Motor Imagery Recognition Based on GMM-JCSFE Model.

Features from EEG microstate models, such as time-domain statistical features and state transition probabilities, are typically manually selected based on experience. However, traditional microstate models assume abrupt transitions between states, and the classification features can vary among individuals due to personal differences. To date, both empirical and theoretical classification results of EEG microstate features have not been entirely satisfactory.

s-TBN: A New Neural Decoding Model to Identify Stimulus Categories From Brain Activity Patterns.

Neural decoding is still a challenging and a hot topic in neurocomputing science. Recently, many studies have shown that brain network patterns containing rich spatiotemporal structural information represent the brain's activation information under external stimuli. In the traditional method, brain network features are directly obtained using the standard machine learning method and provide to a classifier, subsequently decoding external stimuli.

A feature enhanced EEG compression model using asymmetric encoding-decoding network.

Recently, the demand for wearable devices using electroencephalography (EEG) has increased rapidly in many fields. Due to its volume and computation constraints, wearable devices usually compress and transmit EEG to external devices for analysis. However, current EEG compression algorithms are not tailor-made for wearable devices with limited computing and storage.

Decoding Human Interaction Type from Inter-brain Synchronization by Using EEG Brain Network.

Cooperation and competition are two common forms of interpersonal interactions and exploring inter-brain synchronization in these two forms can help to further deliberate the underlying neural mechanisms of interpersonal interactions. Recently, studies revealed that electrode-paired inter-brain synchronization plays an important role in human interactions. This study investigated the neural correlates of interpersonal synchronization at the brain network scale and interaction type.

Advanced Reinforcement Learning and Its Connections with Brain Neuroscience.

In recent years, brain science and neuroscience have greatly propelled the innovation of computer science. In particular, knowledge from the neurobiology and neuropsychology of the brain revolutionized the development of reinforcement learning (RL) by providing novel interpretable mechanisms of how the brain achieves intelligent and efficient decision making. Triggered by this, there has been a boom in research about advanced RL algorithms that are built upon the inspirations of brain neuroscience.

Self-Supervised Node Classification with Strategy and Actively Selected Labeled Set.

To alleviate the impact of insufficient labels in less-labeled classification problems, self-supervised learning improves the performance of graph neural networks (GNNs) by focusing on the information of unlabeled nodes. However, none of the existing self-supervised pretext tasks perform optimally on different datasets, and the choice of hyperparameters is also included when combining self-supervised and supervised tasks. To select the best-performing self-supervised pretext task for each dataset and optimize the hyperparameters with no expert experience needed, we propose a novel auto graph self-supervised learning framework and enhance this framework with a one-shot active learning method.

Applying Interleaving Strategy of Learning Materials and Perceptual Modality to Address Secondary Students' Need to Restore Cognitive Capacity.

Online courses are prevalent around the world, especially during the COVID-19 pandemic. Long hours of highly demanding online learning can lead to mental fatigue and cognitive depletion. According to Attention Restoration Theory, 'being away' or a mental shift could be an important strategy to allow a person to recover from the cognitive overload.

Categorizing objects from MEG signals using EEGNet.

Magnetoencephalography (MEG) signals have demonstrated their practical application to reading human minds. Current neural decoding studies have made great progress to build subject-wise decoding models to extract and discriminate the temporal/spatial features in neural signals. In this paper, we used a compact convolutional neural network-EEGNet-to build a common decoder across subjects, which deciphered the categories of objects (faces, tools, animals, and scenes) from MEG data.

Domain adaptation based on rough adjoint inconsistency and optimal transport for identifying autistic patients.

Background And Objective: Computer aided diagnosis technology has been widely used to diagnose autism spectrum disorder (ASD) from neural images. The performance of the model usually depends largely on a sufficient number of training samples that reflect the real sample distribution. Due to the lack of labelled neural images data, multisite data are often pooled together to expand the sample size.

Domain Adaptation Using a Three-Way Decision Improves the Identification of Autism Patients from Multisite fMRI Data.

Machine learning methods are widely used in autism spectrum disorder (ASD) diagnosis. Due to the lack of labelled ASD data, multisite data are often pooled together to expand the sample size. However, the heterogeneity that exists among different sites leads to the degeneration of machine learning models.

Mitigation Effects of Bentonite and Yeast Cell Wall Binders on AFB, DON, and OTA Induced Changes in Laying Hen Performance, Egg Quality, and Health.

The objective of this study was to evaluate the efficacy of mycotoxin binders in reducing the adverse effects of co-occurring dietary aflatoxin B (AFB), deoxynivalenol (DON) and ochratoxin A (OTA) on laying hens. Three hundred and sixty 26-week-old Roman laying hens were randomly allocated into four experimental groups with 10 replicates of nine birds each. The four groups received either a basal diet (BD; Control), a BD supplemented with 0.

Transcriptomics and flow cytometry reveals the cytotoxicity of aflatoxin B and aflatoxin M in bovine mammary epithelial cells.

Aflatoxin is a known mycotoxin that pollutes various grains widely in the environment. Aflatoxin B (AFB) and Aflatoxin M (AFM) have been shown to induce cytotoxicity in many cells, yet their effects on mammary epithelial cells remain unclear. In this study, we examined the toxicity and the effects of AFB and AFM on bovine mammary epithelial cells (BME cells).

Effects of Thymol Supplementation on Goat Rumen Fermentation and Rumen Microbiota In Vitro.

This study was performed to explore the predominant responses of rumen microbiota with thymol supplementation as well as effective dose of thymol on rumen fermentation. Thymol at different concentrations, i.e.

