Connectivity Network Feature Sharing in Single-Cell RNA Sequencing Data Identifies Rare Cells.

Single-cell RNA sequencing is a valuable technique for identifying diverse cell subtypes. A key challenge in this process is that the detection of rare cells is often missed by conventional methods due to low abundance and subtle features of these cells. To overcome this, we developed SCLCNF (Local Connectivity Network Feature Sharing in Single-Cell RNA sequencing), a novel approach that identifies rare cells by analyzing features uniquely expressed in these cells.

Synthetic datasets for open software development in rare disease research.

Background: Globally, researchers are working on projects aiming to enhance the availability of data for rare disease research. While data sharing remains critical, developing suitable methods is challenging due to the specific sensitivity and uniqueness of rare disease data. This creates a dilemma, as there is a lack of both methods and necessary data to create appropriate approaches initially.

Driver Fatigue Detection Using Heart Rate Variability Features from 2-Minute Electrocardiogram Signals While Accounting for Sex Differences.

Traffic accidents due to fatigue account for a large proportion of road fatalities. Based on simulated driving experiments with drivers recruited from college students, this paper investigates the use of heart rate variability (HRV) features to detect driver fatigue while considering sex differences. Sex-independent and sex-specific differences in HRV features between alert and fatigued states derived from 2 min electrocardiogram (ECG) signals were determined.

Evaluation of Neonatal Cerebral Circulation Under Hypoxic Ischemic Risk Factors Based on Quantitative Analysis of Cerebral Veins with Magnetic Resonance Susceptibility Weighted Imaging.

Purpose: To observe the regulation of cerebral circulation in vivo based on image segmentation algorithms for deep learning in medical imaging to automatically detect and quantify the neonatal deep medullary veins (DMVs) on susceptibility weighted imaging (SWI) images. To evaluate early cerebral circulation self-rescue for neonates undergoing risk of cerebral hypoxia-ischaemia in vivo.

Methods: SWI images and clinical data of 317 neonates with or without risk of cerebral hypoxia-ischaemia were analyzed.

YOLOv8-CML: a lightweight target detection method for color-changing melon ripening in intelligent agriculture.

Color-changing melon is an ornamental and edible fruit. Aiming at the problems of slow detection speed and high deployment cost for Color-changing melon in intelligent agriculture equipment, this study proposes a lightweight detection model YOLOv8-CML.Firstly, a lightweight Faster-Block is introduced to reduce the number of memory accesses while reducing redundant computation, and a lighter C2f structure is obtained.

PHLDA1 contributes to hypoxic ischemic brain injury in neonatal rats via inhibiting FUNDC1-mediated mitophagy.

Hypoxia-ischemia (HI) is one of the main causes of neonatal brain injury. Mitophagy has been implicated in the degradation of damaged mitochondria and cell survival following neonatal brain HI injury. Pleckstrin homology-like domain family A member 1 (PHLDA1) plays vital roles in the progression of various disorders including the regulation of oxidative stress, the immune responses and apoptosis.

Breakfast skipping and risk of all-cause, cardiovascular and cancer mortality among adults: a systematic review and meta-analysis of prospective cohort studies.

: Numerous studies reported inconsistent association between breakfast skipping and all-cause, cardiovascular disease (CVD) and cancer mortality. Therefore, we conducted a systematic review and meta-analysis to elucidate these associations. : PubMed, Embase, and Web of Science databases were searched up to July 2023 for prospective cohort studies that assessed the association between breakfast skipping and all-cause, CVD and cancer mortality in general adults.

Machine learning models predict the emergence of depression in Argentinean college students during periods of COVID-19 quarantine.

Introduction: The COVID-19 pandemic has exacerbated mental health challenges, particularly depression among college students. Detecting at-risk students early is crucial but remains challenging, particularly in developing countries. Utilizing data-driven predictive models presents a viable solution to address this pressing need.

