Biology subjects

Ren, Z.

Publications and source records attributed to Ren, Z..

At least 55 records · Page 3Linked to original sources

bioRxiv2025

High-speed mapping of whole-mouse peripheral nerves at subcellular resolution

In contrast to the rapid advancements in mesoscale connectomic mapping of the mammalian brain, similar mapping the peripheral nervous system has remained challenging due to the bodys size and complexity. Here, we present a high-speed blockface volumetric imaging system with an optimized workflow of whole-body clearing, capable of imaging the entire adult mouse at micrometer resolution within 40 hours. Three-dimensional reconstruction of individual spinal fibers in Thy1-EGFP mice reveals distinct morphological features of sensory and motor projections along the ventral and dorsal rami. Immunostaining facilitates body-wide mapping of sympathetic nerves and their branches, highlighting their perivascular patterns in limb muscles, bones, and most visceral organs. Viral tracing elucidates the fine architecture of vagus nerves and individual vagal fibers, revealing unexpected projection routes to various organs. Our approach offers an effective means to achieve a holistic understanding of cellular-level interactions among different systems that underlie body physiology and disease. HighlightsO_LIImaging of whole adult mouse at uniform subcellular resolution within 40 hours C_LIO_LIReconstruction of 191 thoracic spinal neuron projections for Thy1-EGFP mice C_LIO_LIMapping of immunolabeled sympathetic projections and their perivascular patterns C_LIO_LIResolving virus-labeled architecture of vagus nerves and complex single-fiber routes C_LI

neuroscience↗

bioRxiv2024

An AI-Cyborg System for Adaptive Intelligent Modulation of Organoid Maturation

Recent advancements in flexible bioelectronics have enabled continuous, long-term stable interrogation and intervention of biological systems. However, effectively utilizing the interrogated data to modulate biological systems to achieve specific biomedical and biological goals remains a challenge. In this study, we introduce an AI-driven bioelectronics system that integrates tissue-like, flexible bioelectronics with cyber learning algorithms to create a long-term, real-time bidirectional bioelectronic interface with optimized adaptive intelligent modulation (BIO-AIM). When integrated with biological systems as an AI-cyborg system, BIO-AIM continuously adapts and optimizes stimulation parameters based on stable cell state mapping, allowing for real-time, closed-loop feedback through tissue-embedded flexible electrode arrays. Applied to human pluripotent stem cell-derived cardiac organoids, BIO-AIM identifies optimized stimulation conditions that accelerate functional maturation. The effectiveness of this approach is validated through enhanced extracellular spike waveforms, increased conduction velocity, and improved sarcomere organization, outperforming both fixed and no stimulation conditions.

bioengineering↗

bioRxiv2024

A Multi-Modal Genomic Knowledge Distillation Framework for Drug Response Prediction

Precision oncology utilizes genomic data to tailor treatment to individuals. Cancer drug sensitivity studies can predict the response levels of different drugs for the same cultured cancer cell line, which is beneficial for personalized medicine. Recent studies have demonstrated that integrating multi-modal genomic data, e.g., gene expression, mutation, copy number alteration, methylation, can provide comprehensive knowledge and improve drug response prediction. Although multimodal genomic profiles are generally available from public datasets, only gene expression data is commonly used in clinical settings. In this study, we propose a framework for privileged information knowledge distillation to transfer knowledge from a multi-modal genomic teacher network, using only gene expression for inference. Specifically, we train a teacher network by feature re-weighting based on inter-modality dependencies and align the inter-sample correlations through our proposed relation-aware differentiation distillation. Experiments on the Genomics of Drug Sensitivity in Cancer (GDSC) dataset demonstrate that our framework improves drug response prediction by about 6% compared to the baseline and outperforms state-of-the-art methods. Transferable studies performed on missing GDSC data and clinical datasets further confirm the feasibility of our model for predicting drug responses using only gene expression data.

bioinformatics↗

bioRxiv2024

Polybacterial Intracellular Macromolecules Shape Single-Cell Epikine Profiles in Upper Airway Mucosa

