Corpus Callosum Atrophy in Alcohol-Dependent Men with Memory Disorders and Visual Attention Difficulties.

Background: The earlier research confirm the relationship between structural changes in the corpus callosum and difficulties in attention and memory in the group of patients with alcohol use disorder (AUD). Nevertheless, the image of auditory and visual memory disorders in men with gradual atrophy of the corpus callosum and different alcohol abuse duration, it has not been explained yet. The overriding objective of this study was: (1) to determine whether there are principal and interaction effects of visuospatial and auditory-verbal memory on alcohol consumption and cross-sectional corpus callosum area in men with alcohol use disorder, (2) to assess the impact of callosal changes on the memory and visual attention processes.

Autonomous swab robot for naso- and oropharyngeal COVID-19 screening.

The COVID-19 outbreak has triggered a global health and economic crisis, necessitating widespread testing to control viral spread amidst rising cases and fatalities. The recommended testing method, a combined naso- and oropharyngeal swab, poses risks and demands limited protective gear. In response to the COVID-19 pandemic, we developed and tested the first autonomous swab robot station for Naso- and Oropharyngeal Coronavirus Screening (SR-NOCS).

Brain oscillations in reflecting motor status and recovery induced by action observation-driven robotic hand intervention in chronic stroke.

Hand rehabilitation in chronic stroke remains challenging, and finding markers that could reflect motor function would help to understand and evaluate the therapy and recovery. The present study explored whether brain oscillations in different electroencephalogram (EEG) bands could indicate the motor status and recovery induced by action observation-driven brain-computer interface (AO-BCI) robotic therapy in chronic stroke. The neurophysiological data of 16 chronic stroke patients who received 20-session BCI hand training is the basis of the study presented here.

Tailored Design of a Water-Based Nanoreactor Technology for Producing Processable Sub-40 Nm 3D COF Nanoparticles at Atmospheric Conditions.

Covalent organic frameworks (COFs) are crystalline materials with intrinsic porosity that offer a wide range of potential applications spanning diverse fields. Yet, the main goal in the COF research area is to achieve the most stable thermodynamic product while simultaneously targeting the desired size and structure crucial for enabling specific functions. While significant progress is made in the synthesis and processing of 2D COFs, the development of processable 3D COF nanocrystals remains challenging.

Pedicle drilling force control of a robotic surgical system via spine-soft tissue coupling model and parameters optimization.

Bone drilling is a crucial operation in spinal fusion surgery that requires precise control of the applied force to ensure surgical safety. This manuscript aims to enhance the force servo performance of the orthopedic robot during automatic bone drilling operations. Firstly, an analytical model is introduced to describe the spinal mobility of the spine-soft tissue coupling structure.

Towards Minimizing the LiDAR Sim-to-Real Domain Shift: Object-Level Local Domain Adaptation for 3D Point Clouds of Autonomous Vehicles.

Perception algorithms for autonomous vehicles demand large, labeled datasets. Real-world data acquisition and annotation costs are high, making synthetic data from simulation a cost-effective option. However, training on one source domain and testing on a target domain can cause a domain shift attributed to local structure differences, resulting in a decrease in the model's performance.

Quantitative Detection Technology for Geometric Deformation of Pipelines Based on LiDAR.

This paper introduces a novel method for enhancing underground pipeline inspection, specifically addressing limitations associated with traditional closed-circuit television (CCTV) systems. These systems, commonly used for capturing visual data of sewer system deformations, heavily rely on subjective human expertise, leading to limited accuracy in detection. Furthermore, their inability to perform quantitative analyses of deformation extent hampers overall inspection effectiveness.

Multi-domain feature joint optimization based on multi-view learning for improving the EEG decoding.

Background: Brain-computer interface (BCI) systems based on motor imagery (MI) have been widely used in neurorehabilitation. Feature extraction applied by the common spatial pattern (CSP) is very popular in MI classification. The effectiveness of CSP is highly affected by the frequency band and time window of electroencephalogram (EEG) segments and channels selected.

A human lower-limb biomechanics and wearable sensors dataset during cyclic and non-cyclic activities.

