Emotion recognition of EEG signals based on contrastive learning graph convolutional model.

Electroencephalogram (EEG) signals offer invaluable insights into the complexities of emotion generation within the brain. Yet, the variability in EEG signals across individuals presents a formidable obstacle for empirical implementations. Our research addresses these challenges innovatively, focusing on the commonalities within distinct subjects' EEG data.

EEG-based emotion recognition using graph convolutional neural network with dual attention mechanism.

EEG-based emotion recognition is becoming crucial in brain-computer interfaces (BCI). Currently, most researches focus on improving accuracy, while neglecting further research on the interpretability of models, we are committed to analyzing the impact of different brain regions and signal frequency bands on emotion generation based on graph structure. Therefore, this paper proposes a method named Dual Attention Mechanism Graph Convolutional Neural Network (DAMGCN).

Joint extraction model of entity relations based on decomposition strategy.

Named entity recognition and relation extraction are two important fundamental tasks in natural language processing. The joint entity-relationship extraction model based on parameter sharing can effectively reduce the impact of cascading errors on model performance by performing joint learning of entities and relationships in a single model, but it still cannot essentially get rid of the influence of pipeline models and suffers from entity information redundancy and inability to recognize overlapping entities. To this end, we propose a joint extraction model based on the decomposition strategy of pointer mechanism is proposed.

Exosome and lipid metabolism-related genes in pancreatic adenocarcinoma: a prognosis analysis.

Objective: The purpose of the study was to investigate the role of exosome and lipid metabolism-related genes (EALMRGs) mRNA levels in the diagnosis and prognosis of Pancreatic Adenocarcinoma (PAAD).

Methods: The mRNA expression pattern of PAAD and pan-cancers with prognostic data were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. EALMRGs were acquired from GeneCards and MSigDB database after merging and deduplication.

SGN-B7H4V, an investigational vedotin ADC directed to the immune checkpoint ligand B7-H4, shows promising activity in preclinical models.

Background: SGN-B7H4V is a novel investigational vedotin antibody-drug conjugate (ADC) comprising a B7-H4-directed human monoclonal antibody conjugated to the cytotoxic payload monomethyl auristatin E (MMAE) via a protease-cleavable maleimidocaproyl valine citrulline (mc-vc) linker. This vedotin linker-payload system has been clinically validated in multiple Food and Drug Administration approved agents including brentuximab vedotin, enfortumab vedotin, and tisotumab vedotin. B7-H4 is an immune checkpoint ligand with elevated expression on a variety of solid tumors, including breast, ovarian, and endometrial tumors, and limited normal tissue expression.

Rationally Designing High-Performance Versatile Organic Memristors through Molecule-Mediated Ion Movements.

Organic memory has attracted tremendous attention for next-generation electronic elements for the molecules' striking ease of structural design. However, due to them being hardly controllable and their low ion transport, it is always essential and challenge to effectively control their random migration, pathway, and duration. There are very few effective strategies, and specific platforms with a view to molecules with specific coordination-groups-regulating ions have been rarely reported.

A multi-head residual connection GCN for EEG emotion recognition.

Electroencephalography (EEG) emotion recognition is a crucial aspect of human-computer interaction. However, conventional neural networks have limitations in extracting profound EEG emotional features. This paper introduces a novel multi-head residual graph convolutional neural network (MRGCN) model that incorporates complex brain networks and graph convolution networks.

Modeling the environmental suitability for Bacillus anthracis in the Qinghai Lake Basin, China.

Bacillus anthracis is a gram-positive, rod-shaped and endospore-forming bacterium that causes anthrax, a deadly disease to livestock and, occasionally, to humans. The spores are extremely hardy and may remain viable for many years in soil. Previous studies have identified East Qinghai and neighbouring Gansu in northwest China as a potential source of anthrax infection.

Environmental suitability of and the spatial dynamics of plague in the Qinghai Lake region, China.

Plague, a highly infectious disease caused by , has killed millions of people in history and is still active in the natural foci of the world nowadays. Understanding the spatiotemporal patterns of plague outbreaks in history is critically important, as it may help facilitate the prevention and control for potential future outbreaks. This study's objective was to estimate the effect of the topography, vegetation, climate, and other environmental factors on the ecological niche.

Emotional Brain Network Community Division Study Based on an Improved Immunogenetic Algorithm.

Emotion analysis has emerged as one of the most prominent study areas in the field of Brain Computer Interface (BCI) due to the critical role that the human brain plays in the creation of human emotions. In this study, a Multi-objective Immunogenetic Community Division Algorithm Based on Memetic Framework (MFMICD) was suggested to study different emotions from the perspective of brain networks. To improve convergence and accuracy, MFMICD incorporates the unique immunity operator based on the traditional genetic algorithm and combines it with the taboo search algorithm.

Spatial modeling and ecological suitability of monkeypox disease in Southern Nigeria.

The reemergence of monkeypoxvirus (MPXV) in 2017 after about 39 years of no reported cases in Nigeria, and the recent incidence in countries such as the United States of America, United Kingdom, Singapore, and Israel which have been reportedly linked with travelers from Africa, have heightened concern that MPXV may have emerged to occupy the vacant ecological and immunological niche created by the extinct smallpox virus. This study was carried out to identify environmental conditions and areas that are environmentally suitable (risky areas) for MPXV in southern Nigeria. One hundred and sixteen (116) spatially unique MPXV occurrence data from 2017-2021 and corresponding environmental variables were spatially modeled by a maximum entropy algorithm to evaluate the contribution of the variables to the distribution of the viral disease.

