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Bi, Y.

Publications and source records attributed to Bi, Y..

At least 55 records · Page 3Linked to original sources
bioRxiv2024

Unraveling the impact of congenital deafness on individual brain organization

Research on brain plasticity, particularly in the context of deafness, consistently emphasizes the reorganization of the auditory cortex. However, a critical question arises: to what extent do all individuals with deafness show the same level of reorganization? To address this question, we examined the individual differences in deafness functional connectivity (FC), specifically from the deprived auditory cortex. Our findings demonstrate a remarkable differentiation between individuals deriving from the absence of shared auditory experiences, resulting in heightened FC variability among deaf individuals, compared to more consistent FC in the hearing group. Notably, connectivity to language regions becomes more diverse across individuals in deafness. This does not stem from delayed language acquisition, as it is found in deaf native signers, who are exposed to rich natural language since birth. However, comparing FC diversity between deaf native signers and deaf delayed signers who were deprived of language in early development, we show that language experience also impacts individual differences, although to a more moderate extent. Overall, our research points out the intricate interplay between brain plasticity and individual differences, shedding light on the diverse ways reorganization manifests among individuals. It further joins findings in blindness, showing that individual differences are affected by sensory experience. Finally, these findings highlight the importance of considering individual differences in personalized rehabilitation for hearing loss.

neuroscience↗
bioRxiv2024

Leveraging genomics and temporal high-throughput phenotyping to enhance association mapping and yield prediction in sesame

Sesame (Sesamum indicum) is an important oilseed crop with rising demand due to its high oil quality. To meet these future demands, there is an urgent need to develop and integrate new breeding strategies. While genomic resources have advanced genetic research in sesame, implementation of high-throughput phenotyping and genetic analysis of longitudinal traits remains limited. Here, we combined high-throughput phenotyping and random regression models to investigate the dynamics of plant height, leaf area index, and five spectral vegetation indices throughout the sesame growing seasons in a diversity panel. Modeling the temporal phenotypic and additive genetic trajectories revealed distinct patterns corresponding to the sesame growth cycle. We also conducted longitudinal genomic prediction and association mapping of plant height using various models and cross-validation schemes. Moderate prediction accuracy was obtained when predicting new genotypes at each time point, and moderate to high values were obtained when forecasting future phenotypes. Association mapping revealed three genomic regions in linkage groups 6, 8, and 11 conferring trait variation over time and growth rate. Furthermore, we leveraged correlations between the temporal trait and seed-yield and applied multi-trait genomic prediction. We obtained an improvement over single-trait analysis, especially when phenotypes from earlier time points were used, highlighting the potential of using a high-throughput phenotyping platform as a selection tool. Our results shed light on the genetic control of longitudinal traits in sesame and underscore the potential of high-throughput phenotyping to detect a wide range of traits and genotypes that can inform sesame breeding efforts to enhance yield.

plant biology↗
bioRxiv2024

Gray Matters: An Efficient Vision Transformer GAN Framework for Predicting Functional Network Connectivity Biomarkers from Brain Structure

The field of brain connectivity research has under-gone revolutionary changes thanks to state-of-the-art advancements in neuroimaging, particularly regarding structural and functional magnetic resonance imaging (MRI). To navigate the intricate neural dynamics, one must possess a keen comprehension of the interdependent links between structure and function. Such relationships are understudied as they are complex and likely nonlinear. To address this, we created a new generative deep learning architecture using a conditional efficient vision transformer generative adversarial network (cEViTGAN) to capture the distinct information in structural and functional MRI of the human brain. Our model generates functional network connectivity (FNC) matrices directly from three-dimensional sMRI data. Two pioneering innovations are central to our approach. First, we use a novel linear embedding method for structural MRI (sMRI) data that retains the 3D spatial detail. This embedding is best for representative learning, and when used on a consistent dataset, and shows that it is good at upstream classification assignments. To estimate neural biomarkers, we need to process much smaller patches using ViT-based architectures, which usually makes the computations more difficult because of the self-attention operations. We present a new, lightweight self-attention mechanism to address this challenge. Our mechanism not only overcomes computational shortcomings of traditional softmax self-attention but also surpasses pure linear self-attention models in accuracy and performance. This optimization enables us to analyze even the tiniest neuroanatomical details with exceptional precision. Our model allows for the identification of functional network connectivity (FNC) with 74.2% accuracy and also predicts subject differences in FNC for schizophrenia patients versus controls. The results are intriguing and suggest the links between gray matter volume and brain function may be stronger than previously considered.

