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The study used the expanded Unified Theory of Acceptance and usage of Technology (UTAUT) to identify the variables influencing the usage of technology by medical staff in hospitals in Mogadishu. This study aimed to identify the key factors influencing technology usage among hospital staff in Mogadishu, Somalia, using an expanded Unified Theory of Acceptance and Use of Technology (UTAUT) framework. A quantitative research design was employed, based on survey data collected from 470 hospital staff members across selected hospitals in Mogadishu. Structural equation modeling (SEM) was used to examine the relationships between variables and assess the validity of the measurement model. The findings show convergent validity and good indicator reliability across the majority of components, with outer loadings ranging from 0.742 to 0.897. Overall construct reliability and convergent validity were excellent, supporting its preservation, despite age and technological innovation did not significantly moderate the relationships between UTAUT factors and perceived usefulness, suggesting that these effects are consistent across different user groups. The results also showed the p values of the UTAUT factors are less than 0.05 which means that they significantly enhance perceived usefulness, but Age and technological innovation, had no statistically significant moderating effects on the associations between UTAUT predictors and perceived usefulness, suggesting that the effects were consistent across user groups. The study offers empirical insights for the use of technology in hospital settings and helps validate an expanded UTAUT paradigm in healthcare. Future studies should include other moderating factors, incorporate longitudinal data, and adjust technological innovation criteria to improve model robustness.

Throughout their life cycle, plants continuously face various environmental stresses such as drought, high salinity, and extreme temperatures. To cope with these environmental challenges, plants have evolved a multi-layered regulatory network centered on transcription factors (TFs), whose functions are dynamically and precisely modulated by post-translational modifications (PTMs). Despite extensive documentation of PTM-TF regulatory modules in plant stress responses, current studies remain largely descriptive and lack a unified conceptual framework and critical assessment. This review systematically summarizes the molecular mechanisms of major PTM types-including phosphorylation, ubiquitination, acetylation, and SUMOylation-in regulating key stress-responsive TFs such as NAC, WRKY, MYB, bZIP, AP2/ERF, and HSF. Specifically, we focus on how PTMs affect the DNA-binding ability, subcellular localization, protein stability, and interaction networks of these TFs, thereby enabling rapid response to and precise integration of stress signals. We further highlight landmark conceptual advances in the field, deconstruct the hierarchical regulatory logic and synergistic/antagonistic crosstalk between different PTMs, construct a unified PTM-transcription factor regulatory network, and reveal its physiological functions in enhancing plant stress tolerance and maintaining the balance between growth and defense. Finally, we provide a systematic overview of the historical development and comparative performance of PTM detection methodologies, synthesize major research trends in the field, define unresolved core scientific questions and technical bottlenecks and discuss the potential of PTM research in crop stress-resistant breeding and highlights the application prospects of cutting-edge technologies such as gene editing and multi-omics integration in deciphering dynamic modification networks.

