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Single-cell RNA sequencing (scRNA-seq) has revolutionized the field of cellular diversity research. Unsupervised clustering, a key technique in this exploration, allows for the identification of distinct cell types within a population. Graph-based deep clustering methods have shown promise in preserving the structural relationships between cells (nodes) within the data. However, these methods often neglect the inherent distribution of nodes in the graph, leading to incomplete representations of cell populations. Additionally, conventional graph convolutional networks (GCNs) can suffer from oversmoothing, a phenomenon where the network loses the ability to differentiate between samples with similar expression profiles. To address these limitations, we proposed scG-cluster, an innovative deep structural clustering method. This method incorporates two key innovations: (1) Dual-topology adjacency graph: scG-cluster integrates information about node distribution into the traditional adjacency graph used by GCNs. This enriches the graph representation by capturing the spatial relationships between cells in addition to their pairwise similarities. (2) Dual-topology adaptive graph convolutional network (TAGCN): The framework employs a TAGCN architecture with residual concatenation. This network utilizes an attention mechanism to dynamically weight features within the graph, focusing on the most informative aspects for clustering. Additionally, residual connections are implemented to combat oversmoothing, ensuring the network retains the ability to distinguish between subtle differences in cell expression profiles. Furthermore, scG-cluster iteratively refines the clustering centers, leading to enhanced stability and accuracy in the final cluster assignments. Extensive evaluations on six diverse scRNA-seq datasets demonstrate that scG-cluster consistently outperforms existing state-of-the-art methods in terms of both clustering accuracy and scalability. Ablation studies are also conducted to validate the significant contributions of both the residual connections and the attention mechanism to the overall performance of the model. The source code for scG-cluster is publicly available at https://github.com/xixi-wq/scG-cluster. [ABSTRACT FROM AUTHOR]

Introduction: Immunophenotyping is a powerful tool for grading disease severity, aiding in diagnosis, predicting clinical response, and guiding the development of novel therapeutics. Methods: This pilot study employs high parameter immunophenotyping panels (15 markers for dog, 12 for cat) and leverages unsupervised clustering to identify immune cell populations. Our analysis uses machine learning and statistical algorithms to perform unsupervised clustering, multiple visualizations, and statistical analysis of high parameter flow cytometry data. This method reduces user bias and precisely identifies cell populations, demonstrating its potential to detect variations and differentiate populations effectively. To enhance our understanding of cat and dog biology and test the unsupervised clustering approach on real-world samples, we performed in-depth profiling of immune cell populations in blood collected from client-owned and laboratory animals [dogs (n = 55) and cats (n = 68)]. These animals were categorized based on pruritic behavior or routine check-ups (non-pruritic controls). Results: Unsupervised clustering revealed various immune cell populations, including T-cell subsets distinguished by CD62L expression and distinct monocyte subsets. Notably, there were significant differences in monocyte subsets between pruritic and non-pruritic animals. Pruritic dogs and cats showed significant shifts in CD62LHi T-cell subsets compared to non-pruritic controls, with opposite trends observed between pruritic cats and dogs. Discussion: These findings underscore the importance of advancing veterinary immunophenotyping, expanding our knowledge about marker expression on circulating immune cells and driving progress in understanding veterinary-specific biology and uncovering new insights into various conditions and diseases. [ABSTRACT FROM AUTHOR]

The tumor immune microenvironment (TiME) of human central nervous system (CNS) tumors remains to be comprehensively deciphered. Here, we employed flow cytometry and RNA sequencing analysis for a deep data-driven dissection of a diverse TiME and to uncover noncanonical immune cell types in human CNS tumors by using seven tumors from five patients. Myeloid subsets comprised classical microglia, monocyte-derived macrophages, neutrophils, and two noncanonical myeloid subsets: CD3+ myeloids and CD19+ myeloids. T lymphocyte subsets included double-negative (CD4− CD8−) T cells (DNTs). Noncanonical myeloids and DNTs were explored on independent datasets, suggesting that our DNT phenotype represents γδ T cells. Noncanonical myeloids were validated using orthogonal methods across 73 patients from three independent datasets. While the proportions of classical myeloids agreed with reported malignancy type-associated TiMEs, unexpectedly high lymphocyte frequencies were detected in gliosarcoma, which also showed a unique expression pattern of immune-related genes. Our findings highlight the potential of data-driven approaches in resolving CNS TiME to reveal the mosaic of immune cell types constituting TiME, warranting the need for future studies on the nonclassical immune cell subsets. [ABSTRACT FROM AUTHOR]

Pan-sharpening is very often employed in remote sensing to transform low-resolution multispectral (LMS) images into equivalent high-resolution multispectral images (HMS). Images resulting from pan-sharpening are sharper and more detailed that is resulted by improving spatial features of the multispectral image. One such approach of jointly processing LMS and Panchromatic images is discussed in the present study. The decision-level fusion suggested here involves choosing or combining details from numerous sources by taking decisions while analyzing features recovered from the input images. The proposed methodology is an amalgamation of principal component analysis used for separating spatial and spectral details of LMS, non-subsampled contourlet transform for feature analysis, and Jaccard similarity index and block activity measurement for localized decision-based fusion. The algorithm tries to provide an adaptive approach to address the trade-off between spectral and spatial resolution. Self-Organizing Maps based clustering technique is employed with the intension of grouping the image pixels into three categories soil, water and vegetation. The paper highlights the performance comparison of proposed method with various pixel-level fusion techniques ranging from techniques from Intensity, Hue and Saturation (IHS) transform to Neural Networks based pan-sharpening methods. This comparison is implemented using various reference and non-reference indicators along with Kolmogorov–Smirnov test. Additional analysis using Kolmogorov–Smirnov test is done to statistically analyze spectral degradation. The comparative analysis provides enough evidence that the suggested method yields fused images with enhanced edge details without forgoing the spectral features which was also evident from the mutual information obtained from clustered images. The resulting sharpened images tend to possess good spatial and spectral details that would simplify the automatic image analysis. [ABSTRACT FROM AUTHOR]

One of the principal goals of Precision Medicine is to stratify patients by accounting for individual variability. However, extracting meaningful information from Real-World Data, such as Electronic Health Records, still remains challenging due to methodological and computational issues. A Dynamic Time Warping-based unsupervised-clustering methodology is presented in this paper for the clustering of patient trajectories of multi-modal health data on the basis of shared temporal characteristics. Building on an earlier methodology, a new dimension of time-varying clinical and imaging features is incorporated, through an adapted cost-minimization algorithm for clustering on different, possibly overlapping, feature subsets. The model disease chosen is Huntington's disease (HD), characterized by progressive neurodegeneration. From a wide range of examined user-defined parameters, four case examples are highlighted to demonstrate the identified temporal patterns in multi-modal HD trajectories and to study how these differ due to the combined effects of feature weights and granularity threshold. For each identified cluster, polynomial fits that describe the time behavior of the assessed features are provided for an informative comparison, together with their averaged values. The proposed data-mining methodology permits the stratification of distinct time patterns of multi-modal health data in individuals that share a diagnosis, by employing user-customized criteria beyond the current clinical practice. Overall, this work bears implications for better analysis of individual variability in disease progression, opening doors to personalized preventative, diagnostic and therapeutic strategies. [ABSTRACT FROM AUTHOR]

