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Evidence of within- and between-stock connectivity in Mediterranean fisheries challenges the stock unit paradigm
Abstract Achieving sustainable management of marine resources and implementing biologically realistic assessment depend on accurate identification of stock structure. Most harvested marine species are currently assessed under the common and long-standing assumption of “stock unit” within designated spatiotemporally invariant polygons known as assessment units, despite evidence of passive and/or active dispersal across multiple scales. Our objective here is to investigate the connectivity and spatial structure of red mullet ( Mullus barbatus ) metapopulation system in the Northwestern Mediterranean Sea and compare the delineation of assessment units with biological units. To achieve this, we integrate simulated larval dispersal modelling with tissue stable isotope analysis, encompassing the entire life cycle. We observe misalignment between the present “closed” assessment units and the identified biologically informed units. The identified metapopulation system comprises northern and southern subpopulations with dissimilar demographic roles. This principally stems from the Northern Current mediated larval transport from high-density persistent spawning grounds and, secondarily, likely from the regional differences in the isotopic baseline associated with different productivity regimes. Such an interdisciplinary approach is of elevated importance for species with stark differences across their life history, such as red mullet. Moreover, larval connectivity diagnostics successfully capture the interannual variability of recruitment dynamics of this complex stock, evidencing the capacity of biophysical models to inform fisheries assessment and management. This study highlights the importance of implementing spatial stock assessment frameworks that rely on critical, yet still largely disregarded, ecological information.
Beyond intraocular pressure: introducing a novel glaucoma-specific PROM for evaluating outcomes after IStent inject implantation combined with phacoemulsification cataract surgery
Soliton solutions for the nonlinear Zoomeron equation applying the modified Khater method
Modeling the interpretable geometric-performance relationship of metamaterials on small datasets using Kolmogorov-Arnold operator informed network
VLM-fusion: an intelligent diagnostic system for rural teacher professional development integrating vision-language models and adaptive learning path optimization
The moderating role of emotional intelligence in the relationship between workplace civility and job satisfaction: a multi-university behavioral study
Chaotic and complex dynamics expose the limits of counterfactual reasoning
Abstract Counterfactual reasoning, a cornerstone of human cognition and decision-making, is often seen as the “holy grail” of causal learning, with applications ranging from interpreting machine learning models to promoting algorithmic fairness. While counterfactual reasoning has been extensively studied in contexts with clearly defined static causal models, many real-world scenarios reside in dynamic settings often involving model and parameter uncertainty, observational noise, and chaotic behavior. The reliability of counterfactual analysis in such settings remains largely unexplored. In this work, we investigate the limitations of counterfactual reasoning in dynamic settings. We specifically focus on counterfactual sequence estimation and demonstrate empirically that even modest levels of model uncertainty or observational noise can lead to dramatic deviations between predicted and true counterfactual trajectories. Our findings urge caution when applying counterfactual reasoning in dynamical systems, particularly those that may exhibit complex, chaotic behavior, and highlight fundamental limitations in answering certain counterfactual queries reliably.
Unsupervised multivariate analysis of multiple analysis data fusion combining hydrogen distribution and crystallographic orientation image data of vanadium alloy sample
SQUID-COMM: a Colossal Squid-inspired distributed communication framework for real-time multi-node aquaculture monitoring networks with adaptive bioluminescent signaling and neuromorphic edge intelligence
Abstract Precision aquaculture demands robust communication networks capable of coordinating thousands of distributed sensors across marine and freshwater facilities. Current aquaculture IoT networks face critical challenges including underwater signal attenuation reaching 98% loss at 100 m depth, dynamic topology changes from fish movement and water currents, and severe energy constraints on battery-powered sensor nodes. This paper introduces SQUID-COMM, a novel bio-inspired communication framework emulating the signaling mechanisms of the Colossal Squid ( Mesonychoteuthis hamiltoni ). The framework introduces seven innovative mechanisms: Bioluminescent Pulse-Coded Modulation (BPCM) achieving 34% higher spectral efficiency through adaptive signal encoding; Chromatophore-Inspired Channel Adaptation (CICA) enabling 15ms frequency hopping response time; Distributed Axon-Ganglia Routing Protocol (DAGRP) maintaining 99.7% packet delivery under 40% node mobility; Tentacle-Topology Self-Organization (TTSO) for dynamic mesh network formation; Giant Fiber Emergency Broadcast (GFEB) achieving sub-50ms critical alert propagation; Photophore Synchronization Protocol (PSP) for microsecond-accurate time coordination; and Ink-Cloud Congestion Control (ICCC) reducing packet loss by 82%. The Enhanced SQUID-COMM variant incorporates Neuromorphic Edge Processing reducing cloud communication by 78%, Federated Learning Coordination for distributed model updates, and Quantum-Resistant Encryption for future-proof security. Experimental evaluation across five aquaculture deployment scenarios demonstrates end-to-end latency of 12.3ms representing 78% reduction compared to LoRaWAN, throughput of 2.4 Mbps in turbid conditions spanning 5-150 NTU, energy efficiency of 0.23 mJ/bit constituting 67% improvement over Zigbee, and network lifetime extension of 340%. Real-world deployment at four commercial facilities across Norway, Egypt, Thailand, and Greece over 120 days processed 2.3 billion sensor readings with 99.94% reliability, enabling fish behavior detection at 94.7% accuracy and early disease detection with 4.2-day lead time. Statistical analysis confirms significant improvements with p-values below 0.001 and Cohen’s d exceeding 1.2, while economic evaluation demonstrates annual savings of €89,000-€340,000 per facility.
