Cluster Groups Research Articles

Unraveling the Kinematic and Morphological Evolution of the Small Magellanic Cloud

Abstract We model the kinematics of the Small Magellanic Cloud (SMC) by analyzing the proper motions (PMs) from Gaia DR3 of nine different stellar populations, including young main-sequence (MS) stars (<2 Gyr), red giant branch stars, red clump stars, red giants with line-of-sight velocities, and three groups of star clusters. This analysis is carried out using a robust Markov Chain Monte Carlo method, to derive up to seven kinematic parameters. We trace the evolution from a nonrotating flattened elliptical system, as mapped by the old population, to a rotating highly stretched disk structure, as denoted by the young MS stars and clusters (<400 Myr). We estimate that the inclination i (∼58°–82°) decreases and the position angle Θ (∼180°–240°) increases with age. We estimate an asymptotic velocity of ∼49–89 km s−1 with a scale radius of ∼6–9 kpc for the young MS populations, with velocity dispersion of ∼11 km s−1, suggesting a rotation-supported disk structure. Our models estimate a line-of-sight extension of ∼30 kpc, in agreement with observations. We identify four regions of the SMC showing anomalies in the residual PM: the East Anomaly, the Southeast Anomaly (SEA), the South Anomaly, and the West Anomaly. The SEA appears like an infalling feature and is identified for the first time. The tidal imprints observed in the residual PM of the SMC suggest that its evolution is considerably shaped by the recent interaction with the Large Magellanic Cloud.

Clustering-Based Identification of BMI-Associated Metabolites with Mechanistic Insights from Network Analysis in Korean Men

Background: Epidemiological studies using metabolomics often encounter challenges due to metabolite profiles being influenced by multiple modifiable behavioral factors, including regular exercise, smoking, drinking, and weight control. This study aimed to identify modifiable behavioral factors reflected in metabolites by clustering subjects based on their metabolite profiles. Networks of metabolites were constructed to visualize their relationships and the differences between clustering groups. Methods: Sixty-four healthy men were included in this study. Information on regular exercise, smoking, and drinking was collected by questionnaires, and body mass index (BMI), an indicator of weight control, was calculated based on measured height and weight. Through targeted metabolomics, the concentrations of 149 metabolites were quantified. Subjects were clustered using the k-means method based on metabolite composition. Correlation-based networks were constructed for each cluster using Cytoscape software, followed by network analysis. Results: The subjects were divided into two clusters, with BMI identified as a distinguishing feature. Four lyso-phosphatidylcholines (PCs), six diacyl-PCs, and one acyl-alkyl-PC were positively associated with BMI. In the constructed network, acyl-alkyl-PCs exhibited the highest degrees, suggesting their central role in BMI-associated metabolic pathways. Conclusions: These findings suggest that metabolites can reflect behavioral factors, with BMI exerting a significant influence on metabolite profiles, particularly through its associations with phosphatidylcholines.

Harnessing temporal patterns in administrative patient data to predict risk of emergency hospital admission.

