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Cortical control of movement is a distributed computation spanning multiple densely interconnected regions. Although we have rich anatomical atlases and a coarse understanding of how function maps to areas and subregions, we lack a detailed account of how behaviorally relevant activity is organized across the cortical sheet. Here, we trained head-fixed mice to perform a 15-target reach-to-grasp task while we performed cellular-resolution, two-photon calcium imaging across five regions of sensorimotor cortex (>39,000 layer 2/3 neurons). We characterized each neuron's trial-averaged peri-event activity with interpretable metrics and mapped these response properties across areas, revealing large-scale spatial structure. Neuronal response profiles often shifted abruptly at anatomical borders: motor areas showed sharper tuning and more linear relationships with target location, whereas somatosensory areas displayed more heterogeneous response patterns. Neural response properties also differed according to somatotopic representation. Nonlinear dimensionality reduction of the neural feature matrix revealed that areas varied in their average response profiles, but that areas did not have well-separated feature distributions; instead, each area contained subpopulations. Neurons in each subpopulation had characteristic response profiles and were distributed across multiple cortical areas. The spatial distributions of the subpopulations overlapped, with neurons from different subpopulations salt-and-pepper intermingled in the overlap zones. Together, these results describe novel activity structure across sensorimotor cortex and identify several distinct but spatially overlapping subpopulations with characteristic activity patterns during reach-to-grasp behavior. Reaching out to grab a cup of coffee may feel effortless, but it requires the brain to coordinate a precise sequence of movement commands. This control is supported by the sensorimotor cortex, the part of the brain's wrinkled outer layer involved in integrating incoming sensory feedback with outgoing movement commands. Researchers have traditionally divided the sensorimotor cortex into separate anatomical areas based on the shapes of neurons as well as their connectivity to other brain structures. This area-based view has shaped our understanding of the sensorimotor cortex based on the idea that different areas generally serve different functions. However, it has been less clear whether these functions change sharply at the borders dividing regions or whether groups of neurons within an area might perform different functions. Anatomical areas have long formed the basic unit for studying cortical function, yet recent work shows that movement-related activity is far more widely distributed than this view assumes. Understanding how the cortex controls movement, therefore, requires mapping the responses of large numbers of individual neurons across different areas, and determining how responses relate to known anatomical and somatotopic boundaries. Salimian, Grier and Kaufman sought to characterize how responses of single neurons are distributed across anatomically defined sensorimotor areas in the cortex of mice while they performed reach-to-grasp movements. Salimian et al. characterized the activity of nearly 40,000 individual neurons across different areas of the sensorimotor cortex of mice using interpretable features obtained from their activity during a challenging forelimb control task. Neural response features were then organized into coherent spatial gradients spanning motor and somatosensory cortical areas. The results showed that the spatial patterns possessed sharp transitions that closely aligned with anatomical and somatotopic borders. Clustering cells based on their features identified four unique subpopulations with characteristic response profiles, whose members were widely distributed across all recorded areas, but with different prevalence in each. The spatial distributions of these subpopulations formed overlapping zones, in which neurons from different subpopulations intermingled. The findings of Salimian et al. suggest that a complete understanding of the sensorimotor cortex requires mapping the distributed neuronal networks, instead of just focusing on separate, specialized areas. In the future, this view could inform clinical technologies such as the placement of recording electrodes for brain-computer interfaces that help paralyzed people move, or stimulation-based maps used to guide surgical resection of brain tissue. In addition, they may inform the design of neural networks for controlling robots, where sensory feedback must be integrated into choosing motor commands. However, realizing such benefits would first require confirming that these subpopulations exist in the human brain and elucidating their contributions to movement control.

