Search Results (3,981)

Diagnosing tilapia diseases in complex aquaculture environments is severely hindered by noisy backgrounds and limited high-quality pathological data. To overcome these bottlenecks, this study presents a two-stage diagnostic framework integrating an enhanced Segment Anything Model (SAM) with an Adaptive Neuro-Fuzzy Inference System (ANFIS). In the first stage, SAM is augmented with a Convolutional Block Attention Module (CBAM) feature adapter and a Region Proposal Network (RPN)-based prompt encoder. This design enables the automated and precise extraction of irregular disease lesions by self-generating spatial prompts, thereby isolating water background noise. In the second stage, clinical color features extracted from the lesion masks are classified using ANFIS. To optimize performance on small-scale datasets, ANFIS parameters are trained via Particle Swarm Optimization (PSO) under a numerically stable One-vs-Rest (OvR) binary cross-entropy loss. Validated on the public dataset “Enhancing Disease Detection in Nile Tilapia”, our method delivers an average segmentation Dice coefficient of 86.2% and a classification accuracy of 93.5%. This hybrid approach demonstrates strong potential as a foundational baseline for the automated monitoring of aquaculture diseases. Full article

Background: Electroencephalographic (EEG) functional connectivity analysis requires multiple signal-processing, source-modelling, and statistical steps that can limit its adoption in clinician-led randomised controlled trials (RCTs). NeuroStat was developed as a prototype research tool to integrate this workflow; formal usability validation with clinician end-users has not yet been conducted. Methods: NeuroStat is an open-source Python/PyQt6 desktop application that integrates automated artefact removal (a Generalised Eigenvalue Decomposition for Artefact Identification [GEDAI] pathway and a traditional Artefact Subspace Reconstruction (ASR)/Independent Component Analysis (ICA)/ICLabel pathway), boundary element model (BEM) source localisation using the Desikan–Killiany atlas (68 cortical regions), Phase Lag Index (PLI) connectivity estimation across five canonical frequency bands, and RCT-oriented statistical analysis. Evaluation separated sensor-space and source-space claims: a sensor-level simulation (repeated across five independent random seeds) tested preprocessing robustness, a repeated source-space simulation tested recovery of a known cortical parcel-pair contrast after forward projection and inverse reconstruction, a PhysioNet benchmark tested posterior Desikan–Killiany alpha PLI in 20 healthy adults, and an illustrative application to 20 sessions from a published chiropractic RCT demonstrated real-world workflow applicability. Results: In the sensor-level simulation benchmark, the Traditional pathway achieved a mean absolute error of $0.168\pm 0.017$ PLI units and root mean squared error of $0.219\pm 0.045$ (mean ± SD across five independent random seeds) across all artefact conditions. In the source-space simulation, reconstructed alpha PLI for the known bilateral lateral-occipital parcel pair exceeded anterior control edges across 60 repeated condition runs (mean known-control difference = 0.105 PLI units, 95% CI 0.096–0.114; $t\left(59\right)=22.61$, $p<0.001$). In the PhysioNet source-space benchmark, posterior Desikan–Killiany alpha PLI was higher during eyes-closed than eyes-open rest (Cohen’s $d=0.85$, $p=0.001$; 16/20 subjects showing the expected direction) after ICLabel-enabled preprocessing. In the pilot RCT application, all 20 sessions completed processing without manual intervention, with default-mode network alpha PLI showing a pre-to-post change of +0.071 in the intervention group versus +0.015 in the active control group. Conclusions: NeuroStat integrates preprocessing, source-space construction, connectivity estimation, and statistical reporting within a parameter-logged desktop workflow for EEG functional connectivity studies. Current evidence supports initial technical feasibility, sensor-level preprocessing robustness for one pathway in controlled simulations, source-space recovery of a known parcel-level contrast, source-space sensitivity to an expected posterior alpha resting-state contrast, and error-free processing across 20 real RCT sessions in a pilot workflow demonstration. Formal usability testing, test–retest reliability analysis, participant-specific source-model validation, and clinical-population validation remain necessary before clinician-facing or trial-deployment claims can be made. Full article

