Biology subjects

Sun, R.

Publications and source records attributed to Sun, R..

7 recordsLinked to original sources

bioRxiv2018

Fall Risk Prediction in Multiple Sclerosis Using Postural Sway Measures, A Machine Learning Approach

BackgroundBalance impairment affects over 75% of individuals with multiple sclerosis (MS), and leads to an increased risk of falling. Numerous postural sway metrics have been shown to be sensitive to balance impairment and fall risk in individuals with MS. Yet, there are no guidelines concerning the most appropriate postural sway metrics to monitor impairment. This investigation implemented a machine learning approach to assess the accuracy and feature importance of various postural sway metrics to differentiate individuals with MS from healthy controls as a function of physiological fall risk.\n\nMethodsThis secondary data analysis included 153 participants (50 controls and 103 individuals with MS) who underwent posturography based balance assessment (30s eyes open standing on a force platform) and physiological fall risk assessment (Physiological Profile Assessment - PPA). Participants were further classified into four subgroups based on fall risk: controls (n=50, 64.9 {+/-} 4.9 years old, PPA < 1); low-risk MS (n=34, 54.0 {+/-} 13.1 years old, PPA < 1); moderate-risk MS (n=27, 58.3 {+/-} 8.3 years old, 1 [≤] PPA < 2); high-risk MS (n=42, 56.8 {+/-} 9.7 years old, PPA [≥] 2). Twenty common sway metrics were derived following standard procedures, and subsequently used to train a machine learning algorithm (random forest - RF, with 10-fold cross validation) to predict individuals fall risk grouping. The feature importance from the RF algorithms was used to select the strongest sway metric for fall risk prediction.\n\nResults and DiscussionThe sway-metric based RF classifier had high classification accuracy in discriminating controls from MS individuals (> 86%). Sway sample entropy, a sway regularity metric, was identified as the strongest feature for classification of low-risk MS individuals from healthy controls. Whereas for all other comparisons, mediolateral sway amplitude was identified as the strongest predictor for fall risk groupings. These findings may set the foundation for the development of guidelines for reporting balance impairment in individuals with MS.

bioengineering↗

bioRxiv2018

Powerful gene set analysis in GWAS with the Generalized Berk-Jones statistic

A common complementary strategy in Genome-Wide Association Studies (GWAS) is to perform Gene Set Analysis (GSA), which tests for the association between one phenotype of interest and an entire set of Single Nucleotide Polymorphisms (SNPs) residing in selected genes. While there exist many tools for performing GSA, popular methods often include a number of ad-hoc steps that are difficult to justify statistically, provide complicated interpretations based on permutation inference, and demonstrate poor operating characteristics. Additionally, the lack of gold standard gene set lists can produce misleading results and create difficulties in comparing analyses even across the same phenotype. We introduce the Generalized Berk-Jones (GBJ) statistic for GSA, a permutation-free parametric framework that offers asymptotic power guarantees in certain set-based testing settings. To adjust for confounding introduced by different gene set lists, we further develop a GBJ step-down inference technique that can discriminate between gene sets driven to significance by single genes and those demonstrating group-level effects. We compare GBJ to popular alternatives through simulation and re-analysis of summary statistics from a large breast cancer GWAS, and we show how GBJ can increase power by incorporating information from multiple signals in the same gene. In addition, we illustrate how breast cancer pathway analysis can be confounded by the frequency of FGFR2 in pathway lists. Our approach is further validated on two other datasets of summary statistics generated from GWAS of height and schizophrenia.

genetics↗

bioRxiv2018

Scalable approximate Bayesian inference for particle tracking data

Many important datasets in physics, chemistry, and biology consist of noisy sequences of images of multiple moving overlapping particles. In many cases, the observed particles are indistinguishable, leading to unavoidable uncertainty about nearby particles identities. Exact Bayesian inference is intractable in this setting, and previous approximate Bayesian methods scale poorly. Non-Bayesian approaches that output a single \"best\" estimate of the particle tracks (thus discarding important uncertainty information) are therefore dominant in practice. Here we propose a flexible and scalable amortized approach for Bayesian inference on this task. We introduce a novel neural network method to approximate the (intractable) filter-backward-sample-forward algorithm for Bayesian inference in this setting. By varying the simulated training data for the network, we can perform inference on a wide variety of data types. This approach is therefore highly flexible and improves on the state of the art in terms of accuracy; provides uncertainty estimates about the particle locations and identities; and has a test run-time that scales linearly as a function of the data length and number of particles, thus enabling Bayesian inference in arbitrarily large particle tracking datasets.