The upper airway, particularly the nasal and oral mucosal epithelium, serves as a primary barrier for microbial interactions throughout life. Specialized niches like the anterior nares and the tooth are especially susceptible to dysbiosis and chronic inflammatory diseases. To investigate host-microbial interactions in mucosal epithelial cell types, we reanalyzed our single-cell RNA sequencing atlas of human oral mucosa, identifying polybacterial signatures (20% Gram-positive, 80% Gram-negative) within both epithelial- and stromal-resident cells. This analysis revealed unique responses of bacterial-associated epithelia when compared to two inflammatory disease states of mucosa. Single-cell RNA sequencing, in situ hybridization, and immunohistochemistry detected numerous persistent macromolecules from Gram-positive and Gram-negative bacteria within human oral keratinocytes (HOKs), including bacterial rRNA, mRNA and glycolipids. Epithelial cells with higher concentrations of 16S rRNA and glycolipids exhibited enhanced receptor-ligand signaling in vivo. HOKs with a spectrum of polybacterial intracellular macromolecular (PIM) concentrations were challenged with purified exogenous lipopolysaccharide, resulting in the synergistic upregulation of select innate (CXCL8, TNFSF15) and adaptive (CXCL17, CCL28) epikines. Notably, endogenous lipoteichoic acid, rather than lipopolysaccharide, directly correlated with epikine expression in vitro and in vivo. Application of the Drug2Cell algorithm to health and inflammatory disease data suggested altered drug efficacy predictions based on PIM detection. Our findings demonstrate that PIMs persist within mucosal epithelial cells at variable concentrations, linearly driving single-cell effector cytokine expression and influencing drug responses, underscoring the importance of understanding host-microbe interactions and the implications of PIMs on cell behavior in health and disease at single-cell resolution. One-sentence summaryThis study reveals how persistent intracellular bacterial macromolecules in mucosal epithelial cells drive inflammatory signaling, offering new insights into microbial-host interactions and their potential impact on inflammatory disease treatment and drug efficacy.

cell biology↗

bioRxiv2024

Wheat TaNADPO promotes spot blotch resistance

Bipolaris sorokiniana is a common soil-borne fungal pathogen that can infect various organs of wheat (Triticum aestivum L.), causing diseases such as spot blotch, common root rot, head blight, and black point. The genetic basis of wheat resistance to B. sorokiniana is not yet fully understood. In this study, a natural population of 1,302 global common wheat germplasms was established and inoculated with B. sorokiniana at the seedling stage in a greenhouse. Resistance to spot blotch was assessed, revealing that only about 3.8% of the germplasms exhibited moderate or higher resistance levels. A genome-wide association study (GWAS) using high-density 660K single nucleotide polymorphism (SNP) data identified a region on chromosome 1BL (621.2-674.0 Mb) with 9 SNPs significantly associated (p < 10e-4) with spot blotch resistance, designated as Qsb.hebau-1BL. RNA sequencing and qRT-PCR assays showed that the gene TraesCS1B02G410300, encoding nicotinamide-adenine dinucleotide phosphate-binding oxidoreductase (TaNADPO), was significantly induced by B. sorokiniana. Five SNP variations were found in the promoter region of TaNADPO in wheat lines with or without Qsb.hebau-1BL. Transient expression of TaNADPO in Nicotiana benthamiana leaves showed a cytoplasmic subcellular localization of the fusion protein with a green fluorescent protein (GFP) tag. Wheat transgenic lines overexpressing TaNADPO exhibited significantly enhanced resistance to spot blotch compared to wildtype plants, with higher accumulation of reactive oxygen species (ROS). The knockout EMS mutant of Triticum turgidum NADPO (tdnadpo-K2561, Gln125*) showed significantly reduced resistance to spot blotch and lower ROS accumulation compared to wildtype plants. In summary, TaNADPO has been identified as a crucial gene for resistance to B. sorokiniana, providing valuable insights for developing spot blotch-resistant wheat varieties through molecular breeding techniques.

genetics↗

bioRxiv2024

Genetic variation in the species Arabidopsis thaliana reveals the existence of natural heat resilience factors for meiosis

