A simple yet effective Global Context Adaption (GCA) module facilitates representative feature extraction by learning the input-dependent skeleton topologies. Compared to the mainstream works, MV-IGNet can be readily implemented while with smaller model size and faster inference. Experimental results show the proposed MV-IGNet achieves impressive performance on large-scale benchmarks NTU-RGB+D and NTU-RGB+D 120.Quantitative relationship between the activity/property and the structure of compound is critical in chemical applications. To learn this quantitative relationship, hundreds of molecular descriptors have been designed to describe the structure, mainly based on the properties of vertices and edges of molecular graph. However, many descriptors degenerate to the same values for different compounds with the same molecular graph, resulting in model failure. In this paper, we design a multidimensional signal for each vertex of the molecular graph to derive new descriptors with higher discriminability. We treat the new and traditional descriptors as the signals on the descriptor graph learned from the descriptor data, and enhance descriptor dissimilarity using the Laplacian filter derived from the descriptor graph. Combining these with model learning techniques, we propose a graph signal processing based approach to obtain reliable new models for learning the quantitative relationship and predicting the properties of compounds. We also provide insights from chemistry for the boiling point model. Several experiments are presented to demonstrate the validity, effectiveness and advantages of the proposed approach.Clinical translation of “intelligent” lower-limb assistive technologies relies on robust control interfaces capable of accurately detecting user intent. To date, mechanical sensors and surface electromyography (EMG) have been the primary sensing modalities used to classify ambulation. Ultrasound (US) imaging can be used to detect user-intent by characterizing structural changes of muscle. Our study evaluates wearable US imaging as a new sensing modality for continuous classification of five discrete ambulation modes level, incline, decline, stair ascent, and stair descent ambulation, and benchmarks performance relative to EMG sensing. Ten able-bodied subjects were equipped with a wearable US scanner and eight unilateral EMG sensors. Time-intensity features were recorded from US images of three thigh muscles. Features from sliding windows of EMG signals were analyzed in two configurations one including 5 EMG sensors on muscles around the thigh, and another with 3 additional sensors placed on the shank. Linear discriminate analysis was implemented to continuously classify these phase-dependent features of each sensing modality as one of five ambulation modes. US-based sensing statistically improved mean classification accuracy to 99.8% (99.5-100% CI) compared to 8-EMG sensors (85.8%; 84.0-87.6% CI) and 5-EMG sensors (75.3%; 74.5-76.1% CI). Further, separability analyses show the importance of superficial and deep US information for stair classification relative to other modes. These results are the first to demonstrate the ability of US-based sensing to classify discrete ambulation modes, highlighting the potential for improved assistive device control using less widespread, less superficial and higher resolution sensing of skeletal muscle.

The envelope following response (EFR) is a clinically relevant evoked potential, reflecting the synchronization of the auditory pathway to the temporal envelope of sounds. Since there is no standard analysis of this potential, we here aim at contrasting the relative accuracy of known time-frequency methods and new strategies for the reliable estimation of the EFR amplitude and latency.

The EFR was estimated using explicit time-frequency methods the Short-Term Fourier Transform (STFT) and the Morlet Continuous Wavelet Transform (CWT). Selleckchem MCC950 Furthermore, the Chirp Analyzer (CA) was introduced as a new tool for the reliable estimation of the EFR. The applicability of the methods was tested in animal and human recordings.

Using simulated data for comparing the estimation performance by each method, we found that the CA is able to correctly estimate EFR amplitudes, without the typical bias observed in the STFT estimates. The CA is more robust to noise than the CWT method, although with higher sensitivity to the latency of the response. Thus, the estimation of the EFR amplitude with any of the methods, but especially with CA, should be corrected by using the estimated delay. Analysis of real data confirmed these results and showed that all methods offer estimated EFRs similar to those found in previous studies using the classical Fourier Analyzer.

The CA is a potential valuable tool for the analysis of the EFR, which could be extended for the estimation of oscillatory evoked potentials of other sensory modalities.

The CA is a potential valuable tool for the analysis of the EFR, which could be extended for the estimation of oscillatory evoked potentials of other sensory modalities.Preterm labor and birth are the primary causes of neonatal morbidities and mortalities. The early detection and treatment of preterm uterine muscular contraction are crucial for the management of preterm labor. In this work, a ring electrode with a wireless electrical recording and stimulating (RE-WERS) system was designed, fabricated, and investigated for the non-invasive monitoring of uterine contraction/relaxation as a diagnostic and therapeutic tool for preterm labor. By using an organ bath system, we confirmed that the uterine contraction force in mice can be decreased by the application of electrical stimulation. Then, the RE-WERS system was inserted non-invasively through the vagina to the cervix of a pregnant minipig, and it successfully recorded the uterine contraction and reflect signals when various electrical stimulating conditions were applied. The difference in the uterine signals before and after the injection of a labor induction drug, such as oxytocin and prostaglandin [Formula see text], was recorded, and the difference was remarkable. In addition, the uterine signal that was recorded was well matched with the signal of the electromyography (EMG) kit during open abdominal surgery. It seemed that the continuous and various electrical stimulations affected the delay or inhibition of childbirth in the pregnant minipig.