Clustered Data Research Articles

CyCadas: accelerating interactive annotation and analysis of clustered cytometry data.

Single cell profiling by cytometry has emerged as a key technology in biology, immunology and clinical-translational medicine. The correct annotation, which refers to the identification of clusters as specific cell populations based on their marker expression, of clustered high-dimensional cytometry data, is a critical step of the analysis. Its accuracy determines the correct interpretation of the biological data. Despite the progress in various clustering algorithms, the annotation of clustered data still remains a manual, time consuming and error-prone task. We developed a user-friendly cluster annotation and differential abundance detection tool that can be applied on data generated with Self Organizing Map clustering algorithms, thus simplifying the annotation process of datasets that consist of hundreds or thousands of clusters. We present Cytometry Cluster Annotation and Differential Abundance Suite (CyCadas), a semi-automated software tool that facilitates cluster annotation in cytometry data by offering both visual and computational guidance. CyCadas addresses the critical need for efficient and accurate annotation of high-resolution clustered cytometry data, significantly reducing the time needed to perform the analysis compared to both manual gating approaches and manual annotation of clustered data. The tool features a user-friendly interface, visual tools enabling data exploration and automated threshold estimation to separate negative and positive marker expression. It facilitates the definition and annotation of cell phenotypes among multiple clusters in a tree-based data structure. Finally, it calculates the abundance of various cell populations across the conditions with statistical interpretation. It is an ideal resource for researchers aiming to streamline their cytometry workflow. CyCadas is available as open source at: https://github.com/DII-LIH-Luxembourg/cycadas.

Analysis of the Setomimycin Biosynthetic Gene Cluster from Streptomyces nojiriensis JCM3382 and Evaluation of Its α-Glucosidase Inhibitory Activity Using Molecular Docking and Molecular Dynamics Simulations.

The formation of atroposelective biaryl compounds in plants and fungi is well understood; however, polyketide aglycone synthesis and dimerization in bacteria remain unclear. Thus, the biosynthetic gene cluster (BGC) responsible for antibacterial setomimycin production from Streptomyces nojiriensis JCM3382 was examined in comparison with the BGCs of spectomycin, julichromes, lincolnenins, and huanglongmycin. The setomimycin BGC includes post-polyketide synthase (PKS) assembly/cycling enzymes StmD (C-9 ketoreductase), StmE (aromatase), and StmF (thioesterase) as key components. The heterodimeric TcmI-like cyclases StmH and StmK are proposed to aid in forming the setomimycin monomer. In addition, StmI (P-450) is predicted to catalyze the biaryl coupling of two monomeric setomimycin units, with StmM (ferredoxin) specific to the setomimycin BGC. The roles of StmL and StmN, part of the nuclear transport factor 2 (NTF-2)-like protein family and unique to setomimycin BGCs, could particularly interest biochemists and combinatorial biologists. α-Glucosidase, a key enzyme in type 2 diabetes, hydrolyzes carbohydrates into glucose, thereby elevating blood glucose levels. This study aimed to assess the α-glucosidase inhibitory activity of EtOAc extracts of JCM 3382 and setomimycin. The JCM 3382 EtOAc extract and setomimycin exhibited greater potency than the standard inhibitor, acarbose, with IC50 values of 285.14 ± 2.04 μg/mL and 231.26 ± 0.41 μM, respectively. Molecular docking demonstrated two hydrogen bonds with maltase-glucoamylase chain A residues Thr205 and Lys480 (binding energy = -6.8 kcal·mol-1), two π-π interactions with Trp406 and Phe450, and one π-cation interaction with Asp542. Residue-energy analysis highlighted Trp406 and Phe450 as key in setomimycin's binding to maltase-glucoamylase. These findings suggest that setomimycin is a promising candidate for further enzymological research and potential antidiabetic therapy.

