Uniform Manifold Approximation Research Articles

Single-nucleus analysis reveals microenvironment-specific neuron and glial cell enrichment in Alzheimer’s disease

BackgroundAlzheimer’s disease (AD) is a complicated neurodegenerative disease. Neuron-glial cell interactions are an important but not fully understood process in the progression of AD. We used bioinformatic methods to analyze single-nucleus RNA sequencing (snRNA-seq) data to investigate the cellular and molecular biological processes of AD.MethodsnRNA-seq data were downloaded from Gene Expression Omnibus (GEO) datasets and reprocessed to identify 240,804 single nuclei from healthy controls and patients with AD. The cellular composition of AD was further explored using Uniform Manifold Approximation and Projection (UMAP). Enrichment analysis for the functions of the DEGs was conducted and cell development trajectory analyses were used to reveal underlying cell fate decisions. iTALK was performed to identify ligand-receptor pairs among various cell types in the pathological ecological microenvironment of AD.ResultsSix cell types and multiple subclusters were identified based on the snRNA-seq data. A subcluster of neuron and glial cells co-expressing lncRNA-SNHG14, myocardin-related transcription factor A (MRTFA), and MRTFB was found to be more abundant in the AD group. This subcluster was enriched in mitogen-activated protein kinase (MAPK)-, immune-, and apoptosis-related pathways. Through molecular docking, we found that lncRNA-SNHG14 may bind MRTFA and MRTFB, resulting in an interaction between neurons and glial cells.ConclusionsThe findings of this study describe a regulatory relationship between lncRNA-SNHG14, MRTFA, and MRTFB in the six main cell types of AD. This relationship may contribute to microenvironment remodeling in AD and provide a theoretical basis for a more in-depth analysis of AD.

Explorations about the correlation between biological changes of meninges in periodontitis mice and cognitive impairment via single-cell RNA sequencing

Objective: To clarify the potential correlation between biological changes of meninges in periodontitis mice and cognitive impairment by analyzing the biological changes of meninges in periodontitis mice using single-cell RNA sequencing. Methods: Thirty C57BL/6 mice were divided into two groups by using random number table method (15 mice in each group). Mice in the control group were locally administered 2% carboxyl methyl cellulose (CMC) without Porphyromonas gingivalis (Pg) on both buccal sides. A mixture of Pg and 2% CMC was applied on both buccal sides in the experimental group mice three times a week, lasting for 16 weeks in total. The absorption of alveolar bone, locomotor activity and cognitive function, the activation of microglia and astrocytes in the cortex were observed and assessed. The mRNA expression levels of Occludin in meninges and brain were detected in two groups. Single-cell RNA sequencing data of meninges were processed by uniform manifold approximation and projection (UMAP). Differential genes expressions of endothelial cells were processed by gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. In addition, real-time fluorescence quantitative PCR (RT-qPCR) was used to verify the expressions of Atf3 and Apold1. Results: Methylene blue staining found the distances of buccal and palatal cement-enamel junction-alveolar bone crest (CEJ-ABC) in experimental mice [(185.60±17.60), (206.90±13.37) μm] increased significantly compared with the control group [(135.33±9.57), (163.05±14.98) μm] (t=5.02, P=0.002; t=4.37, P=0.005). Open field experiment showed the total distance and average speed of mice in the experimental group [(971.88±164.57) cm; (3.25±0.55) cm/s] were not statistically significant compared with the control group [(914.24±278.81) cm; (3.05±0.93) cm/s] (t=0.65, P=0.525; t=0.65, P=0.520). The recognition index of the experimental group [(48.02±16.92) %] was lower than the control group [(66.27±17.90) %] (t=2.40, P=0.027) by novel object recognition tests. Compared with the control group [(63.56±11.88) %], the alternation of experimental group [(50.99±14.17) %] was significantly decreased in Y maze tests (t=2.33, P=0.030). Immunohistochemistry results showed microglia and astrocytes were activated in the cortex of experimental mice. Compared with the control group (1.02±0.25, 1.04±0.31), the relative mRNA expressions of Occludin decreased significantly in the meninges and brain of periodontitis mice, respectively (0.61±0.10, 0.64±0.20) (t=3.47, P=0.010; t=2.66, P=0.024). By single-cell RNA sequencing, meninges cells were divided into 11 types, such as endothelial cells, fibroblasts, immune cells and so on. Endothelial cells were the main cell types in meninges [the control group: 26.47% (1 589/6 004), the experimental group: 26.26% (807/3 073)]. Compared with the control group [5.56% (334/6 004)], the percentage of granulocytes increased in the periodontitis mice [11.65% (358/3 073)]. Using clustering analysis to further focus on endothelial cells, GO enrichment analysis revealed differential genes were mainly related to angiogenesis, cell adhesion, apoptosis and so on. KEGG enrichment analysis revealed that differential genes were related to signaling pathways of interleukin-17, relaxin and so on. The relative mRNA expressions of Atf3 and Apold1 in meninges of periodontitis mice (0.42±0.24, 0.54±0.27) were significantly lower than the control group (1.03±0.26, 1.02±0.23) (t=3.88, P=0.005; t=3.02, P=0.017). Conclusions: The mice chronically infected with W83 occurred memory impairment, neuroinflammation and changes of barrier function. In the meninges of periodontitis mice, there were infiltration of immune cells and down-regulation expressions of Atf3 and Apold1 by single-cell RNA sequencing. Meningeal immunity and changes of barrier function may play an important role in the cognitive impairment caused by periodontitis.