Unplanned hospital admissions are associated with worse patient outcomes and cause strain on health systems worldwide. Primary care electronic health records (EHRs) have successfully been used to create prediction models for emergency hospitalisation, but these approaches require a broad range of diagnostic, physiological, and laboratory values. In this study, we aimed to capture temporal patterns of patient activity from EHR data and evaluate their effectiveness in predicting emergency hospital admissions compared with conventional methods. In this retrospective observational study, we used the Secure Anonymised Information Linkage databank to extract temporal patterns of primary care activity from undifferentiated electronic health record timestamp data for 1·37 million patients in Wales aged 18-80 years with at least one recorded Read code between the years 2016 and 2018. Using Gaussian mixture modelling we grouped patients into distinct temporal clusters, performed a three-stage validation of our approach and calculated the risk of emergency hospital admission for each temporal cluster group. Finally, these temporal clusters were combined with five administrative variables and incorporated into four emergency hospital admission prediction models (logistic regression, naive Bayes, XGBoost, and multilayer perceptron [MLP]) and compared with a more traditional, but data-intensive, modelling technique. The primary outcome was emergency hospital admission as the next health-care event. Six distinct temporal cluster patterns of primary care EHR activity were identified, associated with varying risks of future emergency hospital admission risk. These patterns were visually interpretable, repeatable at a population-level, and clinically plausible. The best emergency hospital admission prediction model (MLP) achieved an area under the receiver operating characteristic (AUROC) of 0·82 and precision of 0·94 in regional cohorts. In external validation in regional cohorts, similar model performance was observed (AUROC 0·82 and precision 0·92). This model also matched the performance of a more complex model (extended feature model) requiring 33 clinical parameters (AUROC 0·82 vs 0·83; precision 0·94 vs 0·90) for the same task on the same dataset. We developed a novel machine learning pipeline that extracts interpretable temporal patterns from simple representations of EHR data and can be incorporated into emergency hospital admission predictors. This framework might enable more rapid development of parsimonious clinical prediction models. UKRI CDT in AI for Healthcare, UKRI Turing AI Fellowship, NIHR Imperial Biomedical Research Centre, and Research Capability Funding.

AI-Based Clustering of Numerical Flow Fields for Accelerating the Optimization of an Axial Turbine

The growing number of Renewable Energy Sources has increased the demand for innovative and high-performing turbine designs. Due to the increase in computing resources over recent years, numerical optimization using Evolutionary Algorithm (EAs) has become established. Nevertheless, EAs require many expensive Computational Fluid Dynamics (CFD) simulations, and more computational resources are needed with an increasing number of design parameters. In this work, an adapted optimization algorithm is introduced. By employing an Artificial Intelligence (AI)-based design assistant, turbines with a similar flow field are clustered into groups and provide a dataset to train AI models. These AI models can predict the flow field’s clustering before a CFD simulation is performed. The turbine’s efficiency and cavitation volume are predicted by analyzing the turbine’s properties inside the predicted clustering group. Turbines with properties below a certain threshold are not CFD-simulated but estimated by the design assistant. By this procedure, currently, more than 30% of the cfd

Cognitive Impairment in Psychotic Disorders Is Associated with Brain Reductive Stress and Impaired Energy Metabolism as Measured by 31P Magnetic Resonance Spectroscopy.

Convergent evidence shows the presence of brain metabolic abnormalities in psychotic disorders. This study examined brain reductive stress and energy metabolism in people with psychotic disorders with impaired or average range cognition. We hypothesized that global cognitive impairment would be associated with greater brain metabolic dysregulation. Participants with affective and non-affective psychosis (n = 62) were administered the MATRICS Consensus Cognitive Battery (MCCB) and underwent a 31P-magnetic resonance spectroscopy scan at 4T. We used a cluster-analysis approach to identify 2 clusters of participants with and without cognitive dysfunction. We compared clusters on brain redox balance or reductive stress, measured by the ratio of nicotinamide adenine dinucleotide (NAD+) and its reduced form NADH, in addition to creatine kinase (CK) enzymatic activity and pH. The mean (SD) age of participants was 25.1 (6.3) years. The mean NAD+/NADH ratio differed between groups, with lower NAD+/NADH ratio, suggesting more reductive stress, in the impaired cognitive cluster (t = -2.60, P = .01). There was also a significant reduction in CK activity in the impaired cognitive cluster (t = -2.19, P = .03). Intracellular pH did not differ between the 2 cluster groups (t = 1.31, P = .19). The clusters did not significantly differ on severity of mood and psychotic symptomatology or other measures of illness severity. Our results demonstrate that psychotic disorders with greater cognitive impairment have greater brain metabolic dysregulation, with more reductive stress and decrease in energy metabolic rate markers. This provides new evidence for the potential of emerging metabolic therapies to treat cognitive deficits in psychotic disorders.

