Genomic characterization of normal and aberrant human milk production.

Breastfeeding is essential for reducing infant morbidity and mortality, yet exclusive breastfeeding rates remain low, often because of insufficient milk production. The molecular causes of low milk production are not well understood. Fresh milk samples from 30 lactating individuals, classified by milk production levels across postpartum stages, were analyzed using genomic and microbiome techniques.

Rapid and sensitive acute leukemia classification and diagnosis platform using deep learning-assisted SERS detection.

Rapid identification and accurate diagnosis are critical for individuals with acute leukemia (AL). Here, we propose a combined deep learning and surface-enhanced Raman scattering (DL-SERS) classification strategy to achieve rapid and sensitive identification of AL with various subtypes and genetic abnormalities. More than 390 of cerebrospinal fluid (CSF) samples are collected as targets, encompassing healthy control, AL patients, and individuals with other diseases.

A time-frequency graph fusion framework for Major Depressive Disorder diagnosis in multi-site rsfMRI data.

Major Depressive Disorder (MDD) poses a significant global health threat, impairing individual functioning and increasing socioeconomic burden. Accurate diagnosis is crucial for improving treatment outcomes. This study proposes Time-Frequency Text-Attributed DeepWalk (TF-TADW), a framework for MDD classification using resting-state functional MRI data.

Diversity on the bench: An analysis of gendered biases in the language of Australian Family Law Court judgments.

In this paper we analyse gender-based biases in the language within complex legal judgments. Our aims are: (i) to determine the extent to which purported biases discussed in the literature by feminist legal scholars are identifiable from the language of legal judgments themselves, and (ii) to uncover new forms of bias represented in the data that may promote further analysis and interpretation of the functioning of the legal system. We consider a large set of 2530 judgments in family law in Australia over a 20 year period, examining the way that male and female parties to a case are spoken to and about, by male and female judges, in relation to their capacity to provide care for children subject to the decision.

An enhanced secretary bird optimization algorithm based on precise elimination mechanism and boundary control for numerical optimization and low-light image enhancement.

Metaheuristic optimization algorithms often face challenges such as complex modeling, limited adaptability, and a tendency to get trapped in local optima when solving complex optimization problems. To enhance algorithm performance, this paper proposes an enhanced Secretary Bird Optimization Algorithm (MESBOA) based on a precise elimination mechanism and boundary control. The algorithm integrates three key strategies: a precise population elimination strategy, which optimizes the population structure by eliminating individuals with low fitness and intelligently generating new ones; a lens imaging-based opposition learning strategy, which expands the exploration of the solution space through reflection and scaling to reduce the risk of local optima; and a boundary control strategy based on the best individual, which effectively constrains the search range to avoid inefficient searches and premature convergence.

PreRBP: Interpretable Deep Learning for RNA-Protein Binding Site Prediction with Attention Mechanism.

In the complex process of gene expression and regulation, RNA-binding proteins occupy a pivotal position for RNA. Accurate prediction of RNA-protein binding sites can help researchers better understand RNA-binding proteins and their related mechanisms. And prediction techniques based on machine learning algorithms are both cost-effective and efficient in identifying these binding sites.

Implementation of Fully Automated AI-Integrated System for Body Composition Assessment on Computed Tomography for Opportunistic Sarcopenia Screening: Multicenter Prospective Study.

Background: Opportunistic computed tomography (CT) screening for the evaluation of sarcopenia and myosteatosis has been gaining emphasis. A fully automated artificial intelligence (AI)-integrated system for body composition assessment on CT scans is a prerequisite for effective opportunistic screening. However, no study has evaluated the implementation of fully automated AI systems for opportunistic screening in real-world clinical practice for routine health check-ups.

Prenatal diagnosis of cerebellar hypoplasia in fetal ultrasound using deep learning under the constraint of the anatomical structures of the cerebellum and cistern.

Objective: The objective of this retrospective study is to develop and validate an artificial intelligence model constrained by the anatomical structure of the brain with the aim of improving the accuracy of prenatal diagnosis of fetal cerebellar hypoplasia using ultrasound imaging.

Background: Fetal central nervous system dysplasia is one of the most prevalent congenital malformations, and cerebellar hypoplasia represents a significant manifestation of this anomaly. Accurate clinical diagnosis is of great importance for the purpose of prenatal screening of fetal health.