A predictive framework for PEMFC dynamic load performance degradation based on feature parameter analysis

Accurate and swift estimation of the future performance degradation of Proton Exchange Membrane Fuel Cells (PEMFCs) is crucial for the health management of PEMFC. However, most existing studies primarily concentrate on uncovering the inherent patterns of performance degradation parameters, neglecting the fundamental relationships and pattern variations between performance degradation and other characteristic parameters (such as flow rate, temperature, and pressure). This paper seeks to address this research gap. To that end, we delve into feature parameters, extracting underlying patterns of feature evolution to construct a feature exploration-based framework for predicting PEMFC performance degradation. Initially, Time-Varying Filtered Empirical Mode Decomposition (TVF-EMD) is employed to decompose each feature variable, isolating the core information of the features. Furthermore, to reduce the computational burden caused by redundant features, Uniform Manifold Approximation and Projection (UMAP) is used to perform dimensionality reduction on the decomposed feature subsequences, ensuring the efficiency and representativeness of the extracted features. Finally, to overcome the shortcomings of long short-term memory networks (LSTM) in terms of convergence and the effective use of time series information, we introduce an innovative Mogrifier LSTM (M-LSTM) model to enhance the accuracy of PEMFC performance degradation predictions. The experimental and validation results demonstrate that the extraction of feature parameters significantly enhances the predictive precision of the baseline model. Relative to the compared models, there was an improvement of at least 16.859% in Root Mean Square Error (RMSE), a minimum increase of 23.333% in Mean Absolute Error (MAE), and a boost of no less than 0.913% in the coefficient of determination (R2). Simultaneously, the proposed predictive framework demonstrates good stability and accuracy in predicting PEMFC performance degradation.

Exploring mid‐infrared spectral transfer functions for the prediction of multiple soil properties using a global dataset

AbstractInfrared spectroscopy is increasingly being adopted as a technology for soil analysis. However, laboratories worldwide are equipped with different infrared spectrometers, leading to variations that hinder the global application of soil spectroscopy. This study evaluates the transferability of soil spectra from a global dataset collected using four mid‐infrared spectrometers. To evaluate the efficacy of five spectral transfer functions (direct standardization, piecewise direct standardization, spectral space transformation [SST], principal components‐canonical correlation analysis [PC‐CCA], and domain‐invariant partial least square [DIPLS] regression), two datasets were used: dataset A (n = 224; standardized samples) was scanned using one primary spectrometer and three secondary spectrometers; dataset B (n = 1904; legacy samples) was scanned only using the primary spectrometer. The first set of chemometrics models was developed using dataset A to compare the performance of different spectrometers. The second set of models was developed using dataset B to evaluate the effectiveness of spectral transfer functions. Both models were developed using partial least squares regression. Spectral transfer functions developed using dataset A indicate that the PC‐CCA method was the best in converging spectra collected from four instruments into a similar space projected using Uniform Manifold Approximation and Projection. Spectral transfer did not result in consistent improvement in the prediction of soil properties compared to the direct use of spectra collected from different spectrometers. These findings carry significant implications for the utilization of legacy models, enabling laboratories to concentrate on acquiring new samples and spectral measurements using established protocols without the need for spectral transfer.