Consistent laser cutting quality is one of the problems associated with the nonlinearity of relationships between process parameters and output responses. This problem acquires particular importance when it comes to cutting advanced nanocomposites, which requires precise tuning. Despite the wide adoption of intelligent modelling, few studies have investigated the comparative efficiency of various approaches based on the use of the same dataset. In this research, the effectiveness of three models—Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Fuzzy Logic System (FLS)—was tested on experimental data related to the CO₂ laser cutting of ABS/CNT nanocomposites. Input parameters included laser power and cutting speed, whereas HAZ width, kerf width, and surface electrical resistivity were used as output data. Data was split into training, testing, and validation datasets; models were created using supervised machine learning. Model performance was evaluated using Root Mean Square Error (RMSE). Analysis of results showed that ANN demonstrated acceptable predictive capabilities, yielding correlation coefficients (R) close to 1 (≈0.99) and RMSE values of 0.2956 for HAZ, 0.2061 for kerf width, and 2.3655 for surface electrical resistivity. Prediction by means of FLS was able to identify general tendencies; however, it produced RMSE values of 0.4741 for HAZ, 0.6297 for kerf width, and 1.9258 for surface electrical resistivity. Finally, the ANFIS model proved to be the most reliable model, yielding the lowest RMSE values for HAZ (0.2784), kerf width (0.0450), and surface electrical resistivity (0.0905). In conclusion, this research shows that ANFIS can be used effectively for building models predicting laser cutting processes; therefore, it represents an approach worth using in future investigations in this field. Full article

Emerging-market equity exchanges require regime forecasting systems that are continuous in time, robust to heavy-tailed distributions, and optimised against false alarms. No existing method addresses all three simultaneously, and no prior study has reported a crisis false-alarm rate on JSE equities. We propose S-NODE-ANF-RRC: a stochastic neural ODE within an Adaptive Neuro-Fuzzy Risk-Regime Clustering architecture, integrated by a Milstein scheme with Lyapunov-regularised dual-loss training. The system is evaluated as a one-step-ahead probabilistic forecaster ($h=1$ trading day) on 2696 daily observations across 17 JSE securities (March 2015–March 2026). Gaussian mixture clustering on raw features (kurtosis 54.8) inflates ARI by 1.3×; log-transformation corrects this artefact. Two operational profiles emerge: the N-ODE-ANF-RRC achieves the lowest cost (10,350 bp, 65.1% below GMM) and longest lead time (0.71 days); the S-NODE-ANF-RRC achieves the lowest false alarm rate among probabilistic architectures (FAR = 0.051), with a 42.0% cost reduction versus GMM (McNemar $p=0.027$, power $1-\beta =0.73$; bootstrap CI [5250, 19,600] bp excludes zero). Ablation confirms drift, diffusion, and dual-loss as the minimum viable daily-frequency configuration. Full article

We develop SAFIRE (Self-Attention Fuzzy-Inspired Rule Estimator), a differentiable fuzzy-inspired rule-scoring surrogate for binary medical tabular classification coupling multi-head self-attention, Gaussian membership functions, and Hard Concrete gates for continuous rule scoring. We position SAFIRE as a smooth surrogate of the discrete $L_0$-regularised rule-selection problem and establish five mathematical results and one complexity remark: (1) the relaxed objective is differentiable almost everywhere under positive Gaussian widths (enforced by a Softplus reparameterisation) and fixed batch-normalisation statistics; (2) the deterministic-inference active threshold is strictly stricter than the expected-nonzero training threshold, identifying Hard Concrete gates as continuous rule-scoring devices rather than automatic pruning mechanisms; (3) per-sample forward complexity identifies attention and rule layers as the dominant terms; (4) the Softplus–BatchNorm–linear rule operator violates all four triangular-norm axioms—with necessary and sufficient conditions per axiom and a no-finite-parameterisation impossibility result—while a Softplus reparameterisation restores coordinate-wise monotonicity; (5) a margin-based upper bound characterises disagreement between the full classifier and a top-k rule-only surrogate; and (6) the Softplus-reparameterised constrained variant is provably coordinate-wise monotone with explicit asymptotic regimes. Evaluated on four University of California, Irvine (UCI), medical binary tabular benchmarks under repeated stratified cross-validation, SAFIRE-Prog is statistically competitive with strong interpretable, modern, and gradient-boosting baselines, with one Bonferroni-significant gain over RuleFit on the Diabetic Retinopathy Debrecen corpus. The 48-configuration Hard Concrete sweep, constrained-variant comparison, and a top-k fidelity analysis (per-fold range 0.73–0.95) provide quantitative companion measurements for the mathematical framework. A supplementary large-scale hospital electronic health record (EHR) benchmark (Diabetes 130-US Hospitals, $n=101,766$) shows the rule-scoring mechanism scales to ∼${10}^5$ records and, under severe class imbalance, statistically matches gradient boosting on accuracy while significantly exceeding it on macro-F1. The results offer a mathematically auditable pathway towards interpretable, auditable rule scoring for medical tabular classification, with rule signatures defined in a projected latent space rather than over raw clinical variables. Full article