8159 Single Nuclei RNA-Sequencing Reveals Novel Cellular Diversity in Human Neck Adipose Tissue

Abstract Disclosure: H. Camara: None. S.D. Kodani: Employee; Self; Hoxton Farm. S. Yang: None. V. Efthymiou: None. K. An: None. A. Gupta: None. F. Shamsi: None. A. Streets: None. M.E. Patti: Advisory Board Member; Self; Fractyl Laboratories. Consulting Fee; Self; AstraZeneca, Hamni, MBX Biosciences. Grant Recipient; Self; Dexcom. Y. Tseng: Consulting Fee; Self; LyGenesis. Other; Self; BioHaven. Adipose tissue plays a crucial role in maintaining metabolic health. It fulfills distinct functions through two types of fat: white adipose tissue (WAT), the primary site of triglyceride storage, and brown adipose tissue (BAT) along with related beige fat, specializing in thermogenic energy expenditure. Despite the scarcity of BAT in humans, these thermogenic adipocytes efficiently utilize glucose and triglycerides and act as an endocrine organ by secreting batokines. Hence, increased BAT activity is associated with improved metabolic health in humans. In adults, thermogenic adipose tissue is localized in deep neck planes, exhibiting a gradient from white to beige/brown from superficial to deep layers. Mounting evidence suggests the diverse cell types within the adipose tissue and their dynamic interactions play an important role in its versatile functions. To understand the cellular compositions of human adipose tissue and directly compare the differences between human BAT vs. WAT, we collected 30 paired samples of superficial, intermediate and deep human neck adipose tissue from 15 participants who underwent anterior cervical discectomy fusion or thyroid surgery and subjected the frozen tissues to single-nucleus RNA sequencing (snRNA-seq). The integration of the samples yielded 41,337 good-quality nuclei. As expected, white adipocytes (WAd) were the dominant cell type, comprising 38% of the total cells captured. Further analysis of the WAd cluster revealed increased expression of brown adipocyte-associated genes, such as EBF2 and COBL, and enrichment of oxidative phosphorylation pathway score in cells from deep neck samples, indicating increased thermogenic potential. Bona fide brown adipocytes (BAds), marked by the expression of PPARGC1A, were predominantly located in the deep neck samples (>80%) but comprised less than 2% of the dataset. Despite their low numbers, cell-cell communication analysis by CellChat predicted that BAds account for most of the communication events within this niche, highlighting their endocrine importance. snRNA-seq also captured non-adipocyte cells, including adipocyte progenitors, smooth muscle, immune, Schwann and neuron-associated cells. Among these, a novel Neuron-Associated Cell-1, marked by the expression of CNTNAP2, emerged as the second most abundant in the dataset (∼10% of total). Another neuron-associated cell population marked by expression of DNAH11, though less abundant (∼1%), was enriched in deep neck samples, potentially regulating the high levels of thermogenic potential in this region. Taken together, the identified subpopulations and their gene expression profiles offer new insights into the tissue's role in metabolic regulation. This knowledge could be pivotal for developing targeted therapies for obesity and related disorders. Presentation: 6/1/2024

In silico profiling, docking analysis, and protein interactions of secondary metabolites in Musa spp. Against the SGE1 protein of Fusarium oxysporum f. sp. cubense

Banana Fusarium Wilt (BFW), caused by Fusarium oxysporum f. sp. cubense (Foc), threatens banana crops globally, with the pathogen's virulence partially regulated by the Sge1 transcription factor, which enhances disease severity. Certain Musa species display resistance to Foc, suggesting inherent genetic traits that confer immunity against Sge1Foc. This study utilized bioinformatics tools to investigate the mechanisms underlying this resistance in Musa accuminata subsp. aalaccensis. Through in silico analyses, we explored interactions between Musa spp. and Foc, focusing on the Sge1 protein. Tools such as Anti-SMASH, AutoDockVina 4.0, STRING, and Phoenix facilitated the profiling of secondary metabolites in Musa spp. and the identification of biosynthetic gene clusters involved in defense. Our results indicate that secondary metabolites, including saccharides, terpenes, and polyketides, are crucial to the plant's immune response. Molecular docking studies of selected Musa metabolites, such as 3-Phenylphenol, Catechin, and Epicatechin, revealed 3-Phenylphenol as having the highest binding affinity to the Sge1Foc protein (-6.7 kcal/mol).Further analysis of gene clusters associated with secondary metabolite biosynthesis in Musa spp. identified key domains like Chalcone synthase, Phenylalanine ammonia-lyase, Aminotran 1–2, and CoA-ligase, which are integral to phenylpropanoid production—a critical pathway for secondary metabolites. The study highlights that the phenylpropanoid pathway and secondary metabolite biosynthesis are vital for Musa spp. resistance to Foc. Flavonoids and lignin may inhibit Sge1 protein formation, potentially disrupting Foc's cellular processes. These findings emphasize the role of phenylpropanoid pathways and secondary metabolites in combating BFW and suggest that targeting these pathways could offer innovative strategies for enhancing resistance and controlling BFW in banana crops. This research lays the groundwork for developing sustainable methods to protect banana cultivation and ensure food security.

