Stochastic Neighbor Embedding Research Articles

Malaria cell identification using improved machine learning and modified deep learning architecture

Malaria continues to be a serious problem for public health because of its occurrence in tropical and subtropical areas with inadequate healthcare systems and few resources. For prompt intervention and treatment of malaria, effective and precise diagnosis is essential. Professional pathologists examine blood smear films by hand to get a microscopic diagnosis and another way they will do a rapid antigen malaria test which produces the result of 50% accuracy. Convolutional neural network (CNN) is a type of deep learning (DL) model that has been effectively used for a variety of image recognition applications. Our suggested approach uses, improved machine learning (IML) methods like support vector machine (SVM)+principal component analysis (PCA) fit, SVM+t-distributed stochastic neighbor embedding (t-SNE) fit, and CNN architecture with an accuracy of 86.23%, 88.27%, and 97.16% accuracy respectively, to combine feature extraction, data augmentation, and modify the layers by including the SVM algorithm in the final layer of the CNN architecture. The proposed method will significantly reduce pathologists' burden by automating the identification of malaria and improving diagnosis accuracy in resourceconstrained contexts

Machine Learning Techniques and Hybrid Feature Selection Methods for Efficient Prediction of Cancer

The high-dimensional genomic data presents significant challenges, and traditional analytical methods often struggle to capture the complex, non-linear relationships within these datasets. This study elaborates into the application of machine learning methods for dimensionality reduction and predictive modeling of binary phenotypes using gene expression data. Various dimensionality reduction techniques are explored, including t-distributed stochastic neighbor embedding (t-SNE), Non-negative matrix factorization (NMF), Principal component analysis (PCA), and manifold learning methods. Additionally, various algorithms such as logistic regression, random forests, support vector machines (SVMs)cand naive Bayes models are evaluated for predicting phenotypes. The study employs rigorous cross-validation, permutation testing, and evaluation metrics like the Matthews Correlation Coefficient (MCC) to assess model performance. The study rigorously assesses current genomics strategies, pinpointing their drawbacks and suggesting areas for future investigation, while delving into the potential of machine learning to overcome these hurdles and offer valuable insights in genomics. Keywords- Gene Expression , Dimensionality Reduction, Biomarkers, Cancer Prediction, Epigenetics, Machine Learning, Feature extraction.

Rapid quality assessment and traceability of ginger powder from Northeast India and Indian market based on near infrared spectroscopic fingerprinting.

Ginger (Zingiber officinale Rosc.) varies widely due to varying concentrations of phytochemicals and geographical origin. Rapid non-invasive quality and traceability assessment techniques ensure a sustainable value chain. The objective of this study is the development of suitable machine learning models to estimate the concentration of 6-gingerol and check traceability based on the spectral fingerprints of dried ginger samples collected from Northeast India and the Indian market using near-infrared spectrometry. Samples from the market and Northeast India underwent High Performance Liquid Chromatographic analysis for 6-gingerol content estimation. Near infrared (NIR) Spectrometer acquired spectral data. Quality prediction utilized partial least square regression (PLSR), while fingerprint-based traceability identification employed principal component analysis and t-distributed stochastic neighbor embedding (t-SNE). Model performance was assessed using RMSE and R2 values across selective wavelengths and spectral fingerprints. The standard normal variate pretreated spectral data over the wavelength region of 1,100-1,250 nm and 1,325-1,550 nm showed the optimal calibration model with root mean square error of calibration and R2 C (coefficient of determination for calibration) values of 0.87 and 0.897 respectively. A lower value (0.24) of root mean square error of prediction and a higher value (0.973) of R2 P (coefficient of determination for prediction) indicated the effectiveness of the developed model. t-SNE performed better clustering of samples based on geographical location, which was independent of gingerol content. The developed NIR spectroscopic model for Indian ginger samples predicts the 6-gingerol content and provides geographical traceability-based identification to ensure a sustainable value chain, which can promote efficiency, cost-effectiveness, consumer confidence, sustainable sourcing, traceability, and data-driven decision-making.

