Short Peptide Self-Assembly in the Martini Coarse-Grain Force Field Family.

Pivotal to the success of any computational experiment is the ability to make reliable predictions about the system under study and the time required to yield these results. Biomolecular interactions is one area of research that sits in every camp of resolution vs the time required, from the quantum mechanical level to studies. At an approximate midpoint, there is coarse-grained molecular dynamics, for which the Martini force fields have become the most widely used, fast enough to simulate the entire membrane of a mitochondrion though lacking atom-specific precision.

GEMmaker: process massive RNA-seq datasets on heterogeneous computational infrastructure.

Background: Quantification of gene expression from RNA-seq data is a prerequisite for transcriptome analysis such as differential gene expression analysis and gene co-expression network construction. Individual RNA-seq experiments are larger and combining multiple experiments from sequence repositories can result in datasets with thousands of samples. Processing hundreds to thousands of RNA-seq data can result in challenges related to data management, access to sufficient computational resources, navigation of high-performance computing (HPC) systems, installation of required software dependencies, and reproducibility.

Addressing noise in co-expression network construction.

Gene co-expression networks (GCNs) provide multiple benefits to molecular research including hypothesis generation and biomarker discovery. Transcriptome profiles serve as input for GCN construction and are derived from increasingly larger studies with samples across multiple experimental conditions, treatments, time points, genotypes, etc. Such experiments with larger numbers of variables confound discovery of true network edges, exclude edges and inhibit discovery of context (or condition) specific network edges.

Complete and Resilient Documentation for Operational Medical Environments Leveraging Mobile Hands-free Technology in a Systems Approach: Experimental Study.

Background: Prehospitalization documentation is a challenging task and prone to loss of information, as paramedics operate under disruptive environments requiring their constant attention to the patients.

Objective: The aim of this study is to develop a mobile platform for hands-free prehospitalization documentation to assist first responders in operational medical environments by aggregating all existing solutions for noise resiliency and domain adaptation.

Methods: The platform was built to extract meaningful medical information from the real-time audio streaming at the point of injury and transmit complete documentation to a field hospital prior to patient arrival.

Cellular State Transformations Using Deep Learning for Precision Medicine Applications.

We introduce the Transcriptome State Perturbation Generator (TSPG) as a novel deep-learning method to identify changes in genomic expression that occur between tissue states using generative adversarial networks. TSPG learns the transcriptome perturbations from RNA-sequencing data required to shift from a source to a target class. We apply TSPG as an effective method of detecting biologically relevant alternate expression patterns between normal and tumor human tissue samples.

The Biology of (Lepidoptera: Oecophoridae), a Stem-Boring Moth of (Myrtaceae): Descriptions of the Previously Unknown Adult Female and Immature Stages, and Its Potential as a Biological Control Candidate.

is a perennial shrub native to Southeast Asia and is invasive in South Florida and Hawai'i, USA. During surveys of in Hong Kong from 2013-2018 for potential biological control agents, we collected larvae of the stem borer, . Larvae were shipped in stems to a USDA-ARS quarantine facility where they were reared and subjected to biology studies and preliminary host range examinations.

A generalized deep learning approach for local structure identification in molecular simulations.

Identifying local structure in molecular simulations is of utmost importance. The most common existing approach to identify local structure is to calculate some geometrical quantity referred to as an order parameter. In simple cases order parameters are physically intuitive and trivial to develop (, ion-pair distance), however in most cases, order parameter development becomes a much more difficult endeavor (, crystal structure identification).

Uncovering biomarker genes with enriched classification potential from Hallmark gene sets.

Given the complex relationship between gene expression and phenotypic outcomes, computationally efficient approaches are needed to sift through large high-dimensional datasets in order to identify biologically relevant biomarkers. In this report, we describe a method of identifying the most salient biomarker genes in a dataset, which we call "candidate genes", by evaluating the ability of gene combinations to classify samples from a dataset, which we call "classification potential". Our algorithm, Gene Oracle, uses a neural network to test user defined gene sets for polygenic classification potential and then uses a combinatorial approach to further decompose selected gene sets into candidate and non-candidate biomarker genes.

The complete chloroplast genome of the invasive fern (Cav.) R. Br.

The Old World climbing fern, , is a rapidly spreading environmental weed in Florida, United States. We reconstructed the complete chloroplast genome of from Illumina whole-genome shotgun sequencing, and investigate the phylogenetic placement of this species within the Leptosporangiate ferns. The chloroplast genome is 158,891 bp and contains 87 protein-coding genes, four rRNA genes, and 27 tRNA genes.

Potential role of masting by introduced bamboos in deer mice (Peromyscus maniculatus) population irruptions holds public health consequences.

We hypothesized that the ongoing naturalization of frost/shade tolerant Asian bamboos in North America could cause environmental consequences involving introduced bamboos, native rodents and ultimately humans. More specifically, we asked whether the eventual masting by an abundant leptomorphic ("running") bamboo within Pacific Northwest coniferous forests could produce a temporary surfeit of food capable of driving a population irruption of a common native seed predator, the deer mouse (Peromyscus maniculatus), a hantavirus carrier. Single-choice and cafeteria-style feeding trials were conducted for deer mice with seeds of two bamboo species (Bambusa distegia and Yushania brevipaniculata), wheat, Pinus ponderosa, and native mixed diets compared to rodent laboratory feed.

Maximizing capture of gene co-expression relationships through pre-clustering of input expression samples: an Arabidopsis case study.

Background: In genomics, highly relevant gene interaction (co-expression) networks have been constructed by finding significant pair-wise correlations between genes in expression datasets. These networks are then mined to elucidate biological function at the polygenic level. In some cases networks may be constructed from input samples that measure gene expression under a variety of different conditions, such as for different genotypes, environments, disease states and tissues.

Massive-scale gene co-expression network construction and robustness testing using random matrix theory.

The study of gene relationships and their effect on biological function and phenotype is a focal point in systems biology. Gene co-expression networks built using microarray expression profiles are one technique for discovering and interpreting gene relationships. A knowledge-independent thresholding technique, such as Random Matrix Theory (RMT), is useful for identifying meaningful relationships.