Rewc-GNN Algorithm for the Property Prediction of Large-Scale Crystals.

The space group of a crystal describes the symmetry and periodic arrangement of its structure. As the fundamental element in the structure, it plays a vital role in determining the physical and chemical properties of crystals. The investigation of crystal space group information allows for the prediction of material properties, thereby providing guidance for material design and synthesis to enhance their performance or functionality. Currently prevalent first-principles-based computational methods exhibit good accuracy, but they rely heavily on computing resources, greatly limiting the efficiency of material screening. In this paper, our study is oriented toward the prediction the spatial group of crystals, and an algorithm named Rewc, based on graph neural networks (GNNs) is proposed. This algorithm encodes all atoms and the interactions between atoms in the crystal as features by combining Floyd algorithm and k-hop message passing and employs multilayer convolutional networks to extract connections between k layers. This allows for the automatic learning of more representative atomic vector representations through iterations of feature information for each atom and its neighbors. Experimental results demonstrate that the Rewc framework exhibits reliable accuracy and good generalization capabilities in predicting the crystal structure compared to previous GNN methods.

Enhancing Force Control of Prosthetic Controller for Hand Prosthesis by Mimicking Biological Properties.

Prosthetic hands are frequently rejected due to frustrations in daily uses. By adopting principles of human neuromuscular control, it could potentially achieve human-like compliance in hand functions, thereby improving functionality in prosthetic hand. Previous studies have confirmed the feasibility of real-time emulation of neuromuscular reflex for prosthetic control. This study further to explore the effect of feedforward electromyograph (EMG) decoding and proprioception on the biomimetic controller. The biomimetic controller included a feedforward Bayesian model for decoding alpha motor commands from stump EMG, a muscle model, and a closed-loop component with a model of muscle spindle modified with spiking afferents. Real-time control was enabled by neuromorphic hardware to accelerate evaluation of biologically inspired models. This allows us to investigate which aspects in the controller could benefit from biological properties for improvements on force control performance. 3 non-disabled and 3 amputee subjects were recruited to conduct a "press-without-break" task, subjects were required to press a transducer till the pressure stabilized in an expected range without breaking the virtual object. We tested whether introducing more complex but biomimetic models could enhance the task performance. Data showed that when replacing proportional feedback with the neuromorphic spindle, success rates of amputees increased by 12.2% and failures due to breakage decreased by 26.3%. More prominently, success rates increased by 55.5% and failures decreased by 79.3% when replacing a linear model of EMG with the Bayesian model in the feedforward EMG processing. Results suggest that mimicking biological properties in feedback and feedforward control may improve the manipulation of objects by amputees using prosthetic hands. Clinical and Translational Impact Statement: This control approach may eventually assist amputees to perform fine force control when using prosthetic hands, thereby improving the motor performance of amputees. It highlights the promising potential of the biomimetic controller integrating biological properties implemented on neuromorphic models as a viable approach for clinical application in prosthetic hands.

The Effects of Deep Brain Stimulation on Motor Unit Activities in Parkinson's Disease based on High-Density Surface EMG Analysis.

Tremor in Parkinson's disease (PD) is caused by synchronized activation bursts in limb muscles. Deep Brain Stimulation (DBS) is an effective clinical therapy for inhibiting tremor and improving movement disorders in PD patients. However, the neural mechanism of how tremor symptom is suppressed by DBS at motor unit (MU) level remains unclear. This paper developed a data acquisition platform for collecting physiological data in PD patients. Both high-density surface Electromyography (HD-sEMG) and kinematics data were collected concurrently before and after DBS surgery. The MU behaviors were obtained via HD-sEMG decomposition algorithm to reveal the effect of DBS on PD tremor. A data set of one tremor dominant PD patient acquired in pre-operation and post-operation (DBS-on) phases was analyzed. Preliminary results showed significant changes in MU firing rate and MU synchronization. The analysis approach introduced in this paper provides a novel perspective for studying the neural mechanism of DBS as revealed by MU activities. Clinical Relevance- This study presented an approach to investigate the effect of DBS therapy on improving tremor disorder of PD patients.