Subcellular Feature-Based Classification of α and β Cells Using Soft X-ray Tomography

The dysfunction of α and β cells in pancreatic islets can lead to diabetes. Many questions remain on the subcellular organization of islet cells during the progression of disease. Existing three-dimensional cellular mapping approaches face challenges such as time-intensive sample sectioning and subjective cellular identification. To address these challenges, we have developed a subcellular feature-based classification approach, which allows us to identify α and β cells and quantify their subcellular structural characteristics using soft X-ray tomography (SXT). We observed significant differences in whole-cell morphological and organelle statistics between the two cell types. Additionally, we characterize subtle biophysical differences between individual insulin and glucagon vesicles by analyzing vesicle size and molecular density distributions, which were not previously possible using other methods. These sub-vesicular parameters enable us to predict cell types systematically using supervised machine learning. We also visualize distinct vesicle and cell subtypes using Uniform Manifold Approximation and Projection (UMAP) embeddings, which provides us with an innovative approach to explore structural heterogeneity in islet cells. This methodology presents an innovative approach for tracking biologically meaningful heterogeneity in cells that can be applied to any cellular system.

Visualization strategies to aid interpretation of high-dimensional genotoxicity data.

This article describes a range of high-dimensional data visualization strategies that we have explored for their ability to complement machine learning algorithm predictions derived from MultiFlow® assay results. For this exercise, we focused on seven biomarker responses resulting from the exposure of TK6 cells to each of 126 diverse chemicals over a range of concentrations. Obviously, challenges associated with visualizing seven biomarker responses were further complicated whenever there was a desire to represent the entire 126 chemical data set as opposed to results from a single chemical. Scatter plots, spider plots, parallel coordinate plots, hierarchical clustering, principal component analysis, toxicological prioritization index, multidimensional scaling, t-distributed stochastic neighbor embedding, and uniform manifold approximation and projection are each considered in turn. Our report provides a comparative analysis of these techniques. In an era where multiplexed assays and machine learning algorithms are becoming the norm, stakeholders should find some of these visualization strategies useful for efficiently and effectively interpreting their high-dimensional data.

Vocal complexity in the long calls of Bornean orangutans.

Vocal complexity is central to many evolutionary hypotheses about animal communication. Yet, quantifying and comparing complexity remains a challenge, particularly when vocal types are highly graded. Male Bornean orangutans (Pongo pygmaeus wurmbii) produce complex and variable "long call" vocalizations comprising multiple sound types that vary within and among individuals. Previous studies described six distinct call (or pulse) types within these complex vocalizations, but none quantified their discreteness or the ability of human observers to reliably classify them. We studied the long calls of 13 individuals to: (1) evaluate and quantify the reliability of audio-visual classification by three well-trained observers, (2) distinguish among call types using supervised classification and unsupervised clustering, and (3) compare the performance of different feature sets. Using 46 acoustic features, we used machine learning (i.e., support vector machines, affinity propagation, and fuzzy c-means) to identify call types and assess their discreteness. We additionally used Uniform Manifold Approximation and Projection (UMAP) to visualize the separation of pulses using both extracted features and spectrogram representations. Supervised approaches showed low inter-observer reliability and poor classification accuracy, indicating that pulse types were not discrete. We propose an updated pulse classification approach that is highly reproducible across observers and exhibits strong classification accuracy using support vector machines. Although the low number of call types suggests long calls are fairly simple, the continuous gradation of sounds seems to greatly boost the complexity of this system. This work responds to calls for more quantitative research to define call types and quantify gradedness in animal vocal systems and highlights the need for a more comprehensive framework for studying vocal complexity vis-à-vis graded repertoires.

Identifying Novel Subtypes of Functional Gastrointestinal Disorder by Analyzing Nonlinear Structure in Integrative Biopsychosocial Questionnaire Data.

