EEGFuseNet: Hybrid Unsupervised Deep Feature Characterization and Fusion for High-Dimensional EEG With an Application to Emotion Recognition

How to effectively and efficiently extract valid and reliable features from high-dimensional electroencephalography (EEG), particularly how to fuse the spatial and temporal dynamic brain information into a better feature representation, is a critical issue in brain data analysis. Most current EEG studies work in a task driven manner and explore the valid EEG features with a supervised model, which would be limited by the given labels to a great extent. In this paper, we propose a practical hybrid unsupervised deep convolutional recurrent generative adversarial network based EEG feature characterization and fusion model, which is termed as EEGFuseNet. EEGFuseNet is trained in an unsupervised manner, and deep EEG features covering both spatial and temporal dynamics are automatically characterized. Comparing to the existing features, the characterized deep EEG features could be considered to be more generic and independent of any specific EEG task. The performance of the extracted deep and low-dimensional features by EEGFuseNet is carefully evaluated in an unsupervised emotion recognition application based on three public emotion databases. The results demonstrate the proposed EEGFuseNet is a robust and reliable model, which is easy to train and performs efficiently in the representation and fusion of dynamic EEG features. In particular, EEGFuseNet is established as an optimal unsupervised fusion model with promising cross-subject emotion recognition performance. It proves EEGFuseNet is capable of characterizing and fusing deep features that imply comparative cortical dynamic significance corresponding to the changing of different emotion states, and also demonstrates the possibility of realizing EEG based cross-subject emotion recognition in a pure unsupervised manner

High-resolution X-ray microdiffraction analysis of natural teeth

In situ microzone X-ray diffraction analysis of natural teeth is presented. From our experiment, layer orientation and continuous crystal variations in teeth could be conveniently studied using fast online measurements by high-resolution X-ray microdiffraction equipment

Characteristics of DNA-AuNP networks on cell membranes and real-time movies for viral infection

AbstractThis data article provides complementary data for the article entitled “DNA-AuNP networks on cell membranes as a protective barrier to inhibit viral attachment, entry and budding” Li et al. (2016) [1]. The experimental methods for the preparation and characterization of DNA-conjugated nanoparticle networks on cell membranes were described. Confocal fluorescence images, agarose gel electrophoresis images and hydrodynamic diameter of DNA-conjugated gold nanoparticle (DNA-AuNP) networks were presented. In addition, we have prepared QDs-labeled RSV (QDs-RSV) to real-time monitor the RSV infection on HEp-2 cells in the absence and presence of DNA-AuNP networks. Finally, the cell viability of HEp-2 cells coated by six types of DNA-nanoparticle networks was determined after RSV infection

Neural Activation During Tonic Pain and Interaction Between Pain and Emotion in Bipolar Disorder: An fMRI Study

Objective: Pain and affective disorders have clear clinical relevance; however, very few studies have investigated the association between pain and bipolar disorder. This study investigated the brain activity of patients with bipolar disorder (BPs) undergoing tonic pain and assessed the interaction between pain and emotion.Methods: Ten BPs and ten healthy controls (HCs) were exposed to emotional pictures (positive, neutral, or negative), tonic pain only (pain session), and emotional pictures along with tonic pain (combined session). A moderate tonic pain was induced by the infusion of hypertonic saline (5% NaCl) into the right masseter muscle with a computer-controlled system. Whole-brain blood oxygenation level dependent (BOLD) signals were acquired using 3T functional resonance imaging (fMRI).Results: Ten BPs and ten healthy participants were included in the final analysis. During the pain session, BPs accepted more saline, but showed lower pain rating scores than HCs. When experiencing pain, BPs showed a significant decrease in the BOLD signal in the bilateral insula, left inferior frontal gyrus (IFG), and left cerebellum as compared with HCs. In the combined session, the activated regions for positive mood (pain with positive mood > baseline) in BPs were the left cerebellum, right temporal gyrus, and left occipital gyrus; the activated regions for negative mood (pain with negative mood > baseline) were the right occipital gyrus, left insula, left IFG, and bilateral precentral gyrus.Conclusions: This study presents the preliminary finding of the interaction between pain and emotion in BPs. BPs exhibited lower sensitivity to pain, and the activation of insula and IFG may reflect the interaction between emotion and pain stimulus

Influence of Individual Differences in fMRI-Based Pain Prediction Models on Between-Individual Prediction Performance

