Unsupervised heterogeneous domain adaptation for EEG classification.

Objective.Domain adaptation has been recognized as a potent solution to the challenge of limited training data for electroencephalography (EEG) classification tasks. Existing studies primarily focus on homogeneous environments, however, the heterogeneous properties of EEG data arising from device diversity cannot be overlooked. This motivates the development of heterogeneous domain adaptation methods that can fully exploit the knowledge from an auxiliary heterogeneous domain for EEG classification.Approach.In this article, we propose a novel model named informative representation fusion (IRF) to tackle the problem of unsupervised heterogeneous domain adaptation in the context of EEG data. In IRF, we consider different perspectives of data, i.e. independent identically distributed (iid) and non-iid, to learn different representations. Specifically, from the non-iid perspective, IRF models high-order correlations among data by hypergraphs and develops hypergraph encoders to obtain data representations of each domain. From the non-iid perspective, by applying multi-layer perceptron networks to the source and target domain data, we achieve another type of representation for both domains. Subsequently, an attention mechanism is used to fuse these two types of representations to yield informative features. To learn transferable representations, the maximum mean discrepancy is utilized to align the distributions of the source and target domains based on the fused features.Main results.Experimental results on several real-world datasets demonstrate the effectiveness of the proposed model.Significance.This article handles an EEG classification situation where the source and target EEG data lie in different spaces, and what's more, under an unsupervised learning setting. This situation is practical in the real world but barely studied in the literature. The proposed model achieves high classification accuracy, and this study is important for the commercial applications of EEG-based BCIs.

A distinct Fusobacterium nucleatum clade dominates the colorectal cancer niche.

Fusobacterium nucleatum (Fn), a bacterium present in the human oral cavity and rarely found in the lower gastrointestinal tract of healthy individuals1, is enriched in human colorectal cancer (CRC) tumours2-5. High intratumoural Fn loads are associated with recurrence, metastases and poorer patient prognosis5-8. Here, to delineate Fn genetic factors facilitating tumour colonization, we generated closed genomes for 135 Fn strains; 80 oral strains from individuals without cancer and 55 unique cancer strains cultured from tumours from 51 patients with CRC. Pangenomic analyses identified 483 CRC-enriched genetic factors. Tumour-isolated strains predominantly belong to Fn subspecies animalis (Fna). However, genomic analyses reveal that Fna, considered a single subspecies, is instead composed of two distinct clades (Fna C1 and Fna C2). Of these, only Fna C2 dominates the CRC tumour niche. Inter-Fna analyses identified 195 Fna C2-associated genetic factors consistent with increased metabolic potential and colonization of the gastrointestinal tract. In support of this, Fna C2-treated mice had an increased number of intestinal adenomas and altered metabolites. Microbiome analysis of human tumour tissue from 116 patients with CRC demonstrated Fna C2 enrichment. Comparison of 62 paired specimens showed that only Fna C2 is tumour enriched compared to normal adjacent tissue. This was further supported by metagenomic analysis of stool samples from 627 patients with CRC and 619 healthy individuals. Collectively, our results identify the Fna clade bifurcation, show that specifically Fna C2 drives the reported Fn enrichment in human CRC and reveal the genetic underpinnings of pathoadaptation of Fna C2 to the CRC niche.

INVADEseq to identify cell-adherent or invasive bacteria and the associated host transcriptome at single-cell-level resolution.