Digital technologies for rehabilitation (DT4R), such as robotics and treadmill systems (RobTS), virtual reality and active video gaming (VR-AVG), and telehealth and apps (T&Apps), are promising tools for pediatric motor rehabilitation. Identifying acceptance factors is essential for effective clinical adoption. This study aimed to analyze the use of 3 different technologies for rehabilitation-RobTS, VR-AVG, and T&Apps-through a causal model based on the Unified Theory of Acceptance and Use of Technology (UTAUT). This study was part of RehaTech4child, a cross-sectional survey (2022) supported by the European Academy of Childhood-onset Disability, aimed at professionals working in pediatric motor rehabilitation across Europe. It assessed DT4R use, intention to use, and UTAUT concepts (performance expectancy, effort expectancy, social influence, and barriers). Structural equation modeling was performed to analyze the data and understand relationships between observed and latent variables. A total of 1397 responses were received, and 635 fulfilled the eligibility criteria. The fitness indices suggested a satisfactory fit between the data and the model. The model explained 67% of the variance in the use of RobTS, 62% in VR-AVG, and 57% in T&Apps. Among all studied determinants, access had the strongest impact on use for all 3 categories of DT4R (RobTS: &#x3b2;=0.78, VR-AVG: &#x3b2;=0.73, and T&Apps: &#x3b2;=0.70; P<.001). Intention to use significantly impacted use behavior for all technologies; it was the second determinant after access for VR-AVG (&#x3b2;=0.18, P<.001) and T&Apps (&#x3b2;=0.21, P<.001), with a lower weight for RobTS (&#x3b2;=0.06, P=.007; P<.001). In the subgroup analysis of respondents reporting easy access, intention to use was the strongest determinant of use. The model explained 61% of the variance in intention to use for RobTS, 67% for VR-AVG, and 68% for T&Apps. Performance expectancy had the strongest effect on intention to use for the 3 technologies (RobTS: &#x3b2;=0.81, VR-AVG: &#x3b2;=0.84, and T&Apps: &#x3b2;=0.90; P<.001). For this concept, the items with the highest weights were significantly related to the effectiveness of DT4R on rehabilitation. Social influence and effort expectancy had a slight impact on intention to use. These results underscore the need to ensure easy access as a prerequisite for assessing relevant determinants of acceptance. Developing the evidence base for DT4R effectiveness and ensuring the availability of existing evidence may facilitate DT4R implementation. In our study, within the framework of the UTAUT model, no acceptance barrier was linked to the use of DT4R with children. Gathering families' views may be useful for the implementation of RobTS. T&Apps may be useful for involving parents in their child's rehabilitation. Further studies should focus on children's and families' points of view.

Glycans constitute a structurally diverse and immunologically instructive layer that shapes how transplanted tissues are interpreted by the host immune system. Although glycoengineering approaches and glycocalyx-focused strategies have gained momentum, the mechanistic pathways through which immune cells decode glycan information remain underexplored in transplantation biology. This hybrid Perspective integrates selected mechanistic foundations with a broader conceptual framework that positions glycans as upstream immune checkpoints governing graft recognition and early innate-adaptive integration. We synthesize advances across four major axes of glycan-regulated immunity: Siglec (Sialic acid-binding immunoglobulin-type lectin)-mediated inhibitory circuits that calibrate macrophage, neutrophil, and NK-cell activation; C-type lectin receptor pathways that program antigen-presenting cells and govern antigen routing; NK-cell glycan-sensing mechanisms shaped by sialylation density, glycan topology, and ischemia-reperfusion-induced glycocalyx collapse; and complement regulation through Factor H, which interprets sialic acid motifs to restrain alternative pathway amplification. We further examine how these innate pathways intersect with glycan-dependent modulation of direct, indirect, and semi-direct allorecognition, including effects on MHC stability, exosomal transfer, antigen uptake, and T-cell intrinsic glycan checkpoints. Together, these mechanisms reveal that glycans function as a pre-recognition code that precedes and conditions classical protein-centric checkpoints by initiating, amplifying and sustaining the classical pathways, and influencing whether grafts are classified as self-like, stressed, or foreign. By consolidating these pathways into a unified model, this Perspective highlights glycan composition and architecture as a foundational design parameter for next-generation immune-compatible organ modifications and outlines mechanistic priorities for advancing glycan-informed strategies in transplantation.

