CWL-Based Analysis Pipeline for Hi-C Data: From FASTQ Files to Matrices.

Over a decade has passed since the development of the Hi-C method for genome-wide analysis of 3D genome organization. Hi-C utilizes next-generation sequencing (NGS) technology to generate large-scale chromatin interaction data, which has accumulated across a diverse range of species and cell types, particularly in eukaryotes. There is thus a growing need to streamline the process of Hi-C data analysis to utilize these data sets effectively. Hi-C generates data that are much larger compared to other NGS techniques such as chromatin immunoprecipitation sequencing (ChIP-seq) or RNA-seq, making the data reanalysis process computationally expensive. In an effort to bridge this resource gap, the 4D Nucleome (4DN) Data Portal has reanalyzed approximately 600 Hi-C data sets, allowing users to access and utilize the analyzed data. In this chapter, we provide detailed instructions for the implementation of the common workflow language (CWL)-based Hi-C analysis pipeline adopted by the 4DN Data Portal ecosystem. This reproducible and portable pipeline generates standard Hi-C contact matrices in formats such as .hic or .mcool from FASTQ files. It enables users to output their own Hi-C data in the same format as those registered in the 4DN Data portal, facilitating comparative analysis using data registered in the portal. Our custom-made scripts are available on GitHub at https://github.com/kuzobuta/4dn_cwl_pipeline .

Artificial Intelligence in Identifying Patients With Undiagnosed Nonalcoholic Steatohepatitis.

Background: Although increasing in prevalence, nonalcoholic steatohepatitis (NASH) is often undiagnosed in clinical practice. Objective: This study identified patients in the Veterans Affairs (VA) health system who likely had undiagnosed NASH using a machine learning algorithm. Methods: From a VA data set of 25 million adult enrollees, the study population was divided into NASH-positive, non-NASH, and at-risk cohorts. We performed a claims data analysis using a machine learning algorithm. To build our model, the study population was randomly divided into an 80% training subset and a 20% testing subset and tested and trained using a cross-validation technique. In addition to the baseline model, a gradient-boosted classification tree, naïve Bayes, and random forest model were created and compared using receiver operator characteristics, area under the curve, and accuracy. The best performing model was retrained on the full 80% training subset and applied to the 20% testing subset to calculate the performance metrics. Results: In total, 4 223 443 patients met the study inclusion criteria, of whom 4903 were positive for NASH and 35 528 were non-NASH patients. The remainder was in the at-risk patient cohort, of which 514 997 patients (12%) were identified as likely to have NASH. Age, obesity, and abnormal liver function tests were the top determinants in assigning NASH probability. Conclusions: Utilization of machine learning to predict NASH allows for wider recognition, timely intervention, and targeted treatments to improve or mitigate disease progression and could be used as an initial screening tool.

SCIP: A scalable, reproducible, and open-source pipeline for morphological profiling image cytometry and microscopy data.

Imaging flow cytometry (IFC) provides single-cell imaging data at a high acquisition rate. It is increasingly used in image-based profiling experiments consisting of hundreds of thousands of multi-channel images of cells. Currently available software solutions for processing microscopy data can provide good results in downstream analysis, but are limited in efficiency and scalability, and often ill-adapted to IFC data. In this work, we propose Scalable Cytometry Image Processing (SCIP), a Python software that efficiently processes images from IFC and standard microscopy datasets. We also propose a file format for efficiently storing IFC data. We showcase our contributions on two large-scale microscopy and one IFC datasets, all of which are publicly available. Our results show that SCIP can extract the same kind of information as other tools, in a much shorter time and in a more scalable manner.

Multivariate Scalar on Multidimensional Distribution Regression With Application to Modeling the Association Between Physical Activity and Cognitive Functions.