neuroscience↗
bioRxiv2023

MERITS: a web-based integrated MycobacterialPE/PPE protein database

MotivationPE/PPE proteins, highly abundant in the Mycobacterium genome, play a vital role in virulence and immune modulation. Understanding their functions is key to comprehending the internal mechanisms of Mycobacterium. However, a lack of dedicated resources has limited research into PE/PPE proteins. ResultsAddressing this gap, we introduce MERITS, a comprehensive 3D structure database specifically designed for PE/PPE proteins. MERITS hosts 22,353 non-redundant PE/PPE proteins, encompassing details like physicochemical properties, subcellular localisation, post-translational modification sites, protein functions, and measures of antigenicity, toxicity, and allergenicity. MERITS also includes data on their secondary and tertiary structure, along with other relevant biological information. MERITS is designed to be user-friendly, offering interactive search and data browsing Features to aid researchers in exploring the potential functions of PE/PPE proteins. MERITS is expected to become a crucial resource in the field, aiding in developing new diagnostics and vaccines by elucidating the sequence-structure-functional relationships of PE/PPE proteins. Availability and implementationMERITS is freely accessible at http://merits.unimelb-biotools.cloud.edu.au/.

bioinformatics↗
bioRxiv2023

Allocator is a graph neural network-based framework for mRNA subcellular localization prediction

MotivationThe asymmetrical distribution of expressed mRNAs tightly controls the precise synthesis of proteins within human cells. This non-uniform distribution, a cornerstone of developmental biology, plays a pivotal role in numerous cellular processes. To advance our comprehension of gene regulatory networks, it is essential to develop computational tools for accurately identifying the subcellular localizations of mRNAs. However, considering multi-localization phenomena remains limited in existing approaches, with none considering the influence of RNAs secondary structure. ResultsIn this study, we propose Allocator, a multi-view parallel deep learning framework that seamlessly integrates the RNA sequence-level and structure-level information, enhancing the prediction of mRNA multi-localization. The Allocator models equip four efficient feature extractors, each designed to handle different inputs. Two are tailored for sequence-based inputs, incorporating multilayer perceptron and multi-head self-attention mechanisms. The other two are specialized in processing structure-based inputs, employing graph neural networks. Benchmarking results underscore Allocators superiority over state-of-the-art methods, showcasing its strength in revealing intricate localization associations. AvailabilityThe webserver of Allocator is available at http://Allocator.unimelb-biotools.cloud.edu.au; the source code and datasets are available at https://github.com/lifuyi774/Allocator

bioinformatics↗
bioRxiv2023

Nuclear Pyruvate Dehydrogenase Complex Regulates Histone Acetylation and Transcriptional Regulation in the Ethylene Response

Ethylene plays its essential roles in plant development, growth, and defense responses by controlling the transcriptional reprograming, in which EIN2-C-directed regulation of histone acetylation is the first key-step for chromatin to perceive ethylene signaling. But how the nuclear acetyl coenzyme A (acetyl CoA) is produced to ensure the ethylene-mediated histone acetylation is unknown. Here we report that ethylene triggers the accumulation of the pyruvate dehydrogenase complex (PDC) in the nucleus to synthesize nuclear acetyl CoA to regulate ethylene response. PDC is identified as an EIN2-C nuclear partner, and ethylene triggers its nuclear accumulation. Mutations in PDC lead to an ethylene-hyposensitivity that results from the reduction of histone acetylation and transcription activation. Enzymatically active nuclear PDC synthesize nuclear acetyl CoA for EIN2-C-directed histone acetylation and transcription regulation. These findings uncover a mechanism by which PDC-EIN2 converges the mitochondrial enzyme mediated nuclear acetyl CoA synthesis with epigenetic and transcriptional regulation for plant hormone response.