Does Higher Maturation Make Age-Grouped Swimmers Faster? A Study on Pubertal Female Swimmers

Background: The main aim of this study was to identify the differences between subgroups of swimmers based on physiological (peak oxygen uptake—VO2peak), strength (average tethered swimming force—60Fave), stroke kinematics (v100—swimming speed at 100 m front crawl, stroke rate—SR, stroke length—SL), and anthropometrical (i.e.,: biological age—BA, body height—BH, body mass—BM) factors within swimmers at different levels of maturity (BA). Methods: This study involved 39 female swimmers (age: 12.88 ± 0.54 years, BA: 13.98 ± 1.91 years). Cluster analysis (k-cluster) and stepwise multiple regression was performed. Results: Significant correlations were observed between v100 and BA, 60Fave, AS, VO2peak. Stepwise multiple regression indicated 60Fave and VO2peak as the main explanatory variables of v100 (R2 = 0.60, p < 0.0001). Cluster analysis allowed us to distinguish three groups of swimmers, differing in BA (cluster 1: 14.07 ± 0.96 years, cluster 2: 17.05 ± 1.53, cluster 3: 11.94 ± 0.95) and v100, as well as in BH, FFM, AS. Conclusions: There were differences between cluster groups, with early mature swimmers characterized by the highest BH, FFM, AS, 60Fave, and VO2peak. Probably, biologically younger late mature swimmers (cluster 3) are slower than the other 2 groups (cluster 1 and 2) because of being less somatically developed. Based on these study results, coaches should ensure further development of aerobic and anaerobic conditioning among normal mature swimmers with simultaneous focus on improving technique skills among early mature ones.

Relationship between secondary metabolites and ecological suitability zones for Eucommia ulmoides.

The quality of Chinese medicinal materials is closely related to the types and contents of their secondary metabolites, while ecological adaptability influences the production of secondary metabolites. Therefore, identifying the relationship between ecological adaptability and secondary metabolites is important for enhancing the quality of Chinese medicinal materials. In this study, we collected 10-year-old Cortex Eucommiae (Eucommia ulmoides, EU) samples from 21 plots in eight provinces which are the primary production areas of EU in central China. We used the MaxEnt model to determine the ecological suitability zones for EU at the 21 sampling sites and classified them accordingly. The contents of six pharmacologically active secondary metabolites, including chlorogenic acid, aucubin, geniposidic acid, geniposide, pinoresinol monoglucoside, and pinoresinol diglucoside were measured in the EU bark. The results demonstrated significant variations in the content of the six secondary metabolites in EU bark among different sampling sites. Correlation analysis indicated a close relationship between the content of chlorogenic acid and aucubin in EU bark and the ecological suitability of their respective production areas. The total content of the six secondary metabolites also showed good consistency with the ecological suitability of the production areas. Exploratory factor analysis further revealed a strong consistency between the factor analysis comprehensive scores based on the content of major secondary metabolites and the types of suitability zones at the sample locations. The cluster analysis results demonstrated good consistency between clustering groups and ecological suitability zone groups, with higher consistency as the suitability level of the ecological zone increased. This indicated a significant impact of suitable ecological environments on the content of EU secondary metabolites.