TIGAR exacerbates obesity by triggering LRRK2-mediated defects in macroautophagy and chaperone-mediated autophagy in adipocytes.

Obesity is one of the most common metabolic diseases around the world, which is distinguished by the abnormal buildup of triglycerides within adipose cells. Recent research has revealed that autophagy regulates lipid mobilization to maintain energy balance. TIGAR (Trp53 induced glycolysis regulatory phosphatase) has been identified as a glycolysis inhibitor, whether it plays a role in the metabolism of lipids is unknown.

Heterogeneous graph inference with range constrainted L-collaborative matrix factorization for small molecule-miRNA association prediction.

MicroRNAs (miRNAs) play a vital role in regulating gene expression and various biological processes. As a result, they have been identified as effective targets for small molecule (SM) drugs in disease treatment. Heterogeneous graph inference stands as a classical approach for predicting SM-miRNA associations, showcasing commendable convergence accuracy and speed.

A structure-preserving linearly homomorphic signature scheme with designated combiner.

Linearly homomorphic signature (LHS) allows the acquisition of a new legal signature using the homomorphic operation of the original signatures. However, the public composability of LHS also prevents it from being used in some scenarios where the combiner needs to be designated. The LZZ22 scheme designates a combiner and preserves the signature structure by having the signer and the designated combiner share a secret.

MEG-PPIS: a fast protein-protein interaction site prediction method based on multi-scale graph information and equivariant graph neural network.

Motivation: Protein-protein interaction sites (PPIS) are crucial for deciphering protein action mechanisms and related medical research, which is the key issue in protein action research. Recent studies have shown that graph neural networks have achieved outstanding performance in predicting PPIS. However, these studies often neglect the modeling of information at different scales in the graph and the symmetry of protein molecules within three-dimensional space.

The crosstalk between SUMOylation and immune system in host-pathogen interactions.

Pathogens can not only cause infectious diseases, immune system diseases, and chronic diseases, but also serve as potential triggers or initiators for certain tumors. They directly or indirectly damage human health and are one of the leading causes of global deaths. Small ubiquitin-like modifier (SUMO) modification, a type of protein post-translational modification (PTM) that occurs when SUMO groups bond covalently to particular lysine residues on substrate proteins, plays a crucial role in both innate and adaptive immunologic responses, as well as pathogen-host immune system crosstalk.

Three-dimensional reconstruction of industrial parts from a single image.

This study proposes an image-based three-dimensional (3D) vector reconstruction of industrial parts that can generate non-uniform rational B-splines (NURBS) surfaces with high fidelity and flexibility. The contributions of this study include three parts: first, a dataset of two-dimensional images is constructed for typical industrial parts, including hexagonal head bolts, cylindrical gears, shoulder rings, hexagonal nuts, and cylindrical roller bearings; second, a deep learning algorithm is developed for parameter extraction of 3D industrial parts, which can determine the final 3D parameters and pose information of the reconstructed model using two new nets, CAD-ClassNet and CAD-ReconNet; and finally, a 3D vector shape reconstruction of mechanical parts is presented to generate NURBS from the obtained shape parameters. The final reconstructed models show that the proposed approach is highly accurate, efficient, and practical.

TIGAR reduces neuronal ferroptosis by inhibiting succinate dehydrogenase activity in cerebral ischemia.

Ischemia Stroke (IS) is an acute neurological condition with high morbidity, disability, and mortality due to a severe reduction in local cerebral blood flow to the brain and blockage of oxygen and glucose supply. Oxidative stress induced by IS predisposes neurons to ferroptosis. TP53-induced glycolysis and apoptosis regulator (TIGAR) inhibits the intracellular glycolytic pathway to increase pentose phosphate pathway (PPP) flux, promotes NADPH production and thus generates reduced glutathione (GSH) to scavenge reactive oxygen species (ROS), and thus shows strong antioxidant effects to ameliorate cerebral ischemia/reperfusion injury.

Perfect Matchings with Crossings.