Motor-dominant symptoms predict persistent neurological impairment in mild-to-moderate degenerative cervical myelopathy: a multi-state modeling study
Standardized End Point Definitions for Clinical Trials in Thoracic Aortic Repair: A Consensus Report From the ARCH–Academic Research Consortium
Innovation in the treatment of ascending aorta and arch pathology with novel catheter-based and hybrid procedures has driven the need for a strategy to guide their safe application. The ARCH-ARC (Aortic Arch Academic Research Consortium) was established to pragmatically develop consistent clinical end points and to standardize definitions for use in studies of these new technologies. The ARCH-ARC team, consisting of independent international specialists in cardiac surgery, vascular surgery, vascular medicine, cardiology, neurology, radiology, and clinical trials, along with US Food and Drug Administration, industry, and contract research organization representatives, held virtual meetings from 2021 to 2025. Consensus was used to identify appropriate clinical end points and to standardize definitions of end points for endovascular, hybrid, and open surgical procedures in clinical trials in the ascending aorta and arch. Drawing on previous ARC work in cardiac, neurological, renal, and bleeding end points, the ARCH-ARC focused on definitions and end points related to aortic arch–specific anatomy, pathology, and procedures and clinical, device, and imaging. The adoption of the ARCH-ARC consensus definitions and end points will provide a template for consistent adjudication and event reporting and facilitate comparisons of clinical research studies involving devices for ascending aorta and arch pathology.
Analytical soliton construction and dynamical transition analysis for the Kadomtsev-Petviashvili-Benjamin-Bona-Mahony model
Response by Omland et al to Letter Regarding Article, “Sacubitril/Valsartan and Prevention of Cardiac Dysfunction During Adjuvant Breast Cancer Therapy: The PRADA II Randomized Clinical Trial”
microRNA miR-31-5p confers protection to melanin-deficient vitiligo keratinocytes against UV-B induced DNA damage by activating autophagy machinery
WNT5a-Mediated Aberrant Actin Filament Dynamics Drive Cardiac Pathogenic Phenotypes in <i>LMNA</i> -Related Emery-Dreifuss Muscular Dystrophy
BACKGROUND: Emery-Dreifuss muscular dystrophy (EDMD) is a rare genetic disorder characterized by early-onset joint contractures, progressive muscle atrophy, and cardiac abnormalities. Patients with EDMD carrying LMNA sequence variations often exhibit severe cardiac manifestations, including frequent atrioventricular block and ventricular tachycardia. Approximately 20% of those patients may ultimately require heart transplantation. The molecular mechanisms by which LMNA sequence variations lead to EDMD remain unknown. METHODS: Five clinically diagnosed patients with EDMD carrying LMNA sequence variations were recruited. Patient-specific induced pluripotent stem cells (iPSCs) were generated using a nonintegrating Sendai virus. Previously generated iPSCs, derived from 2 healthy donors, were used as controls. The LMNA L204P sequence variation was corrected by genome editing in EDMD iPSC lines to generate isogenic controls. All iPSC-derived cardiomyocytes (iPSC-CMs) were generated using a monolayer-based differentiation protocol. Three-dimensional, strip-format, and force-generating human engineered heart tissues were generated from iPSC-CMs. A knock-in mouse model carrying the Lmna L204P sequence variation was also generated. RESULTS: EDMD-specific iPSC-CMs exhibited a variety of deleterious phenotypes, including disorganized sarcomeres, abnormal nuclear envelope structure, arrhythmias, and contractile dysfunction, when compared with control and gene-corrected iPSC-CMs. Multi-omics analysis further revealed that LMNA directly binds the WNT5A promoter and the Leu204Pro sequence variation reduces chromatin accessibility and WNT5A transcription in EDMD iPSC-CMs. WNT5a (Wnt family member 5a)/RhoA (Ras homolog family member A) signaling inactivation was shown to lead to actin depolymerization and inhibition of actin polymerization in EDMD iPSC-CMs. This results in a deformed nuclear envelope, contractile dysfunction, and impaired trafficking of Cx43 (connexin 43). The impairment of Cx43 trafficking causes reduced distribution of Cx43 at cell–cell borders, contributing to the arrhythmic phenotype in EDMD iPSC-CMs. Pharmacological interventions of exogenous WNT5a supplementation, RhoA activator, or an actin polymerization stabilizer effectively rescued the pathogenic phenotypes of EDMD iPSC-CMs. EDMD engineered heart tissues displayed dysfunctional contractile force generation, which was significantly alleviated by RhoA activator. Lmna L204P heterozygous knock-in mice exhibited impaired cardiac function and developed cardiac arrhythmias in response to sympathetic stress. CONCLUSIONS: We present WNT5a-mediated aberrant actin filament dynamics as a novel mechanism underlying cardiac pathogenic phenotypes in LMNA -related EDMD. Our findings indicate that activating WNT5a/RhoA and stabilizing actin assembly may serve as novel therapeutic strategies for this condition.
