The influence of helmet size and shape on peak linear decelerations when impacting crash pads

AbstractDuring training and competition in short track speed skating, skaters commonly fall on the ice and slide into crash pads that line the boards of the rink. Skaters wear helmets to protect their heads from such impacts. Nevertheless, concussion injuries are not uncommon, especially from impacts into the crash pads. Basic mechanical principles suggest that, all other things being equal, smaller sized and rounder shaped helmets should reduce peak impact forces when hitting relatively soft crash pads. This study validates these assumptions and determines the magnitude of these effects using drop tests and a 3D accelerometer. Hemispherical head forms of various radii, each weighing approx. 4.5kg, were dropped from four heights (0.3-4.0 m) onto a crash pad. Peak linear decelerations were recorded. In one set of tests, complete hemispheres were used, highlighting the effect of helmet size (radius). In a second set of tests, another set of hemispheres of various radii were sliced to produce caps each with a diameter of 8” but each with a different radius of curvature. Impact tests at four drop heights using these caps revealed the effect of helmet shape. Size was found to be more important than shape, with the greatest effects being in the 10-20cm radius range, a range which is relevant to helmets used in the sport today

beachmat: A Bioconductor C++ API for accessing high-throughput biological data from a variety of R matrix types.

Biological experiments involving genomics or other high-throughput assays typically yield a data matrix that can be explored and analyzed using the R programming language with packages from the Bioconductor project. Improvements in the throughput of these assays have resulted in an explosion of data even from routine experiments, which poses a challenge to the existing computational infrastructure for statistical data analysis. For example, single-cell RNA sequencing (scRNA-seq) experiments frequently generate large matrices containing expression values for each gene in each cell, requiring sparse or file-backed representations for memory-efficient manipulation in R. These alternative representations are not easily compatible with high-performance C++ code used for computationally intensive tasks in existing R/Bioconductor packages. Here, we describe a C++ interface named beachmat, which enables agnostic data access from various matrix representations. This allows package developers to write efficient C++ code that is interoperable with dense, sparse and file-backed matrices, amongst others. We evaluated the performance of beachmat for accessing data from each matrix representation using both simulated and real scRNA-seq data, and defined a clear memory/speed trade-off to motivate the choice of an appropriate representation. We also demonstrate how beachmat can be incorporated into the code of other packages to drive analyses of a very large scRNA-seq data set

FluShuffle and FluResort: new algorithms to identify reassorted strains of the influenza virus by mass spectrometry

Background: Influenza is one of the oldest and deadliest infectious diseases known to man. Reassorted strains of the virus pose the greatest risk to both human and animal health and have been associated with all pandemics of the past century, with the possible exception of the 1918 pandemic, resulting in tens of millions of deaths. We have developed and tested new computer algorithms, FluShuffle and FluResort, which enable reassorted viruses to be identified by the most rapid and direct means possible. These algorithms enable reassorted influenza, and other, viruses to be rapidly identified to allow prevention strategies and treatments to be more efficiently implemented.Results: The FluShuffle and FluResort algorithms were tested with both experimental and simulated mass spectra of whole virus digests. FluShuffle considers different combinations of viral protein identities that match the mass spectral data using a Gibbs sampling algorithm employing a mixed protein Markov chain Monte Carlo (MCMC) method. FluResort utilizes those identities to calculate the weighted distance of each across two or more different phylogenetic trees constructed through viral protein sequence alignments. Each weighted mean distance value is normalized by conversion to a Z-score to establish a reassorted strain.Conclusions: The new FluShuffle and FluResort algorithms can correctly identify the origins of influenza viral proteins and the number of reassortment events required to produce the strains from the high resolution mass spectral data of whole virus proteolytic digestions. This has been demonstrated in the case of constructed vaccine strains as well as common human seasonal strains of the virus. The algorithms significantly improve the capability of the proteotyping approach to identify reassorted viruses that pose the greatest pandemic risk. © 2012 Lun et al.; licensee BioMed Central Ltd.Link_to_subscribed_fulltex

Scater: pre-processing, quality control, normalization and visualization of single-cell RNA-seq data in R.

MOTIVATION: Single-cell RNA sequencing (scRNA-seq) is increasingly used to study gene expression at the level of individual cells. However, preparing raw sequence data for further analysis is not a straightforward process. Biases, artifacts and other sources of unwanted variation are present in the data, requiring substantial time and effort to be spent on pre-processing, quality control (QC) and normalization. RESULTS: We have developed the R/Bioconductor package scater to facilitate rigorous pre-processing, quality control, normalization and visualization of scRNA-seq data. The package provides a convenient, flexible workflow to process raw sequencing reads into a high-quality expression dataset ready for downstream analysis. scater provides a rich suite of plotting tools for single-cell data and a flexible data structure that is compatible with existing tools and can be used as infrastructure for future software development. AVAILABILITY AND IMPLEMENTATION: The open-source code, along with installation instructions, vignettes and case studies, is available through Bioconductor at http://bioconductor.org/packages/scater . CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online

Doublet identification in single-cell sequencing data using scDblFinder

Doublets are prevalent in single-cell sequencing data and can lead to artifactual findings. A number of strategies have therefore been proposed to detect them. Building on the strengths of existing approaches, we developed scDblFinder, a fast, flexible and accurate Bioconductor-based doublet detection method. Here we present the method, justify its design choices, demonstrate its performance on both single-cell RNA and accessibility (ATAC) sequencing data, and provide some observations on doublet formation, detection, and enrichment analysis. Even in complex datasets, scDblFinder can accurately identify most heterotypic doublets, and was already found by an independent benchmark to outcompete alternatives

Detection and removal of barcode swapping in single-cell RNA-seq data.