Massive changes in many aspects related to social groups of different socioeconomic backgrounds were caused by the COVID-19 pandemic and as a result, the overall state of mental health was severely affected globally. This study examined how the pandemic affected Sri Lankan citizens representing a range of socioeconomic backgrounds in terms of their mental health. The data used in this research was gathered from 3,020 households using a nationwide face-to-face survey, from which a processed dataset of 921 responses was considered for the final analysis. Four distinct factors were identified by factor analysis (FA) that was conducted and subsequently, the population was clustered using unsupervised clustering to determine which population subgroups were affected similarly. Two such subgroups were identified where the respective relationships to the retrieved principal factors and their demographics were thoroughly examined and interpreted. This resulted in the identification of contrasting perspectives between the two groups toward the maintenance and the state of social relationships during the pandemic, which revealed that one group was more "socially connected" in nature resulting in their mental state being comparatively better in coping with the pandemic. The other group was seen to be more "socially reserved" showing an opposite reaction toward social connections while their mental well-being declined showing symptoms such as loneliness, and emptiness in response to the pandemic. The study examined the role of social media, and it was observed that social media was perceived as a substitute for the lack of social connections or primarily used as a coping mechanism in response to the challenges of the pandemic and results show that maintaining social connections physically or via online rather than the use of social media has helped one group over the other in decreasing their symptoms such as emptiness, loneliness and fear of death. [ABSTRACT FROM AUTHOR]

Renewable Energy Communities (RECs) are emerging as significant in the global paradigm shift towards a smart and sustainable energy environment. By empowering energy consumers to actively participate in local energy generation, and sharing, using renewable energy sources, energy storage, and flexible loads, REC participants can reduce costs, and also contribute to low-carbon objectives, providing the flexibility needed to address modern smart grid challenges. This article presents a mixed integer linear programming model for optimal sizing of the solar PVs and battery energy storage systems (BESS) of REC participants who engage in P2P energy exchange. The model is formulated using a two-stage stochastic optimization to address load and PV uncertainty, and unsupervised clustering to structure the data for the stochastic optimization process. The model enables sizing solar PVs for different rooftop geometries and the objective function includes comprehensively defined electricity, operational, and scaled investment costs for solar PV and BESS, where economic fairness constraints are analyzed and implemented. The model is validated on real solar and atmospheric measured data from Zagreb, Croatia, and publicly available household consumption data from Northern Germany. The article also analyzes how tariff models, and electricity prices affect PV and BESS sizes, cost reductions, and P2P energy exchange for different REC participants with varying consumption and production profiles. [ABSTRACT FROM AUTHOR]

• There is consistent evidence for the existence of 3 cognitive subgroups in BD • Good-performance subgroup is characterized by better executive functions and normal functioning in all other domains. • Severe-impairment subgroup is associated with more severe course of illness and higher ratio of antipsychotic use. • Severe-impairment subgroup is also chaarctereized by lower educational attainment and later-onset of illness. • The characteristics of the moderate-impairment subgroup was lying between the other two subgroups for most of the measures. The delineation of cognitive subgroups of bipolar disorder (BD) might be helpful for identifying biologically valid subtypes of this disorder. This meta-analysis identified peer-reviewed literature on studies investigating cognitive subgroups of BD with data-driven clustering methods. Relevant studies were searched in PubMed, Scopus, and Web of Science. Random-effects meta-analysis was performed using R software. A total of 14 cross-sectional studies including euthymic or mildly symptomatic patients with BD were included in the current meta-analysis. The available studies have consistently supported a 3-cluster solution. The pooled prevalence of the severe-impairment, moderate-impairment, and major good-functioning groups were 23.1 % (95%CI, 18.5 %–27.7 %), 42.5 % (95%CI, 36.3 %–48.8 %), and 33.5 % (95%CI, 25.9 %–41.1 %) respectively. Compared to healthy controls, both the severe-impairment (g=−1.40 to −1.73) and moderate-impairment groups (g=−0.59 to −0.96) had significant deficits in all six cognitive domains (verbal memory, visual memory, executive functions, working memory, attention and processing speed). The good-performance subgroup had a small increase in the performance of executive functions (g=0.23) and normal functioning in all other domains. Compared to the good-performance subgroup, the severe-impairment subgroup was characterized by more severe functional impairment, more hospital admissions, a higher percentage of type I BD and antipsychotic use. The characteristics of the moderate-impairment subgroup were lying between the other two subgroups for most of the measures. The current findings support the existence of 3 cognitive subgroups in BD including severe-impairment and moderate-impairment groups which are associated with a more severe course of illness. [ABSTRACT FROM AUTHOR]

Abstract Objectives Head and neck squamous cell carcinoma (HNSCC) is the sixth most common cancer worldwide. Four types of RNA modification writers (m6A, m1A, A-I editing, and APA) are widely involved in tumorigenesis and the TME. We aimed to comprehensively explore the role of the four RNA modification writers in the progression and immune microenvironment of HNSCC. Materials and methods We first obtained transcription profile data and transcriptional variation of the four types of RNA modification writers from The Cancer Genome Atlas (TCGA) database. HNSCC patients in TCGA dataset were divided into different clusters based on the four types of RNA modification writers. Univariate Cox and Least absolute shrinkage and selection operator (LASSO) analyses were performed to conduct a Writer-score scoring system, which was successfully verified in the GSE65858 dataset and our clinical sample dataset. Finally, we evaluated the relationship between different RNA modification clusters (Writer-score) and immunological characteristics of HNSCC. Results Two different RNA modification clusters (A and B) were obtained. These RNA modification clusters (Writer-score) were strongly associated with immunological characteristics (immunomodulators, cancer immunity cycles, infiltrating immune cells (TIICs), inhibitory immune checkpoints, and T cell inflamed score (TIS)) of HNSCC. Conclusions This study identified two different RNA modification clusters and explored the potential relationship between RNA modification clusters (Writer-score) and immunological characteristics, offering a new theoretical basis for precision immunotherapy in patients with HNSCC.