Imaging Mass Cytometry (IMC) enables highly multiplexed, spatially resolved single-cell proteomics, providing simultaneous measurement of dozens of protein markers while preserving tissue architecture. Despite its analytical power, IMC data analysis remains fragmented across multiple software environments, requiring researchers to combine independent tools for visualization, preprocessing, segmentation, feature extraction, phenotyping, batch correction, and spatial analysis. This fragmentation increases technical barriers, complicates reproducibility, and limits accessibility for non-computational users. We developed OpenIMC, an open-source platform that integrates the major stages of IMC analysis within a unified graphical and command-line framework. OpenIMC supports image visualization, quality control, preprocessing, segmentation, feature extraction, dimensionality reduction, batch effect correction, clustering, phenotyping, and spatial analysis while maintaining interoperability with established community tools. The platform incorporates automated provenance tracking, records analytical parameters and software versions, and enables export and sharing of complete analytical sessions. Benchmarking demonstrated deterministic behavior across repeated runs, complete concordance between graphical and command-line workflows, and strong agreement with established IMC analysis pipelines. OpenIMC additionally provides support for high-resolution IMC workflows, including signal attenuation modeling and image deconvolution. We apply OpenIMC to two datasets of circulating cells and breast tissue to demonstrate the platform's ability to support integrated single-cell and spatial proteomics analysis. OpenIMC reduces the complexity of IMC data analysis by providing a unified, reproducible, and extensible framework for common IMC workflows. By combining interactive visualization with scalable computational analysis, OpenIMC lowers technical barriers and facilitates reproducible single-cell and spatial proteomics research.

Population genomic workflows frequently rely on fragmented command-line utilities, custom conversion scripts, and programming language-specific environments, complicating computational reproducibility and obscuring data provenance. As analytical workflows become increasingly automated and computationally intensive, dependence on disparate preprocessing tools can introduce friction between raw genotype files, quality-control decisions, statistical analyses, and downstream workflows. We developed SNPio, a Python-native framework that consolidates single nucleotide polymorphism data parsing, filtering, visualization, numerical genotype encoding, and population genomic summary-statistic calculation within a unified software architecture. VCF file parsing and filtering benchmarks were compared against vcfR and SNPfiltR. SNPio demonstrated faster execution times but used more memory than its R-based comparators, reflecting SNPio's retention of genotype arrays, metadata, and provenance-tracking attributes. Pairwise Weir and Cockerham's FST and Nei's genetic distance estimates aligned with HierFstat expectations based on Pearson correlations and aggregate error metrics. D-statistics conformed to theoretical expectations across eleven simulated datasets spanning a range of introgression signal strengths. SNPio provides a reproducible Python-native workflow for processing, filtering, encoding, visualizing, and analyzing SNP datasets. It integrates common early-stage population genomic operations into a transparent, scriptable framework, which ultimately promotes workflow provenance and reduces reliance on disjointed software tools, unsaved terminal commands, and custom scripts. SNPio is particularly suited for population genomic studies of non-model organisms in ecological, evolutionary, and conservation contexts, where reproducible preprocessing and interoperability with downstream analyses are becoming increasingly important.

Brain-computer interfaces (BCIs) promise to extend human movement capabilities by enabling direct neural control of supernumerary effectors, yet integrating augmented commands with multiple degrees of freedom without disrupting natural movement remains a key challenge. Here, we propose a tactile-encoded BCI that leverages sensory afferents through a tactile-evoked P300 paradigm, allowing reliable decoding of supernumerary motor intentions even when superimposed with voluntary actions. The interface was evaluated in a multi-day experiment comprising a single motor recognition task to validate baseline BCI performance and a dual-task paradigm to assess the potential influence between the BCI and natural human movement. The interface achieved real-time and reliable decoding of four supernumerary degrees of freedom, with significant performance improvements after three days of training. After training, performance did not differ significantly between the single-task and dual-task conditions, and natural movement remained unimpaired during concurrent supernumerary control. Lastly, the interface was deployed in a movement augmentation task, demonstrating its ability to command two supernumerary robotic arms for functional assistance during bimanual tasks. These results establish a neural interface paradigm for movement augmentation through stimulation of sensory afferents, expanding motor degrees of freedom without impairing natural movement.

