Analysis of the influence of educational level on the nutritional status and lifestyle habits of the young Spanish population

Aim

This study aims to analyze some nutrition and health habits of young people and the impact of educational attainment on health.

Methods

An observational, descriptive, and cross-sectional study was carried out using surveys. Using non-probabilistic snowball sampling, a previously validated questionnaire was disseminated through networks, collecting a sample of 9,681 people between 18 and 30  years old. Comparative analyses between groups were obtained by clustering and the corresponding statistical tests.

Results

The results showed how young people with higher education generally have a lower BMI, a higher healthy nutrition index, less frequent consumption of sugary drinks, and less smoking than their peers with basic education. These healthier habits are reflected in the higher self-perceived health status of the higher-educated group. While for all the educational levels analyzed, the minutes of physical activity practice are above the 150 min recommended by the WHO.

Conclusion

Our findings suggest that young people's education level is of fundamental importance for health, particularly for nutritional habits. In general, the lifestyle habits of the young Spanish population are healthy, but there is a need for improvement in those aspects related to nutrition and food.

miR-143 promotes cell proliferation, invasion and migration via directly binding to BRD2 in lens epithelial cells

OBJECTIVE

Cataract causes the greatest number of blindnesses worldwide. This study aims to investigate the role of miR-143 in lens epithelial cells.

METHODS

Clustering analysis was conducted to systematically compare miRNA expression levels across cataract and myopia. The levels of miR-143 and Bromodomain containing 2 (BRD2) were determined using real-time quantitative PCR (RT-qPCR) assay in lens epithelial cells. Transwell and wound healing assays were conducted to detect cell invasive and migratory abilities. The regulation relationship between MiR-143 and BRD2 was assessed using dual-luciferase reporter gene assays. BRD2 was knocked down using siRNA-BRD2, and siRNA-BRD2, and miR-143 inhibitors were transfected into cells with lipofectamine 2000.

RESULTS

Through retrieving five databases, 2690 miRNAs were selected. Volcano plot results demonstrated that 200 miRNAs were differentially expressed between cataract and myopia, in which 152 miRNAs were upregulated and 48 miRNAs downregulated in myopia compared with cataract. MiR-143 was upregulated in cataract compared with myopia (P<0.05). MiR-143 inhibitor suppressed the proliferation, invasion and migration of lens epithelial cells (all P<0.05). Luciferase reporter assays confirmed that BRD2 was a miR-143 target gene in SRA01/04 cells. Knockdown of BRD2 promoted SRA01/04 cell proliferation, invasion and migration (all P<0.05). In addition, silencing of BRD2 partially reversed the functions of miR-143 inhibitor on proliferation, invasion and migration (all P<0.05).

CONCLUSION

MiR-143 suppresses lens epithelial cell proliferation, invasion and migration by regulating BRD2, which may support a novel therapeutic strategy for cataract patients.

A data-driven approach to complement the A/T/(N) classification system using CSF biomarkers

AIMS

The AT(N) classification system not only improved the biological characterization of Alzheimer's disease (AD) but also raised challenges for its clinical application. Unbiased, data-driven techniques such as clustering may help optimize it, rendering informative categories on biomarkers' values.

METHODS

We compared the diagnostic and prognostic abilities of CSF biomarkers clustering results against their AT(N) classification. We studied clinical (patients from our center) and research (Alzheimer's Disease Neuroimaging Initiative) cohorts. The studied CSF biomarkers included Aβ(1-42), Aβ(1-42)/Aβ(1-40) ratio, tTau, and pTau.

RESULTS

The optimal solution yielded three clusters in both cohorts, significantly different in diagnosis, AT(N) classification, values distribution, and survival. We defined these three CSF groups as (i) non-defined or unrelated to AD, (ii) early stages and/or more delayed risk of conversion to dementia, and (iii) more severe cognitive impairment subjects with faster progression to dementia.

CONCLUSION

We propose this data-driven three-group classification as a meaningful and straightforward approach to evaluating the risk of conversion to dementia, complementary to the AT(N) system classification.

Efficient nonlinear homogenization of bones using a cluster-based model order reduction technique

We present a reduced order model for efficient nonlinear homogenization of bones, accounting for strength difference effects and containing some well-known plasticity models (like von Mises or Drucker-Prager) as special cases. The reduced order homogenization is done by using a cluster-based model order reduction technique, called cluster-based nonuniform transformation field analysis. For an offline phase, a space-time decomposition is performed on the mesoscopic plastic strain fields, while a clustering analysis is employed for a spatial decomposition of the mesoscale RVE model. A volumetric-deviatoric split is additionally introduced to capture the enriched characteristics of the mesoscopic plastic strain fields. For an online analysis, the reduced order model is formulated in a unified minimization problem, which is compatible with a large variety of material models. Both cortical and trabecular bones are considered for numerical experiments. Compared to conventional FE-based RVE computations, the developed reduced order model renders a considerable acceleration rate beyond10 3 , while maintaining a sufficient accuracy level.

Classification and immunotherapy assessment of lung adenocarcinoma based on coagulation-related genes

Introduction: This study on lung adenocarcinoma (LUAD), a common lung cancer subtype with high mortality. Aims: This study focuses on how tumor cell interactions affect immunotherapy responsiveness. Methods: Using public databases, we used non-negative matrix factorization clustering method, ssGSEA, CIBERSORT algorithm, immunophenotype score, survival analysis, protein-protein interaction network method to analyze gene expression data and coagulation-related genes. Results: We divided LUAD patients into three coagulation-related subgroups with varying immune characteristics and survival rates. A cluster of three patients, having the highest immune infiltration and survival rate, also showed the most potential for immunotherapy. We identified five key genes influencing patient survival using a protein-protein interaction network. Conclusion: This research offers valuable insights for forecasting prognosis and immunotherapy responsiveness in LUAD patients, helping to inform clinical treatment strategies.

A Discrete Mathematics Approach for Understanding Risk Factors in Overactive Bladder Treatment

Introduction Discrete mathematics, a branch of mathematics that includes graph theory, combinatorics, and logic, focuses on discrete mathematical structures. Its application in the medical field, particularly in analyzing patterns in patient data and optimizing treatment methods, is invaluable. This study, focusing on post-void residual (PVR) urine following overactive bladder (OAB) treatment, utilized discrete mathematics techniques to analyze PVR and its associated risk factors. Methods A retrospective study was conducted on 128 OAB patients who received intradetrusor onabotulinum toxin A injections between 2020 and 2022. Network graphs based on graph theory were used to analyze correlations between clinical variables, and clustering analysis was performed with PVR as the primary variable. Results The network graph analysis revealed that frailty, daytime frequency, and nocturia episodes were closely related to PVR. Clustering analysis with PVR as the primary variable divided the patients into three groups, suggesting that the group with particularly high frailty (Cluster 1) is at high risk for PVR. Moreover, significant differences in clinical indicators such as age, voiding efficiency, Overactive Bladder Symptom Score, and International Consultation on Incontinence Questionnaire-Short Form were observed in the remaining two clusters (Cluster 0 and 2). Conclusion This study demonstrates the effectiveness of discrete mathematics methods in identifying risk factors for PVR after OAB treatment and in distinguishing clinical subgroups based on patient characteristics. This approach could contribute to the formulation of individualized treatment strategies and the improvement of patient care quality. Further development and clinical application of this methodology are expected in future research.

Repeated mRNA vaccination sequentially boosts SARS-CoV-2-specific CD8+ T cells in persons with previous COVID-19

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) hybrid immunity is more protective than vaccination or previous infection alone. To investigate the kinetics of spike-reactive T (TS) cells from SARS-CoV-2 infection through messenger RNA vaccination in persons with hybrid immunity, we identified the T cell receptor (TCR) sequences of thousands of index TS cells and tracked their frequency in bulk TCRβ repertoires sampled longitudinally from the peripheral blood of persons who had recovered from coronavirus disease 2019 (COVID-19). Vaccinations led to large expansions in memory TS cell clonotypes, most of which were CD8+ T cells, while also eliciting diverse TS cell clonotypes not observed before vaccination. TCR sequence similarity clustering identified public CD8+ and CD4+ TCR motifs associated with spike (S) specificity. Synthesis of longitudinal bulk ex vivo single-chain TCRβ repertoires and paired-chain TCRɑβ sequences from droplet sequencing of TS cells provides a roadmap for the rapid assessment of T cell responses to vaccines and emerging pathogens.

