Biology subjects

Ding, Y.

Publications and source records attributed to Ding, Y..

At least 163 records · Page 9Linked to original sources

bioRxiv2023

CD4+ T cell senescence is associated with reduced reactogenicity in severe/critical COVID-19

BackgroundAging is a critical risk factor for unfavorable clinical outcomes among COVID-19 patients and may affect vaccine efficacy. However, whether the senescence of T cells impact the progression to severe COVID-19 in the elderly individuals remains unclear. MethodsBy using flow cytometry, we analyzed the frequency of senescent T cells (Tsens) in the peripheral blood from 100 elderly patients hospitalized for COVID-19 and compared the difference between mild/moderate and severe/critical illness. We also assessed correlations between the percentage of Tsens and the quantity and quality of spike-specific antibodies by ELISA, neutralizing antibody test kit and Elispot assay respectively, cytokine production profile of COVID-19 reactive T cells as well as plasma soluble factors by cytometric bead array (CBA). ResultsWe found a significant elevated level of CD4+ Tsens in severe/critical disease compared to mild/moderate illness and patients with a higher level of CD4+ Tsens (>19.78%) showed a decreased survival rate as compared to those with a lower level (<19.78%), especially in the breakthrough infection. The percentage of CD4+ Tsens was negatively correlated with spike-specific antibody titers, neutralization ability and COVID-19 reactive IL-2+ CD4+ T cells. Additionally, IL-2 producing T cells and plasma levels of IL-2 were positively correlated with antibody levels. ConclusionOur data illustrated that the percentage of CD4+ Tsens in the peripheral blood could act as an efficient biomarker for the capacity of spike-specific antibody production and the prognosis of severe COVID-19, especially in the breakthrough infection. Therefore, restoration of the immune response of CD4+ Tsens is one of the key factors to prevent severe illness and improve vaccine efficacy in older adults.

immunology↗

bioRxiv2023

DHX36 binding induces RNA structurome remolding and regulates RNA abundance via m6A/YTHDF1

RNA structure constitutes a new layer of gene regulatory mechanisms. RNA binding proteins can modulate RNA secondary structures, thus participating in post-transcriptional regulation. The DEAH-box helicase 36 (DHX36) is known to bind and unwind RNA G-quadruplex (rG4) structure but the transcriptome-wide RNA structure remodeling induced by DHX36 binding and the impact on RNA fate remain poorly understood. Here, we investigate the RNA structurome alteration induced by DHX36 depletion. Our findings reveal that DHX36 binding induces structure remodeling not only at the localized binding sites but also on the entire mRNA transcript most pronounced in3UTR regions. DHX36 binding increase structural accessibility at 3UTRs which is correlated with decreased post-transcriptional mRNA abundance. Further analyses and experiments uncover that DHX36 binding sites are enriched for N6-methyladenosine (m6A) modification and YTHDF1 binding; and DHX36 induced structural change may facilitate YTHDF1 binding to m6A sites leading to RNA degradation. Altogether, our findings uncover the structural remodeling effect of DHX36 binding and its impact on RNA abundance through regulating m6A dependent YTHDF1 binding.

molecular biology↗

bioRxiv2023

A versatile framework for drug-target interaction prediction by considering domain specific features

Predicting drug-target interaction (DTI) is a critical and rate-limiting step in drug discovery. Traditional wet-lab experiments are reliable but expensive and time-consuming. Recently, deep learning has revealed itself as a new and promising tool for accelerating the DTI prediction process because its powerful performance. Due to the vast chemical space, the DTI prediction models are typically expected to discover drugs or targets that are absent from the training set. However, generalizing prediction performance to novel drug-target pairs that belong to different distributions is a challenge for deep learning methods. In this work, we propose an Ensemble of models that capture both Domain-generIc and domain-Specific features (E-DIS) to learn diversity domain features and adapt to out-of-distribution (OOD) data. We employed Mixture-of-Experts (MOE) as a domain-specific feature extractor for the raw data to prevent the loss of any crucial features by the encoder during the learning process. Multiple experts are trained on different domains to capture and align domain-specific information from various distributions without accessing any data from unseen domains. We evaluate our approach using four benchmark datasets under both in-domain and cross-domain settings and compare it with advanced approaches for solving OOD generalization problems. The results demonstrate that E-DIS effectively improves the robustness and generalizability of DTI prediction models by incorporating diversity domain features.