Diagnosing autism spectrum disorders using a double deep Q-Network framework based on social media footprints.

Introduction: Social media is increasingly used in many contexts within the healthcare sector. The improved prevalence of Internet use via computers or mobile devices presents an opportunity for social media to serve as a tool for the rapid and direct distribution of essential health information. Autism spectrum disorders (ASD) are a comprehensive neurodevelopmental syndrome with enduring effects.

YOLO-ED: An efficient lung cancer detection model based on improved YOLOv8.

In recent years, You Only Look Once (YOLO) models have gradually been applied to medical image object detection tasks due to their good scalability and excellent generalization performance, bringing new perspectives and approaches to this field. However, existing models overlook the impact of numerous consecutive convolutions and the sampling blur caused by bilinear interpolation, resulting in excessive computational costs and insufficient precision in object detection. To address these problems, we propose a YOLOv8-based model using Efficient modulation and dynamic upsampling (YOLO-ED) to detect lung cancer in CT images.

Comparative benchmarking of single-cell clustering algorithms for transcriptomic and proteomic data.

Background: Differences in data distribution, feature dimensions, and quality between different single-cell modalities pose challenges for clustering. Although clustering algorithms have been developed for single-cell transcriptomic or proteomic data, their performance across different omics data types and integration scenarios remains poorly investigated, which limits the selection of methods and future method development.

Results: In this study, we conduct a systematic and comparative benchmark analysis of 28 computational algorithms on 10 paired transcriptomic and proteomic datasets, evaluating their performance across various metrics in terms of clustering, peak memory, and running time.

Accurately Predicting Cell Type Abundance from Spatial Histology Image Through HPCell.

Recent advancements in spatial transcriptomics (ST) have revolutionized our ability to simultaneously profile gene expression, spatial location, and tissue morphology, enabling the precise mapping of cell types and signaling pathways within their native tissue context. However, the high cost of sequencing remains a significant barrier to its widespread adoption. Although existing methods often leverage histopathological images to predict transcriptomic profiles and identify cellular heterogeneity, few approaches directly estimate cell-type abundance from these images.

Automatic detection and prediction of epileptic EEG signals based on nonlinear dynamics and deep learning: a review.

Epilepsy is a neurological disorder affecting ~50 million patients worldwide (30% refractory cases) with complex dynamical behavior governed by nonlinear differential equations. Seizures severely impact patients' quality of life and may lead to serious complications. As a primary diagnostic tool, electroencephalography (EEG) captures brain dynamics through non-stationary time series with measurable chaotic and fractal properties.

Decentralizing video copyright protection: a novel blockchain-enabled framework with performance evaluation.

Introduction: Digital content, including images and videos, is increasingly ruling the online world, and so multimedia services form a part of this modern life. However, the digital resources face significant problems, especially regarding copyright infringement. In such an instance, any modification without authority infringes intellectual property rights.

Proposal of (Artamonova and Krassilnikov 1960) Pridham 1970 (Approved Lists 1980) as a later heterotypic synonym of (Rossi Doria 1891) Waksman 1953 (Approved Lists 1980) and an emended description of .

The taxonomic relationship between and was re-evaluated using comparative genome analysis. The 16S rRNA gene sequence analysis indicated that the type strains of and shared 100% sequence similarity. Phylogenetic trees based on 16S rRNA gene sequences indicated that JCM 4387 was clustered together with CGMCC 4.

One-step bipartite graph cut: A normalized formulation and its application to scalable subspace clustering.

The bipartite graph structure has shown its promising ability in facilitating the subspace clustering and spectral clustering algorithms for large-scale datasets. To avoid the post-processing via k-means during the bipartite graph partitioning, the constrained Laplacian rank (CLR) is often utilized for constraining the number of connected components (i.e.

Improved decisions for unknown behaviours in interactive dynamic influence diagrams.

Unlabelled: Interactive dynamic influence diagrams (I-DIDs) are a general decision framework for a subject agent who interacts with other agents (of either collaborative or competitive) in a common environment with partial observability. The subject agent aims to optimize its decision-making (response strategy) while other agents concurrently adapt their behaviors over time. The I-DID model has faced a long-term challenge when other agents exhibit unknown behaviors that go beyond what the subject agent has planned for prior to their interactions.