Identification and subtype analysis of biomarkers associated with the solute carrier family in acute myocardial infarction

The dysregulation of some solute carrier (SLC) proteins has been linked to a variety of diseases, including diabetes and chronic kidney disease. However, SLC-related genes (SLCs) has not been extensively studied in acute myocardial infarction (AMI). The GSE66360 and GSE60993 datasets, and SLCs geneset were enrolled in this study. Differentially expressed SLCs (DE-SLCs) were screened by overlapping DEGs between the AMI and control groups and SLCs. Next, functional enrichment analysis was carried out to research the function of DE-SLCs. Consistent clustering of samples from the GSE66360 dataset was accomplished based on DE-SLCs selected. Next, the gene set enrichment analysis (GSEA) was performed on the DEGs-cluster (cluster 1 vs cluster 2). Three machine learning models were performed to obtain key genes. Subsequently, biomarkers were obtained through receiver operating characteristic (ROC) curves and expression analysis. Then, the immune infiltration analysis was performed. Afterwards, single-gene GSEA was carried out, and the biomarker-drug network was established. Finally, quantitative real-time fluorescence PCR (qRT-PCR) was performed to verify the expression levels of biomarkers. In this study, 13 DE-SLCs were filtered by overlapping 366 SLCs and 448 DEGs. The functional enrichment results indicated that the genes were implicated with amino acid transport and TNF signaling pathway. After the consistency clustering analysis, the samples were classified into cluster 1 and cluster 2 subtypes. The functional enrichment results showed that DEGs-cluster were implicated with chemokine signaling pathway and so on. Further, SLC11A1 and SLC2A3 were identified as SLC-related biomarkers, which had the strongest negative relationship with resting memory CD4 T cells and the strongest positive association with activated mast cells. In addition, the single-gene GSEA results showed that cytosolic ribosome was enriched by the biomarkers. Five drugs targeting SLC2A3 were predicted as well. Lastly, the experimental results showed that the biomarkers expression trends were consistent with public database. In this study, 2 SLC-related biomarkers (SLC11A1 and SLC2A3) were screened and drug predictions were carried out to explore the prediction and treatment of AMI.

Dissection of action mechanisms of Zuogui Pill in the treatment of liver cancer based on machine learning and network pharmacology: A review

This study aimed to investigate the underlying mechanism of Zuogui Pill in its efficacy against liver cancer, employing a combination of data mining approaches and network pharmacology methods. A novel clustering analysis algorithm was proposed to identify the core gene modules of Zuogui Pill. This algorithm successfully identified 5 core modules, with the first large module comprised of twelve proteins forming a 12-clique, representing the strongest connections among them. By utilizing GEO platform, ten key target proteins were detected, including FOS, PTGS2, and MYC. According to the GO annotation and KEGG analysis, desired target proteins were significantly enriched in various biological processes (BP). The analysis showed that ten key targets were strongly associated with signaling pathways mainly centered on MAPK and PI3K-Akt pathway. Additionally, molecular docking revealed strong binding affinities between core active ingredients of Zuogui Pill and these key targets, and the best affinity modes were observed for PTGS2-Sesamin, PRKCA-Sesamin, FOS-delta-Carotene. In order to establish the relationships between clinical symptoms and drug targets, a heterogeneous targets-related network was constructed. A total of 60 key target-symptom association pairs were detected, exemplified by the strongly association between fever and PTGS2 through the intermediary of Shu Di Huang. In summary, symptom-target associations are valuable in uncovering the underlying molecular mechanisms of Zuogui Pill. Our work reinforced the notion that Zuogui pill exhibits therapeutic potential on liver cancer through network targets, as well as synergistic effects of multi-component and multi-pathway. This study provided specific references for future experiments at the cost of less time.

Necroptosis-related regulatory pattern and scoring system for predicting therapeutic efficacy and prognosis in ovarian cancer

BACKGROUND

Ovarian cancer is difficult to treat and is, therefore, associated with a high fatality rate. Although targeted therapy and immunotherapy have been successfully used clinically to improve the diagnosis and treatment of ovarian cancer, most tumors become drug resistant, and patients experience relapse, meaning that the overall survival rate remains low.

AIMS

There is currently a lack of effective biomarkers for predicting the prognosis and/or outcomes of patients with ovarian cancer. Therefore, we used published transcriptomic data derived from a large ovarian cancer sample set to establish a molecular subtyping model of the core genes involved in necroptosis in ovarian cancer.

METHODS AND RESULTS

Clustering analysis and differential gene expression analyses were performed to establish the genomic subtypes related to necroptosis and to explore the patterns of regulatory gene expression related to necroptosis in ovarian cancer. A necroptosis scoring system (NSS) was established using principal component analysis according to different regulatory patterns of necroptosis. In addition, this study revealed important biological processes with essential roles in the regulation of ovarian tumorigenesis, including external encapsulating structure organization, leukocyte migration, oxidative phosphorylation, and focal adhesion. Patients with high NSS scores had unique immunophenotypes, such as more abundant M2 macrophages, monocytes, CD4+ memory T cells, and regulatory T cells. Immune checkpoint CD274 had a greater expression in patients with high NSS values.

CONCLUSION

This NSS could be used as an independent predictor of prognosis to determine the sensitivity of ovarian cancer to various small-molecule inhibitors, immune checkpoint inhibitors, and platinum-based chemotherapy drugs.

In silico screening combined with bioactivity evaluation to identify AMI-1 as a novel anticancer compound by targeting AXL

Recently, some studies have proven that AXL plays a crucial role in the drug resistance of tumors. At present, no AXL inhibitors on the market and it is essential to discover novel compounds targeting AXL to overcome resistance. In this work, based on the anchor structure, 21,313 compounds were obtained by substructure search from more than 400,000 compounds. Then, the Qvina and Ledock were selected for virtual screening to obtain 17 compounds. Next, four compounds (ARRY614, AMI-1, NG25, and Butein) were selected for bioactivity evaluation after hydrogen bond and cluster analysis. Further activity evaluation suggested that the compound AMI-1 is a novel AXL inhibitor with an IC50 value of 1.13 uM. In addition, molecular dynamics simulation demonstrated that compound AMI-1 contained lower binding energy and more key residues than the other three compounds, showing the best inhibitory activity against AXL. Finally, further MM/PBSA prediction showed that AMI-1 is more sensitive to mutant protein 3IKA than wildtype protein 1M17, which means that the AMI-1 may be helpful to overcome the resistance of EGFRT790M mutations. In conclusion, this work successfully discovered a novel compound with moderate inhibitory activity against AXL by a drug discovery workflow, which also could be applied to discover active compounds for other targets quickly.Communicated by Ramaswamy H. Sarma.

Characterization and Classification In Silico of Peptides with Dual Activity (Antimicrobial and Wound Healing)

The growing challenge of chronic wounds and antibiotic resistance has spotlighted the potential of dual-function peptides (antimicrobial and wound healing) as novel therapeutic strategies. The investigation aimed to characterize and correlate in silico the physicochemical attributes of these peptides with their biological activity. We sourced a dataset of 207 such peptides from various peptide databases, followed by a detailed analysis of their physicochemical properties using bioinformatic tools. Utilizing statistical tools like clustering, correlation, and principal component analysis (PCA), patterns and relationships were discerned among these properties. Furthermore, we analyzed the peptides' functional domains for insights into their potential mechanisms of action. Our findings spotlight peptides in Cluster 2 as efficacious in wound healing, whereas Cluster 1 peptides exhibited pronounced antimicrobial potential. In our study, we identified specific amino acid patterns and peptide families associated with their biological activities, such as the cecropin antimicrobial domain. Additionally, we found the presence of polar amino acids like arginine, cysteine, and lysine, as well as apolar amino acids like glycine, isoleucine, and leucine. These characteristics are crucial for interactions with bacterial membranes and receptors involved in migration, proliferation, angiogenesis, and immunomodulation. While this study provides a groundwork for therapeutic development, translating these findings into practical applications necessitates additional experimental and clinical research.

Bioinformatic Analysis of Secondary Metabolite Biosynthetic Potential in Pathogenic Fusarium

Fusarium species are among the filamentous fungi with the most pronounced impact on agricultural production and human health. The mycotoxins produced by pathogenic Fusarium not only attack various plants including crops, causing various plant diseases that lead to reduced yields and even death, but also penetrate into the food chain of humans and animals to cause food poisoning and consequent health hazards. Although sporadic studies have revealed some of the biosynthetic pathways of Fusarium toxins, they are insufficient to satisfy the need for a comprehensive understanding of Fusarium toxin production. In this study, we focused on 35 serious pathogenic Fusarium species with available genomes and systematically analyzed the ubiquity of the distribution of identified Fusarium- and non-Fusarium-derived fungal toxin biosynthesis gene clusters (BGCs) in these species through the mining of core genes and the comparative analysis of corresponding BGCs. Additionally, novel sesterterpene synthases and PKS_NRPS clusters were discovered and analyzed. This work is the first to systematically analyze the distribution of related mycotoxin biosynthesis in pathogenic Fusarium species. These findings enhance the knowledge of mycotoxin production and provide a theoretical grounding for the prevention of fungal toxin production using biotechnological approaches.

Macroarray expression analysis of cytokines and prostaglandin metabolism-related genes in chronic rhinosinusitis

Background

Chronic rhinosinusitis (CRS) can be divided into endotypes by functional or pathophysiologic findings.

Objective

The aim of this study was to analyze the expression of cytokines, prostaglandin (PG) synthases, and their receptors related to the pathogenesis of CRS, especially those contributing to nasal polyp (NP) formation.

Methods

NPs and uncinate tissue (UT) samples were collected from 90 patients who underwent endoscopic sinus surgery. They included 75 patients with CRS (including 45 with eosinophilic CRS [eCRS] and 30 with non-eCRS) and 15 patients without CRS. A total of 30 genes were selected for our original DNA array plate to analyze the levels of expression of 10 cytokines (IFN-γ, IL-4, IL-5, IL-10, IL-13, IL-17A, IL-22, IL-25, IL-33, and TSLP), 4 prostaglandin synthases (prostaglandin D2 [PGD2] synthase, prostaglandin E2 synthase, COX-1, and COX-2), and their 16 receptors. Clustering analysis was performed according to the expression results, and clinical findings of patients from each cluster were investigated.