Decoding subjective pain perception from functional magnetic resonance imaging (fMRI) data using machine learning technique is gaining a growing interest. Despite the well-documented individual differences in pain experience and brain responses, it still remains unclear how and to what extent these individual differences affect the performance of between-individual fMRI-based pain prediction. The present study is aimed to examine the relationship between individual differences in pain prediction models and between-individual prediction error, and, further, to identify brain regions that contribute to between-individual prediction error. To this end, we collected and analyzed fMRI data and pain ratings in a laser-evoked pain experiment. By correlating different types of individual difference metrics with between-individual prediction error, we are able to quantify the influence of these individual differences on prediction performance and reveal a set of brain regions whose activities are related to prediction error. Interestingly, we found that the precuneus, which does not have predictive capability to pain, could also affect the prediction error. This study elucidates the influence of interindividual variability in pain on the between-individual prediction performance, and the results will be useful for the design of more accurate and robust fMRI-based pain prediction models

Association analysis between the TLR9 gene polymorphism rs352140 and type 1 diabetes

BackgroundTo a great extent, genetic factors contribute to the susceptibility to type 1 diabetes (T1D) development, and by triggering immune imbalance, Toll-like receptor (TLR) 9 is involved in the development of T1D. However, there is a lack of evidence supporting a genetic association between polymorphisms in the TLR9 gene and T1D.MethodsIn total, 1513 individuals, including T1D patients (n=738) and healthy control individuals (n=775), from the Han Chinese population were recruited for an association analysis of the rs352140 polymorphism of the TLR9 gene and T1D. rs352140 was genotyped by MassARRAY. The allele and genotype distributions of rs352140 in the T1D and healthy groups and those in different T1D subgroups were analyzed by the chi-squared test and binary logistic regression model. The chi-square test and Kruskal−Wallis H test were performed to explore the association between genotype and phenotype in T1D patients.ResultsThe allele and genotype distributions of rs352140 were significantly different in T1D patients and healthy control individuals (p=0.019, p=0.035). Specifically, the T allele and TT genotype of rs352140 conferred a higher risk of T1D (OR=1.194, 95% CI=1.029-1.385, p=0.019, OR=1.535, 95% CI=1.108-2.126, p=0.010). The allele and genotype distributions of rs352140 were not significantly different between childhood-onset and adult-onset T1D and between T1D with a single islet autoantibody and T1D with multiple islet autoantibodies (p=0.603, p=0.743). rs352140 was associated with T1D susceptibility according to the recessive and additive models (p=0.015, p=0.019) but was not associated with T1D susceptibility in the dominant and overdominant models (p=0.117, p=0.928). Moreover, genotype-phenotype association analysis showed that the TT genotype of rs352140 was associated with higher fasting C-peptide levels (p=0.017).ConclusionIn the Han Chinese population, the TLR9 polymorphism rs352140 is associated with T1D and is a risk factor for susceptibility to T1D

MiR-592 Promotes Gastric Cancer Proliferation, Migration, and Invasion Through the PI3K/AKT and MAPK/ERK Signaling Pathways by Targeting Spry2

Background/Aims: Gastric cancer (GC) is one of the most prevalent digestive malignancies. MicroRNAs (miRNAs) are involved in multiple cellular processes, including oncogenesis, and miR-592 itself participates in many malignancies; however, its role in GC remains unknown. In this study, we investigated the expression and molecular mechanisms of miR-592 in GC. Methods: Quantitative real-time PCR and immunohistochemistry were performed to determine the expression of miR-592 and its putative targets in human tissues and cell lines. Proliferation, migration, and invasion were evaluated by Cell Counting Kit-8, population doubling time, colony formation, Transwell, and wound-healing assays in transfected GC cells in vitro. A dual-luciferase reporter assay was used to determine whether miR-592 could directly bind its target. A tumorigenesis assay was used to study whether miR-592 affected GC growth in vivo. Proteins involved in signaling pathways and the epithelial–mesenchymal transition (EMT) were detected with western blot. Results: The ectopic expression of miR-592 promoted GC proliferation, migration, and invasion in vitro and facilitated tumorigenesis in vivo. Spry2 was a direct target of miR-592 and Spry2 overexpression partially counteracted the effects of miR-592. miR-592 induced the EMT and promoted its progression in GC via the PI3K/AKT and MAPK/ERK signaling pathways by inhibiting Spry2. Conclusions: Overexpression of miR-592 promotes GC proliferation, migration, and invasion and induces the EMT via the PI3K/AKT and MAPK/ERK signaling pathways by inhibiting Spry2, suggesting a potential therapeutic target for GC