plant biology↗
bioRxiv2023

Geranylgeranyl pyrophosphate (GGPP) is associated with hepatic lipid accumulation and insulin resistance in MAO by prenylating Perilipin4

Metabolically Abnormal Obesity (MAO) is characterized by hepatic steatosis and type 2 diabetes (T2D), in contrast to Metabolically Healthy Obesity (MHO). In this study, we investigated the role of hepatic geranylgeranyl pyrophosphate (GGPP), a metabolite of the mevalonate (MVA) pathway, in regulating the differences in lipid metabolism between MAO and MHO. Our findings revealed that GGPP levels were significantly elevated in individuals with MAO, and deficiency of GGPP in the liver ameliorated the defects associated with MAO. Furthermore, we discovered that the prenylation of the lipid droplet-associated protein Perilipin 4 by GGPP enhances the formation of large lipid droplets, thereby exacerbating hepatic lipid accumulation and insulin resistance. Notably, the inhibitor DGBP, targeting the GGPP synthase Ggpps, effectively attenuated the traits of MAO, offering novel insights into the treatment of this condition.

pathology↗
bioRxiv2023

A Novel Fluorogenic Spin Probe for Real-time Monitoring of Mitochondrial Reactive Oxygen Species (mROS) Generation in an in vitro Stroke Model

The extent of oxidative damage caused by a stroke can be assessed by monitoring mitochondrial reactive oxygen species (mROS), which can further aid in early diagnosis and evaluation of treatment efficacy. Mitochondrial ROS have been identified as a potential biomarker for stroke, as they are known to cause oxidative stress and mitochondrial dysfunction in brain tissue. However, monitoring reversible mROS levels has been challenging due to the lack of appropriate probes. In this study, we developed a fluorogenic spin probe, Mito-RT-ROS, based on the rhodamine skeleton, which demonstrated good sensitivity for reversible monitoring of mROS in a physiological environment such as starvation and recovery. We also evaluated the probes efficacy in an in vitro stroke model for real-time tracking of mROS generation in a pathological setting.

neuroscience↗
bioRxiv2023

Functional connectome through the human life span

The lifespan growth of the functional connectome remains unknown. Here, we assemble task-free functional and structural magnetic resonance imaging data from 33,250 individuals aged 32 postmenstrual weeks to 80 years from 132 global sites. We report critical inflection points in the nonlinear growth curves of the global mean and variance of the connectome, peaking in the late fourth and late third decades of life, respectively. After constructing a fine-grained, lifespan-wide suite of system-level brain atlases, we show distinct maturation timelines for functional segregation within different systems. Lifespan growth of regional connectivity is organized along a primary-to-association cortical axis. These connectome-based normative models reveal substantial individual heterogeneities in functional brain networks in patients with autism spectrum disorder, major depressive disorder, and Alzheimers disease. These findings elucidate the lifespan evolution of the functional connectome and can serve as a normative reference for quantifying individual variation in development, aging, and neuropsychiatric disorders.

neuroscience↗
bioRxiv2023

A Multimodal Vision Transformer for InterpretableFusion of Functional and Structural NeuroimagingData

Deep learning models, despite their potential for increasing our understanding of intricate neuroimaging data, can be hampered by challenges related to interpretability. Multimodal neuroimaging appears to be a promising approach that allows us to extract supplementary information from various imaging modalities. Its noteworthy that functional brain changes are often more pronounced in schizophrenia, albeit potentially less reproducible, while structural MRI effects are more replicable but usually manifest smaller effects. Instead of conducting isolated analyses for each modality, the joint analysis of these data can bolster the effects and further refine our neurobiological understanding of schizophrenia. This paper introduces a novel deep learning model, the multimodal vision transformer (MultiViT), specifically engineered to enhance the accuracy of classifying schizophrenia by using structural MRI (sMRI) and functional MRI (fMRI) data independently and simultaneously leveraging the combined information from both modalities. This study uses functional network connectivity data derived from a fully automated independent component analysis method as the fMRI features and segmented gray matter volume (GMV) as the sMRI features. These offer sensitive, high-dimensional features for learning from structural and functional MRI data. The resulting MultiViT model is lightweight and robust, outperforming unimodal analyses. Our approach has been applied to data collected from control subjects and patients with schizophrenia, with the MultiViT model achieving an AUC of 0.833, which is significantly higher than the average 0.766 AUC for unimodal baselines and 0.78 AUC for multimodal baselines. Advanced algorithmic approaches for predicting and characterizing these disorders have consistently evolved, though subject and diagnostic heterogeneity pose significant challenges. Given that each modality provides only a partial representation of the brain, we can gather more comprehensive information by harnessing both modalities than by relying on either one independently. Furthermore, we conducted a saliency analysis to gain insights into the co-alterations in structural gray matter and functional network connectivity disrupted in schizophrenia. While its clear that the MultiViT model demonstrates differences compared to previous multimodal methods, the specifics of how it compares to methods such as MCCA and JICA are still under investigation, and more research is needed in this area. The findings underscore the potential of interpretable multimodal data fusion models like the MultiViT, highlighting their robustness and potential in the classification and understanding of schizophrenia.