Results

The samples could be divided into 3 clusters. Cluster 1 showed elevated levels of expression of IL4, IL5, IL13, TSLP, IL1RL1 (ST2 [an IL-33 receptor]), HPGDS, and GPR44 (CRTH2, a PGD2 receptor); cluster 2 showed elevated levels of expression of IL17A and PTGES; and cluster 3 showed an elevated level of expression of IL25. Regarding clinical features, the main characteristics of each cluster were as follows: NPs from patients with eCRS for cluster 1, NPs and/or UT samples from patients with non-eCRS for cluster 2, and UTs from patients with non-CRS for cluster 3.

Conclusion

The results suggest that there are associations between type 2 inflammation/PGD2 and eCRS and also between type 3 inflammation/prostaglandin E2 and non-eCRS.

Introducing Diinamic, a flexible and robust method for clustering analysis in single-molecule localization microscopy

Super-resolution microscopy allowed major improvements in our capacity to describe and explain biological organization at the nanoscale. Single-molecule localization microscopy (SMLM) uses the positions of molecules to create super-resolved images, but it can also provide new insights into the organization of molecules through appropriate pointillistic analyses that fully exploit the sparse nature of SMLM data. However, the main drawback of SMLM is the lack of analytical tools easily applicable to the diverse types of data that can arise from biological samples. Typically, a cloud of detections may be a cluster of molecules or not depending on the local density of detections, but also on the size of molecules themselves, the labeling technique, the photo-physics of the fluorophore, and the imaging conditions. We aimed to set an easy-to-use clustering analysis protocol adaptable to different types of data. Here, we introduce Diinamic, which combines different density-based analyses and optional thresholding to facilitate the detection of clusters. On simulated or real SMLM data, Diinamic correctly identified clusters of different sizes and densities, being performant even in noisy datasets with multiple detections per fluorophore. It also detected subdomains ("nanodomains") in clusters with non-homogeneous distribution of detections.

Recent Advances in Single-Molecule Tracking and Imaging Techniques

Since the early 1990s, single-molecule detection in solution at room temperature has enabled direct observation of single biomolecules at work in real time and under physiological conditions, providing insights into complex biological systems that the traditional ensemble methods cannot offer. In particular, recent advances in single-molecule tracking techniques allow researchers to follow individual biomolecules in their native environments for a timescale of seconds to minutes, revealing not only the distinct pathways these biomolecules take for downstream signaling but also their roles in supporting life. In this review, we discuss various single-molecule tracking and imaging techniques developed to date, with an emphasis on advanced three-dimensional (3D) tracking systems that not only achieve ultrahigh spatiotemporal resolution but also provide sufficient working depths suitable for tracking single molecules in 3D tissue models. We then summarize the observables that can be extracted from the trajectory data. Methods to perform single-molecule clustering analysis and future directions are also discussed.

Spatial age-period-cohort analysis of hepatitis B risk in Xinjiang from 2006 to 2019

Objective

The objective of this study was to investigate the spatio-temporal distribution and epidemiological characteristics of hepatitis B in 96 districts and counties of Xinjiang and to give useful information for hepatitis B prevention and treatment.

Methods

Based on the incidence data of hepatitis B in 96 districts and counties of Xinjiang from 2006 to 2019, the global trend analysis method was used to characterize the spatial variability of the disease, and the spatial autocorrelation and spatio-temporal aggregation analysis were used to explore the spatial clustering of hepatitis B and to identify high-risk areas and periods. The Integrated Nested Laplace Approximation (INLA)-based spatial age-period-cohort model was established to further explore the influence of age, period, birth queue effect, and spatial distribution on the incidence risk of hepatitis B, and sum-to-zero constraint was adopted to avoid the issue of model unrecognition.

Results

The risk of hepatitis B in Xinjiang is increasing from west to east and from north to south, with spatial heterogeneity and spatio-temporal scanning statistics yielding five clustering areas. The spatial age-period-cohort model showed two peaks in the average risk of hepatitis B, at [25,30) years old and [50,55) years old, respectively. The mean risk of hepatitis B incidence fluctuated up and down around 1 with time, and the average risk of disease by birth cohort displayed an increasing-decreasing-stabilizing trend. Taking age, period, and cohort effect into consideration, it was found that the areas with a high risk of hepatitis B are Tianshan District, Xinshi District, Shuimogou District, Changji City, Aksu City, Kashi City, Korla City, Qiemo County and Yopurga County in Xinjiang. According to the spatio-temporal effect item, it was found that there are unobserved variables affecting the incidence of hepatitis B in some districts and counties of Xinjiang.

Conclusion

The spatio-temporal characteristics of hepatitis B and the high-risk population needed to be taken into attention. It is suggested that the relevant disease prevention and control centers should strengthen the prevention and control of hepatitis B among young people while paying attention to middle-aged and older adult people, and strengthening the prevention and monitoring of high-risk areas.

PeakForce AFM Analysis Enhanced with Model Reduction Techniques

PeakForce quantitative nanomechanical AFM mode (PF-QNM) is a popular AFM technique designed to measure multiple mechanical features (e.g., adhesion, apparent modulus, etc.) simultaneously at the exact same spatial coordinates with a robust scanning frequency. This paper proposes compressing the initial high-dimensional dataset obtained from the PeakForce AFM mode into a subset of much lower dimensionality by a sequence of proper orthogonal decomposition (POD) reduction and subsequent machine learning on the low-dimensionality data. A substantial reduction in user dependency and subjectivity of the extracted results is obtained. The underlying parameters, or "state variables", governing the mechanical response can be easily extracted from the latter using various machine learning techniques. Two samples are investigated to illustrate the proposed procedure (i) a polystyrene film with low-density polyethylene nano-pods and (ii) a PDMS film with carbon-iron particles. The heterogeneity of material, as well as the sharp variation in topography, make the segmentation challenging. Nonetheless, the underlying parameters describing the mechanical response naturally offer a compact representation allowing for a more straightforward interpretation of the high-dimensional force-indentation data in terms of the nature (and proportion) of phases, interfaces, or topography. Finally, those techniques come with a low processing time cost and do not require a prior mechanical model.

BinderSpace: A package for sequence space analyses for datasets of affinity-selected oligonucleotides and peptide-based molecules

Discovery of target-binding molecules, such as aptamers and peptides, is usually performed with the use of high-throughput experimental screening methods. These methods typically generate large datasets of sequences of target-binding molecules, which can be enriched with high affinity binders. However, the identification of the highest affinity binders from these large datasets often requires additional low-throughput experiments or other approaches. Bioinformatics-based analyses could be helpful to better understand these large datasets and identify the parts of the sequence space enriched with high affinity binders. BinderSpace is an open-source Python package that performs motif analysis, sequence space visualization, clustering analyses, and sequence extraction from clusters of interest. The motif analysis, resulting in text-based and visual output of motifs, can also provide heat maps of previously measured user-defined functional properties for all the motif-containing molecules. Users can also run principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE) analyses on whole datasets and on motif-related subsets of the data. Functionally important sequences can also be highlighted in the resulting PCA and t-SNE maps. If points (sequences) in two-dimensional maps in PCA or t-SNE space form clusters, users can perform clustering analyses on their data, and extract sequences from clusters of interest. We demonstrate the use of BinderSpace on a dataset of oligonucleotides binding to single-wall carbon nanotubes in the presence and absence of a bioanalyte, and on a dataset of cyclic peptidomimetics binding to bovine carbonic anhydrase protein. BinderSpace is openly accessible to the public via the GitHub website: https://github.com/vukoviclab/BinderSpace.

Association of Time-of-Day Physical Activity With Incident Cardiovascular Disease: The UK Biobank Study

INTRODUCTION

Early morning is characterized by an increased risk of cardiovascular events, a sudden rise in blood pressure, impaired endothelial function, and exacerbated hemodynamic changes during physical activity. The study aims to examine whether the time of day of physical activity is associated with incident cardiovascular disease (CVD).

METHODS

We prospectively analyzed 83,053 participants in the UK Biobank with objectively measured physical activity and initially free of CVD. Based on the diurnal patterns of physical activity, participants were categorized into 4 groups: early morning (n = 15,908), late morning (n = 22,371), midday (n = 24,764), and evening (n = 20,010). Incident CVD was defined as the first diagnosis of coronary heart disease or stroke.

RESULTS

During 197.4 million person-years of follow-up, we identified 3454 CVD cases. After adjusting for the overall acceleration average, the hazard ratios and 95% confidence intervals were 0.95 (0.86-1.07) for late morning, 1.15 (1.03-1.27) for midday, and 1.03 (0.92-1.15) for evening, as compared with the early morning group. In the joint analyses, higher levels of physical activity were associated with a lower risk of incident CVD in a similar manner across the early morning, late morning, and evening groups. However, the beneficial association was attenuated in the midday group.

CONCLUSION

In conclusion, early morning, late morning, and evening are all favorable times of day to engage in physical activity for the primary prevention of CVD, while midday physical activity is associated with an increased risk of CVD compared with early morning physical activity after controlling for the levels of physical activity.