Scientists increasingly approach the world through machine learning techniques, but philosophers of science often question their epistemic status. Some philosophers have argued that the use of unsupervised clustering algorithms is more justified than the use of supervised classification, because supervised classification is more biased, and because (parametric) simplicity plays a different and more interesting role in unsupervised clustering. I call these arguments the No-Bias Argument and the Simplicity-Truth Argument. I show how both arguments are fallacious and how, on the contrary, the use of supervised classification is at least as justified as the use of unsupervised clustering. [ABSTRACT FROM AUTHOR]

Psoriasis exhibits a higher incidence of anxiety and depression. However, the diagnostic process heavily relies on subjective evaluation. Fatty acid‐binding protein 7 (FABP7) and serotonin (5‐HT) are considered as potential plasma biomarkers. We aimed to investigate the potentiality of plasma FABP7 and 5‐HT as biomarkers for predicting anxiety and depression in psoriasis. Data were analysed from 140 patients with psoriasis in the Shanghai Psoriasis Effectiveness Evaluation CoHort (SPEECH). Unsupervised clustering was employed to group patients based on their FABP7 and 5‐HT profiles. Subsequently, patients were categorised into Group 1 (lower FABP7 and higher 5‐HT) or Group 2. Multivariate logistic regression was employed to investigate the correlation between the FABP7‐5‐HT pattern and anxiety or depression in psoriasis patients. Patients with psoriasis have a higher incidence of anxiety or depression, as well as higher levels of FABP7 and lower levels of 5‐HT. After clustering patients using K‐means clustering, Group 2 showed a higher body mass index, a higher incidence of hypertension, more severe psoriasis, and more significant anxiety and depression compared to Group 1. Multivariate logistic regression shows that adjusting for covariates except PASI, duration of psoriasis, and psoriatic arthritis, Group 2 had a higher risk of anxiety and depression compared to Group 1. Further adjustment for covariates yielded similar results. Pattern of FABP7‐5‐HT that may indicate an association with psoriasis accompanied by anxiety or depression. [ABSTRACT FROM AUTHOR]

A population of CD133+lin-CD45- and CD34+lin-CD45- very small embryonic-like stem cells (VSELs) has been identified in postnatal human tissues, including bone marrow (BM), mobilized peripheral blood (mPB) and umbilical cord blood (UCB). Under appropriate conditions, VSELs in vitro and in vivo differentiate into tissue-committed stem cells for all three germ layers. Molecular analysis of adult murine BM-purified VSELs revealed that these rare cells deposited during development in adult tissues (i) express a similar transcriptome as embryonic stem cells, (ii) share several markers characteristic for epiblast and migratory primordial germ cells (PGCs), (iii) highly express a polycomb group protein enhancer of zeste drosophila homolog 2 (Ezh2) and finally (iv) display a unique pattern of imprinting at crucial paternally inherited genes that promotes their quiescence. Here, by employing single-cell RNA sequencing we demonstrate for the first time that purified from UCB human VSELs defined by expression of CD34 or CD133 antigens and lack of lineage markers, including CD45 antigen express similar molecular signature as murine BM-derived VSELs. Specifically, unsupervised clustering revealed numerous subpopulations of VSELs including ones i) annotated to germline compartments, ii) regulated by parental imprinting, iii) responding to early developmental fate decisions, iv) transcription factors involved in differentiation and development, including homeobox family of genes, and v) expressing innate immunity and purinergic signaling genes. [ABSTRACT FROM AUTHOR]

Objectives: Head and neck squamous cell carcinoma (HNSCC) is the sixth most common cancer worldwide. Four types of RNA modification writers (m6A, m1A, A-I editing, and APA) are widely involved in tumorigenesis and the TME. We aimed to comprehensively explore the role of the four RNA modification writers in the progression and immune microenvironment of HNSCC. Materials and methods: We first obtained transcription profile data and transcriptional variation of the four types of RNA modification writers from The Cancer Genome Atlas (TCGA) database. HNSCC patients in TCGA dataset were divided into different clusters based on the four types of RNA modification writers. Univariate Cox and Least absolute shrinkage and selection operator (LASSO) analyses were performed to conduct a Writer-score scoring system, which was successfully verified in the GSE65858 dataset and our clinical sample dataset. Finally, we evaluated the relationship between different RNA modification clusters (Writer-score) and immunological characteristics of HNSCC. Results: Two different RNA modification clusters (A and B) were obtained. These RNA modification clusters (Writer-score) were strongly associated with immunological characteristics (immunomodulators, cancer immunity cycles, infiltrating immune cells (TIICs), inhibitory immune checkpoints, and T cell inflamed score (TIS)) of HNSCC. Conclusions: This study identified two different RNA modification clusters and explored the potential relationship between RNA modification clusters (Writer-score) and immunological characteristics, offering a new theoretical basis for precision immunotherapy in patients with HNSCC. [ABSTRACT FROM AUTHOR]

We propose a discrete random effects multinomial regression model to deal with estimation and inference issues in the case of categorical and hierarchical data. Random effects are assumed to follow a discrete distribution with an a priori unknown number of support points. For a K-categories response, the modelling identifies a latent structure at the highest level of grouping, where groups are clustered into subpopulations. This model does not assume the independence across random effects relative to different response categories, and this provides an improvement from the multinomial semi-parametric multilevel model previously proposed in the literature. Since the category-specific random effects arise from the same subjects, the independence assumption is seldom verified in real data. To evaluate the improvements provided by the proposed model, we reproduce simulation and case studies of the literature, highlighting the strength of the method in properly modelling the real data structure and the advantages that taking into account the data dependence structure offers. [ABSTRACT FROM AUTHOR]

Failure mode identification in unreinforced masonry (URM) walls is a challenging task because of the presence of multiple damage mechanisms, including so-called hybrid modes. This paper employed a novel application of supervised and unsupervised learning to link URM wall design and mechanical properties to the failure mode. We used a database with 330 backbone curves from cyclically loaded URM walls as well as the associated images captured during the experiments. Based on the observations documented during the experiments, information on the cyclic curves, and the associated images, the walls were first manually classified into four failure modes: bed-joint sliding, diagonal tension, rocking, and toe crushing. Then k -means clustering was used to group the damaged walls into four classes based on critical points along the backbone curves. A hybridity index was introduced to quantify the contribution of each failure mode based on the distance from the centroid of the clusters. The design and mechanical properties of the URM walls were then used to predict the hybridity index using a multioutput regression model. The hybridity prediction model determines the contribution of the various failure modes to the ultimate behavior of a damaged URM wall. The proposed framework provides a robust approach to quantifying the relative contribution of each failure mechanism to the overall performance of the URM wall. [ABSTRACT FROM AUTHOR]

Vertical stratification in marine sediment profiles indicates physical and chemical sedimentary processes and, thus, is the first step in sedimentary research and in studying their relationship with global climate change. Traditional technologies for studying vertical stratification have low efficiency; thus, new technologies are highly needed. Recently, visible and near-infrared spectroscopy (VNIR) has been explored to rapidly determine sediment parameters, such as clay content, particle size, total carbon (TC), total nitrogen (TN), and so on. Here, we explored vertical stratification in a sediment column in the South China Sea using VNIR. The sediment column was 160 cm and divided into 160 samples by 1 cm intervals. All samples were classified into three layers by depth, that is, 0–50 cm (the upper layer), 50–100 cm (the middle layer), and 100–160 cm (the bottom layer). Concentrations of TC and TN in each sample were measured by Elementa Vario EL III. Visible and near-infrared reflectance spectra of each sample were collected by Agilent Cary 5000. A global model and several classification models for vertical stratification in sediments were established by a Support Vector Machine (SVM) after the characteristic spectra were identified using Competitive Adaptive Reweighted Sampling. In the classification models, K-means clustering and Density Peak Clustering (DPC) were employed as the unsupervised clustering algorithms. The results showed that the stratification was successful by VNIR, especially when using the combination of unsupervised clustering and machine learning algorithms. The correct classification rate (CCR) was much higher in the classification models than in the global model. And the classification models had a higher CCR using K-means combined with SVM (94.8%) and using DPC combined with SVM (96.0%). The higher CCR might be derived from the chemical classification. Indeed, similar results were also found in the chemical stratification. This study provided a theoretical basis for the rapid and synchronous measurement of chemical and physical parameters in sediment profiles by VNIR. [ABSTRACT FROM AUTHOR]

