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Understanding diverse bacterial nutritional requirements and responses is foundational in microbial research and biotechnology. In this study, we employed knowledge-enriched transcriptomic analytics to decipher complex stress responses of Vibrio natriegens to supplied nutrients, aiming to enhance microbial engineering efforts. We computed 64 independently modulated gene sets that comprise a quantitative basis for transcriptome dynamics across a comprehensive transcriptomics dataset containing a broad array of nutrient conditions. Our approach led to the i) identification of novel transporter systems for diverse substrates, ii) a detailed understanding of how trace elements affect metabolism and growth, and iii) extensive characterization of nutrient-induced stress responses, including osmotic stress, low glycolytic flux, proteostasis, and altered protein expression. By clarifying the relationship between the acetate-associated regulon and glycolytic flux status of various nutrients, we have showcased its vital role in directing optimal carbon source selection. Our findings offer deep insights into the transcriptional landscape of bacterial nutrition and underscore its significance in tailoring strain engineering strategies, thereby facilitating the development of more efficient and robust microbial systems for biotechnological applications.

With the advances in smart sensing and data mining technologies of Industry 4.0, condition monitoring of key equipment in manufacturing has brought transformations in production and maintenance management. However, in practical applications, noise from both the working environment and the sensing devices is inevitable, which causes the low performance of data-driven fault diagnosis. To address this challenge, the paper develops a robust two-stage joint denoising method by integrating ensemble empirical mode decomposition (EEMD) and independent component analysis (ICA), with fuzzy entropy discriminant as a threshold. The developed method can filter noisy components from decomposed modal components and reconstruct a new signal with denoised independent components. Moreover, an improved convolutional neural network (CNN) model based on the VGG structure has been constructed as a classifier to achieve end-to-end fault diagnosis. The experimental results demonstrate the high accuracy and superior anti-interference capability of the proposed method for rolling bearing fault diagnosis under various noise levels. Compared with state-of-the-art denoising methods and fault diagnosis methods, the proposed method achieves higher accuracy and robustness under variable noise interference. The proposed method can be applied to broader fault diagnosis tasks of production equipment in complex practical environments. [ABSTRACT FROM AUTHOR]

UNLABELLED: There is growing interest in engineering Pseudomonas putida KT2440 as a microbial chassis for the conversion of renewable and waste-based feedstocks, and metabolic engineering of P. putida relies on the understanding of the functional relationships between genes. In this work, independent component analysis (ICA) was applied to a compendium of existing fitness data from randomly barcoded transposon insertion sequencing (RB-TnSeq) of P. putida KT2440 grown in 179 unique experimental conditions. ICA identified 84 independent groups of genes, which we call fModules (functional modules), where gene members displayed shared functional influence in a specific cellular process. This machine learning-based approach both successfully recapitulated previously characterized functional relationships and established hitherto unknown associations between genes. Selected gene members from fModules for hydroxycinnamate metabolism and stress resistance, acetyl coenzyme A assimilation, and nitrogen metabolism were validated with engineered mutants of P. putida. Additionally, functional gene clusters from ICA of RB-TnSeq data sets were compared with regulatory gene clusters from prior ICA of RNAseq data sets to draw connections between gene regulation and function. Because ICA profiles the functional role of several distinct gene networks simultaneously, it can reduce the time required to annotate gene function relative to manual curation of RB-TnSeq data sets. IMPORTANCE: This study demonstrates a rapid, automated approach for elucidating functional modules within complex genetic networks. While Pseudomonas putida randomly barcoded transposon insertion sequencing data were used as a proof of concept, this approach is applicable to any organism with existing functional genomics data sets and may serve as a useful tool for many valuable applications, such as guiding metabolic engineering efforts in other microbes or understanding functional relationships between virulence-associated genes in pathogenic microbes. Furthermore, this work demonstrates that comparison of data obtained from independent component analysis of transcriptomics and gene fitness datasets can elucidate regulatory-functional relationships between genes, which may have utility in a variety of applications, such as metabolic modeling, strain engineering, or identification of antimicrobial drug targets.

Uncovering slow collective variables (CVs) of self-assembly dynamics is important to elucidate its numerous kinetic assembly pathways and drive the design of novel structures for advanced materials through the bottom-up approach. However, identifying the CVs for self-assembly presents several challenges. First, self-assembly systems often consist of identical monomers, and the feature representations should be invariant to permutations and rotational symmetries. Physical coordinates, such as aggregate size, lack high-resolution detail, while common geometric coordinates like pairwise distances are hindered by the permutation and rotational symmetry challenges. Second, self-assembly is usually a downhill process, and the trajectories often suffer from insufficient sampling of backward transitions that correspond to the dissociation of self-assembled structures. Popular dimensionality reduction methods, such as time-structure independent component analysis, impose detailed balance constraints, potentially obscuring the true dynamics of self-assembly. In this work, we employ GraphVAMPnets, which combines graph neural networks with a variational approach for Markovian process (VAMP) theory to identify the slow CVs of the self-assembly processes. First, GraphVAMPnets bears the advantages of graph neural networks, in which the graph embeddings can represent self-assembly structures in high-resolution while being invariant to permutations and rotational symmetries. Second, it is built upon VAMP theory, which studies Markov processes without forcing detailed balance constraints, which addresses the out-of-equilibrium challenge in the self-assembly process. We demonstrate GraphVAMPnets for identifying slow CVs of self-assembly kinetics in two systems: the aggregation of two hydrophobic molecules and the self-assembly of patchy particles. We expect that our GraphVAMPnets can be widely applied to molecular self-assembly. [ABSTRACT FROM AUTHOR]

We propose a new method of independent component analysis (ICA) in order to extract appropriate features from high-dimensional data. In general, matrix factorization methods including ICA have a problem regarding the interpretability of extracted features. For the improvement of interpretability, sparse constraint on a factorized matrix is helpful. With this background, we construct a new ICA method with sparsity. In our method, the ℓ 1 -regularization term is added to the cost function of ICA, and minimization of the cost function is performed by a difference of convex functions algorithm. For the validity of our proposed method, we apply it to synthetic data and real functional magnetic resonance imaging data. [ABSTRACT FROM AUTHOR]

In recent years, the focus on mental health care in Japan has increased, leading to a rise in companies addressing employee mental well-being. Chronic stress, stemming from various sources including work and interpersonal relationships, can have severe impacts on individuals' happiness and health. To address this, there is a growing demand for technology capable of measuring chronic stress on a daily basis. In this study, we explore the potential of using facial thermal images (FTI) as a method for daily measurement of chronic stress. We conducted experiments over a 3-month period with healthy adult participants, collecting routine data on chronic stress and capturing FTI. Independent component analysis (ICA) was applied to the FTI data to extract relevant features. In addition, psychological questionnaires were administered to assess chronic stress levels. We aggregated the questionnaire scores using principal component analysis (PCA) to obtain a single chronic stress indicator. Multiple regression analysis (MRA) was then employed to model the relationship between the extracted FTI components and chronic stress scores. Our results indicate a moderate to strong correlation between the predicted and actual chronic stress scores, suggesting the potential utility of FTI in estimating stress levels. Identified features in the FTI, particularly around the upper lip and on the right half of the face, showed significant associations with chronic stress. This study provides insights into the feasibility of using FTI as a non-invasive method for daily monitoring of chronic stress levels. However, limitations such as variations in stress levels among participants and questionnaire administration frequency should be considered in future research. [ABSTRACT FROM AUTHOR]

Detecting violence in images has emerged as an important area of research and application as digital imaging and computer vision advance. Detecting and assessing violent content automatically in visual data holds great promise for enhancing public safety, security, and content moderation. Violent incidents are difficult to detect because of disruptions in optical flow, complications caused by camera aperture variations, and difficulties with illumination and feature tracking. In this work, a novel F3DNN-Net has been proposed for violence detection in images. The input videos from the surveillance camera are initially converted into frame sequences and pre-processed. The Yolov8 (You Only Look Once) algorithm is employed to detect objects within pre-processed frames. Simultaneously, the ego algorithm is applied to the pre-processed images for motion estimation, ensuring comprehensive coverage of information. Afterwards, the features are extracted using the fined feature extraction phase as independent component analysis (ICA), and the selected features are selected using the Dingo algorithm. Then, the fine-tuned fused (FF) features are implemented in the deep neural network (DNN) approach for the categorization of violent and non-violent cases. According to the findings, the proposed F3DNN-Net detects violence in images with an accuracy rate of 99.33%. The proposed DNN enhances an overall F1-score of 10.46%, 6.91%, and 4.44%, better than Alex Net, ResNet, and Dense Net. The proposed F3DNN-Net enhances the overall accuracy by 6%, 0.3%, and 29.33% better than Inception-Resnet-V2, CNN, LSTM, and ResNet50, respectively. [ABSTRACT FROM AUTHOR]