Persisting ethnic disparities in pre-eclampsia (PE), cardiovascular disease (CVD), and maternal mortality call for a paradigm shift in how ethnicity is understood as a risk factor for PE. Starting from a migrant perspective, we argue that the transgenerational experience of maternal stress within shared, yet dynamic ecosocial contexts can be linked to core pathophysiological features of PE. A growing body of evidence suggests how a vicious cycle of chronic maternal stress, cardiovascular dysfunction, placental ER stress, and endothelial dysfunction may serve as a catalyst for the transmission of altered cardiovascular and neuro-endocrine stress reactivity patterns across generations, with a seemingly important role for foetal programming and epigenetics. As these alterations in stress reactivity patterns have in turn been associated with an increased risk of PE and CVD later in life, the resulting transgenerational chain reaction may ultimately allow for ethnic disparities in PE to be traced back to historic, stressful moments in the shared ecosocial contexts of ethnic minority women. Reconceptualising ethnicity as a proxy for the stratified and embodied experience of transgenerational maternal stress within its unique ecosocial contexts, rather than a stand-alone, non-modifiable risk factor, will therefore open new directions for future research, clinical care, and policy interventions aimed at advancing maternal health equity. Full article

Background/Objectives: Patients with breast cancer undergoing chemotherapy with taxanes or platin derivates often develop peripheral neuropathy (CIPN). Higher-grade CIPN generally affects the patient’s quality of life. Because the treatment options for CIPN are limited, early diagnosis is desirable to allow for timely modifications of the treatment. This may be facilitated with scoring tools that are ideally usable by the patients. A prospective study suggested that a recently developed self-assessment tool might be able to reveal the difference between the absence of CIPN and higher-grade CIPN. When CIPN has reached an advanced grade, alteration of the chemotherapy regimen may have only a limited effect. Therefore, it is important to know whether the new scoring tool can identify CIPN even when it is still mild. The current trial (NCT07604441) aims to identify the optimal cutoff point value for detecting mild CIPN. Given the limited sample size, the derived cutoff will be considered preliminary and will require validation in a larger independent cohort. Methods: The NEURO-BREAC-02 trial aims to identify the optimal cutoff point value of the new tool to distinguish between absent and mild CIPN after treatment with taxanes for breast cancer. Scores ranging between 0 and 44 points are reported via self-assessment supported by a neuropathy tracker. Secondly, the satisfaction of participants with the self-assessment tool is evaluated. Twenty-six participants (19 with mild CIPN and 7 without CIPN) are required, and 28 must be enrolled. Conclusions: The outcomes of the NEURO-BREAC-02 trial are considered crucial for the creation of a self-assessment tool to identify mild CIPN in patients with breast cancer and are a necessary addition to the preceding NEURO-BREAC study. Full article

