Discrepant changes in structure-function coupling in dancers and musicians.

Dance and music are well known to improve sensorimotor skills and cognitive functions. To reveal the underlying mechanism, previous studies focus on the brain plastic structural and functional effects of dance and music training. However, the discrepancy training effects on brain structure-function relationship are still blurred. Thus, proficient dancers, musicians, and controls were recruited in this study. The graph signal processing framework was employed to quantify the region-level and network-level relationship between brain function and structure. The results showed the increased coupling strength of the right ventromedial putamen in the dance and music groups. Distinctly, enhanced coupling strength of the ventral attention network, increased coupling strength of the right inferior frontal gyrus opercular area, and increased function connectivity of coupling function signal between the right and left middle frontal gyrus were only found in the dance group. Besides, the dance group indicated enhanced coupling function connectivity between the left inferior parietal lobule caudal area and the left superior parietal lobule intraparietal area compared with the music groups. The results might illustrate dance and music training's discrepant effect on the structure-function relationship of the subcortical and cortical attention networks. Furthermore, dance training seemed to have a greater impact on these networks.

The role of the primary sensorimotor system in generalized epilepsy: Evidence from the cerebello-cerebral functional integration.

The interaction between cerebellum and cerebrum participates widely in function from motor processing to high-level cognitive and affective processing. Because of the motor symptom, idiopathic generalized epilepsy (IGE) patients with generalized tonic-clonic seizure have been recognized to associate with motor abnormalities, but the functional interaction in the cerebello-cerebral circuit is still poorly understood. Resting-state functional magnetic resonance imaging data were collected for 101 IGE patients and 106 healthy controls. The voxel-based functional connectivity (FC) between cerebral cortex and the cerebellum was contacted. The functional gradient and independent components analysis were applied to evaluate cerebello-cerebral functional integration on the voxel-based FC. Cerebellar motor components were further linked to cerebellar gradient. Results revealed cerebellar motor functional modules were closely related to cerebral motor components. The altered mapping of cerebral motor components to cerebellum was observed in motor module in patients with IGE. In addition, patients also showed compression in cerebello-cerebral functional gradient between motor and cognition modules. Interestingly, the contribution of the motor components to the gradient was unbalanced between bilateral primary sensorimotor components in patients: the increase was observed in cerebellar cognitive module for the dominant hemisphere primary sensorimotor, but the decrease was found in the cerebellar cognitive module for the nondominant hemisphere primary sensorimotor. The present findings suggest that the cerebral primary motor system affects the hierarchical architecture of cerebellum, and substantially contributes to the functional integration evidence to understand the motor functional abnormality in IGE patients.

Role of dynamic contrast-enhanced MRI in predicting severe acute radiation-induced rectal injury in patients with rectal cancer.

OBJECTIVES: To explore the potential of dynamic contrast-enhanced MRI (DCE-MRI) quantitative parameters in predicting severe acute radiation-induced rectal injury (RRI) in rectal cancer. METHODS: This retrospective study enrolled 49 patients with rectal cancer who underwent neoadjuvant chemoradiotherapy and rectal MRI including a DCE-MRI sequence from November 2014 to March 2021. Two radiologists independently measured DCE-MRI quantitative parameters, including the forward volume transfer constant (Ktrans), rate constant (kep), fractional extravascular extracellular space volume (ve), and the thickness of the rectal wall farthest away from the tumor. These parameters were compared between mild and severe acute RRI groups based on histopathological assessment. Receiver operating characteristic curve analysis was performed to analyze statistically significant parameters. RESULTS: Forty-nine patients (mean age, 54 years ± 12 [standard deviation]; 37 men) were enrolled, including 25 patients with severe acute RRI. Ktrans was lower in severe acute RRI group than mild acute RRI group (0.032 min-1 vs 0.054 min-1; p = 0.008), but difference of other parameters (kep, ve and rectal wall thickness) was not significant between these two groups (all p > 0.05). The area under the receiver operating characteristic curve of Ktrans was 0.72 (95% confidence interval: 0.57, 0.84). With a Ktrans cutoff value of 0.047 min-1, the sensitivity and specificity for severe acute RRI prediction were 80% and 54%, respectively. CONCLUSION: Ktrans demonstrated moderate diagnostic performance in predicting severe acute RRI. CLINICAL RELEVANCE STATEMENT: Dynamic contrast-enhanced MRI can provide non-invasive and objective evidence for perioperative management and treatment strategies in rectal cancer patients with acute radiation-induced rectal injury. KEY POINTS: • To our knowledge, this study is the first to evaluate the predictive value of contrast-enhanced MRI (DCE-MRI) quantitative parameters for severe acute radiation-induced rectal injury (RRI) in patients with rectal cancer. • Forward volume transfer constant (Ktrans), derived from DCE-MRI, exhibited moderate diagnostic performance (AUC = 0.72) in predicting severe acute RRI of rectal cancer, with a sensitivity of 80% and specificity of 54%. • DCE-MRI is a promising imaging marker for distinguishing the severity of acute RRI in patients with rectal cancer.