ABSTRACT Imprinted proteins (IPs) are promising alternatives to natural recognition systems, such as biological receptors or antibodies. One of the crucial stages during development of IPs is removal of the template molecules from its complex with the protein. In this study, bovine serum albumin was imprinted in the presence of 4‐hydroxycoumarin (4‐HC); purification of IPs were carried out by dialysis, and fluorescence 3D spectroscopy was used to monitor the IP purification process. Excitation–emission matrix (EEM) was further investigated via several chemometric algorithms (principal component analysis [PCA], parallel factor analysis [PARAFAC], and independent components analysis [ICA]). We found that the models using PARAFAC and ICA worked better than those of PCA. It was shown that PARAFAC and ICA analyses allow not only to recognize IP sample with signal close to nonimprinted protein, but also to provide recommendations on the optimal dialysis time. [ABSTRACT FROM AUTHOR]

Public gene expression databases are a rapidly expanding resource of organism responses to diverse perturbations, presenting both an opportunity and a challenge for bioinformatics workflows to extract actionable knowledge of transcription regulatory network function. Here, we introduce a five-step computational pipeline, called iModulonMiner, to compile, process, curate, analyze, and characterize the totality of RNA-seq data for a given organism or cell type. This workflow is centered around the data-driven computation of co-regulated gene sets using Independent Component Analysis, called iModulons, which have been shown to have broad applications. As a demonstration, we applied this workflow to generate the iModulon structure of Bacillus subtilis using all high-quality, publicly-available RNA-seq data. Using this structure, we predicted regulatory interactions for multiple transcription factors, identified groups of co-expressed genes that are putatively regulated by undiscovered transcription factors, and predicted properties of a recently discovered single-subunit phage RNA polymerase. We also present a Python package, PyModulon, with functions to characterize, visualize, and explore computed iModulons. The pipeline, available at https://github.com/SBRG/iModulonMiner, can be readily applied to diverse organisms to gain a rapid understanding of their transcriptional regulatory network structure and condition-specific activity. [ABSTRACT FROM AUTHOR]

An overview of the analytical process in this study: ①Resting-state networks (RSNs) were extracted using group independent component analysis (ICA).②Static functional connectivity differences within and between networks were compared.③The sliding window method and k-means cluster analysis were combined to identify seven stable repetitive states and compare the differences in dynamic functional network connectivity (dFNC) and dynamic temporal metrics between the two groups.④Support vector machine (SVM) models based on functional connectivity (FC) and functional network connectivity (FNC) were utilized to distinguish primary angle-closure glaucoma (PACG) patients from healthy controls (HCs). [Display omitted] • We investigated static and dynamic brain networks alterations in PACG patients. • PACG patients showed low-level and higher-order networks changes. • These results offer insights into the neural mechanisms of visual loss in PACG. Primary angle-closure glaucoma (PACG) is a severe and irreversible blinding eye disease characterized by progressive retinal ganglion cell death. However, prior research has predominantly focused on static brain activity changes, neglecting the exploration of how PACG impacts the dynamic characteristics of functional brain networks. This study enrolled forty-four patients diagnosed with PACG and forty-four age, gender, and education level-matched healthy controls (HCs). The study employed Independent Component Analysis (ICA) techniques to extract resting-state networks (RSNs) from resting-state functional magnetic resonance imaging (rs-fMRI) data. Subsequently, the RSNs was utilized as the basis for examining and comparing the functional connectivity variations within and between the two groups of resting-state networks. To further explore, a combination of sliding time window and k-means cluster analyses identified seven stable and repetitive dynamic functional network connectivity (dFNC) states. This approach facilitated the comparison of dynamic functional network connectivity and temporal metrics between PACG patients and HCs for each state. Subsequently, a support vector machine (SVM) model leveraging functional connectivity (FC) and FNC was applied to differentiate PACG patients from HCs. Our study underscores the presence of modified functional connectivity within large-scale brain networks and abnormalities in dynamic temporal metrics among PACG patients. By elucidating the impact of changes in large-scale brain networks on disease evolution, researchers may enhance the development of targeted therapies and interventions to preserve vision and cognitive function in PACG. [ABSTRACT FROM AUTHOR]

Anhedonia is a core symptom of depression that is closely related to prognosis and treatment outcomes. However, accurate and efficient treatments for anhedonia are lacking, mandating a deeper understanding of the underlying mechanisms. A total of 303 patients diagnosed with depression and anhedonia were assessed by the Snaith-Hamilton Pleasure Scale (SHAPS) and magnetic resonance imaging (MRI). The patients were categorized into a low-anhedonia group and a high-anhedonia group using the K -means algorithm. A data-driven approach was used to explore the differences in brain structure and function with different degrees of anhedonia based on MATLAB. A random forest model was used exploratorily to test the predictive ability of differences in brain structure and function on anhedonia in depression. Structural and functional differences were apparent in several brain regions of patients with depression and high-level anhedonia, including in the temporal lobe, paracingulate gyrus, superior frontal gyrus, inferior occipital gyrus, right insular gyrus, and superior parietal lobule. And changes in these brain regions were significantly correlated with scores of SHAPS. These brain regions may be useful as biomarkers that provide a more objective assessment of anhedonia in depression, laying the foundation for precision medicine in this treatment-resistant, relatively poor prognosis group. • rs-fMRI was used to distinguish high and low levels of anhedonia in patients with depression. • The temporal lobe and superior parietal lobule may be an important hub for anhedonia in depression. • Imaging biomarkers for anhedonia in depression can provide more objective assessment, laying a foundation for precision medicine. [ABSTRACT FROM AUTHOR]

Severe acute brain injury (SABI) with suppressed consciousness is a major societal burden, with early prognosis being crucial for life-and-death treatment decisions. Resting-state functional MRI (rs-fMRI) is promising for prognosis and identifying epileptogenic activity in SABI. While established for SABI prognosis and seizure networks (SzNET) identification in epilepsy, the rs-fMRI use for SzNET detection in SABI is limited. This study compared evolution of SzNET and resting-state networks (RSN) pre-to-post treatment in SABI and epilepsy, hypothesizing that changes would align with clinical evolution. Therapies included epilepsy surgery for the epilepsy group and antiseizure medication for the SABI group. Independent component analysis (ICA) was used to identify SzNET and RSNs in all rs-fMRI. High-frequency BOLD (HF-BOLD), an ICA power spectrum-based index, quantified RSN and SzNET changes by the patient. Confidence intervals measured HF-BOLD changes pre-to-post-therapy. Baseline HF-BOLD and HF-BOLD changes were compared using linear-mixed models and interaction tests. Five SABI and ten epilepsy patients were included. SzNET were identified in all SABI's pre-therapy rs-fMRI. The clinical changes in SABI and epilepsy were consistent with rs-fMRI findings across groups. HF-BOLD reduced in the epilepsy group RSN post-therapy (-0.78, 95% CI -3.42 to -0.33), but the evidence was insu cient to determine an HF-BOLD reduction in SABI patients or SzNET. The HF-BOLD change trend in pre-to-post epilepsy surgery scans paralleled the clinical improvement, suggesting that the power spectrum may quantify the degree of abnormality on ICA-derived networks. Despite limitations such as small sample sizes, this exploratory study provides valuable insights into network dysfunction in SABI and epilepsy. [ABSTRACT FROM AUTHOR]

This paper presents an innovative approach for the Feature Extraction method using Self-Attention, incorporating various Feature Selection techniques known as the AtSiftNet method to enhance the classification performance of motor imaginary activities using electrophotography (EEG) signals. Initially, the EEG signals were sorted and then denoised using multiscale principal component analysis to obtain clean EEG signals. However, we also conducted a non-denoised experiment. Subsequently, the clean EEG signals underwent the Self-Attention feature extraction method to compute the features of each trial (i.e., 350 × 18 ). The best 1 or 15 features were then extracted through eight different feature selection techniques. Finally, five different machine learning and neural network classification models were employed to calculate the accuracy, sensitivity, and specificity of this approach. The BCI competition III dataset IV-a was utilized for all experiments, encompassing the datasets of five volunteers who participated in the competition. The experiment findings reveal that the average accuracy of classification is highest among ReliefF (i.e., 99.946 % ), Mutual Information (i.e., 98.902 % ), Independent Component Analysis (i.e., 99.62 % ), and Principal Component Analysis (i.e., 98.884 % ) for both 1 and 15 best-selected features from each trial. These accuracies were obtained for motor imagery using a Support Vector Machine (SVM) as a classifier. In addition, five-fold validation was performed in this paper to assess the fair performance estimation and robustness of the model. The average accuracy obtained through five-fold validation is 99.89 % . The experiments' findings indicate that the suggested framework provides a resilient biomarker with minimal computational complexity, making it a suitable choice for advancing Motor Imagery Brain–Computer Interfaces (BCI). [ABSTRACT FROM AUTHOR]