Book cluster: a new approach to explore the evolution of knowledge organization in ancient China

Abstract Chinese ancient book catalogs have persisted for over two millennia, serving as ancient knowledge organization systems and cultural heritage of great academic value. This article introduces the concept of “book cluster,” a novel approach to studying catalog classification evolution, offering a meso-level perspective that bridges books and categories. Drawing from computational methods, we identify clusters of books with similar categorization trajectories, enabling more efficient and intuitive exploration of knowledge organization trends. We review existing research on catalog classification, highlighting the traditional qualitative analysis and emerging computational methods. We then present our data source and processing methods. The concept of book cluster is defined, along with the algorithm for cluster identification and feature extraction. A case study demonstrates the potential of the book cluster concept in analyzing classification changes and knowledge inheritance. Additionally, we introduce an online analytical platform for further exploration. Through computational analysis of book clusters, we gain insights into the evolution of ancient book recording, circulation, and academic contexts. Book clusters offer clues about the content of lost books. This research lays the groundwork for future iterations. By pioneering the study of knowledge organization evolution using book clusters, we enhance our understanding of Chinese history's academic culture and knowledge preservation.

Factors affecting Kaposi’s sarcoma-associated herpesvirus transmission in rural Ugandan households, a longitudinal study

BackgroundWe report the impact of HIV infection within a household on oral Kaposi’s sarcoma-associated herpesvirus (KSHV) shedding.MethodsWe enrolled 469 individuals from 90 households. Mouthwash rinse samples collected at three monthly visits were analyzed for KSHV DNA using quantitative polymerase chain reaction (qPCR). Generalized linear mixed effects logistic models were applied to analyze factors associated with KSHV ever shedding, and among shedders, always versus intermittent shedding. Linear mixed effects models were applied to models of KSHV viral loads. Intraclass correlation coefficients (ICCs) were calculated to assess the contribution of household-level factors to variations in shedding probabilities. Hotspot analyses of geospatial feature clusters were calculated using Getis-Ord Gi* statistic and visualized using inverse distance weighted interpolation.ResultsAnalyses included 340 KSHV seropositive individuals, aged 3 + years, with qPCR results from 89 households. Forty households had 1 + persons living with HIV (PLWH), while 49 had none. Among participants, 149(44%) were KSHV ever shedders. Of 140 who shed KSHV at two or more visits, 34(24%) were always shedders. Increasing number of KSHV seropositive household members was significantly associated with ever shedding [Odds ratio(OR) (95% Confidence Interval(95%CI)):1.14(1.03,1.26);p = 0.013]. Among KSHV shedders, a statistically significant age-related trend was identified with 10–19 years being more likely to be always shedders (type III test p = 0.039) and to have higher viral loads (type III test p = 0.027). In addition, higher viral loads were significantly associated with increasing number of household members [coefficient(95%CI):0.06(0.01,0.12);p = 0.042], increasing number of KSHV seropositive members [coefficient(95%CI):0.08(0.01,0.15);p = 0.021], and living in households with 1 + PLWH [coefficient(95%CI):0.51(0.04,0.98);p = 0.033]. Always shedders exhibited higher viral loads than intermittent shedders [coefficient(95%CI):1.62(1.19,2.05);p < 0.001], and viral loads increased with the number of visits where KSHV DNA was detected in saliva (type III test p < 0.001). Household-level factors attributed for 19% of the variability in KSHV shedding (ICC:0.191;p = 0.010). Geospatial analysis indicated overlapping hotspots of households with more KSHV seropositive individuals and KSHV shedders, distinct from areas where PLWH were clustered.DiscussionKSHV oral shedding is influenced by multiple factors at the individual, household, and regional levels. To mitigate ongoing KSHV transmission a comprehensive understanding of factors contributing to oral KSHV reactivation and transmission within households is needed.

