A two-stage transformer based network for motor imagery classification.

Brain-computer interfaces (BCIs) are used to understand brain functioning and develop therapies for neurological and neurodegenerative disorders. Therefore, BCIs are crucial in rehabilitating motor dysfunction and advancing motor imagery applications. For motor imagery, electroencephalogram (EEG) signals are used to classify the subject's intention of moving a body part without actually moving it. This paper presents a two-stage transformer-based architecture that employs handcrafted features and deep learning techniques to enhance the classification performance on benchmarked EEG signals. Stage-1 is built on parallel convolution based EEGNet, multi-head attention, and separable temporal convolution networks for spatiotemporal feature extraction. Further, for enhanced classification, in stage-2, additional features and embeddings extracted from stage-1 are used to train TabNet. In addition, a novel channel cluster swapping data augmentation technique is also developed to handle the issue of limited samples for training deep learning architectures. The developed two-stage architecture offered an average classification accuracy of 88.5 % and 88.3 % on the BCI Competition IV-2a and IV-2b datasets, respectively, which is approximately 3.0 % superior over similar recent reported works.

ECG arrhythmia detection in an inter-patient setting using Fourier decomposition and machine learning.

ECG beat classification or arrhythmia detection through artificial intelligence (AI) is an active topic of research. It is vital to recognize and detect the type of arrhythmia for monitoring cardiac abnormalities. The AI-based ECG beat classification algorithms proposed in the literature suffer from two main drawbacks. Firstly, some of the works have not considered any unseen test data to validate the performance of their algorithms. Secondly, the accuracy of detecting superventricular ectopic beats (SVEB) needs to be improved. In this work, we address these issues by considering an inter-patient paradigm where the test dataset is collected from a different set of subjects than the training data. Also, the proposed methodology detects SVEB with an F1 score of 89.35%, which is better than existing algorithms. We have used the Fourier decomposition method (FDM) for multi-scale analysis of ECG signals and extracted time-domain and statistical features from the narrow-band signal components obtained using FDM. Feature selection techniques, including the Kruskal-Wallis test and minimum redundancy maximum relevance (mRMR) have been used to select only the relevant features and rank these features to remove any redundancy. Since the dataset used is highly imbalanced, Mathew's correlation coefficient (MCC) has also been used to analyze the performance of the proposed method. Support vector machine classifier with linear kernel achieves an overall 98.03% accuracy and 91.84% MCC for the MIT-BIH arrhythmia dataset.

Longitudinal single cell atlas identifies complex temporal relationship between type I interferon response and COVID-19 severity.

Due to the paucity of longitudinal molecular studies of COVID-19, particularly those covering the early stages of infection (Days 1-8 symptom onset), our understanding of host response over the disease course is limited. We perform longitudinal single cell RNA-seq on 286 blood samples from 108 age- and sex-matched COVID-19 patients, including 73 with early samples. We examine discrete cell subtypes and continuous cell states longitudinally, and we identify upregulation of type I IFN-stimulated genes (ISGs) as the predominant early signature of subsequent worsening of symptoms, which we validate in an independent cohort and corroborate by plasma markers. However, ISG expression is dynamic in progressors, spiking early and then rapidly receding to the level of severity-matched non-progressors. In contrast, cross-sectional analysis shows that ISG expression is deficient and IFN suppressors such as SOCS3 are upregulated in severe and critical COVID-19. We validate the latter in four independent cohorts, and SOCS3 inhibition reduces SARS-CoV-2 replication in vitro. In summary, we identify complexity in type I IFN response to COVID-19, as well as a potential avenue for host-directed therapy.

Rv0495c regulates redox homeostasis in Mycobacterium tuberculosis.