Heat interferes with multiple meiotic processes leading to genome instability and sterility in flowering plants, including many crops. Despite its importance for food security, the mechanisms underlying heat tolerance of meiosis are poorly understood. In this study, we analyzed different meiotic processes in the Arabidopsis (Arabidopsis thaliana) accessions Columbia (Col) and Landsberg erecta (Ler), their F1 hybrids and F2 offspring under heat stress (37{degrees}C). At 37{degrees}C, Col exhibits significantly reduced formation of double-stand breaks (DSBs) and completely abolished homolog pairing, synapsis and crossover (CO) formation. Strikingly, Ler and Ler/Col hybrids are much less affected than Col. Interestingly, only 10% [~] 20% of F2 offspring exhibit the same heat tolerance of meiotic recombination as parents, indicating that heat resilience in Ler is controlled by the interplay of several loci. Moreover, F2 offspring show defective chromosome condensation in interkinesis, and untimely sister-chromatid segregation and/or chromosome fragmentation, the levels of which exceed those in either inbreds and/or hybrids thus implying a transgressive effect on heat tolerance of meiosis. Furthermore, correlation and cytogenetic analysis suggest that homolog pairing and/or synapsis have an impact on heat tolerance of chromosome morphology and stability during post-recombination stages under heat stress. Taken together, this study reveals the existence of natural heat resilience factors for meiosis in Arabidopsis, which have the great potential to be exploited in breeding programs. Author summaryEnvironmental temperature alterations affect meiotic recombination and/or chromosome segregation thus perturbing genetic makeup and genome stability in plants. We have previously reported that CO formation is fully abolished in Arabidopsis thaliana accession Col under heat stress (36{degrees}C-38{degrees}C) due to reduced DSB formation and impaired homolog pairing. Here, we show that in Arabidopsis thaliana accession Ler under the same high temperature conditions, both DSB and CO formation occur normally, and homolog pairing is mildly impacted, which indicate a striking difference in heat tolerance of meiotic recombination from Col. Remarkably, Col/Ler hybrids display the same heat tolerance as Ler, however, only 10% [~] 20% of F2 offspring behave the same as parents. Moreover, we found higher levels of defects in chromosome morphology and integrity, and sister-chromatid segregation in F2 population than those in both inbreds and hybrids, which suggest a transgressive effect influencing heat tolerance of meiosis. Our findings reveal that heat resilience in Arabidopsis is controlled by the interplay of multiple genomic loci, holding a great potential to be exploited in crop breeding.

plant biology↗

bioRxiv2024

MetaSCDrug: Meta-Transfer Learning for Single-Cell-Level Drug Response Prediction from Transcriptome and Molecular Representations

ObjectiveSingle-cell pharmacogenomic data is crucial for identifying biomarkers and understanding resistance mechanisms. However, the limited availability of those data poses significant challenges for efficient pre-training and thus hinders the generalization capability of the model. This paper aims to develop an efficient predictive model for single-cell drug response inference by leveraging knowledge from a bulk dataset. MethodsTo translate knowledge from bulk cell lines to single-cell analysis, this paper proposes a meta-pretraining and few-shot transfer learning frame-work based on pharmacogenomic embeddings. To enhance feature representation and alignment, genomic information is processed by a position-based feature extraction network to extract contextual features. Simultaneously, a graph-aware Transformer is developed to capture interatomic relations for drug information representation. Underlying this framework, key drug action pathways can be identified at the cellular level through the proposed gene gradient attribution algorithm. ResultsThis model integrates drug response data from 223 drugs across 14 tissues for meta pre-training, followed by transfer and testing on seven single-cell datasets. In comparison to other models, the proposed framework achieves an average accuracy increase of 4.58% for pre-trained drugs and demonstrates a 20% improvement in generalization performance for previously unseen drugs. Case studies further illustrate its capability to differentiate between resistance genes. ConclusionThe proposed framework exhibits enhanced generalization capabilities in predicting cellular responses across multiple drugs, including unseen drugs. This model serves as an effective tool for investigating drug action pathways and elucidating resistance mechanisms from single-cell insights, with significant implications for guiding clinical medication practices.

bioinformatics↗

bioRxiv2024

Towards anchoring evolutionary fitness for protein stability with virtual chemical environment recovery