Tasks of daily living are often sporadic, highly variable, and asymmetric. Analyzing these real-world non-cyclic activities is integral for expanding the applicability of exoskeletons, protheses, wearable sensing, and activity classification to real life, and could provide new insights into human biomechanics. Yet, currently available biomechanics datasets focus on either highly consistent, continuous, and symmetric activities, such as walking and running, or only a single specific non-cyclic task.

Bio-inspired affordance learning for 6-DoF robotic grasping: A transformer-based global feature encoding approach.

The 6-Degree-of-Freedom (6-DoF) robotic grasping is a fundamental task in robot manipulation, aimed at detecting graspable points and corresponding parameters in a 3D space, i.e affordance learning, and then a robot executes grasp actions with the detected affordances. Existing research works on affordance learning predominantly focus on learning local features directly for each grid in a voxel scene or each point in a point cloud scene, subsequently filtering the most promising candidate for execution.

"On-The-Fly" Synthesis of Self-Supported LDH Hollow Structures Through Controlled Microfluidic Reaction-Diffusion Conditions.

Layered double hydroxides (LDHs) are a class of functional materials that exhibit exceptional properties for diverse applications in areas such as heterogeneous catalysis, energy storage and conversion, and bio-medical applications, among others. Efforts have been devoted to produce millimeter-scale LDH structures for direct integration into functional devices. However, the controlled synthesis of self-supported continuous LDH materials with hierarchical structuring up to the millimeter scale through a straightforward one-pot reaction method remains unaddressed.

On-Command Disassembly of Microrobotic Superstructures for Transport and Delivery of Magnetic Micromachines.

Magnetic microrobots have been developed for navigating microscale environments by means of remote magnetic fields. However, limited propulsion speeds at small scales remain an issue in the maneuverability of these devices as magnetic force and torque are proportional to their magnetic volume. Here, a microrobotic superstructure is proposed, which, as analogous to a supramolecular system, consists of two or more microrobotic units that are interconnected and organized through a physical (transient) component (a polymeric frame or a thread).

Machine learning-driven self-discovery of the robot body morphology.

The morphology of a robot is typically assumed to be known, and data from external measuring devices are used mainly for its kinematic calibration. In contrast, we take an agent-centric perspective and ponder the vaguely explored question of whether a robot could learn elements of its morphology by itself, relying on minimal prior knowledge and depending only on unorganized proprioceptive signals. To answer this question, we propose a mutual information-based representation of the relationships between the proprioceptive signals of a robot, which we call proprioceptive information graphs (π-graphs).

Learning context shapes bimanual control strategy and generalization of novel dynamics.

Bimanual movements are fundamental components of everyday actions, yet the underlying mechanisms coordinating adaptation of the two hands remain unclear. Although previous studies highlighted the contextual effect of kinematics of both arms on internal model formation, we do not know how the sensorimotor control system associates the learned memory with the experienced states in bimanual movements. More specifically, can, and if so, how, does the sensorimotor control system combine multiple states from different effectors to create and adapt a motor memory? Here, we tested motor memory formation in two groups with a novel paradigm requiring the encoding of the kinematics of the right hand to produce the appropriate predictive force on the left hand.

Guiding bone cell network formation in 3D via photosensitized two-photon ablation.

A long-standing challenge in skeletal tissue engineering is to reconstruct a three-dimensionally (3D) interconnected bone cell network in vitro that mimics the native bone microarchitecture. While conventional hydrogels are extensively used in studying bone cell behavior in vitro, current techniques lack the precision to manipulate the complex pericellular environment found in bone. The goal of this study is to guide single bone cells to form a 3D network in vitro via photosensitized two-photon ablation of microchannels in gelatin methacryloyl (GelMA) hydrogels.

Explicit learning based on reward prediction error facilitates agile motor adaptations.

Error based motor learning can be driven by both sensory prediction error and reward prediction error. Learning based on sensory prediction error is termed sensorimotor adaptation, while learning based on reward prediction error is termed reward learning. To investigate the characteristics and differences between sensorimotor adaptation and reward learning, we adapted a visuomotor paradigm where subjects performed arm movements while presented with either the sensory prediction error, signed end-point error, or binary reward.

Dermal features derived from optoacoustic tomograms via machine learning correlate microangiopathy phenotypes with diabetes stage.

