When are Quarnets Sufficient to Reconstruct Semi-directed Phylogenetic Networks?

Phylogenetic networks are graphs that are used to represent evolutionary relationships between different taxa. They generalize phylogenetic trees since for example, unlike trees, they permit lineages to combine. Recently, there has been rising interest in semi-directed phylogenetic networks, which are mixed graphs in which certain lineage combination events are represented by directed edges coming together, whereas the remaining edges are left undirected.

A Detection Approach for Wheat Spike Recognition and Counting Based on UAV Images and Improved Faster R-CNN.

This study presents an innovative unmanned aerial vehicle (UAV)-based intelligent detection method utilizing an improved Faster Region-based Convolutional Neural Network (Faster R-CNN) architecture to address the inefficiency and inaccuracy inherent in manual wheat spike counting. We systematically collected a high-resolution image dataset (2000 images, 4096 × 3072 pixels) covering key growth stages (heading, grain filling, and maturity) of winter wheat ( L.) during 2022-2023 using a DJI M300 RTK equipped with multispectral sensors.

Enhanced ResNet-50 with Multi-Feature Fusion for Robust Detection of Pneumonia in Chest X-Ray Images.

Pneumonia is a critical lung infection that demands timely and precise diagnosis, particularly during the evaluation of chest X-ray images. Deep learning is widely used for pneumonia detection but faces challenges such as poor denoising, limited feature diversity, low interpretability, and class imbalance issues. This study aims to develop an optimized ResNet-50 based framework for accurate pneumonia detection.

Copy Number Variants of Uncertain Significance by Chromosome Microarray Analysis from Consecutive Pediatric Patients: Reevaluation Following Current Guidelines and Reanalysis by Genome Sequencing.

Background: Copy number variants of uncertain significance (CNVus) from chromosome microarray analysis (CMA) presents unresolved challenges for clinical geneticists, genetic counselors, and patients. We performed a systematic reevaluation of reported CNVus and reanalysis of selected CNVus by whole genome sequencing (WGS) to assess the diagnostic value and clinical impact on CNVus reclassification.

Methods: We retrospectively reviewed 5277 consecutive pediatric cases by CMA from the Yale Clinical Cytogenetics Laboratory over a 13-year period.

TPOT: TOPOLOGY PRESERVING OPTIMAL TRANSPORT IN RETINAL FUNDUS IMAGE ENHANCEMENT.

Retinal fundus photography enhancement is important for diagnosing and monitoring retinal diseases. However, early approaches to retinal image enhancement, such as those based on Generative Adversarial Networks (GANs), often struggle to preserve the complex topological information of blood vessels, resulting in spurious or missing vessel structures. The persistence diagram, which captures topological features based on the persistence of topological structures under different filtrations, provides a promising way to represent the structure information.

Measuring Fatigue in Multiple Sclerosis: A Rapid Review.

Background: Fatigue is one of the most prevalent and debilitating symptoms of multiple sclerosis (MS), as people with MS describe it. It has a complex pathogenesis and often precedes the clinical symptoms of MS and potentially indicates disease progression. Given its prevalence, impact, and intricate connections to disease pathology, accurate measurement is crucial to manage and study fatigue in people with MS; however, current measurements often lack content validity.

Challenges and opportunities in direct-to-consumer hearing healthcare service delivery: a scoping review.

Despite the rising prevalence of hearing loss worldwide, underutilization of hearing aids persists. Direct-to-consumer (DTC) hearing services have emerged as a potential solution to address barriers in conventional audiology services. This scoping review investigates the challenges and opportunities associated with direct-to-consumer service delivery in audiology.

Hybrid optimization-based deep learning for energy efficiency resource allocation in MIMO-enabled wireless networks.

Resource allocation in multiple-input multiple-output (MIMO)-enabled wireless networks is designated for multiple users, which aims to optimize the distribution of network resources. This network's main intent is to maximize system performance by improving energy efficiency. However, the users of MIMO need many resources for effective operation.

Optimizing long term disease prevention with reinforcement learning: a framework for precision lipid control.

The prevention of chronic disease is a long-term combat with continual fine-tuning to adapt to the course of disease. Without comprehensive insights, prescriptions may prioritize short-term gains but deviate from trajectories toward long-term survival. Here we introduce Duramax, an evidence-based framework empowered by reinforcement learning to optimize long-term preventive strategies.

Helmets Labeling Crops: Kenya Crop Type Dataset Created via Helmet-Mounted Cameras and Deep Learning.

Accurate, up-to-date agricultural monitoring is essential for assessing food production, particularly in countries like Kenya, where recurring climate extremes, including floods and droughts, exacerbate food insecurity challenges. In regions dominated by smallholder farmers, a significant obstacle to effective agricultural monitoring is the limited availability of current, detailed crop-type maps. Creating crop-type maps requires extensive field data.

Innovations in early detection of chronic non-communicable diseases among adolescents through an easy-to-Use AutoML paradigm.

In this research, we present an interpretable AutoML approach for the early diagnosis of hypertension and hyperinsulinemia among adolescents, conditions that are critical to identify during these formative years due to their requirement for lifelong care and monitoring. The dataset, collected from 2019 to 2022 by Serbia's Healthcare Center through an observational cross-sectional study, posed challenges common to medical datasets, including imbalances, data scarcity, and a need for transparent, explainable predictive models. To counter these issues, we utilized three AutoML frameworks - AutoGluon, H2O, and MLJAR - in conjunction with a Tabular Variational Autoencoder (TVAE) to synthetically augment the data points, Prinicipal Component Analysis (PCA) for dimensionality reduction, and SHapley Additive exPlanations (SHAP) and Permutation feature importance analyses to extract insights from the results.