Single-cell RNA sequencing (scRNAseq) technologies have been beneficial in revealing and describing cellular heterogeneity within mammalian tissues, including solid tumors. However, many of these techniques apply poly(A) selection of RNA, and thus have primarily focused on determining the gene signatures of eukaryotic cellular components of the tumor microenvironment. Microbiome analysis has revealed the presence of microbial ecosystems, including bacteria and fungi, within human tumor tissues from major cancer types. Imaging data have revealed that intratumoral bacteria may be located within epithelial and immune cell types. However, as bacterial RNA typically lacks a poly(A) tail, standard scRNAseq approaches have limited ability to capture this microbial component of the tumor microenvironment. To overcome this, we describe the invasion-adhesion-directed expression sequencing (INVADEseq) approach, whereby we adapt 10x Genomics 5' scRNAseq protocol by introducing a primer that targets a conserved region of the bacterial 16S ribosomal RNA gene in addition to the standard primer for eukaryotic poly(A) RNA selection. This 'add-on' approach enables the generation of eukaryotic and bacterial DNA libraries at eukaryotic single-cell level resolution, utilizing the 10x barcode to identify single cells with intracellular bacteria. The INVADEseq method takes 30 h to complete, including tissue processing, sequencing and computational analysis. As an output, INVADEseq has shown to be a reliable tool in human cancer cell lines and patient tumor specimens by detecting the proportion of human cells that harbor bacteria and the identities of human cells and intracellular bacteria, along with identifying host transcriptional programs that are modulated on the basis of associated bacteria.

Fusobacterium sphaericum sp. nov. , isolated from a human colon tumor, is prevalent in various human body sites and induces IL-8 secretion from colorectal cancer cells.

Cancerous tissue is a largely unexplored microbial niche that provides a unique environment for the colonization and growth of specific bacterial communities, and with it, the opportunity to identify novel bacterial species. Here, we report distinct features of a novel Fusobacterium species, F. sphaericum sp. nov. ( Fs ), isolated from primary colon adenocarcinoma tissue. We acquire the complete, closed genome of this organism and phylogenetically confirm its classification into the Fusobacterium genus. Phenotypic and genomic analysis of Fs reveal that this novel organism is of coccoid shape, rare for Fusobacterium members, and has species-distinct gene content. Fs displays a metabolic profile and antibiotic resistance repertoire consistent with other Fusobacterium species. In vitro, Fs has adherent and immunomodulatory capabilities, as it intimately associates with human colon cancer epithelial cells and promotes IL-8 secretion. Analysis of the prevalence and abundance of Fs in ∼1,750 human metagenomic samples shows that it is a moderately prevalent member of the human oral cavity and stool. Intriguingly, analysis of ∼1,270 specimens from patients with colorectal cancer demonstrate that Fs is significantly enriched in colonic and tumor tissue as compared to mucosa or feces. Our study sheds light on a novel bacterial species that is prevalent within the human intestinal microbiota and whose role in human health and disease requires further investigation.

Transferable multi-modal fusion in knee angles and gait phases for their continuous prediction.

Objective.The gait phase and joint angle are two essential and complementary components of kinematics during normal walking, whose accurate prediction is critical for lower-limb rehabilitation, such as controlling the exoskeleton robots. Multi-modal signals have been used to promote the prediction performance of the gait phase or joint angle separately, but it is still few reports to examine how these signals can be used to predict both simultaneously.Approach.To address this problem, we propose a new method named transferable multi-modal fusion (TMMF) to perform a continuous prediction of knee angles and corresponding gait phases by fusing multi-modal signals. Specifically, TMMF consists of a multi-modal signal fusion block, a time series feature extractor, a regressor, and a classifier. The multi-modal signal fusion block leverages the maximum mean discrepancy to reduce the distribution discrepancy across different modals in the latent space, achieving the goal of transferable multi-modal fusion. Subsequently, by using the long short-term memory-based network, we obtain the feature representation from time series data to predict the knee angles and gait phases simultaneously. To validate our proposal, we design an experimental paradigm with random walking and resting to collect data containing multi-modal biomedical signals from electromyography, gyroscopes, and virtual reality.Main results.Comprehensive experiments on our constructed dataset demonstrate the effectiveness of the proposed method. TMMF achieves a root mean square error of0.090±0.022s in knee angle prediction and a precision of83.7±7.7% in gait phase prediction.Significance.We demonstrate the feasibility and validity of using TMMF to predict lower-limb kinematics continuously from multi-modal biomedical signals. This proposed method represents application potential in predicting the motor intent of patients with different pathologies.

