FetchEEG: a hybrid approach combining feature extraction and temporal-channel joint attention for EEG-based emotion classification.

Objective. Electroencephalogram (EEG) analysis has always been an important tool in neural engineering, and the recognition and classification of human emotions are one of the important tasks in neural engineering. EEG data, obtained from electrodes placed on the scalp, represent a valuable resource of information for brain activity analysis and emotion recognition. Feature extraction methods have shown promising results, but recent trends have shifted toward end-to-end methods based on deep learning. However, these approaches often overlook channel representations, and their complex structures pose certain challenges to model fitting.Approach. To address these challenges, this paper proposes a hybrid approach named FetchEEG that combines feature extraction and temporal-channel joint attention. Leveraging the advantages of both traditional feature extraction and deep learning, the FetchEEG adopts a multi-head self-attention mechanism to extract representations between different time moments and channels simultaneously. The joint representations are then concatenated and classified using fully-connected layers for emotion recognition. The performance of the FetchEEG is verified by comparison experiments on a self-developed dataset and two public datasets.Main results. In both subject-dependent and subject-independent experiments, the FetchEEG demonstrates better performance and stronger generalization ability than the state-of-the-art methods on all datasets. Moreover, the performance of the FetchEEG is analyzed for different sliding window sizes and overlap rates in the feature extraction module. The sensitivity of emotion recognition is investigated for three- and five-frequency-band scenarios.Significance. FetchEEG is a novel hybrid method based on EEG for emotion classification, which combines EEG feature extraction with Transformer neural networks. It has achieved state-of-the-art performance on both self-developed datasets and multiple public datasets, with significantly higher training efficiency compared to end-to-end methods, demonstrating its effectiveness and feasibility.

Formulation of arginine-loaded mesoporous silica nanoparticles (Arg@MSNs) modified orthodontic adhesive.

OBJECTIVES: The objective of this study was to synthesize arginine loaded mesoporous silica nanoparticles (Arg@MSNs), develop a novel orthodontic adhesive using Arg@MSNs as modifiers, and investigate the adhesive performance, antibacterial activity, and biocompatibility. METHODS: Arg@MSNs were synthesized by immobilizing arginine into MSNs and characterized using transmission electron microscope (TEM), dynamic light scattering (DLS), and Fourier Transform Infrared Spectrometer (FT-IR). Arg@MSNs were incorporated into Transbond XT adhesive with different mass fraction to form functional adhesives. The degree of conversion (DC), arginine release behavior, adhesive performance, antibacterial activity against Streptococcus mutans biofilm, and cytotoxicity were comprehensively evaluated. RESULTS: TEM, DLS, and FT-IR characterizations confirmed the successful preparation of Arg@MSNs. The incorporation of Arg@MSNs did not significantly affect DC and exhibited clinically acceptable bonding strength. Compared to the commercial control, the Arg@MSNs modified adhesives greatly suppressed the metabolic activity and polysaccharide production while increased the biofilm pH values. The cell counting kit (CCK)-8 test indicated no cytotoxicity. CONCLUSIONS: The novel orthodontic adhesive containing Arg@MSNs exhibited significantly enhanced antibacterial activities and inhibitory effects on acid production compared to the commercial adhesive without compromising their bonding strength or biocompatibility. CLINICAL SIGNIFICANCE: The novel orthodontic adhesive containing Arg@MSNs exhibits potential clinical benefits in preventing demineralization of enamel surfaces around or beneath orthodontic brackets due to its enhanced antibacterial activities and acid-producing inhibitory effects.

Embedding cognitive framework with self-attention for interpretable knowledge tracing.

Recently, deep neural network-based cognitive models such as deep knowledge tracing have been introduced into the field of learning analytics and educational data mining. Despite an accurate predictive performance of such models, it is challenging to interpret their behaviors and obtain an intuitive insight into latent student learning status. To address these challenges, this paper proposes a new learner modeling framework named the EAKT, which embeds a structured cognitive model into a transformer. In this way, the EAKT not only can achieve an excellent prediction result of learning outcome but also can depict students' knowledge state on a multi-dimensional knowledge component(KC) level. By performing the fine-grained analysis of the student learning process, the proposed framework provides better explanatory learner models for designing and implementing intelligent tutoring systems. The proposed EAKT is verified by experiments. The performance experiments show that the EAKT can better predict the future performance of student learning(more than 2.6% higher than the baseline method on two of three real-world datasets). The interpretability experiments demonstrate that the student knowledge state obtained by EAKT is closer to ground truth than other models, which means EAKT can more accurately trace changes in the students' knowledge state.

Effects of GLP-1 Receptor Agonists on Biological Behavior of Colorectal Cancer Cells by Regulating PI3K/AKT/mTOR Signaling Pathway.