LEARNERS’ INDISCIPLINE: CONSTRUCTING MODEL OF SOCIAL CONTROL AMONG PRINCIPALS IN SECONDARY SCHOOLS

This study was conducted in Johannesburg secondary schools and focused on misconducts of students and management approach mitigating misconducts. This study is an extension of ongoing literature covering school discipline and enforcement moral conduct. The problem of learner’s misconducts has assumed panic mode globally and there is now scholarly attention to mitigate the phenomenon. The study is hinged on Skinner’s Operant Condition also known as Reinforcement Theory. Survey qualitative design was used and five schools in Johannesburg district of Central Gauteng were purposively selected using Principals as contact sample. Data were collected and analyzed qualitatively using in-depth interviews and thematic content analysis. Thematic Constructs were classified. On incidents of misconducts, it was evident that Johannesburg schools were deeply affected by learners' misconducts, occurrence was uniformed in the five schools and offenders were both male and females. Misconducts were perpetrated as collective sub group or gangs which strongly reinforced intention among offenders. Construct of management or principal’s approach showed that the schools had structured discipline system which endured as preventive or punitive method. Despite the rigid structure, learner’s misconducts were rampant and defiled management approach in Johannesburg schools. Principals attested to surging incidents of misconducts despite instrument of check and balance. Misconducts of learners in Johannesburg schools were mainly drug ingestions, violence, vandalism, absenteeism and bullying. School Offenders perpetrated misconducts when they operated as cluster group or gangs. Although cases of lone offenders were reported in the schools, sub group offenders were more severe and engaged brutal offending behaviors. There was consensus showing that learners’ misconducts were rampant and brutal when it occurred as sub group offenders. Members of sub groups also gained status identity as hard guy, bad boy and scorpion. This is a dangerous trend in Johannesburg schools like similar case in global society. The study recommended that severe shaming and stigmatization method which extend label beyond school environment should be adopted to block opportunities for misconducts in Johannesburg schools as may be the case for others.

Research on Assessing Comprehensive Competitiveness of Tourist Destinations Within Cities, Based on Field Theory and Competitiveness Theory

The question of how to assess the comprehensive competitiveness of tourist destinations within cities is an important aspect for determining the potential of a city’s tourism development and its ranking among peers in the field. There are four main parts to the content of this article, which consist of the analysis of competition formation motives based on “Field Theory”, the selection of influencing factors by drawing on Porter’s theory of competitiveness, the construction of an assessment model based on the multi-factors weighted comprehensive evaluation method, and an empirical analysis using Nanjing as the research area. The conclusions are as follows: Firstly, the tourist destination field within a city is composed of three interrelated elements, which are actors, rules, and competition. Under the influence of mainstream social and cultural trends, each tourist destination occupies a certain “position” by relying on the attractiveness formed by various types of capital, and then participates in peer competition within the field. Secondly, the three major influencing aspects of the competitiveness of tourist destinations are element conditions, demand characteristics, and supporting conditions. The key points involved in the three aspects can be summarized into four categories of factors, namely, quality evaluation, popularity level, spatial attractiveness, and emotional cognition, which together constitute the indicator system. Thirdly, there are thirteen tourist destinations in Nanjing that are rated above the average, accounting for about 43% of all the popular destinations. The variation coefficient of competitiveness results is about 35%, indicating a moderate to relatively weak degree of dispersion. Finally, the competitiveness of the thirty hot tourist destinations generally presents a spatial order that gradually weakens in an outward direction from the center zone of the city, forming an overall pattern of cluster groups of well-known tourist destinations in the core of the city, relatively random small clusters in the new main city area, and scattered point distribution in the suburbs.

Long-Chain Polysaturated Fatty Acid in Atrial Fibrillation-Associated Stroke: Lipidomic-GWAS Study.

Background and Purpose This study aimed to explore the relationship between lipidomic domains, particularly free fatty acids (FFAs), and the presence of AF in patients with acute stroke, and to identify mechanisms of AF-associated stroke through genetic studies. Methods A total of 483 patients of stroke in patients without AF (n = 391) and with AF (n = 92) were selected from a prospectively collected stroke registry. Lipidomic profiling was conducted and the lipid components associated with AF were explored using fold-change analyses and clustering. Genotyping was conducted through trait comparison. Colocalization was also performed. Results Among the lipidomic domains, the free fatty acid (FFA) class was positively associated with AF. Long-chain fatty acids with 14-24 carbons and unsaturated FFAs distinguished AF. Clustering analysis based on FFAs revealed differences in AF proportion across groups. GWAS identified two loci associated with clustered groups of FFA metabolites: near MIR548F3 associated with FFA 20:1, FFA 20:2, FFA 22:5, and FFA 22:6; and near RPL37A associated with FFA 22:5 and FFA 22:6. These loci were associated with increased fibrinogen levels. In the GWAS for the FFA metabolite-quantitative trial locus analysis, loci near rs28456 and rs3770088, and FFA 20:4-QTLs were co-localized with the eQTLs of FADS2, a gene involved in the peroxisome proliferative-activated receptor gamma-related signaling pathway, in the whole blood, left ventricle, and atrial appendage tissue. Conclusion Elevated FFA levels, especially those of long-chain unsaturated FFAs, are strongly associated with AF-associated stroke. This relationship is regulated by the peroxisome proliferator-activated receptor gamma-related signaling pathway.