Background/Objectives: Given the limited success in treating functional gastrointestinal disorders (FGIDs) through conventional methods, there is a pressing need for tailored treatments that account for the heterogeneity and biopsychosocial factors associated with FGIDs. Here, we considered the potential of novel subtypes of FGIDs based on biopsychosocial information. Methods: We collected data from 198 FGID patients utilizing an integrative approach that included the traditional Korean medicine diagnosis questionnaire for digestive symptoms (KM), as well as the 36-item Short Form Health Survey (SF-36), alongside the conventional Rome-criteria-based Korean Bowel Disease Questionnaire (K-BDQ). Multivariate analyses were conducted to assess whether KM or SF-36 provided additional information beyond the K-BDQ and its statistical relevance to symptom severity. Questions related to symptom severity were selected using an extremely randomized trees (ERT) regressor to develop an integrative questionnaire. For the identification of novel subtypes, Uniform Manifold Approximation and Projection and spectral clustering were used for nonlinear dimensionality reduction and clustering, respectively. The validity of the clusters was assessed using certain metrics, such as trustworthiness, silhouette coefficient, and accordance rate. An ERT classifier was employed to further validate the clustered result. Results: The multivariate analyses revealed that SF-36 and KM supplemented the psychosocial aspects lacking in K-BDQ. Through the application of nonlinear clustering using the integrative questionnaire data, four subtypes of FGID were identified: mild, severe, mind-symptom predominance, and body-symptom predominance. Conclusions: The identification of these subtypes offers a framework for personalized treatment strategies, thus potentially enhancing therapeutic outcomes by tailoring interventions to the unique biopsychosocial profiles of FGID patients.

Characterizing Healthy Dietary Practices in Japan: Insights from a 2024 Nationwide Survey and Cluster Analysis.

The increasing burden of lifestyle-related diseases highlights the need to address unhealthy dietary habits. This study aims to explore the latest dietary patterns in Japan following the COVID-19 pandemic, focusing on trends in health-promoting food choices. A web-based survey was conducted among 27,154 Japanese adults, selected via quota sampling to mirror national demographics. The study evaluated dietary diversity, measured through the Dietary Variety Score (Outcome 1), and the prioritization of nutritional and health considerations in food selection, assessed via a Likert scale (Outcome 2). Uniform Manifold Approximation and Projection (UMAP) and Ordering Points To Identify the Clustering Structure (OPTICS) algorithms were used to delineate patterns in health-centric food selections. OPTICS clustering revealed four distinct clusters for each outcome. Cluster 3, with a diverse diet, comprised older, predominantly female individuals with higher well-being and lower social isolation compared to Cluster 4, which lacked distinct dietary patterns. Cluster 3 also engaged more in snacking, treat foods, home cooking, and frozen meals. Similarly, a divide emerged between those prioritizing dietary considerations (Cluster C) and those indifferent to such aspects (Cluster D). The findings underscore the need for holistic post-COVID-19 public health initiatives addressing socioeconomic and cultural barriers to healthier dietary practices.

Using single-cell RNA sequencing and bulk RNA sequencing data to reveal a correlation between smoking and neutrophil activation in esophageal carcinoma patients.

Cigarette smoking is considered as a major risk factor for esophageal carcinoma (ESCA) patients. Neutrophil activation plays a key role in cancer development and progression. However, the relationship between cigarette smoking and neutrophils in ESCA patients remained unclear. Single-cell RNA sequencing (scRNA-seq) and bulk RNA sequencing data were obtained from public databases. Uniform manifold approximation and projection (UMAP) was used to perform downscaling and clustering based on scRNA-seq data. The module genes associated with smoking in ESCA patients were filtered by weighted gene co-expression network analysis (WGCNA). Using the "AUCell" package, the enrichment of different cell subpopulations and gene collections were assessed. "CellChat" and "CellphoneDB" were used to infer the probability and significance of ligand-receptor interactions between different cell subpopulations. WGCNA was performed to screened module genes associated with smoking in ESCA patients from MEdarkquosie, MEturquoise, and MEgreenyellow. Next, eight cell clusters were identified, and using the AUCell score, we determined that neutrophil clusters were more active in the gene modules associated with smoking in ESCA patients. Two neutrophil subtypes, Neutrophils 1 and Neutrophils 2, exhibited greater enrichment in inflammatory response regulation, intercellular adhesion, and regulation of T cell activation. Furthermore, we found that neutrophils may pass through AMPT-(ITGA5 + ITGB1) and ICAM1-AREG in order to promote the development of ESCA, and that the expression levels of the receptor genes insulin-degrading enzyme and ITGB1 were significantly and positively correlated with cigarette smoking per day. Combining smoking-related gene modules and scRNA-seq, the current findings revealed the heterogeneity of neutrophils in ESCA and a tumor-promoting role of neutrophils in the tumor microenvironment of smoking ESCA patients.