Multi-view data visualisation via manifold learning

Non-linear dimensionality reduction can be performed by manifold learning approaches, such as stochastic neighbour embedding (SNE), locally linear embedding (LLE) and isometric feature mapping (ISOMAP). These methods aim to produce two or three latent embeddings, primarily to visualise the data in intelligible representations. This manuscript proposes extensions of Student’s t-distributed SNE (t-SNE), LLE and ISOMAP, for dimensionality reduction and visualisation of multi-view data. Multi-view data refers to multiple types of data generated from the same samples. The proposed multi-view approaches provide more comprehensible projections of the samples compared to the ones obtained by visualising each data-view separately. Commonly, visualisation is used for identifying underlying patterns within the samples. By incorporating the obtained low-dimensional embeddings from the multi-view manifold approaches into the K-means clustering algorithm, it is shown that clusters of the samples are accurately identified. Through extensive comparisons of novel and existing multi-view manifold learning algorithms on real and synthetic data, the proposed multi-view extension of t-SNE, named multi-SNE, is found to have the best performance, quantified both qualitatively and quantitatively by assessing the clusterings obtained. The applicability of multi-SNE is illustrated by its implementation in the newly developed and challenging multi-omics single-cell data. The aim is to visualise and identify cell heterogeneity and cell types in biological tissues relevant to health and disease. In this application, multi-SNE provides an improved performance over single-view manifold learning approaches and a promising solution for unified clustering of multi-omics single-cell data.

Unsupervised machine learning defines subgroups in ischemic heart disease patients presenting with ventricular tachycardia

Abstract Background Ventricular tachycardia (VT) in ischemic heart disease (IHD) is the leading cause of sudden cardiac death (SCD). Despite the well-defined recommendations, tailoring therapies individually is often a challenge owing to the great heterogeneity in this population. We hypothesized that unsupervised machine learning can uncover hidden patterns to identify patient subgroups with similar characteristics. Aims To use unsupervised machine learning methods on a database of patients with ischemic VT, to discover subgroups with similar characteristics and compare the survival of the identified groups. Methods We collected the data of 566 patients with IHD hospitalized for sustained monomorphic VT, excluding those with acute coronary syndrome. The 17 recorded features included medical history and echocardiography. All-cause mortality was registered with one-year follow-up. We performed dimension reduction using T-distributed Stochastic Neighbor Embedding (TSNE) to produce 3 dimensions of the data. Then, we applied spectral clustering to create three groups within the population. Each group was compared to the rest of the population for each variable. Finally, we compared the survival across the three groups using Kaplan-Meyer analysis, univariate Cox-regression, and Log-rank test. Results We identified three groups with distinct characteristics. Group 1 represented the healthiest population; patients had no previous VT hospitalizations, and none of HD instability, LBBB, SCD, diabetes, VT storm or ICD shocks; additionally, they always had a single VT episode at presentation. They showed better left ventricular ejection fraction (LVEF) (38% vs. 33%, p<0.001) and TAPSE (20 mm vs. 18 mm, p=0.01), less severe mitral and tricuspid regurgitation (MR, TR), smaller left ventricular end-systolic diameter, and longer DT. Conversely, patients in Group 2 (n=349) had more previous VT hospitalizations (30% vs. 18%, p=0,003), more often had diabetes (46% vs. 18%, p<0.001), left bundle branch block (23% vs. 13%, p=0.006), and more often presented with HD instability (47% vs. 28%, p<0.001). Group 3 contained all seriously ill patients with VT storm or incessant VT, more commonly presenting with ICD shocks (34% vs. 21%, p=0.004), SCD (24% vs. 15%, p=0.004), hemodynamic instability (50% vs. 37%, p=0.01). This group had the poorest LVEF (30% vs. 35%, <0.001) and TAPSE (17 mm vs. 19 mm p=0.001), more severe MR and TR, and the shortest DT (140 ms vs. 173 ms p<0.001). Finally, we compared survival across the three groups. Significantly better survival was found in Group 1 (HR: 0.52 CI [0.28 - 0.98]) and Group 2 (HR: 0.61 CI [0.42 - 0.88]), while Group 3 had higher mortality (HR: 2.7 CI [1.83 - 3.97]). Conclusions Using spectral clustering we identified three groups in our cohort of ischemic VT patients. This method delineates the group of ischemic VT patients with the highest risk of mortality who might need more aggressive treatment and closer follow-up.