Introduction: Having multiple pharmacological effects is a characteristic of Traditional Chinese Medicine (TCM). Currently, there is a lack of suitable methods to explore and discover modern diseases suitable for TCM treatment using this characteristic. Unsupervised machine learning technology is an efficient strategy to predict the pharmacological activity of drugs. This study takes Yuxuebi Tablet (YXB) as the research object. Using the unsupervised machine learning technology of drug cell functional fingerprint similarity research, the potential pharmacological effects of YXB were discovered and verified. Methods: LC-MS combined with the in vitro intestinal absorption method was used to identify components of YXB that could be absorbed by the intestinal tract of rats. Unsupervised learning hierarchical clustering was used to calculate the degree of similarity of cellular functional fingerprints between these components and 121 marketed Western drugs whose indications are diseases and symptoms that YXB is commonly used to treat. Then, based on the Library of Integrated Network-based Cellular Signatures database, pathway analysis was performed for selected Western drugs with high similarity in cellular functional fingerprints with the components of YXB to discover the potential pharmacological effects of YXB, which were validated by animal experiments. Results: We identified 40 intestinally absorbed components of YXB. Through predictive studies, we found that they have pharmacological effects very similar to non-steroidal anti-inflammatory drugs (NSAIDs) and corticosteroids. In addition, we found that they have very similar pharmacological effects to antineuropathic pain medications (such as gabapentin, duloxetine, and pethidine) and may inhibit the NF-B signaling pathway and biological processes related to pain perception. Therefore, YXB may have an antinociceptive effect on neuropathic pain. Finally, we demonstrated that YXB significantly reduced neuropathic pain in a rat model of sciatic nerve chronic constriction injury (CCI). Transcriptome analysis further revealed that YXB regulates the expression of multiple genes involved in nerve injury repair, signal transduction, ion channels, and inflammatory response, with key regulatory targets including Sgk1, Sst, Isl1, and Shh. Conclusion: This study successfully identified and confirmed the previously unknown pharmacological activity of YXB against neuropathic pain through unsupervised learning prediction and experimental verification. [ABSTRACT FROM AUTHOR]

IntroductionImmunophenotyping is a powerful tool for grading disease severity, aiding in diagnosis, predicting clinical response, and guiding the development of novel therapeutics.MethodsThis pilot study employs high parameter immunophenotyping panels (15 markers for dog, 12 for cat) and leverages unsupervised clustering to identify immune cell populations. Our analysis uses machine learning and statistical algorithms to perform unsupervised clustering, multiple visualizations, and statistical analysis of high parameter flow cytometry data. This method reduces user bias and precisely identifies cell populations, demonstrating its potential to detect variations and differentiate populations effectively. To enhance our understanding of cat and dog biology and test the unsupervised clustering approach on real-world samples, we performed in-depth profiling of immune cell populations in blood collected from client-owned and laboratory animals [dogs (n = 55) and cats (n = 68)]. These animals were categorized based on pruritic behavior or routine check-ups (non-pruritic controls).ResultsUnsupervised clustering revealed various immune cell populations, including T-cell subsets distinguished by CD62L expression and distinct monocyte subsets. Notably, there were significant differences in monocyte subsets between pruritic and non-pruritic animals. Pruritic dogs and cats showed significant shifts in CD62LHi T-cell subsets compared to non-pruritic controls, with opposite trends observed between pruritic cats and dogs.DiscussionThese findings underscore the importance of advancing veterinary immunophenotyping, expanding our knowledge about marker expression on circulating immune cells and driving progress in understanding veterinary-specific biology and uncovering new insights into various conditions and diseases.

Massive changes in many aspects related to social groups of different socioeconomic backgrounds were caused by the COVID-19 pandemic and as a result, the overall state of mental health was severely affected globally. This study examined how the pandemic affected Sri Lankan citizens representing a range of socioeconomic backgrounds in terms of their mental health. The data used in this research was gathered from 3,020 households using a nationwide face-to-face survey, from which a processed dataset of 921 responses was considered for the final analysis. Four distinct factors were identified by factor analysis (FA) that was conducted and subsequently, the population was clustered using unsupervised clustering to determine which population subgroups were affected similarly. Two such subgroups were identified where the respective relationships to the retrieved principal factors and their demographics were thoroughly examined and interpreted. This resulted in the identification of contrasting perspectives between the two groups toward the maintenance and the state of social relationships during the pandemic, which revealed that one group was more “socially connected” in nature resulting in their mental state being comparatively better in coping with the pandemic. The other group was seen to be more “socially reserved” showing an opposite reaction toward social connections while their mental well-being declined showing symptoms such as loneliness, and emptiness in response to the pandemic. The study examined the role of social media, and it was observed that social media was perceived as a substitute for the lack of social connections or primarily used as a coping mechanism in response to the challenges of the pandemic and results show that maintaining social connections physically or via online rather than the use of social media has helped one group over the other in decreasing their symptoms such as emptiness, loneliness and fear of death.

Background: In recent times, disulfidptosis, an intricate form of cellular demise, has garnered attention due to its impact on prognosis, tumor progression and treatment response. Nevertheless, the exact significance of disulfidptosis-related genes (DisRGs) in glioblastoma (GBM) remains enigmatic. Methods: The GEO and TCGA databases provided transcriptional and clinically relevant data on tumor samples, while the GTEx database provided data on healthy tissues. Disulfidptosis-related genes (DisRGs) were procured from previous scholarly investigations. The expression profile of DisRGs was initially scrutinized among patients diagnosed with GBM, subsequent to which their prognostic value was explored. Through consensus clustering, we constructed DisRGs-related clusters and gene subtypes. Our results established that the DisRG-related clusters had differentially expressed genes, resulting in a DisulfidptosisScore model, which had a positive prognostic value. Results: The differential expression profile of 24 DisRGs between GBM samples and healthy samples was acquired. Through consensus cluster analysis, two distinct disulfidptosis subtypes, namely DisRGcluster A and DisRGcluster B, were identified. Then, the DisulfidptosisScore model including 4 characteristic genes was constructed.Notably, patients with GBM assigned with lower score demonstrated a considerably longer overall survival (OS) compared to those with higher score. Conclusion: We have effectively devised a prognostic model associated with disulfidptosis, presenting autonomous prognostic predictions for patients with GBM. These findings serve as a valuable addition to the current comprehension of disulfidptosis and offer fresh theoretical substantiation for the development of enhanced treatment strategies.