We develop a new method for multivariate scalar on multidimensional distribution regression. Traditional approaches typically analyze isolated univariate scalar outcomes or consider unidimensional distributional representations as predictors. However, these approaches are suboptimal because (i) they fail to utilize the dependence between the distributional predictors and (ii) neglect the correlation structure of the response. To overcome these limitations, we propose a multivariate distributional analysis framework that harnesses the power of multivariate density functions and multitask learning. We develop a computationally efficient semiparametric estimation method for modeling the effect of the latent joint density on the multivariate response of interest. Additionally, we introduce a new conformal prediction algorithm for quantifying the uncertainty of our multivariate predictions based on subject characteristics and individualized distributional predictors, providing valuable insights into the conditional distribution of the response. We validate the effectiveness of our proposed method through comprehensive numerical simulations, clearly demonstrating its superior performance compared to traditional methods. The application of the proposed method is demonstrated on triaxial accelerometer data from the National Health and Nutrition Examination Survey 2011-2014 for modeling the association between cognitive scores across various domains and distributional representation of physical activity among the older adult population. Our results highlight the advantages of the proposed approach, emphasizing the significance of incorporating multidimensional distributional information in the triaxial accelerometer data.

Interpretable discriminant analysis for functional data supported on random nonlinear domains with an application to Alzheimer's disease.

We introduce a novel framework for the classification of functional data supported on nonlinear, and possibly random, manifold domains. The motivating application is the identification of subjects with Alzheimer's disease from their cortical surface geometry and associated cortical thickness map. The proposed model is based upon a reformulation of the classification problem as a regularized multivariate functional linear regression model. This allows us to adopt a direct approach to the estimation of the most discriminant direction while controlling for its complexity with appropriate differential regularization. Our approach does not require prior estimation of the covariance structure of the functional predictors, which is computationally prohibitive in our application setting. We provide a theoretical analysis of the out-of-sample prediction error of the proposed model and explore the finite sample performance in a simulation setting. We apply the proposed method to a pooled dataset from Alzheimer's Disease Neuroimaging Initiative and Parkinson's Progression Markers Initiative. Through this application, we identify discriminant directions that capture both cortical geometric and thickness predictive features of Alzheimer's disease that are consistent with the existing neuroscience literature.

Bangla news article dataset.

In this research, we present an updated standard Bangla dataset based on gathered Bangla news articles. In total, more than 1.9 million articles from nine Bangla news websites were gathered; the selection process was led by a number of categories, including sports, economy, politics, local news, tech, tourism, entertainment, education, health, the arts, and many more. The dataset per newspaper contains varying attributes, such as title, content, time, tags, meta, category, etc. This dataset will enable data scientists to investigate and assess theories related to Bangla natural language processing. Furthermore, there is a greater chance that the dataset will be utilized for domain-specific large language models in the context of Bangladesh, and it may be used to develop deep learning and machine learning models that categorize articles according to subjects.

Super-resolution microscopy to study membrane nanodomains and transport mechanisms in the plasma membrane.

Biological membranes are complex, heterogeneous, and dynamic systems that play roles in the compartmentalization and protection of cells from the environment. It is still a challenge to elucidate kinetics and real-time transport routes for molecules through biological membranes in live cells. Currently, by developing and employing super-resolution microscopy; increasing evidence indicates channels and transporter nano-organization and dynamics within membranes play an important role in these regulatory mechanisms. Here we review recent advances and discuss the major advantages and disadvantages of using super-resolution microscopy to investigate protein organization and transport within plasma membranes.

What explains adolescents' physical activity and sports participation during the COVID-19 pandemic? - an interpretable machine learning approach.

Adolescents' physical activity (PA) and sports participation declined due to the COVID-19 pandemic. This study aimed to determine the critical socio-ecological factors for PA and sports participation using a machine learning approach. We did a cross-sectional secondary data analysis utilising the 2021 National Survey of Children's Health (NSCH) dataset (N=16,166; 49.0% female). We applied an interpretable machine learning approach (e.g. decision tree-based models) that examined the critical factors associated with PA and sports participation. The factors related to the intrapersonal, interpersonal, organisational, and community levels of the socio-ecological model. Out of the 25 factors examined, our findings unveiled the 11 critical factors associated with PA and the 10 critical factors associated with sports participation. Factors at the intrapersonal levels (e.g. age, screen time, and race) held greater importance to PA than those at the other three levels. While interpersonal factors (e.g. parent participation in children's events/activities, family's highest educational level, and family income level) were most important for sports participation. This study identified that the common critical factors of physical activity and sports participation during the COVID-19 pandemic mainly relied on intrapersonal and interpersonal levels. Unique factors were discussed.