This work provides a self-contained derivation of several fundamental results in stochastic thermodynamics, including the Jarzynski equality, Crooks fluctuation theorem, and the Clausius inequality. Although the principal theoretical conclusions are well established in the literature, the present approach departs from conventional formulations of stochastic entropy by employing a trajectory-independent probability density constructed as the marginal of the full path-space distribution. This construction establishes a direct connection between microscopic path statistics and macroscopic state probabilities, thereby providing a natural framework for quantifying the relative statistical weights of distinct dynamical histories leading to the same microstate. We demonstrate that entropy production and the fluctuation theorems emerge directly from the Gibbs inequality under the assumption of microscopic reversibility of trajectories along with the extension of the Gibbs-Shannon entropy to time-evolving ensembles. For reduced stochastic descriptions such as Langevin dynamics, the counterpart of microscopic reversibility is local detailed balance, which is inherited from the underlying time-reversal symmetry of the full system-reservoir dynamics and is the condition under which the present framework applies to effective open-system descriptions. We show that the change in microscopic entropy can be attributed to both underlying stochastic fluctuations and the statistical uncertainties inherent in thermodynamic ensembles. This dual perspective ensures that the average entropy production remains non-negative, providing a consistent microscopic basis for thermodynamic irreversibility through the statistical preference of forward over time-reversed trajectories. The connection between microscopic entropy flow and heat, as well as the subsequent derivation of the Clausius inequality and Jarzynski-Crooks relations, requires the additional assumption of an ideal thermal reservoir at a fixed temperature. While this formulation introduces no new physical or mathematical insights, it illuminates the universality of fluctuation theorems across diverse systems, offering a unified perspective that may serve a pedagogical purpose for those studying the statistical-mechanical foundations of non-equilibrium thermodynamics.

The hantavirus outbreak linked to the MV Hondius cruise ship, managed by Spanish authorities in May 2026, provides a timely opportunity to examine how Spain prepares and responds to imported high-consequence infectious diseases (HCID). This crisis bears greater resemblance to Spain's 2014 Ebola response than to the early stages of the COVID-19 pandemic. A decade after Ebola, Spain has built substantially stronger HCID capacities: a network of High Level Isolation and Treatment Units, improved clinical protocols, diagnostic capacity, and more consistent technical communication. Coordination with international partners functioned effectively. However, structural challenges persist: political tensions between central and regional authorities complicated real-time operational coordination and spilled into public communication, undermining the perception of a unified, technically-led response. Finalisation of a National Preparedness and Response Plan and full operationalisation of the National Public Health Agency (AESAP) remain urgent priorities. These domestic challenges sit within a deteriorating global health financing environment, directly eroding the surveillance and response capacities that HCID containment depends upon globally. Spain's Estrategia Espa&#xf1;ola de Salud Global 2025-2030, its seat on the WHO Executive Board and its commitment to increasing ODA, position it well to advocate for stronger HCID-specific frameworks internationally, building on the genuine progress this response demonstrated.

Health digital twins (HDTs) promise patient-specific modeling and decision support but current approaches remain structurally fragmented: monolithic models that address a single organ or task lack cross-scale fidelity, while system-level twins lack generalizable architectural frameworks. We propose OmniBioTwin, a System-of-Twinned-Systems (SoTS) framework that organizes HDTs as modular computational entities coupled through explicit interaction operators within a multi-layer network architecture. The framework comprises seven coordinated layers - spanning data integration, autonomous twin modeling, cross-scale coupling, temporal synchronization, and human-in-the-loop decision support. We demonstrate OmniBioTwin by instantiating a multiscale twin for glucagon-like peptide-1 (GLP-1) signaling pathways in Alzheimer's disease, illustrating how molecular, cellular, and organ-level twins can be composed and coupled within a unified system.