bioinformatics↗

bioRxiv2023

The within-person association of relative left frontal activity and vagally mediated heart rate variability not moderated by history of depression

Motivated by the Neurovisceral Integration Model (NVI) of cardiac vagal control, we investigated the relationship between relative left frontal activity (rLFA) and vagally mediated heart rate variability or respiratory sinus arrhythmia (RSA) in 287 participants, half of whom had a history of depression. We hypothesized that there would be a within-person association of rLFA and RSA such that when RSA is lower rLFA would also be lower (Hypothesis I). Moreover, it was hypothesized that this within-subject association would be moderated by a history of depression (Hypothesis II). Metrics of rLFA and RSA were derived from concurrent electroencephalogram and electrocardiogram recordings. The logarithmic difference in EEG alpha power between the homologous right and left electrodes (Ln (Right/Left)) in the frontal region was used to index rLFA. A Hilbert transform was applied to the mean-centered and bandpass-filtered (0.12-.40 Hz) inter-beat interval (IBI) time series to get a fine-grained measure (in the time domain) of RSA. A linear mixed ANOVA model with rLFA as the dependent variable and RSA as the main fixed effect found that participants had less rLFA during epochs when they had lower RSA, which was consistent with the prediction from Hypothesis I. Contrary to the prediction from Hypothesis II, the within-person association of RSA and rLFA was not moderated by a history of depression. However, the association between RSA and rLFA varied across the four pairs of frontal electrodes that we examined. Thus, more research is needed to determine the spatial extent of this association, e.g., examining the relationship between source-localized rLFA and RSA.

neuroscience↗

bioRxiv2023

Comparison of robotic automated and manual injection methods in zebrafish embryos for high throughput RNA silencing using CRISPR-CasRx

Recently, the CRISPR-RfxCas13d (CasRx) system was proven to induce efficient mRNA knockdown in animal embryos. Here we compared the efficiency of CasRx-based RNA depletion with that of Cas9-mediated DNA targeting under the same conditions, using automated robotic and manual injection methods. As a proof-of-principle target, we used the no tail (tbxta) gene in zebrafish embryos, for which knockdown and knockout embryonic phenotypes were easy to be scored. Both Cas9 and CasRx systems induced loss of function phenotypes of tbxta gene. Higher percentage of severe phenotype was observed using Cas9 protein compared to the mRNA while the efficiency was similar in terms of Cas13d protein and mRNA. In addition, both the robotic and manual injection approaches yielded similar percentages of phenotypes and mortality rates. Therefore, our study not only showcases the potential of RNA-targeting CRISPR effectors for precise and potent gene knockdown, but also emphasizes automated microinjection in zebrafish embryos as an excellent alternative to manual methods for achieving gene knockdown at a high throughput level.

bioengineering↗

bioRxiv2023

BertNDA: a Model Based on Graph-Bert and Multi-scale Information Fusion for ncRNA-disease Association Prediction

Non-coding RNAs (ncRNAs) are a class of RNA molecules that lack the ability to encode proteins in human cells, yet play crucial roles in various biological process. Understanding these relationships and how different ncRNAs interact with each other to affect diseases can vastly contribute to their diagnosis, prevention, and treatment. However, predicting tertiary interactions between ncRNA-disease associations by utilizing structural information across multiple scales remains a challenging task. It should be noted that research on predicting tertiary interaction between trinary ncRNA-disease associations is scarce, highlighting the need for further studies in this area. In this work, we propose a predictive framework, called BertNDA, which aims to predict association between miRNA, lncRNA and disease. The framework employs Laplace transform of graph structure and WL (Weisfeiler-Lehman) absolute role coding to extract global information. Local information is identified by the connectionless subgraph which aggregates neighbor feature. Moreover, an EMLP (Element-wise MLP) structure is designed to fuse the multi-scale feature representation of nodes. Furtherly, feature representation is encoded by using a Transformer-encoder structure, the prediction-layer outputs the final correlation between miRNA, lncRNA and diseases. The 5-fold cross-validation result furtherly demonstrates that BertNDA outperforms the state-of-the-art method in predicting assignment. Furthermore, an online prediction platform that embeds our prediction model is designed for users to experience. Overall, our model provides an efficient, accurate, and comprehensive tool for predicting ncRNA-disease associations. The code of our method is available in: https://github.com/zhiweining/BertNDA-main.

