Learning Graph Attentions via Replicator Dynamics.

Graph Attention (GA) which aims to learn the attention coefficients for graph edges has achieved impressive performance in GNNs on many graph learning tasks. However, existing GAs are usually learned based on edges' (or connected nodes') features which fail to fully capture the rich structural information of edges. Some recent research attempts to incorporate the structural information into GA learning but how to fully exploit them in GA learning is still a challenging problem. To address this challenge, in this work, we propose to leverage a new Replicator Dynamics model for graph attention learning, termed Graph Replicator Attention (GRA). The core of GRA is our derivation of replicator dynamics based sparse attention diffusion which can explicitly learn context-aware and sparse preserved graph attentions via a simple self-supervised way. Moreover, GRA can be theoretically explained from an energy minimization model. This provides a more theoretical justification for the proposed GRA method. Experiments on several graph learning tasks demonstrate the effectiveness and advantages of the proposed GRA method on ten benchmark datasets.

Within-Session Reliability of fNIRS in Robot-Assisted Upper-Limb Training.

Functional near-infrared spectroscopy (fNIRS) seems opportune for neurofeedback in robot-assisted rehabilitation training due to its noninvasive, less physical restriction, and no electromagnetic disturbance. Previous research has proved the cross-session reliability of fNIRS responses to non-motor tasks (e.g., visual stimuli) and fine-motor tasks (e.g., finger tapping). However, it is still unknown whether fNIRS responses remain reliable 1) in gross-motor tasks, 2) within a training session, and 3) for different training parameters. Hence, this study aimed to investigate the within-session reliability of fNIRS responses to gross-motor tasks for different training parameters. Ten healthy participants were recruited to conduct right elbow extension-flexion in three robot-assisted modes. The Passive mode was fully motor-actuated, while Active1 and Active2 modes involved active engagement with different resistance levels. FNIRS data of three identical runs were used to assess the within-session reliability in terms of the map- ( R2 ) and cluster-wise ( Roverlap ) spatial reproducibility and the intraclass correlation (ICC) of temporal features. The results revealed good spatial reliability ( R2 up to 0.69, Roverlap up to 0.68) at the subject level. Besides, the within-session temporal reliabilities of Slope, Max/Min, and Mean were between good and excellent ( ICC < 0.86). We also found that the within-session reliability was positively correlated with the intensity of the training mode, except for the temporal reliability of HbO in Active2 mode. Overall, our results demonstrated good within-session reliability of fNIRS responses, suggesting fNIRS as reliable neurofeedback for constructing closed-loop robot-assisted rehabilitation systems.

Poor vitamin D status was associated with increased appendicular fat deposition in US Adults: Data from 2011-2018 National Health and Nutrition Examination Survey.

The aim of the study was to explore the relationship between serum 25-hydroxyvitamin D [25(OH)D] concentrations and regional body fat deposition in 2011-2018 National Health and Nutrition Examination Survey participants aged 18 to 59 years. We hypothesized that serum 25(OH)D concentrations were negatively associated with total, appendicular, and truncal fat deposition. Serum 25(OH)D concentration was categorized into sufficient (≥75.0 nM), insufficient (50.0-74.9 nM), and deficient (<50.0 nM) groups. Fat mass (FM) was measured by dual-energy X-ray absorptiometry, and FM index (FMI) was calculated by dividing FM (kg) with height2 (m2). Multivariant linear regression and Granger causal analysis were performed to assess the causal relationship between vitamin D status and regional FMIs. Overall serum 25(OH)D concentrations were negatively associated with total (ß = -0.029, standard error [SE] = 0.002), trunk (ß = -0.015, SE = 0.001), arms (ß = -0.004, SE = 3.09 × 10-4), and legs (ß = -0.010, SE = 0.001) FMIs in all participants (P < .001, respectively); however, after stratified by vitamin D status and BMI, the negative associations were only observed in individuals with vitamin D deficiency and obesity. The causal analysis indicated that serum 25(OH)D concentrations may causally reduce the arms (F = 4.917, probability [P] = 0.007), legs (F = 5.783, P = 0.003), and total (F = 3.202, P = 0.041) FMIs except for trunk FMI but not vice versa. In conclusion, poor vitamin D status was associated with increased total and appendicular body fat deposition in US adults, particularly in participants with obesity.