Predicting the protein stability changes upon mutations is one of the effective ways to improve the efficiency of protein engineering. Here, we propose a dual-view ensemble learning-based framework, DVE-stability, for mutation-induced protein stability change prediction from single sequence. DVE-stability integrates the global and local dependencies of mutations to capture the intramolecular interactions from two views through ensemble learning, in which a structural microenvironment simulation module is designed to indirectly introduce the information of structural microenvironment at the sequence level. DVE-stability achieved state-of-the-art prediction performance on 7 single-point mutation benchmark datasets, and comprehensively surpassed other methods on 5 of them. Furthermore, DVE-stability outperformed other methods comprehensively through zero-shot inference on multiple-point mutation prediction task, demonstrating superior model generalizability to capture the epistasis of multiple-point mutations. More importantly, DVE-stability exhibited superior generalization performance in predicting rare beneficial mutations that are crucial for practical protein directed evolution scenarios. In addition, DVE-stability identified important intramolecular interactions via attention scores, demonstrating interpretable. Overall, DVE-stability provides a flexible and efficient tool for mutation-induced protein stability change prediction in an interpretable ensemble learning manner.

bioinformatics↗

bioRxiv2024

Comprehensive analysis across mammalian tissues and cells decipher the underlying mechanism of m6A specificity

N6-methyladenine (m6A) stands out as the most prevalent internal chemical modification on mammalian mRNA, playing a vital role in diverse biological processes. Despite considerable advancements in individual cell line studies, the characteristics of m6A sites across distinct cell lines or tissues remain elusive. In this study, we have successfully identified approximately 1.5 million high-confidence m6A sites in human and mouse cell lines or tissues using published m6A-seq data. By categorizing m6A sites into different consistency levels, we observe that those of high consistency are notably enriched near the stop codon. Furthermore, they exhibit a higher likelihood of interaction with known m6A binding proteins such as YTHDF1-3, RBM15, YTHDC1, and IGF2BP1, thereby influencing gene expression homeostasis. Additionally, these sites display a higher CpG density in the promoter region of the genes they mark, with METTL3 demonstrating a preference for binding to the promoter region of its marked genes. m6A sites of low consistency levels, including unique m6A sites, show a significant enrichment near the start codon. These sites are more prone to binding by newly discovered m6A-binding proteins such as DDX3X, PRPF8, and EIF3G. The identification of these distinct features of m6A sites lays a foundational understanding for unraveling the functional roles of m6A.

bioinformatics↗

bioRxiv2024

DOPAMINE D4 RECEPTOR DOWN-REGULATES RENAL SODIUM CHLORIDE COTRANSPORTER VIA UBIQUITINATION-ASSOCIATED LYSOSOME DEGRADATION

BackgroundThe thiazide-sensitive sodium chloride cotransporter (NCC) is the major apical sodium transporter located in the mammalian renal distal convoluted tubule (DCT). The amount of sodium reabsorbed in the DCT through NCC plays an important role in the regulation of extracellular fluid volume and blood pressure. Dopamine and its receptors constitute a renal antihypertensive system in mammals. The disruption of Drd4 in mice causes kidney-related hypertension. However, the pathogenesis of D4R-deficiency associated hypertension is not well documented. MethodWe assessed the effects of D4R on NCC protein abundances and activities of DCT in mice with renal or global Drd4-deficiencies and expressing human D4.7 variant and in cultured mouse DCT cells, and explored the molecular mechanism. ResultsNCC inhibitor hydrochlorothiazide enhanced the natriuresis in Drd4-/- mice. Renal NCC protein was greater while ubiquitination of NCC was less in Drd4-/- than Drd4+/+ mice. Silencing of D4R in cultured mouse DCT cells increased NCC protein but decreased NCC ubiquitination. D4R agonist had opposite effects that were blocked by the antagonist. In mouse kidneys and DCT cells D4R and NCC colocalized and co-immunoprecipitated. Moreover, D4R-agonist promoted the binding between the two proteins demonstrated by fluorescence resonance energy transfer. D4R agonism internalized NCC, decreased NCC in the plasma membrane, increased NCC in lysosomes and reduced NCC-dependent-intracellular-sodium transport. The lysosomal inhibitor chloroquine prevented the D4R-induced NCC-reduction. A shortened NCC half-life was suggested by its decay under cycloheximide-chase. Ubiquitin-specific-protease 48 (USP48, a deubiquitinating enzyme) was increased in the kidneys and cells with Drd4-deficiency while D4R stimulation decreased it in vitro and reduction of USP48 with siRNA decreased NCC expression. The mice carrying human D4.7 variant or with renal reduction of D4R developed hypertension with increased NCC. ConclusionOur data demonstrates that D4R downregulates NCC by promoting USP48-associated deubiquitination and subsequent internalization, lysosome relocation and degradation.