Directing the Linkage of Small Polyoxometalate Building Blocks Using (Benzene)ruthenium Cations.

Oligomerization of monomeric molybdate and tungstate oxyanions in the presence of organometallic cations produces a group of organometallic-polyoxometalate clusters, which have diverse structures based on cubane-like {M4O4} units resembling the structure of oxide surfaces. This work investigated the oligomerization of [MoO4]2- and [WO4]2- oxyanions in aqueous solutions in the presence of {Ru(C6H6)}2+ as the organometallic structure-directing agents. The reactions produced a mixture of several species, and fractional crystallization by adjusting crystallization temperature and molar ratios of Ru:Mo or Ru:W allowed the isolation of seven types of (benzene)ruthenium-polyoxometalate complexes: [{Ru(C6H6)}4Mo4O16] (1), [{Ru(C6H6)(OH)}3HMoO4][MoO4] (2), [{Ru(C6H6)}4H2Mo5O20] (3), [{Ru(C6H6)}4W2O10] (4), Na6[{Ru(C6H6)}2H2W8O30] (5), [{Ru(C6H6)}5H2W6O24] (6), and [{Ru(C6H6)(OH)}6W2O7][{Ru(C6H6)}2{Ru(C6H6)(OH2)}2H2W8O30]2 (7). They comprise small, reactive iso-polyoxometalate building blocks that are capped by {Ru(C6H6)}2+ cations, resulting in cubane-like {Ru3MO4}, {Ru2M2O4}, or {RuM3O4} (M = Mo or W) structural motifs. Moreover, the molecular crystal of 1 contains extensive C-H···O hydrogen bonds, and it undergoes a reversible crystal-to-amorphous-to-crystal transition upon dehydration-hydration cycles.

An efficient unsupervised classification model for galaxy morphology: Voting clustering based on coding from ConvNeXt large model

By combining unsupervised and supervised machine learning methods, we have proposed a framework, called USmorph to carry out automatic classifications of galaxy morphologies. In this work, we update the unsupervised machine learning (UML) step by proposing an algorithm based on ConvNeXt large model coding to improve the efficiency of unlabeled galaxy morphology classifications. The method can be summarized into three key aspects as follows: (1) a convolutional autoencoder is used for image denoising and reconstruction and the rotational invariance of the model is improved by polar coordinate extension; (2) utilizing a pre-trained convolutional neural network (CNN) named ConvNeXt for encoding the image data. The features were further compressed via a principal component analysis (PCA) dimensionality reduction; (3) adopting a bagging-based multi-model voting classification algorithm to enhance robustness. We applied this model to I-band images of a galaxy sample with $I_ mag < 25$ in the COSMOS field. Compared to the original unsupervised method, the number of clustering groups required by the new method is reduced from 100 to 20. Finally, we managed to classify about 53<!PCT!> galaxies, significantly improving the classification efficiency. To verify the validity of the morphological classification, we selected massive galaxies with $M_ M_ sun $ for morphological parameter tests. The corresponding rules between the classification results and the physical properties of galaxies on multiple parameter surfaces are consistent with the existing evolution model. Our method has demonstrated the feasibility of using large model encoding to classify galaxy morphology, which not only improves the efficiency of galaxy morphology classification, but also saves time and manpower. Furthermore, in comparison to the original UML model, the enhanced classification performance is more evident in qualitative analysis and has successfully surpassed a greater number of parameter tests. The enhanced UML method will support the Chinese space station telescope in the future.