Associations Between Acute COVID-19 Symptom Profiles and Long COVID Prevalence: Population-Based Cross-Sectional Study.

Growing evidence suggests that severe acute COVID-19 illness increases the risk of long COVID (also known as post-COVID-19 condition). However, few studies have examined associations between acute symptoms and long COVID onset. This study aimed to examine associations between acute COVID-19 symptom profiles and long COVID prevalence using a population-based sample. We used a dual mode (phone and web-based) population-based probability survey of adults with polymerase chain reaction-confirmed SARS-CoV-2 between June 2020 and May 2022 in the Michigan Disease Surveillance System to examine (1) how acute COVID-19 symptoms cluster together using latent class analysis, (2) sociodemographic and clinical predictors of symptom clusters using multinomial logistic regression accounting for classification uncertainties, and (3) associations between symptom clusters and long COVID prevalence using modified Poisson regression. In our sample (n=4169), 15.9% (n=693) had long COVID, defined as new or worsening symptoms at least 90 days post SARS-CoV-2 infection. We identified 6 acute COVID-19 symptom clusters resulting from the latent class analysis, with flu-like symptoms (24.7%) and fever (23.6%) being the most prevalent in our sample, followed by nasal congestion (16.4%), multi-symptomatic (14.5%), predominance of fatigue (10.8%), and predominance of shortness of breath (10%) clusters. Long COVID prevalence was highest in the multi-symptomatic (39.7%) and predominance of shortness of breath (22.4%) clusters, followed by the flu-like symptom (15.8%), predominance of fatigue (14.5%), fever (6.4%), and nasal congestion (5.6%) clusters. After adjustment, females (vs males) had greater odds of membership in the multi-symptomatic, flu-like symptom, and predominance of fatigue clusters, while adults who were Hispanic or another race or ethnicity (vs non-Hispanic White) had greater odds of membership in the multi-symptomatic cluster. Compared with the nasal congestion cluster, the multi-symptomatic cluster had the highest prevalence of long COVID (adjusted prevalence ratio [aPR] 6.1, 95% CI 4.3-8.7), followed by the predominance of shortness of breath (aPR 3.7, 95% CI 2.5-5.5), flu-like symptom (aPR 2.8, 95% CI 1.9-4.0), and predominance of fatigue (aPR 2.2, 95% CI 1.5-3.3) clusters. Researchers and clinicians should consider acute COVID-19 symptom profiles when evaluating subsequent risk of long COVID, including potential mechanistic pathways in a research context, and proactively screen high-risk patients during the provision of clinical care.

Analysis of COVID-19 nosocomial clusters in an Omicron strain epidemic: importance of patient education on infection control measures

Analysis of COVID-19 nosocomial clusters in an Omicron strain epidemic: importance of patient education on infection control measures

Influence of Non-Economic Factors on the Formation and Development of the Design of Financial Systems

The purpose of this scientific work is to investigate the impact of non-economic factors on the design of financial systems, focusing on the concept of institutional quality, which is measured using six indices according to the World Bank's methodology. To assess this impact, we utilized data from 1996 to 2022 for a wide range of countries, grouped into five clusters based on per capita income. The comparative analysis of these country clusters revealed a direct and consistent relationship between per capita income dynamics and financial development with changes in institutional quality. It also highlighted the significant influence of this relationship on the structural features of national financial systems. The study demonstrates that institutional quality is the starting point of this entire process, determining the effectiveness of the link between financial development and economic growth through changes in the financial structure. The findings confirm the convergence of financial development levels among countries with different financial system structures and legal traditions, provided they maintain high-quality institutions. The study underscores the importance of institutional quality in minimizing the consequences of structural distortions in the financial system and addressing gaps in financial and economic development. These results are crucial for economic policymakers in developing countries and those with low per capita incomes. Doi: 10.28991/ESJ-2024-08-05-08 Full Text: PDF

