Search Results (4,929)

The phase-sensitive optical time-domain reflectometry (Φ-OTDR) system has shown substantial potential in distributed acoustic sensing applications. Accurate event classification is crucial for effective deployment of Φ-OTDR systems, and various methods have been proposed for event classification in Φ-OTDR systems. However, most existing methods typically rely on sufficient labeled signal data for model training, which poses a major bottleneck in applying these methods due to the expensive and laborious process of labeling extensive data. To address this limitation, we propose CLWTNet, a novel contrastive representation learning method enhanced with wavelet transform convolution for event classification in Φ-OTDR systems. CLWTNet learns robust and discriminative representations directly from unlabeled signal data by transforming time-domain signals into STFT images and employing contrastive learning to maximize inter-class separation while preserving intra-class similarity. Furthermore, CLWTNet incorporates wavelet transform convolution to enhance its capacity to capture intricate features of event signals. The experimental results demonstrate that CLWTNet achieves competitive performance with the supervised representation learning methods and superior performance to unsupervised representation learning methods, even when training with unlabeled signal data. These findings highlight the effectiveness of CLWTNet in extracting discriminative representations without relying on labeled data, thereby enhancing data efficiency and reducing the costs and effort involved in extensive data labeling in practical Φ-OTDR system applications. Full article

Global warming has resulted in an increase in the frequency of extreme high-temperature events. High temperatures can increase cell membrane permeability, elevate levels of osmotic adjustment substances, reduce photosynthetic capacity, impair plant growth and development, and even result in plant death. Under high-temperature stress, plants mitigate damage through physiological and biochemical adjustments, heat signal transduction, the regulation of transcription factors, and the synthesis of heat shock proteins. However, different plants exhibit varying regulatory abilities and temperature tolerances. Investigating the heat-resistance and regulatory mechanisms of plants can facilitate the development of heat-resistant varieties for plant genetic breeding and landscaping applications. This paper presents a systematic review of plant physiological and biochemical responses, regulatory substances, signal transduction pathways, molecular mechanisms—including the regulation of heat shock transcription factors and heat shock proteins—and the role of plant hormones under high-temperature stress. The study constructed a molecular regulatory network encompassing Ca²⁺ signaling, plant hormone pathways, and heat shock transcription factors, and it systematically elucidated the mechanisms underlying the enhancement of plant thermotolerance, thereby providing a scientific foundation for the development of heat-resistant plant varieties. Full article

This study is the first to systematically integrate supervised machine learning (decision tree) and association rule mining techniques to analyze accident data from the Taipei Metro system, conducting a large-scale data-driven investigation into both passenger injury and train malfunction events. The research demonstrates strong novelty and practical contributions. In the passenger injury analysis, a dataset of 3331 cases was examined, from which two highly explanatory rules were extracted: (i) elderly passengers (aged > 61) involved in station incidents are more likely to suffer moderate to severe injuries; and (ii) younger passengers (aged ≤ 61) involved in escalator incidents during off-peak hours are also at higher risk of severe injury. This is the first study to quantitatively reveal the interactive effect of age and time of use on injury severity. In the train malfunction analysis, 1157 incidents with delays exceeding five minutes were analyzed. The study identified high-risk condition combinations—such as those involving rolling stock, power supply, communication, and signaling systems—associated with specific seasons and time periods (e.g., a lift value of 4.0 for power system failures during clear mornings from 06:00–12:00, and 3.27 for communication failures during summer evenings from 18:00–24:00). These findings were further cross-validated with maintenance records to uncover underlying causes, including brake system failures, cable aging, and automatic train operation (ATO) module malfunctions. Targeted preventive maintenance recommendations were proposed. Additionally, the study highlighted existing gaps in the completeness and consistency of maintenance records, recommending improvements in documentation standards and data auditing mechanisms. Overall, this research presents a new paradigm for intelligent metro system maintenance and safety prediction, offering substantial potential for broader adoption and practical application. Full article

