ObjectiveThis work establishes a methodological framework for sources of pilot distraction and interruptions in a structured model that can be used as a tool for cockpit design/procedure assessment.BackgroundPilots must complete complex tasks, and distractions can impair performance and lead to errors that can cause aircraft accidents. Although various cockpit distractors are examined individually, there is no integrated approach.MethodDistraction and interruption sources were identified through a literature review and confirmed/extended by interviews with airline pilots. Associated weights were determined through pairwise comparisons, yielding a hierarchical model using the Analytic Hierarchy Process.Results26 sources of pilot distraction and interruptions were quantified and categorized into four categories: communication, head-down time, responding to abnormal conditions & unexpected situations, and searching for traffic.ConclusionA taxonomic structure for assessment is achieved with the top 5 sources identified as communications, technical interruptions, experience in type, environmental factors, operational irregularities, and airspace high terrain, accounting for 63.07%.ApplicationThe structured system is a flexible assessment scale that provides a taxonomic framework for airline risk management, supports future research, and cockpit design efforts.

Importance: Medical time series constitute the largest data type in electronic health records and are often irregularly sampled in real-world clinical settings. Such irregularly sampled medical time series exhibit uneven time intervals, missing observations, and heterogeneous sampling rates, posing substantial challenges for deep learning models. Highlights: In this paper, from an irregularity-aware and data-centric perspective, we categorize existing deep learning methods for irregularly sampled medical time series into missing-data-based and raw-data-based approaches. We analyze their theoretical foundations and practical implications and conduct experiments on benchmark and real-world medical datasets to compare their strengths and limitations. Conclusion: Based on these analyses, we provide practical recommendations and discuss open problems and future research directions for modeling irregularly sampled medical time series.

Modeling irregularly sampled multivariate time series is a persistent challenge in domains like healthcare and sensor networks. While recent works have explored a variety of complex learning architectures to solve the prediction problems for irregularly sampled time series, it remains unclear what the true benefits of some of these architectures are, and whether clever modifications of simpler and more efficient RNN-based algorithms are still competitive, i.e. they are on par with or even superior to these methods. In this work, we propose and study GRUwE: Gated Recurrent Unit with Exponential basis functions, that builds upon RNN-based architectures for observations made at irregular times. GRUwE supports both regression-based and event-based predictions in continuous time. GRUwE works by maintaining a Markov state representation of the time series that updates with the arrival of irregular observations. The Markov state update relies on two reset mechanisms: (i) observation-triggered reset to account for the new observation, and (ii) time-triggered reset that relies on learnable exponential decays, to support the predictions in continuous time. Our empirical evaluations across several real-world benchmarks on next-observation and next-event prediction tasks demonstrate that GRUwE can indeed achieve competitive or superior performance compared to the recent state-of-the-art (SOTA) methods. Thanks to its simplicity, GRUwE offers compelling advantages: it is easy to implement, requires minimal hyper-parameter tuning efforts, and significantly reduces the computational overhead in the online deployment.

Large neuronal networks demonstrate complex dynamics across multiple scales, ranging from single-neuron excitability and spike-train variability to mesoscopic rhythms and whole-brain activity. Different types of differential equation models have been developed to comprehend these phenomena, connecting deterministic, stochastic, and mean-field descriptions. At the deterministic level, ordinary differential equation (ODE) models, including conductance-based neuron models, neural-mass systems, and whole-brain networks, summarize neural behavior through a reduced set of macroscopic variables. At the population level, mean-field partial differential equation (PDE) models such as Fokker-Planck, age-structured, kinetic, and neural field equations describe the evolution of probability or population densities over membrane-potentials, synaptic states, and other kinetic variables. These PDEs link single-neuron mechanisms to population-level activity and allow one to analyze bifurcations, oscillations and other collective patterns. Stochastic differential equation (SDE) models and their extensions that include jump-diffusion processes and stochastic PDEs (SPDEs) are widely used to describe random membrane fluctuations, irregular spike trains, synaptic plasticity and large-scale variability in neural activity. These stochastic models are also applied to neural data analysis, for example to quantify noise in electro-physiological recordings and to infer latent neural dynamics. Because variability and noise are central in neural systems, we devote more space to stochastic models but always relate them back to the surrounding ODE and PDE frameworks. This hierarchy of ODE, PDE, and SDE-SPDE models shows that the versatility of differential-equation-based approaches in neuroscience offers unified tools for multiscale modeling, neural signal processing, cognitive modeling, and the analysis of noisy neural systems. We also discuss some known numerical and computational approaches, especially for stochastic models and conclude by outlining open challenges, such as multiscale inference, control-oriented formulations and the integration of differential-equation models with modern machine-learning methods.