Hypergraph Collaborative Network on Vertices and Hyperedges.

In many practical datasets, such as co-citation and co-authorship, relationships across the samples are more complex than pair-wise. Hypergraphs provide a flexible and natural representation for such complex correlations and thus obtain increasing attention in the machine learning and data mining communities. Existing deep learning-based hypergraph approaches seek to learn the latent vertex representations based on either vertices or hyperedges from previous layers and focus on reducing the cross-entropy error over labeled vertices to obtain a classifier. In this paper, we propose a novel model called Hypergraph Collaborative Network (HCoN), which takes the information from both previous vertices and hyperedges into consideration to achieve informative latent representations and further introduces the hypergraph reconstruction error as a regularizer to learn an effective classifier. We evaluate the proposed method on two cases, i.e., semi-supervised vertex and hyperedge classifications. We carry out the experiments on several benchmark datasets and compare our method with several state-of-the-art approaches. Experimental results demonstrate that the performance of the proposed method is better than that of the baseline methods.

Multi-Source Transfer Learning for EEG Classification Based on Domain Adversarial Neural Network.

Electroencephalogram (EEG) classification has attracted great attention in recent years, and many models have been presented for this task. Nevertheless, EEG data vary from subject to subject, which may lead to the performance of a classifier degrades due to individual differences. To collect enough labeled data to model would address the issue, but it is often time-consuming and labor-intensive. In this paper, we propose a new multi-source transfer learning method based on domain adversarial neural network for EEG classification. Specifically, we design a domain adversarial neural network, which includes a feature extractor, a classifier, and a domain discriminator, and therefore reduce the domain shift to achieve the purpose. In addition, a unified multi-source optimization framework is constructed to further improve the performance, and the result for EEG classification is induced by the weighted combination of the predictions from multiple source domains. Experiments on three publicly available EEG datasets validate the advantages of the proposed method.

Effect of the intratumoral microbiota on spatial and cellular heterogeneity in cancer.

The tumour-associated microbiota is an intrinsic component of the tumour microenvironment across human cancer types1,2. Intratumoral host-microbiota studies have so far largely relied on bulk tissue analysis1-3, which obscures the spatial distribution and localized effect of the microbiota within tumours. Here, by applying in situ spatial-profiling technologies4 and single-cell RNA sequencing5 to oral squamous cell carcinoma and colorectal cancer, we reveal spatial, cellular and molecular host-microbe interactions. We adapted 10x Visium spatial transcriptomics to determine the identity and in situ location of intratumoral microbial communities within patient tissues. Using GeoMx digital spatial profiling6, we show that bacterial communities populate microniches that are less vascularized, highly immuno­suppressive and associated with malignant cells with lower levels of Ki-67 as compared to bacteria-negative tumour regions. We developed a single-cell RNA-sequencing method that we name INVADEseq (invasion-adhesion-directed expression sequencing) and, by applying this to patient tumours, identify cell-associated bacteria and the host cells with which they interact, as well as uncovering alterations in transcriptional pathways that are involved in inflammation, metastasis, cell dormancy and DNA repair. Through functional studies, we show that cancer cells that are infected with bacteria invade their surrounding environment as single cells and recruit myeloid cells to bacterial regions. Collectively, our data reveal that the distribution of the microbiota within a tumour is not random; instead, it is highly organized in microniches with immune and epithelial cell functions that promote cancer progression.

Subject adaptation convolutional neural network for EEG-based motor imagery classification.