Natural swarms can move cohesively while continuously reshaping their spatial shape, yet reproducing this behavior with large flying robot swarms in obstacle environments remains difficult, especially in achieving uniformity and safety. Here, we present a distributed 3D shape-assembly controller for flying robot swarms that forms arbitrary target shapes with uniform coverage and safe inter-robot distance. Inspired by bubble-rafts, each robot is associated with a non-overlapping region inside the target shape and updates its velocity by considering region exploration, region balancing and distance fine-tuning. The proposed region-based shape-assembly method is able to drive the swarm toward uniform formations and enable fast shape transformation, as well as resilience to robot joining and failure. For navigation, we design a lightweight velocity-obstacle mechanism that adjusts the nominal shape-assembly velocity to collision-free commands with minimal formation disruption. We validate the proposed methods in extensive simulations and real-world swarm experiments, demonstrating efficient formation of complex 3D shapes, robust maintenance of safety distances, and reliable obstacle traversal.

Cognitive motor dissociation (CMD) is associated with long-term recovery in acute brain injury, but CMD testing is only available in few centers. Our objective was to identify surface EEG patterns with high sensitivity or positive predictive value (PPV) for CMD in patients with acute disorders of consciousness to refine allocation of this resource-intensive test. In this observational cohort study, we enrolled clinically unresponsive, acutely brain injured patients who underwent continuous surface EEG and CMD assessments. CMD was detected by applying a machine learning algorithm to EEG acquired during a motor command paradigm presentation. Electroencephalographers blinded to CMD test results applied standardized ACNS criteria to the EEGs acquired during CMD assessments. We calculated accuracy measures of surface EEG findings for CMD test results using generalized estimating equations, with an exchangeable matrix and accounting for repeated measures per patient. We included 185 patients (mean age: 62 ± 17; 85 [46%] female) and 282 CMD assessments. CMD testing was positive in 39 (14%) assessments. Sensitivity and PPV of normal background voltage, symmetry, and continuity were, respectively, 77% (95% CI: 60%-88%) and 19% (95% CI: 13%-26%), 74% (95% CI: 58%-86%) and 14% (95% CI: 10%-20%), and 74% (95% CI: 58%-86%) and 14% (95% CI: 9%-19%). All EEGs with burst suppression, suppression, sporadic epileptiform discharges, lateralized periodic discharges, bilateral independent periodic discharges, electrographic seizures, and brief potentially ictal rhythmic discharges had negative CMD tests. Surface EEG findings are not reliable to screen for CMD or to identify patterns conferring higher CMD pretest probability.

The purpose of this study was to investigate the clinical status of active monitoring [active surveillance (AS)] or surgical resection for adult low-risk papillary thyroid microcarcinoma (cT1aN0M0 PTMC) in the military and some local medical institutions in southern China. A questionnaire survey was conducted on the actual treatment mode of adult PTMC patients in the member institutions of the Southern Theater Command General Surgery Specialty Alliance. The respondents mainly included surgeons engaged in the surgical treatment of thyroid diseases. A total of 36 medical institutions received replies, and the average annual volume of thyroid surgery in these institutions exceeded 100 cases. For suspicious nodules detected via ultrasound, routine fine-needle aspiration cytology (FNAC) for all suspicious nodules was recommended by six institutions (16.7%); nine (25.0%) performed FNAC only for nodules larger than 10 mm. After diagnosis, AS was recommended by six institutions (16.7%), immediate surgery was recommended by six institutions (16.7%), and 13 institutions (36.1%) left the treatment decision to the patient. For the AS protocol, 22 institutions recommended initial monitoring every 3 months, and 11 recommended every 6 months. When obvious clinical symptoms, new lymph node metastasis, or extrathyroidal invasion appeared, these institutions tended to convert from AS to surgery. During the continuous 3-month period selected by each institution from 2024 to 2025, among 474 PTMC patients, 228 (48.1%) underwent immediate surgery as initial treatment, 175 (36.9%) chose initial AS, and 63 (13.3%) received radiofrequency/microwave ablation. Of the 175 patients initially managed with AS, 49 (28.0% of the AS group) subsequently underwent surgery. Correlation analysis showed that the number of patients undergoing surgery was significantly higher in general surgery departments and in institutions with a larger number of thyroid surgeons (p < 0.05). At the same time, the number of patients receiving AS was significantly higher in tertiary hospitals (p < 0.05). Within our specialized alliance member institutions, more than one-third of low-risk PTMC patients have adopted AS as their management strategy. However, significant variability exists across medical institutions regarding the indications and implementation protocols for AS. To facilitate the broader and more standardized application of AS, it is essential to strengthen educational efforts directed at both clinicians and patients and to develop more refined, population-specific clinical guidelines or expert consensus for the management of low-risk PTMC in the Chinese population.