In this study, we identified 11 critical factors for PA, with the top five being age, neighbourhood amenities, screen time, missed school days, and family income level. Additionally, we identified 10 critical factors for sports participation, with the top five factors being parent participation in a child's events/activities, family's highest educational level, family income level, screen time, and school engagement. These findings emphasise the shared significance of intrapersonal and interpersonal factors as common determinants of both PA and sports participation. Notably, PA appears to be primarily influenced by intrapersonal factors (e.g. age, screen time, and race), reflecting its more internally driven nature. In contrast, sports participation appears to be more externally driven, primarily shaped by interpersonal factors (e.g. parent participation in the child's events/activities, family's highest educational level, and family income level). This distinction underscores the need for educators and policymakers to carefully consider these common and unique factors when devising promotion strategies during the COVID-19 pandemic. By recognising these distinctions, interventions can be better tailored to encourage both PA and sports participation among adolescents.

An insight from National Family Health Surveys on changing trends in prevalence and knowledge regarding T.B. in India.

BACKGROUND/OBJECTIVES: Addressing gaps in knowledge about T.B. is a vital component of T.B.'s elimination to achieve the End T.B. strategy by 2025 in India. The present study compares the correct knowledge regarding T.B. by analysis of the nationally-representative secondary data of NFHS-4 (2015-16) and NFHS-5 (2019-20) data in India. METHODS: NFHS-4 and NFHS-5 secondary data on eleven T.B.-related questions analysis was done after seeking permission to use datasets from the Demographic and Health Surveys (DHS) program-sociodemographic details and Responses exported and analysed using M.S. Excel. Descriptive variables were represented as frequency and percentages. Z tests for proportions were used to compare and determine differences between NFHS-4 and NFHS-5 knowledge. Statistical significance was set at a p-value of <0.05. RESULTS: The correct knowledge regarding T.B. significantly rose from 457,399 (56.3%) in NFHS-4 to 507,517 (61.4%) in NFHS-5. However, a significant increase in incorrect knowledge about the other modes of transmission of T.B. and T.B. courtesy stigma in households from 95,985 (13.4%) in 2015-16 to 113,978 (14.9%) in 2019-20 was observed. CONCLUSIONS: The correct knowledge of T.B. has significantly increased from NFHS-4 (2015-16) to NFHS-5 (2019-20). However, there is a significant increase in incorrect knowledge regarding the modes of transmission and stigmatising attitudes towards T.B. through improvement in the communication efforts in the National T.B. Elimination Programme (NTEP).

Investigating drug-induced urinary retention: a pharmacovigilance analysis of FDA adverse event reports from 2004 to 2024.

BACKGROUND: Drug-induced urinary retention (DIUR) can severely impact patient quality of life and complicate treatment. This study investigates the incidence and characteristics of DIUR using data from the FDA Adverse Event Reporting System (FAERS) over 20 years. METHODS: FAERS reports related to urinary retention (UR) from Q1 2004 to Q1 2024 were analyzed. Potential causative drugs were identified, and the top 30 drugs with the most UR reports were ranked. Statistical disproportionality analyses, including Proportional Reporting Ratio (PRR) and Reporting Odds Ratio (ROR), were conducted to detect significant safety signals. RESULTS: Out of 17,703,515 reports in the FAERS database 28,423 cases of UR were identified. Anticholinergics, antidepressants, and opioids were the most frequently implicated drug classes. The highest ROR and PRR values were observed for drugs like ezogabine. Additionally, less commonly associated drugs, such as adalimumab and others, were implicated, suggesting potential under-recognition of this adverse effect. However, these associations should be interpreted with caution, as they do not confirm a direct causal relationship. CONCLUSION: This study underscores the importance of pharmacovigilance in identifying and understanding DIUR. Further research is needed to confirm these findings and develop strategies to manage and reduce the risk, improving patient outcomes.

