Change point detection to analyze air pollution and its economic effects: an exponentially weighted moving average perspective.

Air pollution has a direct impact on every society, leading to consequential effects on the economy of a nation. Poor air quality adversely affects human health, resulting in various economic outcomes such as rising healthcare costs, diminished labor productivity, negative impacts on tourism and living standards, increased regulatory expenses for businesses, and heightened economic disparities. Effective control methods are essential to monitor factors influencing the economy, including air quality.

Superficial and deep white matter abnormalities in temporal lobe epilepsy.

Non-invasive neuroimaging is important in epilepsy to help identify cerebral abnormalities. Abnormally reduced fractional anisotropy (FA) in deep white matter (WM) from diffusion-weighted imaging (DWI) is widely reported in large multi-cohort studies across all types of epilepsies. However, abnormalities in FA for superficial WM are rarely investigated in epilepsy.

Estimating resting energy expenditure in severely obese patients with laparoscopic sleeve gastrectomy: Normalized lean weight appears to be more accurate-A prospective observational study.

Background: The accurate estimation of energy requirements in severely obese patients poses a significant challenge. This study compared the measured resting energy expenditure (mREE) obtained using indirect calorimetry with the estimated energy target in severely obese patients who underwent laparoscopic sleeve gastrectomy.

Methods: This study enrolled patients who underwent elective bariatric surgery for metabolic syndrome and were admitted to the ICU between September 2023 and October 2024 because of severe clinical complications.

Privacy-preserving dementia classification from EEG via hybrid-fusion EEGNetv4 and federated learning.

As global life expectancy rises, a growing proportion of the population is affected by dementia, particularly Alzheimer's disease (AD) and Frontotemporal dementia (FTD). Electroencephalography (EEG) based diagnosis presents a non-invasive, cost effective alternative for early detection, yet existing methods are challenged by data scarcity, inter-subject variability, and privacy concerns. This study proposes lightweight and privacy-preserving EEG classification framework combining deep learning and Federated Learning (FL).

A two-phase hybrid clustering framework exploring transitional activities in HAR.

Human Activity Recognition (HAR) using data streams from wearable sensors is challenging due to high data dimensionality, noise, and the lack of labeled data in unsupervised settings. Our prior work proved that traditional clustering models, which achieve state-of-the-art performance on simulated datasets, perform poorly on time-series numeric sensor data. This paper explores different autoencoder (AE) architectures to extract latent features with reduced dimensionality from streaming HAR datasets, which is then clustered using a clustering model to identify different activity patterns.

Non-negative connectivity causes bow-tie architecture in neural circuits.

Bow-tie architecture (BTA) is widely observed in biological neural systems, yet the underlying mechanism driving its spontaneous emergence remains unclear. In this study, we identify a novel formation mechanism by training multi-layer neural networks under biologically inspired non-negative connectivity constraints across diverse classification tasks. We show that non-negative weights reshape network dynamics by amplifying back-propagated error signals and suppressing hidden-layer activity, leading to the self-organization of BTA without pre-defined architecture.

High-precision machine learning identifies a reproducible functional connectivity signature of autism spectrum diagnosis in a subset of individuals.

Background: Discovery of predictive biomarkers is essential for understanding the neurobiological underpinnings of autism spectrum diagnosis (ASD) and improving identification. Resting-state functional connectivity analyses of individuals with ASD have established sensitivity of brain connectivity at the group level. However, the extensive heterogeneity in ASD limits the translation of these findings into reliable individual-level biomarkers.

Polysubstance use in early adulthood and associated factors in the Republic of Ireland: An analysis of a nationally representative cohort.

Background And Aims: Alcohol and other drug use is common in early adulthood; however, research on contemporary polysubstance use patterns-defined as use of multiple psychoactive substances-and their associated factors is limited. This study aimed to identify groups with differing polysubstance use patterns and to examine associations with individual, family and socio-environmental factors.

Design: This is a cohort study based on data from the Growing Up in Ireland (GUI) study.