Parkinson’s Disease Recognition Using Decorrelated Convolutional Neural Networks: Addressing Imbalance and Scanner Bias in rs-fMRI Data

Parkinson’s disease (PD) is a neurodegenerative and progressive disease that impacts the nerve cells in the brain and varies from person to person. The exact cause of PD is still unknown, and the diagnosis of PD does not include a specific objective test with certainty. Although deep learning has made great progress in medical neuroimaging analysis, these methods are very susceptible to biases present in neuroimaging datasets. An innovative decorrelated deep learning technique is introduced to mitigate class bias and scanner bias while simultaneously focusing on finding distinguishing characteristics in resting-state functional MRI (rs-fMRI) data, which assists in recognizing PD with good accuracy. The decorrelation function reduces the nonlinear correlation between features and bias in order to learn bias-invariant features. The publicly available Parkinson’s Progression Markers Initiative (PPMI) dataset, referred to as a single-scanner imbalanced dataset in this study, was used to validate our method. The imbalanced dataset problem affects the performance of the deep learning framework by overfitting to the majority class. To resolve this problem, we propose a new decorrelated convolutional neural network (DcCNN) framework by applying decorrelation-based optimization to convolutional neural networks (CNNs). An analysis of evaluation metrics comparisons shows that integrating the decorrelation function boosts the performance of PD recognition by removing class bias. Specifically, our DcCNN models perform significantly better than existing traditional approaches to tackle the imbalance problem. Finally, the same framework can be extended to create scanner-invariant features without significantly impacting the performance of a model. The obtained dataset is a multiscanner dataset, which leads to scanner bias due to the differences in acquisition protocols and scanners. The multiscanner dataset is a combination of two publicly available datasets, namely, PPMI and FTLDNI—the frontotemporal lobar degeneration neuroimaging initiative (NIFD) dataset. The results of t-distributed stochastic neighbor embedding (t-SNE) and scanner classification accuracy of our proposed feature extraction–DcCNN (FE-DcCNN) model validated the effective removal of scanner bias. Our method achieves an average accuracy of 77.80% on a multiscanner dataset for differentiating PD from a healthy control, which is superior to the DcCNN model trained on a single-scanner imbalanced dataset.

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.

Evaluation of Density Functional Theory-Generated Data for Infrared Spectroscopy of Novel Psychoactive Substances Using Unsupervised Learning

Novel psychoactive substances (NPSs) are compounds plotted to modify the chemical structures of prohibited substances, offering alternatives for consumption and evading legislation. The prompt emergence of these substances presents challenges in health concerns and forensic assessment because of the lack of analytical standards. A viable alternative for establishing these standards involves leveraging in silico methods to acquire spectroscopic data. This study assesses the efficacy of utilizing infrared spectroscopy (IRS) data derived from density functional theory (DFT) for analyzing NPSs. Various functionals were employed to generate infrared spectra for five distinct NPS categories including the following: amphetamines, benzodiazepines, synthetic cannabinoids, cathinones, and fentanyls. PRISMA software was conceived to rationalize data management. Unsupervised learning techniques, including Hierarchical Cluster Analysis (HCA), Principal Component Analysis (PCA), and t-distributed stochastic neighbor embedding (t-SNE), were utilized to refine the assessment process. Our findings reveal no significant disparities among the different functionals used to generate infrared spectra data. Additionally, the application of unsupervised learning demonstrated adequate segregation of NPSs within their respective groups. In conclusion, integrating theoretical data and dimension reduction techniques proves to be a powerful strategy for evaluating the spectroscopic characteristics of NPSs. This underscores the potential of this combined methodology as a diagnostic tool for distinguishing IR spectra across various NPS groups, facilitating the evaluation of newly unknown compounds.

Efficient identification of broad absorption line quasars using dimensionality reduction and machine learning

Efficient identification of broad absorption line quasars using dimensionality reduction and machine learning

Predicting Alloying Element Yield in Converter Steelmaking Using t-SNE-WOA-LSTM

The performance and quality of steel products are significantly impacted by the alloying element control. The efficiency of alloy utilization in the steelmaking process was directly related to element yield. This study analyses the factors that influence the yield of elements in the steelmaking process using correlation analysis. A yield prediction model was developed using a t-distributed stochastic neighbor embedding (t-SNE) algorithm, a whale optimization algorithm (WOA), and a long short-term memory (LSTM) neural network. The t-SNE algorithm was used to reduce the dimensionality of the original data, while the WOA optimization algorithm was employed to optimize the hyperparameters of the LSTM neural network. The t-SNE-WOA-LSTM model accurately predicted the yield of Mn and Si elements with hit rates of 71.67%, 96.67%, and 99.17% and 57.50%, 89.17%, and 97.50%, respectively, falling within the error range of ±1%, ±2%, and ±3% for Mn and ±1%, ±3%, and ±5% for Si. The results demonstrate that the t-SNE-WOA-LSTM model outperforms the backpropagation (BP), LSTM, and WOA-LSTM models in terms of prediction accuracy. The model was applied to actual production in a Chinese plant. The actual performance of the industrial application is within a ±3% error range, with an accuracy of 100%. Furthermore, the elemental yield predicted by the model and then added the ferroalloys resulted in a reduction in the elemental content of the product by 0.017%. The model enables accurate prediction of alloying element yields and was effectively applied in industrial production.