Mycobacterium tuberculosis (Mtb) has evolved sophisticated surveillance mechanisms to neutralize the ROS-induces toxicity which otherwise would degrade a variety of biological molecules including proteins, nucleic acids and lipids. In the present study, we find that Mtb lacking the Rv0495c gene (ΔRv0495c) is presented with a highly oxidized cytosolic environment. The superoxide-induced lipid peroxidation resulted in altered colony morphology and loss of membrane integrity in ΔRv0495c. As a consequence, ΔRv0495c demonstrated enhanced susceptibility when exposed to various host-induced stress conditions. Further, as expected, we observed a mutant-specific increase in the abundance of transcripts that encode proteins involved in antioxidant defence. Surprisingly, despite showing a growth defect phenotype in macrophages, the absence of the Rv0495c enhanced the pathogenicity and augmented the ability of the Mtb to grow inside the host. Additionally, our study revealed that Rv0495c-mediated immunomodulation by the pathogen helps create a favorable niche for long-term survival of Mtb inside the host. In summary, the current study underscores the fact that the truce in the war between the host and the pathogen favours long-term disease persistence in tuberculosis. We believe targeting Rv0495c could potentially be explored as a strategy to potentiate the current anti-TB regimen.

A mitochondrial quality control mechanism reverses the phagosome maturation arrest caused by Mycobacterium tuberculosis.

Phagosome maturation arrest (PMA) imposed by Mycobacterium tuberculosis ( Mtb ) is a classic tool that helps Mtb evade macrophage anti-bacterial responses. The exclusion of RAB7, a small GTPase, from Mtb -phagosomes underscores PMA. Here we report an unexpected mechanism that triggers crosstalk between the mitochondrial quality control (MQC) and the phagosome maturation pathways that reverses the PMA. CRISPR-mediated p62/SQSTM1 depletion ( p62 KD ) blocks mitophagy flux without impacting mitochondrial quality. In p62 KD cells, Mtb growth and survival are diminished, mainly through witnessing an increasingly oxidative environment and increased lysosomal targeting. The lysosomal targeting of Mtb is facilitated by enhanced TOM20 + mitochondria-derived vesicles (MDVs) biogenesis, a key MQC mechanism. In p62 KD cells, TOM20 + -MDVs biogenesis is MIRO1/MIRO2-dependent and delivered to lysosomes for degradation in a RAB7-dependent manner. Upon infection in p62 KD cells, TOM20 + -MDVs get extensively targeted to Mtb -phagosomes, inadvertently facilitating RAB7 recruitment, PMA reversal and lysosomal targeting of Mtb . Triggering MQC collapse in p62 KD cells further diminishes Mtb survival signifying cooperation between redox- and lysosome-mediated mechanisms. The MQC-anti-bacterial pathway crosstalk could be exploited for host-directed anti-tuberculosis therapies.

HIV-1 active and latent infections induce disparate chromatin reorganization and transcriptional regulation of mRNAs and lncRNAs in SupT1 cells.

IMPORTANCE: HIV-1 infection of T-lymphocytes depends on co-opting cellular transcriptional and translational machineries for viral replication. This requires significant changes in the cellular microenvironment. We have characterized and compared the changes in cellular chromatin structures as well as gene expression landscapes in T cells that are either actively or latently infected with HIV-1. Our results reveal that chromatin accessibility and expression of both protein-coding mRNAs and non-coding lncRNAs are uniquely regulated in HIV-1-infected T cells, depending on whether the virus is actively transcribing or remains in a transcriptionally silent, latent state. HIV-1 latent infection elicits more robust changes in the cellular chromatin organization than active viral infection. Our analysis also identifies the effects of such epigenomic changes on the cellular gene expression and subsequent biological pathways. This study comprehensively characterizes the cellular epigenomic and transcriptomic states that support active and latent HIV-1 infection in an in vitro model of SupT1 cells.

Deep temporal networks for EEG-based motor imagery recognition.