A novel technique for ransomware detection using image based dynamic features and transfer learning to address dataset limitations.

The increasing frequency of ransomware attacks necessitates the development of more effective detection methods. Existing image-based ransomware detection approaches have largely focused on static analysis, overlooking specialized ransomware behaviors such as encryption, privilege escalation, and system recovery disruption. Although dynamic and memory forensics-based visualization methods exist in the broader malware domain, they primarily target generic malware families and often rely on memory dumps or system snapshots without transforming behavioral features into spatially meaningful representations.

Paternal prepubertal passive smoke exposure is related to impaired lung function trajectories from childhood to middle age in their offspring.

Introduction: Paternal prepubertal passive smoke exposure may increase the risk of childhood asthma. However, its association with impaired lung function trajectories at risk of chronic obstructive pulmonary disease in offspring was not investigated. We assessed the association between paternal prepubertal passive smoke exposure and lung function from childhood to middle age in their offspring.

VideoA11y: Method and Dataset for Accessible Video Description.

Video descriptions are crucial for blind and low vision (BLV) users to access visual content. However, current artificial intelligence models for generating descriptions often fall short due to limitations in the quality of human annotations within training datasets, resulting in descriptions that do not fully meet BLV users' needs. To address this gap, we introduce VideoA11y, an approach that leverages multimodal large language models (MLLMs) and video accessibility guidelines to generate descriptions tailored for BLV individuals.

High-speed 3D DNA PAINT and unsupervised clustering for unlocking 3D DNA origami cryptography.

DNA origami information storage is a promising alternative to silicon-based data storage, offering a secure molecular cryptography technique that conceals information within arbitrarily folded DNA origami nanostructures. Routing, sliding, and interlacing staple strands lead to the creation of a large 700-bit key size. The realization of practical DNA data storage requires high information density, robust security, and accurate and rapid information retrieval.

AGECovP: identifying ageism and analyzing COVID-19 discourse on older adults in YouTube.

The COVID-19 pandemic significantly impacted older adults, generating widespread online discussions that revealed how this at-risk population was perceived. Understanding these portrayals is essential, as public discourse influences societal perceptions of aging and impacts policies and practices affecting older adults. Past research highlights that ageist stereotypes and attitudes frequently surface in public discussions, shaping the experiences of older individuals.

Research of text paraphrase generation based on self-contrastive learning.

The goal of this study is to improve the quality and diversity of text paraphrase generation, a critical task in Natural Language Generation (NLG) that requires producing semantically equivalent sentences with varied structures and expressions. Existing approaches often fail to generate paraphrases that are both high-quality and diverse, limiting their applicability in tasks such as machine translation, dialogue systems, and automated content rewriting. To address this gap, we introduce two self-contrastive learning models designed to enhance paraphrase generation: the Contrastive Generative Adversarial Network (ContraGAN) for supervised learning and the Contrastive Model with Metrics (ContraMetrics) for unsupervised learning.

Overcoming Site Variability in Multisite fMRI Studies: an Autoencoder Framework for Enhanced Generalizability of Machine Learning Models.

Harmonizing multisite functional magnetic resonance imaging (fMRI) data is crucial for eliminating site-specific variability that hinders the generalizability of machine learning models. Traditional harmonization techniques, such as ComBat, depend on additive and multiplicative factors, and may struggle to capture the non-linear interactions between scanner hardware, acquisition protocols, and signal variations between different imaging sites. In addition, these statistical techniques require data from all the sites during their model training which may have the unintended consequence of data leakage for ML models trained using this harmonized data.

Spaces of ranked tree-child networks.

Ranked tree-child networks are a recently introduced class of rooted phylogenetic networks in which the evolutionary events represented by the network are ordered so as to respect the flow of time. This class includes the well-studied ranked phylogenetic trees (also known as ranked genealogies). An important problem in phylogenetic analysis is to define distances between phylogenetic trees and networks in order to systematically compare them.

Host-Gated Enzymatic Release (H-GER) Enables Colorimetric Transduction for Enzyme Measurement.