Risk assessment and soil heavy metal contamination near marble processing plants (MPPs) in district Malakand, Pakistan.

Soil heavy metals (HMs) pollution is a growing global concern, mainly in regions with rapid industrial growth. This study assessed the concentrations, potential sources, and health risks of HMs in agricultural soils near marble processing plants in Malakand, Pakistan. A total of 21 soil samples were analyzed for essential and toxic HMs via inductively coupled plasma‒optical emission spectrometry (ICP‒OES), and probabilistic health risks were evaluated via Monte Carlo simulation. The concentrations (mg/kg) of Ca (29,250), P (805.5) and Cd (4.5) exceeded the average shale limits of 22,100, 700, and 3.0 mg/kg, respectively, and indices such as Nemerow's synthetic contamination index (NSCI) and the geoaccumulation index (Igeo) categorized the soil sites as moderately polluted. The potential ecological risk index (PERI) indicated considerable to high ecological risk for As and Cd. The deterministic analysis indicated non-carcinogenic risks for children (HI > 1), whereas the probabilistic analysis suggested no significant risk (HI < 1) for both adults and children. Both methods indicated that the total cancer risk for Cr, Ni, Cd, and As exceeded the USEPA safety limits of 1.0E-06 and 1.0E-04. Sensitivity analysis identified heavy metal concentration, exposure duration, and frequency as key risk factors. The study suggested that HM contamination is mainly anthropogenic, poses a threat to soil and human health, and highlights the need for management strategies and surveillance programs to mitigate these risks.

Prognostic factors in post-prostatectomy salvage radiotherapy setting with and without hormonotherapy: An individual patient data analysis of randomized trials from ICECaP database.

BACKGROUND: Early salvage radiotherapy (SRT) is the standard of care for biochemical recurrence post-prostatectomy but outcomes are heterogeneous. OBJECTIVE: To develop a risk scoring system based on relevant standard-of-care clinico-pathological prognostic factors for patients treated with SRT with and without hormonal therapy (HT). DESIGN, SETTING, AND PARTICIPANTS: The Intermediate Clinical Endpoints in Cancer of the Prostate (ICECaP) database included three randomized trials (Individual patients' data from 1647 subjects) assessing SRT (GETUG-AFU-16; NRG/RTOG-9601, and a subset of EORTC-22911). OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Outcomes were clinical progression (CP). metastasis free-survival (MFS) and overall survival (OS). Clinico-pathological factors, including pathological Gleason Score (GS), PSA at SRT start, margin status, persistent PSA post-RP and time from RP to SRT were evaluated by multivariable models stratified by type of treatment. RESULTS AND LIMITATIONS: On multivariable analysis PSA ≥ 0.5 ng/mL at SRT start, GS ≥ 8 and negative margin status were the three strongest prognostic factors. Three prognostic groups defined by number of these risk features (high risk: 2 or 3; intermediate risk: 1 and low risk: 0) were strongly associated with OS, MFS and CP outcomes with SRT alone or with HT. This prognostic group definition was also relevant for patients with persistent PSA post RP and for patients treated < 1 year from RP to SRT and with and without HT. CONCLUSION: A risk score for patients receiving SRT with or without HT, using three standard-of-care clinico-pathological risk factors provides refined prognostic information for individual patient counselling. PATIENT SUMMARY: By using a composite score of pathology grading (Gleason Score), PSA at start of salvage radiation and margin status data, physicians can provide patients with more refined information on the risk of a second relapse after receiving radiation to the prostate bed after a prostatectomy for a rising or persistent PSA, both with and without hormonal therapy.

Examining how customers perceive community pharmacies based on Google maps reviews: Multivariable and sentiment analysis.

