Multimodal brain-controlled system for rehabilitation training: Combining asynchronous online brain-computer interface and exoskeleton.

BACKGROUND: Traditional therapist-based rehabilitation training for patients with movement impairment is laborious and expensive. In order to reduce the cost and improve the treatment effect of rehabilitation, many methods based on human-computer interaction (HCI) technology have been proposed, such as robot-assisted therapy and functional electrical stimulation (FES). However, due to the lack of active participation of brain, these methods have limited effects on the promotion of damaged nerve remodeling. NEW METHOD: Based on the neurofeedback training provided by the combination of brain-computer interface (BCI) and exoskeleton, this paper proposes a multimodal brain-controlled active rehabilitation system to help improve limb function. The joint control mode of steady-state visual evoked potential (SSVEP) and motor imagery (MI) is adopted to achieve self-paced control and thus maximize the degree of brain involvement, and a requirement selection function based on SSVEP design is added to facilitate communication with aphasia patients. COMPARISON WITH EXISTING METHODS: In addition, the Transformer is introduced as the MI decoder in the asynchronous online BCI to improve the global perception of electroencephalogram (EEG) signals and maintain the sensitivity and efficiency of the system. RESULTS: In two multi-task online experiments for left hand, right hand, foot and idle states, subject achieves 91.25% and 92.50% best accuracy, respectively. CONCLUSION: Compared with previous studies, this paper aims to establish a high-performance and low-latency brain-controlled rehabilitation system, and provide an independent and autonomous control mode of the brain, so as to improve the effect of neural remodeling. The performance of the proposed method is evaluated through offline and online experiments.

Recent advances in the role of dissolved organic matter during antibiotics photodegradation in the aquatic environment.

The presence of residual antibiotics in the environment is a prominent issue. Photodegradation behavior is an important way of antibiotics reduction, which is closely related to dissolved organic matter (DOM) in water. The review provides an overview of the latest advancements in the field. Classification, characterization of DOM, and the dominant mechanisms for antibiotic photodegradation were discussed. Furthermore, it summarized and compared the effects of DOM on different antibiotics photodegradation. Moreover, the review comprehensively considered the factors influencing the photodegradation of antibiotics in the aquatic environment, including the characteristics of light, temperature, dosage of DOM, concentration of antibiotics, solution pH, and the presence of coexisting ions. Finally, potential directions were proposed for the development of predictive models for the photodegradation of antibiotics. Based on the review of existing literature, this paper also considered several pathways for the future study of antibiotic photodegradation. This study allows for a better understanding of the DOM's environmental role and provides important new insights into the photochemical fate of antibiotics in the aquatic environment.

A study on the combination of functional connection features and Riemannian manifold in EEG emotion recognition.

Introduction: Affective computing is the core for Human-computer interface (HCI) to be more intelligent, where electroencephalogram (EEG) based emotion recognition is one of the primary research orientations. Besides, in the field of brain-computer interface, Riemannian manifold is a highly robust and effective method. However, the symmetric positive definiteness (SPD) of the features limits its application. Methods: In the present work, we introduced the Laplace matrix to transform the functional connection features, i.e., phase locking value (PLV), Pearson correlation coefficient (PCC), spectral coherent (COH), and mutual information (MI), to into semi-positive, and the max operator to ensure the transformed feature be positive. Then the SPD network is employed to extract the deep spatial information and a fully connected layer is employed to validate the effectiveness of the extracted features. Particularly, the decision layer fusion strategy is utilized to achieve more accurate and stable recognition results, and the differences of classification performance of different feature combinations are studied. What's more, the optimal threshold value applied to the functional connection feature is also studied. Results: The public emotional dataset, SEED, is adopted to test the proposed method with subject dependent cross-validation strategy. The result of average accuracies for the four features indicate that PCC outperform others three features. The proposed model achieve best accuracy of 91.05% for the fusion of PLV, PCC, and COH, followed by the fusion of all four features with the accuracy of 90.16%. Discussion: The experimental results demonstrate that the optimal thresholds for the four functional connection features always kept relatively stable within a fixed interval. In conclusion, the experimental results demonstrated the effectiveness of the proposed method.

The association between dietary branched-chain amino acids and the risk of cardiovascular diseases in Chinese patients with type 2 diabetes: A hospital-based case-control study.