This study offers an in-depth analysis of the COVID-19 pandemic’s trajectory in several member countries of the European Union (EU) in order to assess similarities in their crisis experiences. We also examine data from the United States to facilitate a larger comparison across continents. We introduce our new approach, which uses a spatiotemporal algorithm to identify five distinct and recurring phases that each country underwent at different times during the COVID-19 pandemic. These stages include: Comfort Period, characterized by minimal COVID-19 activity and limited impacts; Preventive Situation, demonstrating the implementation of proactive measures, with relatively low numbers of cases, deaths, and Intensive Care Unit (ICU) admissions; Worrying Situation, is defined by high levels of concern and preparation as deaths and cases begin to rise and reach substantial levels; Panic Situation, marked by a high number of deaths relative to the number of cases and a rise in ICU admissions, denoting a critical and alarming period of the pandemic; and finally, Epidemic Control Situation, distinguished by limited numbers of COVID-19 deaths despite a high number of new cases. By examining these phases, we identify the various waves of the pandemic, indicating periods where the health crisis had a significant impact. This comparative analysis highlights the time lags between countries as they transitioned through these different critical stages and navigated the waves of the COVID-19 pandemic.

Single-cell RNA sequencing (scRNA-seq) data, susceptible to noise arising from biological variability and technical errors, can distort gene expression analysis and impact cell similarity assessments, particularly in heterogeneous populations. Current methods, including deep learning approaches, often struggle to accurately characterize cell relationships due to this inherent noise. To address these challenges, we introduce scAMF (Single-cell Analysis via Manifold Fitting), a framework designed to enhance clustering accuracy and data visualization in scRNA-seq studies. At the heart of scAMF lies the manifold fitting module, which effectively denoises scRNA-seq data by unfolding their distribution in the ambient space. This unfolding aligns the gene expression vector of each cell more closely with its underlying structure, bringing it spatially closer to other cells of the same cell type. To comprehensively assess the impact of scAMF, we compile a collection of 25 publicly available scRNA-seq datasets spanning various sequencing platforms, species, and organ types, forming an extensive RNA data bank. In our comparative studies, benchmarking scAMF against existing scRNA-seq analysis algorithms in this data bank, we consistently observe that scAMF outperforms in terms of clustering efficiency and data visualization clarity. Further experimental analysis reveals that this enhanced performance stems from scAMF's ability to improve the spatial distribution of the data and capture class-consistent neighborhoods. These findings underscore the promising application potential of manifold fitting as a tool in scRNA-seq analysis, signaling a significant enhancement in the precision and reliability of data interpretation in this critical field of study. [ABSTRACT FROM AUTHOR]

Background: Gut bacteria are related to colorectal cancer (CRC) and its clinicopathologic characteristics. Objective: To develop gut bacterial subtypes and explore potential microbial targets for CRC. Methods: Stool samples from 914 volunteers (376 CRCs, 363 advanced adenomas, and 175 normal controls) were included for 16S rRNA sequencing. Unsupervised learning was used to generate gut microbial subtypes. Gut bacterial community composition and clustering effects were plotted. Differences of gut bacterial abundance were analyzed. Then, the association of CRC‐associated bacteria with subtypes and the association of gut bacteria with clinical information were assessed. The CatBoost models based on gut differential bacteria were constructed to identify the diseases including CRC and advanced adenoma (AA). Results: Four gut microbial subtypes (A, B, C, D) were finally obtained via unsupervised learning. The characteristic bacteria of each subtype were Escherichia‐Shigella in subtype A, Streptococcus in subtype B, Blautia in subtype C, and Bacteroides in subtype D. Clinical information (e.g., free fatty acids and total cholesterol) and CRC pathological information (e.g., tumor depth) varied among gut microbial subtypes. Bacilli, Lactobacillales, etc., were positively correlated with subtype B. Positive correlation of Blautia, Lachnospiraceae, etc., with subtype C and negative correlation of Coriobacteriia, Coriobacteriales, etc., with subtype D were found. Finally, the predictive ability of CatBoost models for CRC identification was improved based on gut microbial subtypes. Conclusion: Gut microbial subtypes provide characteristic gut bacteria and are expected to contribute to the diagnosis of CRC. [ABSTRACT FROM AUTHOR]

Background: The TGx-DDI biomarker identifies transcripts specifically induced by primary DNA damage. Profiling similarity of TGx-DDI signatures can allow clustering compounds by genotoxic mechanism. This transcriptomics-based approach complements conventional toxicology testing by enhancing mechanistic resolution. Methods: Unsupervised hierarchical clustering and t-distributed stochastic neighbor embedding (tSNE) were utilized to assess similarity of publicly-available per- and polyfluoroalkyl substances (PFAS) and ToxCast chemicals based on TGx-DDI modulation. TempO-seq transcriptomic data after highest chemical concentrations were analyzed. Results: Clustering discriminated between genotoxic and non-genotoxic compounds while drawing similarity among chemicals with shared mechanisms. PFAS largely clustered distinctly from classical mutagens. However, dynamic range across PFAS types and durations indicated variable potential for DNA damage. tSNE visualization reinforced phenotypic groupings, with genotoxins clustering separately from non-DNA damaging agents. Discussion: Unsupervised learning approaches applied to TGx-DDI profiles effectively categorizes chemical genotoxicity potential, aiding elucidation of biological response pathways. This transcriptomics-based strategy gives further insight into the role and effect of individual TGx-DDI biomarker genes and complements existing assays by enhancing mechanistic resolution. Overall, TGx-DDI biomarker profiling holds promise for predictive safety screening. [ABSTRACT FROM AUTHOR]

Simple Summary: Due to the rapid development of single-cell RNA sequencing technology, the volume of single-cell RNA sequencing data has grown exponentially. Traditional clustering methods have proven increasingly difficult to cluster this large-scale and highly complex single-cell RNA sequencing data. Although many unsupervised clustering methods based on deep neural networks have been developed to cluster cell subpopulations, these methods are complex in models and poor in scalability. In this paper, we propose a novel clustering method for single-cell RNA sequencing, which successfully combines the advantages of these two clustering models, maintaining high clustering performance while also preserving the stable computational efficiency of traditional clustering methods. Experiments conducted on nine public datasets have demonstrated that our proposed novel clustering method for single-cell RNA sequencing outperforms both classic and state-of-the-art clustering methods. Single-cell RNA sequencing (scRNA-seq) is now a successful technology for identifying cell heterogeneity, revealing new cell subpopulations, and predicting developmental trajectories. A crucial component in scRNA-seq is the precise identification of cell subsets. Although many unsupervised clustering methods have been developed for clustering cell subpopulations, the performance of these methods is prone to be affected by dropout, high dimensionality, and technical noise. Additionally, most existing methods are time-consuming and fail to fully consider the potential correlations between cells. In this paper, we propose a novel unsupervised clustering method called scVGATAE (Single-cell Variational Graph Attention Autoencoder) for scRNA-seq data. This method constructs a reliable cell graph through network denoising, utilizes a novel variational graph autoencoder model integrated with graph attention networks to aggregate neighbor information and learn the distribution of the low-dimensional representations of cells, and adaptively determines the model training iterations for various datasets. Finally, the obtained low-dimensional representations of cells are clustered using kmeans. Experiments on nine public datasets show that scVGATAE outperforms classical and state-of-the-art clustering methods. [ABSTRACT FROM AUTHOR]