physiology↗

bioRxiv2024

Identification of differentially methylated single-nucleotide m6A sites by incorporating site-specific antibody specificity

Various genome-wide and transcriptome-wide technologies are based on antibodies, however, the specificity of antibodies on different targets has not been characterized or considered in the analyses. The antibody-based MeRIP-seq is the most widely used method to determine the locations of N6-methyladenosine (m6A) on RNAs, especially for differential m6A analyses. However, the antibody specificities in different RNA regions and their resulting technical biases in differential m6A analyses have not been evaluated. Here, we evaluated the m6A antibody specificities using 100 pairs of spike-in RNAs with known m6A levels at single sites. Based on two replicates with different m6A levels on spike-in RNAs, we realized the m6A antibody specificities of the m6A sites on spike-in RNAs were greatly varied and mainly determined by the surrounding sequences of the m6A sites. Moreover, the MeRIP-seq signal fold change is the function of the real difference in m6A levels as well as the m6A antibody specificity. We then trained a machine learning model to predict the m6A antibody specificities of given sequences and predicted the m6A specificities of all RNA sequences surrounding the known m6A motif DRACH throughout the human transcriptome. Finally, we developed a Hierarchical statistic model for Differential Analysis of m6A Sites (HDAMS) by taking advantage of the predicted m6A specificities. We found that HDAMS can accurately determine the differentially methylated single-nucleotide m6A sites and the output more functionally relevant results. Our study not only provides a powerful tool for differential m6A analyses but also provides a methodological framework for other antibody-based studies to incorporate antibody specificities.

bioinformatics↗

bioRxiv2024

Endogenous labeling empowers accurate detection of m6A from single long reads of direct RNA sequencing

Although plenty of machine learning models have been developed to detect m6A RNA modification sites using the electric current signals of ONT direct RNA sequencing (DRS) reads, the landscape of m6A on different RNA isoforms is still a mystery due to their limited capacity to distinguish the m6A on individual long reads and RNA isoforms. The primary challenge in training the model with single-read accuracy is the difficulty of obtaining the training data from individual DRS reads that comprehensively represent the m6A on endogenous RNAs. Here, we endogenously label the methylated m6A sites on single ONT DRS reads by APOBEC1-YTH induced C-to-U mutations, strategically positioned 10-100 nt away from the known m6A sites on the same reads. Adopting a semi-supervised leaning strategy, we obtain 700,438 reliable 5-mer single-read level m6A signals, providing a comprehensive representation of m6A on endogenous RNAs. Leveraging this dataset, we develop m6Aiso, a deep residual neural network model that not only accurately identifies and quantifies known m6A sites but also reveals unknown, subtly methylated m6A sites responsive to METTL3 depletion. Analyzing m6Aiso-determined m6A on single reads and isoforms uncovers distance-dependent linkages of m6A sites along single molecules, as well as differential methylation of identical m6A sites on different isoforms. Moreover, we find wide-spread functionally important dynamic changes of m6A sites on specific isoforms during epithelial-mesenchymal transition (EMT). The pivotal utilization of the endogenous labeling strategy empowers m6Aiso to achieve remarkable precision in pinpointing m6A on individual molecules, underscores its effectiveness in elucidating the intricate dynamics and complexities of m6A across RNA isoforms.

bioinformatics↗

bioRxiv2023

Short-chain carboxylates facilitate the counting of yeasts in Sub-high temperature Daqu.

Sub-high temperature Daqu is a traditional solid fermenting agent that produces Chinese strong-aroma Baijiu. It is abundant in various microorganisms, including bacteria, yeasts, molds, and actinomycetes, of which yeasts play a crucial role in ethanol production and flavor formation. Counting yeasts in Daqu is difficult due to the interference of molds and bacteria. Antibiotics are employed to inhibit bacterial growth, but there is no effective way to suppress molds without affecting the growth of yeasts. In this study, short-chain carboxylates (C1-C6) were added to the culture medium at various pH conditions to investigate their effects on the growth of molds and yeasts. Results showed that they have distinct inhibitory effects in a pH-and concentration-dependent manner. A few of these tested short-chain carboxylates effectively suppress mold growth in agar plates without affecting yeast growth. Herein, a simple and feasible method for improving the efficiency of yeast isolation and counting in Daqu has been proposed, which may be useful for studying yeasts from complex habitats.