neuroscience↗
bioRxiv2023

Structure-based virtual screening identifies small molecule inhibitors of O-fucosyltransferase SPINDLY

Protein O-glycosylation is a nutrient-signaling mechanism that plays essential roles in maintaining cellular homeostasis across different species. In plants, SPINDLY (SPY) and SECRET AGENT (SEC) catalyze posttranslational modifications of hundreds of intracellular proteins by O-fucose and O-linked N-acetylglucosamine, respectively. SPY and SEC play overlapping roles in cellular regulation and loss of both SPY and SEC causes embryo lethality in Arabidopsis. Using structure-based virtual screening of chemical libraries followed by in vitro and in planta assays, we identified a SPY O-fucosyltransferase inhibitor (SOFTI). Computational analyses predicted that SOFTI binds to the GDP-fucose-binding pocket of SPY and competitively inhibits GDP-fucose binding. In vitro assays confirmed that SOFTI interacts with SPY and inhibits its O-fucosyltransferase activity. Docking analysis identified additional SOFTI analogs that showed stronger inhibitory activities. SOFTI treatment of Arabidopsis seedlings decreased protein O-fucosylation and caused phenotypes similar to the spy mutants, including early seed germination, increased root hair density, and defect in sugar-dependent growth. By contrast, SOFTI had no visible effect on the spy mutant. Similarly, SOFTI inhibited sugar-dependent growth of tomato seedlings. These results demonstrate that SOFTI is a specific SPY O-fucosyltransferase inhibitor and a useful chemical tool for functional studies of O-fucosylation and potentially for agricultural management.

plant biology↗
bioRxiv2023

The neural organization of visual information in the auditory cortex of the congenitally deaf

Neuroplasticity is the ability of the human brain to reorganize and modify its activity throughout life. In congenital deafness, sensory-deprived cortex can be recruited to represent sensory information belonging to other modalities, a process known as cross-modal plasticity. Previous studies have indicated that the auditory cortex of congenitally deaf, but not of hearing individuals, is recruited during visual tasks. However, it is not clear to what extent, and how, these cross-modal responses in the deprived auditory cortex represent low-level visual spatial information or map the visual field. Here, we addressed this question directly in an fMRI case-study, aiming to map retinotopic features in the auditory cortex. Two congenitally deaf and one hearing participant went through a conventional retinotopy fMRI experiment with visual stimuli designed to map the visual system. Using population receptive field (pRF) modelling, we revealed retinotopic-related responses in the auditory cortex of the deaf, but not in the hearing. These responses, that were mostly lateralized to the right hemisphere, represented the contralateral visual field, and were characterized by large receptive fields, centred to near foveal areas. Interestingly, we found that these responses to visual stimuli predominantly reflected negative BOLD signals in the auditory cortex of the deaf, suggesting that visual information might be represented through cross-modal deactivation signals.

neuroscience↗
bioRxiv2022

Multi-environment analysis enhances genomic prediction accuracy of agronomic traits in sesame