Investigating Emotion EEG Patterns for Depression Detection with Attentive Simple Graph Convolutional Network.

Depression severely limits daily functioning, diminishes quality of life and possibly leads to self-harm and suicide. Noninvasive electroencephalography (EEG) has been shown effective as biomarkers for objective depression diagnose and treatment response prediction, and dry EEG electrodes further extend its availability for clinical use. Even though many efforts have been made to identify depression biomarkers, searching reliable EEG biomarkers for depression detection remains challenging. This work presents a systematic investigation of capabilities of emotion EEG patterns for depression detection using a dry EEG electrode system. We design an emotion elicitation paradigm with happy, neutral and sad emotions and collect EEG signals during film watching from 33 depressed patients and 40 healthy controls. The mean activation levels at frontal and temporal sites in the alpha, beta and gamma bands of the depressed group are different to those of the healthy group, indicating the impacts of depressive symptoms on the emotion experiences. To leverage the topology information among EEG channels for emotion recognition and depression detection, an Attentive Simple Graph Convolutional network is built. The deep depression-health classifier achieves a sensitivity of 81.93% and a specificity of 91.69% on the happy emotions, suggesting the promising use of the emotion neural patterns for distinguishing the depressed patients from the healthy controls.

Objective Depression Detection Using EEG and Eye Movement Signals Induced by Oil Paintings.

Depression is a mental disorder characterized by persistent sadness and loss of interest, which has become one of the leading causes of disability worldwide. There are currently no objective diagnostic standards for depression in clinical practice. Previous studies have shown that depression causes both brain abnormalities and behavioral disorders. In this study, both electroencephalography (EEG) and eye movement signals were used to objectively detect depression. By presenting 40 carefully selected oil paintings-20 positive and 20 negative-as stimuli, we were able to successfully evoke emotions in 48 depressed patients (DPs) and 40 healthy controls (HCs) from three centers. We then used Transformer, a deep learning model, to conduct emotion recognition and depression detection. The experimental results demonstrate that: a) Transformer achieves the best accuracies of 89.21% and 92.19% in emotion recognition and depression detection, respectively; b) The HC group has higher accuracies than the DP group in emotion recognition for both subject-dependent and subject-independent experiments; c) The neural pattern differences do exist between DPs and HCs, and we find the consistent asymmetry of the neural patterns in DPs; d) For depression detection, using single oil painting achieves the best accuracies, and using negative oil paintings has higher accuracies than using positive oil paintings. These findings suggest that EEG and eye movement signals induced by oil paintings can be used to objectively identify depression.

Tagging Continuous Labels for EEG-based Emotion Classification.

EEG-based emotion classification has long been a critical task in the field of affective brain-computer interface (aBCI). The majority of leading researches construct supervised learning models based on labeled datasets. Several datasets have been released, including different kinds of emotions while utilizing various forms of stimulus materials. However, they adopt discrete labeling methods, in which the EEG data collected during the same stimulus material are given a same label. These methods neglect the fact that emotion changes continuously, and mislabeled data possibly exist. The imprecision of discrete labels may hinder the progress of emotion classification in concerned works. Therefore, we develop an efficient system in this paper to support continuous labeling by giving each sample a unique label, and construct a continuously labeled EEG emotion dataset. Using our dataset with continuous labels, we demonstrate the superiority of continuous labeling in emotion classification through experiments on several classification models. We further utilize the continuous labels to identify the EEG features under induced and non-induced emotions in both our dataset and a public dataset. Our experimental results reveal the learnability and generality of the relation between the EEG features and their continuous labels.