We introduce Host-Gated Enzymatic Release (H-GER) as an alternative colorimetric signal transduction mechanism for measuring amylase activity. This assay uses a visually colored complex formed when hydroxypropyl-γ-cyclodextrin (HP-γ-CD) binds to the aggregachromic dye CRANAD-2, with the HP side chains playing a key role in the complexation. The analytical capability of this visually addressable assay relies on changes in dye dispersity, triggered by the enzymatic release of gated CRANAD-2 from HP-γ-CD host.

A deception detection model by using integrated LLM with emotion features.

Traditional lie detection relies on the experience of human interrogators, making it susceptible to subjective factors and leading to misjudgments. To solve this problem, we propose an emotion-enhanced deception detection model, Lie Detection using XGBoost with RoBERTa-based Emotion Features (LieXBerta). In this framework, the Robustly Optimized BERT Pretraining Approach (RoBERTa) is used to extract emotional features from interrogation texts.

Gastrointestinal tract disease classification from wireless capsule endoscopy images based on deep learning information fusion and Newton Raphson controlled marine predator algorithm.

Worldwide, cancer is one of the leading causes of death in humans. Interobserver variability and specialized experience are key factors in diagnosing gastrointestinal tract (GIT) abnormalities using endoscopic procedures. Due to this diversity, small lesions may go unnoticed, leading to a delay in early diagnosis.

Improving lung cancer detection with enhanced convolutional sequential networks.

Lung cancer is the most common cause of cancer-related deaths worldwide, and early detection is extremely important for improving survival. According to the National Institute of Health Sciences, lung cancer has the highest rate of cancer mortality, according to the National Institute of Health Sciences. Medical professionals are usually based on clinical imaging methods such as MRI, X-ray, biopsy, ultrasound, and CT scans.

A comprehensive evaluation of large language models for information extraction from unstructured electronic health records in residential aged care.

Despite rapid healthcare digitization, extracting information from unstructured electronic health records (EHRs), such as nursing notes, remains challenging due to inconsistencies and ambiguities in clinical documentation. Generative large language models (LLMs) have emerged as promising tools for automating information extraction (IE); however, their application in real-world clinical settings, such as residential aged care (RAC), is limited by critical gaps. Prior studies have often focused on structured EHR data and conventional evaluation metrics such as accuracy and F1 score, overlooking critical aspects like robustness, fairness, bias, and contextual relevance, particularly in unstructured clinical narratives.

Predicting sugar-sweetened beverage intake from the brain and known risk factors in adolescents.

Background: Low socio-economic status, male sex, and body mass index (BMI) are known risk factors for high sugar sweetened beverage (SSB) consumption in adolescents. The present analysis aimed to predict SSB intake based on known risk factors and resting-state functional magnetic resonance (rsfMRI) connectivity from the Adolescent Brain Cognitive Development study.

Methods: Using the year-2 follow up visit Block Kids Food Screener data, participants were categorized as low SSB consumers (<8 floz/day) or high SSB consumers (>16 floz/day).

Exploring Clinician Experiences With a Digital Platform Supporting Orthopedic Care That Integrates Patient-Generated Health Data: Qualitative Study of Early Users.

Background: Digital care platforms that integrate patient-generated health data (PGHD) alongside education and communication tools have been recognized as potential instruments in transforming health care from clinician-centered to a more patient-centered approach. This transformation is driven by the potential of PGHD to provide deeper insights into patients' conditions, facilitate personalized care, improve patient quality of life, reduce inefficiencies in data collection, and empower patients. Yet, actual implementation within clinical settings is still at early stages; therefore, impacts on clinical care remain limited.

Systematic benchmarking of 13 AI methods for predicting cyclic peptide membrane permeability.

Cyclic peptides are promising drug candidates due to their ability to modulate intracellular protein-protein interactions, a property often inaccessible to small molecules. However, their typically poor membrane permeability limits therapeutic applicability. Accurate computational prediction of permeability can accelerate the identification of cell-permeable candidates, reducing reliance on time-consuming and costly experimental screening.