Sesame is an ancient oilseed crop containing many valuable nutritional components. Recently, the demand for sesame seeds and their products has increased worldwide, making it necessary to enhance the development of high-yielding cultivars. One approach to enhance genetic gain in breeding programs is genomic selection. However, studies on genomic selection and genomic prediction in sesame are limited. In this study, we performed genomic prediction for agronomic traits using the phenotypes and genotypes of a sesame diversity panel grown under Mediterranean climatic conditions over two growing seasons. We aimed to assess the accuracy of prediction for nine important agronomic traits in sesame using single- and multi-environment analyses. In single-environment analysis, genomic best linear unbiased prediction, BayesB, BayesC, and reproducing kernel Hilbert spaces models showed no substantial differences. The average prediction accuracy of the nine traits across these models ranged from 0.39-0.79 for both growing seasons. In the multi-environment analysis, the marker-by-environment interaction model, which decomposed the marker effects into components shared across environments and environment-specific deviations, improved the prediction accuracies for all traits by 15%-58% compared to the single-environment model, particularly when borrowing information from other environments was made possible. Our results showed that single-environment analysis produced moderate-to-high genomic prediction accuracy for agronomic traits in sesame. The multi-environment analysis further enhanced this accuracy by exploiting marker-by-environment interaction. We concluded that genomic prediction using multi-environmental trial data could improve efforts for breeding cultivars adapted to the semi-arid Mediterranean climate.

genetics↗
bioRxiv2022

Dynamic Interaction Learning and MultimodalRepresentation for Drug Response Prediction

Mining multimodal pharmaceutical data is crucial for in-silico drug candidate screening and discovery. A daunting challenge of integrating multimodal data is to enable dynamic feature modeling generalizable for real-world applications. Unlike conventional approaches using a simple concatenation with fixed parameters, in this paper, we develop a dynamic interaction learning network to adaptively integrate drug and different reactants on multimodal tasks towards robust drug response prediction. The primary objective of dynamic learning falls into two key aspects: at micro-level, we aim to dynamically search specific relational patterns on the whole reactant range for each drug-reactant pair; at macro-level, drug features can be used to adaptively correlate with different reactants. Extensive experiments demonstrate the validity of our approach in both drug protein interaction (DPI) and cancer drug response (CDR) tasks. Our approach achieves superior performance on both DPI (AUC = 0.967) and CDR (AUC = 0.932) tasks, outperforming competitive baselines from four real-world, drug-outcome datasets. In addition, the performance on the challenging blind subsets is remarkably improved, where AUC value increases from 0.843 to 0.937 on blind protein set of DPI task, and Pearsons correlation value increases from 0.516 to 0.566 on blind drug set of CDR task. A series of case studies highlight the potential generalization and interpretability of dynamic learning in the in-silico drug response assessment.

bioinformatics↗
bioRxiv2022

Early-life language deprivation affects specific neural mechanisms of semantic representations

One signature of the human brain is its ability to derive knowledge from language inputs, in addition to nonlinguistic sensory channels such as vision and touch. How does human language experience specifically modulate the way in which semantic knowledge is stored in the human brain? We investigated this question using a unique early-life language-deprivation human model: early deaf adults who are born to hearing parents and thus had delayed acquisition of any natural human language (speech or sign), with early deaf adults who acquired sign language from birth as nonlinguistic sensory experience controls. Neural responses in a meaning judgment task with 90 written words that were familiar to both groups were measured using fMRI. The early language-deprived group, compared with the deaf control group, showed reduced semantic sensitivity, in both multivariate pattern (semantic structure encoding) and univariate (abstractness effect) analyses, in the left dorsal anterior temporal lobe (dATL). These results provide positive, causal evidence that the neural semantic representation in dATL is specifically supported by language, as a unique mechanism of representing (abstract) semantic space, beyond the sensory-derived semantic representations distributed in the other cortical regions.

neuroscience↗
bioRxiv2022

Induction of Autophagy Prevents Mutant SOD1G93A Related Neurotoxicity in Experimental Models of Amyotrophic Lateral Sclerosis (ALS)