Deep Depression Detection with Resting-State and Cognitive-Task EEG.

Depression is a common mental disorder that negatively affects physical health and personal, social and occupational functioning. Currently, accurate and objective diagnosis of depression remains challenging, and electroencephalography (EEG) provides promising clinical practice or home use due to considerable performance and low cost. This work investigates the capabilities of deep neural networks with EEG-based neural patterns from both resting states and cognitive tasks for depression detection. We collect EEG signals from 33 depressed patients and 40 healthy controls using wearable dry electrodes and build Attentive Simple Graph Convolutional network and Transformer neural network for objective depression detection. Four experiment stages, including two resting states and two cognitive tasks, are designed to characterize the alteration of relevant neural patterns in the depressed patients, in terms of decreased energy and impaired performance in sustained attention and response inhibition. The Transformer model achieves an AUC of 0.94 on the Continuous Performance Test-Identical Pairs version (sensitivity: 0.87, specificity: 0.91) and the Stroop Color Word Test (sensitivity: 0.93, specificity: 0.88), and an AUC of 0.89 on the two resting states (sensitivity: 0.85 and 0.87, specificity: 0.88 and 0.90, respectively), indicating the potential of EEG-based neural patterns in identifying depression. These findings provide new insights into the research of depression mechanisms and EEG-based depression biomarkers.

MRI-detected tumor deposits in cT3 and cT4 rectal cancer following neoadjuvant chemoradiotherapy.

OBJECTIVES: To evaluate the identification of tumor deposits (TDs) and the prognostic significance of an MRI tumor regression grade for TDs in patients with rectal cancer treated with neoadjuvant chemoradiotherapy (nCRT). METHODS: Ninety-one patients with cT3 or cT4 rectal cancer who underwent surgery following nCRT between August 2014 and June 2020 were retrospectively analyzed. Changes in pre-nCRT MRI-detected TDs (mrTDs) were described as mrTD regression grade. The diagnostic performance of post-nCRT MRI-detected TDs (ymrTDs) was compared with histopathological reference standard. The correlation between ymrTDs, mrTD regression grade, and disease-free survival (DFS) was assessed. RESULTS: The sensitivity and specificity of ymrTDs were 88.00% and 89.39%, respectively. The area under the receiver operating characteristic curve was 0.887 (95% confidence interval [CI]: 0.803-0.944). The 3-year DFS of patients with positive ymrTDs was significantly lower than of the negative group (44.83% vs 82.73%, p < 0.001). The 3-year DFS was 33.33% for patients with poor regression of mrTDs following nCRT and 55.56% for those with moderate regression, compared to 69.23% in good responders and 83.97% in patients without mrTDs (p < 0.001). On multivariable Cox regression, mrTD regression grade was the only independent MRI factor associated with DFS (p = 0.042). CONCLUSIONS: Diagnostic performance of ymrTDs was moderate. The mrTD regression grade was independently correlated with DFS, which may have a prognostic implication for treatment and follow-up. CLINICAL RELEVANCE STATEMENT: Patients with poor regression of MRI-detected tumor deposits may benefit from more aggressive treatments, such as chemoradiation therapy plus induction or consolidation chemotherapy. KEY POINTS: • MRI provides a preoperative and noninvasive way to visualize tumor deposits (TDs) after neoadjuvant chemoradiotherapy (nCRT). • Post-nCRT MRI-detected TDs are a poor prognostic marker in cT3 and cT4 rectal cancer patients. • The regression of MRI-detected TDs after nCRT is associated with an improved disease-free survival.

Identifying sex differences in EEG-based emotion recognition using graph convolutional network with attention mechanism.