bioinformatics↗

bioRxiv2023

Integrated multimodal cell atlas of Alzheimer's disease

Alzheimers disease (AD) is the most common cause of dementia in older adults. Neuropathological and imaging studies have demonstrated a progressive and stereotyped accumulation of protein aggregates, but the underlying molecular and cellular mechanisms driving AD progression and vulnerable cell populations affected by disease remain coarsely understood. The current study harnesses single cell and spatial genomics tools and knowledge from the BRAIN Initiative Cell Census Network to understand the impact of disease progression on middle temporal gyrus cell types. We used image-based quantitative neuropathology to place 84 donors spanning the spectrum of AD pathology along a continuous disease pseudoprogression score and multiomic technologies to profile single nuclei from each donor, mapping their transcriptomes, epigenomes, and spatial coordinates to a common cell type reference with unprecedented resolution. Pseudo-progression analysis showed two major epochs corresponding with a slow early increase in pathology and a later exponential increase that correlated with cognitive decline. The early phase included inflammatory microglial and reactive astrocyte component, as well as a selective loss of Sst+ inhibitory neuron types in superficial cortical layers, loss of myelinating oligodendrocytes, and up-regulation of a re-myelination program by OPCs. The later phase involved loss of excitatory neurons and Pvalb and Vip neuron subtypes also predominantly in superficial layers. These cell vulnerabilities were also seen in prefrontal cortex and replicated by other independent studies when integrated with the BRAIN Initiative reference. Study data and exploratory tools are freely available to accelerate progress in AD research at SEA-AD.org.

genomics↗

bioRxiv2023

KGETCDA: an efficient representation learning framework based on knowledge graph encoder from transformer for predicting circRNA-disease associations

Recent studies have demonstrated the significant role that circRNA plays in the progression of human diseases. Identifying circRNA-disease associations (CDA) in an efficient manner can offer crucial insights into disease diagnosis. While traditional biological experiments can be time-consuming and labor-intensive, computational methods have emerged as a viable alternative in recent years. However, these methods are often limited by data sparsity and their inability to explore high-order information. In this paper, we introduce a novel method named Knowledge Graph Encoder from Transformer for predicting CDA (KGETCDA). Specifically, KGETCDA first integrates more than 10 databases to construct a large heterogeneous non-coding RNA dataset, which contains multiple relationships between circRNA, miRNA, lncRNA and disease. Then, a biological knowledge graph is created based on this dataset and Transformer-based knowledge representation learning and attentive propagation layers are applied to obtain high-quality embeddings with accurately captured high-order interaction information. Finally, multilayer perceptron is utilized to predict the matching scores of CDA based on their embeddings. Our empirical results demonstrate that KGETCDA significantly outperforms other state-of-the-art models. To enhance user experience, we have developed an interactive web-based platform named HNRBase that allows users to visualize, download data and make predictions using KGETCDA with ease.

bioinformatics↗

bioRxiv2023

Duckweed evolution: from land back to water

Terrestialization is supposedly an important evolutionary process plant experience. However, directions of land back to water acquired little attention. Here we integrate multiproxy evidence to elucidate the evolution of duckweed. Three genera of duckweed show chronologically gradient degeneration in roots structure and stomatal function and decrease in lignocellulose content, accompanied by gradual contraction in relevant gene numbers and/or decline in transcription. The gene numbers in the main phytohormonal pathway are also gradually decreased. The co-action of auxin and rhizoid development gene causes a gradual decrease in adventitious roots. The significant expansion of the flavonoid pathway is also related to the adaptation of duckweed to floating growth. This study reconstructs the evolution history of duckweeds from land back to water, reverse to that of early land plants. SummaryWith terrestrialization being the popularly acknowledged plant evolutionary process, little is known about the evolution of higher plant from land back to water. Here we integrate multiproxy evidence to elucidate the gradual reverse evolution of duckweed. Three genera of duckweed show chronologically gradient degeneration in the structure of roots, the function of stomata, and decrease in lignocellulose content, accompanied by gradient contraction in relevant gene numbers and/or decline in transcript expression. The gene numbers in the main phytohormonal pathway are also gradually decreased. The co-action of auxin and rhizoid development gene causes a gradual decrease in adventitious roots. The significant expansion of the flavonoid pathway is also highly related to the adaptation of duckweed to floating growth. Our study combined with the fossil evidence reconstruct the evolution history of duckweeds from land back to water, reverse to that of early land plants. This study reconstructed the process of how a land plant returns to water, a reverse evolutionary approach which is different from what we studied in textbook about plant terrestrialization. This finding could be helpful for us to deeply and widely understand the adaptation of plant to the environment, and to expand and deepen the knowledge of evolution theory.