Regression equation for predicting dietary phosphorus intake based on estimated dietary protein intake: A multicenter cross-sectional study in China.

BACKGROUND: Protein and phosphorus intake, which affect chronic kidney disease (CKD), is assessed using cumbersome food diaries. Therefore, more straightforward and accurate methods of assessing protein and phosphorus intake are needed. We decided to investigate the nutrition status and dietary protein and phosphorus intake of patients with stages 3, 4, 5, or 5D CKD. METHODS: This cross-sectional survey included outpatients with CKD at seven class A tertiary hospitals in Beijing, Shanghai, Sichuan, Shandong, Liaoning, and Guangdong in China. Protein and phosphorus intake levels were calculated using 3-day food records. Protein levels and calcium and phosphorus serum concentrations were measured; urinary urea nitrogen was determined using a 24-h urine test. Protein and phosphorus intakes were calculated using the Maroni and Boaz formulas, respectively. The calculated values were compared with the recorded dietary intakes. An equation that regressed phosphorus intake on protein intake was constructed. RESULTS: The average recorded energy and protein intake was 1637.5 ± 595.74 kcal/day and 56.97 ± 25.25 g/day, respectively. Overall, 68.8% of patients had a good nutrition status (grade A on the Subjective Global Assessment). The correlation coefficient between protein intake and calculated intake was 0.145 (P = 0.376) and that between phosphorus intake and calculated intake was 0.713 (P < 0.001). CONCLUSION: Protein and phosphorus intakes correlated linearly. Chinese patients with stage 3-5 CKD had low daily energy intake but high protein intake. Malnutrition was present in 31.2% of patients with CKD. The phosphorus intake could be estimated from the protein intake.

Cross Domain Correlation Maximization for Enhancing the Target Recognition of SSVEP-Based Brain-Computer Interfaces.

The target recognition performance of steady-state visual evoked potential (SSVEP)-based brain-computer interfaces can be significantly improved with a training-based approach. However, the training procedure is time consuming and often causes fatigue. Consequently, the number of training data should be limited, which may reduce the classification performance. Thus, how to improve classification accuracy without increasing the training time is crucial to SSVEP-based BCI system. This study proposes a transfer-related component analysis (TransRCA) method for addressing the above issue. In this method, the SSVEP-related components are extracted from a small number of training data of the current individual and combined with those extracted from a large number of existing training data of other individuals. The TransRCA method maximizes not only the inter-trial covariances between the source and target subjects, but also the correlation between the reference signals and SSVEP signals from the source and target subjects. The proposed method was validated on the SSVEP public Benchmark and BETA datasets, and the classification accuracy and information transmission rate of the ensemble version of the proposed TransRCA method were compared with those of the state-of-the-art eCCA, eTRCA, ttCCA, LSTeTRCA, and eIISMC methods on both datasets. The comparison results indicate that the proposed method provides a superior performance compared with these state-of-the-art methods, and thus has high potential for the development of a SSVEP-based brain-computer interface system with high classification performance that only uses a small number of training data.

Postoperative hypoxemia for patients undergoing Stanford type A aortic dissection.

Clinically, it is widely recognized that surgical treatment is the preferred and reliable option for Stanford type A aortic dissection. Stanford type A aortic dissection is an emergent and serious cardiovascular disease characterized with an acute onset, poor prognosis, and high mortality. However, the incidences of postoperative complications are relatively higher due to the complexity of the disease and its intricate procedure. It has been considered that hypoxemia, one of the most common postoperative complications, plays an important role in having a worse clinical prognosis. Therefore, the effective intervention of postoperative hypoxemia is significant for the improved prognosis of patients with Stanford type A aortic dissection.