In situ spacecraft observations are critical to our study and understanding of the various phenomena that couple mass, momentum, and energy throughout near‐Earth space and beyond. However, on‐orbit telemetry constraints can severely limit the capability of spacecraft to transmit high‐cadence data, and missions are often only able to telemeter a small percentage of their captured data at full rate. This presents a programmatic need to prioritize intervals with the highest probability of enabling the mission's science goals. Larger missions such as the Magnetospheric Multiscale mission (MMS) aim to solve this problem with a Scientist‐In‐The‐Loop (SITL), where a domain expert flags intervals of time with potentially interesting data for high‐cadence data downlink and subsequent study. Although suitable for some missions, the SITL solution is not always feasible, especially for low‐cost missions such as CubeSats and NanoSats. This manuscript presents a generalizable method for the detection of anomalous data points in spacecraft observations, enabling rapid data prioritization without substantial computational overhead or the need for additional infrastructure on the ground. Specifically, Principal Components Analysis and One‐Class Support Vector Machines are used to generate an alternative representation of the data and provide an indication, for each point, of the data's potential for scientific utility. The technique's performance and generalizability is demonstrated through application to intervals of observations, including magnetic field data and plasma moments, from the CASSIOPE e‐POP/Swarm‐Echo and MMS missions. Plain Language Summary: Measurements captured by spacecraft are necessary to our understanding the space environment near Earth and throughout our solar system. However, spacecraft can often only transmit a small portion of the data they capture back to Earth. This means that many spacecraft must prioritize intervals of data that have the highest probability of helping to further our understanding of these environments. Some missions utilize humans, on Earth, to help select these scientifically important intervals. This solution, called the Scientist‐In‐The‐Loop, can be too expensive or programmatically complex for many small missions to implement. This manuscript presents a technique for the detection of anomalous events in spaceflight measurements using statistical analysis and machine learning. These detected anomalies can be used to prioritize data that has a high probability of scientific relevance. Further, the proposed technique is highly generalizable and computationally lightweight, making it suitable for a variety of missions. Several case studies from multiple existing missions will be analyzed throughout this paper. Key Points: Spacecraft often cannot transmit all measurements to Earth at full cadence due to telemetry bandwidth limitationsMany missions must implement complex data prioritization schemes to ensure only the highest‐priority data is transmitted at high cadenceThe proposed data prioritization technique is highly generic, compatible with inexpensive hardware, and suitable for low‐cost missions [ABSTRACT FROM AUTHOR]

With the increased availability of chemical data in public databases, innovative techniques and algorithms have emerged for the analysis, exploration, visualization, and extraction of information from these data. One such technique is chemical grouping, where chemicals with common characteristics are categorized into distinct groups based on physicochemical properties, use, biological activity, or a combination. However, existing tools for chemical grouping often require specialized programming skills or the use of commercial software packages. To address these challenges, we developed a user-friendly chemical grouping workflow implemented in KNIME, a free, open-source, low/no-code, data analytics platform. The workflow serves as an all-encompassing tool, expertly incorporating a range of processes such as molecular descriptor calculation, feature selection, dimensionality reduction, hyperparameter search, and supervised and unsupervised machine learning methods, enabling effective chemical grouping and visualization of results. Furthermore, we implemented tools for interpretation, identifying key molecular descriptors for the chemical groups, and using natural language summaries to clarify the rationale behind these groupings. The workflow was designed to run seamlessly in both the KNIME local desktop version and KNIME Server WebPortal as a web application. It incorporates interactive interfaces and guides to assist users in a step-by-step manner. We demonstrate the utility of this workflow through a case study using an eye irritation and corrosion dataset. Scientific contributions This work presents a novel, comprehensive chemical grouping workflow in KNIME, enhancing accessibility by integrating a user-friendly graphical interface that eliminates the need for extensive programming skills. This workflow uniquely combines several features such as automated molecular descriptor calculation, feature selection, dimensionality reduction, and machine learning algorithms (both supervised and unsupervised), with hyperparameter optimization to refine chemical grouping accuracy. Moreover, we have introduced an innovative interpretative step and natural language summaries to elucidate the underlying reasons for chemical groupings, significantly advancing the usability of the tool and interpretability of the results. [ABSTRACT FROM AUTHOR]

Complex systems are typically characterized by intricate internal dynamics that are often hard to elucidate. Ideally, this requires methods that allow to detect and classify in an unsupervised way the microscopic dynamical events occurring in the system. However, decoupling statistically relevant fluctuations from the internal noise remains most often nontrivial. Here, we describe "Onion Clustering": a simple, iterative unsupervised clustering method that efficiently detects and classifies statistically relevant fluctuations in noisy time-series data. We demonstrate its efficiency by analyzing simulation and experimental trajectories of various systems with complex internal dynamics, ranging from the atomic-to the microscopic-scale, in- and out-of-equilibrium. The method is based on an iterative detect-classify-archive approach. In a similar way as peeling the external (evident) layer of an onion reveals the internal hidden ones, the method performs a first detection/classification of the most populated dynamical environment in the system and of its characteristic noise. The signal of such dynamical cluster is then removed from the time-series data and the remaining part, cleared-out from its noise, is analyzed again. At every iteration, the detection of hidden dynamical subdomains is facilitated by an increasing (and adaptive) relevance-to-noise ratio. The process iterates until no new dynamical domains can be uncovered, revealing, as an output, the number of clusters that can be effectively distinguished/classified in a statistically robust way as a function of the time-resolution of the analysis. Onion Clustering is general and benefits from clear-cut physical interpretability. We expect that it will help analyzing a variety of complex dynamical systems and time-series data. [ABSTRACT FROM AUTHOR]

Background: The clinical presentation of Community-acquired pneumonia (CAP) in hospitalized patients exhibits heterogeneity. Inflammation and immune responses play significant roles in CAP development. However, research on immunophenotypes in CAP patients is limited, with few machine learning (ML) models analyzing immune indicators. Methods: A retrospective cohort study was conducted at Xinhua Hospital, affiliated with Shanghai Jiaotong University. Patients meeting predefined criteria were included and unsupervised clustering was used to identify phenotypes. Patients with distinct phenotypes were also compared in different outcomes. By machine learning methods, we comprehensively assess the disease severity of CAP patients. Results: A total of 1156 CAP patients were included in this research. In the training cohort (n=809), we identified three immune phenotypes among patients: Phenotype A (42.0%), Phenotype B (40.2%), and Phenotype C (17.8%), with Phenotype C corresponding to more severe disease. Similar results can be observed in the validation cohort. The optimal prognostic model, SuperPC, achieved the highest average C-index of 0.859. For predicting CAP severity, the random forest model was highly accurate, with C-index of 0.998 and 0.794 in training and validation cohorts, respectively. Conclusion: CAP patients can be categorized into three distinct immune phenotypes, each with prognostic relevance. Machine learning exhibits potential in predicting mortality and disease severity in CAP patients by leveraging clinical immunological data. Further external validation studies are crucial to confirm applicability. [ABSTRACT FROM AUTHOR]