evolutionary biology↗

bioRxiv2023

Artificially cultivated duckweed: a high-efficiency starch producer

The increasing demand for starch has been a social struggle. We report a new technology that efficiently produces starch from duckweed. Although Landoltia punctata has a dramatic contraction of gene families, its starch content and productivity reached 72.2% (dry basis) and 10.4 g m-2 d-1 in 10 days, equivalent to a yield of 38.0 t ha-1 y-1 under nutrient limitation and CO2 elevation treatment. Meanwhile, we also examined the mechanism of duckweeds high starch accumulation. This is exhibited in the regulation of DNA methylation and transcription factors as well as the significantly up-regulated transcription levels and the increased enzyme activities of key genes in starch biosynthesis. Meantime, while nitrogen redistribution was enhanced, sucrose biosynthesis and transportation, and lignocellulose biosynthesis were all reduced. These alterations led to a reduction in lignocellulose and protein content and ultimately an increase in an accumulation of starch in the chloroplast. This work demonstrates duckweeds potential of being a highly efficient starch producer.

plant biology↗

bioRxiv2023

Genomic clustering by geography not species in taxonomically complex British and Irish eyebrights (Euphrasia)

Genomic studies of incipient speciation are fundamental to understand the origin and establishment of species. However, a wide range of evolutionary processes and complex evolutionary interactions remain to be explored outside of genetically tractable evolutionary and ecological model systems. Here, we study taxonomically complex British and Irish eyebrights (Euphrasia), as a test case for how different evolutionary factors influence species boundaries across geographic space. Euphrasia is a plant genus that has remarkable diversity in ploidy, mating system and ecology. There are 21 British and Irish Euphrasia species, but with species that are exceptionally difficult to identify based on morphology or DNA barcoding. Here, we test the hypothesis that species boundaries are highly permeable, and taxa experience extensive gene flow despite potential barriers such as ploidy and contrasting mating systems. To understand geographic genetic structure and the nature of species differences, we applied genotyping-by-sequencing (GBS) and spatial-aware clustering methods to 378 population samples from 18 British and Irish species. We find the selfing heathland specialist E. micrantha demonstrates genome-wide divergence in Northern Scotland, indicative of a distinct post-glacial colonisation history and the role of a highly selfing mating system in divergence. In contrast, all other genetic clusters correspond to geographic regions, with extensive gene flow between species and a complete absence of species-specific SNPs. Our results reveal the highly permeable species boundaries present in a recently diverging group, with an overriding signal of geographic genetic structure over and above genetic clustering by species.

evolutionary biology↗

bioRxiv2023

Microbially-derived indole-3-acetate alleviates diet induced steatosis and inflammation in mice

Non-alcoholic fatty liver disease (NAFLD) is the most common chronic liver disease in Western countries. There is growing evidence that dysbiosis of the intestinal microbiota and disruption of microbiota-host interactions contribute to the pathology of NAFLD. We previously demonstrated that gut microbiota derived tryptophan metabolite indole-3-acetate (I3A) was decreased in both cecum and liver of high-fat diet-fed mice and attenuated the expression of inflammatory cytokines in macrophages and TNF-a and fatty acid induced inflammatory responses in an aryl-hydrocarbon receptor (AhR) dependent manner in hepatocytes. In this study, we investigated the effect of orally administered I3A in a mouse model of diet induced NAFLD. Western diet (WD)-fed mice given sugar water (SW) with I3A showed dramatically decreased serum ALT, hepatic TG, liver steatosis, hepatocyte ballooning, lobular inflammation, and hepatic production of inflammatory cytokines, compared to WD-fed mice given only SW. Metagenomic analysis show that I3A administration did not significantly modify the intestinal microbiome, suggesting that I3As beneficial effects likely reflect the metabolites direct actions on the liver. Administration of I3A partially reversed WD induced alterations of liver metabolome and proteome, notably, decreasing expression of several enzymes in hepatic lipogenesis and {beta}- oxidation. Mechanistically, we also show that AMP-activated protein kinase (AMPK) mediates the anti-inflammatory effects of I3A in macrophages. The potency of I3A in alleviating liver steatosis and inflammation clearly demonstrates its potential as a therapeutic modality for preventing the progression of steatosis to NASH.