The electroencephalogram (EEG) based motor imagery (MI) signal classification, also known as motion recognition, is a highly popular area of research due to its applications in robotics, gaming, and medical fields. However, the problem is ill-posed as these signals are non-stationary and noisy. Recently, a lot of efforts have been made to improve MI signal classification using a combination of signal decomposition and machine learning techniques but they fail to perform adequately on large multi-class datasets. Previously, researchers have implemented long short-term memory (LSTM), which is capable of learning the time-series information, on the MI-EEG dataset for motion recognition. However, it can not model very long-term dependencies present in the motion recognition data. With the advent of transformer networks in natural language processing (NLP), the long-term dependency issue has been widely addressed. Motivated by the success of transformer algorithms, in this article, we propose a transformer-based deep learning neural network architecture that performs motion recognition on the raw BCI competition III IVa and IV 2a datasets. The validation results show that the proposed method achieves superior performance than the existing state-of-the-art methods. The proposed method produces classification accuracy of 99.7% and 84% on the binary class and the multi-class datasets, respectively. Further, the performance of the proposed transformer-based model is also compared with LSTM.

Glycerol contributes to tuberculosis susceptibility in male mice with type 2 diabetes.

Diabetes mellitus increases risk for tuberculosis disease and adverse outcomes. Most people with both conditions have type 2 diabetes, but it is unknown if type 1 and type 2 diabetes have identical effects on tuberculosis susceptibility. Here we show that male mice receiving a high-fat diet and streptozotocin to model type 2 diabetes, have higher mortality, more lung pathology, and higher bacterial burden following Mycobacterium tuberculosis infection compared to mice treated with streptozotocin or high-fat diet alone. Type 2 diabetes model mice have elevated plasma glycerol, which is a preferred carbon source for M. tuberculosis. Infection studies with glycerol kinase mutant M. tuberculosis reveal that glycerol utilization contributes to the susceptibility of the type 2 diabetes mice. Hyperglycemia impairs protective immunity against M. tuberculosis in both forms of diabetes, but our data show that elevated glycerol contributes to an additional adverse effect uniquely relevant to type 2 diabetes.

Diagnosis and biomarkers for ocular tuberculosis: From the present into the future.

Tuberculosis is an airborne disease caused by Mycobacterium tuberculosis (Mtb) and can manifest both pulmonary and extrapulmonary disease, including ocular tuberculosis (OTB). Accurate diagnosis and swift optimal treatment initiation for OTB is faced by many challenges combined with the lack of standardized treatment regimens this results in uncertain OTB outcomes. The purpose of this study is to summarize existing diagnostic approaches and recently discovered biomarkers that may contribute to establishing OTB diagnosis, choice of anti-tubercular therapy (ATT) regimen, and treatment monitoring. The keywords ocular tuberculosis, tuberculosis, Mycobacterium, biomarkers, molecular diagnosis, multi-omics, proteomics, genomics, transcriptomics, metabolomics, T-lymphocytes profiling were searched on PubMed and MEDLINE databases. Articles and books published with at least one of the keywords were included and screened for relevance. There was no time limit for study inclusion. More emphasis was placed on recent publications that contributed new information about the pathogenesis, diagnosis, or treatment of OTB. We excluded abstracts and articles that were not written in the English language. References cited within the identified articles were used to further supplement the search. We found 10 studies evaluating the sensitivity and specificity of interferon-gamma release assay (IGRA), and 6 studies evaluating that of tuberculin skin test (TST) in OTB patients. IGRA (Sp = 71-100%, Se = 36-100%) achieves overall better sensitivity and specificity than TST (Sp = 51.1-85.7%; Se = 70.9-98.5%). For nuclear acid amplification tests (NAAT), we found 7 studies on uniplex polymerase chain reaction (PCR) with different Mtb targets, 7 studies on DNA-based multiplex PCR, 1 study on mRNA-based multiplex PCR, 4 studies on loop-mediated isothermal amplification (LAMP) assay with different Mtb targets, 3 studies on GeneXpert assay, 1 study on GeneXpert Ultra assay and 1 study for MTBDRplus assay for OTB. Specificity is overall improved but sensitivity is highly variable for NAATs (excluding uniplex PCR, Sp = 50-100%; Se = 10.5-98%) as compared to IGRA. We also found 3 transcriptomic studies, 6 proteomic studies, 2 studies on stimulation assays, 1 study on intraocular protein analysis and 1 study on T-lymphocyte profiling in OTB patients. All except 1 study evaluated novel, previously undiscovered biomarkers. Only 1 study has been externally validated by a large independent cohort. Future theranostic marker discovery by a multi-omics approach is essential to deepen pathophysiological understanding of OTB. Combined these might result in swift, optimal and personalized treatment regimens to modulate the heterogeneous mechanisms of OTB. Eventually, these studies could improve the current cumbersome diagnosis and management of OTB.