Cauchy non-convex sparse feature selection method for the high-dimensional small-sample problem in motor imagery EEG decoding

IntroductionThe time, frequency, and space information of electroencephalogram (EEG) signals is crucial for motor imagery decoding. However, these temporal-frequency-spatial features are high-dimensional small-sample data, which poses significant challenges for motor imagery decoding. Sparse regularization is an effective method for addressing this issue. However, the most commonly employed sparse regularization models in motor imagery decoding, such as the least absolute shrinkage and selection operator (LASSO), is a biased estimation method and leads to the loss of target feature information.MethodsIn this paper, we propose a non-convex sparse regularization model that employs the Cauchy function. By designing a proximal gradient algorithm, our proposed model achieves closer-to-unbiased estimation than existing sparse models. Therefore, it can learn more accurate, discriminative, and effective feature information. Additionally, the proposed method can perform feature selection and classification simultaneously, without requiring additional classifiers.ResultsWe conducted experiments on two publicly available motor imagery EEG datasets. The proposed method achieved an average classification accuracy of 82.98% and 64.45% in subject-dependent and subject-independent decoding assessment methods, respectively.ConclusionThe experimental results show that the proposed method can significantly improve the performance of motor imagery decoding, with better classification performance than existing feature selection and deep learning methods. Furthermore, the proposed model shows better generalization capability, with parameter consistency over different datasets and robust classification across different training sample sizes. Compared with existing sparse regularization methods, the proposed method converges faster, and with shorter model training time

Biodegradable Silk Fibroin Nanocarriers to Modulate Hypoxia Tumor Microenvironment Favoring Enhanced Chemotherapy

Biopolymer silk fibroin (SF) is a great candidate for drug carriers characterized by its tunable biodegradability, and excellent biocompatibility properties. Recently, we have constructed SF-based nano-enabled drug delivery carriers, in which doxorubicin (Dox) and atovaquone (Ato) were encapsulated with Arg-Gly-Asp-SF-Polylactic Acid (RSA) to form micellar-like nanoparticles (RSA-Dox-Ato NPs). The RGD peptide was decorated on micellar-like nanoparticles, promoting tumor accumulation of the drug. Meanwhile, Ato, as a mitochondrial complex III inhibitor inhibiting mitochondrial respiration, would reverse the hypoxia microenvironment and enhance chemotherapy in the tumor. In vitro, the biopolymer alone showed extremely low cytotoxicity to 4T1 cell lines, while the RSA-Dox-Ato demonstrated a higher inhibition rate than other groups. Most significantly, the ROS levels in cells were obviously improved after being treated with RSA-Dox-Ato, indicating that the hypoxic microenvironment was alleviated. Eventually, SF-based targeted drug carrier provides biocompatibility to reverse hypoxia microenvironment in vivo for enhancing chemotherapy, strikingly suppressing tumor development, and thereby suggesting a promising candidate for drug delivery system

Transcriptomic profiling suggests candidate molecular responses to waterlogging in cassava

Owing to climate change impacts, waterlogging is a serious abiotic stress that affects crops, resulting in stunted growth and loss of productivity. Cassava (Manihot esculenta Grantz) is usually grown in areas that experience high amounts of rainfall; however, little research has been done on the waterlogging tolerance mechanism of this species. Therefore, we investigated the physiological responses of cassava plants to waterlogging stress and analyzed global gene transcription responses in the leaves and roots of waterlogged cassava plants. The results showed that waterlogging stress significantly decreased the leaf chlorophyll content, caused premature senescence, and increased the activities of superoxide dismutase (SOD), catalase (CAT) and peroxidase (POD) in the leaves and roots. In total, 2538 differentially expressed genes (DEGs) were detected in the leaves and 13364 in the roots, with 1523 genes shared between the two tissues. Comparative analysis revealed that the DEGs were related mainly to photosynthesis, amino metabolism, RNA transport and degradation. We also summarized the functions of the pathways that respond to waterlogging and are involved in photosynthesis, glycolysis and galactose metabolism. Additionally, many transcription factors (TFs), such as MYBs, AP2/ERFs, WRKYs and NACs, were identified, suggesting that they potentially function in the waterlogging response in cassava. The expression of 12 randomly selected genes evaluated via both quantitative real-time PCR (qRT-PCR) and RNA sequencing (RNA-seq) was highly correlated (R2 = 0.9077), validating the reliability of the RNA-seq results. The potential waterlogging stress-related transcripts identified in this study are representatives of candidate genes and molecular resources for further understanding the molecular mechanisms underlying the waterlogging response in cassava