An integrated complete-genome sequencing and systems biology approach to predict antimicrobial resistance genes in the virulent bacterial strains of Moraxella catarrhalis

Moraxella catarrhalis is a symbiotic as well as mucosal infection-causing bacterium unique to humans. Currently, it is considered as one of the leading factors of acute middle ear infection in children. As M. catarrhalis is resistant to multiple drugs, the treatment is unsuccessful; therefore, innovative and forward-thinking approaches are required to combat the problem of antimicrobial resistance (AMR). To better comprehend the numerous processes that lead to antibiotic resistance in M. catarrhalis, we have adopted a computational method in this study. From the NCBI-Genome database, we investigated 12 strains of M. catarrhalis. We explored the interaction network comprising 74 antimicrobial-resistant genes found by analyzing M. catarrhalis bacterial strains. Moreover, to elucidate the molecular mechanism of the AMR system, clustering and the functional enrichment analysis were assessed employing AMR gene interactions networks. According to the findings of our assessment, the majority of the genes in the network were involved in antibiotic inactivation; antibiotic target replacement, alteration and antibiotic efflux pump processes. They exhibit resistance to several antibiotics, such as isoniazid, ethionamide, cycloserine, fosfomycin, triclosan, etc. Additionally, rpoB, atpA, fusA, groEL and rpoL have the highest frequency of relevant interactors in the interaction network and are therefore regarded as the hub nodes. These genes can be exploited to create novel medications by serving as possible therapeutic targets. Finally, we believe that our findings could be useful to advance knowledge of the AMR system present in M. catarrhalis.

Early-Life Slow Enteral Feeding Progression Pattern Is Associated with Longitudinal Head-Size Growth Faltering and Neurodevelopmental Impairment Outcomes in Extremely Preterm Infants

OBJECTIVE

To determine whether feeding progression patterns in the first eight postnatal weeks, depicted by clustering analysis of daily enteral feeding volume, are associated with longitudinal head-circumference (HC) growth and neurodevelopmental outcomes in extremely preterm (EP) infants.

METHODS

200 infants who were admitted at gestational ages 23-27 weeks between 2011 and 2018; survived to discharge; and underwent longitudinal HC growth measurements at birth, term-equivalent age (TEA), corrected age (CA) 6-month, 12-month, and 24-month; and neurodevelopmental assessment using the Bayley Scales of Infant Development at CA 24 months were included for analysis.

RESULTS

kmlShape analysis identified two distinct enteral feeding progression patterns: fast progression in 131 (66%) infants and slow progression in 69 (34%) infants. Compared to the fast progression group, the slow progression group showed significantly lower daily enteral volumes after day 13, was older in postnatal age reaching full feeding, had a higher rate of Delta z scores of HC (zHC) < -1 (p < 0.001) between birth and TEA, and displayed lower longitudinal zHC from TEA to CA 24 months. The slow progression group also showed higher rates of microcephaly [42% vs. 16%, p < 0.001; adjusted odd ratio (aOR): 3.269, p = 0.001] and neurodevelopmental impairment (NDI) (38% vs. 19%, p = 0.007; aOR: 2.095, p = 0.035) at CA 24 months. For NDI, the model including feeding progression patterns showed a lower Akaike information criterion score and a better goodness of fit than the model that did not include feeding patterns.

CONCLUSION

Characterizing feeding progression pattern may help identify EP infants at high-risk of head-size growth faltering and NDI at early childhood.

The effect of different mobile uses on crash frequency among young drivers: application of statistical models and clustering analysis

This study focuses on investigating the use of mobile phones among young drivers by employing an online questionnaire survey data. Ordinal logistic regression model was used for modelling the probabilities of crashes due to different uses of mobile phone while driving. Moreover, binary logistic regression models were used for predicting the probabilities of different uses of mobile phone. Logistic regression models revealed that texting and internet use have the same likelihood of causing crashes. Drivers having prior experience of being fined for mobile phone use, also showed a higher tendency to be involved in 2 crashes. Moreover, these drivers had a higher likelihood of being involved in texting, as compared to other uses of mobile phones. Drivers with more education had a higher tendency for internet use during driving. Drivers who use mobile phone for long periods during driving have a lesser tendency to get involved in texting, internet use or GPS navigation. Moreover, drivers with a previous crash record have less likelihood of being involved in texting. The models of this study can be useful in developing effective road safety measures. Clustering was also applied in this study which reinforced the findings of the statistical analysis and models.

The cellular basis of mechanosensation in mammalian tongue

Mechanosensory neurons that innervate the tongue provide essential information to guide feeding, speech, and social grooming. We use in vivo calcium imaging of mouse trigeminal ganglion neurons to identify functional groups of mechanosensory neurons innervating the anterior tongue. These sensory neurons respond to thermal and mechanical stimulation. Analysis of neuronal activity patterns reveal that most mechanosensory trigeminal neurons are tuned to detect moving stimuli across the tongue. Using an unbiased, multilayer hierarchical clustering approach to classify pressure-evoked activity based on temporal response dynamics, we identify five functional classes of mechanosensory neurons with distinct force-response relations and adaptation profiles. These populations are tuned to detect different features of touch. Molecular markers of functionally distinct clusters are identified by analyzing cluster representation in genetically marked neuronal subsets. Collectively, these studies provide a platform for defining the contributions of functionally distinct mechanosensory neurons to oral behaviors crucial for survival in mammals.

Crash Risk Predictors in Older Drivers: A Cross-Sectional Study Based on a Driving Simulator and Machine Learning Algorithms

The ability to drive depends on the motor, visual, and cognitive functions, which are necessary to integrate information and respond appropriately to different situations that occur in traffic. The study aimed to evaluate older drivers in a driving simulator and identify motor, cognitive and visual variables that interfere with safe driving through a cluster analysis, and identify the main predictors of traffic crashes. We analyzed the data of older drivers (n = 100, mean age of 72.5 ± 5.7 years) recruited in a hospital in São Paulo, Brazil. The assessments were divided into three domains: motor, visual, and cognitive. The K-Means algorithm was used to identify clusters of individuals with similar characteristics that may be associated with the risk of a traffic crash. The Random Forest algorithm was used to predict road crash in older drivers and identify the predictors (main risk factors) related to the outcome (number of crashes). The analysis identified two clusters, one with 59 participants and another with 41 drivers. There were no differences in the mean of crashes (1.7 vs. 1.8) and infractions (2.6 vs. 2.0) by cluster. However, the drivers allocated in Cluster 1, when compared to Cluster 2, had higher age, driving time, and braking time (p < 0.05). The random forest performed well (r = 0.98, R² = 0.81) in predicting road crash. Advanced age and the functional reach test were the factors representing the highest risk of road crash. There were no differences in the number of crashes and infractions per cluster. However, the Random Forest model performed well in predicting the number of crashes.

Brain Degeneration in Synucleinopathies Based on Analysis of Cognition and Other Nonmotor Features: A Multimodal Imaging Study

BACKGROUND

We aimed to characterize subtypes of synucleinopathies using a clustering approach based on cognitive and other nonmotor data and to explore structural and functional magnetic resonance imaging (MRI) brain differences between identified clusters.

METHODS

Sixty-two patients (n = 6 E46K-SNCA, n = 8 dementia with Lewy bodies (DLB) and n = 48 idiopathic Parkinson's disease (PD)) and 37 normal controls underwent nonmotor evaluation with extensive cognitive assessment. Hierarchical cluster analysis (HCA) was performed on patients' samples based on nonmotor variables. T1, diffusion-weighted, and resting-state functional MRI data were acquired. Whole-brain comparisons were performed.

RESULTS

HCA revealed two subtypes, the mild subtype (n = 29) and the severe subtype (n = 33). The mild subtype patients were slightly impaired in some nonmotor domains (fatigue, depression, olfaction, and orthostatic hypotension) with no detectable cognitive impairment; the severe subtype patients (PD patients, all DLB, and the symptomatic E46K-SNCA carriers) were severely impaired in motor and nonmotor domains with marked cognitive, visual and bradykinesia alterations. Multimodal MRI analyses suggested that the severe subtype exhibits widespread brain alterations in both structure and function, whereas the mild subtype shows relatively mild disruptions in occipital brain structure and function.

CONCLUSIONS

These findings support the potential value of incorporating an extensive nonmotor evaluation to characterize specific clinical patterns and brain degeneration patterns of synucleinopathies.

An Iterative Unsupervised Method for Gene Expression Differentiation

For several decades, intensive research for understanding gene activity and its role in organism's lives is the research focus of scientists in different areas. A part of these investigations is the analysis of gene expression data for selecting differentially expressed genes. Methods that identify the interested genes have been proposed on statistical data analysis. The problem is that there is no good agreement among them, as different results are produced by distinct methods. By taking the advantage of the unsupervised data analysis, an iterative clustering procedure that finds differentially expressed genes shows promising results. In the present paper, a comparative study of the clustering methods applied for gene expression analysis is presented to explicate the choice of the clustering algorithm implemented in the method. An investigation of different distance measures is provided to reveal those that increase the efficiency of the method in finding the real data structure. Further, the method is improved by incorporating an additional aggregation measure based on the standard deviation of the expression levels. Its usage increases the gene distinction as a new amount of differentially expressed genes is found. The method is summarized in a detailed procedure. The significance of the method is proved by an analysis of two mice strain data sets. The differentially expressed genes defined by the proposed method are compared with those selected by the well-known statistical methods applied to the same data set.