Rapidly Decoding Image Categories From MEG Data Using a Multivariate Short-Time FC Pattern Analysis Approach.

Recent advances in the development of multivariate analysis methods have led to the application of multivariate pattern analysis (MVPA) to investigate the interactions between brain regions using graph theory (functional connectivity, FC) and decode visual categories from functional magnetic resonance imaging (fMRI) data from a continuous multicategory paradigm. To estimate stable FC patterns from fMRI data, previous studies required long periods in the order of several minutes, in comparison to the human brain that categories visual stimuli within hundreds of milliseconds. Constructing short-time dynamic FC patterns in the order of milliseconds and decoding visual categories is a relatively novel concept.

Individual-specific and shared representations during episodic memory encoding and retrieval.

Although human memories seem unique to each individual, they are shared to a great extent across individuals. Previous studies have examined, separately, subject-specific and cross-subject shared representations during memory encoding and retrieval, but how shared memories are formed from individually encoded representations is not clearly understood. Using a unique fMRI design involving memory encoding and retrieval, and representational similarity analysis to link representations from different individuals, brain regions, and processing stages, the current study revealed that distributed brain regions showed both subject-specific and shared neural representations during both memory encoding and retrieval.

Yeast culture promotes the production of aged laying hens by improving intestinal digestive enzyme activities and the intestinal health status.

Yeast culture (YC) positively affects the performance of laying hens. The purpose of the present study was to explore the underlying mechanism for the YC-mediated performance improvement. Sixty 67-week-old Hy-Line Brown laying hens were randomly allocated into 2 experimental groups with 5 replicates of 6 birds each.

Decoding disparity categories in 3-dimensional images from fMRI data using functional connectivity patterns.

Humans use binocular disparity to extract depth information from two-dimensional retinal images in a process called stereopsis. Previous studies usually introduce the standard univariate analysis to describe the correlation between disparity level and brain activity within a given brain region based on functional magnetic resonance imaging (fMRI) data. Recently, multivariate pattern analysis has been developed to extract activity patterns across multiple voxels for deciphering categories of binocular disparity.

Sodium Butyrate Protects the Intestinal Barrier by Modulating Intestinal Host Defense Peptide Expression and Gut Microbiota after a Challenge with Deoxynivalenol in Weaned Piglets.

This study aims to determine whether sodium butyrate (SB) could antagonize deoxynivalenol (DON)-induced intestinal epithelial dysfunction. In a four-week feeding trial, twenty-eight barrows were randomly divided into four treatments: (1) uncontaminated basal diet (control); (2) 4 mg/kg DON-contaminated diet (DON); (3) basal diet supplemented with 0.2% SB (SB); and (4) 4 mg/kg DON + 0.

Predicting Brain Age of Healthy Adults Based on Structural MRI Parcellation Using Convolutional Neural Networks.

Structural magnetic resonance imaging (MRI) studies have demonstrated that the brain undergoes age-related neuroanatomical changes not only regionally but also on the network level during the normal development and aging process. In recent years, many studies have focused on estimating age using structural MRI measurements. However, the age prediction effects on different structural networks remain unclear.

The paradoxical effects of progesterone on the eggshell quality of laying hens.

This study demonstrates the effects of progesterone on eggshell quality and ultrastructure by injecting progesterone into laying hens 2 and 5 h post-oviposition, respectively. Progesterone injected 2 h post-oviposition (P-2 h) improved eggshell quality with a significant decrease (P < 0.01) in the thickness of the mammillary layer and a significant increase (P < 0.

Deoxynivalenol Inhibits Porcine Intestinal Trefoil Factors Expression in Weanling Piglets and IPEC-J2 Cells.

Trefoil factors (TFFs) are regulatory peptides playing critical roles in mucosal repair and protection against a variety of insults within the gastrointestinal tract. This work aimed to explore the effects of deoxynivalenol (DON) on intestinal TFFs expression using in vivo and in vitro models. In an animal trial, twenty-four 28-d-old barrows (Duroc × Landrace × Large White; initial body weight = 7.

Decoding Brain States From fMRI Signals by Using Unsupervised Domain Adaptation.

With the development of deep learning in medical image analysis, decoding brain states from functional magnetic resonance imaging (fMRI) signals has made significant progress. Previous studies often utilized deep neural networks to automatically classify brain activity patterns related to diverse cognitive states. However, due to the individual differences between subjects and the variation in acquisition parameters across devices, the inconsistency in data distributions degrades the performance of cross-subject decoding.

The differences of gonadal hormones and uterine transcriptome during shell calcification of hens laying hard or weak-shelled eggs.

Background: Eggshell breaking strength is critical to reduce egg breaking rate and avoid economic loss. The process of eggshell calcification initiates with the egg entering the uterus and lasts about 18 h. It follows a temporal sequence corresponding to the initiation, growth and termination periods of shell calcification.

Potential Link between Gut Microbiota and Deoxynivalenol-Induced Feed Refusal in Weaned Piglets.

This study investigated the potential link between gut microbiota and deoxynivalenol (DON)-induced feed refusal. A total of 24 barrows were randomly divided into one of three diets containing 0.61 (control diet), 1.

Long-term Chinese calligraphic handwriting reshapes the posterior cingulate cortex: A VBM study.

As a special kind of handwriting with a brush, Chinese calligraphic handwriting (CCH) requires a large amount of practice with high levels of concentration and emotion regulation. Previous studies have showed that long-term CCH training has positive effects physically (induced by handwriting activities) and psychologically (induced by the state of relaxation and concentration), the latter of which is similar to the effects of meditation. The aim of this study was to investigate the long-term CCH training effect on anxiety and attention, as well as brain structure.