Automated EEG pre‐processing pipelines provide several key advantages over traditional manual data cleaning approaches; primarily, they are less time‐intensive and remove potential experimenter error/bias. Automated pipelines also require fewer technical expertise as they remove the need for manual artefact identification. We recently developed the fully automated Reduction of Electroencephalographic Artefacts (RELAX) pipeline and demonstrated its performance in cleaning EEG data recorded from adult populations. Here, we introduce the RELAX‐Jr pipeline, which was adapted from RELAX and designed specifically for pre‐processing of data collected from children. RELAX‐Jr implements multi‐channel Wiener filtering (MWF) and/or wavelet‐enhanced independent component analysis (wICA) combined with the adjusted‐ADJUST automated independent component classification algorithm to identify and reduce all artefacts using algorithms adapted to optimally identify artefacts in EEG recordings taken from children. Using a dataset of resting‐state EEG recordings (N = 136) from children spanning early‐to‐middle childhood (4–12 years), we assessed the cleaning performance of RELAX‐Jr using a range of metrics including signal‐to‐error ratio, artefact‐to‐residue ratio, ability to reduce blink and muscle contamination, and differences in estimates of alpha power between eyes‐open and eyes‐closed recordings. We also compared the performance of RELAX‐Jr against four publicly available automated cleaning pipelines. We demonstrate that RELAX‐Jr provides strong cleaning performance across a range of metrics, supporting its use as an effective and fully automated cleaning pipeline for neurodevelopmental EEG data. [ABSTRACT FROM AUTHOR]

Purpose: While fMRI provides information on the temporal changes in blood oxygenation, 2- [18F]fluoro-2-deoxy-D-glucose ([18F]FDG)-PET has traditionally offered a static snapshot of brain glucose consumption. As a result, studies investigating metabolic brain networks as potential biomarkers for neurodegeneration have primarily been conducted at the group level. However, recent pioneering studies introduced time-resolved [18F]FDG-PET with constant infusion, which enables metabolic connectivity studies at the individual level. Methods: In the current study, this technique was employed to explore Parkinson's disease (PD)-related alterations in individual metabolic connectivity, in comparison to inter-subject measures and hemodynamic connectivity. Fifteen PD patients and 14 healthy controls with comparable cognition underwent sequential resting-state dynamic PET with constant infusion and functional MRI. Intrinsic networks were identified by independent component analysis and interregional connectivity calculated for summed static PET images, PET time series and functional MRI. Results: Our findings revealed an intrinsic sensorimotor network in PD patients that has not been previously observed to this extent. In PD, a significantly higher number of connections in cortical motor areas was observed compared to elderly control subjects, as indicated by both static PET and functional MRI (pBonferroni−Holm = 0.027), as well as constant infusion PET and functional MRI connectomes (pBonferroni−Holm = 0.012). This intensified coupling was associated with disease severity (ρ = 0.56, p = 0.036). Conclusion: Metabolic connectivity, as revealed by both static and dynamic PET, provides unique information on metabolic network activity. Subject-level metabolic connectivity based on constant infusion PET may serve as a potential marker for the metabolic network signature in neurodegeneration. [ABSTRACT FROM AUTHOR]

Background: Juvenile myoclonic epilepsy (JME) is characterized by altered patterns of brain functional connectivity (FC). However, the nature and extent of alterations in the spatiotemporal characteristics of dynamic FC in JME patients remain elusive. Dynamic networks effectively encapsulate temporal variations in brain imaging data, offering insights into brain network abnormalities and contributing to our understanding of the seizure mechanisms and origins. Methods: Resting-state functional magnetic resonance imaging (rs-fMRI) data were procured from 37 JME patients and 37 healthy counterparts. Forty-seven network nodes were identified by group-independent component analysis (ICA) to construct the dynamic network. Ultimately, patients' and controls' spatiotemporal characteristics, encompassing temporal clustering and variability, were contrasted at the whole-brain, large-scale network, and regional levels. Results: Our findings reveal a marked reduction in temporal clustering and an elevation in temporal variability in JME patients at the whole-brain echelon. Perturbations were notably pronounced in the default mode network (DMN) and visual network (VN) at the large-scale level. Nodes exhibiting anomalous were predominantly situated within the DMN and VN. Additionally, there was a significant correlation between the severity of JME symptoms and the temporal clustering of the VN. Conclusions: Our findings suggest that excessive temporal changes in brain FC may affect the temporal structure of dynamic brain networks, leading to disturbances in brain function in patients with JME. The DMN and VN play an important role in the dynamics of brain networks in patients, and their abnormal spatiotemporal properties may underlie abnormal brain function in patients with JME in the early stages of the disease. [ABSTRACT FROM AUTHOR]

Gonadotropin releasing hormone (GnRH) synthesis and secretion regulates seasonal fertility. In the brain, the distribution of GnRH-positive neurons is diffuse, hindering efforts to monitor variations in its cellular and tissue levels. Here, we aim at assessing GnRH immunoreactivity in nuclei responsible for seasonal fertility regulation (SFR) within the posterior, anterior, and preoptic areas of the basal hypothalamus during estrous in ewes. We detected reaction products in the ventromedial basal hypothalamus in neurons, nerve fibers, non-neuronal immunoreactive bodies, and diffuse interstitial areas. Immunoreactivity correlated with the distribution of the main SFR nuclei in the arcuate, retrochiasmatic, periventricular, medial preoptic, supraoptic, and preoptic areas. By independent component analysis density segmentation and by interferential contrast, we identified GnRH non-neuronal positive bodies as microglial cells encapsulated within a dense halo of reaction products. These GnRH-positive microglial cells were distributed in patches and rows throughout the basal ventromedial hypothalamus, suggesting their role in paracrine or juxtacrine signaling. Moreover, as shown by ionized calcium-binding adaptor molecule 1 (IBA1) immunocytochemistry, the distribution of GnRH reaction products overlapped with the microglial dense reactive zones. Therefore, our findings support the assertion that a combined densitometric analysis of GnRH and IBA1 immunocytochemistry enables activity mapping for monitoring seasonal changes following experimental interventions. • GnRH reaction products overlap with areas of reactive microglia in the basal ventromedial hypothalamus of ewes. • GnRH-positive microglial cells support their role in activity-dependent juxtacrine signaling. • Comparative densitometric mapping of GnRH and IBA1 may be a suitable method for monitoring seasonal changes. [ABSTRACT FROM AUTHOR]

The early Paleogene, including the Paleocene and Eocene, is characterized by Warmhouse and Hothouse climate states with superimposed transient warming events known as hyperthermals. While these paleoenvironmental changes are well‐documented in the Pacific and Atlantic Oceans, records of such changes in the Indian Ocean are limited. Here, we present a new data set of bulk chemical composition and stable isotopic ratios from the late Paleocene to middle Eocene sediments on the Exmouth Plateau in the mid‐latitude eastern Indian Ocean. The bulk δ13C and δ18O suggest a warming period called the Early Eocene Climatic Optimum (EECO) and cooling toward the middle Eocene in a long‐term perspective. In the short‐term, we identified at least six hyperthermals (Paleocene–Eocene Thermal Maximum, H2, I1, J, ETM3, and L) in the studied sections. By applying independent component (IC) analyses (ICA) to the bulk chemical composition data, we identified six ICs corresponding to sediment source materials and post‐depositional processes. The ICA result infers an increase in detrital materials or a decrease in carbonate rain flux, and an increased population of higher‐order consumers in the oceanic ecosystem during both long‐term (EECO) and short‐term (hyperthermal) warmings around the Exmouth Plateau. Furthermore, ICs representing diagenetic processes and post‐depositional remobilization of elements showed excursions across the hyperthermal horizons, indicating that changes in the redox state of pore or bottom water are associated with hyperthermals. This study provides critical insights into the paleoceanography of the Indian Ocean, highlighting the response of marine environments to both long‐term and short‐term climatic events during the early Paleogene. Plain Language Summary: This study examined marine sediments from the mid‐latitude eastern Indian Ocean dating back to the early Paleogene period, approximately 57 to 40 million years ago. During this time, Earth's climate was much warmer than today, with several short‐term warming events known as hyperthermals. By analyzing the stable carbon and oxygen isotope ratios and the bulk chemical composition of these sediments, we identified six distinct hyperthermals, including the Paleocene–Eocene Thermal Maximum, which was the severest global warming event among the early Paleogene hyperthermals. By applying a multivariate statistical method called independent component analysis to the bulk chemical composition data set, we extracted geochemical features of sediments, including the contributions of source materials (detrital materials, biogenic materials), and signatures of post‐depositional chemical processes. The analysis indicated that inputs of detrital materials from continents and populations of higher consumers in the ocean, such as fish, increased during both long‐ and short‐term warmings. Additionally, several elements were remobilized within the sediment because of changes in the redox state of pore or bottom water. This research provides valuable insights into the link between past climate events and changes in ocean and sediment chemistry, helping us understand how warm periods shaped marine ecosystems and oceanic environments. Key Points: Here, we report bulk δ13C, δ18O, and chemical compositions of early Paleogene sediments from Hole 762C in the Indian OceanWe updated age model of Hole 762C by multiple Eocene hyperthermal horizons, indicated by the bulk δ13C recordIndependent component analysis on bulk compositions revealed Paleogene climate upheavals affected depositional environments at the site [ABSTRACT FROM AUTHOR]