Neuropsychiatric feature-based subgrouping reveals neural sensory processing spectrum in female FMR1 premutation carriers: A pilot study

Introduction

Fragile X Syndrome (FXS) is rare genetic condition characterized by a repeat expansion (CGG) in the Fragile X messenger ribonucleoprotein 1 (FMR1) gene where individuals with greater than 200 repeats are defined as full mutation. FXS clinical presentation often includes intellectual disability, and autism-like symptoms, including anxiety and sensory hypersensitivities. Individuals with 55 to <200 CGG repeats are said to have the FMR1 premutation, which is not associated with primary characteristics of the full mutation, but with an increased risk for anxiety, depression, and other affective conditions, as well as and impaired cognitive processing differences that vary in severity. Defining subgroups of premutation carriers based on distinct biological features may identify subgroups with varying levels of psychiatric, cognitive, and behavioral alterations.

Methods

The current pilot study utilized 3 cluster subgroupings defined by previous k means cluster analysis on neuropsychiatric, cognitive, and resting EEG variables in order to examine basic sensory auditory chirp task-based EEG parameters from 33 females with the FMR1 premutation (ages 17-78).

Results

Based on the predefined, neuropsychiatric three-cluster solution, premutation carriers with increased neuropsychiatric features and higher CGG repeat counts (cluster 1) showed decreased stimulus onset response, similar to previous ERP findings across a number of psychiatric disorders but opposite to findings in individuals with full mutation FXS. Premutation carriers with increased executive dysfunction and resting gamma power (cluster 2) exhibited decreased gamma phase locking to a chirp stimulus, similar to individuals with full mutation FXS. Cluster 3 members, who were relatively unaffected by psychiatric or cognitive symptoms, showed the most normative task-based EEG metrics.

Discussion

Our findings suggest a spectrum of sensory processing characteristics present in subgroups of premutation carriers that have been previously understudied due to lack of overall group differences. Our findings also further validate the pre-defined clinical subgroups by supporting links between disturbances in well-defined neural pathways and behavioral alterations that may be informative for identifying the mechanisms supporting specific risk factors and divergent therapeutic needs in individuals with the FMR1 premutation.

Serum antibodies to periodontal pathogens are related to allergic symptoms

BACKGROUND

The association between periodontitis and allergic symptoms has been investigated. However, the difference in immune signatures between them remains poorly understood. This cross-sectional study assessed the relationship between serum immunoglobulin G (IgG) antibodies to periodontal pathogens and allergic symptoms in a nationwide population cohort.

METHODS

Two phases of the Third National Health and Nutrition Examination Survey (NHANES III) were used as discovery dataset (n = 3700) and validation dataset (n = 4453), respectively. Based on the antibodies against 19 periodontal pathogens, we performed an unsupervised hierarchical clustering to categorize the population into three clusters. In the discovery dataset, cluster 1 (n = 2847) had the highest level of IgG antibodies, followed by clusters 2 (n = 588) and 3 (n = 265). Data on allergic symptoms (asthma, hay fever, and wheezing) were obtained using a self-reported questionnaire. Survey-weighted multivariable logistic regression evaluated the association between these clusters and allergic symptoms.

RESULTS

In the discovery dataset, the participants with lower levels of antibodies to periodontal pathogens exhibited a higher risk of asthma (odds ratio [OR]_{cluster 3 vs. cluster 1} = 1.820, 95% confidence interval [CI]: 1.153-2.873) and wheezing (OR_{cluster 3 vs. cluster 1} = 1.550, 95% CI: 1.095-2.194) compared to those with higher periodontal antibodies, but the non-significant association with hay fever. Consistent results were found in the validation dataset.

CONCLUSIONS

Serum IgG titers to periodontal pathogens were inversely associated with the risk of asthma and wheezing, suggesting the potentially protective role against allergic conditions.

Identifying Immune-Specific Subtypes of Adrenocortical Carcinoma Based on Immunogenomic Profiling

BACKGROUND

The tumor immune microenvironment (TIME) of adrenocortical carcinoma (ACC) is heterogeneous. However, a classification of ACC based on the TIME remains unexplored.

METHODS

We hierarchically clustered ACC based on the enrichment levels of twenty-three immune signatures to identify its immune-specific subtypes. Furthermore, we comprehensively compared the clinical and molecular profiles between the subtypes.

RESULTS

We identified two immune-specific subtypes of ACC: Immunity-H and Immunity-L, which had high and low immune signature scores, respectively. We demonstrated that this subtyping method was stable and reproducible by analyzing five different ACC cohorts. Compared with Immunity-H, Immunity-L had lower levels of immune cell infiltration, worse overall and disease-free survival prognosis, and higher tumor stemness, genomic instability, proliferation potential, and intratumor heterogeneity. Furthermore, the ACC driver gene CTNNB1 was more frequently mutated in Immunity-L than in Immunity-H. Several proteins, such as mTOR, ERCC1, Akt, ACC1, Cyclin_E1, β-catenin, FASN, and GAPDH, were more highly expressed in Immunity-L than in Immunity-H. In contrast, p53, Syk, Lck, PREX1, and MAPK were more highly expressed in Immunity-H. Pathway and gene ontology analysis showed that the immune, stromal, and apoptosis pathways were highly enriched in Immunity-H, while the cell cycle, steroid biosynthesis, and DNA damage repair pathways were highly enriched in Immunity-L.

CONCLUSIONS

ACC can be classified into two stable immune-related subtypes, which have significantly different antitumor responses, molecular characteristics, and clinical outcomes. This subtyping may provide clinical implications for prognostic and immunotherapeutic stratification of ACC.

[Analysis of differentiated regional dietary patterns of adults aged 18-64 years in 15 provinces( autonomous regions, municipalities) in 2018]

OBJECTIVE

To analyze the dietary patterns of Chinese adults in 15 provinces and cities in China.

METHODS

A total of 9208 adults aged 18-64 from the "China Health and Nutrition Survey" in 2018 were selected as subjects. Food consumption data were collected through 3 d-24 h dietary recalls and weighing household cooking oil and condiments, and the average daily dietary nutrient intake was calculated by the food composition table. Clustering analysis was used to analyze dietary patterns in different regions, and data from the 2009 China Health and Nutrition Survey were used for comparison.

RESULTS

Three dietary patterns were obtained in this study, including northern pattern, coastal pattern and southern pattern. The analysis of demographic characteristics showed that the intake of cereals, vegetables and livestock were statistically different among different age groups, and livestock and poultry intake was higher in the 18-49 years; Fruit and milk were higher in women and other foods were higher in men; The intakes of cereals, tubers, legumes and vegetables were higher in rural areas, people with low income and low education levels, and other foods were higher in urban areas, people with high income and high education levels, especially milk. Compared with the dietary guidelines, it was found that the proportion of insufficient intake of dark vegetables, fruits and nuts in three patterns and milk, eggs in the southern pattern and aquatic products in the northern pattern was up to 80%, the other food intake was in 50%-80%; The proportion of excessive intake of livestock and poultry meat in the southern mode was significantly higher, reaching 78%. Nutrient analysis showed that the intake of carbohydrate, dietary fiber and carbohydrate energy ratio, vitamin E, magnesium and manganese were higher in the northern pattern; The intakes of protein, protein energy ratio, riboflavin, potassium, sodium, calcium, phosphorus, copper and selenium were higher in the coastal pattern; The southernpattern had a higher intake of energy, fat, fat energy ratio, niacin, retinol, zinc and vitamin C. In addition, the comparison between 2009 and 2018 showed that the intake of most foods except milk has decreased to varying degrees in the past decade, while the intake of some foods has increased.

CONCLUSION

The 15 provinces in China were divided into three dietary patterns. Unbalanced intake of food and nutrients was common among them, and the intake was influenced by different demographic characteristics. In addition, most food intake has declined over the past decade.

The characteristics of postprandial glycemic response patterns to white rice and glucose in healthy adults: Identifying subgroups by clustering analysis

OBJECTIVES

Large interpersonal variability in postprandial glycemic response (PGR) to white rice has been reported, and differences in the PGR patterns during the oral glucose tolerance test (OGTT) have been documented. However, there is scant study on the PGR patterns of white rice. We examined the typical PGR patterns of white rice and glucose and the association between them.

MATERIALS AND METHODS

We analyzed the data of 3-h PGRs to white rice (WR) and glucose (G) of 114 normoglycemic female subjects of similar age, weight status, and same ethnic group. Diverse glycemic parameters, based on the discrete blood glucose values, were calculated over 120 and 180 min. K-means clustering based on glycemic parameters calculated over 180 min was applied to identify subgroups and representative PGR patterns. Principal factor analysis based on the parameters used in the cluster analysis was applied to characterize PGR patterns. Simple correspondence analysis was performed on the clustering categories of WR and G.

RESULTS

More distinct differences were found in glycemic parameters calculated over 180 min compared with that calculated over 120 min, especially in the negative area under the curve and Nadir. We identified four distinct PGR patterns to WR (WR1, WR2, WR3, and WR4) and G (G1, G2, G3, and G4), respectively. There were significant differences among the patterns regard to postprandial hyperglycemia, hypoglycemic, and glycemic variability. The WR1 clusters had significantly lower glycemic index (59 ± 19), while no difference was found among the glycemic index based on the other three clusters. Each given G subgroup presented multiple patterns of PGR to WR, especially in the largest G subgroup (G1), and in subgroup with the greatest glycemic variability (G3).