Application of Two-Step Entropy–TOPSIS Method and Complete Linkage Clustering for Water-Pumping Windmill Investment on Thailand Peninsula

This study focuses on identifying suitable areas for the installation of water-pumping windmills in Thailand, which require wind speeds of at least 4 m/s to operate efficiently. A simple combined approach is introduced, integrating the Entropy–TOPSIS method complete linkage clustering to prioritize and categorize potential locations. Out of 271 initial areas, 28 have been selected based on their ability to meet the 4 m/s wind speed threshold. The Entropy–TOPSIS method first evaluates these areas based on monthly wind speed and agricultural area. The analysis reveals that regions with higher wind speeds generally score better for wind energy potential, while areas with larger agricultural spaces tend to score higher for farming suitability. The final integrated scores show that agricultural area is more significant, with a weight of 0.7788, compared to the wind speed weight of 0.2212. The areas are then ranked, and complete linkage clustering groups them into six categories, from the most to the least suitable for windmill installation. A sensitivity analysis confirms the robustness of the clustering method, as the group composition remains stable despite minor changes in weight adjustments. This approach simplifies decision-making for sustainable energy investments in Thailand agriculture sector.

Dynamic spectrum resource allocations in wireless senor networks for improving packet transmission

Spectrum allocation has gained a lot of attention in cognitive wireless networks and research as one of the key problems for enhancing spectrum quality in the communication processes in the contemporary communication-dependent wireless environment. By effectively managing the restricted spectrum resources, adjusting to dynamic network conditions, lowering interference, and increasing energy efficiency, the study on dynamic spectrum allocation in wireless sensor networks seeks to improve packet transmission. In a variety of applications, this improves network performance, dependability, and quality of service. This is the reason we apply the dynamic source allocation to the sensor nodes in the network visualization for the packet transmission performance study in the 5G spectrum region. The primary goal of the study conducted for this article is to improve packet delivery ratios and data packet throughputs from the source to the destinations. Compared to previous research, this work has achieved 100% of its aims. In this context, certain DRL concerns are also addressed. The spectrum is allotted such that, in the event of phishing or malicious nodal assaults on the cluster groups of the wireless sensor nodal points in the WSN, efficient packet transmission will occur, beginning at the source and terminating at the sink. The simulation results demonstrate the efficacy of the approach described in the research paper and its application to data transmission.

Elevated Expression of Cell Adhesion, Metabolic, and Mucus Secretion Gene Clusters Associated with Tumorigenesis, Metastasis, and Poor Survival in Pancreatic Ductal Adenocarcinoma.

Technological advances in identifying gene expression profiles are being applied to study an array of cancers. The goal of this study was to identify differentially expressed genes in pancreatic ductal adenocarcinoma (PDAC) and examine their potential role in tumorigenesis and metastasis. The transcriptomic profiles of PDAC and non-tumorous tissue samples were derived from the gene expression omnibus (GEO), which is a public repository. The GEO2R tool was used to further derive differentially expressed genes from those profiles. In this study, a total of 68 genes were derived from upregulated PDAC genes in three or more transcriptomic profiles and were considered PDAC gene sets. The identified PDAC gene sets were examined in the molecular signatures database (MSigDB) for ontological investigation, which revealed that these genes were involved in the extracellular matrix and associated with the cell adhesion process in PDAC tumorigenesis. The gene set enrichment analysis showed greater enrichment scores for the gene sets. Moreover, the identified gene sets were examined for protein-protein interaction using the STRING database. Based on functional k-means clustering, the following three functional cluster groups were identified in this study: extracellular matrix/cell adhesion, metabolic, and mucus secretion-related protein groups. The receiver operating characteristic (ROC) curve revealed greater specificity and sensitivity for these cluster genes in predicting PDAC tumorigenesis and metastases. In addition, the expression of the cluster genes affects the overall survival rate of PDAC patients. Using the cancer genome atlas (TCGA) database, the associations between expression levels and clinicopathological features were validated. Overall, the genes identified in this study appear to be critical in PDAC development and can serve as potential diagnostic and prognostic targets for pancreatic cancer treatment.