cell biology↗

bioRxiv2023

Orthogonal inducible control of Cas13 circuits enables programmable RNA regulation in mammalian cells

RNA plays an indispensable role in mammalian cell functions. Cas13, a class of RNA-guided ribonuclease, is a flexible tool for modifying and regulating coding and non-coding RNAs, with enormous potential for creating new cell functions. However, the lack of control over Cas13 activity has limited its cell engineering capability. Here, we present the CRISTAL (Control of RNA with Inducible SpliT CAs13 Orthologs and Exogenous Ligands) platform. CRISTAL is powered by a collection (10 total) of orthogonal split inducible Cas13s that can be turned ON or OFF via small molecules in multiple cell types, providing precise temporal control. Also, we engineered Cas13 logic circuits that can respond to endogenous signaling and exogenous small molecule inputs. Furthermore, the orthogonality, low leakiness, and high dynamic range of our inducible Cas13d and Cas13b enable the design and construction of a robust incoherent feedforward loop, leading to near-perfect and tunable adaptation response. Finally, using our inducible Cas13s, we achieve simultaneous multiplexed control of multiple genes in vitro and in mice. Together, our CRISTAL design represents a powerful platform for precisely regulating RNA dynamics to advance cell engineering and elucidate RNA biology.

synthetic biology↗

bioRxiv2023

Identification of RNA-Binding Protein Targets with HyperTRIBE in Saccharomyces cerevisiae

As a mater regulator in cells, RNA-binding protein (RBP) plays critical roles in organismal development, metabolism and various diseases. It controls gene expression at multiple levels mostly by specific recognition of target RNA. The traditional CLIP-seq method to detect transcriptome-wide RNA targets of RBP is less efficient in yeasts due to their cell walls. Here, we established an efficient HyperTRIBE (Targets of RNA-binding proteins Identified By Editing) in yeast, by fusing a RBP to the hyper active catalytic domain of human RNA editing enzyme ADAR2 and expressing the fusion protein in yeast cells. The target transcripts of RBP were marked with new RNA editing events and identified by high-throughput sequencing. We successfully applied TRIBE to identifying the RNA targets of two yeast RBPs, KHD1 and BFR1. The antibody-free HyperTRIBE has competitive advantages including low background, high sensitivity and reproducibility, and a simple library preparation procedure, which provides a reliable strategy for RBP target identification in Saccharomyces cerevisiae.

molecular biology↗

bioRxiv2023

RNA Sensing and Innate Immunity Constitutes a Barrier for Interspecies Chimerism

Interspecies chimera formation with human pluripotent stem cells (PSCs) holds great promise to generate humanized animal models and provide donor organs for transplant. However, the approach is currently limited by low levels of human cells ultimately represented in chimeric embryos. Different strategies have been developed to improve chimerism by genetically editing donor human PSCs. To date, however, it remains unexplored if human chimerism can be enhanced in animals through modifying the host embryos. Leveraging the interspecies PSC competition model, here we discovered retinoic acid-inducible gene I (RIG-I)-like receptor (RLR) signaling, an RNA sensor, in "winner" cells plays an important role in the competitive interactions between co-cultured mouse and human PSCs. We found that genetic inactivation of Ddx58/Ifih1-Mavs-Irf7 axis compromised the "winner" status of mouse PSCs and their ability to outcompete PSCs from evolutionarily distant species during co-culture. Furthermore, by using Mavs-deficient mouse embryos we substantially improved unmodified donor human cell survival. Comparative transcriptome analyses based on species-specific sequences suggest contact-dependent human-to-mouse transfer of RNAs likely plays a part in mediating the cross-species interactions. Taken together, these findings establish a previously unrecognized role of RNA sensing and innate immunity in "winner" cells during cell competition and provides a proof-of-concept for modifying host embryos, rather than donor PSCs, to enhance interspecies chimerism.

cell biology↗

bioRxiv2023

Dissecting embryonic and extra-embryonic lineage crosstalk with stem cell co-culture