Colorectal cancer (CRC) has become one of the top ten malignant tumors with a high incidence rate and mortality. Due to the lack of a good CRC screening program, most of the CRC patients are being transferred at the time of treatment. The conventional treatment cannot effectively improve the prognosis of CRC patients, and the target drugs can significantly prolong the overall survival of patients in the advanced stage. However, the use of single drug may lead to acquired drug resistance and various serious complications. Therefore, combined targeted drug therapy is the main alternative treatment with poor effect of single targeted drug therapy, which has important research significance for the treatment of CRC. Therefore, this study intends to culture CRC cell lines in vitro at the cell level and intervene with the GLP-1 receptor agonist liraglutide. The effects of liraglutide on the PI3K/Akt/mTOR signal pathway and CRC cell proliferation, cycle, migration, invasion, and apoptosis are explored by detecting cell proliferation, cycle, migration, invasion, and apoptosis and the expression of related mRNA and protein. The results showed that liraglutide, a GLP-1 receptor agonist, could block the CRC cell cycle, reduce cell proliferation, migration, and invasion and promote apoptosis by inhibiting the PI3K/Akt/mTOR signal pathway.

HELP-DKT: an interpretable cognitive model of how students learn programming based on deep knowledge tracing.

Student cognitive models are playing an essential role in intelligent online tutoring for programming courses. These models capture students' learning interactions and store them in the form of a set of binary responses, thereby failing to utilize rich educational information in the learning process. Moreover, the recent development of these models has been focused on improving the prediction performance and tended to adopt deep neural networks in building the end-to-end prediction frameworks. Although this approach can provide an improved prediction performance, it may also cause difficulties in interpreting the student's learning status, which is crucial for providing personalized educational feedback. To address this problem, this paper provides an interpretable cognitive model named HELP-DKT, which can infer how students learn programming based on deep knowledge tracing. HELP-DKT has two major advantages. First, it implements a feature-rich input layer, where the raw codes of students are encoded to vector representations, and the error classifications as concept indicators are incorporated. Second, it can infer meaningful estimation of student abilities while reliably predicting future performance. The experiments confirm that HELP-DKT can achieve good prediction performance and present reasonable interpretability of student skills improvement. In practice, HELP-DKT can personalize the learning experience of novice learners.

A Novel Approach for Acoustic Signal Processing of a Drum Shearer Based on Improved Variational Mode Decomposition and Cluster Analysis.

During operation, the acoustic signal of the drum shearer contains a wealth of information. The monitoring or diagnosis system based on acoustic signal has obvious advantages. However, the signal is challenging to extract and recognize. Therefore, this paper proposes an approach for acoustic signal processing of a shearer based on the parameter optimized variational mode decomposition (VMD) method and a clustering algorithm. First, the particle swarm optimization (PSO) algorithm searched for the best parameter combination of the VMD. According to the results, the approach determined the number of modes and penalty parameters for VMD. Then the improved VMD algorithm decomposed the acoustic signal. It selected the ideal component through the minimum envelope entropy. The PSO was designed to optimize the clustering analysis, and the minimum envelope entropy of the acoustic signal was regarded as the feature for classification. We then use a shearer simulation platform to collect the acoustic signal and use the approach proposed in this paper to process and classify the signal. The experimental results show that the approach proposed can effectively extract the features of the acoustic signal of the shearer. The recognition accuracy of the acoustic signal was high, which has practical application value.

Antioxidative Effects and Mechanism Study of Bioactive Peptides from Defatted Walnut ( Juglans regia L.) Meal Hydrolysate.

The peptide components of defatted walnut ( Juglans regia L.) meal hydrolysate (DWMH) remain unclear, hindering the investigation of biological mechanisms and exploitation of bioactive peptides. The present study aims to identify the peptide composition of DWMH, followed by to evaluate in vitro antioxidant effects of selected peptides and investigate mechanisms of antioxidative effect. First, more than 1 000 peptides were identified by de novo sequencing in DWMH. Subsequently, a scoring method was established to select promising bioactive peptides by structure based screening. Eight brand new peptides were selected due to their highest scores in two different batches of DWMH. All of them showed potent in vitro antioxidant effects on H2O2-injured nerve cells. Four of them even possessed significantly stronger effects than DWMH, making the selected bioactive peptides useful for further research as new bioactive entities. Two mechanisms of hydroxyl radical scavenging and ROS reduction were involved in their antioxidative effects at different degrees. The results showed peptides possessing similar capacity of hydroxyl radical scavenging or ROS reduction may have significantly different in vitro antioxidative effects. Therefore, comprehensive consideration of different antioxidative mechanisms were suggested in selecting antioxidative peptides from DWMH.