Transcription factor mce3R modulates antibiotics and disease persistence in Mycobacteriumtuberculosis.

Transcription factors (TFs) of Mycobacterium tuberculosis (Mtb), an etiological agent of tuberculosis, regulate a network of pathways that help prolong the survival of Mtb inside the host. In this study, we have characterized a transcription repressor gene (mce3R) from the TetR family, that encodes for Mce3R protein in Mtb. We demonstrated that the mce3R gene is dispensable for the growth of Mtb on cholesterol. Gene expression analysis suggests that the transcription of genes belonging to the mce3R regulon is independent of the carbon source. We found that, in comparison to the wild type, the mce3R deleted strain (Δmce3R) generated more intracellular ROS and demonstrated reduced susceptibility to oxidative stress. Total lipid analysis suggests that mce3R regulon encoded proteins modulate the biosynthesis of cell wall lipids in Mtb. Interestingly, the absence of Mce3R increased the frequency of generation of antibiotic persisters in Mtb and imparted in-vivo growth advantage phenotype in guinea pigs. In conclusion, genes belonging to the mce3R regulon modulate the frequency of generation of persisters in Mtb. Hence, targeting mce3R regulon encoded proteins could potentiate the current regimen by eliminating persisters during Mtb infection.

Fusion of pattern-based and statistical features for Schizophrenia detection from EEG signals.

Schizophrenia (SZ) is a chronic disorder affecting the functioning of the brain. It can lead to irrational behaviour amongst the patients suffering from this disease. A low-cost diagnostic needs to be developed for SZ so that timely treatment can be provided to the patients. In this work, we propose an accurate and easy-to-implement system to detect SZ using electroencephalogram (EEG) signals. The signal is divided into sub-band components by a Fourier-based technique that can be implemented in real-time using fast Fourier transform. Thereafter, statistical features are computed from these components. Further, look ahead pattern (LAP) is developed as a feature to capture local variations in the EEG signal. The fusion of these two distinct schemes enables a thorough examination of EEG signals. Kruskal-Wallis test is utilized for the selection of significant features. Various machine learning classifiers are employed and the proposed framework achieves 98.62% and 99.24% accuracy in identifying SZ cases, considering two distinct datasets, using boosted trees classifier. This method provides a promising candidate for widespread deployment in efficient real-time systems for SZ detection.

Identification of small molecules targeting homoserine acetyl transferase from Mycobacterium tuberculosis and Staphylococcus aureus.

There is an urgent need to validate new drug targets and identify small molecules that possess activity against both drug-resistant and drug-sensitive bacteria. The enzymes belonging to amino acid biosynthesis have been shown to be essential for growth in vitro, in vivo and have not been exploited much for the development of anti-tubercular agents. Here, we have identified small molecule inhibitors targeting homoserine acetyl transferase (HSAT, MetX, Rv3341) from M. tuberculosis. MetX catalyses the first committed step in L-methionine and S-adenosyl methionine biosynthesis resulting in the formation of O-acetyl-homoserine. Using CRISPRi approach, we demonstrate that conditional repression of metX resulted in inhibition of M. tuberculosis growth in vitro. We have determined steady state kinetic parameters for the acetylation of L-homoserine by Rv3341. We show that the recombinant enzyme followed Michaelis-Menten kinetics and utilizes both acetyl-CoA and propionyl-CoA as acyl-donors. High-throughput screening of a 2443 compound library resulted in identification of small molecule inhibitors against MetX enzyme from M. tuberculosis. The identified lead compounds inhibited Rv3341 enzymatic activity in a dose dependent manner and were also active against HSAT homolog from S. aureus. Molecular docking of the identified primary hits predicted residues that are essential for their binding in HSAT homologs from M. tuberculosis and S. aureus. ThermoFluor assay demonstrated direct binding of the identified primary hits with HSAT proteins. Few of the identified small molecules were able to inhibit growth of M. tuberculosis and S. aureus in liquid cultures. Taken together, our findings validated HSAT as an attractive target for development of new broad-spectrum anti-bacterial agents that should be effective against drug-resistant bacteria.