Analysis and validation of biomarkers of immune cell-related genes in postmenopausal osteoporosis: An observational study.

Postmenopausal osteoporosis (PMOP) is a common metabolic inflammatory disease. In conditions of estrogen deficiency, chronic activation of the immune system leads to a hypo-inflammatory phenotype and alterations in its cytokine and immune cell profile, although immune cells play an important role in the pathology of osteoporosis, studies on this have been rare. Therefore, it is important to investigate the role of immune cell-related genes in PMOP. PMOP-related datasets were downloaded from the Gene Expression Omnibus database. Immune cells scores between high bone mineral density (BMD) and low BMD samples were assessed based on the single sample gene set enrichment analysis method. Subsequently, weighted gene co-expression network analysis was performed to identify modules highly associated with immune cells and obtain module genes. Differential analysis between high BMD and low BMD was also performed to obtain differentially expressed genes. Module genes are intersected with differentially expressed genes to obtain candidate genes, and functional enrichment analysis was performed. Machine learning methods were used to filter out the signature genes. The receiver operating characteristic (ROC) curves of the signature genes and the nomogram were plotted to determine whether the signature genes can be used as a molecular marker. Gene set enrichment analysis was also performed to explore the potential mechanism of the signature genes. Finally, RNA expression of signature genes was validated in blood samples from PMOP patients and normal control by real-time quantitative polymerase chain reaction. Our study of PMOP patients identified differences in immune cells (activated dendritic cell, CD56 bright natural killer cell, Central memory CD4 T cell, Effector memory CD4 T cell, Mast cell, Natural killer T cell, T follicular helper cell, Type 1 T-helper cell, and Type 17 T-helper cell) between high and low BMD patients. We obtained a total of 73 candidate genes based on modular genes and differential genes, and obtained 5 signature genes by least absolute shrinkage and selection operator and random forest model screening. ROC, principal component analysis, and t-distributed stochastic neighbor embedding down scaling analysis revealed that the 5 signature genes had good discriminatory ability between high and low BMD samples. A logistic regression model was constructed based on 5 signature genes, and both ROC and column line plots indicated that the model accuracy and applicability were good. Five signature genes were found to be associated with proteasome, mitochondria, and lysosome by gene set enrichment analysis. The real-time quantitative polymerase chain reaction results showed that the expression of the signature genes was significantly different between the 2 groups. HIST1H2AG, PYGM, NCKAP1, POMP, and LYPLA1 might play key roles in PMOP and be served as the biomarkers of PMOP.

Single-nucleus RNA transcriptome profiling reveals murine adipose tissue endothelial cell proliferation gene networks involved in obesity development

Endothelial cells play an important role in the metabolism of adipose tissue (AT). This study aimed to analyze the changes that adipose tissue in AT endothelial cells undergo during the development of obesity, using single-nucleus RNA sequence (snRNA-seq). Mouse paraepididymal AT cells were subjected to snRNA-seq with the 10X Genomics platform. The cell types were then clustered using t-distributed stochastic neighbor embedding and unbiased computational informatics analyses. Protein–protein interactions network was established using the STRING database and visualized using Cytoscape. The dataset was subjected to differential gene enrichment analysis. In total, 21,333 cells acquired from 24 mouse paraepididymal AT samples were analyzed using snRNA-seq. This study identified 18 distinct clusters and annotated macrophages, fibroblasts, epithelial cells, T cells, endothelial cells, stem cells, neutrophil cells, and neutrophil cell types based on representative markers. Cluster 12 was defined as endothelial cells. The proportion of endothelial cells decreased with the development of obesity. Inflammatory factors, such as Vegfa and Prdm16 were upregulated in the medium obesity group but downregulated in the obesity group. Genes, such as Prox1, Erg, Flt4, Kdr, Flt1, and Pecam1 promoted the proliferation of AT endothelial cells and maintained the internal environment of AT. This study established a reference model and general framework for studying the mechanisms, biomarkers, and therapeutic targets of endothelial cell dysfunction-related diseases at the single-cell level.