OBJECTIVE: Sex differences in emotions have been widely perceived via self-reports, peripheral physiological signals and brain imaging techniques. However, how sex differences are reflected in the EEG neural patterns of emotions remains unresolved. In this paper, we detect sex differences in emotional EEG patterns, investigate the consistency of such differences in various emotion datasets across cultures, and study how sex as a factor affects the performance of EEG-based emotion recognition models. APPROACH: We thoroughly assess sex differences in emotional EEG patterns on five public datasets, including SEED, SEED-IV, SEED-V, DEAP and DREAMER, systematically examine the sex-specific EEG patterns for happy, sad, fearful, disgusted and neutral emotions, and implement deep learning models for sex-specific emotion recognition. MAIN RESULTS: (1) Sex differences exist in various emotion types and both Western and Eastern cultures; (2) The emotion patterns of females are more stable than those of males, and the patterns of happiness from females are in sharp contrast with the patterns of sadness, fear and disgust, while the energy levels are more balanced for males; (3) The key features for emotion recognition are mainly located at the frontal and temporal sites for females and distributed more evenly over the whole brain for males, and (4) The same-sex emotion recognition models outperform the corresponding cross-sex models. SIGNIFICANCE: These findings extend efforts to characterize sex differences in emotional brain activation, provide new physiological evidence for sex-specific emotion processing, and reinforce the message that sex differences should be carefully considered in affective research and precision medicine.

ERK/MAPK Signalling Pathway Regulates MMP2 Through ETS1 in Renal Clear Cell Carcinoma.

BACKGROUND: The c-ETS-1 (ETS1) expression is high in clear cell renal cell carcinoma (ccRCC) tissues; however, how it impacts ccRCC is currently unknown. METHOD: The online STRING web source was used to construct a protein network interacting with ETS1. The Cell Counting Kit-8 was used to detect the cell viability. A clonogenic assay, a wound-healing assay, and a Transwell assay were used to detect cell proliferation, invasion and migration abilities. Western blot was used to detect the expression of proteins. RESULT: The data showed the expression of ETS1 in ccRCC tissues to be significantly increased compared to adjacent tissues (p<0.05). The positive expression of ETS1 in ccRCC patients aged 20-100 was statistically significant compared to adjacent normal tissues (p<0.05). The grade of ETS1 positive expression (1-4) and lymph node metastasis (N1) in ccRCC were significantly higher than those in adjacent normal tissues (p<0.05). The tumour stage (stages 1-4) in ccRCC patients with positive ETS1 expression was significantly higher than that in adjacent normal tissues (p<0.05). Knockdown of ETS1 and PERK inhibitors significantly inhibited the proliferation, migration and invasion of ccRCC cells. Knockdown of ETS1 inhibited MMP-2 expression, and an extracellular signal-related kinase (ERK) inhibitor inhibited both ETS1 and MMP-2 expression. CONCLUSION: A high expression of ETS1 is associated with the progression of ccRCC. This study suggests that ETS1 promotes proliferation by increasing MMP2 expression in ccRCC, and combined knockdown of ETS1 and inhibition of ERK can significantly inhibit the proliferation, migration and invasion of ccRCC. ETS1 may be a therapeutic and prognostic target for renal cell carcinoma.

Crop-Weed Introgression Plays Critical Roles in Genetic Differentiation and Diversity of Weedy Rice: A Case Study of Human-Influenced Weed Evolution.

As an important driving force, introgression plays an essential role in shaping the evolution of plant species. However, knowledge concerning how introgression affects plant evolution in agroecosystems with strong human influences is still limited. To generate such knowledge, we used InDel (insertion/deletion) molecular fingerprints to determine the level of introgression from japonica rice cultivars into the indica type of weedy rice. We also analyzed the impact of crop-to-weed introgression on the genetic differentiation and diversity of weedy rice, using InDel (insertion/deletion) and SSR (simple sequence repeat) molecular fingerprints. Results based on the STRUCTURE analysis indicated an evident admixture of some weedy rice samples with indica and japonica components, suggesting different levels of introgression from japonica rice cultivars to the indica type of weedy rice. The principal coordinate analyses indicated indica-japonica genetic differentiation among weedy rice samples, which was positively correlated with the introgression of japonica-specific alleles from the rice cultivars. In addition, increased crop-to-weed introgression formed a parabola pattern of dynamic genetic diversity in weedy rice. Our findings based on this case study provide evidence that human activities, such as the frequent change in crop varieties, can strongly influence weed evolution by altering genetic differentiation and genetic diversity through crop-weed introgression in agroecosystems.