Multiple sclerosis (MS) frequently leads to mobility impairment, fatigue and a significant decline in health-related quality of life (QoL). Home-based assistive technology, such as robotic exoskeletons, offers a promising solution to enhance independent mobility and increase the intensity of motor training. Long-term functional and quality of life benefits of light lower-limb exoskeleton home use have yet to be determined. The primary objective of this study is to determine the efficacy of an 8-week period, home-based use of a robotic exoskeleton in improving QoL in individuals with MS, compared with a no-device control period. This is a multicentre, randomised, controlled and single-blinded cross-over trial. A total of 28 patients with confirmed MS (Expanded Disability Status Scale (EDSS) score 5.0-7.0)&#x2009;will be recruited across three rehabilitation centres. Participants will be randomly assigned to two 8-week phases: intervention (daily home-based exoskeleton use) or control (physical activity advice), separated by an 8-week wash-out period. The primary outcome is the change in the MS Quality of Life assessed by the Functional Assessment of Multiple Sclerosis (FAMS) physical composite score from baseline to the end of each phase. Secondary outcomes include changes in walking performance (2-minute Walk, 10 M Walk, Timed Up-and-Go, Four Square Step Test and Sit-to-Stand Test), fatigue severity (Fatigue Severity Scale and Fatigue Impact Measurement Scale (EMIF)-MS), and strength capacity (Manual Muscle Testing, Tardieu scale), self-confidence (Rosenberg), anxiety and depression (Hospital Anxiety and Depression Scale), satisfaction to use (Technical Aid Satisfaction Scale) and intention to use (Unified Theory of Acceptance and Use of Technology). This study was registered on ClinicalTrials.gov on 1 February 2024 (Trial registration number: NCT05835622 https://clinicaltrials.gov/ct2/show/NCT05835622). Patient recruitment is currently underway and is anticipated to be completed by January 2026. Primary endpoint data collection is expected to be completed in June 2026.This study protocol describes a rigorous trial designed to provide high-level evidence on the impact of a home-based robotic exoskeleton on QoL in individuals with MS. By determining intervention effectiveness, the results will provide clinical guidelines, potentially facilitating the widespread adoption of home-based assistive robotics to substantially improve the independence and overall QoL for patients with MS. NCT05835622.

Medical Referring Image Segmentation (MRIS) involves segmenting target regions in medical images based on natural language descriptions. While achieving promising results, recent approaches usually involve complex design of multimodal fusion or multi-stage decoders. In this work, we propose NTP-MRISeg, a novel framework that reformulates MRIS as an autoregressive next-token prediction task over a unified multimodal sequence of tokenized image, text, and mask representations. This formulation streamlines model design by eliminating the need for modality-specific fusion and external segmentation models, and supports a unified architecture for end-to-end training. It also enables the use of pretrained tokenizers from emerging large-scale multimodal models, enhancing generalization and adaptability. More importantly, to address challenges under this formulation-such as exposure bias, long-tail token distributions, and fine-grained lesion edges-we propose three novel strategies: (1) a Next-k Token Prediction (NkTP) scheme to reduce cumulative prediction errors, (2) Token-level Contrastive Learning (TCL) to enhance boundary sensitivity and mitigate long-tail distribution effects, and (3) a memory-based Hard Error Token (HET) optimization strategy that emphasizes difficult tokens during training. Extensive experiments on the QaTa-COV19 and MosMedData+ datasets demonstrate that NTP-MRISeg achieves new state-of-the-art performance, offering a streamlined and effective alternative to traditional MRIS pipelines. The code is available at: https://github.com/c1oTTpD/MRIS.

Accurate prediction of drug-target interactions (DTIs) plays a crucial role in modern drug discovery and repositioning. Despite recent advances in deep learning, existing methods often fail to effectively integrate heterogeneous data, such as molecular structures and protein sequences, into a unified representation. To address this limitation, we propose MMCA (Multi-Modal Co-Attention), a novel deep learning framework that introduces a multi-modal co-attention mechanism to dynamically align and fuse graph-based drug features with sequence-based protein embeddings. Our model leverages parallel encoding pathways to capture both structural and semantic information, followed by a context-aware fusion module that adaptively weighs cross-modal dependencies. Evaluation on three benchmark datasets-BioSNAP, BindingDB, and Human STRING-demonstrates that MMCA outperforms state-of-the-art methods in terms of AUC, AUPR, and F1-score, achieving up to 98.4% AUC. Ablation studies confirm the significance of our co-attention fusion mechanism in enhancing both accuracy and robustness. Case studies of high-confidence predictions reveal biologically plausible drug-protein interactions, supporting MMCA's potential for prioritizing candidates for experimental validation. By enabling end-to-end multi-modal reasoning, MMCA provides a powerful framework for advancing DTI prediction systems and offers broad applicability for various bioinformatics tasks. The source code of MMCA is publicly available at https://github.com/Join-xiaobai/MMCA.