Bioimage frameworks based on artificial intelligence (AI) offer powerful tools for image segmentation, but their technical overhead often creates a gap between developers and the broader bioimaging community. Biom3d addresses this challenge by providing a modular, PyTorch-based architecture designed to enable reproducible and interoperable 2D and 3D segmentation pipelines while maintaining strict adherence to FAIR principles. The framework's engineering quality is demonstrated through low intra-module complexity, high modularity, and seamless interoperability, exemplified by the successful substitution of transformer-based MONAI models. Its architecture is organized around seven core module types, allowing fine-grained control over data handling, model configuration, optimization, and evaluation. Its default configuration, nnCore, autoconfigures optimal pipelines based on new datasets and competes with state-of-the-art 3D segmentation methods, outperforming classical tools such as NucleusJ/NODeJ and matching the robustness of nnU-Net across diverse modalities. Biom3d is accessible through multiple interfaces including a graphical user interface, a Jupyter Notebook, a command-line tool, a Docker image and as a Python library. Biom3d is compatible with OMERO (Open Microscopy Environment Remote Objects) for streamlined data management and reproducible computation, including execution on HPC servers. Collectively, these results position Biom3d as a sustainable and extensible framework for building, sharing, and reusing advanced bioimage analysis solutions.

Eye-hand coordination (EHC) is central to everyday behavior. This is often described as a sequential process in which the eyes move first to guide the hand. However, converging behavioral and neurophysiological evidence supports a fundamentally different view: Coordination arises from a distributed network that issues parallel commands and aligns effectors through structured interareal communication. Saccade and reach planning are typically initiated concurrently, with apparent timing differences driven largely by effector dynamics. Experimental dissociations reveal that coupling enhances performance but is not obligatory, particularly in bimanual or naturalistic contexts. Here we emphasize the posterior parietal cortex as a key hub integrating sensory and motor signals for planning and coordination through effector-specific subregions and their interareal interactions. Oscillatory dynamics, notably beta-band coherence, consistently associate with coordination between oculomotor and manual circuits, although whether they causally implement routing or instead index structured interactions remains unresolved. Together, these findings point to a distributed, intercommunicating network that flexibly aligns eye and hand control.

High-throughput crop phenotyping requires accurate and smooth path tracking during inter-row travel and row transitions. For four-wheel-independent-drive and independent-steering agricultural robots, conventional single-reference geometric controllers usually use only one axle or one reference point for feedback, which can cause front-rear posture lag and oscillatory correction under disturbances. Here we propose a dual-reference-point fuzzy-gain Stanley controller that computes coordinated front and rear steering commands from the lateral and heading errors at both axles. The key distinction from conventional single-reference controllers is that the proposed strategy treats the front and rear axles as coordinated control objects, rather than using one axle or one reference point as the sole feedback source. An online fuzzy scheduler adaptively tunes the Stanley gain, and Ackermann relations map axle commands to individual wheel angles and speeds for four-wheel steering implementation. The method was evaluated using MATLAB/Simulink simulations and through pavement, potted-wheat, and rapeseed field tests. In straight-line simulation, settling time decreased from 11.03&#xa0;s with conventional Stanley and 16.16&#xa0;s with Pure Pursuit to 8.27&#xa0;s, and further to 5.12&#xa0;s with fuzzy gain scheduling, without noticeable overshoot. In field experiments, maximum/mean lateral deviation remained within 63/17&#xa0;mm and maximum/mean heading error within 3.4&#xb0;/0.9&#xb0;. During row transitions, the corresponding initial deviations were within 35/29&#xa0;mm and 2.9&#xb0;/2.6&#xb0;. These results show that the proposed controller enables accurate, smooth, and real-time tracking for practical crop phenotyping.