Purpose: Accurate detection of Atrial Fibrillation (AF) has great significance in the field of medical science which can reduce the rate of mortality and morbidity. The present study focuses on Electrocardiography (ECG) signal classification using dimensionality reduction techniques combined with R wave to R wave interval (RR interval) features. Materials and Methods: In the first approach, Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Independent Component Analysis (ICA), and Probabilistic Principal Component Analysis (PPCA) are performed independently on denoised ECG signal using Discrete Wavelet Transform (DWT) for the classification of ECG signal. In the second approach, the dimensionality reduction techniques combined with RR interval features are used for the classification of ECG signal. Results: Machine Learning (ML) algorithms such as Decision Tree (DT), Support Vector Machine (SVM), and Deep Learning (DL) algorithms such as Long Short Term Memory (LSTM) and Bi-Directional LSTM (BiLSTM) are used for classification purposes. Conclusion: The proposed methodology provided an overall accuracy of 93.65% with PCA and LSTM classifier and an overall accuracy of 99.45% with PCA combined with RR interval features and LSTM classifier. The developed technology has potential applications in many practical solutions. [ABSTRACT FROM AUTHOR]

With the advances in Internet-of-Things and data mining technologies, deep learning-based approaches have been widely used for intelligent fault diagnosis of manufacturing assets. However, uncertainty caused by the non-stationary process data such as vibration signal and noise interference in practical working environments will greatly affect the performance and reliability of predictions. The present paper develops a trustworthy and intelligent fault diagnosis framework based on a two-stage joint denoising method and evidential neural networks. The proposed denoising method integrating the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and the independent component analysis (ICA) method can effectively reduce data uncertainty caused by noise interference. The stacked gated recurrent unit (SGRU) model has been incorporated into the evidential neural networks as a deep classifier. The proposed evidential SGRU (ESGRU) method can quantify the prediction uncertainty, which estimates the prediction trustworthiness. Predictive entropy and reliability diagrams are used as calibration methods to validate the effectiveness of uncertainty estimation. The proposed framework is validated by two case studies of rolling bearing fault diagnosis in variable noise conditions. Experimental results demonstrate that the proposed method can achieve a high denoising effect and provide reliable uncertainty prediction results which are significant for practical applications. [ABSTRACT FROM AUTHOR]

Background: Childhood maltreatment (CM) is increasingly recognized as a significant risk factor for major depressive disorder (MDD), yet the neural mechanisms underlying the connection between CM and depression are not fully understood. This study aims to deepen our understanding of this relationship through neuroimaging, exploring how CM correlates with depression. Methods: The study included 56 MDD patients (33 with CM experiences and 23 without) and 23 healthy controls. Participants were assessed for depression severity, CM experiences, and underwent resting-state functional MRI scans. Independent Component Analysis was used to examine differences in functional connectivity (FC) within the Default Mode Network (DMN) among the groups. Results: MDD patients with CM experiences exhibited significantly stronger functional connectivity in the left Superior Frontal Gyrus (SFG) and right Anterior Cingulate Cortex (ACC) within the DMN compared to both MDD patients without CM experiences and healthy controls. FC in these regions positively correlated with Childhood Trauma Questionnaire scores. Receiver Operating Characteristic (ROC) curve analysis underscored the diagnostic value of FC in the SFG and ACC for identifying MDD related to CM. Additionally, MDD patients with CM experiences showed markedly reduced FC in the left medial Prefrontal Cortex (mPFC) relative to MDD patients without CM experiences, correlating negatively with Childhood Trauma Questionnaire scores. Conclusion: Our findings suggest that increased FC in the ACC and SFG within the DMN is associated with CM in MDD patients. This enhanced connectivity in these brain regions is key to understanding the predisposition to depression related to CM. [ABSTRACT FROM AUTHOR]

This article develops statistical inference methods for a class of set‐identified models, where the errors are known functions of observations and the parameters satisfy either serial or/and cross‐sectional independence conditions. This class of models includes the independent component analysis (ICA), Structural Vector Autoregressive (SVAR), and multi‐variate mixed causal–non‐causal models. We use the Generalized Covariance (GCov) estimator to compute the residual‐based portmanteau statistic for testing the error independence hypothesis. Next, we build the confidence sets for the identified sets of parameters by inverting the test statistic. We also discuss the choice (design) of these statistics. The approach is illustrated by simulations examining the under‐identification condition in an ICA model and an application to financial return series. [ABSTRACT FROM AUTHOR]

Objective: Research indicates that cognitive control is compromised in individuals with internet gaming disorder (IGD). However, the neural mechanisms behind it are still unclear. This study aims to investigate alterations in resting-state brain networks in adolescents with IGD and the potential neurobiological mechanisms underlying cognitive dysfunction. Materials and methods: A total of 44 adolescent IGD subjects (male/female: 38/ 6) and 50 healthy controls (male/female: 40/10) were enrolled. Participants underwent demographic assessments, Young's Internet Addiction Scale, Barratt Impulsiveness Scale 11 Chinese Revised Version, the Chinese Adolescents' Maladaptive Cognitions Scale, exploratory eye movement tests, and functional magnetic resonance imaging (fMRI). FMRI data were analyzed using the GIFT software for independent component analysis, focusing on functional connectivity within and between resting-state brain networks. Results: In comparison to the control group, impulsivity in adolescent IGD subjects showed a positive correlation with the severity of IGD (r=0.6350, p < 0.001), linked to impairments in the Executive Control Network (ECN) and a decrease in functional connectivity between the Salience Network (SN) and ECN (r=0.4307, p=0.0021; r=-0.5147, p=0.0034). Decreased resting state activity of the dorsal attention network (DAN) was associated with attentional dysregulation of IGD in adolescents (r=0.4071, p=0.0017), and ECN increased functional connectivity with DAN. The degree of IGD was positively correlated with enhanced functional connectivity between the ECN and DAN (r=0.4283, p=0.0037). Conclusions: This research demonstrates that changes in the ECN and DAN correlate with heightened impulsivity and attentional deficits in adolescents with IGD. The interaction between cognitive control disorders and resting-state brain networks in adolescent IGD is related. [ABSTRACT FROM AUTHOR]

Synechococcus elongatus is an important cyanobacterium that serves as a versatile and robust model for studying circadian biology and photosynthetic metabolism. Its transcriptional regulatory network (TRN) is of fundamental interest, as it orchestrates the cell's adaptation to the environment, including its response to sunlight. Despite the previous characterization of constituent parts of the S. elongatus TRN, a comprehensive layout of its topology remains to be established. Here, we decomposed a compendium of 300 high-quality RNA sequencing datasets of the model strain PCC 7942 using independent component analysis. We obtained 57 independently modulated gene sets, or iModulons, that explain 67% of the variance in the transcriptional response and 1) accurately reflect the activity of known transcriptional regulations, 2) capture functional components of photosynthesis, 3) provide hypotheses for regulon structures and functional annotations of poorly characterized genes, and 4) describe the transcriptional shifts under dynamic light conditions. This transcriptome-wide analysis of S. elongatus provides a quantitative reconstruction of the TRN and presents a knowledge base that can guide future investigations. Our systems-level analysis also provides a global TRN structure for S. elongatus PCC 7942. [ABSTRACT FROM AUTHOR]

Radar surveillance of noncooperative UAV swarm is challenging and is involved in many critical surveillance scenarios. The multimodality property of dynamic UAV swarm targets presents larger radar signature complexity and elevates the radar detection difficulty. The swarm unit number ambiguity from dense UAV grouping also inhibits radar monitoring accuracy. Inspired by the coherent integration essence of swarm target signals, this paper proposes a radar signal processing framework based on complex valued independent component analysis (cICA) for swarm target identification and quantification. The target detection threshold is determined from pure clutter signals after cICA processing. A customized clustering algorithm is applied on independent components for swarm target quantification. Target detection and quantification methods are verified with various multimodality UAV swarm flight plans. The results indicate that the detection performance of the proposed method is comparable with conventional CFAR algorithms with better stability performance. The target quantification procedure could estimate swarm unit numbers with acceptable numerical deviations. More discussions are given on the relevance between quantification accuracy and swarm configurations with respect to signal independency mechanisms. Efficiency discussions reveal the bottleneck of the proposed method for future optimization works. [ABSTRACT FROM AUTHOR]