CONCLUSION

Multiple subgroups could be classified based on the PGR patterns to white rice and glucose even in seemingly homogeneous subjects. Extending the monitoring time to 180 min was conducive to more effective discrimination of PGR patterns. It may not be reliable to extrapolate the patterns of PGR to rice from that to glucose, suggesting a need of combining OGTT and meal tolerance test for individualized glycemic management.

CD8+ T cell clonotypes from prior SARS-CoV-2 infection predominate during the cellular immune response to mRNA vaccination

Almost three years into the SARS-CoV-2 pandemic, hybrid immunity is highly prevalent worldwide and more protective than vaccination or prior infection alone. Given emerging resistance of variant strains to neutralizing antibodies (nAb), it is likely that T cells contribute to this protection. To understand how sequential SARS-CoV-2 infection and mRNA-vectored SARS-CoV-2 spike (S) vaccines affect T cell clonotype-level expansion kinetics, we identified and cross-referenced TCR sequences from thousands of S-reactive single cells against deeply sequenced peripheral blood TCR repertoires longitudinally collected from persons during COVID-19 convalescence through booster vaccination. Successive vaccinations recalled memory T cells and elicited antigen-specific T cell clonotypes not detected after infection. Vaccine-related recruitment of novel clonotypes and the expansion of S-specific clones were most strongly observed for CD8+ T cells. Severe COVID-19 illness was associated with a more diverse CD4+ T cell response to SARS-CoV-2 both prior to and after mRNA vaccination, suggesting imprinting of CD4+ T cells by severe infection. TCR sequence similarity search algorithms revealed myriad public TCR clusters correlating with human leukocyte antigen (HLA) alleles. Selected TCRs from distinct clusters functionally recognized S in the predicted HLA context, with fine viral peptide requirements differing between TCRs. Most subjects tested had S-specific T cells in the nasal mucosa after a 3rd mRNA vaccine dose. The blood and nasal T cell responses to vaccination revealed by clonal tracking were more heterogeneous than nAb boosts. Analysis of bulk and single cell TCR sequences reveals T cell kinetics and diversity at the clonotype level, without requiring prior knowledge of T cell epitopes or HLA restriction, providing a roadmap for rapid assessment of T cell responses to emerging pathogens.

Transcriptome data reveal gene clusters and key genes in pepper response to heat shock

Climate change and global warming pose a great threat to plant growth and development as well as crop productivity. To better study the genome-wide gene expression under heat, we performed a time-course (0.5 to 24 h) transcriptome analysis in the leaf and root of 40-day-old pepper plants under 40°C as well as in control plants. Clustering analysis (K-means) showed that the expression of 29,249 genes can be grouped into 12 clusters with distinct expression dynamics under stress. Gene ontology (GO) enrichment analysis and transcription factor (TF) identification were performed on the clusters with certain expression patterns. Comparative analysis between the heat-treated and control plants also identified differentially expressed genes (DEGs), which showed the largest degree of change at 24 h. Interestingly, more DEGs were identified in the root than in the leaf. Moreover, we analyzed the gene expression of 25 heat shock factor genes (HSFs) in pepper after heat stress, identified five of these HSFs that responded to heat stress, and characterized the role of these genes in heat-tolerant (17CL30) and heat-susceptible (05S180) pepper lines. The findings of this study improve our understanding of the genome-wide heat stress response in pepper.

MDO: A Computational Protocol for Prediction of Flexible Enzyme-Ligand Binding Mode

AIM

Developing a method for use in computer aided drug design Background: Predicting the structure of enzyme-ligand binding mode is essential for understanding the properties, functions, and mechanisms of the bio-complex, but is rather difficult due to the enormous sampling space involved.

OBJECTIVE

Accurate prediction of enzyme-ligand binding mode conformation.

METHOD

A new computational protocol, MDO, is proposed for finding the structure of ligand binding pose. MDO consists of sampling enzyme sidechain conformations via molecular dynamics simulation of enzyme-ligand system and clustering of the enzyme configurations, sampling ligand binding poses via molecular docking and clustering of the ligand conformations, and the optimal ligand binding pose prediction via geometry optimization and ranking by the ONIOM method. MDO is tested on 15 enzyme-ligand complexes with known accurate structures.

RESULTS

The success rate of MDO predictions, with RMSD < 2 Å, is 67%, substantially higher than the 40% success rate of conventional methods. The MDO success rate can be increased to 83% if the ONIOM calculations are applied only for the starting poses with ligands inside the binding cavities.

CONCLUSION

The MDO protocol provides high quality enzyme-ligand binding mode prediction with reasonable computational cost. The MDO protocol is recommended for use in the structure-based drug design.

A Semi-Supervised Methodology for Fishing Activity Detection Using the Geometry behind the Trajectory of Multiple Vessels

Automatic Identification System (AIS) messages are useful for tracking vessel activity across oceans worldwide using radio links and satellite transceivers. Such data play a significant role in tracking vessel activity and mapping mobility patterns such as those found during fishing activities. Accordingly, this paper proposes a geometric-driven semi-supervised approach for fishing activity detection from AIS data. Through the proposed methodology, it is shown how to explore the information included in the messages to extract features describing the geometry of the vessel route. To this end, we leverage the unsupervised nature of cluster analysis to label the trajectory geometry, highlighting changes in the vessel's moving pattern, which tends to indicate fishing activity. The labels obtained by the proposed unsupervised approach are used to detect fishing activities, which we approach as a time-series classification task. We propose a solution using recurrent neural networks on AIS data streams with roughly 87% of the overall F-score on the whole trajectories of 50 different unseen fishing vessels. Such results are accompanied by a broad benchmark study assessing the performance of different Recurrent Neural Network (RNN) architectures. In conclusion, this work contributes by proposing a thorough process that includes data preparation, labeling, data modeling, and model validation. Therefore, we present a novel solution for mobility pattern detection that relies upon unfolding the geometry observed in the trajectory.

Clinical Characteristics of Inpatients With New-Onset Diabetes Mellitus in Eastern China: Based on Novel Clustering Analysis

Introduction

This study aimed to explore the novel classification of inpatients with new-onset diabetes in Eastern China by the cluster-based classification method and compare the clinical characteristics among the different subgroups.

Methods

A total of 1017 Inpatients with new-onset diabetes of five hospitals in Eastern China were included in the study. Clustering analysis was used to cluster the data into five subgroups according to six basic variables. The differences in clinical characteristics, treatments, and the prevalence of diabetes-related diseases among the five subgroups were analyzed by multiple groups comparisons and pairwise comparisons. The risk of diabetes-related diseases in the five subgroups was compared by calculating odd ratio (OR). P value < 0.05 was considered significant.

Results

Five subgroups were obtained by clustering analysis with the highest proportion of patients with severe insulin-deficient diabetes (SIDD) 451 (44.35%), followed by patients with mild age-related diabetes (MARD) 236 (23.21%), patients with mild obesity-related diabetes (MOD) 207 (20.35%), patients with severe insulin-resistant diabetes (SIRD) 81 (7.96%), and patients with severe autoimmune diabetes (SAID) 42 (4.13%). Five subtypes had their own unique characteristics and treatments. The prevalence and risk of diabetes-related complications and comorbidities were also significantly different among the five subtypes. Diabetic kidney disease (DKD) was the most common in SIRD group. Patients in SIDD, SIRD, and MARD groups were more likely to develop cardiovascular disease (CVD) and/or stroke, diabetic peripheral vascular disease (DPVD), and diabetic distal symmetric polyneuropathy (DSPN). The prevalence and risk of metabolic syndrome (MS) were the highest in MOD and SIRD groups. Patients in SAID group had the highest prevalence and risk of diabetic ketoacidosis (DKA). Patients with MOD were more likely to develop non-alcoholic fatty liver disease (NAFLD).

Conclusions

The inpatients with new-onset diabetes in Eastern China had the unique clustering distribution. The clinical characteristics, treatments, and diabetes-related complications and comorbidities of the five subgroups were different, which may provide the basis for precise treatments of diabetes.

Cluster Analysis Assessment in Proposing a Surgical Technique for Benign Prostatic Enlargement

Benign prostatic enlargement (BPE) is a common disease in men over 50 years old. The phenotype of patients with BPE is heterogenous, regarding both baseline patient characteristics and disease-related parameters. Treatment can be either medical-conservative or surgical. A great variety of surgical techniques are available for surgical management, with three of the most common being monopolar transurethral resection of the prostate (mTUR-P), bipolar transurethral resection of the prostate (bTUR-P), and bipolar transurethral vaporization of the prostate (bTUVis). The selection of each one of these depends on surgeon reasoning, equipment availability, patient characteristics, and preferences. Since all of these techniques are available in our Urology Department, and surgeons are skilled to perform each one of them, we performed a clustering analysis according to patient pre-operative characteristics, using the k-means algorithm, to compare clustering-related technique assignment with the real-life technique used.