Background: Previous studies showed conflicting evidence on the association between the intake of dietary branched-chain amino acid (BCAA) and the risk of cardiovascular disease (CVD). However, this relationship has not been studied in patients with type 2 diabetes. Therefore, we evaluated the effects of total and individual dietary BCAA (leucine, isoleucine, and valine) intake on CVD risk among individuals with type 2 diabetes in China. Materials and methods: A total of 419 patients with type 2 diabetes who have been diagnosed with CVD (within 2 weeks) were recruited between March 2013 and September 2015 in China. Cases with CVD were 1:1 matched to controls with type 2 diabetes but without CVD by age (±5 years) and sex. A validated 79-item semiquantitative food frequency questionnaire (FFQ) was administered to assess the participants' dietary data. Total dietary BCAA per individual was the summation of the daily intake of isoleucine, leucine, and valine. OR and corresponding CIs were computed by conditional logistic regression models adjusted for potential confounders. Results: Median values of the daily intake of total BCAA were 11.87 g, with an interquartile range of 10.46-13.15 g for cases, and 12.47 g, with an interquartile range of 11.08-13.79 g for controls (P = 0.001). Dietary BCAA was inversely related to CVD risk after multivariable adjustment (OR Q4-Q1 = 0.23, 95%CI = 0.10, 0.51, P trend <0.001 for total BCAA; OR Q4-Q1 = 0.20, 95%CI = 0.07, 0.53, P trend = 0.001 for leucine). For each 1-S.D. increase in total dietary BCAA, leucine or valine intake was associated with 54% (95%CI = 29%, 70%, P = 0.001), 64% (95%CI = 29%, 82%, P = 0.003), or 54% (95%CI = 1%, 79%, P = 0.049) decrease in the risk of CVD, respectively. Whole grains, starchy vegetables, mushrooms, fruit, eggs, and dairy and dairy product-derived BCAA were found to attenuate CVD risk (P ranged: = 0.002-0.027). Conclusion: Higher BCAA intake, in particular leucine and valine, might be associated with a lower risk of CVD.

Translation and validation of the Chinese version of the orthorexia nervosa assessment questionnaires among college students.

PURPOSE: The main objective of the study was to translate, validate, and compare the Chinese ORTO scales (ORTO-15 and ORTO-R). The secondary objective was to assess factors that may be related with risk of orthorexia nervosa (ON). METHODS: Two cross-sectional surveys were conducted on March-to-June 2021 for ORTO-15 and April 2022 for ORTO-R. ORTO questionnaires were translated into Chinese using the forward-backward-forward method. Exploratory factor analysis (EFA), discriminant validity and confirmatory factor analysis (CFA) were used to examine the construct validity of the questionnaires. The internal consistency was assessed using the Cronbach alpha coefficient and the test-retest reliability. Multivariate linear regression analysis was used to explore potential factors related with ON scores. RESULTS: Totally, 1289 and 1084 eligible participants were included for assessment of ORTO-15 and ORTO-R, with the mean age of 20.9 ± 2.0 years and 21.0 ± 2.3 years. The internal consistency of Chinese ORTO-15 scale and ORTO-R scale were both satisfactory (α = 0.79, ICC = 0.79; α = 0.77, ICC = 0.82). However, all ORTO-15 models showed a poor fit using CFA whereas the ORTO-R was characterized by acceptable goodness-of-fit. Multivariate linear regression indicated that physical activities and mental disorders were positively associated with ON risk assessed by both ORTO-R and ORTO-15. CONCLUSION: The Chinese ORTO-R scale was a more reliable tool to screen for ON tendencies than the Chinese version of ORTO-15. Mental disorders and physical activities might be associated with the increased ON risk. LEVEL OF EVIDENCE: Level V (descriptive cross-sectional study).

Global, Regional, and National Burden of Cancer in Children Younger Than 5 Years, 1990-2019: Analysis of the Global Burden of Disease Study 2019.

Objectives: To quantify the burden and variation trends of cancers in children under 5 years at the global, regional, and national levels from 1990 to 2019. Methods: Epidemiological data for children under 5 years who were diagnosed with any one childhood cancer were obtained from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) from 1990 to 2019. The outcomes were the absolute numbers and rates of incidence, prevalence, mortality, and disability-adjusted life-years (DALYs) for different types of cancer. Results: In 2019, 8,774,979.1 incident cases (95% uncertainty interval [UI]: 6,243,599.2 to11,737,568.5) and 8,956,583.8 (6,446,323.9 to 12,364,520.8) prevalent cases of cancer in children under 5 years were identified worldwide; these cancers resulted in 44,451.6 (36,198.7 to 53,905.9) deaths and 3,918,014.8 (3,196,454.9 to 4,751,304.2) DALYs. From 1990 to 2019, although the numbers of incident and prevalent cases only decreased by -4.6% (-7.0 to -2.2) and -8.3% (-12.6 to -3.4), respectively, the numbers of deaths and DALYs clearly declined by -47.8% (-60.7 to -26.4) and -47.7% (-60.7 to -26.2), respectively. In 2019, the middle sociodemographic index (SDI) regions had the highest incidence and prevalence, whereas the low SDI regions had the most mortality and DALYs. Although all of the SDI regions displayed a steady drop in deaths and DALYs between 1990 and 2019, the low-middle and low SDI regions showed increasing trends of incidence and prevalence. Leukemia remained the most common cancer globally in 2019. From 1990 to 2019, the burdens of leukemia, liver cancer, and Hodgkin's lymphoma declined, whereas the incidence and prevalence of other cancers grew, particularly testicular cancer. Conclusions: The global childhood cancer burden in young children has been steadily decreasing over the past three decades. However, the burdens and other characteristics have varied across different regions and types of cancers. This highlights the need to reorient current treatment strategies and establish effective prevention methods to reduce the global burden of childhood cancer.