Non-target feature filtering for weakly supervised semantic segmentation

Weakly supervised semantic segmentation (WSSS) utilizes weak labels to learn semantic segmentation models, significantly reducing reliance on pixel-level annotations. WSSS typically employs a multi-label classification network to extract image features for constructing localization maps. The quality of the localization map critically influences the performance of WSSS. However, non-target semantic noise within the features impedes the improvement of localization map quality. To address this issue, we propose a non-target feature filtering class activation mapping (NFF-CAM) for WSSS, which can reduce non-target semantic signals and generate higher-quality localization maps. Specifically, the class-constrained dual cosine clustering (CDCC) and channel identification (CI) modules are introduced in NFF-CAM. CDCC effectively addresses the issue of unsuitability in the clustering group relationships of the original features under specified class conditions. CI can efficiently identify channel features containing non-target semantic information. We conduct extensive evaluations of NFF-CAM on popular datasets, including PASCAL VOC 2012 and MS COCO 2014. Experimental results show that NFF-CAM can effectively improve the segmentation performance of off-the-shelf methods.

Arbuscular mycorrhizal fungi improve morphological and yield performance of Eragrostis tef genotypes in Tigray, Ethiopia

Arbuscular mycorrhizal fungi (AMF) form beneficial partnerships with most plant species, helping to improve crop resilience in tough environmental conditions. This paper analyzed how different genotypes responded to AMF inoculation, focusing on root colonization percentage (RCP) and the impact of AMF on above-ground agronomic traits. However, RCP alone may not fully explain genotype variation, and AMF effects cannot be judged solely on above-ground traits. This research aims to explore the variation in ninety tef genotypes under AMF conditions and assess how AMF and genotypes affect root and shoot morphology traits. Genotypes were sorted into six cluster groups, showing varied responsiveness to AMF, with RCP ranging from 25.03 to 72.29%. Despite similar RCP, variations in morphological traits were observed, and groups with lower RCP exhibited important traits not found in those with higher RCP, indicating RCP alone cannot indicate genotype variability. Wider Mahalanobis distance (D2) between clusters IV and VI, I and VI, and V and VI were crucial for developing different varieties and advancing root traits through hybridization. Among the tested genotypes, Wehni and Tsaeda zezew, followed by Gureaza, exhibited higher scores for plant height (PH), panicle length (PL), shoot biomass yield (SBY), root length (RL), and specific root length (SRL) compared to Simada. However, Wehni, Tsaeda zezew, and Gureaza showed similar results for days to maturity (DM), grain yield (GY), harvest index (HI), root dry weight (RDW), and root depth distribution (RDD) but differed from Simada genotype. Moreover, the inoculated Wehni genotype increased in days to panicle emergence (DPE) by 72%, DM by 84.11%, PH by 73.93%, PL by 73.68%, SBY by 144.17%, GY by 254.58%, HI by 133.33%, RL by 74.16%, RDW by 216.92%, SRL by 220%, and RDD by 93.28% as compared to the non-inoculated Simada. Improved performance of inoculated genotypes despite genotype variability could be because AMF enhances nutrient and water uptake by increasing root and shoot growth and the inherent growth strategy of the genotypes. Small-seeded crops planted shallowly benefit from AMF, which promotes deeper root growth for better nutrient and water uptake.

T-cell receptor and B-cell receptor repertoires profiling in pleural tuberculosis.