Breast cancer promotes the expression of neurotransmitter receptor related gene groups and image simulation of prognosis model

Breast cancer (BC), a prevalent and severe malignancy, detrimentally affects women globally. Its prognostic implications are profoundly influenced by gene expression patterns. This study retrieved 509 BC-associated oncogenes and 1,012 neurotransmitter receptor-related genes from the GSEA and KEGG databases, intersecting to identify 98 relevant genes. Clinical and transcriptomic expression data related to BC were downloaded from the TCGA, and differential genes were identified based on an FDR value <0.05 & |log2FC| ≥ 0.585. Univariate analysis of these genes revealed that high expression of NSF and low expression of HRAS, KIF17, and RPS6KA1 are closely associated with BC survival prognosis. A prognostic model constructed for these four genes demonstrated significant prognostic relevance for BC-TCGA patients (P < 0.001). Subsequently, an immunofunctional analysis of the BC oncogene-neurotransmitter receptor-related gene cluster revealed the involvement of immune cells such as T cells CD8, T cells CD4 memory resting, and Macrophages M2. Further analysis indicated that immune functions were primarily concentrated in APC_co_inhibition, APC_co_stimulation, CCR, and Check-point, among others. Lastly, a prognostic nomogram model was established, and ROC curve analysis revealed that the nomogram is a vital indicator for assessing BC prognosis, with 1-year, 3-year, and 5-year survival rates of 0.981, 0.897, and 0.802, respectively. This model demonstrates high calibration, clinical utility, and predictive capability, promising to offer an effective preliminary tool for clinical diagnostics.

Topic identification and content analysis of internet medical policies under the background of Healthy China 2030.

This study aims to analyse the content of internet medical policies, grasp the inherent laws of the development of internet medical policies and provide references for improving the policy system. On the basis of web crawler technology, 436 internet medical policies issued by the central and local governments after the Healthy China Strategy was proposed were collected. The BERTopic model was used to extract topics, and a comprehensive analysis of China's internet medical policy texts was conducted through the analysis of topic content, topic hierarchy and topic clusters. A total of 27 topics were identified in China's internet medical policies, mainly focussing on five topic clusters: medical service regulation, elderly nursing and children's healthcare using traditional Chinese medicine, user safety guarantees, health education and communication and infectious disease treatment and recovery guidance. Different topic contents play a role in promoting the comprehensive and standardized development of internet medical services. However, there is still room for further improvement in policy integrity, consideration of multiple types of users, and implementation effectiveness. Continuous efforts are needed to optimize the effectiveness of policies.

Digital color analysis and machine learning for ballpoint pen ink clustering and aging investigation

Fraudulent activities often involve document manipulation, which poses a significant challenge to forensic science. To address this issue, a novel method was developed that combines intended artificial UV pre-degradation, digital color analysis (DCA) of stroke images, and various machine learning (ML) models. This method can cluster blue ballpoint pen inks and predict their photodegradation time. The results of the study indicate that the k-shape clustering method is highly effective in differentiating between inks based on their degradation curve patterns and HSV or RBS color features, aligning well with results from chromatography analyses. Furthermore, the random forest regression model demonstrated superior performance in predicting age, exhibiting the highest coefficients of determination. The DCA-ML method is a straightforward, cost-effective, and highly accurate solution for clustering blue pen inks. Using photodegradation curves to predict document age could eliminate the need for conventional physicochemical analysis techniques.