Faithful embryogenesis requires precise coordination between embryonic and extraembryonic tissues. Although stem cells from embryonic and extraembryonic origins have been generated for several mammalian species(Bogliotti et al., 2018; Choi et al., 2019; Cui et al., 2019; Evans and Kaufman, 1981; Kunath et al., 2005; Li et al., 2008; Martin, 1981; Okae et al., 2018; Tanaka et al., 1998; Thomson et al., 1998; Vandevoort et al., 2007; Vilarino et al., 2020; Yu et al., 2021b; Zhong et al., 2018), they are grown in different culture conditions with diverse media composition, which makes it difficult to study cross-lineage communication. Here, by using the same culture condition that activates FGF, TGF-{beta} and WNT signaling pathways, we derived stable embryonic stem cells (ESCs), extraembryonic endoderm stem cells (XENs) and trophoblast stem cells (TSCs) from all three founding tissues of mouse and cynomolgus monkey blastocysts. This allowed us to establish embryonic and extraembryonic stem cell co-cultures to dissect lineage crosstalk during early mammalian development. Co-cultures of ESCs and XENs uncovered a conserved and previously unrecognized growth inhibition of pluripotent cells by extraembryonic endoderm cells, which is in part mediated through extracellular matrix signaling. Our study unveils a more universal state of stem cell self-renewal stabilized by activation, as opposed to inhibition, of developmental signaling pathways. The embryonic and extraembryonic stem cell co-culture strategy developed here will open new avenues for creating more faithful embryo models and developing more developmentally relevant differentiation protocols.

developmental biology↗

bioRxiv2023

Secretory IgM (sIgM) is an ancient master regulator of microbiota homeostasis and metabolism

The co-evolution between secretory immunoglobulins (sIgs) and microbiota began with the emergence of IgM over half a billion years ago. Yet, IgM function in vertebrates is mostly associated with systemic immunity against pathogens. sIgA and sIgT are the only sIgs known to be required in the control of microbiota homeostasis in warm- and cold-blooded vertebrates respectively. Recent studies have shown that sIgM coats a large proportion of the gut microbiota of humans and teleost fish, thus suggesting an ancient and conserved relationship between sIgM and microbiota early in vertebrate evolution. To test this hypothesis, we temporarily and selectively depleted IgM from rainbow trout, an old bony fish species. IgM depletion resulted in a drastic reduction in microbiota IgM coating levels and losses in gutassociated bacteria. These were accompanied by bacterial translocation, severe gut tissue damage, inflammation and dysbiosis predictive of metabolic shifts. Furthermore, depletion of IgM resulted in body weight loss and lethality in an experimental colitis model. Recovery of sIgM to physiological levels restores tissue barrier integrity, while microbiome homeostasis and their predictive metabolic capabilities are not fully restituted. Our findings uncover a previously unrecognized role of sIgM as an ancient master regulator of microbiota homeostasis and metabolism and challenge the current paradigm that sIgA and sIgT are the key vertebrate sIgs regulating microbiome homeostasis. One-Sentence SummaryIgM, the most ancient and conserved immunoglobulin in jawed vertebrates, is required for successful symbiosis with the gut microbiota.

immunology↗

bioRxiv2023

Integrating simultaneous motor imagery and spatial attention for EEG-BCI control

Objective: EEG-based brain-computer interfaces (BCI) are non-invasive approaches for replacing or restoring motor functions in impaired patients, and direct brain-to-device communication in the general population. Motor imagery (MI) is one of the most used BCI paradigms, but its performance varies across individuals and certain users require substantial training to develop control. In this study, we propose to integrate a MI paradigm simultaneously with a recently proposed Overt Spatial Attention (OSA) paradigm, to accomplish BCI control. Methods: We evaluated a cohort of 25 human subjects ability to control a virtual cursor in one- and two-dimensions over 5 BCI sessions. The subjects used 5 different BCI paradigms: MI alone, OSA alone, MI and OSA simultaneously towards the same target (MI+OSA), and MI for one axis while OSA controls the other (MI/OSA and OSA/MI). Results: Our results show that MI+OSA reached the highest average online performance in 2D tasks at 49% Percent Valid Correct (PVC), statistically outperforms MI alone (42%), and was higher, but not statistically significant, than OSA alone (45%). MI+OSA had a similar performance to each subjects best individual method between MI alone and OSA alone (50%) and 9 subjects reached their highest average BCI performance using MI+OSA. Conclusion: Integrating MI and OSA leads to improved performance over MI alone at the group level and is the best BCI paradigm option for some subjects. Significance: This work proposes a new BCI control paradigm that integrates two existing paradigms and demonstrates its value by showing that it can improve users BCI performance.

bioengineering↗