Block Distributed Joint Temporal-Frequency-Phase Modulation for Steady-State Visual Evoked Potential Based Brain-Computer Interface With a Limited Number of Frequencies.

How to encode as many targets as possible with limited frequency resources is a grave problem that restricts the application of steady-state visual evoked potential (SSVEP) based brain-computer interfaces (BCIs). In the current study, we propose a novel block-distributed joint temporal-frequency-phase modulation method for a virtual speller based on SSVEP-based BCI. A 48-target speller keyboard array is virtually divided into eight blocks and each block contains six targets. The coding cycle consists of two sessions: in the first session, each block flashes at different frequencies while all the targets in the same block flicker at the same frequency; in the second session, all the targets in the same block flash at different frequencies. Using this method, 48 targets can be coded with only eight frequencies, which greatly reduces the frequency resources required, and average accuracies of 86.81  ± 9.41% and 91.36  ± 6.41% were obtained for both the offline and online experiments. This study provides a new coding approach for a large number of targets with a small number of frequencies, which can further expand the application potential of SSVEP-based BCI.

Ultrafast Microfluidic PCR Thermocycler for Nucleic Acid Amplification.

The polymerase chain reaction (PCR) is essential in nucleic acid amplification tests and is widely used in many applications such as infectious disease detection, tumor screening, and food safety testing; however, most PCR devices have inefficient heating and cooling ramp rates for the solution, which significantly limit their application in special scenarios such as hospital emergencies, airports, and customs. Here, we propose a temperature control strategy to significantly increase the ramp rates for the solution temperature by switching microfluidic chips between multiple temperature zones and excessively increasing the temperature difference between temperature zones and the solution; accordingly, we have designed an ultrafast thermocycler. The results showed that the ramp rates of the solution temperature are a linear function of temperature differences within a range, and a larger temperature difference would result in faster ramp rates. The maximum heating and cooling ramp rates of the 25 µL solution reached 24.12 °C/s and 25.28 °C/s, respectively, and the average ramp rate was 13.33 °C/s, 6-8 times higher than that of conventional commercial PCR devices. The thermocycler achieved 9 min (1 min pre-denaturation + 45 PCR cycles) ultrafast nucleic acid amplification, shortening the time by 92% compared to the conventional 120 min nucleic acid amplification, and has the potential to be used for rapid nucleic acid detection.

Capped L21-norm-based common spatial patterns for EEG signals classification applicable to BCI systems.

The common spatial patterns (CSP) technique is an effective strategy for the classification of multichannel electroencephalogram (EEG) signals. However, the objective function expression of the conventional CSP algorithm is based on the L2-norm, which makes the performance of the method easily affected by outliers and noise. In this paper, we consider a new extension to CSP, which is termed capped L21-norm-based common spatial patterns (CCSP-L21), by using the capped L21-norm rather than the L2-norm for robust modeling. L21-norm considers the L1-norm sum which largely alleviates the influence of outliers and noise for the sake of robustness. The capped norm is further used to mitigate the effects of extreme outliers whose signal amplitude is much higher than that of the normal signal. Moreover, a non-greedy iterative procedure is derived to solve the proposed objective function. The experimental results show that the proposed method achieves the highest average recognition rates on the three real data sets of BCI competitions, which are 91.67%, 85.07%, and 82.04%, respectively. Capped L21-norm-based common spatial patterns-a robust model for EEG signals classification.

Major depression disorder may causally associate with the increased breast cancer risk: Evidence from two-sample mendelian randomization analyses.