This study presents a comprehensive symmetry-breaking analysis framework for a twelve-legged Jansen walking robot, integrating finite element analysis (FEA) with adaptive neuro-fuzzy inference system (ANFIS) surrogate modeling. A systematic dataset of 210 cases was generated by combining 21 single- and multi-leg failure scenarios across 10 load levels (20–200 N) on the PLA-based 3D-printed prototype. Two novel dimensionless metrics are introduced: the Resilience Index (RI), quantifying the proportional stress increase relative to the baseline, and the Asymmetry Index (AI), measuring leg-reaction force distribution imbalance. Results identify a clear fault-tolerance threshold between two- and four-leg failures: single-leg failures remain at LOW risk (RI < 0.20), while three-leg asymmetric failures (S18) reach CRITICAL level (RI = 1.13, ~97% of PLA yield strength). A hybrid machine learning framework is proposed, applying ANFIS to maximum stress (R² = 0.817) and safety factor (R² = 0.936) predictions, while reserving FEA tables for bimodal outputs. The ANFIS surrogate achieves approximately 10⁶× speedup over FEA (262.6 μs vs. 5–8 min), enabling real-time fault diagnosis and digital twin applications. The framework is generalizable to other multi-legged robotic systems requiring fault-tolerance evaluation. Full article

Background: The click-evoked Auditory Brainstem Response (ABR) is the gold standard electrophysiological tool for assessing auditory pathway integrity in infants and young children. As normative data are inherently equipment-specific, the absence of pediatric reference values for the NeuroAudio system (Neurosoft, Ivanovo, Russia) represents a significant gap in clinical practice, given that existing normative datasets for this system are restricted to adult populations. Objective: To establish normative data for click ABR recorded with the NeuroAudio system in children under three years of age, stratified by age group according to auditory maturation patterns. Methods: A prospective, cross-sectional study was conducted at the Electrophysiology Laboratory of the Department of Speech Therapy, Paulista School of Medicine, Federal University of São Paulo (UNIFESP/EPM), under the approval of the Research Ethics Committee (protocol 7.939.564). A total of 203 children (121 males, 82 females; age range: 2 weeks to 36 months) with confirmed normal peripheral auditory function were included. Click stimuli (0.1 ms, rarefaction polarity) were delivered monaurally via ER-3A insert earphones at 80 dB nHL and a repetition rate of 19.3/s. Two average runs of 2000 artifact-free sweeps were recorded per ear. Absolute latencies of waves I, III, and V, interpeak intervals I–III, III–V, and I–V, and amplitudes of waves I and V were analyzed. Results: Statistical modeling supported the consolidation of 12 initial age bins into three clinically and statistically validated categories: 0–3, 4–12, and 13–36 months. Wave I latency remained stable across age groups, whereas waves III and V and all interpeak intervals showed progressive shortening with increasing age. Wave V amplitude increased progressively with age, while wave I amplitude remained unchanged. Females presented shorter latencies than males for waves III and V and for all interpeak intervals. The right ear exhibited a shorter III–V interpeak interval than the left ear, with a significant ear × age interaction indicating that this asymmetry is modulated during early maturation. Age, sex, and ear-stratified normative values (two SD and three SD reference limits) are reported. Conclusion: This study provides the first pediatric normative dataset for click-evoked ABR acquired with the NeuroAudio system in children under three years of age. The proposed three age stratifications, together with sex- and ear-specific reference values for the III–V interpeak interval, offer a clinically actionable framework for the accurate interpretation of pediatric ABR recordings and for the early identification of auditory pathway abnormalities. Full article

Fatty acid-binding protein 7 (FABP7) is a multifunctional lipid chaperone that is enriched in radial glia and astrocytes within the central nervous system (CNS) and is frequently upregulated in glioma. Beyond its established roles in glial development, lipid homeostasis, and circadian regulation, growing evidence positions FABP7 at the intersection of tumor metabolism, neuronal activity, and immune modulation in the brain. In this review, we integrate the physiological functions of FABP7 in glial cells with its tumor-intrinsic and microenvironmental roles in glioma. We summarize how gliomas co-opt FABP7-dependent metabolic, transcriptional, and post-transcriptional programs to promote stemness, lipid remodeling (e.g., altered fatty acid composition, lipid droplet formation, and lipid peroxidation resistance), inflammatory signaling, and invasive growth, including nuclear FABP7-mediated transcriptional activation linked to oncogene status. Furthermore, we discuss the role of FABP7 in shaping the tumor–neuro–immune interface, including regulating immunosuppressive gene networks, pro-tumoral macrophage polarization, resistance to T-cell-induced ferroptosis and immunotherapy, and tumor microtube-mediated integration into neuronal circuits to support glioma progression. Finally, we highlight therapeutic opportunities and challenges, including small-molecule FABP7 inhibitors, brain-directed delivery strategies, chronotherapeutic considerations, and combination approaches with immunotherapy. Collectively, this work positions FABP7-centered metabolic, circadian, and neuro-immune networks as potential vulnerabilities in glioma, linking fundamental glial biology to glioma therapeutics. Full article