As a critical component in industrial production, pipelines face the risk of failure due to long-term corrosion. In recent years, acoustic emission (AE) technology has demonstrated significant potential in online pipeline monitoring. However, the interference of flow-induced noise seriously hinders the application of acoustic emission technology in pipeline corrosion monitoring. Therefore, a pattern-recognition model for online pipeline AE monitoring signals based on blind source separation (BSS) and a convolutional neural network (CNN) is proposed. First, the singular spectrum analysis (SSA) was employed to transform the original AE signal into multiple observed signals. An independent component analysis (ICA) was then utilized to separate the source signals from the mixed signals. Subsequently, the Hilbert–Huang transform (HHT) was applied to each source signal to obtain a joint time–frequency domain map and to construct and compress it. Finally, the mapping relationship between the pipeline sources and AE signals was established based on the CNN for the precise identification of corrosion signals. The experimental data indicate that when the average amplitude of flow-induced noise signals is within three times that of corrosion signals, the separation of mixed signals is effective, and the overall recognition accuracy of the model exceeds 90%. [ABSTRACT FROM AUTHOR]

Background: Functional magnetic resonance imaging (fMRI) studies have revealed extensive functional reorganization in patients with sensorineural hearing loss (SNHL). However, almost no study focuses on the dynamic functional connectivity after hearing loss. Objective: This study aimed to investigate dynamic functional connectivity changes in children with profound bilateral congenital SNHL under the age of 3 years. Materials and methods: Thirty-two children with profound bilateral congenital SNHL and 24 children with normal hearing were recruited for the present study. Independent component analysis identified 18 independent components composing five resting-state networks. A sliding window approach was used to acquire dynamic functional matrices. Three states were identified using the k-means algorithm. Then, the differences in temporal properties and the variance of network efficiency between groups were compared. Results: The children with SNHL showed longer mean dwell time and decreased functional connectivity between the auditory network and sensorimotor network in state 3 (P < 0.05), which was characterized by relatively stronger functional connectivity between high-order resting-state networks and motion and perception networks. There was no difference in the variance of network efficiency. Conclusions: These results indicated the functional reorganization due to hearing loss. This study also provided new perspectives for understanding the state-dependent connectivity patterns in children with SNHL. [ABSTRACT FROM AUTHOR]

While self-construal and posttraumatic stress disorder (PTSD) are independently associated with altered self-referential processes and underlying default mode network (DMN) functioning, no study has examined how self-construal affects DMN connectivity in PTSD. A final sample of 93 refugee participants (48 with DSM-5 PTSD or sub-syndromal PTSD and 45 matched trauma-exposed controls) completed a 5-minute resting state fMRI scan to enable the observation of connectivity in the DMN and other core networks. A self-construal index was calculated by substracting scores on the collectivistic and individualistic sub-scales of the Self Construal Scale. Independent components analysis identified 9 active networks-of-interest, and functional network connectivity was determined. A significant interaction effect between PTSD and self-construal index was observed in the anterior ventromedial DMN, with spatial maps localizing this to the left ventromedial prefrontal cortex (vmPFC), extending to the ventral anterior cingulate cortex. This effect revealed that connectivity in the vMPFC showed greater reductions in those with PTSD with higher levels of collectivistic self-construal. This is an observational study and causality cannot be assumed. The specialized sample of refugees means that the findings may not generalize to other trauma-exposed populations. Such a finding indicates that self-construal may shape the core neural architecture of PTSD, given that functional disruptions to the vmPFC underpin the core mechanisms of extinction learning, emotion dysregulation and self-referential processing in PTSD. Results have important implications for understanding the universality of neural disturbances in PTSD, and suggest that self-construal could be an important consideration in the assessment and treatment of post-traumatic stress reactions. • Studies that consider cultural influences on PTSD brain mechanisms are rare. • PTSD interacted with self-construal to affect default mode network connectivity. • The ventromedial prefrontal cortex was the site of interaction. • Findings suggest cultural variables shape the neural substrates of PTSD. [ABSTRACT FROM AUTHOR]

Featured Application: This work can be applied to estimate acceleration using measured inclination, withoutpartially measuring the target acceleration. Moreover, this is a finiteelement model-free method and can be conveniently applied to beam bridges. Data fusion is an important issue in bridge health monitoring. Through data fusion, specific unknown bridge responses can be estimated with measured responses. However, existing data fusion methods always require a precise finite element model of the bridge or partially measured target responses, which are hard to realize in actual engineering. In this study, we propose a novel data fusion method. Measured inclinations across multiple cross-sections of the target bridge and accelerations at a subset of these sections were used to estimate accelerations at the remaining sections. Theoretical analysis of a typical vehicle-bridge interaction (VBI) system has shown parallels with the blind source separation (BSS) problem. Based on this, Independent Component Analysis (ICA) was applied to derive surrogate inclination mode shapes. This was followed by calculating surrogate displacement mode shapes through numerical integration. Finally, a surrogate inter-section transfer matrix for both measured and unmeasured accelerations was constructed, enabling the estimation of the target accelerations. This paper presents three key principles involving the relationship between the surrogate and actual inter-section transfer matrices, the integration of mode shape functions, and the consistency of transfer matrices for low- and high-frequency responses, which form the basis of the proposed method. A series of numerical simulations and a large-scale laboratory experiment were proposed to validate the proposed method. Compared to existing approaches, our proposed method stands out as a purely data-driven technique, eliminating the need for finite element analysis assessment. By incorporating the ICA algorithm and surrogate mode shapes, this study addresses the challenges associated with obtaining accurate mode shape functions from low-frequency responses. Moreover, our method does not require partial measurements of the target responses, simplifying the data collection process. The validation results demonstrate the method's practicality and convenience for real-world engineering applications, showcasing its potential for broad adoption in the field. [ABSTRACT FROM AUTHOR]

This paper studied the problem of multi-user blind signal separation (BSS) in wireless communications. The existing separation algorithms work on quadrature phase shift keying (QPSK). Through our work, two proposed algorithms were presented to enhance the BSS performance. The first proposed algorithm uses wavelet denoising to remove noise from the received signals in time domain. It adopts different modulation techniques such as minimum shift keying (MSK), QPSK, and Gaussian minimum shift keying (GMSK). Then several BSS algorithms such as independent component analysis (ICA), principle component analysis (PCA), and multi-user kurtosis (MUK) algorithms were implemented. The second proposed algorithm transferred the problem of BSS to transform domain and used wavelet denoising to reduce noise effect on received mixture. The BSS with Discrete Sine Transform (DST) and Discrete Cosine Transform (DCT) were investigated and compared to time domain performance. Minimum square error (MSE) and signal to noise ratio (SNR) were used as the evaluating metrics for comparison. Simulation results proved that in time domain, MUK with QPSK gave best performance and wavelet denoising was found to enhance the performance of BSS under all conditions. Signal separation in transform domain was found to give better performance than that in time domain due to the energy compaction process of these transforms and noise reduction due to their averaging effect. [ABSTRACT FROM AUTHOR]

Background: Thyroid-associated ophthalmopathy (TAO) is a prevalent autoimmune disease characterized by ocular symptoms like eyelid retraction and exophthalmos. Prior neuroimaging studies have revealed structural and functional brain abnormalities in TAO patients, along with central nervous system symptoms such as cognitive deficits. Nonetheless, the changes in the static and dynamic functional network connectivity of the brain in TAO patients are currently unknown. This study delved into the modifications in static functional network connectivity (sFNC) and dynamic functional network connectivity (dFNC) among thyroid-associated ophthalmopathy patients using independent component analysis (ICA). Methods: Thirty-two patients diagnosed with thyroid-associated ophthalmopathy and 30 healthy controls (HCs) underwent resting-state functional magnetic resonance imaging (rs-fMRI) scanning. ICA method was utilized to extract the sFNC and dFNC changes of both groups. Results: In comparison to the HC group, the TAO group exhibited significantly increased intra-network functional connectivity (FC) in the right inferior temporal gyrus of the executive control network (ECN) and the visual network (VN), along with significantly decreased intra-network FC in the dorsal attentional network (DAN), the default mode network (DMN), and the left middle cingulum of the ECN. On the other hand, FNC analysis revealed substantially reduced connectivity intra-VN and inter-cerebellum network (CN) and highlevel cognitive networks (DAN, DMN, and ECN) in the TAO group compared to the HC group. Regarding dFNC, TAO patients displayed abnormal connectivity across all five states, characterized by notably reduced intra-VN connectivity and CN connectivity with high-level cognitive networks (DAN, DMN, and ECN), alongside compensatory increased connectivity between DMN and low-level perceptual networks (VN and basal ganglia network). No significant differences were observed between the two groups for the three dynamic temporal metrics. Furthermore, excluding the classification outcomes of FC within VN (with an accuracy of 51.61% and area under the curve of 0.35208), the FC-based support vector machine (SVM) model demonstrated improved performance in distinguishing between TAO and HC, achieving accuracies ranging from 69.35 to 77.42% and areas under the curve from 0.68229 to 0.81667. The FNCbased SVM classification yielded an accuracy of 61.29% and an area under the curve of 0.57292. Conclusion: In summary, our study revealed that significant alterations in the visual network and high-level cognitive networks. These discoveries contribute to our understanding of the neural mechanisms in individuals with TAO, offering a valuable target for exploring future central nervous system changes in thyroidassociated eye diseases. [ABSTRACT FROM AUTHOR]