Barcode swapping results in the mislabelling of sequencing reads between multiplexed samples on patterned flow-cell Illumina sequencing machines. This may compromise the validity of numerous genomic assays; however, the severity and consequences of barcode swapping remain poorly understood. We have used two statistical approaches to robustly quantify the fraction of swapped reads in two plate-based single-cell RNA-sequencing datasets. We found that approximately 2.5% of reads were mislabelled between samples on the HiSeq 4000, which is lower than previous reports. We observed no correlation between the swapped fraction of reads and the concentration of free barcode across plates. Furthermore, we have demonstrated that barcode swapping may generate complex but artefactual cell libraries in droplet-based single-cell RNA-sequencing studies. To eliminate these artefacts, we have developed an algorithm to exclude individual molecules that have swapped between samples in 10x Genomics experiments, allowing the continued use of cutting-edge sequencing machines for these assays

Assessing the reliability of spike-in normalization for analyses of single-cell RNA sequencing data.

By profiling the transcriptomes of individual cells, single-cell RNA sequencing provides unparalleled resolution to study cellular heterogeneity. However, this comes at the cost of high technical noise, including cell-specific biases in capture efficiency and library generation. One strategy for removing these biases is to add a constant amount of spike-in RNA to each cell and to scale the observed expression values so that the coverage of spike-in transcripts is constant across cells. This approach has previously been criticized as its accuracy depends on the precise addition of spike-in RNA to each sample. Here, we perform mixture experiments using two different sets of spike-in RNA to quantify the variance in the amount of spike-in RNA added to each well in a plate-based protocol. We also obtain an upper bound on the variance due to differences in behavior between the two spike-in sets. We demonstrate that both factors are small contributors to the total technical variance and have only minor effects on downstream analyses, such as detection of highly variable genes and clustering. Our results suggest that scaling normalization using spike-in transcripts is reliable enough for routine use in single-cell RNA sequencing data analyses.This work was supported by Cancer Research UK (core funding to JCM, award no. A17197), the University of Cambridge and Hutchison Whampoa Limited. JCM was also supported by core funding from EMBL. LHV was supported by an EMBL Interdisciplinary Postdoctoral fellowship. Work in the G ottgens group was supported by Cancer Research UK, Bloodwise, the National Institute of Diabetes and Digestive and Kidney Diseases, the Leukemia and Lymphoma Society and core infrastructure grants from the Wellcome Trust and the Medical Research Council to the Cambridge Stem Cell Institute

Pooling across cells to normalize single-cell RNA sequencing data with many zero counts.

Normalization of single-cell RNA sequencing data is necessary to eliminate cell-specific biases prior to downstream analyses. However, this is not straightforward for noisy single-cell data where many counts are zero. We present a novel approach where expression values are summed across pools of cells, and the summed values are used for normalization. Pool-based size factors are then deconvolved to yield cell-based factors. Our deconvolution approach outperforms existing methods for accurate normalization of cell-specific biases in simulated data. Similar behavior is observed in real data, where deconvolution improves the relevance of results of downstream analyses.All authors were supported by core funding from Cancer Research UK (code: SW73).This is the final version of the article. It first appeared from Biomed Central via https://doi.org/10.1186/s13059-016-0947-

Specificity of RNAi, LNA and CRISPRi as loss-of-function methods in transcriptional analysis

ABSTRACT Loss-of-function (LOF) methods, such as RNA interference (RNAi), antisense oligonucleotides or CRISPR-based genome editing, provide unparalleled power for studying the biological function of genes of interest. When coupled with transcriptomic analyses, LOF methods allow researchers to dissect networks of transcriptional regulation. However, a major concern is nonspecific targeting, which involves depletion of transcripts other than those intended. The off-target effects of each of these common LOF methods have yet to be compared at the whole-transcriptome level. Here, we systematically and experimentally compared non-specific activity of RNAi, antisense oligonucleotides and CRISPR interference (CRISPRi). All three methods yielded non-negligible offtarget effects in gene expression, with CRISPRi exhibiting clonal variation in the transcriptional profile. As an illustrative example, we evaluated the performance of each method for deciphering the role of a long noncoding RNA (lncRNA) with unknown function. Although all LOF methods reduced expression of the candidate lncRNA, each method yielded different sets of differentially expressed genes upon knockdown as well as a different cellular phenotype. Therefore, to definitively confirm the functional role of a transcriptional regulator, we recommend the simultaneous use of at least two different LOF methods and the inclusion of multiple, specifically designed negative controls