Lipophilic marine toxins (LMTs) are important toxic risk factors in marine ecosystems and seafood safety, yet the comparative cytotoxicity-associated molecular responses of different LMT classes remain unclear. Here, Neuro-2a cells were exposed to four representative LMTs—dinophysistoxin-1 (DTX1), azaspiracid-3 (AZA3), yessotoxin (YTX), and pectenotoxin-2 (PTX2)—and acute cytotoxicity was evaluated together with integrated transcriptomic, proteomic, and metabolomic analyses. Cell viability assays showed a cytotoxic potency order of DTX1 > AZA3 > YTX > PTX2. Integrated multi-omics analysis revealed that DTX1, the most cytotoxic toxin, caused the broadest molecular perturbations, mainly involving mitochondrial energy metabolism, p53-mediated stress responses, and multilayered metabolic networks. AZA3 and YTX induced intermediate cytotoxicity and showed partially similar perturbation patterns, particularly affecting cytoskeleton-related, immune-related, and metabolism-related processes. In contrast, PTX2, the least cytotoxic toxin, produced more limited responses mainly involving tyrosine metabolism and the cGMP–PKG signaling network. Overall, molecular perturbation patterns generally corresponded to acute cytotoxic potencies, while each toxin exhibited distinct key pathways and functional modules. These findings provide a multi-omics basis for cytotoxic responses of representative LMT classes and guide subsequent functional validation. Full article

Background/Objectives: Severe brain injury (SBI) in very preterm infants includes heterogeneous lesions with distinct timing, burden and outcomes. We used cranial ultrasound (CUS) to describe SBI entity, documented timing, three-domain burden, deaths following documented withdrawal, withholding or non-escalation of life-sustaining treatment for poor neurological prognosis (neuro-WWLST), and survivor outcomes. Methods: Retrospective single-center cohort (1991–2020) of 2841 very preterm infants (<32 weeks’ gestation and/or birth weight ≤ 1500 g) with complete CUS within 48 h after birth. CUS was summarized by four windows, three domains—parenchymal lesion, intraventricular hemorrhage (IVH) and ventriculomegaly—and three mutually exclusive entities: periventricular hemorrhagic infarction (PVHI), cystic periventricular leukomalacia (cPVL and grade 3 IVH without PVHI/cPVL (IVH3 entity). Cross-outcome analyses used common maximal-burden CUS. Results: SBI occurred in 286/2841 infants (10.1%) and neuro-WWLST death in 45/2841 infants (1.6%); 43/45 occurred within SBI, and 43/89 SBI deaths (48.3%) followed documented neuro-WWLST. Using common maximal-burden CUS, severe three-domain involvement was more frequent among neuro-WWLST deaths than survivors (37.2% vs. 8.6%). Among SBI survivors with follow-up, cerebral palsy (CP) occurred in 87/176 (49.4%) and clinically classified school-age cognitive sequelae in 50/155 (32.3%). Outcomes varied by entity, with mainly ambulatory unilateral CP after PVHI, more frequent non-ambulatory bilateral CP after cPVL, and a heterogeneous IVH3 profile. Severe three-domain involvement identified a small subgroup with higher outcome burden, but outcomes were not deterministic. Conclusions: A structured, descriptive CUS approach separating lesion entity, documented timing and multidomain burden may support transparent cohort-level description of SBI trajectories, documented neuro-WWLST deaths and survivor outcomes. Full article

