family structures, car-owning households, and residents in areas with abundant activity opportunities actively engage in non-commuting activities. These findings can help policymakers optimize travel services and develop heterogeneous commuting and transportation policies.