Advances in the individual authentication of cocoa beans: Vis/NIR spectroscopy as a tool to distinguish fermented from unfermented beans and classify genotypes in the eastern Amazonia

Reliable analytical methods to authenticate high-quality and economically valuable cocoa beans are highly desirable, and NIR spectroscopy stands out as a rapid and non-destructive alternative. This study employs NIR for the authentication and differentiation of 19 whole Forastero cocoa beans from Eastern Amazonia (Pará, Brazil), based on their fermentation status and genetic profiles. Partial Least Squares Discriminant Analysis (PLS-DA) models in wavelength ranges of 400–700 nm, 1400–1600 nm, and 1900–2500 nm, demonstrated high sensitivity and specificity in discriminating fermented from unfermented beans, regardless of genotype. Various compounds, including proteins, lipids, carbohydrates, anthocyanins, and theobromine, provide crucial insights into the spectral regions essential for distinction. The variable importance in projection (VIP) score value greater than 1 was used to select relevant variables, and Linear Discriminant Analysis (LDA) was performed in both the visible (472 nm and 636 nm) and infrared ranges (2096 nm and 2278 nm), demonstrating that absorbances at two specific wavelengths are sufficient for discrimination. The t-distributed stochastic neighbor embedding (t-SNE) indicated a segregation trend of the genotypes based on classification by major genetic groups in unfermented beans, suggesting that the biochemical characteristics shared by them are more prominent before fermentation. The PLS-DA models based on complete vis/NIR spectra showed comparable results in discriminating the 19 cocoa genotypes in both fermented (0.14% prediction error) and unfermented beans (0.16% prediction error). The model's classification errors can be attributed to shared genetic ancestry among the samples, primarily in unfermented beans. This research corroborates the effectiveness of vis/NIR spectroscopy as a straightforward tool for whole cocoa bean authentication, providing rapid insights into genetic diversity regardless of their fermentation state.

A PD-1highCD4+ T Cell Population With a Cytotoxic Phenotype is Associated With Interstitial Lung Disease in Systemic Sclerosis.

T cells contribute to tissue injury in systemic sclerosis (SSc), yet the specific T cell subsets expanded in patients with SSc remain incompletely defined. Here we evaluated specific phenotypes and functions of peripheral helper T (Tph) and follicular helper T (Tfh) cells, which have been implicated in autoantibody production, and assessed their associations with clinical features in a well-characterized cohort of patients with SSc. Mass cytometry of T cells from peripheral blood mononuclear cells of patients with SSc and controls were evaluated using t-distributed stochastic neighbor embedding visualization, biaxial gating, and marker expression levels. Findings were validated with flow cytometry and in vitro assays. The frequencies of PD-1highCXCR5+ Tfh cells and PD-1highCXCR5- Tph cells were similar in patients with SSc and controls. t-distributed stochastic neighbor embedding visualization (tSNE) revealed distinct populations within the PD-1highCXCR5- cells distinguished by expression of HLA-DR and inducible costimulator (ICOS). Among PD-1highCXCR5- cells, only the HLA-DR+ICOS- cell population was expanded in patients with SSc. Cytometric and RNA sequencing analyses indicated that these cells expressed cytotoxic rather than B cell helper features. HLA-DR+ICOS- PD-1highCXCR5- cells were less potent in inducing B cell plasmablast differentiation and antibody production than comparator T helper cell populations. HLA-DR+ICOS-PD-1highCXCR5- cells were significantly associated with the presence and severity of interstitial lung disease among patients with SSc. Among PD-1highCXCR5- T cells, a subset of HLA-DR+ICOS- cells with cytotoxic features is specifically expanded in patients with SSc and is significantly associated with interstitial lung disease severity. This potential cytotoxicity appearing in the CD4 T cell population can be evaluated as a prognostic disease biomarker in patients with SSc.