Comparison of three malnutrition risk screening tools in identifying malnutrition according to Global Leadership Initiative on Malnutrition criteria in gastrointestinal cancer.

Background: Malnutrition is common in patients with gastrointestinal cancer. The first step in the diagnosis of malnutrition is to evaluate the malnutrition risk by validated screening tools according to the Global Leadership Initiative on Malnutrition (GLIM). This study aimed to determine the best nutritional screening tool for identifying GLIM malnutrition and validate the performance of these tools in different age subgroups.

Clinical characteristics of pneumonia patients of long course of illness infected with SARS-CoV-2.

Epidemiological and clinical characteristics of patients with COVID-19 have been reported in the last two years. A few studies reported clinical course of illness of median 22 days, including viral shedding of median 20 days, but there are several cases with a longer time of viral shedding. In this study, we included four cases with a longer illness course of more than 40 days who had been discharged or still in hospital by March 15, 2020.

Using a Novel Functional Brain Network Approach to Locate Important Nodes for Working Memory Tasks.

Working Memory (WM) is a short-term memory for processing and storing information. When investigating WM mechanisms using Electroencephalogram (EEG), its rhythmic synchronization properties inevitably become one of the focal features. To further leverage these features for better improve WM task performance, this paper uses a novel algorithm: Weight K-order propagation number (WKPN) to locate important brain nodes and their coupling characteristic in different frequency bands while subjects are proceeding French word retaining tasks, which is an intriguing but original experiment paradigm.

Predicting the possibility of African horse sickness (AHS) introduction into China using spatial risk analysis and habitat connectivity of Culicoides.

African horse sickness (AHS) is a devastating equine infectious disease. On 17 March 2020, it first appeared in Thailand and threatened all the South-East Asia equine industry security. Therefore, it is imperative to carry out risk warnings of the AHS in China.

Identification of Survival-Associated Hub Genes in Pancreatic Adenocarcinoma Based on WGCNA.

Pancreatic adenocarcinoma is one of the leading causes of cancer-related death worldwide. Since little clinical symptoms were shown in the early period of pancreatic adenocarcinoma, most patients were found to carry metastases when diagnosis. The lack of effective diagnosis biomarkers and therapeutic targets makes pancreatic adenocarcinoma difficult to screen and cure.

Effects of a high-phosphorus diet on the gut microbiota in CKD rats.

Objective: To investigate whether high-phosphorus diets alter gut microbiota in healthy rats and chronic kidney disease (CKD) rats.

Methods: In this 4-week randomized controlled trial, healthy rats and CKD rats were fed a regular-phosphorus (Pi: 0.8%) and high-phosphorus (Pi: 1.

Emergency Braking Intention Detect System Based on K-Order Propagation Number Algorithm: A Network Perspective.

In order to avoid erroneous braking responses when vehicle drivers are faced with a stressful setting, a is developed in this paper to detect emergency braking intentions in simulated driving scenarios using electroencephalography (EEG) signals. Two approaches are employed in KFCS to extract EEG features and to improve classification performance: the K-Order Propagation Number Algorithm is the former, calculating the node importance from the perspective of brain networks as a novel approach; the latter uses a set of feature extraction algorithms to adjust the thresholds. Working with the data collected from seven subjects, the highest classification accuracy of a single trial can reach over 90%, with an overall accuracy of 83%.

Transboundary spread of peste des petits ruminants virus in western China: A prediction model.

In pan Pamir Plateau countries, Peste des petits ruminants (PPR) has brought huge losses to the livestock industry and threaten the endangered wildlife. In unknown regions, revealing PPRV transmission among countries is the premise of effective prevention and control, therefore calls for quantified monitoring on disease communication among countries. In this paper, a MaxEnt model was built for the first time to predict the PPR risk within the research area.

A predictive analysis on the risk of peste des petits ruminants in livestock in the Trans-Himalayan region and validation of its transboundary transmission paths.

Although the Trans-Himalayan region (THR) is an important endemic and rendezvous area of peste des petits ruminants (PPR), monitoring and prevention measurements are difficult to execute because of the rough geographical conditions. Besides, a heterogeneous breeding system and the poor veterinary service of susceptible animals compound the existing problems. Here, we propose a forecasting system to define the key points of PPR prevention and aid the countries in saving time, labor, and products to achieve the goal of the global eradication project of PPR.

[Corrigendum] SiRNA‑mediated PIAS1 silencing promotes inflammatory response and leads to injury of cerulein‑stimulated pancreatic acinar cells via regulation of the P38MAPK signaling pathway.

Following the publication of the above article, an interested reader drew to the authors' attention that the data shown in Fig. 2D representing the P53 and Bax data were strikingly similar. After having re‑examined their raw data, the authors have realized that this error arose inadvertently; the data shown for Bax in the original figure were selected incorrectly.

A Feature Tensor-Based Epileptic Detection Model Based on Improved Edge Removal Approach for Directed Brain Networks.

Electroencephalograph (EEG) plays a significant role in the diagnostics process of epilepsy, but the detection rate is unsatisfactory when the length of interictal EEG signals is relatively short. Although the deliberate attacking theories for undirected brain network based on node removal method can extract potential network features, the node removal method fails to sufficiently consider the directionality of brain electrical activities. To solve the problems above, this study proposes a feature tensor-based epileptic detection method of directed brain networks.