Low-quality training data detection method of EEG signals for motor imagery BCI system.

BACKGROUND: The design and implementation of high-performance motor imagery-based brain computer interface (MI-BCI) requires high-quality training samples. However, fluctuation in subjects' physiological and mental states as well as artifacts can produce the low-quality motor imagery electroencephalogram (EEG) signal, which will damage the performance of MI-BCI system. NEW METHOD: In order to select high-quality MI-EEG training data, this paper proposes a low-quality training data detection method combining independent component analysis (ICA) and weak classifier cluster. we also design and implement a new online BCI system based on motor imagery to verify the online processing performance of the proposed method. RESULT: In order to verify the effectiveness of the proposed method, we conducted offline experiments on the public dataset called BCI Competition IV Data Set 2b. Furthermore, in order to verify the processing performance of the online system, we designed 60 groups of online experiments on 12 subjects. The online experimental results show that the twelve subjects can complete the system task efficiently (the best experiment is 135.6 s with 9 trials of subject S1). CONCLUSION: This paper demonstrated that the proposed low-quality training data detection method can effectively screen out low-quality training samples, so as to improve the performance of the MI-BCI system.

Optical SAR data processing configuration with simultaneous azimuth and range matching filtering.

The real-time processing of synthetic aperture radar (SAR) data has a high requirement for the processor, which is a difficult problem in SAR real-time processing. With the rapid development of optoelectronic devices, traditional electrical SAR data processing can be converted into optoelectronic processing to improve the processing speed. In this paper, a new type of optical device is proposed to improve the processing speed of SAR data. With the help of a spatial light modulator (SLM), the initial SAR signal and matched filter function are loaded on the input plane and spectrum plane of the 4f system, respectively. Using an optical lens with the function of the Fourier transform, the Fourier transform and inverse Fourier transform of the SAR signal are carried out to realize the fast imaging of SAR. In theory, the processing speed of SAR data is the speed of light. Compared with traditional methods such as the range-Doppler (RD) algorithm, it is no longer necessary to carry out a one-dimensional Fourier transform but to carry out matching filtering for the azimuth and range of the spectrum plane of 4f system at the same time. In this way, it is not necessary to introduce a cylindrical lens, only a spherical lens is needed to realize the Fourier transform imaging of SAR. Finally, a two-dimensional SAR processing optical system is built to obtain the SAR image in real time.

A High Optical Throughput Spectral Imaging Technique Using Broadband Filters.

To address the miniaturization of the spectral imaging system required by a mounted platform and to overcome the low luminous flux caused by current spectroscopic technology, we propose a method for the multichannel measurement of spectra using a broadband filter in this work. The broadband filter is placed in front of a lens, and the spectral absorption characteristics of the broadband filter are used to achieve the modulation of the incident spectrum of the detection target and to establish a mathematical model for the detection of the target. The spectral and spatial information of the target can be obtained by acquiring data using a push-broom method and reconstructing the spectrum using the GCV-based Tikhonov regularization algorithm. In this work, we compare the accuracy of the reconstructed spectra using the least-squares method and the Tikhonov algorithm based on the L-curve. The effect of errors in the spectral modulation function on the accuracy of the reconstructed spectra is analyzed. We also analyze the effect of the number of overdetermined equations on the accuracy of the reconstructed spectra and consider the effect of detector noise on the spectral recovery. A comparison between the known data cubes and our simulation results shows that the spectral image quality based on broadband filter reduction is better, which validates the feasibility of the method. The proposed method of combining broadband filter-based spectroscopy with a panchromatic imaging process for measurement modulation rather than spectroscopic modulation provides a new approach to spectral imaging.