Enhanced Fault Detection in Bearings Using Machine Learning and Raw Accelerometer Data: A Case Study Using the Case Western Reserve University Dataset

This study introduces a novel approach for fault classification in bearing components utilizing raw accelerometer data. By employing various neural network models, including deep learning architectures, we bypass the traditional preprocessing and feature-extraction stages, streamlining the classification process. Utilizing the Case Western Reserve University (CWRU) bearing dataset, our methodology demonstrates remarkable accuracy, particularly in deep learning networks such as the three variant convolutional neural networks (CNNs), achieving above 98% accuracy across various loading levels, establishing a new benchmark in fault-detection efficiency. Notably, data exploration through principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE) provided valuable insights into feature relationships and patterns, aiding in effective fault detection. This research not only proves the efficacy of neural network classifiers in handling raw data but also opens avenues for more straightforward yet effective diagnostic methods in machinery health monitoring. These findings suggest significant potential for real-world applications, offering a faster yet reliable alternative to conventional fault-classification techniques.

Fault diagnosis of power transformers based on t-SNE and ECOC-TEWSO-SVM

Support Vector Machines (SVMs) have achieved significant success in the field of power transformer fault diagnosis. However, challenges such as determining SVM hyperparameters and their suitability for binary classification still exist. This paper proposes a novel method for power transformer fault diagnosis, called ECOC-WSO-SVM, which utilizes a White Shark Optimizer (WSO) and error correcting output codes to optimize SVMs. First, t-distributed Stochastic Neighbor Embedding (t-SNE) is employed to reduce the dimensionality of Dissolved Gas Analysis (DGA) features constructed using the correlation ratio method, from 26 dimensions. In addition, to effectively solve the hyperparameters of SVMs, a multi-strategy fusion method is proposed to improve the WSO, incorporating tent chaos initialization, elite opposite learning, and selection strategies, forming TEWSO, and its superior optimization performance is validated using IEEE CEC2021 test functions. Furthermore, to address the limitations of SVMs as a binary classifier, an error correcting output code fusion SVM is introduced, thus constructing a multi-class SVM model. Finally, the diagnostic performance of the ECOC-TEWSO-SVM model is validated using real-world data. Results demonstrate that the proposed model exhibits the best diagnostic performance compared to traditional models and those in the literature, thereby proving the significance and effectiveness of the proposed model.

Assessment and accumulation of microplastics in the Indian riverine systems: Risk assessment and implications of translocation across the water-to-fish continuum.

Microplastic (MP) pollution has engulfed global aquatic systems, and the concerns about microplastic translocation and bioaccumulation in fish and other aquatic organisms are now an unpleasant truth. In the past few years, MP pollution in freshwater systems, particularly rivers and subsequently in freshwater organisms, especially in fish, has caught the attention of researchers. Rivers provide livelihood to approximately 40 % of the global population through food and potable water. Hence, assessment of emerging contaminants like microplastics in rivers and the associated fauna is crucial. This study assessed microplastics (MPs) in fish, sediment and freshwater samples across the third largest riverine system of peninsular India, the Mahanadi River. The number concentrations of MPs measured in water, sediment and fish ranged from 337.5±54.4-1333.3±557.2 MPs/m3, 14.7±3.7-69.3±10.1 MPs/kg. Dry weight and 0.4-3.2 MPs/Fish, respectively. Surprisingly, MPs were found in every second fish sample, with a higher MP number in the gut than in the gills. Black and blue coloured filaments with <0.5 mm size were the dominant MPs with polypropylene and polyethylene polymers in abundance. The Polymer Hazard Index (PHI) and the Potential Ecological Risk Index (PERI) studies revealed that the majority of the sampling sites fell in Risk category V (dangerous category). An irregular trend in the MP concentration was observed downstream of the river, though relatively elevated MP concentrations in water and fish samples were observed downstream of the river. t-Distributed Stochastic Neighbour Embedding (t-SNE) unveiled distinct patterns in MP distribution with a higher similarity exhibited in the MPs found in fish gill and gut samples, unlike water and sediment, which shared certain characteristics. The findings in the current study contribute to filling the knowledge gap of MP assessment and accumulation in global freshwater systems and highlight the microplastic contamination and accumulation in fish with its potential implications on human health.

Comprehensive analysis of human monocyte subsets using full-spectrum flow cytometry and hierarchical marker clustering.