bioinformatics↗

bioRxiv2017

High-throughput fitness profiling of Zika virus E protein reveals different roles for N-linked glycosylation during infection of mammalian and mosquito cells

Zika virus (ZIKV) infection causes Guillain-Barre syndrome and severe birth defects. ZIKV envelope (E) protein is the major viral protein involved in cell receptor binding and entry and therefore considered one of the major determinants in ZIKV pathogenesis. Here, we report a gene-wide mapping of functional residues of ZIKV E protein using a mutant library with changes covering every nucleotide position. By comparing the replication fitness of every viral mutant between mosquito and human cells, we identified that mutations affecting N-linked glycosylation at N154 position display the most divergence. Through characterizing individual mutants, we show that, while ablation of N-linked glycosylation selectively benefits ZIKV infection of mosquito cells by enhancing cell entry, it either had little impact on ZIKV infection on certain human cells or decreased infection through entry factor DC-SIGN. In conclusion, we define the roles of individual residues of ZIKV envelope protein, which contribute to ZIKV replication fitness in human and mosquito cells.\n\nHighlightsO_LIGene-wide mapping of functional residues of E protein in human and mosquito cells.\nC_LIO_LIMutations affecting N-linked glycosylation display the most dramatic difference.\nC_LIO_LIN-linked glycosylation decreases ZIKV entry into mosquito cells.\nC_LIO_LIN-linked glycosylation is important for DC-SIGN mediated infection of human cells.\nC_LI

microbiology↗

bioRxiv2017

Conformational dynamics of Cas9 governing DNA cleavage revealed by single molecule FRET

Off-target binding and cleavage by Cas9 pose as major challenges in its applications. How conformational dynamics of Cas9 governs its nuclease activity under on- and off-target conditions remains largely unknown. Here, using intra-molecular single molecule fluorescence resonance energy transfer measurements, we revealed that Cas9 in apo, sgRNA-bound, and dsDNA/sgRNA-bound forms all spontaneously transits between three major conformational states, mainly reflecting significant conformational mobility of the catalytic HNH domain. We furthermore uncovered a surprising long-range allosteric communication between the HNH domain and RNA/DNA heteroduplex at the PAM-distal end to ensure correct positioning of the catalytic site, which demonstrated a unique proofreading mechanism served as the last checkpoint before DNA cleavage. Several Cas9 residues were likely to mediate the allosteric communication and proofreading step. Modulating interactions between Cas9 and heteroduplex at the distal end by introducing mutations on these sites provides an alternative route to improve and optimize the CRISPR/Cas9 toolbox.

biophysics↗

bioRxiv2017

Pathogenesis of Zika Virus Infection via Rectal Route

Introduction Introduction Methods Competing interests References Zika virus (ZIKV) is a mosquito-borne flavivirus originally confined to Africa and Asia that has spread to islands located in Southeast Asia, and most recently to the Americas and the Caribbean. Approximately 80% of infected individuals are asymptomatic, while the remaining infected population exhibit mild febrile syndrome such as rash, conjunctivitis, and arthralgia. In some adults, ZIKV causes neurotropic Guillain-Barre syndrome1. Vertical transmission of ZIKV in infected mothers causes fetal growth restriction, microcephaly, and congenital eye disease2-5. Cases of ZIKV sexual transmission from male to female6-8, male to male9, ...

microbiology↗

bioRxiv2016

Scalable variational inference for super resolution microscopy

Super-resolution microscopy methods (e.g. STORM or PALM imaging) have become essential tools in biology, opening up a variety of new questions that were previously inaccessible with standard light microscopy methods. In this paper we develop new Bayesian image processing methods that extend the reach of super-resolution microscopy even further. Our method couples variational inference techniques with a data summarization based on Laplace approximation to ensure computational scalability. Our formulation makes it straightforward to incorporate prior information about the underlying sample to further improve accuracy. The proposed method obtains dramatic resolution improvements over previous methods while retaining computational tractability.

bioinformatics↗