PhyImpute and UniFracImpute: two imputation approaches incorporating phylogeny information for microbial count data.

Sequencing-based microbial count data analysis is a challenging task due to the presence of numerous non-biological zeros, which can impede downstream analysis. To tackle this issue, we introduce two novel approaches, PhyImpute and UniFracImpute, which leverage similar microbial samples to identify and impute non-biological zeros in microbial count data. Our proposed methods utilize the probability of non-biological zeros and phylogenetic trees to estimate sample-to-sample similarity, thus addressing this challenge.

SPADE: spatial deconvolution for domain specific cell-type estimation.

Understanding gene expression in different cell types within their spatial context is a key goal in genomics research. SPADE (SPAtial DEconvolution), our proposed method, addresses this by integrating spatial patterns into the analysis of cell type composition. This approach uses a combination of single-cell RNA sequencing, spatial transcriptomics, and histological data to accurately estimate the proportions of cell types in various locations.

An FPGA implementation of Bayesian inference with spiking neural networks.

Spiking neural networks (SNNs), as brain-inspired neural network models based on spikes, have the advantage of processing information with low complexity and efficient energy consumption. Currently, there is a growing trend to design hardware accelerators for dedicated SNNs to overcome the limitation of running under the traditional von Neumann architecture. Probabilistic sampling is an effective modeling approach for implementing SNNs to simulate the brain to achieve Bayesian inference.

Semi-reference based cell type deconvolution with application to human metastatic cancers.

Bulk RNA-seq experiments, commonly used to discern gene expression changes across conditions, often neglect critical cell type-specific information due to their focus on average transcript abundance. Recognizing cell type contribution is crucial to understanding phenotype and disease variations. The advent of single-cell RNA sequencing has allowed detailed examination of cellular heterogeneity; however, the cost and analytic caveat prohibits such sequencing for a large number of samples.

SPADE: Spatial Deconvolution for Domain Specific Cell-type Estimation.

The advent of spatial transcriptomics technology has allowed for the acquisition of gene expression profiles with multi-cellular resolution in a spatially resolved manner, presenting a new milestone in the field of genomics. However, the aggregate gene expression from heterogeneous cell types obtained by these technologies poses a significant challenge for a comprehensive delineation of cell type-specific spatial patterns. Here, we propose SPADE (SPAtial DEconvolution), an in-silico method designed to address this challenge by incorporating spatial patterns during cell type decomposition.

Diverse role of NMDA receptors for dendritic integration of neural dynamics.

Neurons, represented as a tree structure of morphology, have various distinguished branches of dendrites. Different types of synaptic receptors distributed over dendrites are responsible for receiving inputs from other neurons. NMDA receptors (NMDARs) are expressed as excitatory units, and play a key physiological role in synaptic function.

Attention-Based Graph Neural Network for Label Propagation in Single-Cell Omics.

Single-cell data analysis has been at forefront of development in biology and medicine since sequencing data have been made available. An important challenge in single-cell data analysis is the identification of cell types. Several methods have been proposed for cell-type identification.

A Distribution-Free Model for Longitudinal Metagenomic Count Data.

Longitudinal metagenomics has been widely studied in the recent decade to provide valuable insight for understanding microbial dynamics. The correlation within each subject can be observed across repeated measurements. However, previous methods that assume independent correlation may suffer from incorrect inferences.

NISC: Neural Network-Imputation for Single-Cell RNA Sequencing and Cell Type Clustering.

Single-cell RNA sequencing (scRNA-seq) reveals the transcriptome diversity in heterogeneous cell populations as it allows researchers to study gene expression at single-cell resolution. The latest advances in scRNA-seq technology have made it possible to profile tens of thousands of individual cells simultaneously. However, the technology also increases the number of missing values, i.

Nrf2 signaling in heart failure: expression of Nrf2, Keap1, antioxidant, and detoxification genes in dilated or ischemic cardiomyopathy.

Increased levels of oxidative stress have been found with heart failure. Whether failing hearts express antioxidant and detoxification enzymes have not been addressed systematically. Nrf2 gene encodes a transcription factor that regulates the expression of antioxidant and detoxification genes.

N-Acetylcysteine for Cardiac Protection During Coronary Artery Reperfusion: A Systematic Review and Meta-Analysis of Randomized Controlled Trials.

Coronary artery reperfusion is essential for the management of symptoms in the patients with myocardial ischemia. However, the benefit of reperfusion often comes at an expense of paradoxical injury, which contributes to the adverse events, and sometimes heart failure. Reperfusion is known to increase the production of reactive oxygen species (ROS).

Loss of Endothelial Hypoxia Inducible Factor-Prolyl Hydroxylase 2 Induces Cardiac Hypertrophy and Fibrosis.

Background Cardiac hypertrophy and fibrosis are common adaptive responses to injury and stress, eventually leading to heart failure. Hypoxia signaling is important to the (patho)physiological process of cardiac remodeling. However, the role of endothelial PHD2 (prolyl-4 hydroxylase 2)/hypoxia inducible factor (HIF) signaling in the pathogenesis of cardiac hypertrophy and heart failure remains elusive.

Phonological Measures for Bilingual Spanish-English-Speaking Preschoolers: The Language Combination Effect.