INTRODUCTION: Major depression disorder (MDD) has been associated with increased breast cancer risk in epidemiological studies; however, it is still unknown whether this association is causal or not. The aim of this study is to determine the causal relationship between MDD and breast cancer risk. METHODS: Two-sample Mendelian randomization (MR) analyses with 92 single-nucleotide polymorphisms (SNPs) significantly associated with MDD as instrumental variables (IVs) were performed. Effects of these SNPs on breast cancer in women were estimated in the Breast Cancer Association Consortium (122,977 cases and 105,974 controls) using inverse variance weighted (IVW), weighted median and multivariable MR models. Heterogeneity and pleiotropy effects were assessed based on IVW and MR-Egger regression model, respectively. RESULTS: An 8.7% increased risk of overall breast cancer [odds ratio (OR) = 1.087; 95% confidence interval (CI) 1.011-1.170; P = 0.025] per log-odds ratio increment of MDD risk based on the IVW model was noticed. Similar results were obtained with the multivariable MR model (OR = 1.118, 95% CI = 1.010-1.237; P = 0.031). An increment but not statistically significant causality association was noticed between MDD and risk of ER+ (OR = 1.098, 95% CI = 0.984-1.227; P = 0.093) or ER- (OR = 1.129, 95% CI = 0.982-1.297; P = 0.089) breast cancer under multivariable MR model. No significant pleiotropy effects were observed for the IVs in the two-sample MR studies. CONCLUSIONS: The results suggested that a genetic predisposition of MDD is causally associated with overall breast cancer risk; however, the underlying biological mechanisms are worthy of further study.

Effective Connectivity Analysis and Classification of Action Observation From Different Perspectives: An fMRI Study.

OBJECTIVE: Analyzing the effective connectivity characteristics of brain networks in the process of action observation is helpful for understanding the neurodynamic mechanisms during action observation. METHOD: In this study, functional magnetic resonance imaging (fMRI) images were obtained from 20 participants who performed hand-object interaction observation tasks from the first-person perspective (1PP) and third-person perspective (3PP). On the basis of a meta-analysis, 11 key brain regions were extracted as nodes to build an action observation network. The weighted and directional connections between all of the nodes were investigated using partial directional coherence (PDC) analysis in five narrow frequency bands. RESULTS: The statistical analysis indicated that the ultra-low frequency band ( ≤ 0.04 Hz) exhibited significant activation compared with other frequency bands for both 1PP and 3PP. In addition, it was found that 3PP induced significantly stronger brain activation than 1PP in the ultra-low frequency band. Moreover, this study attempted to classify fMRI data corresponding to different perspectives using brain network features. A comparative analysis revealed that the weighted and binary PDC matrix methods achieved classification accuracies of 86.3% and 80.8%, respectively. SIGNIFICANCE: The weighted PDC analysis exhibits a more comprehensive understanding of neural mechanisms during action observation in different visual perspectives. It also has potential applications value in human-computer interaction in the future.

oppOntology: a MATLAB Toolbox for Enrichment Analysis.

Biologists often use systems of ontologies to classify gene lists obtained by high-throughput gene or protein-sequencing instruments, and then enrichment scores were used to rank the ontology system. Therefore, the important molecular functional categories related to the phenotype can be conveniently viewed in the ontology system. Since the birth of GO (Gene Ontology) organization, various types of ontology software have been developed to calculate enrichment scores for the target gene list in the GO system. Herein, we provide an enrichment calculation application oppOntology (Omics Pilot Platform for Ontology) developed by MATLAB. oppOntology supports simultaneous calculation of multiple samples with manifold enrichment scores (GeneCount, GeneRatio, EnrichFactor, HypergeometricTest, and FisherExactTest). oppOntology can not only calculate enrichment scores for generic functional databases, such as GO, KEGG, HPO, and MsigDB, but also for self-defined functional category databases and customized GO Slim. Moreover, oppOntology supports online mapping of KEGG pathway diagrams in a batch way. The GUI (Graphical User Interface) of oppOntology is developed on the architecture of AppDesigner in MATLAB, and all input and output files are Microsoft Excel. oppOntology is an independent, easy-to-use enrichment calculation software, that can be available at https://github.com/HangZhouSheep/oppOntology .