Skin microangiopathy has been associated with diabetes. Here we show that skin-microangiopathy phenotypes in humans can be correlated with diabetes stage via morphophysiological cutaneous features extracted from raster-scan optoacoustic mesoscopy (RSOM) images of skin on the leg. We obtained 199 RSOM images from 115 participants (40 healthy and 75 with diabetes), and used machine learning to segment skin layers and microvasculature to identify clinically explainable features pertaining to different depths and scales of detail that provided the highest predictive power.

Recent Developments in Metallic Degradable Micromotors for Biomedical and Environmental Remediation Applications.

Synthetic micromotor has gained substantial attention in biomedicine and environmental remediation. Metal-based degradable micromotor composed of magnesium (Mg), zinc (Zn), and iron (Fe) have promise due to their nontoxic fuel-free propulsion, favorable biocompatibility, and safe excretion of degradation products Recent advances in degradable metallic micromotor have shown their fast movement in complex biological media, efficient cargo delivery and favorable biocompatibility. A noteworthy number of degradable metal-based micromotors employ bubble propulsion, utilizing water as fuel to generate hydrogen bubbles.

Do robots outperform humans in human-centered domains?

The incessant progress of robotic technology and rationalization of human manpower induces high expectations in society, but also resentment and even fear. In this paper, we present a quantitative normalized comparison of performance, to shine a light onto the pressing question, "How close is the current state of humanoid robotics to outperforming humans in their typical functions (e.g.

Robotic world models-conceptualization, review, and engineering best practices.

The term "world model" (WM) has surfaced several times in robotics, for instance, in the context of mobile manipulation, navigation and mapping, and deep reinforcement learning. Despite its frequent use, the term does not appear to have a concise definition that is consistently used across domains and research fields. In this review article, we bootstrap a terminology for WMs, describe important design dimensions found in robotic WMs, and use them to analyze the literature on WMs in robotics, which spans four decades.

Study on the Prediction Model of Wound Infection After Spinal Fusion and Internal Fixation Based on Logistic Regression.

With the development of the times, spinal problems are not only one of the diseases that older people pay close attention to, but also gradually spread among teenagers. Therefore, it is very important to predict the possibility of wound infection in patients after spinal fusion and internal fixation. The method is to statistically analyze the clinical data of patients with clinical spinal disease, and to propose individualized treatment and recovery plan for each patient's pathological characteristics and postoperative recovery, so as to realize humanized service and minimize the possibility of wound infection.

Exploring the Association Linking Head Position and Sleep Architecture to Motor Impairment in Parkinson's Disease: An Exploratory Study.

Patients with Parkinson's disease (PD) tend to sleep more frequently in the supine position and less often change head and body position during sleep. Besides sleep quality and continuity, head and body positions are crucial for glymphatic system (GS) activity. This pilot study evaluated sleep architecture and head position during each sleep stage in idiopathic PD patients without cognitive impairment, correlating sleep data to patients' motor and non-motor symptoms (NMS).

Powering Disturb-Free Reconfigurable Computing and Tunable Analog Electronics with Dual-Port Ferroelectric FET.

Single-port ferroelectric FET (FeFET) that performs write and read operations on the same electrical gate prevents its wide application in tunable analog electronics and suffers from read disturb, especially in the high-threshold voltage () state as the retention energy barrier is reduced by the applied read bias. To address both issues, we propose to adopt a read disturb-free dual-port FeFET where the write is performed on the gate featuring a ferroelectric layer and the read is done on a separate gate featuring a nonferroelectric dielectric. Combining the unique structure and the separate read gate, read disturb is eliminated as the applied field is aligned with polarization in the high- state, thus improving its stability, while it is screened by the channel inversion charge and exerts no negative impact on the low- state stability.

The Evolution of Hand Proprioceptive and Motor Impairments in the Sub-Acute Phase After Stroke.

Background: Hand proprioception is essential for fine movements and therefore many activities of daily living. Although frequently impaired after stroke, it is unclear how hand proprioception evolves in the sub-acute phase and whether it follows a similar pattern of changes as motor impairments.

Objective: This work investigates whether there is a corresponding pattern of changes over time in hand proprioception and motor function as comprehensively quantified by a combination of robotic, clinical, and neurophysiological assessments.