Objective: This study aims to understand customer perceptions of community pharmacies utilizing publicly available data from Google Maps platform. Materials and methods: Python was used to scrape data with Google Maps APIs. As a result, 17,237 reviews were collected from 512 pharmacies distributed over Riyadh city, Saudi Arabia. Logistic regression was conducted to test the relationships between multiple variables and the given score. In addition, sentiment analysis using VADER (Valence Aware Dictionary for Sentiment Reasoning) model was conducted on written reviews, followed by cross-tabulation and chi-square tests. Results: The Logistic regression model implies that a unit increase in the Pharmacy score enhances the odds of attaining a higher score by approximately 3.734 times. The Mann-Whitney U test showed that a notable and statistically significant difference between "written reviews" and "unwritten reviews" (U = 39,928,072.5, p < 0.001). The Pearson chi-square test generated a value of 2991.315 with 8 degrees of freedom, leading to a p value of 0.000. Discussion: Our study found that the willingness of reviewers to write reviews depends on their perception. This study provides a descriptive analysis of conducted sentiment analysis using VADAR. The chi-square test indicates a significant relationship between rating scores and review sentiments. Conclusion: This study offers valuable findings on customer perception of community pharmacies using a new source of data.

Application of a molecular networking approach using LC-HRMS combined with the MetWork webserver for clinical and forensic toxicology.

Backgrounds and aims: In toxicology, LC-HRMS for untargeted screening yields a great deal of high quality spectral data. However, there we lack tools to visualize/organize the MS data. We applied molecular networking (MN) to untargeted screening interpretation. Our aims were to compare theoretical MS libraries obtained in silico with our experimental dataset in patients to broaden its application, and to use the MetWork web application for metabolite identification. Methods: Samples were analyzed using an LC-HRMS system. For MN, data was generated using MZmine, and analyzed and visualized using MetGem. MetWork annotations were filtered and this file was used for annotation of the previously obtained MN. Results: 155 compounds including drugs found in patients were recorded. Using this dataset, we confirmed in 60 patients intake of tramadol, amitriptyline bromazepam, and cocaine. The results obtained by the reference methods were confirmed by MN approaches. Eighty percent of the compounds were common to both conventional and MN approaches. Using MetWork, metabolites and parent drugs such as amitriptyline, its metabolite nortriptyline and amitriptyline glucuronide phase 2 metabolites were anticipated and proposed as putative annotations. Conclusion: The workflow increases confidence in toxicological screening by highlighting putative structures in biological matrices in combination with CFM-ID (Competitive Fragmentation Modeling for Metabolite Identification) and MetWork to extend the annotation of potential drugs even without a reference standard.

Enriched traffic datasets for the city of Madrid: Integrating data from traffic sensors, the road infrastructure, calendar data and weather data.

The proliferation of urban areas and the concurrent increase in vehicular mobility have escalated the urgency for advanced traffic management solutions. This data article introduces two traffic datasets from Madrid, collected between June 2022 and February 2024, to address the challenges of traffic management in urban areas. The first dataset provides detailed traffic flow measurements (vehicles per hour) from urban sensors and road networks, enriched with weather data, calendar data and road infrastructure details from OpenStreetMap. This combination allows for an in-depth analysis of urban mobility. Through preprocessing, data quality is ensured by eliminating inconsistent sensor readings. The second dataset is enhanced for advanced predictive modelling. It includes time-based transformations and a tailored preprocessing pipeline that standardizes numeric data, applies one-hot encoding to categorical features, and uses ordinal encoding for specific features. In constructing the datasets, we initially employed the k-means algorithm to cluster data from multiple sensors, thereby highlighting the most representative ones. This clustering can be adapted or modified according to the user's needs, ensuring flexibility for various analyses and applications. This work underscores the importance of advanced datasets in urban planning and highlights the versatility of these resources for multiple practical applications. We highlight the relevance of the collected data for a variety of essential purposes, including traffic prediction, infrastructure planning, studies on the environmental impact of traffic, event planning, and conducting simulations. These datasets not only provide a solid foundation for academic research but also for designing and implementing more effective and sustainable traffic policies. Furthermore, all related datasets, source code, and documentation have been made publicly available, encouraging further research and practical applications in traffic management and urban planning.