[Spatio-temporal Characteristics of Organic Aggregates and the Driving Factors in Typical Lakes].

Organic aggregates (OA) are the important circulation hub of matter and energy in aquatic ecosystems. However, the comparison studies on OA in lakes with different nutrient levels are limited. In this study, spatio-temporal abundances of OA and OA-attached bacteria (OAB) in oligotrophic Lake Fuxian, mesotrophic Lake Tianmu, middle-eutrophic Lake Taihu, and hyper-eutrophic Lake Xingyun were investigated in different seasons during 2019-2021 using a scanning electron microscope, epi-fluorescence microscope, and flow cytometry. The results showed that:① the annual average abundances of OA in Lake Fuxian, Lake Tianmu, Lake Taihu, and Lake Xingyun were 1.4×104, 7.0×104, 27.7×104, and 16.0×104 ind·mL-1, whereas the annual average abundances of OAB in the four lakes were 0.3×106, 1.9×106, 4.9×106, and 6.2×106 cells·mL-1. The ratios of OAB:total bacteria (TB) in the four lakes were 30%, 31%, 50%, and 38%, respectively. ② OA abundance in summer was significantly higher than that in autumn and winter; however, the ratio of OAB:TB in summer was approximately 26%, which was significantly lower than that in the other three seasons. ③ Lake nutrient status was the most important environmental factor that affected the abundance variations of OA and OAB, accounting for 50% and 68% of the spatio-temporal variations in OA and OAB abundances. ④ Nutrient and organic matters were enriched in OA, especially in Lake Xingyun; the proportions of particle phosphorous, particle nitrogen, and organic matters in this lake were as high as 69%, 59%, and 79%, respectively. Under the circumstance of future climate change and the expansion of lake algal blooms, the effects of algal-originated OA in the degradation of organic matters and nutrient recycling would be increased.

Reduced Carbon Dioxide by Overexpressing EPSPS Transgene in Arabidopsis and Rice: Implications in Carbon Neutrality through Genetically Engineered Plants.

With the increasing challenges of climate change caused by global warming, the effective reduction of carbon dioxide (CO2) becomes an urgent environmental issue for the sustainable development of human society. Previous reports indicated increased biomass in genetically engineered (GE) Arabidopsis and rice overexpressing the 5-enolpyruvylshikimate-3-phosphate synthase (EPSPS) gene, suggesting the possibility of consuming more carbon by GE plants. However, whether overexpressing the EPSPS gene in GE plants consumes more CO2 remains a question. To address this question, we measured expression of the EPSPS gene, intercellular CO2 concentration, photosynthetic ratios, and gene expression (RNA-seq and RT-qPCR) in GE (overexpression) and non-GE (normal expression) Arabidopsis and rice plants. Results showed substantially increased EPSPS expression accompanied with CO2 consumption in the GE Arabidopsis and rice plants. Furthermore, overexpressing the EPSPS gene affected carbon-fixation related biological pathways. We also confirmed significant upregulation of four key carbon-fixation associated genes, in addition to increased photosynthetic ratios, in all GE plants. Our finding of significantly enhanced carbon fixation in GE plants overexpressing the EPSPS transgene provides a novel strategy to reduce global CO2 for carbon neutrality by genetic engineering of plant species, in addition to increased plant production by enhanced photosynthesis.

Reducing Seed Shattering in Weedy Rice by Editing SH4 and qSH1 Genes: Implications in Environmental Biosafety and Weed Control through Transgene Mitigation.