Most existing cross-domain recommendation (CDR) systems apply the embedding and mapping idea to tackle the cold-start user problem and, to this end, they learn a common bridge function to transfer the user preferences from the source domain into the target domain. However, sharing a bridge function for all users inevitably leads to biased recommendations. This paper proposes a novel method, named CDR to cold-start users via categorized preference transfer (CDRCPT), to overcome the shortcomings of existing approaches. First, the embeddings of users and items in both the source and target domain are learned through pretraining and we utilize preference encoder to obtain the preference embeddings of users in the source domain. Second, mini-batch clustering is applied in the source domain to group users according to their preferences; here, each cluster identifies a specific class of users, and each cluster is represented by its center. Finally, the general representation is fed into a meta network to learn a bridge function for each available class of users. Experiments on two real data sets show that our CDRCPT method is effective in improving the accuracy and robustness of recommendations. [ABSTRACT FROM AUTHOR]

In recent times, disulfidptosis, an intricate form of cellular demise, has garnered attention due to its impact on prognosis, tumor progression and treatment response. Nevertheless, the exact significance of disulfidptosis-related genes (DisRGs) in glioblastoma (GBM) remains enigmatic. The GEO and TCGA databases provided transcriptional and clinically relevant data on tumor samples, while the GTEx database provided data on healthy tissues. Disulfidptosis-related genes (DisRGs) were procured from previous scholarly investigations. The expression profile of DisRGs was initially scrutinized among patients diagnosed with GBM, subsequent to which their prognostic value was explored. Through consensus clustering, we constructed DisRGs-related clusters and gene subtypes. Our results established that the DisRG-related clusters had differentially expressed genes, resulting in a DisulfidptosisScore model, which had a positive prognostic value. The differential expression profile of 24 DisRGs between GBM samples and healthy samples was acquired. Through consensus cluster analysis, two distinct disulfidptosis subtypes, namely DisRGcluster A and DisRGcluster B, were identified. Then, the DisulfidptosisScore model including 4 characteristic genes was constructed.Notably, patients with GBM assigned with lower score demonstrated a considerably longer overall survival (OS) compared to those with higher score. We have effectively devised a prognostic model associated with disulfidptosis, presenting autonomous prognostic predictions for patients with GBM. These findings serve as a valuable addition to the current comprehension of disulfidptosis and offer fresh theoretical substantiation for the development of enhanced treatment strategies. [ABSTRACT FROM AUTHOR]

This study offers an in-depth analysis of the COVID-19 pandemic's trajectory in several member countries of the European Union (EU) in order to assess similarities in their crisis experiences. We also examine data from the United States to facilitate a larger comparison across continents. We introduce our new approach, which uses a spatiotemporal algorithm to identify five distinct and recurring phases that each country underwent at different times during the COVID-19 pandemic. These stages include: Comfort Period, characterized by minimal COVID-19 activity and limited impacts; Preventive Situation, demonstrating the implementation of proactive measures, with relatively low numbers of cases, deaths, and Intensive Care Unit (ICU) admissions; Worrying Situation, is defined by high levels of concern and preparation as deaths and cases begin to rise and reach substantial levels; Panic Situation, marked by a high number of deaths relative to the number of cases and a rise in ICU admissions, denoting a critical and alarming period of the pandemic; and finally, Epidemic Control Situation, distinguished by limited numbers of COVID-19 deaths despite a high number of new cases. By examining these phases, we identify the various waves of the pandemic, indicating periods where the health crisis had a significant impact. This comparative analysis highlights the time lags between countries as they transitioned through these different critical stages and navigated the waves of the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

El Niño Southern Oscillation (ENSO) diversity is characterized based on the longitudinal location of maximum sea surface temperature anomalies (SSTA) and amplitude in the tropical Pacific, as Central Pacific events are typically weaker than Eastern Pacific events. SSTA pattern and intensity undergo low‐frequency modulations, affecting ENSO prediction skill and remote impacts, and resulting in low‐frequency changes in ENSO variance. Yet, how different ENSO types contribute to these decadal variance changes remains unclear. Here, we decompose the low‐frequency changes of ENSO variance into contributions from ENSO diversity categories. We propose a fuzzy clustering of monthly SSTA to allow for non‐binary event category memberships, where each event can belong to different clusters. Our approach identifies two La Niña and three El Niño categories and shows that the major shift of ENSO variance in the mid‐1970s was associated with an increasing likelihood of strong La Niña and extreme El Niño events. Plain Language Summary: The El Niño Southern Oscillation (ENSO) is a climate phenomenon that involves changes in ocean temperatures in the central and eastern tropical Pacific and greatly influences the weather around the globe. ENSO events are usually categorized based on where these temperature variations are the strongest and how intense they are. However, these categories are at times ambiguous and the way ENSO behaves can change over decades. Our study introduces a new way to estimate how different kinds of ENSO events contribute to its overall variation over time. We base our variability analysis on a fuzzy categorization of ENSO events, which results in three different kinds of El Niño events and two kinds of La Niña events. We find that the decadal changes in ENSO variance, including the decadal shift in the mid‐1970s, were primarily associated with strong La Niña and extreme El Niño events. Key Points: Using a fuzzy categorization, five different El Niño Southern Oscillation (ENSO) types are identified in observationsExtreme El Niño events emerge as a distinct categoryStrong La Niña and Extreme El Niño categories mainly contributed to the shift in decadal ENSO variance around 1970 [ABSTRACT FROM AUTHOR]

Background: Accumulating evidence reveals mitochondrial dysfunction exacerbates intestinal barrier dysfunction and inflammation. Despite the growing knowledge of mitochondrial dysfunction and ulcerative colitis (UC), the mechanism of mitochondrial dysfunction in UC remains to be fully explored. Methods: We integrated 1137 UC colon mucosal samples from 12 multicenter cohorts worldwide to create a normalized compendium. Differentially expressed mitochondria-related genes (DE-MiRGs) in individuals with UC were identified using the "Limma" R package. Unsupervised consensus clustering was utilized to determine the intrinsic subtypes of UC driven by DE-MiRGs. Weighted gene coexpression network analysis was employed to investigate module genes related to UC. Four machine learning algorithms were utilized for screening DE-MiRGs in UC and construct MiRGs diagnostic models. The models were developed utilizing the over-sampled training cohort, followed by validation in both the internal test cohort and the external validation cohort. Immune cell infiltration was assessed using the Xcell and CIBERSORT algorithms, while potential biological mechanisms were explored through GSVA and GSEA algorithms. Hub genes were selected using the PPI network. Results: The study identified 108 DE-MiRGs in the colonic mucosa of patients with UC compared to healthy controls, showing significant enrichment in pathways associated with mitochondrial metabolism and inflammation. The MiRGs diagnostic models for UC were constructed based on 17 signature genes identified through various machine learning algorithms, demonstrated excellent predictive capabilities. Utilizing the identified DE-MiRGs from the normalized compendium, 941 patients with UC were stratified into three subtypes characterized by distinct cellular and molecular profiles. Specifically, the metabolic subtype demonstrated enrichment in epithelial cells, the immuneinflamed subtype displayed high enrichment in antigen-presenting cells and pathways related to pro-inflammatory activation, and the transitional subtype exhibited moderate activation across all signaling pathways. Importantly, the immune-inflamed subtype exhibited a stronger correlation with superior response to four biologics: infliximab, ustekinumab, vedolizumab, and golimumab compared to the metabolic subtype. Conclusion: This analysis unveils the interplay between mitochondrial dysfunction and the immune microenvironment in UC, thereby offering novel perspectives on the potential pathogenesis of UC and precision treatment of UC patients, and identifying new therapeutic targets. [ABSTRACT FROM AUTHOR]