The Mediterranean region, identified by scientists as a ‘climate hot spot’, is experiencing warmer and drier conditions, along with an increase in the intensity and frequency of extreme weather events. One such extreme phenomena is droughts. The recent wildfires in this region are a concerning consequence of this phenomenon, causing severe environmental damage and transforming natural landscapes. However, droughts involve a two-way interaction: On the one hand, climate change and various human activities, such as urbanization and deforestation, influence the development and severity of droughts. On the other hand, droughts have a significant impact on various sectors, including ecology, agriculture, and the local economy. This study investigates drought dynamics in four Mediterranean countries, Greece, France, Italy, and Spain, each of which has experienced severe wildfire events in recent years. Using satellite-based Earth observation data, we monitored drought conditions across these regions over a five-year period that includes the dates of major wildfires. To support this analysis, we derived and assessed key indices: the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and Normalized Difference Drought Index (NDDI). High-resolution satellite imagery processed within the Google Earth Engine (GEE) platform enabled the spatial and temporal analysis of these indicators. Our findings reveal that, in all four study areas, peak drought conditions, as reflected in elevated NDDI values, were observed in the months leading up to wildfire outbreaks. This pattern underscores the potential of satellite-derived indices for identifying regional drought patterns and providing early signals of heightened fire risk. The application of GEE offered significant advantages, as it allows efficient handling of long-term and large-scale datasets and facilitates comprehensive spatial analysis. Our methodological framework contributes to a deeper understanding of regional drought variability and its links to extreme events; thus, it could be a valuable tool for supporting the development of adaptive management strategies. Ultimately, such approaches are vital for enhancing resilience, guiding water resource planning, and implementing early warning systems in fire-prone Mediterranean landscapes. Full article

Brain ischemia is a severe condition caused by reduced cerebral blood flow, leading to the disruption of ion gradients in brain tissue. This imbalance triggers spreading depolarizations, which are waves of neuronal and glial depolarization propagating through the gray matter. Microelectrode arrays (MEAs) are essential for real-time monitoring of these electrophysiological processes both in vivo and in vitro, but their sensitivity and signal quality are critical for accurate detection of extracellular brain activity. In this study, we evaluate the performance of a flexible microelectrode array based on gold-coated zinc oxide nanorods (ZnO NRs), referred to as nano-fMEA, specifically for high-fidelity electrophysiological recording under pathological conditions. Acute mouse brain slices were tested under two ischemic models: oxygen–glucose deprivation (OGD) and hyperkalemia. The nano-fMEA demonstrated significant improvements in event detection rates and in capturing subtle fluctuations in neural signals compared to flat fMEAs. This enhanced performance is primarily attributed to an optimized electrode–tissue interface that reduces impedance and improves charge transfer. These features enabled the nano-fMEA to detect weak or transient electrophysiological events more effectively, making it a valuable platform for investigating neural dynamics during metabolic stress. Overall, the results underscore the promise of ZnO NRs in advancing electrophysiological tools for neuroscience research. Full article

RNA-seq analysis of the highly differentiated human retinal pigment epithelial (RPE) cell-line ARPE-19, cultured on transwells for ≥4 months, yielded 44,909 genes showing 83.35% alignment with the human reference genome. These included mRNA transcripts of RPE-specific genes and those involved in retinopathies. Monolayers were fed photoreceptor outer segments (POS), designed to be synchronously internalised, mimicking homeostatic RPE activity. Cells were subsequently fixed at 4, 6, 24 and 48 h when POS were previously shown to maximally co-localise with Rab5, Rab7, LAMP/lysosomes and LC3b/autophagic compartments. A comprehensive analysis of differentially expressed genes involved in proteolysis revealed a pattern of gene orchestration consistent with POS breakdown in the autophagy-lysosomal pathway. At 4 h, these included elevated upstream signalling events promoting early stages of cargo transport and endosome maturation compared to RPE without POS exposure. This transcriptional landscape altered from 6 h, transitioning to promoting cargo degradation in autolysosomes by 24–48 h. Longitudinal scrutiny of mRNA transcripts revealed nuanced differences even within linked gene networks. POS exposure also initiated transcriptional upregulation in ubiquitin proteasome and chaperone-mediated systems within 4–6 h, providing evidence of cross-talk with other proteolytic processes. These findings show detailed evidence of transcriptome-level responses to cargo trafficking and processing in RPE cells. Full article