This article presents a gaze-enabled measurement framework designed to quantify visual attention during interactive digital art experiences and to link attention dynamics to experiential outcomes. Unlike traditional static art viewing, interactive digital art involves dynamic media, multi-source information, and continuous feedback loops, which challenge standard eye-tracking methodologies. The proposed system integrates eye-tracking acquisition, interaction logging, timestamp synchronization, dynamic Area of Interest (AOI) mapping, and metric computation into a unified pipeline. The protocol outlines the development of a reproducible data workflow that aligns gaze data with specific interaction events, enabling precise calculations of attention allocation, switching costs, and exploratory entropy. The experimental design involves both free exploration and goal-directed tasks, as demonstrated in a study with 37 participants. Results from applying this protocol indicate high perceived user-friendliness and satisfaction, with measurement modeling supporting a two-construct model of usability and satisfaction. Furthermore, outcome modeling confirms that perceived usability acts as a foundational predictor for overall user satisfaction, while the single-case demonstration validates the pipeline's capacity to quantify context-aware attention shifts. This protocol provides researchers and designers with a rigorous, reproducible toolset for analyzing how interaction design elements-such as feedback latency and guidance intensity-shape user attention and subsequent subjective experiences in immersive digital environments.

Electrical Submersible Pumps (ESPs) are widely used in oil and gas production but are highly vulnerable to mechanical, electrical, hydraulic, and thermal degradation under harsh downhole conditions. Unexpected ESP failures can result in severe production losses, costly interventions, and reduced operational reliability, highlighting the importance of predictive maintenance and Remaining Useful Life (RUL) estimation. Existing prognostic approaches often rely on single-modality data and lack physical consistency in degradation modeling. This study proposes HF-ESPNet, a physics-informed multi-modal transformer framework for joint ESP failure prediction and RUL estimation. The proposed model integrates heterogeneous operational data, including electrical telemetry, pressure, temperature, vibration indicators, well metadata, and maintenance history within a unified transformer-based architecture. Domain-specific physics-informed constraints related to hydraulic behavior, thermal dynamics, and vibration degradation are incorporated into the learning objective to improve physical consistency and prognostic reliability. The framework was evaluated using an anonymized real-world ESP dataset containing more than 58,000 multivariate time-series samples under diverse operational conditions. Experimental results demonstrate that HF-ESPNet outperforms conventional machine learning, deep learning, and transformer-based baseline models in both failure prediction and RUL estimation tasks. Ablation analysis further confirms the importance of vibration-derived features and physics-informed constraints in improving predictive robustness and degradation modeling accuracy. The results demonstrate that combining multi-modal representation learning, transformer-based temporal modeling, and physics-informed learning provides a robust and interpretable framework for ESP prognostics, supporting improved maintenance scheduling, reduced unplanned downtime, and enhanced operational reliability in oil and gas production systems.

The brain's remarkable ability to process continuous sensory inputs with adaptive efficiency - balancing flexibility while minimizing metabolic cost - is thought to rely on predictive mechanisms that generate and update internal models that leverage statistical regularities in the environment. However, it remains unclear whether this efficiency arises from prioritizing reliable, expected events or informative, unexpected ones, as they offer complementary adaptive advantages. To isolate genuine expectation effects, we combined electroencephalography (EEG), pupillometry, and behavioural measures in a paradigm that independently manipulated task relevance (selective attention) and stimulus predictability, while minimizing stimulus repetition at identical spatial locations to control for low-level adaptation. Human participants (both sexes) responded faster and more accurately to expected events, which was enhanced when attention was engaged; however, these events were reproduced with lower precision, independent of attention. Feature-specific neural decoding revealed pre-stimulus effects of attention and post-stimulus effects of expectation, with no interaction between the two. Attention increased decoding accuracy, while expectation reduced accuracy. The reduced representational fidelity for expected events appeared rapidly (&#x223c;100-200 ms after stimulus onset) and correlated with individual differences in perceptual precision. Collectively, our findings indicate two complementary processes that define how the brain leverages redundancy in the environment: an early (pre-stimulus) mechanism, which supports rapid motor responses to expected events and is mediated by attention, and a later (post-stimulus) process, which dampens sensory responses to expected events and is unaffected by attention.Significance statement The brain must process vast amounts of sensory information efficiently while remaining flexible enough to detect important changes. Competing theories propose that the brain saves energy either by prioritizing expected events or by enhancing responses to surprising ones, but experimental evidence has been inconsistent. Our findings show that the brain uses both strategies, each serving a different purpose. Expected events are processed quickly to support rapid, accurate actions, whereas expected events are also encoded with reduced precision, prioritising unexpected events to update internal models and guide future behaviour. By cleanly separating expectation from attention and low-level adaptation, we provide a unified explanation of how the brain balances efficiency with adaptability across multiple timescales.