IntroductionThe HEART-UA (Healthcare Extracorporeal Assessment and Resource Tracking in Ukraine) survey represents the first nationwide evaluation of extracorporeal life support (ECLS) and renal replacement therapy (RRT) availability in Ukraine. The study aimed to systematically characterize national ECLS and RRT capacity, assess temporal changes before and during the conflict, and identify major operational limitations affecting access to extracorporeal organ support.MethodsA structured cross-sectional survey was conducted among Ukrainian hospitals providing ECLS and/or RRT. Data collection included institutional characteristics, ECMO device availability, case volumes in 2021 and 2023, staffing models, barriers to ECLS provision, and the availability of CRRT and intermittent hemodialysis. Responses were clustered at the institutional level, and duplicates were merged. Categorical variables were compared between the pre-war and wartime periods using Fisher's exact or chi-squared tests where appropriate.ResultsTwenty-two institutional responses were analyzed, including 15 ECLS centers, 16 CRRT centers, and 19 IHD-capable institutions. Geographical distribution showed regional clustering of ECLS resources. No statistically significant expansion in ECMO device availability or procedural volumes was observed between 2021 and 2023. The predominant barriers to ECLS provision included shortages of consumables (60%), insufficient trained personnel (53%), and inadequate numbers of ECMO devices (47%). CRRT availability was higher, but most centers reported major limitations due to shortages of consumable circuits, machine availability, and infrastructural instability. Humanitarian aid related to extracorporeal support was received by only 27% of institutions and was irregular, fragmented, and insufficient to meet national needs.ConclusionsThe HEART-UA survey demonstrates that Ukraine's capacity to provide extracorporeal organ support has remained critically constrained during the war. Device availability, trained personnel, and consumable supply chains are still major limiting factors. Although many centers continue to provide lifesaving ECLS, CRRT, and IHD despite extreme operational challenges, the overall system is highly dependent on inconsistent humanitarian aid.

Convolutional neural networks demonstrate strong performance in medical image segmentation but face clinically significant challenges due to the morphological diversity of anatomical targets-including substantial variations in shape, scale, and position. To overcome these limitations, we propose shape perception and multi-scale features enhancement UNet, a novel architecture designed to jointly learn discriminative geometric features (shapes and scales) for robust target perception. The proposed framework designs two synergistic core modules to address the challenges of anatomical shape and scale variability: a shape perception module (SPM) that employs a dynamic gating mechanisms to adaptively sharpen crucial contour features and suppress irrelevant background interference, and a multi-scale features enhancement module (MFEM) which leverages a parallel multi-branch convolutional architecture with varied receptive fields to capture and intelligently fuse hierarchical patterns, from local textures to global semantics. These co-optimized modules form an integrated feature learning pipeline where the SPM purifies shape-related features and the MFEM empowers them with rich contextual information, enabling joint geometric perception for robust and accurate segmentation across heterogeneous clinical imaging scenarios. Experiments demonstrate competitive performance across three datasets: on Synapse, our method achieves a Dice score of 84.67% (surpassing CCViM by 2.02%) and an HD95 of 16.37 mm; on ISIC-2017, it attains a Dice of 92.36% (outperforming EMCAD by 1.15%) and an mean Intersection over Union (mIoU) of 86.93%; and on ISIC-2018, it reaches a Dice of 90.81% (exceeding CCViM by 0.75%) and an mIoU of 84.31%. Our method effectively mitigates mis-segmentation artifacts stemming from scale mismatches and shape irregularities, ultimately delivering superior robustness and accuracy in complex clinical imaging scenarios.

Quantitative assessment of collagen fibril diameter is essential for understanding ultrastructural changes in aging, connective tissue disorders, and extracellular matrix remodeling. Although transmission electron microscopy (TEM) is widely used for this purpose, existing methods for fibril measurement are predominantly manual, operator-dependent, and prone to inconsistent reporting. To address this, we developed a semi-automated Fiji/ImageJ (IJ1) macro that standardizes fibril diameter measurements from two-dimensional TEMs. The macro uses a wand-based region-of-interest (ROI) detection strategy with integrated geometric validation. It calculates multiple metrics, including area-equivalent diameters, ellipse-derived major and minor axes, shape consistency indices, and spatial localization across the image field. Built-in quality assurance thresholds exclude oblique or irregular profiles, ensuring accurate identification of true fibril cross-sections. Real-time visual overlays support live validation and user feedback during analysis. We detail the implementation, analytical workflow, and validation approach, provide practical guidance for reproducible use across operators and datasets, and show proof-of-concept utility for analysis of collagen fibrils in vascular Ehlers-Danlos syndrome (VEDS) subject dermal samples. This open-source, extensible tool enhances standardization and reproducibility in collagen fibril morphometry for ultrastructural research.

Shift work is associated with an increased risk of metabolic disorders, including type 2 diabetes, largely due to circadian misalignment, irregular meal patterns, and suboptimal diet quality. Chrononutrition, which focuses on aligning nutrient intake with circadian rhythms, has emerged as a promising strategy to improve metabolic health. Protein intake plays a key role in glucose homeostasis, and high-protein hypocaloric diets have shown benefits in people with type 2 diabetes. However, the effects of higher evening protein intake in shift workers remain unclear. This trial aims to analyze the effect of three hypocaloric diets differing in macronutrient composition and distribution throughout the day on glycemic control, body composition, and other secondary outcomes in shift workers with overweight or obesity and prediabetes or type 2 diabetes. This is a 12-week, three-arm, parallel-group, single-blind randomized controlled trial including 126 shift workers. Participants are randomized equally to: (A) a high-protein diet with protein-enriched dinner (50-60% of daily protein); (B) a high-protein diet with protein-restricted dinner (10-20% of daily protein); or (C) a normoproteic control diet with usual protein distribution. Primary outcomes include changes in glycemic control and DXA-derived body composition. Secondary outcomes are lipid profile, sleep quality, and quality of life. Other clinical and lifestyle parameters are evaluated to monitor changes throughout the intervention. Assessments are performed at baseline, week 6, and week 12. All analyses will follow the intention-to-treat principle. This study will provide new evidence on how evening protein intake may influence metabolic health in populations exposed to circadian disruption.