[Acupuncture combined with repetitive transcranial magnetic stimulation for post-stroke depression: a randomized controlled trial].

OBJECTIVE: To observe the curative effect of Shugan Tiaoshen (soothing liver and regulating mind) acupuncture combined with repetitive transcranial magnetic stimulation (rTMS) in the treatment of post-stroke depression (PSD), and to explore its mechanism. METHODS: Ninety patients of PSD were randomly divided into an acupuncture+rTMS combination group (30 cases), a rTMS combination group (30 cases, 1 case dropped off) and a western medication group (30 cases, 1 case dropped off). The western medication group was treated with escitalopram oxalate tablets, 10 mg orally each time, once a day; on the basis of the treatment in the western medication group, the rTMS combination group was additionally given rTMS, and the frequency was 20 Hz, 20 min each time, once a day, 5 times a week; on the basis of the treatment in the rTMS combination group, the acupuncture+rTMS combination group was additionally treated with Shugan Tiaoshen acupuncture at Baihui (GV 20), Sishencong (EX-HN 1), Yintang (GV 24+), Shenting (GV 24), etc. for 40 min each time, once a day, weekly 5 times, and each group was treated for 4 weeks. Before and after treatment, the scores of Hamilton depression scale-17 (HAMD-17), Montreal cognitive assessment scale (MoCA), Pittsburgh sleep quality index (PSQI) were observed, and serum levels of 5-hydroxytryptamine (5-HT) and brain-derived neurotrophic factor (BDNF) were detected in each group. RESULTS: After treatment, the HAMD-17 scores in the three groups were lower than those before treatment (P<0.01), and the PSQI scores in the acupuncture+rTMS combination group and the rTMS combination group were lower than those before treatment (P<0.01); the above indexes in the acupuncture+rTMS combination group and the rTMS combination group were lower than those in the western medication group (P<0.05), and the PSQI score in the acupuncture+rTMS combination group was lower than that in the rTMS combination group (P<0.05). After treatment, the MoCA scores and serum BDNF levels in the acupuncture+rTMS combination group and the rTMS combination group were higher than those before treatment (P<0.01), and the serum 5-HT levels in the three groups were higher than those before treatment (P<0.01); and the above indexes in the acupuncture+rTMS combination group and the rTMS combination group were higher than those in the western medication group (P<0.05), and which in the acupuncture+rTMS combination group were higher than those in the rTMS combination group (P<0.05). CONCLUSION: On the basis of western medication escitalopram oxalate, the addition of Shugan Tiaoshen acupuncture combined with rTMS therapy can effectively improve cognitive function and sleep quality in patients with PSD, and the effect is better than that of western medication alone or rTMS combined with western medication. Its mechanism of action may be related to the increase of peripheral serum 5-HT and BDNF levels.

Free-space dissemination of time and frequency with 10-19 instability over 113 km.

Networks of optical clocks find applications in precise navigation1,2, in efforts to redefine the fundamental unit of the 'second'3-6 and in gravitational tests7. As the frequency instability for state-of-the-art optical clocks has reached the 10-19 level8,9, the vision of a global-scale optical network that achieves comparable performances requires the dissemination of time and frequency over a long-distance free-space link with a similar instability of 10-19. However, previous attempts at free-space dissemination of time and frequency at high precision did not extend beyond dozens of kilometres10,11. Here we report time-frequency dissemination with an offset of 6.3 × 10-20 ± 3.4 × 10-19 and an instability of less than 4 × 10-19 at 10,000 s through a free-space link of 113 km. Key technologies essential to this achievement include the deployment of high-power frequency combs, high-stability and high-efficiency optical transceiver systems and efficient linear optical sampling. We observe that the stability we have reached is retained for channel losses up to 89 dB. The technique we report can not only be directly used in ground-based applications, but could also lay the groundwork for future satellite time-frequency dissemination.

AR-regulated ZIC5 contributes to the aggressiveness of prostate cancer.