GraphDL: A visibility graph-based structural representation framework for flow-based cyber-attack detection
Health Care Affordability in the United States, From Crisis to Action: A Presidential Advisory From the American Heart Association
The United States is facing a growing health care affordability crisis. In 2024, national health expenditures totalled $5.3 trillion, or $15 474 per person, accounting for 18.0% of the U.S. economy. Spending on health care continues to rise, propelled by high prices for services, drugs, and devices; growing administrative complexity; chronic underinvestment in prevention, primary care, and public health; and the mounting burden of chronic conditions such as cardiovascular disease. Patients, even those with insurance, frequently face financial hardship, delayed or foregone care, and medical debt because of gaps in coverage and inadequate consumer protections. Addressing this crisis will require coordinated action across the health care system, guided by evidence and a commitment to shared responsibility among key stakeholders. This Presidential Advisory from the American Heart Association draws on interviews and listening sessions with patients, clinicians, payers, employers, health system leaders, and public health experts to examine the many dimensions of affordability and offer a practical framework for action. The Advisory presents 5 core principles to guide efforts to address the affordability crisis: ensuring access to high-quality care without financial hardship; minimizing cost sharing for high-value services; creating shared accountability across the health care system; investing in the workforce, infrastructure, and data systems needed to support progress; and addressing the social and structural factors that make care less affordable for many communities. The evidence, tools, and expertise to combat the health care affordability crisis already exist. What is needed now is the collective will to act.
A multi-scale supervised contrastive framework for cross-domain soybean disease classification using leaf and UAV imagery
Abstract Accurate and scalable soybean crop health monitoring remains a major challenge in precision agriculture due to environment variability, inconsistent lighting conditions, and significant differences between the ground-level leaf imagery and UAV-based aerial imagery. Most existing deep learning approaches treat these two sensing modalities separately without properly exploring cross-scale feature transferability or measuring the domain gap that exists between the sensing scales. As a result, developing unified and deployment-ready crop health monitoring systems that can effectively leverage the more accessible leaf-level datasets, collected without specialized equipment or regulatory constraints, to improve UAV-scale inference remains difficult. In order to address this limitation, we propose a multi-scale soybean crop health assessment framework that integrates ground-level leaf imagery and UAV-based aerial imagery from the MH-SoyaHealthVision dataset across four health conditions, which include Healthy, Mosaic Virus, Pest attack, and Rust. CLAHE, Gray-World color constancy correction, and illumination normalization is incorporated into a structured pre-processing pipeline and further applied to reduce illumination bias and enhance cross-domain feature consistency. Six deep learning backbones were comprehensively evaluated for leaf-level classification, with MaxViT and ConvNeXt achieving the best performance. Their static weighted ensemble further improved accuracy to 87.08%. Cross-scale evaluation showed that zero-shot leap-to-UAV transfer achieved only 40% accuracy, thus highlighting the presence of a substantial domain shift. Fine-tuning improved UAV classification performance to about 97%, while a supervised contrastive learning framework specifically designed for cross-scale feature alignment further increased accuracy to approximately 98% with better convergence stability. Feature embedding analysis using PCA, t-SNE, and silhouette metrics demonstrated considerable improvements in inter-class separability (0.59 vs. 0.19) and reduced domain discrepancy (0.0336 vs. 0.114) under contrastive learning. These findings suggest that supervised alignment can generate more class-discriminative representations with lower cross-scale domain discrepancy, making them more suitable for scalable multi-scale cross-health monitoring.