Exploring monocytes' roles within the tumor microenvironment is crucial for crafting targeted cancer treatments. This study unveils a novel methodology utilizing four 20-color flow cytometry panels for comprehensive peripheral immune system phenotyping, specifically targeting classical, intermediate, and non-classical monocyte subsets. By applying advanced dimensionality reduction techniques like t-distributed stochastic neighbor embedding (tSNE) and FlowSom analysis, we performed an extensive profiling of monocytes, assessing 50 unique cell surface markers related to a wide range of immunological functions, including activation, differentiation, and immune checkpoint regulation. This in-depth approach significantly refines the identification of monocyte subsets, directly supporting the development of personalized immunotherapies and enhancing diagnostic precision. Our pioneering panel for monocyte phenotyping marks a substantial leap in understanding monocyte biology, with profound implications for the accuracy of disease diagnostics and the success of checkpoint-inhibitor therapies. Key findings include revealing distinct marker expression patterns linked to tumor progression and providing new avenues for targeted therapeutic interventions.

Power quality disturbances classification with imbalanced/insufficient samples based on WGAN-GP-SA and DCNN

The accurate identification of power quality disturbance signals (PQDs) is of great importance to ensure the safe and stable operation of power systems. Considering that there are insufficient samples and imbalance samples of power quality disturbance signals in real systems, the hybrid classification method based on WGAN-GP-SA (Wasserstein generative adversarial networks with gradient penalty and Squeeze-and-Excitation block with atrous spatial pyramid pooling module) and DCNN (deep convolutional neural network) is proposed for recognition of PQDs. Firstly, time-frequency features are extracted by combining improved Kalman and continuous wavelet transforms (CWT); secondly, the original training time-frequency maps are expanded by using WGAN-GP-SA, which can learn the distribution of real data and generate similar samples, and PSNR, SSIM, FID and other indexes are introduced for evaluation of generated data; finally, classification experiments are conducted on two data sets using DCNN, and simulation results show that the accuracy is improved by 2.82 % compared with the original data without augmentation. Furthermore, t-distributed stochastic neighbor embedding algorithm (t-SNE) is introduced for visually analysing of classification performance of various datasets, and the visualization indicates the superiority of the hybrid classification method.

Comparative single-cell multiplex immunophenotyping of therapy-naive patients with rheumatoid arthritis, systemic sclerosis, and systemic lupus erythematosus shed light on disease-specific composition of the peripheral immune system.

Systemic autoimmune diseases (SADs) are a significant burden on the healthcare system. Understanding the complexity of the peripheral immunophenotype in SADs may facilitate the differential diagnosis and identification of potential therapeutic targets. Single-cell mass cytometric immunophenotyping was performed on peripheral blood mononuclear cells (PBMCs) from healthy controls (HCs) and therapy-naive patients with rheumatoid arthritis (RA), progressive systemic sclerosis (SSc), and systemic lupus erythematosus (SLE). Immunophenotyping was performed on 15,387,165 CD45+ live single cells from 52 participants (13cases/group), using an antibody panel to detect 34 markers. Using the t-SNE (t-distributed stochastic neighbor embedding) algorithm, the following 17 main immune cell types were determined: CD4+/CD57- T cells, CD4+/CD57+ T cells, CD8+/CD161- T cells, CD8+/CD161+/CD28+ T cells, CD8dim T cells, CD3+/CD4-/CD8- T cells, TCRγ/δ T cells, CD4+ NKT cells, CD8+ NKT cells, classic NK cells, CD56dim/CD98dim cells, B cells, plasmablasts, monocytes, CD11cdim/CD172dim cells, myeloid dendritic cells (mDCs), and plasmacytoid dendritic cells (pDCs). Seven of the 17 main cell types exhibited statistically significant frequencies in the investigated groups. The expression levels of the 34 markers in the main populations were compared between HCs and SADs. In summary, 59 scatter plots showed significant differences in the expression intensities between at least two groups. Next, each immune cell population was divided into subpopulations (metaclusters) using the FlowSOM (self-organizing map) algorithm. Finally, 121 metaclusters (MCs) of the 10 main immune cell populations were found to have significant differences to classify diseases. The single-cell T-cell heterogeneity represented 64MCs based on the expression of 34 markers, and the frequency of 23 MCs differed significantly between at least twoconditions. The CD3- non-T-cell compartment contained 57 MCs with 17 MCs differentiating at least two investigated groups. In summary, we are the first to demonstrate the complexity of the immunophenotype of34markers over 15 million single cells in HCs vs. therapy-naive patients withRA, SSc, and SLE. Disease specific population frequencies or expression patterns of peripheral immune cells provide a single-cell data resource to the scientific community.