Risk analysis of enfortumab vedotin: A real-world approach based on the FAERS database.

Purpose: To analyze the risk of enfortumab vedotin (EV), a targeted therapy for advanced bladder cancer, using real-world data from the U.S. Food and Drug Administration's Federal Adverse Event Reporting System (FAERS). Methods: A retrospective pharmacovigilance analysis was conducted using FAERS data from Q1 2020 to Q1 2024. Adverse drug events (ADEs) related to EV were identified and categorized according to the System Organ Classes (SOCs) and specific events. Statistical methods, such as the proportional reporting ratio, reporting odds ratio (ROR), Bayesian confidence propagation neural network, and empirical Bayesian geometric mean were used to detect safety signals. Results: Of the 7,449,181 FAERS case reports, 1,617 EV-related ADEs were identified, including 101 preferred terms and 22 SOCs. The key SOCs included skin and subcutaneous tissue, metabolic, and nutritional disorders. Rare ADEs, such as lichenoid keratosis (n = 4; ROR 26.89), small intestinal perforation (n = 3; ROR 24.51), pigmentation disorder (n = 9; ROR 18.16), and cholangitis (n = 8; ROR 17.48), showed significant disproportionality. Conclusion: While most findings aligned with the existing data, new signs such as lichenoid keratosis and small intestinal perforation were identified. Further studies are necessary to validate these findings and emphasize the need for the clinical monitoring of EV-related ADEs.

The prospective associations of 24-hour movement behaviors and domain-specific activities with executive function and academic achievement among school-aged children in Singapore.

Background: Physical activity (PA), sedentary behavior (SB), and sleep are collectively referred to as 24-h movement behaviors, which may be linked to cognitive development in children. However, most of the evidence was based on cross-sectional studies and/or solely relied on parent-reported information on children's behaviors, and it remains uncertain whether all domains/contexts of PA and SB are similarly associated with executive function and academic achievement. Objective: We investigated the prospective associations of accelerometer-measured 24 h-movement behaviors and domain-specific PA and SB with executive function and academic achievement among school-aged children in Singapore. Methods: The Growing Up in Singapore Toward healthy Outcomes (GUSTO) cohort used a wrist-worn accelerometer (Actigraph-GT3x+) to measure 24 h-movement behaviors data at ages 5.5 and 8 years. Executive function and academic achievement were assessed using NEuroPSYchology (NEPSY) and Wechsler Individual Achievement Tests at ages 8.5 and 9-years, respectively. Compositional data analyses were conducted to explore the associations of 24 h-movement behavior with outcomes, and multiple linear regression models to examine the associations of domain-specific PA and SB with outcomes (n = 432). Results: Among 432 children whose parents agreed to cognitive assessments (47% girls and 58% Chinese), the composition of 24 h-movement behaviors at ages 5.5 and 8 years was not associated with executive function and academic achievement. However, higher moderate-to-vigorous PA (MVPA) relative to remaining movement behaviors at age 5.5 years was associated with lower academic achievement [Mean difference (95% confidence interval): -0.367 (-0.726, -0.009) z-score], and reallocating MVPA time to sleep showed higher academic achievement scores [30 min from MVPA to sleep: 0.214 (0.023, 0.404) z-score]. Certain domains of PA and SB, notably organized PA/sports, outdoor play, and reading books were favorably associated with outcomes of interest, while indoor play and screen-viewing were unfavorably associated. Conclusion: The associations between movement behaviors and cognitive outcomes are multifaceted, influenced by specific domains of PA and SB. This study underscores the importance of participation in organized PA/sports, outdoor active play, and reading books, while ensuring adequate sleep and limiting screen viewing, to enhance cognitive outcomes. These findings underscore the need for further research into time-use trade-offs. Such studies could have major implications for revising current guidelines or strategies aimed at promoting healthier 24 h-movement behaviors in children. Study registration: https://clinicaltrials.gov/, NCT01174875.