Introduction: Burnout syndrome, long described as an “occupational phenomenon”, now affects 15–20% of the general workforce and more than 50% of clinicians, teachers, social-care staff and first responders. Its precise nosological standing remains disputed. We conducted a mechanistic review of electroencephalography (EEG) studies to determine whether burnout is accompanied by reproducible brain-function alterations that justify disease-level classification. Methods: Following PRISMA-adapted guidelines, two independent reviewers searched PubMed/MEDLINE, Scopus, Google Scholar, Cochrane Library and reference lists (January 1980–May 2025) using combinations of “burnout,” “EEG”, “electroencephalography” and “event-related potential.” Only English-language clinical investigations were eligible. Eighteen studies (n = 2194 participants) met the inclusion criteria. Data were synthesised across three domains: resting-state spectra/connectivity, event-related potentials (ERPs) and longitudinal change. Results: Resting EEG consistently showed (i) a 0.4–0.6 Hz slowing of individual-alpha frequency, (ii) 20–35% global alpha-power reduction and (iii) fragmentation of high-alpha (11–13 Hz) fronto-parietal coherence, with stage- and sex-dependent modulation. ERP paradigms revealed a distinctive “alarm-heavy/evaluation-poor” profile; enlarged N2 and ERN components signalled hyper-reactive conflict and error detection, whereas P3b, Pe, reward-P3 and late CNV amplitudes were attenuated by 25–50%, indicating depleted evaluative and preparatory resources. Feedback processing showed intact or heightened FRN but blunted FRP, and affective tasks demonstrated threat-biassed P3a latency shifts alongside dampened VPP/EPN to positive cues. These alterations persisted in longitudinal cohorts yet normalised after recovery, supporting trait-plus-state dynamics. The electrophysiological fingerprint differed from major depression (no frontal-alpha asymmetry, opposite connectivity pattern). Conclusions: Across paradigms, burnout exhibits a coherent neurophysiological signature comparable in magnitude to established psychiatric disorders, refuting its current classification as a non-disease. Objective EEG markers can complement symptom scales for earlier diagnosis, treatment monitoring and public-health surveillance. Recognising burnout as a clinical disorder—and funding prevention and care accordingly—is medically justified and economically imperative. Full article

T-cells constitute an essential component of the adaptive immune response, mount a protective response against foreign pathogens and are important regulators of anti-tumor immunotherapy. In this context, the activation of T-cells and chimeric antigen receptor (CAR)-expressing T-cells is orchestrated by various signaling pathways, involving the initiation of a protein tyrosine phosphorylation cascade. For T-cells, this involves initiation of the phosphorylation cascade via src-related protein-tyrosine kinase p56^lck, which we show to associate with the co-receptors CD4 and CD8 for the induction of a phosphorylation cascade needed for the activation of T-cells. Likewise, p56^lck phosphorylation of the antigen receptor immunoreceptor tyrosine-based activation motifs (ITAMs) and key CD28 tyrosine motifs ensures the functionality and the survival of CARs, while their phospho-targets are also inhibited by PD-1, a key component of the immune checkpoint blockade. This review covers historic and current elements of our knowledge of CD4/CD8–p56^lck-induced activation events and their importance to the development of CAR T-cell immunotherapies. Full article

The early detection of Alzheimer’s disease (AD) is important for effective therapeutic interventions and optimized enrollment for clinical trials. Recent studies have shown high accuracy in identifying mild AD by applying visibility graph and machine learning methods to electroencephalographic (EEG) data. We present a novel analytical framework combining Weighted Visibility Graphs (WVG) and ensemble learning to detect individuals in the “preclinical” stage of AD (preAD) using a word repetition EEG paradigm, where WVG is an advanced variant of natural Visibility Graph (VG), incorporating weighted edges based on the visibility degree between corresponding data points. The EEG signals were recorded from 40 cognitively unimpaired elderly participants (20 preclinical AD and 20 normal old) during a word repetition task. Event-related potential (ERP) and oscillatory signals were extracted from each EEG channel and transformed into a WVG network, from which relevant topological features were extracted. The features were selected using t-tests to reduce noise. Subsequent statistical analysis reveals significant disparities in the structure of WVG networks between preAD and normal subjects. Furthermore, Principal Component Analysis (PCA) was applied to condense the input data into its principal features. Leveraging these PCA components as input features, several machine learning algorithms are used to classify preAD vs. normal subjects. To enhance classification accuracy and robustness, an ensemble method is employed alongside the classifiers. Our framework achieved an accuracy of up to 92% discriminating preAD from normal old using both linear and non-linear classifiers, signifying the efficacy of combining WVG and ensemble learning in identifying very early AD from EEG signals. The framework can also improve clinical efficiency by reducing the amount of data required for effective classification and thus saving valuable clinical time. Full article

This paper proposes a multi-hop peer-to-peer (P2P) video streaming architecture designed to support dynamic, conversation-aware communication. The primary contribution is a decentralized system built on WebRTC that eliminates reliance on a central media server by employing super node aggregation. In this architecture, video streams from multiple peer nodes are dynamically routed through a group of super nodes, enabling real-time reconfiguration of the network topology in response to conversational changes. To support this dynamic behavior, the system leverages WebRTC data channels for control signaling and overlay restructuring, allowing efficient dissemination of topology updates and coordination messages among peers. A key focus of this study is the rapid and efficient reallocation of network resources immediately following conversational events, ensuring that the streaming overlay remains aligned with ongoing interaction patterns. While the automatic detection of such events is beyond the scope of this work, we assume that external triggers are available to initiate topology updates. To validate the effectiveness of the proposed system, we construct a simulation environment using Docker containers and evaluate its streaming performance under dynamic network conditions. The results demonstrate the system’s applicability to adaptive, naturalistic communication scenarios. Finally, we discuss future directions, including the seamless integration of external trigger sources and enhanced support for flexible, context-sensitive interaction frameworks. Full article