Exploring UMAP in hybrid models of entropy-based and representativeness sampling for active learning in biomedical segmentation

In this work, we study various hybrid models of entropy-based and representativeness sampling techniques in the context of active learning in medical segmentation, in particular examining the role of UMAP (Uniform Manifold Approximation and Projection) as a technique for capturing representativeness. Although UMAP has been shown viable as a general purpose dimension reduction method in diverse areas, its role in deep learning-based medical segmentation has yet been extensively explored. Using the cardiac and prostate datasets in the Medical Segmentation Decathlon for validation, we found that a novel hybrid combination of Entropy-UMAP sampling technique achieved a statistically significant Dice score advantage over the random baseline (3.2% for cardiac, 4.5% for prostate), and attained the highest Dice coefficient among the spectrum of 10 distinct active learning methodologies we examined. This provides preliminary evidence that there is an interesting synergy between entropy-based and UMAP methods when the former precedes the latter in a hybrid model of active learning.

Exercise Mitigates Angiotensin II-Mediated Sympathetic Nerve-Macrophage Interaction in Aortic Adventitia Leading to Vascular Remodeling

Large artery stiffness is a significant risk factor for cardiovascular diseases. Our previous study suggested that angiotensin II (Ang II) stimulates sympathetic innervation in aortic adventitia (AA) leading to elevated mean arterial blood pressure and increased arterial fibrosis, while voluntary wheel-running (VWR) reduces them. The aim of this study is to explore mechanism under Ang II-induced the aortic remodeling through sympathetic nerves and immune cell interactions using transcriptomics. We isolated cells in AA from Ang II (1.5 mg/kg/day) vs. Vehicle (Saline) infused C57BL/6 male mice (n=3 of each group), and we performed single-cell RNA-sequencing (scRNAseq). We profiled 23,419 cells after quality control and filtering steps and annotated 9 cell types in the AA employing a uniform manifold approximation and projection (UMAP) to visualize the remaining cells and the Leiden cluster algorithm. Firstly, we identified the Adrb2 gene for β2-adrenergic receptor (β2-ADR) was predominantly expressed in macrophages/monocytes as compared to other cell types, and significantly more expressed in macrophages vs. monocytes (adjusted p-value, <0.05). We validated the transcriptomic results using immunostaining for β2-ADR and synapsin with CD68 in AA. In addition, Ang II increased AA macrophage proportion which displays M1 phenotype (pro-inflammatory) (adjusted p-value, <0.05). These findings suggest that Ang II promotes M1 like macrophages which interact with sympathetic nerves in AA. Next, we performed Gene Ontology (GO) enrichment test in macrophages and fibroblasts. Ang II Macrophages were enriched in GO terms with inflammation and cytokines, and Ang II fibroblasts were enriched in GO terms involved in extracellular matrix deposition. The GO results indicate that Ang II induced inflammatory macrophages activates fibroblasts to accumulate ECM deposition in AA. Congruent with the scRNAseq results, we found that number of macrophages, ECM accumulation, and vascular stiffness measured by pulse wave velocity were increased with Ang II vs. Vehicle (all, p <0.05), whereas interestingly VWR ameliorated the Ang II effects. This study provides the evidence that Ang II promotes aortic remodeling by interaction between sympathetic nerves and macrophage which results in ECM deposition in AA using transcriptomics approach, while VWR countermeasure to these detrimental effects. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

K-nearest-neighbors induced topological PCA for single cell RNA-sequence data analysis