Mitigating the function of acquired transgenes in crop wild/weedy relatives can provide an ideal strategy to reduce the possible undesired environmental impacts of pollen-mediated transgene flow from genetically engineered (GE) crops. To explore a transgene mitigation system in rice, we edited the seed-shattering genes, SH4 and qSH1, using a weedy rice line ("C9") that originally had strong seed shattering. We also analyzed seed size-related traits, the total genomic transcriptomic data, and RT-qPCR expression of the SH4 or qSH1 gene-edited and SH4/qSH1 gene-edited weedy rice lines. Substantially reduced seed shattering was observed in all gene-edited weedy rice lines. The single gene-edited weedy rice lines, either the SH4 or qSH1 gene, did not show a consistent reduction in their seed size-related traits. In addition, reduced seed shattering was closely linked with the weakness and absence of abscission layers and reduced abscisic acid (ABA). Additionally, the genes closely associated with ABA biosynthesis and signaling transduction, as well as cell-wall hydrolysis, were downregulated in all gene-edited weedy rice lines. These findings facilitate our deep insights into the underlying mechanisms of reduced seed shattering in plants in the rice genus Oryza. In addition, such a mitigating technology also has practical applications for reducing the potential adverse environmental impacts caused by transgene flow and for managing the infestation of weedy rice by acquiring the mitigator from GE rice cultivars through natural gene flow.

Quantitative assessment of the microstructure of the mesorectum with different prognostic statuses by intravoxel incoherent motion diffusion-weighed magnetic resonance imaging.

BACKGROUND: The mesorectum surrounding the rectum provides an ideal substrate for tumour spread. However, preoperative risk assessment is still an issue. This study aimed to investigate the microstructural features of mesorectum with different prognostic statuses by intravoxel incoherent motion diffusion-weighted imaging (IVIM DWI). METHODS: Patients with pathologically proven rectal adenocarcinoma underwent routine high-resolution rectal magnetic resonance imaging (MRI) and IVIM DWI sequences were acquired. The MRI-detected circumferential resection margin (mrCRM) and extramural vascular invasion (mrEMVI) were evaluated. IVIM parameters of the mesorectum adjacent to (MAT) and distant from (MDT) the tumour were measured and compared between and within the prognostic factor groups. RESULTS: The positive mrCRM (pMAT < 0.001; pMDT = 0.013) and mrEMVI (pMAT = 0.001; pMDT < 0.001) groups demonstrated higher D values in the MAT and MDT than the corresponding negative groups. Conversely, the positive mrCRM (p = 0.001) and mrEMVI (p < 0.001) groups both demonstrated lower f values in the MAT. Similarly, in the self-comparison between the MAT and MDT in the above subgroups, D showed a significant difference in all subgroups (p < 0.001 for all), and f showed a significant difference in the positive mrCRM (p = 0.001) and mrEMVI (p = 0.002) groups. Moreover, the MAT displayed a higher D* in the positive mrCRM (p = 0.014), negative mrCRM (p = 0.009) and negative mrEMVI groups (p < 0.001). CONCLUSION: The microstructure of the mesorectum in patients with rectal cancer with poor prognostic status shows changes based on IVIM parameters. IVIM parameters might be promising imaging biomarkers for risk assessment of tumour spread in mesorectum preoperatively.

Multimodal Emotion Recognition in Response to Oil Paintings.

Most previous affective studies use facial expression pictures, music or movie clips as emotional stimuli, which are either too simplified without contexts or too dynamic for emotion annotations. In this work, we evaluate the effectiveness of oil paintings as stimuli. We develop an emotion stimuli dataset with 114 oil paintings selected from subject ratings to evoke three emotional states (i.e., negative, neutral and positive), and acquire both EEG and eye tracking data from 20 subjects while watching the oil paintings. Furthermore, we propose a novel affective model for multimodal emotion recognition by 1) extracting informative features of EEG signals from both the time domain and the frequency domain, 2) exploring topological information embedded in EEG channels with graph neural networks (GNNs), and 3) combining EEG and eye tracking data with a deep autoencoder neural network. From the exper-iments, our model obtains an averaged classification accuracy of 94.72 % ± 1.47 %, which demonstrates the feasibility of using oil paintings as emotion elicitation material.