Objective.Deep transfer learning has been widely used to address the nonstationarity of electroencephalogram (EEG) data during motor imagery (MI) classification. However, previous deep learning approaches suffer from limited classification accuracy because the temporal and spatial features cannot be effectively extracted.Approach.Here, we propose a novel end-to-end deep subject adaptation convolutional neural network (SACNN) to handle the problem of EEG-based MI classification. Our proposed model jointly optimizes three modules, i.e. a feature extractor, a classifier, and a subject adapter. Specifically, the feature extractor simultaneously extracts the temporal and spatial features from the raw EEG data using a parallel multiscale convolution network. In addition, we design a subject adapter to reduce the feature distribution shift between the source and target subjects by using the maximum mean discrepancy. By minimizing the classification loss and the distribution discrepancy, the model is able to extract the temporal-spatial features to the prediction of a new subject.Main results.Extensive experiments are carried out on three EEG-based MI datasets, i.e. brain-computer interface (BCI) competition IV dataset IIb, BCI competition III dataset IVa, and BCI competition IV dataset I, and the average accuracy reaches to 86.42%, 81.71% and 79.35% on the three datasets respectively. Furthermore, the statistical analysis also indicates the significant performance improvement of SACNN.Significance.This paper reveals the importance of the temporal-spatial features on EEG-based MI classification task. Our proposed SACNN model can make fully use of the temporal-spatial information to achieve the purpose.

A new biomarker for the early diagnosis of gastric cancer: gastric juice- and serum-derived SNCG.

Aim: To explore the possibility of gastric juice (GJ)- and serum-derived SNCG as a potential biomarker for the early diagnosis of gastric cancer (GC). Materials & methods: GJ and serum samples were collected from 87 patients with GC, 38 patients with gastric precancerous lesions and 44 healthy volunteers. The levels of SNCG in GJ and serum samples were detected by ELISA. Results: The levels of SNCG in GJ and serum were significantly higher in the GC group when compared with the GPL group or the control group. The expression of SNCG in GJ and serum was associated with tumor node metastasis stage, lymph node metastasis, tumor size and drinking, and it is important for the diagnosis and prognosis of GC (p < 0.05). Conclusion: The findings highlight the significance of SNCG in GC diagnosis and prognosis and implicate SNCG as a promising candidate for GC treatment.

Gastric cancer (GC) has high morbidity and mortality rates due to its concealment in the early stage. At present, CEA, CA19-9, CA125, CA724, AFP, CA242 and CA50 are commonly used for the diagnosis of GC, but the effects are not satisfactory. Thus, a better biomarker for the diagnosis of GC is required. This study found that SNCG is highly expressed in the gastric juice and serum of GC patients and contributes to GC's progression. Detection of SNCG in gastric juice and serum is an ideal method for early diagnosis of GC with high specificity and sensitivity. Furthermore, SNCG has great value in the prognosis evaluation of GC, and high expression of SNCG predicts shorter survival for patients with GC, which provides a valuable reference for the clinical diagnosis and treatment of GC.

Mechanism of One-Step Hydrothermally Synthesized Titanate Catalysts for Ozonation.

A titanate nanotube catalyst for ozonation was synthesized with a simple one-step NaOH hydrothermal treatment without energy-consuming calcination. The synthesized titania catalysts were characterized by X-ray diffraction (XRD), Raman, porosimetry analysis, high-resolution transmission electron microscopy (HR-TEM), Fourier transformed infrared (FTIR), and electron paramagnetic resonance (EPR) analysis. The catalyst treated with a higher concentration of NaOH was found to be more catalytically active for phenol removal due to its higher titanate content that would facilitate more OH groups on its surface. Furthermore, the main active oxidizing species of the catalytic ozonation process were recognized as singlet oxygen and superoxide radical, while the hydroxyl radical may only play a minor role. This work provides further support for the correlation between the properties of titania and catalytic performance, which is significant for understanding the mechanism of catalytic ozonation with titania-based materials.

Behavior-Dependent Corticocortical Contributions to Imagined Grasping: a BCI-triggered TMS study.