Single-cell RNA sequencing (scRNA-seq) is widely used to reveal heterogeneity in cells, which has given us insights into cell–cell communication, cell differentiation, and differential gene expression. However, analyzing scRNA-seq data is a challenge due to sparsity and the large number of genes involved. Therefore, dimensionality reduction and feature selection are important for removing spurious signals and enhancing downstream analysis. Traditional PCA, a main workhorse in dimensionality reduction, lacks the ability to capture geometrical structure information embedded in the data, and previous graph Laplacian regularizations are limited by the analysis of only a single scale. We propose a topological Principal Components Analysis (tPCA) method by the combination of persistent Laplacian (PL) technique and L2,1 norm regularization to address multiscale and multiclass heterogeneity issues in data. We further introduce a k-Nearest-Neighbor (kNN) persistent Laplacian technique to improve the robustness of our persistent Laplacian method. The proposed kNN-PL is a new algebraic topology technique which addresses the many limitations of the traditional persistent homology. Rather than inducing filtration via the varying of a distance threshold, we introduced kNN-tPCA, where filtrations are achieved by varying the number of neighbors in a kNN network at each step, and find that this framework has significant implications for hyper-parameter tuning. We validate the efficacy of our proposed tPCA and kNN-tPCA methods on 11 diverse benchmark scRNA-seq datasets, and showcase that our methods outperform other unsupervised PCA enhancements from the literature, as well as popular Uniform Manifold Approximation (UMAP), t-Distributed Stochastic Neighbor Embedding (tSNE), and Projection Non-Negative Matrix Factorization (NMF) by significant margins. For example, tPCA provides up to 628%, 78%, and 149% improvements to UMAP, tSNE, and NMF, respectively on classification in the F1 metric, and kNN-tPCA offers 53%, 63%, and 32% improvements to UMAP, tSNE, and NMF, respectively on clustering in the ARI metric.

Comparing Open-Access Database and Traditional Intensive Care Studies Using Machine Learning: Bibliometric Analysis Study.

Intensive care research has predominantly relied on conventional methods like randomized controlled trials. However, the increasing popularity of open-access, free databases in the past decade has opened new avenues for research, offering fresh insights. Leveraging machine learning (ML) techniques enables the analysis of trends in a vast number of studies. This study aims to conduct a comprehensive bibliometric analysis using ML to compare trends and research topics in traditional intensive care unit (ICU) studies and those done with open-access databases (OADs). We used ML for the analysis of publications in the Web of Science database in this study. Articles were categorized into "OAD" and "traditional intensive care" (TIC) studies. OAD studies were included in the Medical Information Mart for Intensive Care (MIMIC), eICU Collaborative Research Database (eICU-CRD), Amsterdam University Medical Centers Database (AmsterdamUMCdb), High Time Resolution ICU Dataset (HiRID), and Pediatric Intensive Care database. TIC studies included all other intensive care studies. Uniform manifold approximation and projection was used to visualize the corpus distribution. The BERTopic technique was used to generate 30 topic-unique identification numbers and to categorize topics into 22 topic families. A total of 227,893 records were extracted. After exclusions, 145,426 articles were identified as TIC and 1301 articles as OAD studies. TIC studies experienced exponential growth over the last 2 decades, culminating in a peak of 16,378 articles in 2021, while OAD studies demonstrated a consistent upsurge since 2018. Sepsis, ventilation-related research, and pediatric intensive care were the most frequently discussed topics. TIC studies exhibited broader coverage than OAD studies, suggesting a more extensive research scope. This study analyzed ICU research, providing valuable insights from a large number of publications. OAD studies complement TIC studies, focusing on predictive modeling, while TIC studies capture essential qualitative information. Integrating both approaches in a complementary manner is the future direction for ICU research. Additionally, natural language processing techniques offer a transformative alternative for literature review and bibliometric analysis.

Proteomics-Derived Biomarker Panel Facilitates Distinguishing Primary Lung Adenocarcinomas With Intestinal or Mucinous Differentiation From Lung Metastatic Colorectal Cancer

The diagnosis of primary lung adenocarcinomas with intestinal or mucinous differentiation (PAIM) remains challenging due to the overlapping histomorphological, immunohistochemical (IHC), and genetic characteristics with lung metastatic colorectal cancer (lmCRC). This study aimed to explore the protein biomarkers that could distinguish between PAIM and lmCRC. To uncover differences between the two diseases, we used tandem mass tagging–based shotgun proteomics to characterize proteomes of formalin-fixed, paraffin-embedded tumor samples of PAIM (n = 22) and lmCRC (n = 17).Then three machine learning algorithms, namely support vector machine (SVM), random forest, and the Least Absolute Shrinkage and Selection Operator, were utilized to select protein features with diagnostic significance. These candidate proteins were further validated in an independent cohort (PAIM, n = 11; lmCRC, n = 19) by IHC to confirm their diagnostic performance. In total, 105 proteins out of 7871 proteins were significantly dysregulated between PAIM and lmCRC samples and well-separated two groups by Uniform Manifold Approximation and Projection. The upregulated proteins in PAIM were involved in actin cytoskeleton organization, platelet degranulation, and regulation of leukocyte chemotaxis, while downregulated ones were involved in mitochondrial transmembrane transport, vasculature development, and stem cell proliferation. A set of ten candidate proteins (high-level expression in lmCRC: CDH17, ATP1B3, GLB1, OXNAD1, LYST, FABP1; high-level expression in PAIM: CK7 (an established marker), NARR, MLPH, S100A14) was ultimately selected to distinguish PAIM from lmCRC by machine learning algorithms. We further confirmed using IHC that the five protein biomarkers including CDH17, CK7, MLPH, FABP1 and NARR were effective biomarkers for distinguishing PAIM from lmCRC. Our study depicts PAIM-specific proteomic characteristics and demonstrates the potential utility of new protein biomarkers for the differential diagnosis of PAIM and lmCRC. These findings may contribute to improving the diagnostic accuracy and guide appropriate treatments for these patients.

