SARS-CoV-2 Illumina GeNome Assembly Line (SIGNAL), a Snakemate workflow for rapid and bulk analysis of Illumina sequencing of SARS-CoV-2 genomes.

The incorporation of sequencing technologies in frontline and public health healthcare settings was vital in developing virus surveillance programs during the Coronavirus Disease 2019 (COVID-19) pandemic caused by transmission of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, increased data acquisition poses challenges for both rapid and accurate analyses. To overcome these hurdles, we developed the SARS-CoV-2 Illumina GeNome Assembly Line (SIGNAL) for quick bulk analyses of Illumina short-read sequencing data.

b-move: Faster Lossless Approximate Pattern Matching in a Run-Length Compressed Index.

Background: Due to the increasing availability of high-quality genome sequences, pan-genomes are gradually replacing single consensus reference genomes in many bioinformatics pipelines to better capture genetic diversity. Traditional bioinformatics tools using the FM-index face memory limitations with such large genome collections. Recent advancements in run-length compressed indices like Gagie et al.

b-move: faster bidirectional character extensions in a run-length compressed index.

Due to the increasing availability of high-quality genome sequences, pan-genomes are gradually replacing single consensus reference genomes in many bioinformatics pipelines to better capture genetic diversity. Traditional bioinformatics tools using the FM-index face memory limitations with such large genome collections. Recent advancements in run-length compressed indices like Gagie et al.

Violence on the front line: a qualitative comparative analysis of the causes of patient violence towards medical staff in China during the COVID-19 pandemic.

COVID-19, reduced funding and a shortage of healthcare workers has led to growing international concern about patient violence towards medical staff in medical settings. As the number of reported physical and verbal assaults increases, many medical staff are considering leaving their positions due to the resulting impact on their mental and physical wellbeing, creating a critical need to understand the causes for violence towards medical staff working on the front line. This study aims to examine the causes for patient violence towards medical staff in China during the COVID-19 pandemic.

Machine learning for contour classification in TG-263 noncompliant databases.

A large volume of medical data are labeled using nonstandardized nomenclature. Although efforts have been made by the American Association of Physicists in Medicine (AAPM) to standardize nomenclature through Task Group 263 (TG-263), there remain noncompliant databases. This work aims to create an algorithm that can analyze anatomical contours in patients with head and neck cancer and classify them into TG-263 compliant nomenclature.

Plankton classification with high-throughput submersible holographic microscopy and transfer learning.

Background: Plankton are foundational to marine food webs and an important feature for characterizing ocean health. Recent developments in quantitative imaging devices provide in-flow high-throughput sampling from bulk volumes-opening new ecological challenges exploring microbial eukaryotic variation and diversity, alongside technical hurdles to automate classification from large datasets. However, a limited number of deployable imaging instruments have been coupled with the most prominent classification algorithms-effectively limiting the extraction of curated observations from field deployments.

Metagenome-assembled genome binning methods with short reads disproportionately fail for plasmids and genomic Islands.

Metagenomic methods enable the simultaneous characterization of microbial communities without time-consuming and bias-inducing culturing. Metagenome-assembled genome (MAG) binning methods aim to reassemble individual genomes from this data. However, the recovery of mobile genetic elements (MGEs), such as plasmids and genomic islands (GIs), by binning has not been well characterized.

Genomic signatures and correlates of widespread population declines in salmon.

Global losses of biodiversity are occurring at an unprecedented rate, but causes are often unidentified. Genomic data provide an opportunity to isolate drivers of change and even predict future vulnerabilities. Atlantic salmon (Salmo salar) populations have declined range-wide, but factors responsible are poorly understood.

Cellulase-Hemicellulase Activities and Bacterial Community Composition of Different Soils from Algerian Ecosystems.

Soil microorganisms are important mediators of carbon cycling in nature. Although cellulose- and hemicellulose-degrading bacteria have been isolated from Algerian ecosystems, the information on the composition of soil bacterial communities and thus the potential of their members to decompose plant residues is still limited. The objective of the present study was to describe and compare the bacterial community composition in Algerian soils (crop, forest, garden, and desert) and the activity of cellulose- and hemicellulose-degrading enzymes.

A Future with Ubiquitous Sensing and Intelligent Systems.

In this paper, we discuss the relevance of sensing and biosensing for the ongoing revolution in science and technology as a product of the merging of machine learning and Big Data into affordable technologies and accessible everyday products. Possible scenarios for the next decades are described with examples of intelligent systems for various areas, most of which will rely on ubiquitous sensing. The technological and societal challenges for developing the full potential of such intelligent systems are also addressed.

Emergent Solutions to High-Dimensional Multitask Reinforcement Learning.

Algorithms that learn through environmental interaction and delayed rewards, or reinforcement learning (RL), increasingly face the challenge of scaling to dynamic, high-dimensional, and partially observable environments. Significant attention is being paid to frameworks from deep learning, which scale to high-dimensional data by decomposing the task through multilayered neural networks. While effective, the representation is complex and computationally demanding.

A Data Mining Framework for Glaucoma Decision Support Based on Optic Nerve Image Analysis Using Machine Learning Methods.

Ocular imaging instruments, such as Confocal Scanning Laser Ophthalmoscopy (CSLO), captures high-quality images of the optic disc (also known as optic nerve head) that help clinicians to diagnose glaucoma. We present an integrated data analytics framework to aid clinicians in interpreting CSLO optic nerve images to diagnose and monitor the progression of glaucoma. To distinguish between healthy and glaucomatous optic discs, our framework derives shape information from CSLO images using image processing (Zernike moment method), selects salient features (hybrid feature selection), and then trains image classifiers (Multilayer Perceptron, Support Vector Machine, Bayesian Network).