The chronic heart failure evolutions: Different fates and routes.

AIMS: Individual prognostic assessment and disease evolution pathways are undefined in chronic heart failure (HF). The application of unsupervised learning methodologies could help to identify patient phenotypes and the progression in each phenotype as well as to assess adverse event risk. METHODS AND RESULTS: From a bulk of 7948 HF patients included in the MECKI registry, we selected patients with a minimum 2-year follow-up. We implemented a topological data analysis (TDA), based on 43 variables derived from clinical, biochemical, cardiac ultrasound, and exercise evaluations, to identify several patients' clusters. Thereafter, we used the trajectory analysis to describe the evolution of HF states, which is able to identify bifurcation points, characterized by different follow-up paths, as well as specific end-stages conditions of the disease. Finally, we conducted a 5-year survival analysis (composite of cardiovascular death, left ventricular assist device, or urgent heart transplant). Findings were validated on internal (n = 527) and external (n = 777) populations. We analyzed 4876 patients (age = 63 [53-71], male gender n = 3973 (81.5%), NYHA class I-II n = 3576 (73.3%), III-IV n = 1300 (26.7%), LVEF = 33 [25.5-39.9], atrial fibrillation n = 791 (16.2%), peak VO2% pred = 54.8 [43.8-67.2]), with a minimum 2-year follow-up. Nineteen patient clusters were identified by TDA. Trajectory analysis revealed a path characterized by 3 bifurcation and 4 end-stage points. Clusters survival rate varied from 44% to 100% at 2 years and from 20% to 100% at 5 years, respectively. The event frequency at 5-year follow-up for each study cohort cluster was successfully compared with those in the validation cohorts (R = 0.94 and R = 0.84, P < 0.001, for internal and external cohort, respectively). Finally, we conducted a 5-year survival analysis (composite of cardiovascular death, left ventricular assist device, or urgent heart transplant observed in 22% of cases). CONCLUSIONS: Each HF phenotype has a specific disease progression and prognosis. These findings allow to individualize HF patient evolutions and to tailor assessment.

Advanced Methods for Analyzing in-Situ Observations of Magnetic Reconnection.

There is ample evidence for magnetic reconnection in the solar system, but it is a nontrivial task to visualize, to determine the proper approaches and frames to study, and in turn to elucidate the physical processes at work in reconnection regions from in-situ measurements of plasma particles and electromagnetic fields. Here an overview is given of a variety of single- and multi-spacecraft data analysis techniques that are key to revealing the context of in-situ observations of magnetic reconnection in space and for detecting and analyzing the diffusion regions where ions and/or electrons are demagnetized. We focus on recent advances in the era of the Magnetospheric Multiscale mission, which has made electron-scale, multi-point measurements of magnetic reconnection in and around Earth's magnetosphere.

Multimodal functional deep learning for multiomics data.

With rapidly evolving high-throughput technologies and consistently decreasing costs, collecting multimodal omics data in large-scale studies has become feasible. Although studying multiomics provides a new comprehensive approach in understanding the complex biological mechanisms of human diseases, the high dimensionality of omics data and the complexity of the interactions among various omics levels in contributing to disease phenotypes present tremendous analytical challenges. There is a great need of novel analytical methods to address these challenges and to facilitate multiomics analyses. In this paper, we propose a multimodal functional deep learning (MFDL) method for the analysis of high-dimensional multiomics data. The MFDL method models the complex relationships between multiomics variants and disease phenotypes through the hierarchical structure of deep neural networks and handles high-dimensional omics data using the functional data analysis technique. Furthermore, MFDL leverages the structure of the multimodal model to capture interactions between different types of omics data. Through simulation studies and real-data applications, we demonstrate the advantages of MFDL in terms of prediction accuracy and its robustness to the high dimensionality and noise within the data.