Cluster analysis from EEG signals recorded from electrodes placed over the sensorimotor cortex of control participants showed that predicting either an upcoming hand movement or the occurrence of a tactile event yielded specific neural signatures. In BPI participants, the EEG signals from the sensorimotor cortex contralateral to the dominant hand in the hand movement condition were different compared to the other conditions. Furthermore, there were no differences between ball movement and no movement conditions in the sensorimotor cortex contralateral to the dominant hand, suggesting that BPI blurred specifically the ability to predict upcoming tactile events for the dominant hand. These results highlight the role of the sensorimotor cortex in creating estimates of both actions and tactile interactions in the space around the body and suggest plastic effects on prediction coding following peripheral sensorimotor loss.

The prevalence of comorbid pain after spinal cord injury (SCI) is relatively high in clinical observations and has continued to increase over time. Neuropathic pain (70.14%) is the most popular subject in academic journals after SCI. However, studies that used the bibliometric method to analyze comorbid pain after SCI are still lacking. This study is aimed at combining and integrating acquired information to analyze the global trends of research on the comorbidity of pain after SCI in the last three decades (1990-2019).

Systematic works of literature published from 1990 to 2019 were obtained from the Web of Science Core Collection. CiteSpace software was used to analyze the relationship of publication year with the country, institution, journals, authors, references, and keywords. The regression analysis is used to evaluate the percentage of the category increase or decrease over time significantly. IBM SPSS Statistics was used in the statistical analysis.

A total of 730 publications were included in thtions.

Overall, this study provides the latest research direction for pain after SCI. This historical overview of research into pain after SCI will be a useful basis for further research into development trends, focus issues, cooperators, and cooperative institutions.Complex question answering in real world is a comprehensive and challenging task due to its demand for deeper question understanding and deeper inference. Information retrieval is a common solution and easy to implement, but it cannot answer questions which need long-distance dependencies across multiple documents. Knowledge base (KB) organizes information as a graph, and KB-based inference can employ logic formulas or knowledge embeddings to capture such long-distance semantic associations. However, KB-based inference has not been applied to real-world question answering well, because there are gaps among natural language, complex semantic structure, and appropriate hypothesis for inference. We propose decoupling KB-based inference by transforming a question into a high-level triplet in the KB, which makes it possible to apply KB-based inference methods to answer complex questions. In addition, we create a specialized question answering dataset only for inference, and our method is proved to be effective by conducting experiments on both AI2 Science Questions dataset and ours.

Brain-computer interface (BCI) based on functional near-infrared spectroscopy (fNIRS) is expected to provide an optional active rehabilitation training method for patients with walking dysfunction, which will affect their quality of life seriously. Sparse representation classification (SRC) oxyhemoglobin (HbO) concentration was used to decode walking imagery and idle state to construct fNIRS-BCI based on walking imagery.

15 subjects were recruited and fNIRS signals were collected during walking imagery and idle state. Firstly, band-pass filtering and baseline drift correction for HbO signal were carried out, and then the mean value, peak value, and root mean square (RMS) of HbO and their combinations were extracted as classification features; SRC was used to identify the extracted features and the result of SRC was compared with those of support vector machine (SVM), K-Nearest Neighbor (KNN), linear discriminant analysis (LDA), and logistic regression (LR).

The experimental results showed that the averawalking imagery and idle state. It also showed that different time windows for feature extraction have an impact on the classification results, and the time window of 2-8 s achieved a better classification accuracy (94.33±2.60%) than other time windows. Significance. The study was expected to provide a new and optional active rehabilitation training method for patients with walking dysfunction. In addition, the experiment was also a rare study based on fNIRS-BCI using SRC to decode walking imagery and idle state.The global oxidation capacity, defined as the tropospheric mean concentration of the hydroxyl radical (OH), controls the lifetime of reactive trace gases in the atmosphere such as methane and carbon monoxide (CO). Models tend to underestimate the methane lifetime and CO concentrations throughout the troposphere, which is consistent with excessive OH. Sodiumbutyrate Approximately half of the oxidation of methane and non-methane volatile organic compounds (VOCs) is thought to occur over the oceans where oxidant chemistry has received little validation due to a lack of observational constraints. We use observations from the first two deployments of the NASA ATom aircraft campaign during July-August 2016 and January-February 2017 to evaluate the oxidation capacity over the remote oceans and its representation by the GEOS-Chem chemical transport model. The model successfully simulates the magnitude and vertical profile of remote OH within the measurement uncertainties. Comparisons against the drivers of OH production (water vapoAA is unlikely to fully resolve previously reported model global biases in OH and methane lifetime, suggesting that future work should examine the sources and sinks of OH over land.Occupational factors can lead to breast cancer, though the relationship between these variables is not well established. The objective of this study was to search the relevant literature for information on the association between breast cancer and exposure to occupational risk factors. For that purpose, electronic databases were searched using the following keywords breast cancer and occupational exposures. A total of 40 articles published in the 10-year period from 2009 to 2019 were included in this review. Workers exposed to metals such as cadmium, chemical products, radiation and night work were more susceptible to breast cancer. The findings showed significant evidence to support an association between breast cancer and some chemical products, ionizing radiation and night work. However, most studies have difficulty establishing a causal relationship between these variables, pointing to the need for further investigation of these issues.