A pilot and comparative study between pathological and serological levels of immunoglobulin and complement among three kinds of primary glomerulonephritis.

BACKGROUND: Immunoglobulin A nephropathy (IgAN), membranous nephropathy (MN) and minimal-change disease (MCD) are three common types of glomerulonephritis in China. Pathological diagnosis based on renal biopsy is the criterion and the golden standard for diagnosing the sub-types of primary or secondary glomerulonephritis. Immunoglobulin and complements might be used in the differential diagnosis of glomerulonephritis without renal biopsies. However, the relationship between IF intensities of immune proteins and the corresponding serum levels remained unclear, and seldom studies combine histopathological examination results and blood tests together for a predictive purpose. This study was considered as a pilot study for integrating histopathological indicators into serum parameters for exploring the relationship of IF intensity and serum values of immunoglobulin and complement, and for screening and investigating effective indicators inIgAN, MN and MCD. METHODS: Renal tissue immunofluorescence (IF) intensity grades and serum levels of immunoglobulin and complements (IgG, IgA, IgM, C3 and C4) were retrospectively analyzed in 236 cases with IgAN, MN or MCD. IF grades were grouped as negative (-), positive (+) or strong positive (++) with both high and low magnification of microscope. Other serum indicators such as urea nitrogen (BUN), creatinine (Crea) and estimated glomerular filtration rate (eGFR) were also evaluated among the groups. RESULTS: There were difference in IgA, IgG and C3 IF intensity grades among IgAN, MN and MCD groups (p = 9.82E-43, 4.60E-39, 7.45E-15, respectively). Serum values of BUN, Crea, eGFR, IgG, IgA, IgM and C4 showed difference in three groups (BUN: p = 0.045, Crea: p = 3.45E-5, eGFR: p = 0.005, IgG: p = 1.68E-14, IgA: p = 9.14E-9, IgM: p = 0.014, C4: p = 0.026). eGFR had the trend to decrease with enhanced IgA IF positive grades (p = 8.99E-4); Crea had trends to decrease with both enhanced IgA and IgG IF intensity grades (p = 2.06E-6, 2.94E-5, respectively). In all subjects, serum IgA levels was inversely correlated with eGFR(r = - 0.216, p = 0.001) and correlated with Crea levels(r = 0.189, p = 0.004); serum IgG and Crea showed no correlation which were discordance with inverse correlation of IgG IF grade and Crea(r = 0.058,p = 0.379). IgG serum level was inverse correlated with its IF grades (p = 3.54E-5, p = 7.08E-6, respectively); C3 serum levels had significantly difference between Neg and positive (+) group (p = 0.0003). IgA serum level was positive correlated with its IF grades (Neg-(+): p = 0.0001; (+)-(++): p = 0.022; Neg-(++): p = 2.01E-10). After matching comparison among C3 groups, C3 Neg. group and C3 ++ group had difference (*p = 0.017). C4 had all negative IF expression in all pathological groups. In IgAN subjects, there were statistical differences of serum C3 levels between its pathological Neg and positive (+) group(p = 0.026), and serum IgA levels showed difference between IgA pathological positive(+) and (++)(p = 0.007). In MN subjects, sIgG levels showed difference between IgG pathological IF grade positive (+) and (++)(p = 0.044); serum C3 levels showed difference between C3 pathological IF grade Neg and positive(+)(p = 0.005); and serum IgA levels showed difference between Neg and positive(+)(p = 0.040). In IgAN, eGFR showed serum IgA levels had significant differences among groups (p = 0.007) and had increasing trend with enhanced its IF grades(Ptrend = 0.016). There were also difference between IgG group Neg and positive (+) (p = 0.005, Ptrend = 0.007) in IgAN. In MN, serum IgG levels had significant differences among IF groups (p = 0.034) and had decreasing trend with its enhanced IF grades (Ptrend = 0.014). Serum C3 concentrations also were found distinctive among IF groups (p = 0.016) and had in inverse correlation with its enhanced IF grades (Ptrend = 0.004). DISCUSSION: Our research cross contrasts several immunoprotein IF intensities and relevant serum levels in three kinds of primary glomerular nephritis, and finally acquired helpful results for understanding the relationships between pathological presentation and serological presentation of immunoproteins in kidney diseases. Furthermore, this pilot study is offering a possible method for the analysis of combination of pathology and serology. CONCLUSION: Different pathological types of nephritis presented different expression patterns of immunoglobulin and complement, especially IgA and IgG, which suggested different pathogenesis involved in the development of IgAN and MN. Furthermore, either in tissue or in serum, increased IgA level was closely related with renal function in all of the patients.