Geohash coding-powered deep learning network for vessel trajectory prediction using clustered AIS data in maritime Internet of Things industries

The increasing traffic flow and frequent navigational activities in maritime transportation raise accidents, such as collision and grounding. To mitigate such safety issues, high precision and stable vessel trajectory prediction play an essential role in developing accident prevention techniques in maritime Internet of Things (IoT) industries. However, vessel trajectory prediction is a challenge because conventional deep learning technologies struggle to capture complex changes in vessel trajectories. To address this challenge, this study designs a novel prediction method (namely DBSCAN-GeoCLSTM) comprising two steps. The first step employs the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to perform clustering analysis on trajectories. The second step feds trajectory data from different categories into a Long Short-Term Memory (LSTM) optimization network with Geohash Coding (called GeoCLSTM netework) for training. This GeoCLSTM network encodes the trajectory points using Geohash Coding to construct one-hot vectors, which captures the positional correlation information among trajectory points. The GeoCLSTM model further introduces a dimensionality reduction module by converting the one-hot vectors into feature vectors. Subsequently, the GeoCLSTM model employs LSTM to use these feature vectors from multiple consecutive time slots to accurately predict trajectory data. Results of comparative experiments demonstrate that the proposed DBSCAN-GeoCLSTM method achieved accurate and stable predictions, and outperformed eight state-of-the-art methods in the vessel trajectory prediction task.

EsxA, a type VII secretion system-dependent effector, reveals a novel function in the sporulation of Bacillus cereus ATCC14579

BackgroundBacillus cereus is a Gram-positive, spore-forming bacterium that produces a spectrum of effectors integral to bacterial niche adaptation and the development of various infections. Among those is EsxA, whose secretion depends on the EssC component of the type VII secretion system (T7SS). EsxA’s roles within the bacterial cell are poorly understood, although postulations indicate that it may be involved in sporulation. However, the T7SS repertoire in B. cereus has not been reported, and its functions are unestablished.MethodsWe used the type strain, B. cereus ATCC14579, to generate ΔessC mutant through homologous recombination using the homing endonuclease I-SceI mediated markerless gene replacement. Comparatively, we analyzed the culture supernatant of type strain and the ΔessC mutant through Liquid chromatography-tandem mass spectrometry (LC-MS/MS). We further generated T7SSb-specific gene mutations to explore the housekeeping roles of the T7SSb-dependent effectors. The sporulation process of B. cereus ATCC14579 and its mutants was observed microscopically through the classic Schaeffer-Fulton staining method. The spore viability of each strain in this study was established by enumerating the colony-forming units on LB agar.ResultsThrough LC-MS/MS, we identified a pair of nearly identical (94%) effector proteins named EsxA belonging to the sagEsxA-like subfamily of the WXG100 protein superfamily in the culture supernatant of the wild type and none in the ΔessC mutant. Homology analysis of the T7SSb gene cluster among B. cereus strains revealed diversity from the 3’ end of essC, encoding additional substrates. Deletions in esxA1 and esxA2 neither altered cellular morphology nor growth rate, but the ΔesxA1ΔesxA2 deletion resulted in significantly fewer viable spores and an overall slower sporulation process. Within 24 h culture, more than 80% of wild-type cells formed endospores compared to less than 5% in the ΔesxA1ΔesxA2 mutant. The maximum spore ratios for the wild type and ΔesxA1ΔesxA2 were 0.96 and 0.72, respectively. Altogether, these results indicated that EsxA1 and EsxA2 work cooperatively and are required for sporulation in B. cereus ATCC14567.ConclusionB. cereus ATCC14579 possesses two nearly identical T7SSb-dependent effectors belonging to the sagEsxA-like proteins. Simultaneous deletion of genes encoding these effectors significantly delayed and reduced sporulation, a novel finding for EsxA.

Making sense of a pandemic: reasoning about COVID-19 in the intellectual dark web.