The mechanisms by which prostate cancer (PCa) progresses to the aggressive castration-resistant stage remain uncertain. Zinc finger of the cerebellum 5 (ZIC5), a transcription factor belonging to the ZIC family, is involved in the pathology of various cancers. However, the potential effect of ZIC5 on PCa malignant progression has not been fully defined. Here, we show that ZIC5 is upregulated in PCa, particularly in metastatic lesions, in positive association with poor prognosis. Genetic inhibition of ZIC5 in PCa cells obviously attenuated invasion and metastasis and blunted the oncogenic properties of colony formation. Mechanistically, ZIC5 functioned as a transcription factor to promote TWIST1-mediated EMT progression or as a cofactor to strengthen the ß-catenin-TCF4 association and stimulate Wnt/ß-catenin signaling. Importantly, ZIC5 and the androgen receptor (AR) form a positive feed-forward loop to mutually stimulate each other's expression. AR, in cooperation with its steroid receptor coactivator 3 (SRC-3), increased ZIC5 expression through binding to the miR-27b-3p promoter and repressing miR-27b-3p transcription. In turn, ZIC5 potentiated AR, AR-V7, and AR targets' expression. Besides, ZIC5 inhibition reduced AR and AR-V7 protein expression and enhanced the sensitivity of PCa to enzalutamide (Enz) treatment, both in vitro and in vivo. These findings indicate that the reciprocal activation between AR and ZIC5 promotes metastasis and Enz resistance of PCa and suggest the therapeutic value of cotargeting ZIC5 and AR for the treatment of advanced PCa.

opplncRNA: A MATLAB Package for Comprehensive Pathway Analysis of lncRNA-miRNA-mRNA in Humans.

The discovery of new lncRNAs (long noncoding RNAs) and their regulatory pathways has always been a hotspot in the field of ceRNA (competing endogenous RNA). Herein, we report opplncRNA (Omics Pilot Platform of lncRNA), a novel and rapid tool for investigating lncRNA-miRNA-mRNA interactions based on the architecture of MATLAB AppDesigner. opplncRNA is useful to analyze the regulatory interaction networks of lncRNA with a friendly GUI (graphical user interface). There are three lncRNA databases (ENCORI, LncBase, and miRcode) about lncRNA-miRNA interactions that have been integrated into opplncRNA, as well as seven miRNA databases (miRcode, ENCORI, TarBase, miRTarBase, miRDB, miRanda, and miRecords) about miRNA-mRNA interactions as also. opplncRNA can read expression data from any profile techniques, such as microarray or RNA-seq. Then, the relationships between lncRNA-miRNA and miRNA-mRNA can be directly calculated through the profile data of lncRNA, miRNA, and mRNA by the threshold of correlation coefficients. Integrated databases can be used to filter calculation outcomes to obtain more reliable pathways. Moreover, opplncRNA has the functionality of directly demonstrating 3 layers network from lncRNA to mRNA in command line form.

Optimal Cutoffs for the Diagnosis of Sarcopenia in Older Chinese Adults.