Background/Objectives: The RTK-RAS signaling cascade is a central axis in colorectal cancer (CRC) pathogenesis, governing cellular proliferation, survival, and therapeutic resistance. Somatic alterations in key pathway genes—including KRAS, NRAS, BRAF, and EGFR—are pivotal to clinical decision-making in precision oncology. However, the integration of these genomic events with clinical and demographic data remains hindered by fragmented resources and a lack of accessible analytical frameworks. To address this challenge, we developed AI-HOPE-RTK-RAS, a domain-specialized conversational artificial intelligence (AI) system designed to enable natural language-based, integrative analysis of RTK-RAS pathway alterations in CRC. Methods: AI-HOPE-RTK-RAS employs a modular architecture combining large language models (LLMs), a natural language-to-code translation engine, and a backend analytics pipeline operating on harmonized multi-dimensional datasets from cBioPortal. Unlike general-purpose AI platforms, this system is purpose-built for real-time exploration of RTK-RAS biology within CRC cohorts. The platform supports mutation frequency profiling, odds ratio testing, survival modeling, and stratified analyses across clinical, genomic, and demographic parameters. Validation included reproduction of known mutation trends and exploratory evaluation of co-alterations, therapy response, and ancestry-specific mutation patterns. Results: AI-HOPE-RTK-RAS enabled rapid, dialogue-driven interrogation of CRC datasets, confirming established patterns and revealing novel associations with translational relevance. Among early-onset CRC (EOCRC) patients, the prevalence of RTK-RAS alterations was significantly lower compared to late-onset disease (67.97% vs. 79.9%; OR = 0.534, p = 0.014), suggesting the involvement of alternative oncogenic drivers. In KRAS-mutant patients receiving Bevacizumab, early-stage disease (Stages I–III) was associated with superior overall survival relative to Stage IV (p = 0.0004). In contrast, BRAF-mutant tumors with microsatellite-stable (MSS) status displayed poorer prognosis despite higher chemotherapy exposure (OR = 7.226, p < 0.001; p = 0.0000). Among EOCRC patients treated with FOLFOX, RTK-RAS alterations were linked to worse outcomes (p = 0.0262). The system also identified ancestry-enriched noncanonical mutations—including CBL, MAPK3, and NF1—with NF1 mutations significantly associated with improved prognosis (p = 1 × 10⁻⁵). Conclusions: AI-HOPE-RTK-RAS exemplifies a new class of conversational AI platforms tailored to precision oncology, enabling integrative, real-time analysis of clinically and biologically complex questions. Its ability to uncover both canonical and ancestry-specific patterns in RTK-RAS dysregulation—especially in EOCRC and populations with disproportionate health burdens—underscores its utility in advancing equitable, personalized cancer care. This work demonstrates the translational potential of domain-optimized AI tools to accelerate biomarker discovery, support therapeutic stratification, and democratize access to multi-omic analysis. Full article

Cancer is no longer considered as an isolated event. Rather, it occurs because of a complex biological drive orchestrating different cell types, growth factors, cytokines, and signaling pathways within the tumor microenvironment (TME). Cancer-associated fibroblasts (CAFs) are the most populous stromal cells within the complex ecosystem of TME, with significant heterogeneity and plasticity in origin and functional phenotypes. Very enigmatic cells, CAFs determine the progress and outcomes of tumors through extensive reciprocal signaling with different tumors infiltrating immune cells in the TME. In their biological drive, CAFs release numerous chemical mediators and utilize various signaling pathways to recruit and modulate tumor-infiltrating immune cells. The CAF-induced secretome and exosomes render immune cells ineffective for their antitumor activities. Moreover, by upregulating immune inhibitory checkpoints, CAFs create an immunosuppressive TME that impedes the susceptibility of tumor cells to tumor-infiltrating lymphocytes (TILs). Further, by depositing and remodeling extracellular matrix (ECM), CAFs reshape the TME, which enhances tumor growth, invasion, metastasis, and chemoresistance. Understanding of CAF biology and its crosstalk with tumor-infiltrating immune cells is crucial not only to gain insight in tumorigenesis but to optimize the potential of novel targeted immunotherapies for cancers. The complex relationships between CAFs and tumor-infiltrating immune cells remain unclear and need further study. Herein, in this narrative review we have focused on updates of CAF biology and its interactions with tumor-infiltrating immune cells in generating immunosuppressive TME and resistance to cell death. Full article