BackgroundGlaucoma is a leading cause of irreversible vision loss and is characterized by subtle structural changes in the optic disc and optic cup. However, existing automated detection systems often suffer from weak boundary delineation, dataset variability, and unstable feature learning, which limit their generalizability and clinical reliability.ObjectiveThis study aims to develop a unified and anatomically guided framework for accurate and reliable automated glaucoma detection from fundus images.MethodsThe proposed pipeline begins with contrast-enhanced preprocessing to improve image quality, followed by an Attention-guided Multi-scale Edge-aware Segmentation Network (AME-SegNet) for precise segmentation of the optic disc and optic cup. Both deep convolutional features and clinically relevant geometric features are extracted and optimized using Bitterling Colony Optimization (BCO) to select the most discriminative attributes. A Convolutional Transformer (CT) is then employed to integrate local convolutional representations with global attention mechanisms for robust classification. Additionally, the Honey Badger Algorithm (HBA) is used for automatic parameter tuning to ensure stable convergence.ResultsExperimental evaluation demonstrates high segmentation performance with Dice scores of 97.36% for the optic disc and 96.72% for the optic cup on the Drishti-GS1 dataset. The classification model achieves accuracies of 98.63% on RIM-ONE and 98.96% on ORIGA-Light datasets, indicating strong generalization capability.ConclusionsThe proposed framework exhibits robust performance, high accuracy, and strong generalization across multiple datasets. These results highlight its effectiveness and clinical potential for reliable automated glaucoma screening and early diagnosis.

The large diversity of neuronal and glial cell types in the human brain is underpinned by foundational cell populations known as neural progenitor cells (NPCs). The dentate gyrus (DG) of the hippocampus, a key structure in learning and memory, maintains a tightly organized NPC population into adulthood across many mammalian species. However, the emergence, organization and persistence of NPCs in the human hippocampus remain poorly characterized. Reports of NPCs in the juvenile, adult, and aged periods have been variable, reflecting differences in identification criteria and highlighting the need for a unified framework across development. In this study, we provide a spatial and molecular map of the developmental trajectory of NPCs in the human DG, combining multimodal transcriptomic analysis within a neuroanatomical context. At mid-gestation, we observed changes in the structural and cellular arrangement of the hippocampus, coinciding with the emergence of a multicellular NPC layer within the DG, herein named the granular-hilar progenitor zone (GHPZ). Neurogenic transcriptomic signatures in the GHPZ were diminished by early infancy, coinciding with a reduction in NPC number as they progressed toward an astrocytic program. At childhood, the GHPZ dissolved with only sparse radial NPCs remaining in the DG. Lastly, we validated WNT signaling pathway-associated genes as NPC identity markers in the developing human DG, observing a decline in their expression after infancy. Our study defines the steep decline of NPCs from gestation to the postnatal period, identifies their progression to an astrocytic nature, and sets the molecular blueprint for NPC identification in the human DG. Multimodal mapping of neural progenitor cells from gestational to postnatal stages in the human hippocampusFormation of the granular-hilar progenitor zone within the dentate gyrus at mid-gestationNeurogenic potential declines sharply from the prenatal period to childhood, with radial glia cells progressively acquiring astrocytic featuresDevelopmental modulation of the WNT signaling pathway accompanies radial glia cell transitions.