The development of the sensory brain relies on early periphery-generated spontaneous neural activity and later sensory-evoked activity. To investigate activity sources in the auditory cortex (ACtx) during development, we performed in vivo imaging in neonatal mouse pups. We found self-vocalization-associated ACtx activity even before ear opening and that this activity was stronger than tone-evoked activity. Self-vocalization-associated activity also existed in deaf pups, suggesting a top-down activity source. We revealed projections from the anterior cingulate cortex (ACC) and secondary motor cortex (M2) to the ACtx and that ACC/M2 showed vocalization-driven activity correlated with ACtx activity. ACC/M2 inactivation reduced self-vocalization and ACtx activity. Thus, self-vocalizations activate the developing ACtx even before ear opening, potentially via ACC/M2 motor commands. Our results reveal a previously unidentified early ACtx activity source that can shape development.

Access to real-world healthcare data is increasingly hindered by privacy concerns and stringent legal frameworks, including the General Data Protection Regulation (GDPR) and the Health Insurance Portability and Accountability Act (HIPAA). Consequently, synthetic healthcare data generators, notably Synthea&#x2122;, have emerged as essential tools to generate high-quality datasets with minimal privacy and legal concerns. However, many simulation engines including, Synthea&#x2122;, predominantly rely on command-line interfaces (CLI), imposing significant technical barriers for clinical researchers. Furthermore, raw output formats such as HL7 Fast Healthcare Interoperability Resources (FHIR) JSON create a substantial "interpretability gap" for end-users. To address these challenges, we present SyntheaWeb, a web-based platform that simplifies the generation and interactive inspection of synthetic patient cohorts. It provides a user interface, visual cohort dashboard, structured longitudinal patient records, semantic terminology linking (e.g., SNOMED CT, LOINC), and the capability for selective subset export.

Insomnia and obstructive sleep apnea (OSA) are the most prevalent sleep disorders in military personnel. People with both sleep problems (co-morbid insomnia and sleep apnea [COMISA]) experience higher rates of medical and psychiatric comorbidities. Few studies have examined the comorbidity of nightmares among those with insomnia, OSA, or COMISA. This secondary data analysis of data collected in a large sample of military men and women seeking treatment for sleep disturbances aimed to examine the occurrence and impact of nightmares in military personnel with insomnia, OSA, and COMISA. Data collected from a convenience sample of 372 active-duty U.S. military personnel purposefully recruited following a diagnosis based on a clinical evaluation and in-lab video-polysomnography (118 with insomnia, 118 with OSA, 136 with COMISA) were analyzed. The Nightmare Disorder Index (NDI) was used to measure clinically significant nightmares. Self-reported symptoms of insomnia, excessive daytime sleepiness, sleep-related impairment, anxiety, depression, and posttraumatic stress disorder (PTSD) were also analyzed. Chi-squared tests and 2-way analyses of variance were used to address the study aims. This study is a secondary analysis of data collected in the conduct of a prospective observational study originally approved and overseen by the 59th Medical Wing Institutional Review Board, and the U.S. Army Medical Research and Development Command Human Research Protection Office monitored the regulatory approvals. Nightmares were significantly more likely in those with insomnia (35.6%; n = 42/118) or COMISA (38.2%; n&#x2009;=&#x2009;52/136) than in those with OSA (14.4%; n&#x2009;=&#x2009;17/118; both Ps <&#x2009;.001). Although there was not a significant interaction between group and nightmares, planned post hoc analyses found nightmares were associated with worse PTSD symptoms in the insomnia group; anxiety, depression, and insomnia symptoms in the OSA group; and insomnia, sleep-related impairment, anxiety, depression, and PTSD symptoms in the COMISA group. Nightmares are associated with increased sleep and mental health symptoms among military personnel with OSA, insomnia, and COMISA. Those with COMISA and nightmares (named COMISA-MARES) exhibited the worst symptoms.