The perception of musical phrase boundaries is a critical aspect of human musical experience: It allows us to organize, understand, derive pleasure from, and remember music. Identifying boundaries is a prerequisite for segmenting music into meaningful chunks, facilitating efficient processing and storage while providing an enjoyable, fulfilling listening experience through the anticipation of upcoming musical events. Expanding on Sridharan et al.'s [Neuron 55, 521-532 (2007)] work on coarse musical boundaries between symphonic movements, we examined finer-grained boundaries. We measured the fMRI responses of 18 musicians and 18 nonmusicians during music listening. Using general linear model, independent component analysis, and Granger causality, we observed heightened auditory integration in anticipation to musical boundaries, and an extensive decrease within the fronto-temporal-parietal network during and immediately following boundaries. Notably, responses were modulated by musicianship. Findings uncover the intricate interplay between musical structure, expertise, and cognitive processing, advancing our knowledge of how the brain makes sense of music. [ABSTRACT FROM AUTHOR]

Background: Patients with anti-N-methyl-D-aspartate receptor (anti-NMDAR) encephalitis often experience severe symptoms. Resting-state functional MRI (rs-fMRI) has revealed widespread impairment of functional networks in patients. However, the changes in information flow remain unclear. This study aims to investigate the intrinsic functional connectivity (FC) both within and between resting-state networks (RSNs), as well as the alterations in effective connectivity (EC) between these networks. Methods: Resting-state functional MRI (rs-fMRI) data were collected from 25 patients with anti-NMDAR encephalitis and 30 healthy controls (HCs) matched for age, sex, and educational level. Changes in the intrinsic functional connectivity (FC) within and between RSNs were analyzed using independent component analysis (ICA). The functional interaction between RSNs was identified by granger causality analysis (GCA). Results: Compared to HCs, patients with anti-NMDAR encephalitis exhibited lower performance on the Wisconsin Card Sorting Test (WCST), both in terms of correct numbers and correct categories. Additionally, these patients demonstrated decreased scores on the Montreal Cognitive Assessment (MoCA). Neuroimaging studies revealed abnormal intra-FC within the default mode network (DMN), increased intra-FC within the visual network (VN) and dorsal attention network (DAN), as well as increased inter-FC between VN and the frontoparietal network (FPN). Furthermore, aberrant effective connectivity (EC) was observed among the DMN, DAN, FPN, VN, and somatomotor network (SMN). Conclusion: Patients with anti-NMDAR encephalitis displayed noticeable deficits in both memory and executive function. Notably, these patients exhibited widespread impairments in intra-FC, inter-FC, and EC. These results may help to explain the pathophysiological mechanism of anti-NMDAR encephalitis. [ABSTRACT FROM AUTHOR]

The serotonin transporter (SERT) is a member of the Solute Carrier 6 (SLC6) family and is responsible for maintaining the appropriate level of serotonin in the brain. Dysfunction of SERT has been linked to several neuropsychiatric disorders, including depression, anxiety and obsessive‐compulsive disorder. Therefore, an in‐depth understanding of the mechanism on an atomistic level, coupled with a quantification of transporter dynamics and the associated free energies is required. Here, we constructed Markov state models (MSMs) from extensive unbiased molecular dynamics simulations to quantify the free energy profile of serotonin (5HT) triggered SERT occlusion and explored the driving forces of the mechanism of occlusion. Our results reveal that SERT occludes via multiple intermediate conformations and show that the motion of occlusion is energetically downhill for the 5HT‐bound transporter. Force distribution analyses show that the interactions of 5HT with the bundle domain are crucial. During occlusion, attractive forces steadily increase and pull on the bundle domain, which leads to SERT occlusion. Some interactions become repulsive upon full occlusion, suggesting that SERT creates pressure on 5HT to promote its movement towards the cytosol. [ABSTRACT FROM AUTHOR]

Parkinson's disease (PD) patients are impaired in word production when the word has to be selected among competing alternatives requiring higher attentional resources. In PD, word selection processes are correlated with the structural integrity of the inferior frontal gyrus, which is critical for response selection, and the uncinate fasciculus, which is necessary for processing lexical information. In early PD, we investigated the role of the main cognitive large-scale networks, namely the salience network (SN), the central executive networks (CENs), and the default mode network (DMN), in word selection. Eighteen PD patients and sixteen healthy controls were required to derive nouns from verbs or generate verbs from nouns. Participants also underwent a resting-state functional MRI. Functional connectivity (FC) was examined using independent component analysis. Functional seeds for the SN, CENs, and DMN were defined as spheres, centered at the local activation maximum. Correlations were calculated between the FC of each functional seed and word production. A significant association between SN connectivity and task performance and, with less evidence, between CEN connectivity and the task requiring selection among a larger number of competitors, emerged in the PD group. These findings suggest the involvement of the SN and CEN in word selection in early PD, supporting the hypothesis of impaired executive control. [ABSTRACT FROM AUTHOR]

When analyzing GPS time series, common mode errors (CME) often obscure the actual crustal movement signals, leading to deviations in the velocity estimates of station coordinates. Therefore, mitigating the impact of CME on station positioning accuracy is crucial to ensuring the precision and reliability of GNSS time series. The current approach to separating CME mainly uses signal filtering methods to decompose the residuals of the observation network into multiple signals, from which the signals corresponding to CME are identified and separated. However, this method overlooks the spatial correlation of the stations. In this paper, we improved the Independent Component Analysis (ICA) method by introducing correlation coefficients as weighting factors, allowing for more accurate emphasis or attenuation of the contributions of the GNSS network's spatial distribution during the ICA process. The results show that the improved Weighted Independent Component Analysis (WICA) method can reduce the root mean square (RMS) of the coordinate time series by an average of 27.96%, 15.23%, and 28.33% in the E, N, and U components, respectively. Compared to the ICA method, considering the spatial distribution correlation of stations, the improved WICA method shows enhancements of 12.53%, 3.70%, and 8.97% in the E, N, and U directions, respectively. This demonstrates the effectiveness of the WICA method in separating CMEs and provides a new algorithmic approach for CME separation methods. [ABSTRACT FROM AUTHOR]

Speech emotion recognition is key to many fields, including human–computer interaction, healthcare, and intelligent assistance. While acoustic features extracted from human speech are essential for this task, not all of them contribute to emotion recognition effectively. Thus, reduced numbers of features are required within successful emotion recognition models. This work aimed to investigate whether splitting the features into two subsets based on their distribution and then applying commonly used feature reduction methods would impact accuracy. Filter reduction was employed using the Kruskal–Wallis test, followed by principal component analysis (PCA) and independent component analysis (ICA). A set of features was investigated to determine whether the indiscriminate use of parametric feature reduction techniques affects the accuracy of emotion recognition. For this investigation, data from three databases—Berlin EmoDB, SAVEE, and RAVDES—were organized into subsets according to their distribution in applying both PCA and ICA. The results showed a reduction from 6373 features to 170 for the Berlin EmoDB database with an accuracy of 84.3%; a final size of 130 features for SAVEE, with a corresponding accuracy of 75.4%; and 150 features for RAVDESS, with an accuracy of 59.9%. [ABSTRACT FROM AUTHOR]

In this study, a method for the monitoring of internal combustion engine operation by vibration signals is proposed. The work falls within the context of the increasingly stringent standards relating to the environmental impact of engines and the development of monitoring and control techniques to ensure increased engine performance as well as fuel saving and reduction of pollutant emissions. Experimentation was performed on a turbocharged light-duty compression ignition direct-injection engine. Two monoaxial accelerometers were installed on the engine compressor case, the speed of which has been demonstrated to be closely related to the engine operation. Vibration measurements of the engine compressor case have been processed by combining the Empirical Mode Decomposition technique with Independent Component Analysis and Short Time Fourier Transform to indirectly estimate the turbocharger speed. The obtained traces have been compared to the direct turbocharger velocity measures during the stationary running of the engine (speed and load conditions varied in the complete engine's range of operation). The results point out the potentiality of the methodology in algorithms devoted to identifying modifications of the combustion development regarding regular operation via indirect turbocharger speed monitoring. [ABSTRACT FROM AUTHOR]