For sets of points, even, in general position in the plane, we consider straight-line drawings of perfect matchings on them. It is well known that such sets admit at least different plane perfect matchings, where is the /2-th Catalan number. Generalizing this result we are interested in the number of drawings of perfect matchings which have crossings.

Knowledge-based recommender systems: overview and research directions.

Recommender systems are decision support systems that help users to identify items of relevance from a potentially large set of alternatives. In contrast to the mainstream recommendation approaches of collaborative filtering and content-based filtering, knowledge-based recommenders exploit semantic user preference knowledge, item knowledge, and recommendation knowledge, to identify user-relevant items which is of specific relevance when dealing with complex and high-involvement items. Such recommenders are primarily applied in scenarios where users specify (and revise) their preferences, and related recommendations are determined on the basis of constraints or attribute-level similarity metrics.

The cortical representation of language timescales is shared between reading and listening.

Language comprehension involves integrating low-level sensory inputs into a hierarchy of increasingly high-level features. Prior work studied brain representations of different levels of the language hierarchy, but has not determined whether these brain representations are shared between written and spoken language. To address this issue, we analyze fMRI BOLD data that were recorded while participants read and listened to the same narratives in each modality.

Accurate and fast implementation of soybean pod counting and localization from high-resolution image.

Introduction: Soybean pod count is one of the crucial indicators of soybean yield. Nevertheless, due to the challenges associated with counting pods, such as crowded and uneven pod distribution, existing pod counting models prioritize accuracy over efficiency, which does not meet the requirements for lightweight and real-time tasks.

Methods: To address this goal, we have designed a deep convolutional network called PodNet.

'Better see a doctor?' Status quo of symptom checker apps in Germany: A cross-sectional survey with a mixed-methods design (CHECK.APP).

Background: Symptom checker apps (SCAs) offer symptom classification and low-threshold self-triage for laypeople. They are already in use despite their poor accuracy and concerns that they may negatively affect primary care. This study assesses the extent to which SCAs are used by medical laypeople in Germany and which software is most popular.

Prediction of Treatment Effect of SLE-ITP Patients Based on Cost-Sensitive Neural Network and Variational Autoencoder.

Objective: The aim of the study was to examine the factors influencing the therapeutic effect of patients with systemic lupus erythematosus combined with immune thrombocytopenia (SLE-ITP) and develop a prediction model to predict the therapeutic effect of SLE-ITP.

Methods: Three hundred twenty-four SLE-ITP patients were retrieved from the electronic health record database of SLE patients in Jiangsu Province according to the latest treatment response criteria for ITP. We adopted the Cox model based on the least absolute shrinkage and selection operator to explore the impact factors affecting patient therapeutic effect, and we developed neural network model to predict therapeutic effect, and in prediction model, cost-sensitivity was introduced to address data category imbalance, and variational autoencoder was used to achieve data augmentation.

Exploring global and local processes underlying alterations in resting-state functional connectivity and dynamics in schizophrenia.

Introduction: We examined changes in large-scale functional connectivity and temporal dynamics and their underlying mechanisms in schizophrenia (ScZ) through measurements of resting-state functional magnetic resonance imaging (rs-fMRI) data and computational modelling.

Methods: The rs-fMRI measurements from patients with chronic ScZ (n=38) and matched healthy controls (n=43), were obtained through the public schizConnect repository. Computational models were constructed based on diffusion-weighted MRI scans and fit to the experimental rs-fMRI data.

Robust Reconstruction of the Void Fraction from Noisy Magnetic Flux Density Using Invertible Neural Networks.

Electrolysis stands as a pivotal method for environmentally sustainable hydrogen production. However, the formation of gas bubbles during the electrolysis process poses significant challenges by impeding the electrochemical reactions, diminishing cell efficiency, and dramatically increasing energy consumption. Furthermore, the inherent difficulty in detecting these bubbles arises from the non-transparency of the wall of electrolysis cells.