microbiology↗

bioRxiv2023

A distinctive evolution of alveolar T cell responses is associated with clinical outcomes in unvaccinated patients with SARS-CoV-2 pneumonia

Pathogen clearance and resolution of inflammation in patients with pneumonia require an effective local T cell response. Nevertheless, local T cell activation may drive lung injury, particularly during prolonged episodes of respiratory failure characteristic of severe SARS-CoV-2 pneumonia. While T cell responses in the peripheral blood are well described, the evolution of T cell phenotypes and molecular signatures in the distal lung of patients with severe pneumonia caused by SARS-CoV-2 or other pathogens is understudied. Accordingly, we serially obtained 432 bronchoalveolar lavage fluid samples from 273 patients with severe pneumonia and respiratory failure, including 74 unvaccinated patients with COVID-19, and performed flow cytometry, transcriptional, and T cell receptor profiling on sorted CD8+ and CD4+ T cell subsets. In patients with COVID-19 but not pneumonia secondary to other pathogens, we found that early and persistent enrichment in CD8+ and CD4+ T cell subsets correlated with survival to hospital discharge. Activation of interferon signaling pathways early after intubation for COVID-19 was associated with favorable outcomes, while activation of NF-{kappa}B-driven programs late in disease was associated with poor outcomes. Patients with SARS-CoV-2 pneumonia whose alveolar T cells preferentially targeted the Spike and Nucleocapsid proteins tended to experience more favorable outcomes than patients whose T cells predominantly targeted the ORF1ab polyprotein complex. These results suggest that in patients with severe SARS-CoV-2 pneumonia, alveolar T cell interferon responses targeting structural SARS-CoV-2 proteins characterize patients who recover, yet these responses progress to NF-{kappa}B activation against non-structural proteins in patients who go on to experience poor clinical outcomes.

systems biology↗

bioRxiv2023

Neuronal dynamics of cerebellum and medial prefrontal cortex in adaptive motor timing

Precise temporal control of sensorimotor coordination and adaptation is a fundamental basis of animal behavior. How different brain regions are involved in regulating the flexible temporal adaptation remains elusive. Here we investigated the neuronal dynamics of cerebellar interposed nucleus (IpN) and medial prefrontal cortex (mPFC) neurons during temporal adaptation between delay eyeblink conditioning (DEC) and trace eyeblink conditioning (TEC). When mice trained for either DEC or TEC and subsequently subjected to a new paradigm, their conditioned responses (CRs) adapted virtually instantaneously. Changes in the activity of the IpN neurons to CR timing were prominent during DEC-to-TEC adaptation, but less so during TEC-to-DEC adaptation. In contrast, mPFC neurons could rapidly alter their modulation patterns during both adaptation paradigms. Accordingly, silencing of mPFC blocked the adaptation of CR timing. These results illuminate how cerebral and cerebellar mechanisms may play differential roles during adaptive control of associative motor timing.

neuroscience↗

bioRxiv2023

NASTRA: Innovative Short Tandem Repeat Analysis through Cluster-Based Structure-Aware Algorithm in Nanopore Sequencing Data

Forensic short-tandem repeats (STR) genetic markers are multi-allelic and widely utilized for individual identification, kinship testing, and cell-line authentication. Nanopore sequencing, known for its portability, is emerging as a promising approach for STR typing, facilitating real-time and in-field testing. However, its efficacy is often hampered by sequencing noise. Previous methods rely on alignment-based genotyping, necessitating known alleles, which limits their applicability to unknown alleles. Here, we introduced NASTRA, an innovative allele reference-free tool for precise germline analysis of STR genetic markers. NASTRA incorporates a recursive algorithm to infer repeat structures of allele sequences using only known repeat motifs. Our tests, conducted on 80 individual samples and 8 DNA standards, have demonstrated NASTRAs exceptional 100% accuracy in genotyping nearly all diploid STRs across various multiplex kits and flow cells. It surpasses alignment-based methods in accuracy and speed. In a paternity testing case study, NASTRA accurately identified three relationships among six individuals within an 18-minute sequencing duration. These results underscore NASTRAs ability to perform STR analysis on both NGS and nanopore sequencing platforms, significantly enhancing the utility of nanopore sequencing in relevant applications. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=151 SRC="FIGDIR/small/565630v3_ufig1.gif" ALT="Figure 1"> View larger version (36K): org.highwire.dtl.DTLVardef@296e89org.highwire.dtl.DTLVardef@180b6forg.highwire.dtl.DTLVardef@96bf21org.highwire.dtl.DTLVardef@de1b1d_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2023

Asynchronous Detection of Erroneous Behaviors in Human-Robot Interaction with EEG: A Comparative Analysis of Machine Learning Models.