Exploring Sex Differences in Key Frequency Bands and Channel Connections for EEG-based Emotion Recognition.

Previous studies have demonstrated the existence of sex differences in emotion recognition by comparing the performance of same-sex and cross-sex training strategies. However, the EEG properties behind the sex differences have not been fully explored. To fill this research gap, we aim to investigate the sex differences in key frequency bands and channel connections of EEG signals. The single-modality attentive simple graph convolutional network (ASGC) is applied to three datasets SEED, SEED-IV and SEED-V under subject-dependence conditions. The classification rates are 90.86 ±4.84%, 83.14 ± 8.84% and 78.33±7.83%, respectively. The adjacency matrices learned by ASGC indicate that females and males have similar channel-connection patterns, but the degree of importance of channel connections varies by sex. Additionally, by comparing the classification results of 5 frequency bands, we find that males and females represent similar frequency band characteristics, i.e., high-frequency bands achieve better performance, indicating that these frequency bands are more related to emotion processing. Finally, we conduct the cross-subject experiment using ASGC and find that the same-sex strategy outperforms the cross-sex strategy, which is consistent with previous studies. The results also imply that males may be more suitable for sex generalization. However, this finding needs the support of more samples and advanced algorithms.

A Cross-modality Deep Learning Method for Measuring Decision Confidence from Eye Movement Signals.

Electroencephalography (EEG) signals can effectively measure the level of human decision confidence. However, it is difficult to acquire EEG signals in practice due to the ex-pensive cost and complex operation, while eye movement signals are much easier to acquire and process. To tackle this problem, we propose a cross-modality deep learning method based on deep canoncial correlation analysis (CDCCA) to transform each modality separately and coordinate different modalities into a hyperspace by using specific canonical correlation analysis constraints. In our proposed method, only eye movement signals are used as inputs in the test phase and the knowledge from EEG signals is learned in the training stage. Experimental results on two human decision confidence datasets demonstrate that our proposed method achieves advanced performance compared with the existing single-modal approaches trained and tested on eye movement signals and maintains a competitive accuracy in comparison with multimodal models.

Combined Administration of Curcumin and Chondroitin Sulfate Alleviates Cartilage Injury and Inflammation via NF-κB Pathway in Knee Osteoarthritis Rats.

Objective: Osteoarthritis (OA) is a degenerative chronic disease that most often occurs in the knee joint. Studies have shown that some food supplements, such as curcumin and chondroitin sulfate, are effective in treating knee osteoarthritis (KOA) by exhibiting different protective effects. In this study, we further investigated the combined therapeutic effects of curcumin and chondroitin sulfate on cartilage injury in rats with arthritis. Methods: An experimental KOA model was induced by monosodium iodoacetate (MIA) in rats. All rats were randomly divided into five groups: Ctrl (control), model (saline), Cur (20 mg/kg curcumin in saline), CS (100 mg/kg chondroitin sulfate in saline), and CA (20 mg/kg curcumin and 100 mg/kg chondroitin sulfate in saline); drugs were given 2 weeks after MIA injection. The histomorphological changes of cartilage were observed by safranin fast green staining, H&E staining, and micro-CT scanning. Also, the levels of PGE2, TNF-α and IL-1ß in the arthral fluid and serum were determined by the ELISA kits. The activities of SOD, CAT, COMP, MMP-3, and type II collagen were detected by biochemical kits. The expressions of TLR4, p-NF-κB, NF-κB, and COX-2 in cartilage were detected by Western blot. Results: Data show that serum levels of IL-1ß (p < 0.05), SOD (p < 0.0001), and MMP-3 (p < 0.001) were downregulated significantly in the CA group when compared to those in the model group. Meanwhile, obvious repair of cartilage with higher contains collagen II (p < 0.0001) could be observed in the CA group than the ones in Cur or CS group. In addition, significant downregulation of the expression of p-p65/p65 (p < 0.05) was found in the CA group. Conclusion: Our findings showed that combined administration of curcumin and chondroitin sulfate could exert better repair for KOA in rat models. This may hold great promise for discovering potential drugs to treat KOA and may improve treatment options for it.