Previous studies have indicated that corticocortical neural mechanisms differ during various grasping behaviors. However, the literature rarely considers corticocortical contributions to various imagined grasping behaviors. To address this question, we examine their mechanisms by transcranial magnetic stimulation (TMS) triggered when detecting event-related desynchronization during right-hand grasping behavior imagination through a brain-computer interface (BCI) system. Based on the BCI system, we designed two experiments. In Experiment 1, we explored differences in motor evoked potentials (MEPs) between power grip and resting conditions. In Experiment 2, we used the three TMS coil orientations (lateral-medial (LM), posterior-anterior (PA), and anterior-posterior (AP) directions) over the primary motor cortex to elicit MEPs during imagined index finger abduction, precision grip, and power grip. We found that larger MEP amplitudes and shorter latencies were obtained in imagined power grip than in resting.We also detected lower MEP amplitudes during imagined power grip, while MEP amplitudes remained similar across imagined precision grip and index finger abduction in each TMS coil orientation. Differences in AP-LM latency were longer when subjects imagined a power grip compared with precision grip and index finger abduction. Based on our results, higher cortical excitability may be achieved when humans imagine precision grip and index finger abduction. Our results suggests that higher cortical excitability may be achieved when humans imagine precision grip and index finger abduction. We also propose that preferential recruitment of late synaptic inputs to corticospinal neurons may occur when humans imagine a power grip.

Heterogeneous Domain Adaptation by Information Capturing and Distribution Matching.

Heterogeneous domain adaptation (HDA) is a challenging problem because of the different feature representations in the source and target domains. Most HDA methods search for mapping matrices from the source and target domains to discover latent features for learning. However, these methods barely consider the reconstruction error to measure the information loss during the mapping procedure. In this paper, we propose to jointly capture the information and match the source and target domain distributions in the latent feature space. In the learning model, we propose to minimize the reconstruction loss between the original and reconstructed representations to preserve information during transformation and reduce the Maximum Mean Discrepancy between the source and target domains to align their distributions. The resulting minimization problem involves two projection variables with orthogonal constraints that can be solved by the generalized gradient flow method, which can preserve orthogonal constraints in the computational procedure. We conduct extensive experiments on several image classification datasets to demonstrate that the effectiveness and efficiency of the proposed method are better than those of state-of-the-art HDA methods.

Investigating Shared Genetic Basis Across Tourette Syndrome and Comorbid Neurodevelopmental Disorders Along the Impulsivity-Compulsivity Spectrum.

BACKGROUND: Tourette syndrome (TS) is often found comorbid with other neurodevelopmental disorders across the impulsivity-compulsivity spectrum, with attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder (ASD), and obsessive-compulsive disorder (OCD) as most prevalent. This points to the possibility of a common etiological thread along an impulsivity-compulsivity continuum. METHODS: Investigating the shared genetic basis across TS, ADHD, ASD, and OCD, we undertook an evaluation of cross-disorder genetic architecture and systematic meta-analysis, integrating summary statistics from the latest genome-wide association studies (93,294 individuals, 6,788,510 markers). RESULTS: As previously identified, a common unifying factor connects TS, ADHD, and ASD, while TS and OCD show the highest genetic correlation in pairwise testing among these disorders. Thanks to a more homogeneous set of disorders and a targeted approach that is guided by genetic correlations, we were able to identify multiple novel hits and regions that seem to play a pleiotropic role for the specific disorders analyzed here and could not be identified through previous studies. In the TS-ADHD-ASD genome-wide association study single nucleotide polymorphism-based and gene-based meta-analysis, we uncovered 13 genome-wide significant regions that host single nucleotide polymorphisms with a high posterior probability for association with all three studied disorders (m-value > 0.9), 11 of which were not identified in previous cross-disorder analysis. In contrast, we also identified two additional pleiotropic regions in the TS-OCD meta-analysis. Through conditional analysis, we highlighted genes and genetic regions that play a specific role in a TS-ADHD-ASD genetic factor versus TS-OCD. Cross-disorder tissue specificity analysis implicated the hypothalamus-pituitary-adrenal gland axis in TS-ADHD-ASD. CONCLUSIONS: Our work underlines the value of redefining the framework for research across traditional diagnostic categories.