This paper explores the hierarchical optimal control strategy for high-order nonlinear systems with unknown dynamics, with a focus on two-player scenarios. The Stackelberg differential game theory provides a leader-follower framework to investigate the optimal control problem for players within a predefined sequential decision order. The optimized controllers for the corresponding subsystems of each player are designed using the command filtered backstepping method. In each subsystem, an adaptive dynamic programming architecture is employed to derive the Stackelberg equilibrium solution for the player. To this end, actor-critic functions are introduced to approximate the cost function and execute the control behavior for all players. An identifier based on fuzzy logic systems is utilized to estimate the unknown nonlinear dynamics arising from modeling inaccuracies. To avoid repeated differentiation of the virtual control law, a command filter is adopted to reduce the computational burden by directly obtaining virtual control without differentiation. Finally, simulation results demonstrate that the proposed optimal control scheme can achieve the desired control objective under the hierarchical performance index.

This paper addresses the distributed predefined-time optimal adaptive formation control problem in heterogeneous systems consisting of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs). To cope with asymmetric state constraints in the planar subsystem, a nonlinear mapping along with a relaxation function is employed to transform the constrained states into an equivalent unconstrained form. Then, the single critic structure is integrated into the command filtered backstepping design framework to derive the distributed predefined-time optimal formation controller, where the adaptive fuzzy approximation is utilized to approximate unknown nonlinearities and the auxiliary performance function. It is demonstrated that the formation errors converge within a predefined time, while the positions of vehicles remain within the prescribed constraints. Finally, simulation results are provided to validate the effectiveness of the proposed control scheme.

Moving targets pose a fundamental challenge for sensorimotor control: by the time a target's position is transformed into neural signals, the target has already moved. Primates compensate for this delay and accurately intercept objects by accounting for target velocity. However, the neural mechanisms by which sensorimotor structures represent target velocity remain poorly understood. Here we recorded spikes and local field potentials from the superior colliculus (SC) of male macaques viewing or directing saccades to stationary and moving targets and used time-resolved dimensionality reduction and classification to characterize SC population dynamics. Target velocity emerged rapidly within the visual response and was robustly decodable despite weak and inconsistent single-neuron motion sensitivity. In low-dimensional state space, activity formed a V-shaped manifold in which speed was organized continuously along direction-specific arms. This geometry generalized across animals and signal modalities, could not be reproduced by displacement-based simulations, and remained largely dissociable from the axis encoding target location. Velocity information was strongest near the border between superficial and intermediate layers, identifying a laminar locus where visual motion signals may be transformed into commands for interception. These findings reveal separate, partially overlapping geometries for representation of target speed and position in the SC and overturn the longstanding view that it encodes only target location and movement goals.

The trade-off between mechanical robustness and ionic conductivity in gel materials impedes their application in flexible electronics. Herein, a eutectogel is engineered via a synergistic strategy that integrates a ternary deep eutectic solvent (DES) (choline chloride/ethylene glycol/zinc chloride) with dynamic Zn2 + coordination. Through in situ photopolymerization of 1-vinylimidazole in the ternary DES, a dynamically cross-linked organic-inorganic hybrid network is constructed. Crucially, Zn2 + ions play a dual role: they form reversible Zn2 +-imidazole coordination sites, enhancing the mechanical properties with an elongation at break of 1100% and a Young's modulus of 0.23&#xa0;MPa, while inducing coordination-driven densification of the amorphous network. This compaction effect tightens the polymer network without triggering crystallization, while accessible ion-transport pathways are retained within the amorphous network. Consequently, the eutectogel exhibits a high ionic conductivity of 0.38 mS cm- 1, overcoming the typical conductivity loss in high-strength gels. Using these properties, a flexible strain-sensing system with Bluetooth transmission is developed. It can capture real-time motor signals and convert them into visual commands, highlighting its potential for wireless assistive monitoring, particularly in rehabilitation for hemiplegic patients. This work provides a promising strategy for achieving a balance between mechanical robustness and ionic conductivity in soft materials by regulating the amorphous structure.