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disorder resulting from the progressive loss of both upper and lower motor neurons in the cerebral cortex, brainstem, and spinal cord. Currently, there are only two drugs, Riluzole (Rilutek) and Edaravone (Radicava), approved by FDA for ALS treatment. These two drugs are very expensive with only a few months of life extension. So far, there is no cure for ALS. Aberrant protein aggregation in motor neurons is an intracellular hallmark of ALS. The disturbance in protein homeostasis may contribute to the onset and progression of ALS. Autophagy plays an important role in degrading misfolded proteins, thereby preventing their aggregation. Pharmacological manipulation of autophagy has been proposed as a new therapeutic approach for treating ALS. IADB, a novel indole alkaloid derivative, has been reported to exert mitochondrial protection and cardioprotection through its autophagy-modulating potential. Our present study attempted to examine whether IADB has therapeutic potential in SOD1G93A-associated experimental models of ALS. We found that IADB could promote the clearance of SOD1G93A aggregates and reduce the overproduction of mitochondrial reactive oxygen species (mtROS) in motor neuron-like NSC-34 cells transfected with SOD1G93A. We further examined the IADB in a SOD1-G93A mouse model of ALS. Administration of IADB started at the age of 55 days until the end stage of the disease. IADB treatment significantly increased LC3-II levels and decreased human SOD1 levels and p62 expression in the spinal cords of SOD1G93A mice, suggesting that IADB treatment could induce autophagy activation and promote clearance of mutant SOD1 aggregates in this mouse model of ALS. Moreover, IADB treatment could alleviate the activation of microglia and astrocytes and reduce mitochondrial oxidative damage in the spinal cord of SOD1G93A mice. Finally, we demonstrated that IADB treatment could improve motor performance and delay the onset and progression of the disease in a mouse model of ALS. The neuroprotective effects of IADB may mainly originate from its autophagy-promoting property.

neuroscience↗
bioRxiv2022

Evaluating metabolic and genomic data for predicting grain traits under high night temperature stress in rice

The asymmetric increase in average nighttime temperatures relative to increase in average daytime temperatures due to climate change is decreasing grain yield and quality in rice. Therefore, a better understanding of the impact of higher night temperature on single grain at whole genome level is essential for future development of more resilient rice. We investigated the utility of metabolites obtained from grains to classify high night temperature conditions of genotypes, and metabolites and single nucleotide polymorphisms to predict grain length, width, and perimeter phenotypes using a rice diversity panel. We found that the metabolic profiles of rice genotypes alone could be used to classify control and high night temperature conditions with high accuracy using random forest or extreme gradient boosting. The best linear unbiased prediction and BayesC showed greater metabolic prediction performance than machine learning models for grain-size phenotypes. Metabolic prediction was most effective for grain width, resulting in the highest prediction performance. Genomic prediction performed better than metabolic prediction. Integrating metabolites and genomics simultaneously in a prediction model slightly improved prediction performance. We did not observe a difference in prediction between the control and high night temperature conditions. Several metabolites were identified as auxiliary phenotypes that could be used to enhance the multi-trait genomic prediction of grain-size phenotypes. Our results showed that, in addition to single nucleotide polymorphisms, metabolites collected from grains offer rich information to perform predictive analyses, including classification modeling of high night temperature responses and regression modeling of grain size-related phenotypes in rice.

genetics↗
bioRxiv2022

Neural correlates of an illusive sense of agency caused by virtual reality

Sense of agency (SoA) is the sensation that self-actions lead to ensuing perceptual consequences. The prospective mechanism emphasizes that SoA arises from motor prediction and its comparison with actual action outcomes, while the reconstructive mechanism stresses that SoA emerges from retrospective causal processing about the action outcomes. Consistent with the prospective mechanism, motor planning regions were identified by neuroimaging studies using the temporal binding effect, a behavioral measure often linked to implicit SoA. Yet, temporal binding also occurs during passive observation of anothers action, lending support to the reconstructive mechanism, but its neural correlates remain unexplored. Here, we employed virtual reality (VR) to modulate such observation-based SoA and examined it with functional magnetic resonance imaging. After manipulating an avatar hand in VR, participants passively observed an avatars "action" and showed a significant increase in temporal binding. The binding effect was associated with the right angular gyrus and inferior parietal lobule, which are critical nodes for inferential and agency processing. These results suggest that the experience of controlling an avatar may potentiate inferential processing within the right inferior parietal cortex and give rise to the illusionary sense of agency without voluntary action.

neuroscience↗