Tuberculosis (TB) is a leading cause of death worldwide from a single infectious agent. In China the most common extra-pulmonary TB (EPTB) is pleural tuberculosis (PLTB). An important clinical feature of PLTB is that the lymphocytes associated with TB will accumulate in the pleural fluid. The adaptive immune repertoires play important roles in Mycobacterium tuberculosis (Mtb) infection. In this study, 10 PLTB patients were enrolled, and their Peripheral Blood Mononuclear Cells(PBMCs) and Pleural Effusion Mononuclear Cells(PEMCs) were collected. After T cells were purified from PBMCs and PEMCs, high-throughput immunosequencing of the TCRβ chain (TRB), TCRγ chain(TRG), and B cell receptor(BCR) immunoglobulin heavy chain (IGH) were conducted on these samples. The TRB, TRG, and BCR IGH repertoires were characterized between the pleural effusion and blood in PLTB patients, and the shared clones were analyzed and collected. The binding activity of antibodies in plasma and pleural effusion to Mtb antigens was tested which indicates that different antibodies responses to Mtb antigens in plasma and pleural effusion in PLTB patients. Moreover, GLIPH2 was used to identify the specificity groups of TRB clusters and Mtb-specific TRB sequences were analyzed and collected by VJ mapping. We characterize the adaptive immune repertoires and identify the shared clones and Mtb-specific clones in pleural effusion and blood in PLTB patients which can give important clues for TB diagnosis, treatment, and vaccine development.

Phenotypic Differentiation of Streptococcus thermophilus and Lactobacillus delbrueckii subsp. bulgaricus Isolates Found in Yogurt Starter Cultures

The mutualistic relationship between Streptococcus thermophilus (S. thermophilus) and L. delbrueckii subsp. bulgaricus (L. bulgaricus) is responsible for milk coagulation, gel formation, and the flavour of yogurt. Under set-style yogurt processing conditions, the performance of a mixed culture composed of these species depends on key technological parameters such as the capacity for acidification and proteolytic activity. This study aimed to determine the extent of phenotypic diversity by comparing the key traits of acidification and proteolytic activity among isolates found in yogurt starter cultures. Seventy-two isolates from three industrial starter cultures were ranked by either their fast or slow acidification activity (time to reach pH 4.6, 16 h), proteolytic activity, cell envelope proteinase (CEP) activity, redox potential and titratable acidity. The integration of multiple phenotype measures by hierarchical clustering and non-metric dimensional scaling (NMDS) clustered groups of isolates by multifactor similarity. A significant difference (p-value < 0.05) was observed between the clusters regarding redox potential and the proteolytic activity of both S. thermophilus and L. bulgaricus. The integration of multiple phenotypes points to the diversification that may have occurred over repeated culturing of yogurt starter bacteria. The phenotypic diversity may explain the divergence in starter performance and be used to refine the formulation of new starter cultures. Future work will investigate the correlation between the activity of specific enzymes based on the phenotype to explain the separation between the fast and slow acidification of isolates.

Application of MRI imaging technology based on magnetic nanoparticles in diagnosis and prognosis evaluation of prostate cancer

Objective: Objective: Prostate cancer is one of the most common malignant tumors in men. Early diagnosis and prognosis evaluation are of great significance for the treatment and prevention of prostate cancer. The purpose of this study was to explore the application of magnetic nanoparticle-based MRI imaging technology in the diagnosis and prognosis assessment of prostate cancer. A total of 81 patients in our hospital from September 2018 to January 2021 were selected as the study objects, all suspected prostate cancer patients, and prostate detection was performed under the guidance of MRI and rectal ultrasound.According to the pathological results, the patients were divided into prostate cancer cluster group and benign prostatic hyperplasia group. Imaging of prostate cancer is achieved by the response of magnetic nanoparticles to magnetic fields. MRI images of patients were collected and analyzed using professional software. It can provide high-resolution images that enable accurate detection and localization of tumors, and the technology can also assess the severity of prostate cancer and predict a patient's prognosis.