A multi-modal assessment of sleep stages using adaptive Fourier decomposition and machine learning.

Healthy sleep is essential for the rejuvenation of the body and helps in maintaining good health. Many people suffer from sleep disorders that are characterized by abnormal sleep patterns. Automated assessment of such disorders using biomedical signals has been an active subject of research. Electroencephalogram (EEG) is a popular diagnostic used in this regard. We consider a widely-used publicly available database and process the signals using the Fourier decomposition method (FDM) to obtain narrowband signal components. Statistical features extracted from these components are passed on to machine learning classifiers to identify different stages of sleep. A novel feature measuring the non-stationarity of the signal is also used to capture salient information. It is shown that classification results can be improved by using multi-channel EEG instead of single-channel EEG data. Simultaneous utilization of multiple modalities, such as Electromyogram (EMG), Electrooculogram (EOG) along with EEG data leads to further enhancement in the obtained results. The proposed method can be efficiently implemented in real-time using fast Fourier transform (FFT), and it provides better classification results than the other algorithms existing in the literature. It can assist in the development of low-cost sensor-based setups for continuous patient monitoring and feedback.

Histone acetylome-wide associations in immune cells from individuals with active Mycobacterium tuberculosis infection.

Host cell chromatin changes are thought to play an important role in the pathogenesis of infectious diseases. Here we describe a histone acetylome-wide association study (HAWAS) of an infectious disease, on the basis of genome-wide H3K27 acetylation profiling of peripheral blood granulocytes and monocytes from persons with active Mycobacterium tuberculosis (Mtb) infection and healthy controls. We detected >2,000 differentially acetylated loci in either cell type in a Singapore Chinese discovery cohort (n = 46), which were validated in a subsequent multi-ethnic Singapore cohort (n = 29), as well as a longitudinal cohort from South Africa (n = 26), thus demonstrating that HAWAS can be independently corroborated. Acetylation changes were correlated with differential gene expression. Differential acetylation was enriched near potassium channel genes, including KCNJ15, which modulates apoptosis and promotes Mtb clearance in vitro. We performed histone acetylation quantitative trait locus (haQTL) analysis on the dataset and identified 69 candidate causal variants for immune phenotypes among granulocyte haQTLs and 83 among monocyte haQTLs. Our study provides proof-of-principle for HAWAS to infer mechanisms of host response to pathogens.

Single-cell immune profiling reveals functional diversity of T cells in tuberculous pleural effusion.

Orchestration of an effective T lymphocyte response at infection sites is critical for protection against Mycobacterium tuberculosis (Mtb) infection. However, the local T cell immunity landscape in human tuberculosis is poorly defined. Tuberculous pleural effusion (TPE), caused by Mtb, is characterized by an influx of leukocytes to the pleural space, providing a platform suitable for delineating complex tissue responses to Mtb infection. Using single-cell transcriptomics and T cell receptor sequencing, we analyzed mononuclear cell populations in paired pleural fluid and peripheral blood of TPE patients. While all major cell clusters were present in both tissues, their relative proportions varied significantly by anatomic location. Lineage tracking analysis revealed subsets of CD8 and CD4 T cell populations with distinct effector functions specifically expanded at pleural sites. Granzyme K-expressing CD8 T cells were preferentially enriched and clonally expanded in pleural fluid from TPE, suggesting that they are involved in the pathogenesis of the disease. The findings collectively reveal the landscape of local T cell immunity in tuberculosis.

The Eye of the Storm: COVID-19 Vaccination and the Eye.