Identification of severity related mutation hotspots in SARS-CoV-2 using a density-based clustering approach.

Background: The immune response to SARS-CoV-2 varies greatly among individuals yielding highly varying severity levels among the patients. While there are various methods to spot severity associated biomarkers in COVID-19 patients, we investigated highly mutated regions, or mutation hotspots, within the SARS-CoV-2 genome that correlate with patient severity levels. SARS-CoV-2 mutation hotspots were searched in the GISAID database using a density based clustering algorithm, Mutclust, that searches for loci with high mutation density and diversity.

Automatic and accurate reconstruction of long-range axonal projections of single-neuron in mouse brain.

Single-neuron axonal projections reveal the route map of neuron output and provide a key cue for understanding how information flows across the brain. Reconstruction of single-neuron axonal projections requires intensive manual operations in tens of terabytes of brain imaging data and is highly time-consuming and labor-intensive. The main issue lies in the need for precise reconstruction algorithms to avoid reconstruction errors, yet current methods struggle with densely distributed axons, focusing mainly on skeleton extraction.

TIPs: Tooth instance and pulp segmentation based on hierarchical extraction and fusion of anatomical priors from cone-beam CT.

Accurate instance segmentation of tooth and pulp from cone-beam computed tomography (CBCT) images is essential but highly challenging due to the pulp's small structures and indistinct boundaries. To address these critical challenges, we propose TIPs designed for Tooth Instance and Pulp segmentation. TIPs initially employs a backbone model to segment a binary mask of the tooth from CBCT images, which is then utilized to derive position prior of the tooth and shape prior of the pulp.

A robust deep learning framework for cerebral microbleeds recognition in GRE and SWI MRI.

Cerebral microbleeds (CMB) are small hypointense lesions visible on gradient echo (GRE) or susceptibility-weighted (SWI) MRI, serving as critical biomarkers for various cerebrovascular and neurological conditions. Accurate quantification of CMB is essential, as their number correlates with the severity of conditions such as small vessel disease, stroke risk and cognitive decline. Current detection methods depend on manual inspection, which is time-consuming and prone to variability.

Edge computing with federated learning for early detection of citric acid overdose and adjustment of regional citrate anticoagulation.

Regional citrate anticoagulation (RCA) is critical for extracorporeal anticoagulation in continuous renal replacement therapy done at the bedside. To make patients' data more secure and to help with computer-based monitoring of dosages, we suggest a system that uses machine learning. This system will give early alerts about citric acid overdose and advise changes to how much citrate and calcium gluconate are infused into the patient's body.

Evaluation of deep learning models using explainable AI with qualitative and quantitative analysis for rice leaf disease detection.

Deep learning models have shown remarkable success in disease detection and classification tasks, but lack transparency in their decision-making process, creating reliability and trust issues. Although traditional evaluation methods focus entirely on performance metrics such as classification accuracy, precision and recall, they fail to assess whether the models are considering relevant features for decision-making. The main objective of this work is to develop and validate a comprehensive three-stage methodology that combines conventional performance evaluation with qualitative and quantitative evaluation of explainable artificial intelligence (XAI) visualizations to assess both the accuracy and reliability of deep learning models.

Harnessing smartphone RGB imagery and LiDAR point cloud for enhanced leaf nitrogen and shoot biomass assessment - Chinese spinach as a case study.

Accurate estimation of leaf nitrogen concentration and shoot dry-weight biomass in leafy vegetables is crucial for crop yield management, stress assessment, and nutrient optimization in precision agriculture. However, obtaining this information often requires access to reliable plant physiological and biophysical data, which typically involves sophisticated equipment, such as high-resolution sensors and cameras. In contrast, smartphone-based sensing provides a cost-effective, manual alternative for gathering accurate plant data.

Hybrid Bayesian deep learning model for predicting urban heat island intensity in African cities.

Urban Heat Islands (UHIs), in which urban areas have higher temperatures than adjacent rural areas, lead to increased energy demand, poor air quality, and greater public health risks. These impacts are particularly significant in rapidly urbanizing regions of Africa, where climate-adaptive infrastructure has yet to be fully established. Here, we propose a hybrid predictive model derived from Bayesian Neural Networks (BNNs) for deep learning-based forecasting of Surface Urban Heat Island (SUHI) intensities across the African continent.