In this study, I examine how users of an online Reddit community, r/IntellectualDarkWeb, forged an anti-establishment collective identity through practices of "heterodox scientific" reasoning. I do so through a discursive analysis of comments and posts made to r/IntellectualDarkWeb during the COVID-19 pandemic. First, I deploy the BERTopic algorithm to cluster my corpus and surface topics pertaining to COVID-19. Second, I engage in a qualitative content analysis of the relevant clusters to understand how discourses about COVID-19 were mobilized by subreddit users. I show that discussions about COVID-19 were polarized along "contrarian" and "anti-contrarian" lines, with significant implications for the subreddit's process of collective identity. Overwhelmingly, contrarian content that expressed skepticism towards vaccines, mistrust towards experts, and cynicism about the medical establishment was affirmed by r/IntellectualDarkWeb users. By contrast, anti-contrarian content that sought to counter anti-vaccine rhetoric, defend expertise, or criticize subreddit users for their contrarianism was penalized. A key factor in this dynamic was Reddit's scoring mechanism, which empowered users to publicly upvote contrarian affirming content while simultaneously downvoting anti-contrarian content. As users participated in sense making about COVID-19, they deployed Reddit's scoring mechanism to reinforce a contrarian collective identity oriented around a practice of heterodox science. My research shows the continued relevance of the concept of collective identity in the digital age and its utility for understanding contemporary reactionary social movements.

Molecular cluster, transmission characteristics, origin and dynamics analysis of HIV-1 CRF59_01B in China: A molecular epidemiology study

PurposeThis study investigated for the HIV-1 CRF59_01B epidemic's spatiotemporal dynamics and its transmission networks in China. MethodsBetween 2007 and 2020, a total of 250 partial pol gene sequences of HIV-1 CRF59_01B were collected from four regions (10 Chinese provinces). Phylogenetic tree construction and cluster identification were then performed. The Bayesian skyline and birth–death susceptible–infected–removed models were employed for the phylodynamic analyses of subtypes and large clusters, respectively. Phylogenetic analyses and trait diffusion of these sequences were performed using Bayesian phylogenetic methods (beast-classic package). Distance-based molecular network analyses were performed to identify putative relationships. ResultsUsing a genetic distance threshold of 1.3 %, We identified 45 clusters that included 62.40 % (156/250) of the sequences. Three clusters (6.67 %, 3/45) had 10 or more sequences, and were considered "large clusters". Six clusters (13.33 %) included sequences from different regions (Southeast, Northeast, Southeast, and Central China). Thirteen clusters (28.89 %) included sequences of men who had sex with men only, three clusters (6.67 %) included sequences of heterosexuals only, and 12 clusters (26.67 %) included sequences of both groups. The substitution rate of CRF59_01B was 1.91 × 10–3 substitutions per site per year [95 % highest posterior density (HPD) interval: 1.39 × 10−3–2.49 × 10−3)], the time to the most recent common ancestor of CRF59_01B was to be 1992.83 (95 % HPD: 1977.97–2002.81). A Bayesian skyline plot revealed that the effective population size of CRF59_01B increased from 2000 to 2015 and remained stable after 2015. The large clusters showed continuous growth from 2013 to 2020. Phylogeographic analysis showed that CRF59_01B B most likely originated in Southeast China, with a posterior probability of 97.44 %, and then spread to other regions. ConclusionsOur study revealed the temporal and geographical origins of HIV-1 CRF59_01B as well as the process of transmission among various regions and risk groups in China, which can help develop targeted HIV prevention strategies.

Correction: Design and analysis of cluster randomized trials

Correction: Design and analysis of cluster randomized trials

Based on Gated Recurrent network analysis of advanced manufacturing cluster and unified large market to promote regional economic development

This study evaluates the catalytic effects of advanced manufacturing industry clusters and unified large markets on regional economic development from a computer science perspective, revealing their underlying mechanisms. It employs a Gated Recurrent Network (GRN) model optimized with Gradient Boosting Decision Tree (GBDT) technology to conduct empirical analysis through comprehensive data collection and analysis. The primary objectives are to assess these catalytic effects, highlight the importance of innovation and environmental indicators, determine the contribution levels of various factors, and test the computational fit and predictive accuracy of the model. Key findings indicate that the GBDT-GRN model demonstrates a significant improvement in data computation accuracy, ranging from 20% to 52%, and an increase in response time by 23% to 52%. The model achieves a computational fit of 92% to 99% when analyzing regional economic development. The proposed GBDT-GRN model is highly accurate and reliable in evaluating catalytic effects, providing strong support for policy-making and business decision-making. Innovation and environmental indicators play a crucial role, with varying contributions from different factors. This study offers an effective solution for sequence data prediction problems, supports policy-making and business decisions, and points to promising directions for future research.