Background: For Rheumatoid Arthritis (RA), a long-term chronic illness, it is essential to identify and describe patient subtypes with comparable goal status and molecular biomarkers. This study aims to develop and validate a new subtyping scheme that integrates genome-scale transcriptomic profiles of RA peripheral blood genes, providing a fresh perspective for stratified treatments. Methods: We utilized independent microarray datasets of RA peripheral blood mononuclear cells (PBMCs). Up-regulated differentially expressed genes (DEGs) were subjected to functional enrichment analysis. Unsupervised cluster analysis was then employed to identify RA peripheral blood gene expression-driven subtypes. We defined three distinct clustering subtypes based on the identified 404 up-regulated DEGs. Results: Subtype A, named NE-driving, was enriched in pathways related to neutrophil activation and responses to bacteria. Subtype B, termed interferondriving (IFN-driving), exhibited abundant B cells and showed increased expression of transcripts involved in IFN signaling and defense responses to viruses. In Subtype C, an enrichment of CD8+ T-cells was found, ultimately defining it as CD8+ T-cells-driving. The RA subtyping scheme was validated using the XGBoost machine learning algorithm. We also evaluated the therapeutic outcomes of biological disease-modifying anti-rheumatic drugs. Conclusions: The findings provide valuable insights for deep stratification, enabling the design of molecular diagnosis and serving as a reference for stratified therapy in RA patients in the future. [ABSTRACT FROM AUTHOR]

This paper presents an innovative technique, Advanced Predictor of Electrical Parameters, based on machine learning methods to predict the degradation of electronic components under the effects of radiation. The term degradation refers to the way in which electrical parameters of the electronic components vary with the irradiation dose. This method consists of two sequential steps defined as 'recognition of degradation patterns in the database' and 'degradation prediction of new samples without any kind of irradiation'. The technique can be used under two different approaches called 'pure data driven' and 'model based'. In this paper, the use of Advanced Predictor of Electrical Parameters is shown for bipolar transistors, but the methodology is sufficiently general to be applied to any other component. [ABSTRACT FROM AUTHOR]

Modeled geospatial Lagrangian trajectories are widely used in Earth Science, including in oceanography, atmospheric science and marine biology. The typically large size of these data sets makes them arduous to analyze, and their underlying pathways challenging to identify. Here, we show that we can use a machine learning unsupervised k‐means++ clustering method combined with expert aggregation of clusters to identify the pathways of the Labrador Current from a large set of modeled Lagrangian trajectories. The presented method requires simple pre‐processing of the data, including a Cartesian correction on longitudes and a principal component analysis reduction. The clustering is performed in a kernelized space and uses a larger number of clusters than the number of expected pathways. To identify the main pathways, similar clusters are grouped into pathway categories by experts in the circulation of the region of interest. We find that the Labrador Current mainly follows a westward‐flowing and an eastward retroflecting pathway (20% and 50% of the flow, respectively) that compensate each other through time in a see‐saw behavior. These pathways experience a strong variability (representing through time 4%–42% and 24%–73% of the flow, respectively). Two thirds of the retroflection occurs at the tip of the Grand Banks, and one quarter at Flemish Cap. The westward pathway is mostly fed by the on‐shelf branch of the Labrador Current, and the eastward pathway by the shelf‐break branch. Among the pathways of secondary importance, we identify a previously unreported one that feeds the subtropics across the Gulf Stream. Plain Language Summary: Lagrangian trajectories, in which parcels of a fluid or objects are tracked as they move, are widely used in Earth Science, including in oceanography, atmospheric science and marine biology. They typically come in very large data sets containing chaotic trajectories, from which it is difficult to identify the main pathways of the flow. Here, we use a machine learning based algorithm, more specifically an unsupervised clustering algorithm, to identify the main pathways of the Labrador Current in the North Atlantic based on a large set of Lagrangian trajectories obtained from an ocean model. This study shows the power of such a method to help analyze this type of data, and provides a detailed description of the method so it can be used by a broad community on various applications. We find that, when it reached the Grand Banks of Newfoundland, most of the Labrador Current flows either westward toward the Slope Sea or eastward toward the North Atlantic Ocean, in a see‐saw behavior. We also identify a previously unknown minor pathway that brings Labrador Current waters south of the Gulf Stream front. Key Points: Unsupervised clustering followed by expert aggregation can identify the main pathways in geospatial Lagrangian trajectoriesThe clusters provide information on the properties, origin and time‐scale of the pathwaysAt the tip of the Grand Banks, the Labrador Current breaks into an east‐west see‐saw system [ABSTRACT FROM AUTHOR]

Classifications of forest vegetation types and characterization of related species assemblages are important analytical tools for mapping and diversity monitoring of forest communities. The discrimination of forest communities is often based on β‐diversity, which can be quantified via numerous indices to derive compositional dissimilarity between samples. This study aims to evaluate the applicability of unsupervised classification for National Forest Inventory data from Georgia by comparing two cluster hierarchies. We calculated the mean basal area per hectare for each woody species across 1059 plot observations and quantified interspecies distances for all 87 species. Following an unspuervised cluster analysis, we compared the results derived from the species‐neutral dissimilarity (Bray‐Curtis) with those based on the Discriminating Avalanche dissimilarity, which incorporates interspecies phylogenetic variation. Incorporating genetic variation in the dissimilarity quantification resulted in a more nuanced discrimination of woody species assemblages and increased cluster coherence. Favorable statistics include the total number of clusters (23 vs. 20), mean distance within clusters (0.773 vs. 0.343), and within sum of squares (344.13 vs. 112.92). Clusters derived from dissimilarities that account for genetic variation showed a more robust alignment with biogeographical units, such as elevation and known habitats. We demonstrate that the applicability of unsupervised classification of species assemblages to large‐scale forest inventory data strongly depends on the underlying quantification of dissimilarity. Our results indicate that by incorporating phylogenetic variation, a more precise classification aligned with biogeographic units is attained. This supports the concept that the genetic signal of species assemblages reflects biogeographical patterns and facilitates more precise analyses for mapping, monitoring, and management of forest diversity. [ABSTRACT FROM AUTHOR]