Multi-track synthetic aperture radar interferometry (InSAR) provides a good approach for the monitoring of long-term multi-dimensional earthquake deformation, including pre-, co-, and post-seismic data. However, the removal of atmospheric errors in both single- and multi-track InSAR data presents significant challenges. In this paper, a method of spatio-temporal correlation analysis using independent component analysis (ICA) is proposed, which can extract multi-track deformation components for the accurate retrieval of earthquake deformation time series. Sentinel-1 data covering the double earthquakes in Turkey and Syria in 2023 are used to demonstrate the effectiveness of the proposed method. The results show that co-seismic displacement in the east–west and up–down directions ranged from −114.7 cm to 82.8 cm and from −87.0 cm to 63.9 cm, respectively. Additionally, the deformation rates during the monitoring period ranged from −137.9 cm/year to 123.3 cm/year in the east–west direction and from −51.8 cm/year to 45.7 cm/year in the up–down direction. A comparative validation experiment was conducted using three GPS stations. Compared with the results of the original MSBAS method, the proposed method provides results that are smoother and closer to those of the GPS data, and the average optimization efficiency is 43.08% higher. The experiments demonstrated that the proposed method could provide accurate two-dimensional deformation time series for studying the pre-, co-, and post-earthquake events of the 2023 Turkey–Syria Earthquakes. [ABSTRACT FROM AUTHOR]

Dyslexia is a specific learning disability that is neurobiological in origin and is characterized by reading and/or spelling problems affecting the development of language-related skills. The aim of this study is to reveal functional markers based on dyslexia by examining the functions of brain regions in resting state and reading tasks and to analyze the effects of special education given during the treatment process of dyslexia. A total of 43 children, aged between 7 and 12, whose native language was Turkish, participated in the study in three groups including those diagnosed with dyslexia for the first time, those receiving special education for dyslexia, and healthy children. Independent component analysis method was employed to analyze functional connectivity variations among three groups both at rest and during the continuous reading task. A whole-brain scanning during task fulfillment and resting states revealed that there were significant differences in the regions including lateral visual, default mode, left frontoparietal, ventral attention, orbitofrontal and lateral motor network. Our results revealed the necessity of adding motor coordination exercises to the training of dyslexic participants and showed that training led to functional connectivity in some brain regions similar to the healthy group. Additionally, our findings confirmed that impulsivity is associated with motor coordination and visuality, and that the dyslexic group has weaknesses in brain connectivity related to these conditions. According to our preliminary results, the differences obtained between children with dyslexia, group of dyslexia with special education and healthy children has revealed the effect of education on brain functions as well as enabling a comprehensive examination of dyslexia. [ABSTRACT FROM AUTHOR]

Precipitation is momentous role in the hydrological cycle, crop irrigation, drinking, and ecosystem maintenance. Excessive and inadequate precipitation leads to flooding, landslides, loss of human lives and properties, and drought. Therefore, precipitation prediction is necessary for planning and protecting water resources and handling natural disasters like floods and drought. This paper proposed a cloud segmentation and precipitation prediction using Machine Learning (ML) algorithms. ML cloud segmentation model is implemented to segment the INSAT-3D Thermal Infra-Red (TIR) image into No-cloud, Low-Level (L-L), Mid-Level (M-L), and High-Level (H-L) clouds and determine the percentage of different clouds over the study area. Fuzzy C-means(FCM) clustering, Gaussian Naïve Bayes (GNB), K-Nearest Neighbor (KNN), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), Adaptive Boosting (AdaBoost), eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Categorical Boosting (CatBoost) are the ML algorithms considered for cloud segmentation. The precipitation intensity was predicted on the weather dataset using ML regression models: KNN, DT, and RF. The estimated cloud percentage is validated with cloud cover attributes of the weather dataset. Qualitative and quantitative results of cloud segmentation show that XGBoost, LightGBM, and CatBoost are the best performing models. Similarly, quantitative results of precipitation prediction show that RF and KNN are the best and least performing ML regression models. Also, the performance of the ML regression model is improved by including dimensionality reduction techniques such as Principal Component Analysis (PCA), t-distributed Stochastic Neighbor Embedding (t-SNE), and Independent Component Analysis (ICA). [ABSTRACT FROM AUTHOR]

Leaks are a constant problem in water distribution systems, resulting in wasted resources, environmental impacts, and financial losses. Thus, it is crucial to develop effective and agile methods to detect network leaks. In this context, this study proposes a leak detection methodology using three different processes. The first consists of treating monitoring data through independent component analysis, and the other two detection processes use the interquartile range (IQR) and matrix profile (MP) techniques, respectively. The methodology is evaluated based on a set of benchmark data. The results indicate that the proposed approach is effective in detecting leaks, with some cases being detected in a few minutes after the beginning of the leak. It is worth mentioning that the IQR method presented better performance in detecting leaks with abrupt onset, whereas the MP method was more efficient in leaks with gradual increase in flow. In summary, the proposed methodology offers a robust and promising approach for fast and accurate leak detection in water distribution networks. [ABSTRACT FROM AUTHOR]

After surgical resection of pancreatic ductal adenocarcinoma (PDAC), patients are predominantly treated with adjuvant chemotherapy, commonly consisting of gemcitabine (GEM)-based regimens or the modified FOLFIRINOX (mFFX) regimen. While mFFX regimen has been shown to be more effective than GEM-based regimens, it is also associated with higher toxicity. Current treatment decisions are based on patient performance status rather than on the molecular characteristics of the tumor. To address this gap, the goal of this study was to develop drug-specific transcriptomic signatures for personalized chemotherapy treatment. We used PDAC datasets from preclinical models, encompassing chemotherapy response profiles for the mFFX regimen components. From them we identified specific gene transcripts associated with chemotherapy response. Three transcriptomic artificial intelligence signatures were obtained by combining independent component analysis and the least absolute shrinkage and selection operator-random forest approach. We integrated a previously developed GEM signature with three newly developed ones. The machine learning strategy employed to enhance these signatures incorporates transcriptomic features from the tumor microenvironment, leading to the development of the 'Pancreas-View' tool ultimately clinically validated in a cohort of 343 patients from the PRODIGE-24/CCTG PA6 trial. Patients who were predicted to be sensitive to the administered drugs (n = 164; 47.8%) had longer disease-free survival (DFS) than the other patients. The median DFS in the mFFX-sensitive group treated with mFFX was 50.0 months [stratified hazard ratio (HR) 0.31, 95% confidence interval (CI) 0.21-0.44, P < 0.001] and 33.7 months (stratified HR 0.40, 95% CI 0.17-0.59, P < 0.001) in the GEM-sensitive group when treated with GEM. Comparatively patients with signature predictions unmatched with the treatments (n = 86; 25.1%) or those resistant to all drugs (n = 93; 27.1%) had shorter DFS (10.6 and 10.8 months, respectively). This study presents a transcriptome-based tool that was developed using preclinical models and machine learning to accurately predict sensitivity to mFFX and GEM. • Transcriptomic signatures were developed for key pancreatic cancer drugs to enable personalized treatment. • The Pancreas-View tool integrates four drug signatures to assist informed therapeutic decisions. • Signatures accurately identify high responder patients, indicative of improved DFS and cancer-specific survival. • Clinical validation involving a cohort of 343 patients confirms the efficacy of this signature approach. • Transcriptomic signatures derived from preclinical models and machine learning offer a rationalized treatment strategy. [ABSTRACT FROM AUTHOR]

As the concentration of fine dust has recently increased, numerous related studies are being conducted to address this issue. Aerosol optical depth (AOD) is a vital atmospheric parameter for measuring the optical properties of aerosols in the atmosphere, providing crucial information related to fine dust. In this paper, we apply three dimension reduction methods, nonnegative matrix factorization (NMF), empirical orthogonal functions (EOF) analysis and independent component analysis (ICA), to AOD data to analyze the patterns of fine dust in the East Asia region. Through a comparison of three dimension reduction methods, we observe that some patterns are observed in all three method, while some information are only extracted in a specific method. Additionally, we forecast AOD levels based on three methods, and compare the predictive performance of the three methodologies. [ABSTRACT FROM AUTHOR]