Factors affecting Kaposi's sarcoma-associated herpesvirus transmission in rural Ugandan households, a longitudinal study.

We report the impact of HIV infection within a household on oral Kaposi's sarcoma-associated herpesvirus (KSHV) shedding. We enrolled 469 individuals from 90 households. Mouthwash rinse samples collected at three monthly visits, were analyzed for KSHV DNA using quantitative polymerase chain reaction (qPCR). Generalized linear mixed effects logistic models were applied to analyze factors associated with KSHV ever shedding, and among shedders, always versus intermittent shedding. Linear mixed effects models were applied to models of KSHV viral loads. Intraclass correlation coefficients (ICCs) were calculated to assess the contribution of household-level factors to variations in shedding probabilities. Hotspot analyses of geospatial feature clusters were calculated using Getis-Ord Gi* statistic and visualized using inverse distance weighted interpolation. Analyses included 340 KSHV seropositive individuals, aged 3 + years, with qPCR results from 89 households. Forty households had 1 + persons living with HIV (PLWH), while 49 had none. Among participants, 149(44%) were KSHV ever shedders. Of 140 who shed KSHV at two or more visits, 34(24%) were always shedders. Increasing number of KSHV seropositive household members was significantly associated with ever shedding [Odds ratio(OR) (95% Confidence Interval(95%CI)):1.14(1.03,1.26);p = 0.013]. Among KSHV shedders, a statistically significant age-related trend was identified with 10-19 years being more likely to be always shedders (type III test p = 0.039) and to have higher viral loads (type III test p = 0.027). In addition, higher viral loads were significantly associated with increasing number of household members [coefficient(95%CI):0.06(0.01,0.12);p = 0.042], increasing number of KSHV seropositive members [coefficient(95%CI):0.08(0.01,0.15);p = 0.021], and living in households with 1 + PLWH [coefficient(95%CI):0.51(0.04,0.98);p = 0.033]. Always shedders exhibited higher viral loads than intermittent shedders [coefficient(95%CI):1.62(1.19,2.05);p < 0.001], and viral loads increased with the number of visits where KSHV DNA was detected in saliva (type III test p < 0.001). Household-level factors attributed for 19% of the variability in KSHV shedding (ICC:0.191;p = 0.010). Geospatial analysis indicated overlapping hotspots of households with more KSHV seropositive individuals and KSHV shedders, distinct from areas where PLWH were clustered. KSHV oral shedding is influenced by multiple factors at the individual, household, and regional levels. To mitigate ongoing KSHV transmission a comprehensive understanding of factors contributing to oral KSHV reactivation and transmission within households is needed.

Observation of magnetic dimers in the diluted triangular-lattice spin liquid candidate NaYbO2

The triangular-lattice spin liquid (QSL) candidate NaYbO2 is magnetically diluted by Lu doping, forming homogeneous solid solutions NaYb1-xLuxO2 (0≤ x<1.0). A combination of structural, magnetic, and pulsed high-field electron spin resonance analysis shows that the lattice parameters and the Curie-Weiss temperature θCW vary almost linearly with x, and the crystal electric field environment of the Yb/Lu ions persists during the Lu doping. The statistical distribution analysis of clusters and the deduced linear θCW ∼ x relation indicate that the nearest neighbor exchange J does not change with magnetic dilution. Interestingly, with the magnetization measurement at 0.39 K, we have observed Seff = 1/2 antiferromagnetic Yb-Yb dimers in NaYb0.02Lu0.98O2, which is manifested as a magnetization step at μ0Hc = 2.68 T due to the transition between singlet and triplet states. Thus, the intradimer interaction is directly determined as J/kB = −5.15 K. Surprisingly, this value is close to the interaction of undoped NaYbO2, supporting the theoretical analysis of clusters. Our study provides a method of estimating the magnetic interaction of QSL materials through magnetic dilution. The statistical distribution analysis of magnetic clusters suggests that through a small amount of Lu doping (x < 0.5), the QSL state in NaYbO2 can continue to be maintained.