Background/Objectives: Vedolizumab is a gut-selective anti-integrin monoclonal antibody approved for the treatment of inflammatory bowel disease (IBD). While clinical trials have demonstrated a favorable safety profile, real-world studies are essential for identifying rare adverse events (AEs) and evaluating post-marketing safety. This study assessed vedolizumab’s safety in a real-world cohort and supported the detection of potential safety signals. Methods: A retrospective chart review was conducted on adult IBD patients treated with vedolizumab at a tertiary center in the Republic of Serbia between October 2021 and August 2022. Data included demographics, AEs, and newly reported extraintestinal manifestations (EIMs). Exposure-adjusted incidence rates were calculated per 100 patient-years (PYs). Disproportionality analysis using the FDA Adverse Event Reporting System (FAERS) was performed to identify safety signals, employing reporting odds ratios (RORs) and proportional reporting ratios (PRRs) for AEs also observed in the cohort. Prior IBD therapies and reasons for discontinuation were evaluated. Results: A total of 107 patients (42.1% Crohn’s disease, 57.9% ulcerative colitis) were included, with a median vedolizumab exposure of 605 days. There were 92 AEs (56.51/100 PYs), most frequently infections (23.95/100 PYs), gastrointestinal disorders (4.30/100 PYs), and skin disorders (4.30/100 PYs). The most frequently reported preferred terms (PTs) included COVID-19, COVID-19 pneumonia, nephrolithiasis, and nasopharyngitis. Arthralgia (12.90/100 PYs) was the most frequent newly reported EIM. No discontinuations due to vedolizumab AEs occurred. FAERS analysis revealed potential signals for events not listed in prescribing information but observed in the cohort: nephrolithiasis, abdominal pain, diarrhea, malaise, cholangitis, gastrointestinal infection, blood pressure decreased, weight decreased, female genital tract fistula, respiratory symptom, and appendicectomy. Most patients had received three prior therapies, often stopping one due to AEs. Conclusions: Vedolizumab demonstrated a favorable safety profile in the IBD cohort. However, FAERS-identified signals, such as nephrolithiasis, gastrointestinal infections, and decreased blood pressure, warrant further investigation in larger, more diverse populations. Full article

Peanut (Arachis hypogaea L.) is a globally important oilseed cash crop, yet its limited genetic diversity and unique reproductive biology present persistent challenges for conventional crossbreeding. Traditional breeding approaches are often time-consuming and inadequate, mitigating the pace of cultivar development. Essential for double fertilization and programmed cell death (PCD), DUF679 membrane proteins (DMPs) represent a membrane protein family unique to plants. In the present study, a comprehensive analysis of the DMP gene family in peanuts was conducted, which included the identification of 21 family members. Based on phylogenetic analysis, these genes were segregated into five distinct clades (I–V), with AhDMP8A, AhDMP8B, AhDMP9A, and AhDMP9B in clade IV exhibiting high homology with known haploid induction genes. These four candidates also displayed significantly elevated expression in floral tissues compared to other organs, supporting their candidacy for haploid induction in peanuts. Subcellular localization prediction, confirmed through co-localization assays, demonstrated that AhDMPs primarily localize to the plasma membrane, consistent with their proposed roles in the reproductive signaling process. Furthermore, chromosomal mapping and synteny analyses revealed that the expansion of the AhDMP gene family is largely driven by whole-genome duplication (WGD) and segmental duplication events, reflecting the evolutionary dynamics of the tetraploid peanut genome. Collectively, these findings establish a foundational understanding of the AhDMP gene family and highlight promising targets for future applications in haploid induction-based breeding strategies in peanuts. Full article

Localized ionization enhancements (LIEs) in altitude range corresponding to the D-region ionosphere, disrupting Very-Low-Frequency (VLF) signal propagation. This case study focuses on Lightning-induced Electron Precipitation (LEP), analyzing amplitude and phase variations in VLF signals recorded in Belgrade, Serbia, from worldwide transmitters. Due to the localized, transient nature of Energetic Electron Precipitation (EEP) events and the path-dependence of VLF responses, research relies on event-specific case studies to model reflection height and sharpness via numerical simulations. Findings show LIEs are typically under 1000 × 500 km, with varying internal structure. Accumulated case studies and corresponding data across diverse conditions contribute to a broader understanding of ionospheric dynamics and space weather effects. These findings enhance regional modeling, support aerosol–electricity climate research, and underscore the value of VLF-based ionospheric monitoring and collaboration in Europe. Full article