Continouous unmanned aerial vehicle (UAV) missions are fundamentally limited by Lithium-Polymer (LiPo) battery endurance under intermittent and power-constrained renewable energy conditions. This paper proposes an integrated energy management and charging framework for a photovoltaic (PV)-powered mobile station equipped with a hybrid energy storage system (HESS) and an automated battery replacement (ABR) mechanism. A lexicographic priority-based allocator sequentially serves ABR actuation, multi-slot LiPo charging, and Brushless DC (BLDC) propulsion, while the HESS compensates for PV intermittency. At the charging level, a constraint-aware constant current–constant voltage (CC–CV) strategy is enhanced by an adaptive neuro-fuzzy inference system (ANFIS) trained on optimization-derived labels using battery temperature and its rate of change, thus enabling anticipatory thermal current derating with smooth, discontinuity-free control action. Anti-windup proportional–integral (PI) regulation and bumpless mode transfer ensure stable CC-to-CV transitions. An event-triggered emergency mode accelerates battery readiness via a max-first selection policy. Comparative simulations against a PSO/DE-optimized PID benchmark over a full diurnal PV cycle demonstrate that the ANFIS controller reduces the CC-mode current tracking root-mean-square error (RMSE) by up to 96.9%, delivers higher charge throughput, and lowers battery degradation proxies, including SOC-weighted thermal dose and equivalent full cycles (EFC). The proposed framework reliably sustains continuous charge–swap–recharge logistics under fluctuating renewable generation. Full article

To address the limitations of vision-only defect detection, image–semantic misalignment, and spatial-logic conflicts in complex substation inspection scenarios, this paper proposes a camera-sensor-based multimodal defect sensing framework with cross-modal feature mapping for substation equipment monitoring. The proposed framework integrates field inspection images acquired by camera sensors, defect textual descriptions, and equipment topology knowledge and establishes a unified domain-adaptive pre-training–bidirectional cross-modal mapping–hierarchical reasoning workflow. First, a Contrastive Language–Image Pre-training (CLIP)-based domain-adaptive pre-training strategy is developed to enhance the representation of equipment categories, defect attributes, and inspection-scene semantics. Second, a bidirectional cross-modal feature mapping network is constructed to model fine-grained interactions between candidate visual regions and textual semantics, where uncertainty-aware fusion and prototype constraints are introduced to improve semantic alignment and defect discrimination. Third, a hierarchical neuro-symbolic reasoning module incorporates equipment topology and spatial rules for posterior verification, logical consistency checking, and false-positive suppression. Experiments on a substation inspection image dataset demonstrate that the proposed method achieves 90.8% mAP@0.5, 68.7% mAP@0.5:0.95, and 89.4% F1-score, outperforming mainstream and recent detection models. Full article

The prevention and treatment of cardiovascular disease (CVD) are undergoing a paradigm shift from a lipid-centric approach to a holistic metabolic perspective. Central to this evolution is the gut–fat–heart axis, a sophisticated three-dimensional communication network that integrates neural, endocrine, and immunometabolic signaling to regulate systemic lipid homeostasis. This manuscript systematically explores how the gut microbiota acts as a “metabolic organ” to remotely control host health through the production of bioactive metabolites and the modulation of molecular communication networks. At the physiological level, microbial products such as short-chain fatty acids (SCFAs) and modified bile acids regulate energy balance and lipid synthesis via the FXR-FGF15/19 axis and G protein-coupled receptors. Furthermore, gut hormones like GLP-1 and neuro-reflex pathways involving the vagus nerve provide rapid control over postprandial lipid clearance and feeding behavior. Conversely, pathological dysbiosis triggers the accumulation of harmful metabolites, such as trimethylamine N-oxide (TMAO) and lipopolysaccharides (LPS), which drive lipotoxicity, vascular inflammation, and “dysfunctional HDL” formation. These processes accelerate the progression of atherosclerosis, heart failure, and metabolic syndrome. Finally, the article outlines promising clinical translation strategies, including the development of TMA lyase inhibitors, next-generation probiotics, and the use of phytochemicals to reshape the microbial landscape. By decoding the molecular dialogues within the gut–fat–heart axis, this research provides a novel strategic vantage point for the integrated management of cardiovascular–kidney–metabolic (CKM) syndrome. Full article