Purpose The purpose of this study was to examine the diagnostic accuracy of traditional measures of phonological ability developed for monolingual English-speaking children with their bilingual peers in both English and Spanish. We predicted that a composite measure, derived from a combination of English and Spanish phonological measures, would result in higher diagnostic accuracy than examining the individual phonological measures of bilingual children separately by language. Method Sixty-six children, ages 3;3-6;3 (years;months), participated in this study: 29 typically developing bilingual Spanish-English-speaking children ( = 5;3), five bilingual Spanish-English-speaking children with speech sound disorders ( = 4;6), 26 typically developing monolingual English-speaking children ( = 4;8), and six monolingual English-speaking children with speech sound disorders ( = 4;9).

Regulating synchronous oscillations of cerebellar granule cells by different types of inhibition.

Synchronous oscillations in neural populations are considered being controlled by inhibitory neurons. In the granular layer of the cerebellum, two major types of cells are excitatory granular cells (GCs) and inhibitory Golgi cells (GoCs). GC spatiotemporal dynamics, as the output of the granular layer, is highly regulated by GoCs.

Comparison and impact of COVID-19 for patients with cancer: a survival analysis of fatality rate controlling for age, sex and cancer type.

Objectives: Prior research has reported an increased risk of fatality for patients with cancer, but most studies investigated the risk by comparing cancer to non-cancer patients among COVID-19 infections, where cancer might have contributed to the increased risk. This study is to understand COVID-19's imposed HR of fatality while controlling for covariates, such as age, sex, metastasis status and cancer type.

Methods: We conducted survival analyses of 4606 cancer patients with COVID-19 test results from 16 March to 11 October 2020 in UK Biobank and estimated the overall HR of fatality with and without COVID-19 infection.

Modulation of the dynamics of cerebellar Purkinje cells through the interaction of excitatory and inhibitory feedforward pathways.

The dynamics of cerebellar neuronal networks is controlled by the underlying building blocks of neurons and synapses between them. For which, the computation of Purkinje cells (PCs), the only output cells of the cerebellar cortex, is implemented through various types of neural pathways interactively routing excitation and inhibition converged to PCs. Such tuning of excitation and inhibition, coming from the gating of specific pathways as well as short-term plasticity (STP) of the synapses, plays a dominant role in controlling the PC dynamics in terms of firing rate and spike timing.

Ensemble dimensionality reduction and feature gene extraction for single-cell RNA-seq data.

Single-cell RNA sequencing (scRNA-seq) technologies allow researchers to uncover the biological states of a single cell at high resolution. For computational efficiency and easy visualization, dimensionality reduction is necessary to capture gene expression patterns in low-dimensional space. Here we propose an ensemble method for simultaneous dimensionality reduction and feature gene extraction (EDGE) of scRNA-seq data.

Microbial community dissimilarity for source tracking with application in forensic studies.

Microbial source-tracking is a useful tool for trace evidence analysis in Forensics. Community-wide massively parallel sequencing profiles can bypass the need for satellite microbes or marker sets, which are unreliable when handling unstable samples. We propose a novel method utilizing Aitchison distance to select important suspects/sources, and then integrate it with existing algorithms in source tracking to estimate the proportions of microbial sample coming from important suspects/sources.

Intrinsic and Synaptic Properties Shaping Diverse Behaviors of Neural Dynamics.

The majority of neurons in the neuronal system of the brain have a complex morphological structure, which diversifies the dynamics of neurons. In the granular layer of the cerebellum, there exists a unique cell type, the unipolar brush cell (UBC), that serves as an important relay cell for transferring information from outside mossy fibers to downstream granule cells. The distinguishing feature of the UBC is that it has a simple morphology, with only one short dendritic brush connected to its soma.

scDoc: correcting drop-out events in single-cell RNA-seq data.

Motivation: Single-cell RNA-sequencing (scRNA-seq) has become an important tool to unravel cellular heterogeneity, discover new cell (sub)types, and understand cell development at single-cell resolution. However, one major challenge to scRNA-seq research is the presence of 'drop-out' events, which usually is due to extremely low mRNA input or the stochastic nature of gene expression. In this article, we present a novel single-cell RNA-seq drop-out correction (scDoc) method, imputing drop-out events by borrowing information for the same gene from highly similar cells.

An Information-Based Approach for Mediation Analysis on High-Dimensional Metagenomic Data.

The human microbiome plays a critical role in the development of gut-related illnesses such as inflammatory bowel disease and clinical pouchitis. A mediation model can be used to describe the interaction between host gene expression, the gut microbiome, and clinical/health situation (e.g.

On fusion methods for knowledge discovery from multi-omics datasets.

Recent years have witnessed the tendency of measuring a biological sample on multiple omics scales for a comprehensive understanding of how biological activities on varying levels are perturbed by genetic variants, environments, and their interactions. This new trend raises substantial challenges to data integration and fusion, of which the latter is a specific type of integration that applies a uniform method in a scalable manner, to solve biological problems which the multi-omics measurements target. Fusion-based analysis has advanced rapidly in the past decade, thanks to application drivers and theoretical breakthroughs in mathematics, statistics, and computer science.