Experimental Assessment of Conventional Features, CNN-Based Features and Ensemble Schemes for Discriminating Benign Versus Malignant Lesions on Breast Ultrasound Images.

Breast ultrasound images play a pivotal role in assessing the nature of suspicious breast lesions, particularly in patients with dense tissue. Computerized analysis of breast ultrasound images has the potential to assist the physician in the clinical decision-making and improve subjective interpretation. We assess the performance of conventional features, deep learning features and ensemble schemes for discriminating benign versus malignant breast lesions on ultrasound images.

AI-Powered Adaptive Disability Prediction and Healthcare Analytics Using Smart Technologies.

: By leveraging advanced wireless technologies, Healthcare Industry 5.0 promotes the continuous monitoring of real-time medical acquisition from the physical environment. These systems help identify early diseases by collecting health records from patients' bodies promptly using biosensors.

Comparative Analysis of a Rapid Quantitative Immunoassay to the Reference Methodology for the Measurement of Blood Vitamin D Levels.

Vitamin D is the only vitamin that is conditionally essential, as it is synthesized from precursors after UV light exposure, whilst also being obtained from the diet. It has numerous health benefits, with deficiency becoming a major concern globally, such that dietary supplementation has more recently achieved vital importance to maintain satisfactory levels. In recent years, measurements made from blood have, therefore, become critical to determine the status of vitamin D levels in individuals and the larger population.

Convolutional neural network and wavelet composite against geometric attacks a watermarking approach.

Digital e-governance has grown tremendously due to the massive information technology revolution. Banking, Healthcare, and Insurance are some sectors that rely on ownership identification during various stages of service provision. Watermarking has been employed as a primary factor in authenticating stakeholders in such circumstances.

Semiparametric Regression Analysis of Interval-Censored Multi-State Data with An Absorbing State.

In studies of chronic diseases, the health status of a subject can often be characterized by a finite number of transient disease states and an absorbing state, such as death. The times of transitions among the transient states are ascertained through periodic examinations and thus interval-censored. The time of reaching the absorbing state is known or right-censored, with the transient state at the previous instant being unobserved.

Enhancing the early detection of Alzheimer's disease using an integrated CNN-LSTM framework: A robust approach for fMRI-based multi-stage classification.

Alzheimer's Disease poses a significant challenge as a progressive and irreversible neurological condition striking the elderly population. Its incurable nature correlates with a significant rise in death rates. However, early detection can slow its progression and facilitate prompt intervention, thereby mitigating mortality risks.

Using Vibration for Secure Pairing With Implantable Medical Devices: Development and Usability Study.

Background: Implantable medical devices (IMDs), such as pacemakers, increasingly communicate wirelessly with external devices. To secure this wireless communication channel, a pairing process is needed to bootstrap a secret key between the devices. Previous work has proposed pairing approaches that often adopt a "seamless" design and render the pairing process imperceptible to patients.

Multi-centre normative brain mapping of intracranial EEG lifespan patterns in the human brain.

Understanding healthy human brain function is crucial to identify and map pathological tissue within it. Whilst previous studies have mapped intracranial EEG (icEEG) from non-epileptogenic brain regions, they often neglect age and sex effects. Further, they are limited by small sample sizes due to the modality's invasive nature.

Cecal microbiome transplantation without antibiotic preconditioning standardizes murine microbiomes.

Introduction: Translation of nonclinical findings from laboratory mice to the clinic may be confounded by un-controlled variance in bacterial gut content, as a driver of immune maturation and recruitment, as well as drug metabolism. Understanding and controlling for microbiome variation in animal experiments can lead to better reproducibility of animal findings, more translatable characterization of efficacy and toxicity end-points and time and cost savings associated with pharmaceutical development. Microbiome composition has been linked to failure of translation of drug responses.

Accounting for white matter uptake improves between tracer agreement in amyloid PET.

Introduction: Amyloid positron emission tomography (PET) allows in vivo measurement of amyloid plaque deposition; however, different tracers lead to different results. We test the hypothesis that the variability in amyloid measurements is related to white matter retention, and accounting for this variability can improve agreements.

Methods: Data from the Centiloid project was downloaded and processed for four F18 tracer-to-Pittsburgh Compound B (PiB) pairs to obtain mean cortical standardized uptake value ratio (MCSUVR).

On-device AI for climate-resilient farming with intelligent crop yield prediction using lightweight models on smart agricultural devices.

In Recent time, with the utilization of Artificial Intelligence (AI), AI applications have proliferated across various domains where agricultural consumer electronics are no exception. These innovations have significantly enhanced the intelligence of agricultural processes, leading to increased efficiency and sustainability. This study introduces an intelligent crop yield prediction system that utilizes Random Forest (RF) classifier to optimize the usage of water based on environmental factors.

Bovine milk extracellular vesicles prepared by ultracentrifugation contain microbial mRNAs that do not accumulate in human plasma following milk consumption.

Small extracellular vesicles (sEVs) and their RNA cargo are not exclusively derived from endogenous synthesis but can also be absorbed from milk and gut bacteria. Given the high rate of bacterial fermentation in the gastrointestinal tract of ruminants, we hypothesized that preparations of bovine milk sEVs (BMEs) contain bacterial mRNAs whose bioavailability in humans remains unknown. BMEs were purified from chilled antibiotics-treated raw milk (RM) and store-bought skim milk (SBM) using sequential ultracentrifugation.