The COVID-19 pandemic has galvanized the global response towards the development of new vaccines based on novel technologies at an unprecedented pace. Since the widespread implementation of vaccination campaigns, case reports on vaccines' systemic side effects, including ocular manifestations, have emerged. Since administered vaccines are generally not able to cause the disease in the recipient, or induce an immune response against the pathogen, we hypothesize that the development of ocular phenomena post-COVID-19 vaccination may occur via an immune response elicited by the vaccine. Of many, the most common ocular adverse events include facial nerve palsy, central venous sinus thrombosis and acute anterior uveitis. These COVID-19 vaccine-induced ocular (CVIO) adverse events could resemble the ocular findings in some of the COVID-19 patients. This review will provide a comprehensive overview of published ocular side effects potentially associated with COVID-19 vaccination and serve as a springboard for further research into CVIO adverse events.

Gut-Evolved Candida albicans Induces Metabolic Changes in Neutrophils.

Serial passaging of the human fungal pathogen Candida albicans in the gastrointestinal tract of antibiotics-treated mice selects for virulence-attenuated strains. These gut-evolved strains protect the host from infection by a wide range of pathogens via trained immunity. Here, we further investigated the molecular and cellular mechanisms underlying this innate immune memory. Both Dectin-1 (the main receptor for ß-glucan; a well-described immune training molecule in the fungal cell wall) and Nod2 (a receptor described to mediate BCG-induced trained immunity), were redundant for the protection induced by gut-evolved C. albicans against a virulent C. albicans strain, suggesting that gut-evolved C. albicans strains induce trained immunity via other pathways. Cytometry by time of flight (CyTOF) analysis of mouse splenocytes revealed that immunization with gut-evolved C. albicans resulted in an expansion of neutrophils and a reduction in natural killer (NK) cells, but no significant numeric changes in monocytes, macrophages or dendritic cell populations. Systemic depletion of phagocytes or neutrophils, but not of macrophages or NK cells, reduced protection mediated by gut-evolved C. albicans. Splenocytes and bone marrow cells of mice immunized with gut-evolved C. albicans demonstrated metabolic changes. In particular, splenic neutrophils displayed significantly elevated glycolytic and respiratory activity in comparison to those from mock-immunized mice. Although further investigation is required for fully deciphering the trained immunity mechanism induced by gut-evolved C. albicans strains, this data is consistent with the existence of several mechanisms of trained immunity, triggered by different training stimuli and involving different immune molecules and cell types.

Single cell analysis of M. tuberculosis phenotype and macrophage lineages in the infected lung.

In this study, we detail a novel approach that combines bacterial fitness fluorescent reporter strains with scRNA-seq to simultaneously acquire the host transcriptome, surface marker expression, and bacterial phenotype for each infected cell. This approach facilitates the dissection of the functional heterogeneity of M. tuberculosis-infected alveolar (AMs) and interstitial macrophages (IMs) in vivo. We identify clusters of pro-inflammatory AMs associated with stressed bacteria, in addition to three different populations of IMs with heterogeneous bacterial phenotypes. Finally, we show that the main macrophage populations in the lung are epigenetically constrained in their response to infection, while inter-species comparison reveals that most AMs subsets are conserved between mice and humans. This conceptual approach is readily transferable to other infectious disease agents with the potential for an increased understanding of the roles that different host cell populations play during the course of an infection.

Immune cell phenotypes associated with disease severity and long-term neutralizing antibody titers after natural dengue virus infection.

Prior immunological exposure to dengue virus can be both protective and disease-enhancing during subsequent infections with different dengue virus serotypes. We provide here a systematic, longitudinal analysis of B cell, T cell, and antibody responses in the same patients. Antibody responses as well as T and B cell activation differentiate primary from secondary responses. Hospitalization is associated with lower frequencies of activated, terminally differentiated T cells and higher percentages of effector memory CD4 T cells. Patients with more severe disease tend to have higher percentages of plasmablasts. This does not translate into long-term antibody titers, since neutralizing titers after 6 months correlate with percentages of specific memory B cells, but not with acute plasmablast activation. Overall, our unbiased analysis reveals associations between cellular profiles and disease severity, opening opportunities to study immunopathology in dengue disease and the potential predictive value of these parameters.