We present a comparative analysis of two distinct machine-learning models designed to detect asynchronous errors during Human-Robot Interaction (HRI). The models under scrutiny are a customized ResNet model and an ensemble model, both trained and validated using EEG data. The ResNet model is a unique adaptation of the Residual Network, comprising a one-dimensional convolutional layer followed by batch normalization and ReLU activation. It also features a custom Residual Block structure and an adaptive average pooling layer, concluding with a fully connected linear layer for binary classification. The ensemble model, on the other hand, incorporates various machine learning models such as MLP, logistic regression, SVM, random forest, and XGBoost, unified in a pipeline with feature extraction and transformation steps. A critical component of our research is the innovative probability map, which maintains a granularity of 0.1 seconds. This map forecasts the likelihood of forthcoming one-second intervals being classified as either Error or Non-error. Our comparative analysis reveals significant variations in the performance of the two models, both of which exhibit promising results in detecting erroneous behaviors during HRI. We provide detailed validation results, including the accuracy, F1 score, and confusion matrix for each model. This study offers valuable insights into the potential of machine learning in enhancing HRI efficiency and accuracy, indicating promising directions for future research.

neuroscience↗

bioRxiv2023

Nuclear Lipid Droplets Exit to the Cytoplasm: A Novel Mechanism of non-nuclear pore-dependent material exchange

In cellular biology, the extrusion of large molecular complexes from the nucleus has consistently represented a considerable challenge. In this investigation, we reveal a hitherto unexplored mechanism facilitating the efflux of nuclear lipid droplets (nLDs) from the nucleus directly into the cytoplasm, a process integral to comprehending cellular processes and nuclear dynamics. Through high-throughput sequencing, immunoprecipitation, and immunofluorescence experimentation, we demonstrated that the type I nucleoplasmic reticulum engages with nLDs, enabling the dynamic protein Rab8as transfer to the nLDs and fostering their translocation to the nuclear membrane. Phosphorylation of LMNA/C at the S392 site, adjacent to the nLD-nuclear membrane contact site, induces local depolymerization of lamins, creating an aperture that enables gradual nuclear exit of nLDs. Following this, nLDs, carrying DNA fragments, are released into the cytoplasm through budding and subsequently undergo autophagy-mediated degradation. Furthermore, we demonstrate that RhoA-PKC signaling enhances nLD movement and LMNA/C phosphorylation, while its inhibition impedes nuclear nLD efflux. Our groundbreaking investigation illuminates non-nuclear pore-dependent mechanisms of material exchange and offers fresh perspectives on nuclear metabolic pathways. In Brief O_FIG O_LINKSMALLFIG WIDTH=197 HEIGHT=200 SRC="FIGDIR/small/556761v1_ufig1.gif" ALT="Figure 1"> View larger version (107K): org.highwire.dtl.DTLVardef@38a213org.highwire.dtl.DTLVardef@1898caorg.highwire.dtl.DTLVardef@d7270forg.highwire.dtl.DTLVardef@14582a2_HPS_FORMAT_FIGEXP M_FIG C_FIG In this study, Jin et al. elucidate a novel mechanism for the expulsion of nuclear lipid droplets (nLDs) from the nucleus. They demonstrate that the activation of RhoA within the nucleus recruits Rab8a to the surface of nLDs. Concurrently, PKC phosphorylation triggers the phosphorylation of LMNA/C at the S392 site, leading to a localized depolymerization of lamins. This creates an aperture that allows for the gradual exit of nLDs from the nucleus. The efflux process of nLDs involves three sequential steps: rupture of the nuclear fibril layer, fusion of nLDs with the inner nuclear membrane (INM), and subsequent fusion with the outer nuclear membrane (ONM). This enables nLDs to enter the cytoplasm, shedding light on a previously unknown process of large molecular entity expulsion from the nucleus.

molecular biology↗