Improvements Achieved by Multiple Imputation for Single-Cell RNA-Seq Data in Clustering Analysis and Differential Expression Analysis

In a single-cell RNA-seq (scRNA-seq) data set, a high proportion of missing values (or an excessive number of zeroes) are frequently observed. For the related follow-up tasks, such as clustering analysis and differential expression analysis, a data set without missing values is generally required. Many imputation approaches have been proposed for this purpose. Multiple imputation (MI) is a well-established approach to address possible biases in a follow-up analysis result based on one-time imputed data. There is a lack of investigation on this in the analysis of scRNA-seq data. In this study, we have investigated how to efficiently apply the MI approach to the clustering analysis and the differential expression analysis of scRNA-seq data. We proposed an MI procedure for clustering analysis and an MI procedure for differential expression analysis. To demonstrate the improvements achieved by MI in clustering analysis and differential expression analysis of scRNA-seq data, we analyzed three well-known scRNA-seq data sets. scIGANs, an scRNA-seq imputation method based on the generative adversarial networks (GANs), has been recently proposed for scRNA-seq data imputation. Multiple randomly imputed data sets can be conveniently generated by this method. We implemented our MI procedures based on scIGANs. We demonstrated that MI yielded improved performances on the clustering analysis and differential expression analysis results. Our applications to experimental scRNA-seq data illustrated the advantages of MI over one-time imputation of missing values in scRNA-seq data.

Study on Air Quality and Its Annual Fluctuation in China Based on Cluster Analysis

Exploring the spatial and temporal distribution characteristics of air quality has become an important topic for the harmonious development of human and nature. Based on the hourly data of CO, O₃, NO₂, SO₂, PM_2.5 and PM₁₀ of 1427 air quality monitoring stations in China in 2016, this paper calculated the annual mean and annual standard deviation of six air quality indicators at each station to obtain 12 variables. Self-Organizing Maps (SOM) and K-means clustering algorithms were carried out based on MATLAB and SPSS Statistics, respectively. Kriging interpolation was used to get the clustering distribution of air quality and fluctuation in China, and Principal Component Analysis (PCA) was used to analyze the main factors affecting the clustering results. The results show that: (1) Most areas in China are low-value regions, while the high-value region is the smallest and more concentrated. Air quality in northern China is worse, and the annual fluctuations of the indicators are more dramatic. (2) Compared with AQI, AQFI has a strong indication significance for the comprehensive situation of air quality and its fluctuation. (3) The spatial distribution of SOM clustering results is more discriminative, while K-means clustering results have a large proportion of low-mean regions. (4) PM_2.5, PM₁₀ and CO are the main pollutants affecting air quality and fluctuation, followed by SO₂, NO₂ and O₃.

Morpho-Agronomic and Biochemical Characterization of Accessions of Tiger Nut (Cyperus esculentus) Grown in the North Temperate Zone of China

Tiger nut (Cyperus esculentus L.) has recently attracted increasing interest from scientific and technological communities because of its potential for serving as additional source of food, oil, and feed. The present study reports morphology and biochemical characterization of 42 tiger nut accessions collected from China and other counties performed in the 2020 and 2021 growing seasons at Nongan, Jilin Province. Assessment of variability of 14 agronomic traits including plant height, maturation, leaf width, tilling number, color, size, and shape: 100-tuber weight showed a wide range of phenotypic variation. The color, size, and shape and maturation of the tubers, as well as the leaf width, were the most distinct characteristics describing variation among the accessions. Compositional analyses of major nutritional components of the tubers reveals that this crop could be a source of high-value proteins, fatty acids, and carbohydrates. Specifically, tiger nut tubers contained high levels of starch, oil, and sugars, and significant amounts of fiber, Ca, P, and Na. Furthermore, the tubers appeared to be a good source of proteins as they contain 16 amino acids, including the essential ones. Amino acid profiles were dominated by aspartic acid followed by glutamic acid, leucine, alanine, and arginine. Overall, these results demonstrated that tiger nut is well adapted to the temperature and light conditions in the north temperate zone of China, even with a shorter growth season. The tiger nut accessions collected here exhibited wide variations for agronomical and biochemical traits, suggesting potential for potential for breeding improvement by maximizing the fresh tuber and grass yield based on the optimal selection of genetic characteristics in climate and soil conditions of northern China.

[Analysis of volatile constituents and chemical relationship of cultivated and wild Angelica dahurica]

OBJECTIVE

To determine the volatile constituents and their contents in the roots of 5 cultivated Angelica dahurica and one wild A. dahurica and analyze the chemical relationship among the plants of A. dahurica.

METHODS

The essential oil was extracted from the roots of 5 cultivated plants of Angelica dahurica and one wild A. dahurica by water steam distillation. Gas chromatography-mass spectrometry (GC-MS) was used to separate and identify all the volatile oil components in the extracts, and their relative contents were calculated with area normalization method. We also conducted clustering analysis and principal component analysis of the volatile oil components.

RESULTS

We identified a total of 81 compounds from the roots of the 6 plants of Angelica dahurica, including 27 in Chuanbaizhi (Angelica dahurica cv. 'Hangbaizhi'), 34 in Hangbaizhi (Angelica dahurica cv. 'Hangbaizhi'), 24 in Qibaizhi (Angelica dahurica cv. 'Qibaizhi'), 32 in Yubaizhi (Angelica dahurica cv.'Qibaizhi'), 28 in Bobahizhi (Angelica dahurica cv.'Qibaizhi'), and 34 in Xinganbaizhi (Angelica dahuirca). These compounds included, in the order of their relative contents (from high to low), alkanes, olefins, esters, organic acids and alcohols. Among the common components found in the roots of all the plants of A. dahurica, nonylcyclopropane, cyclododecane and hexadecanoic acid were identified as the volatile oil components that showed the highest relative contents. Clustering analysis of the volatile oil components showed that wild Angelica dahurica (Xing'anbaizhi) and the 5 cultivated Angelica dahurica (Chuanbaizhi, Hangbaizhi, Qibaizhi, Yubaizhi, Bobaizhi) could be divided into two groups, and the cultivated Angelica dahurica could be divided into two subgroups: Chuanbaizhi, Yubaizhi and Hangbahizhi were clustered in one subgroup, and Qibaizhi and Bobaizhi in another. The results of principal component analysis was consistent with those of clustering analysis.

CONCLUSION

The main volatile oil components and their contents vary among the 6 plants of A. dahurica. Nonylcyclopropane, cyclododecane and hexadecanoic acid are the most abundant volatile oil components in all the plants of A. dahurica, which can be divided into two clusters.

Gestational Age-Related Associations between Early-Life Feeding Trajectories and Growth Outcomes at Term Equivalent Age in Very Preterm Infants

Establishing the different feeding trajectories based on daily enteral feeding data in preterm infants at different gestational ages (GAs), may help to identify the risks and extrauterine growth restriction (EUGR) outcomes associated with the adverse feeding pattern. In a single center, we retrospectively included 625 infants born at 23-30 weeks of gestation who survived to term-equivalent age (TEA) from 2009 to 2020. The infants were designated into three GA groups: 23-26, 27-28, and 29-30 weeks. The daily enteral feeding amounts in the first 56 postnatal days were analyzed to determine the feeding trajectories. The primary outcomes were EUGR in body weight and head circumference calculated, respectively, by the changes between birth and TEA. Clustering analysis identified two feeding trajectories, namely the improving and adverse patterns in each GA group. The adverse feeding pattern that occurred in 49%, 20%, and 17% of GA 23-26, 27-28, and 29-30 weeks, respectively, was differentiated from the improving feeding pattern as early as day 7 in infants at GA 23-26 and 27-28 weeks, in contrast to day 21 in infants at GA 29-30 weeks. The adverse feeding patterns were associated with sepsis, respiratory, and gastrointestinal morbidities at GA 23-26 weeks; sepsis, hemodynamic and gastrointestinal morbidities at GA 27-28 weeks; and preeclampsia, respiratory, and gastrointestinal morbidities at GA 29-30 weeks. Using the improving feeding group as a reference, the adverse feeding group showed significantly higher adjusted odds ratios of EUGR in body weight and head circumference in infants at GA 23-26 and 27-28 weeks. Identifying the early-life adverse feeding trajectories may help recognize the related EUGR outcomes of preterm infants in a GA-related manner.

Variability in Physical Inactivity Responses of University Students during COVID-19 Pandemic: A Monitoring of Daily Step Counts Using a Smartphone Application

This study investigated the changes in physical inactivity of university students during the COVID-19 pandemic, with reference to their academic calendar. We used the daily step counts recorded by a smartphone application (iPhone Health App) from April 2020 to January 2021 (287 days) for 603 participants. The data for 287 days were divided into five periods based on their academic calendar. The median value of daily step counts across each period was calculated. A k-means clustering analysis was performed to classify the 603 participants into subgroups to demonstrate the variability in the physical inactivity responses. The median daily step counts, with a 7-day moving average, dramatically decreased from 5000 to 2000 steps/day in early April. It remained at a lower level (less than 2000 steps/day) during the first semester, then increased to more than 5000 steps/day at the start of summer vacation. The clustering analysis demonstrated the variability in physical inactivity responses. The inactive students did not recover daily step counts throughout the year. Consequently, promoting physical activity is recommended for inactive university students over the course of the whole semester.