The integration of advanced control systems in prostheses necessitates the accurate identification of human locomotion activities, a task that can significantly benefit from EEG-based measurements combined with machine learning techniques. The main contribution of this study is the development of a novel framework for the recognition and classification of locomotion activities using electroencephalography (EEG) data by comparing the performance of different machine learning algorithms. Data of the lower limb movements during level ground walking as well as going up stairs, down stairs, up ramps, and down ramps were collected from 10 healthy volunteers. Time- and frequency-domain features were extracted by applying independent component analysis (ICA). Successively, they were used to train and test random forest and k-nearest neighbors (kNN) algorithms. For the classification, random forest revealed itself as the best-performing one, achieving an overall accuracy up to 92%. The findings of this study contribute to the field of assistive robotics by confirming that EEG-based measurements, when combined with appropriate machine learning models, can serve as robust inputs for prosthesis control systems. [ABSTRACT FROM AUTHOR]

Quantifying seasonal deformation is essential for accurately determining the thickness of the active layer and the distribution of water content within it, providing insights into the freeze‐thaw dynamics of permafrost environments and their sensitivity to climate change. Due to the limited hydraulic conductivity of the underlying permafrost, the freeze‐thaw processes are largely confined to the active layer, allowing for predictable seasonal deformations. This study employed Independent Component Analysis to isolate large‐scale seasonal deformation from Interferometric Synthetic Aperture Radar (InSAR) measurements taken from 2016 to 2020 in the Yangtze River Source Region (YRSR) of the Qinghai‐Tibet Plateau (QTP), covering 18,500 km2. We developed dedicated machine learning (ML) models that integrate these InSAR‐derived measurements with various environmental proxies. By applying these models to the YRSR, we generated a comprehensive, full‐coverage deformation map for permafrost terrains, achieving an R2 value of 0.91 and an Root Mean Squared Error of approximately 0.5 cm, thus confirming the model's strong predictability of seasonal deformation in permafrost regions. Deformation magnitude varied from less than 1 cm to over 10 cm. Our analysis suggests that terrain attributes, influenced by climate and soil conditions, are the primary factors driving these deformations. This research provides valuable insights into quantifying permafrost‐related seasonal deformation across expansive and rural landscapes. It also aids in assessing subsurface hydrological processes and the resilience and vulnerability of permafrost. The developed ML algorithm, with access to precise environmental data, is capable of forecasting seasonal deformations across the entire QTP and potentially throughout the Arctic. Plain Language Summary: Seasonal ground deformation, including both subsidence and uplift, is common in areas with a layer of ground that freezes and thaws seasonally, underlain by permafrost‐a type of ground that remains at or below 0°C for at least 2 years. These deformations are crucial indicators of changes in water content and thickness of this layer, offering insights into the freeze‐thaw dynamics of cold environments and their sensitivity to climate change. However, accurately mapping ground deformation over large areas has been challenging. In this study, we developed machine learning (ML) models that use radar remote sensing data, statistical methods, and a set of environmental variables to predict these seasonal ground movements. Our models can accurately forecast seasonal deformation using readily available environmental data. We find that slope of the terrain is the main factor influencing seasonal deformation, with climate and soil conditions also playing significant roles. This research offers new ways to measure and understand ground deformation in remote permafrost regions and demonstrates how ML can be used to predict such deformations on a continental or even global scale large. Our findings provide valuable insights for environmental scientists and could help inform strategies for managing these regions under changing climatic conditions. Key Points: Our results underscore the predictability of seasonal deformation with high accuracy in permafrost terrainsMachine learning models predict full‐coverage seasonal deformation with high accuracy (R2 = 0.91, Root Mean Squared Error [RMSE] = 0.5 cm)Seasonal deformation is primarily determined by terrain slope and regulated by climate and soil conditions [ABSTRACT FROM AUTHOR]

Spatiotemporal axle temperature forecasting is crucial for real-time failure detection in locomotive control systems, significantly enhancing reliability and facilitating early maintenance. Motivated by the need for more accurate and reliable prediction models, this paper proposes a novel ensemble graph convolutional recurrent unit network. This innovative approach aims to develop a highly reliable and accurate spatiotemporal axle temperature forecasting model, thereby increasing locomotive safety and operational efficiency. The modeling structure involves three key steps: (1) the GCN module extracts and aggregates spatiotemporal temperature data and deep feature information from the raw data of different axles; (2) these features are fed into GRU and BiLSTM networks for modeling and forecasting; (3) the ICA algorithm optimizes the fusion weight coefficients to combine the forecasting results from GRU and BiLSTM, achieving superior outcomes. Comparative experiments demonstrate that the proposed model achieves RMSE values of 0.2517 °C, 0.2011 °C, and 0.2079 °C across three temperature series, respectively, indicating superior prediction accuracy and reduced errors compared to benchmark models in all experimental scenarios. The Wilcoxon signed-rank test further confirms the statistical significance of the result improvements with high confidence. [ABSTRACT FROM AUTHOR]

Metal dichalcogenides are a kind of chemical substance that consists of a metal atom paired with chalcogen elements such as selenium and sulphur. These materials have distinctive electrical and optical characteristics, making them fascinating for a variety of applications, including electronics and optoelectronics. Growth examination of metal dichalcogenide thin films entails analyzing their controlled deposition and crystallization. Understanding growth processes, substrate interactions and controlling parameters like as temperature and precursor concentration are critical for producing high-quality films with the appropriate characteristics, establishing the way for developments in nanotechnology and device manufacturing. Throughout this research, we employed the Machine learning (ML) enabled Reflection High-Energy Electron Diffraction (RHEED) analytical approach to examine the development of two-dimensional (2D thin layers of dichalcogenides (ReSe2) made of transition metals on graphene substrates using Molecular Beam Epitaxy (MBE). Independent Component Analysis (ICA) and the Fuzzy C-Means approach were implemented to determine different patterns and represent the pattern growths. To decrease the original dataset's dimensionality, we employed 20 Independent Components (ICs) and each RHEED image was distributed to the closest centroid, which resulted in the dataset being clustered using Fuzzy C-Means. [ABSTRACT FROM AUTHOR]

Despite the known benefits of data-driven approaches, the lack of approaches for identifying functional neuroimaging patterns that capture both individual variations and inter-subject correspondence limits the clinical utility of rsfMRI and its application to single-subject analyses. Here, using rsfMRI data from over 100k individuals across private and public datasets, we identify replicable multi-spatial-scale canonical intrinsic connectivity network (ICN) templates via the use of multi-model-order independent component analysis (ICA). We also study the feasibility of estimating subject-specific ICNs via spatially constrained ICA. The results show that the subject-level ICN estimations vary as a function of the ICN itself, the data length, and the spatial resolution. In general, large-scale ICNs require less data to achieve specific levels of (within- and between-subject) spatial similarity with their templates. Importantly, increasing data length can reduce an ICN's subject-level specificity, suggesting longer scans may not always be desirable. We also find a positive linear relationship between data length and spatial smoothness (possibly due to averaging over intrinsic dynamics), suggesting studies examining optimized data length should consider spatial smoothness. Finally, consistency in spatial similarity between ICNs estimated using the full data and subsets across different data lengths suggests lower within-subject spatial similarity in shorter data is not wholly defined by lower reliability in ICN estimates, but may be an indication of meaningful brain dynamics which average out as data length increases.

Background: Sleep deprivation (SD) can impair an individual's alertness, which is the basis of attention and the mechanism behind continuous information processing. However, research concerning the effects of total sleep deprivation (TSD) on alertness networks is inadequate. In this study, we investigate the cognitive neural mechanism of alertness processing after TSD. Methods: Twenty-four college students volunteered to participate in the study. The resting-state electroencephalogram (EEG) data were collected under two conditions (rested wakefulness [RW], and TSD). We employed isolated effective coherence (iCoh) analysis and functional independent component analysis (fICA) to explore the effects of TSD on participants' alertness network. Results: This study found the existence of two types of effective connectivity after TSD, as demonstrated by iCoh: from the left cuneus to the right middle frontal gyrus in the β3 and γ bands, and from the left angular gyrus to the left insula in the δ, θ, α, β1, β3, and γ bands. Furthermore, Pearson correlation analysis showed that increased effective connectivity between all the bands had a positive correlation with increases in the response time in the psychomotor vigilance task (PVT). Finally, fICA revealed that the neural oscillations of the cuneus in the α2 bands increased, and of the angular gyrus in the α and β1 bands decreased in TSD. Conclusions: TSD impairs the alertness function among individuals. Increased effective connectivity from the cuneus to the middle frontal gyrus may represent overloads on the alertness network, resulting in participants strengthening top-down control of the attention system. Moreover, enhanced effective connectivity from the angular gyrus to the insula may indicate a special perception strategy in which individuals focus on salient and crucial environmental information while ignoring inessential stimuli to reduce the heavy burden on the alertness network. [ABSTRACT FROM AUTHOR]