A climate-associated multispecies cryptic cline in the northwest Atlantic.

The spatial genetic structure of most species in the open marine environment remains largely unresolved. This information gap creates uncertainty in the sustainable management, recovery, and associated resilience of marine communities and our capacity to extrapolate beyond the few species for which such information exists. We document a previously unidentified multispecies biogeographic break aligned with a steep climatic gradient and driven by seasonal temperature minima in the northwest Atlantic.

A new way to contemplate Darwin's tangled bank: how DNA barcodes are reconnecting biodiversity science and biomonitoring.

Encompassing the breadth of biodiversity in biomonitoring programmes has been frustrated by an inability to simultaneously identify large numbers of species accurately and in a timely fashion. Biomonitoring infers the state of an ecosystem from samples collected and identified using the best available taxonomic knowledge. The advent of DNA barcoding has now given way to the extraction of bulk DNA from mixed samples of organisms in environmental samples through the development of high-throughput sequencing (HTS).

Discovery of an expanded set of avian leukosis subgroup E proviruses in chickens using Vermillion, a novel sequence capture and analysis pipeline [corrected].

Transposable elements (TEs), such as endogenous retroviruses (ERVs), are common in the genomes of vertebrates. ERVs result from retroviral infections of germ-line cells, and once integrated into host DNA they become part of the host's heritable genetic material. ERVs have been ascribed positive effects on host physiology such as the generation of novel, adaptive genetic variation and resistance to infection, as well as negative effects as agents of tumorigenesis and disease.

Determination of Flow Rates in Capillary Liquid Chromatography Coupled to a Nanoelectrospray Source using Droplet Image Analysis Software.

Liquid chromatography coupled to electrospray tandem mass spectrometry (LC-ESI-MS/MS) is widely used in proteomic and metabolomic workflows. Considerable analytical improvements have been observed when the components of LC systems are scaled down. Currently, nano-ESI is typically done at capillary LC flow rates ranging from 200 to 300 nL/min.

megasat: automated inference of microsatellite genotypes from sequence data.

megasat is software that enables genotyping of microsatellite loci using next-generation sequencing data. Microsatellites are amplified in large multiplexes, and then sequenced in pooled amplicons. megasat reads sequence files and automatically scores microsatellite genotypes.

Aims and methods of biosteganography.

Applications of biotechnology to information security are now possible and have potentially far reaching political and technological implications. This change in information security practices, initiated by advancements in molecular biological and biotechnology, warrants reasonable and widespread consideration by biologists, biotechnologists and philosophers. I offer an explication of the landmark contributions, developments and current possibilities of biosteganography-the process of transmitting secure messages via biological mediums.

Microbial malaise: how can we classify the microbiome?

The names and lineages of microorganisms are critical to our understanding of the microbiome. However, microbial taxonomy and phylogeny are in perpetual flux, with emerging criteria being used to rename and reshape our views of the microbial world. Different candidate molecular and nonmolecular criteria are often broadly consistent with one another, which underpins the pluralistic approach to taxonomy.

Phylogenetic approaches to microbial community classification.

Background: The microbiota from different body sites are dominated by different major groups of microbes, but the variations within a body site such as the mouth can be more subtle. Accurate predictive models can serve as useful tools for distinguishing sub-sites and understanding key organisms and their roles and can highlight deviations from expected distributions of microbes. Good classification depends on choosing the right combination of classifier, feature representation, and learning model.

Detection of event-related potentials in individual subjects using support vector machines.

Event-related potentials (ERPs) are tiny electrical brain responses in the human electroencephalogram that are typically not detectable until they are isolated by a process of signal averaging. Owing to the extremely smallsize of ERP components (ranging from less than 1 μV to tens of μV), compared to background brain rhythms, statistical analyses of ERPs are predominantly carried out in groups of subjects. This limitation is a barrier to the translation of ERP-based neuroscience to applications such as medical diagnostics.

Supertrees Based on the Subtree Prune-and-Regraft Distance.

Supertree methods reconcile a set of phylogenetic trees into a single structure that is often interpreted as a branching history of species. A key challenge is combining conflicting evolutionary histories that are due to artifacts of phylogenetic reconstruction and phenomena such as lateral gene transfer (LGT). Many supertree approaches use optimality criteria that do not reflect underlying processes, have known biases, and may be unduly influenced by LGT.

SPANNER: taxonomic assignment of sequences using pyramid matching of similarity profiles.

Background: Homology-based taxonomic assignment is impeded by differences between the unassigned read and reference database, forcing a rank-specific classification to the closest (and possibly incorrect) reference lineage. This assignment may be correct only to a general rank (e.g.

Lateral gene transfer of an ABC transporter complex between major constituents of the human gut microbiome.

Background: Several links have been established between the human gut microbiome and conditions such as obesity and inflammatory bowel syndrome. This highlights the importance of understanding what properties of the gut microbiome can affect the health of the human host. Studies have been undertaken to determine the species composition of this microbiome and infer functional profiles associated with such host properties.

Rapid identification of high-confidence taxonomic assignments for metagenomic data.

Determining the taxonomic lineage of DNA sequences is an important step in metagenomic analysis. Short DNA fragments from next-generation sequencing projects and microbes that lack close relatives in reference sequenced genome databases pose significant problems to taxonomic attribution methods. Our new classification algorithm, RITA (Rapid Identification of Taxonomic Assignments), uses the agreement between composition and homology to accurately classify sequences as short as 50 nt in length by assigning them to different classification groups with varying degrees of confidence.