Background: The optimal criteria for sarcopenia in the older Chinese population have not been defined. Consequently, this study aims to determine the optimal cutoffs of grip strength, appendicular skeletal muscle index (ASMI) using bioelectrical impedance analysis (BIA), and gait speed, comprising the best definition of sarcopenia for older Chinese populations. Methods: A total of 2,821 (1,398 men and 1,423 women) community-dwelling older people (≥60 years) and 409 (205 men and 204 women) young healthy adults (25-34 years) were recruited from three big cities in China. Besides gait speed and grip strength, we examined ASMI by BIA and dual-energy X-ray absorptiometry (DXA), comprising the three components of sarcopenia. DXA classification for low ASMI, 20th percentile among older adults in the study sample, was found to be best compared with the other existing classification, 1 SD and 2 SD below the mean for the young population, and was used as the gold standard to determine the optimal cutoffs of BIA using receiver operating characteristic curves (ROC). The cutoffs of handgrip strength and gait speed were determined following the same rule. Results: Using gender-specific 20th percentiles of DXA (6.53 kg/m2 for men and 5.40 kg/m2 for women), the cutoffs 7.05 kg/m2 for men and 5.85 kg/m2 for women were determined as optimal cutoffs of BIA by achieving the largest sensitivity (0.81, 95% CI: 0.63-0.93 for men and 0.90, 95% CI: 0.73-0.98 for women) and specificity greater than 0.80 (0.80, 95% CI: 0.72-0.87 for men and 0.81, 95% CI: 0.72-0.87 for women) in the ROC analysis. The 28.5 kg and 1.05 m/s for men and 18.6 kg and 1.01 m/s for women were determined as the cutoffs for handgrip strength and gait speed, respectively. Based on the derived cutoffs, 14.2% of men and 15.7% of women in the older Chinese study population were classified as sarcopenia. Conclusion: Notably, 7.05 kg/m2, 28.5 kg, and 1.05 m/s for men and 5.85 kg/m2, 18.6 kg, and 1.01 m/s for women were selected as the optimal cutoffs for low ASMI by BIA, handgrip strength, and gait speed, respectively. These optimal cutoffs will enhance practicability for screening sarcopenia in primary care and clinical settings.

Aggregation-Induced Emission Boosting the Study of Polymer Science.

The past one hundred years have witnessed the great development of polymer science. The advancement of polymer science is closely related with the development of characterization techniques and methods, from viscometry in molecular weight determination to advanced techniques including differential scanning calorimetry, nuclear magnetic resonance, and scanning electron microscopy. However, these techniques are normally constrained to tedious sample preparation, high costs, harsh experimental conditions, or ex situ characterization. Fluorescence technology has the merits of high sensitivity and direct visualization. Contrary to conventional aggregation-causing quenching fluorophores, those dyes with aggregation-induced emission (AIE) characteristics show high emission efficiency in aggregate states. Based on the restriction of intramolecular motions for AIE properties, the AIE materials are very sensitive to the surrounding microenvironments owing to the twisted propeller-like structures and therefore offer great potential in the study of polymers. The AIE concept has been successfully used in polymer science and provides a deeper understanding on polymer structure and properties. In this review, the applications of AIEgens in polymer science for visualizing polymerization, glass transition, dissolution, crystallization, gelation, self-assembly, phase separation, cracking, and self-healing are exemplified and summarized. Lastly, the challenges and perspectives in the study of polymer science using AIEgens are addressed.

Characterization of Bimanual Cyclical Tasks From Single-Trial EEG-fNIRS Measurements.

Robot-assisted bimanual training is promising to improve motor function and cortical reorganization for hemiparetic stroke patients. Closing the rehabilitation training loop with neurofeedback can help refine training protocols in time for better engagements and outcomes. However, due to the low signal-to-noise ratio (SNR) and non-stationary properties of neural signals, reliable characterization of bimanual training-induced neural activities from single-trial measurement is challenging. In this study, ten human participants were recruited conducting robot-assisted bimanual cyclical tasks (in-phase, 90° out-of-phase, and anti-phase) when concurrent electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) were recorded. A unified EEG-fNIRS bimodal signal processing framework was proposed to characterize neural activities induced by three types of bimanual cyclical tasks. In this framework, novel artifact removal methods were used to improve the SNR and the task-related component analysis (TRCA) was introduced to increase the reproducibility of EEG-fNIRS bimodal features. The optimized features were transformed into low-dimensional indicators to reliably characterize bimanual training-induced neural activation. The SVM classification results of three bimanual cyclical tasks revealed a good discrimination ability of EEG-fNIRS bimodal indicators (90.1%), which was higher than that using EEG (74.8%) or fNIRS (82.2%) alone, supporting the proposed method as a feasible technique to characterize neural activities during robot-assisted bimanual training.