Anomaly detection of wind turbine based on norm-linear-ConvNeXt-TCN

The supervisory control and data acquisition (SCADA) system of wind turbines continuously collects a large amount of monitoring data during their operation. These data contain abundant information about the operating status of the turbine components. Utilizing this information makes it feasible to provide early warnings and predict the health status of the wind turbine. However, due to the strong coupling between the various components of the wind turbine, the data exhibits complex spatiotemporal relationships, multiple state parameters, strong non-linearity, and noise interference, which brings great difficulty to anomaly detection of the wind turbine. This paper proposes a new method for detecting abnormal operating conditions of wind turbines, based on a cleverly designed multi-layer linear residual module and the improved temporal convolutional network (TCN) with a new norm-linear-ConvNeXt architecture (NLC-TCN). Initially, the NLC-TCN deep learning reconstruction model is trained with historical data of normal behavior to extract the spatiotemporal features of state parameters under normal operational conditions. Subsequently, the condition score of the unit is determined by calculating the average normalized root mean square error between the reconstructed data and actual data. The streaming peaks-over-threshold real-time calculation of the anomaly warning threshold, based on extreme value theory, is then used for preliminary fault monitoring. Moreover, by shielding the fault alarm for low wind speeds and implementing a continuous delay perception mechanism, issues related to wind speed fluctuations and internal and external interference are addressed, enabling early warning for faulty units. Finally, the effectiveness and reliability of the proposed method are validated through comparative experiments using actual offshore wind farm SCADA data. The performance of the proposed method surpasses that of other compared methods. Additionally, the results of the proposed method were evaluated using the uniform manifold approximation and projection dimensionality reduction technique and kernel density estimation.

Cardiometabolic and immune response to exercise training in patients with metabolic syndrome: retrospective analysis of two randomized clinical trials.

Metabolic syndrome (MetS) is defined by the presence of central obesity plus ≥two metabolic/cardiovascular risk factors (RF), with inflammation being a major disease-driving mechanism. Structured endurance exercise training (ET) may positively affect these traits, as well as cardiorespiratory fitness (V̇O2peak). We explore individual ET-mediated improvements of MetS-associated RF in relation to improvements in V̇O2peak and inflammatory profile. MetS patients from two randomized controlled trials, ExMET (n = 24) and OptimEx (n = 34), had performed 4- or 3-months supervised ET programs according to the respective trial protocol. V̇O2peak, MetS-defining RFs (both RCTs), broad blood leukocyte profile, cytokines and plasma proteins (ExMET only) were assessed at baseline and follow-up. Intra-individual changes in RFs were analysed for both trials separately using non-parametric approaches. Associations between changes in each RF over the exercise period (n-fold of baseline values) were correlated using a non-parametrical approach (Spearman). RF clustering was explored by uniform manifold approximation and projection (UMAP) and changes in RF depending on other RF or exercise parameters were explored by recursive partitioning. Four months of ET reduced circulating leukocyte counts (63.5% of baseline, P = 8.0e-6), especially effector subtypes. ET response of MetS-associated RFs differed depending on patients' individual RF constellation, but was not associated with individual change in V̇O2peak. Blood pressure lowering depended on cumulative exercise duration (ExMET: ≥102 min per week; OptimEx-MetS: ≥38 min per session) and baseline triglyceride levels (ExMET: <150 mg/dl; OptimEx-MetS: <174.8 mg/dl). Neuropilin-1 plasma levels were inversely associated with fasting plasma triglycerides (R: -0.4, P = 0.004) and changes of both parameters during the ET phase were inversely correlated (R: -0.7, P = 0.0001). ET significantly lowered effector leukocyte blood counts. The improvement of MetS-associated cardiovascular RFs depended on individual basal RF profile and exercise duration but was not associated with exercise-mediated increase in V̇O2peak. Neuropilin-1 may be linked to exercise-mediated triglyceride lowering.