Identification of Breast Cancer Immune Subtypes by Analyzing Bulk Tumor and Single Cell Transcriptomes

Background: The histological and molecular classification of breast cancer (BC) is being used in the clinical management of this disease. However, subtyping of BC based on the tumor immune microenvironment (TIME) remains insufficiently explored, although such investigation may provide new insights into intratumor heterogeneity in BC and potential clinical implications for BC immunotherapy.

Methods: Based on the enrichment scores of 28 immune cell types, we performed clustering analysis of transcriptomic data to identify immune-specific subtypes of BC using six different datasets, including five bulk tumor datasets and one single-cell dataset. We further analyzed the molecular and clinical features of these subtypes.

Results: Consistently in the six datasets, we identified three BC subtypes: BC-ImH, BC-ImM, and BC-ImL, which had high, medium, and low immune signature scores, respectively. BC-ImH displayed a significantly better survival prognosis than BC-ImL. Triple-negative BC (TNBC) and human epidermal growth factor receptor-2-positive (HER2+) BC were likely to have the highest proportion in BC-ImH and the lowest proportion in BC-ImL. In contrast, hormone receptor-positive (HR+) BC had the highest proportion in BC-ImL and the lowest proportion in BC-ImH. Furthermore, BC-ImH had the highest tumor mutation burden (TMB) and predicted neoantigens, while BC-ImL had the highest somatic copy number alteration (SCNA) scores. It is consistent with that TMB and SCNA correlate positively and negatively with anti-tumor immune response, respectively. TP53 had the highest mutation rate in BC-ImH and the lowest mutation rate in BC-ImL, supporting that TP53 mutations promote anti-tumor immune response in BC. In contrast, PIK3CA displayed the highest mutation rate in BC-ImM, while GATA3 had the highest mutation rate in BC-ImL. Besides immune pathways, many oncogenic pathways were upregulated in BC-ImH, including ErbB, MAPK, VEGF, and Wnt signaling pathways; the activities of these pathways correlated positively with immune signature scores in BC.

Conclusions: The tumors with the strong immune response (“hot” tumors) have better clinical outcomes than the tumors with the weak immune response (“cold” tumors) in BC. TNBC and HER2+ BC are more immunogenic, while HR + BC is less immunogenic. Certain HER2+ or HR + BC patients could be propitious to immunotherapy in addition to TNBC.

Clustering Analysis and Machine Learning algorithms in the prediction of dietary patterns: Cross-sectional results of the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil)

BACKGROUND

Machine learning investigates how computers can automatically learn. This study aimed to predict dietary patterns and compare algorithm performance in making predictions of dietary patterns.

METHODS

We analyzed the data of public employees (n=12,667) participating in the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil). The K-means clustering algorithm and six other classifiers (support vector machines, naïve Bayes, K-nearest neighbors, decision tree, random forest, and xgboost) were used to predict the dietary patterns.

RESULTS

K-means clustering identified two dietary patterns. Cluster 1, labeled the Western pattern, was characterized by a higher energy intake and consumption of refined cereals, beans and other legumes, tubers, pasta, processed and red meats, high-fat milk and dairy products, and sugary beverages; Cluster 2, labeled the Prudent pattern, was characterized by higher intakes of fruit, vegetables, whole cereals, white meats, and milk and reduced-fat milk derivatives. The most important predictors were age, sex, per capita income, education level, and physical activity. The accuracy of the models varied from moderate to good (69-72%).

CONCLUSIONS

The algorithms' performance in dietary pattern prediction was similar, and the models presented may provide support in screener tasks and guide health professionals in the analysis of dietary data. This article is protected by copyright. All rights reserved.

Factors affecting driver injury severity in fatigue and drowsiness accidents: a data mining framework

BACKGROUND

Fatigue and drowsiness accidents are more likely to cause serious injuries and fatalities than other accidents. Statistics revealed that 20 to 40 percent of traffic accidents in Iran are due to drivers' fatigue. This study identified the most important factors affecting driver injuries in fatigue and drowsiness accidents.

METHODS

The Classification and Regression Tree method (CART) was applied 11,392 drivers were in-volved in fatigue and drowsiness accidents in three provinces of Iran, over the 7 years from 2011-2018. A two-level target variable was used to increase the accuracy of the model. First, dataset in each of three provinces was classified into homogeneous clusters using a two-step clus-tering algorithm. Oversampling method was used for imbalanced accident severity datasets. Then, classification was improved by boosting method.

RESULTS

The classification tree reveals that the month, time of day, collision type, and vehicle type were common factors. Also, driver's age was important in female drivers cluster; the geometry of the place and seat belt/helmet usage were important in urban roads cluster; and area type, road type, road direction, and vehicle factor were important in rural roads cluster. Also, the combination of the CART algorithm with oversampling and boosting increased the accuracy of the models.

CONCLUSIONS

The analysis results revealed motorcycles, lack of using a helmet or seat belt, curvy roads, roads with two-way undivided and one-way movement direction increased the injury and death of drivers. Collision with fixed object, run-off-road, overturning, falling, and defective vehicles increased the severity of accidents. Female drivers older than 44 years old have a higher probability of fatality. Identifying the factors affecting the severity of driver injuries in such accidents in each province could assist in determining engineering countermeasures and training educational programs to mitigate these crash severities.

Physical and pulmonary capacities of individuals with severe coronavirus disease after hospital discharge: A preliminary cross-sectional study based on cluster analysis

OBJECTIVE

This study aimed to analyze the physical and pulmonary capacities of hospitalized patients with severe coronavirus disease and its correlation with the time of hospitalization and complications involved.

METHODS

A total of 54 patients, aged ≥18 years of both sexes, were evaluated 2-4 months after hospital discharge in São Paulo, Brazil. The physical characteristics analyzed were muscle strength, balance, flexibility, and pulmonary function. The K-means cluster algorithm was used to identify patients with similar physical and pulmonary capacities, related to the time of hospitalization.

RESULTS

Two clusters were derived using the K-means algorithm. Patients allocated in cluster 1 had fewer days of hospitalization, intensive care, and intubation than those in cluster 2, which reflected a better physical performance, strength, balance, and pulmonary condition, even 2-4 months after discharge. Days of hospitalization were inversely related to muscle strength, physical performance, and lung function: hand grip D (r=-0.28, p=0.04), Short Physical Performance Battery score (r=-0.28, p=0.03), and forced vital capacity (r=-0.29, p=0.03).

CONCLUSION

Patients with a longer hospitalization time and complications progressed with greater loss of physical and pulmonary capacities.

Proteomic analysis of metabolic mechanisms associated with fatty acid biosynthesis during Styrax tonkinensis kernel development

BACKGROUND

Styrax tonkinensis is a white-flowered tree with considerable potential as a feedstock source for biodiesel production from the oily seed contained within its nutlike drupes. Transcriptome changes during oil accumulation have been previously reported, but not concurrent changes in the proteome.

RESULTS

Using proteomic analysis of samples collected at 50, 70, 100 and 130 days after flowering (DAF), we identified 1472 differentially expressed proteins (DEPs). Based on their expression patterns, we grouped the DEPs into nine clusters and analyzed the pathway enrichment. Proteins related to starch and sucrose metabolism were most abundant at 50 DAF. Proteins involved in fatty acid (FA) biosynthesis were mainly grouped into a cluster that peaked at 70 DAF. Proteins related to protein processing in endoplasmic reticulum had two major patterns, trending either upwards or downwards, while proteins involved in amino acid biosynthesis showed more complex relationships. We identified 42 key enzymes involved in lipid accumulation during kernel development, including the acetyl-CoA carboxylase complex (ACC) and the pyruvate dehydrogenase complex (PDC). One oil body membrane protein, oleosin, continuously increased during kernel development.

CONCLUSION

A regulatory network of oil accumulation processes was built based on protein and available transcriptome expression data, which were in good temporal agreement. This analysis placed ACC and PDC in the center of the network, suggesting that the glycolytic provision of substrate plays a central regulatory role in FA biosynthesis and oil accumulation. © 2021 Society of Chemical Industry.

A Semi-Automated SNP-Based Approach for Contaminant Identification in Biparental Polyploid Populations of Tropical Forage Grasses

Artificial hybridization plays a fundamental role in plant breeding programs since it generates new genotypic combinations that can result in desirable phenotypes. Depending on the species and mode of reproduction, controlled crosses may be challenging, and contaminating individuals can be introduced accidentally. In this context, the identification of such contaminants is important to avoid compromising further selection cycles, as well as genetic and genomic studies. The main objective of this work was to propose an automated multivariate methodology for the detection and classification of putative contaminants, including apomictic clones (ACs), self-fertilized individuals, half-siblings (HSs), and full contaminants (FCs), in biparental polyploid progenies of tropical forage grasses. We established a pipeline to identify contaminants in genotyping-by-sequencing (GBS) data encoded as allele dosages of single nucleotide polymorphism (SNP) markers by integrating principal component analysis (PCA), genotypic analysis (GA) measures based on Mendelian segregation, and clustering analysis (CA). The combination of these methods allowed for the correct identification of all contaminants in all simulated progenies and the detection of putative contaminants in three real progenies of tropical forage grasses, providing an easy and promising methodology for the identification of contaminants in biparental progenies of tetraploid and hexaploid species. The proposed pipeline was made available through the polyCID Shiny app and can be easily coupled with traditional genetic approaches, such as linkage map construction, thereby increasing the efficiency of breeding programs.