Data reduction for SVM training using density-based border identification.

Numerous classification and regression problems have extensively used Support Vector Machines (SVMs). However, the SVM approach is less practical for large datasets because of its processing cost. This is primarily due to the requirement of optimizing a quadratic programming problem to determine the decision boundary during training. As a result, methods for selecting data instances that have a better likelihood of being chosen as support vectors by the SVM algorithm have been developed to help minimize the bulk of training data. This paper presents a density-based method, called Density-based Border Identification (DBI), in addition to four different variations of the method, for the lessening of the SVM training data through the extraction of a layer of border instances. For higher-dimensional datasets, the extraction is performed on lower-dimensional embeddings obtained by Uniform Manifold Approximation and Projection (UMAP), and the resulting subset can be repetitively used for SVM training in higher dimensions. Experimental findings on different datasets, such as Banana, USPS, and Adult9a, have shown that the best-performing variations of the proposed method effectively reduced the size of the training data and achieved acceptable training and prediction speedups while maintaining an adequate classification accuracy compared to training on the original dataset. These results, as well as comparisons to a selection of related state-of-the-art methods from the literature, such as Border Point extraction based on Locality-Sensitive Hashing (BPLSH), Clustering-Based Convex Hull (CBCH), and Shell Extraction (SE), suggest that our proposed methods are effective and potentially useful.

Predicting possible recommendations related to causes and consequences in the HAZOP study worksheet using natural language processing and machine learning: BERT, clustering, and classification

A set of recommendations is one of the most valuable outputs of the hazard and operability (HAZOP) study. The HAZOP study team provides recommendations when deficiencies are detected in the chemical process plant. These deficiencies can cause chemical process accidents and operability issues. This study employed a data-driven approach using natural language processing (NLP) and machine learning (ML) to predict potential recommendations based on causes and consequences. The dataset had no label; thus, clustering was used to label it. Firstly, bidirectional encoder representations from transformers (BERT) converted recommendation sentences into vectors. Secondly, uniform manifold approximation and projection (UMAP) and hierarchical density-based spatial clustering of applications with noise (HDBSCAN) were utilized to determine recommendation categories and label the dataset. Then, BERT was used to convert causes and consequences into vectors. Finally, a multi-layer perceptron (MLP) classifier was employed to predict possible recommendations based on causes and consequences. The class imbalance problem was handled by random over-sampling. The prediction accuracy of possible recommendations based on causes and consequences equals 93.7% and 89.5%, respectively. As a result of predicting potential recommendations utilizing causes and consequences, major recommendations will not be overlooked during the HAZOP study. This can further expand NLP and ML applications in HAZOP study automation.

ScCRT: a contrastive-based dimensionality reduction model for scRNA-seq trajectory inference.

Trajectory inference is a crucial task in single-cell RNA-sequencing downstream analysis, which can reveal the dynamic processes of biological development, including cell differentiation. Dimensionality reduction is an important step in the trajectory inference process. However, most existing trajectory methods rely on cell features derived from traditional dimensionality reduction methods, such as principal component analysis and uniform manifold approximation and projection. These methods are not specifically designed for trajectory inference and fail to fully leverage prior information from upstream analysis, limiting their performance. Here, we introduce scCRT, a novel dimensionality reduction model for trajectory inference. In order to utilize prior information to learn accurate cells representation, scCRT integrates two feature learning components: a cell-level pairwise module and a cluster-level contrastive module. The cell-level module focuses on learning accurate cell representations in a reduced-dimensionality space while maintaining the cell-cell positional relationships in the original space. The cluster-level contrastive module uses prior cell state information to aggregate similar cells, preventing excessive dispersion in the low-dimensional space. Experimental findings from 54 real and 81 synthetic datasets, totaling 135 datasets, highlighted the superior performance of scCRT compared with commonly used trajectory inference methods. Additionally, an ablation study revealed that both cell-level and cluster-level modules enhance the model's ability to learn accurate cell features, facilitating cell lineage inference. The source code of scCRT is available at https://github.com/yuchen21-web/scCRT-for-scRNA-seq.