Biology subjects

Rathore, A.

Publications and source records attributed to Rathore, A..

3 recordsLinked to original sources

bioRxiv2020

Multi-Object Tracking in Heterogeneous environments (MOTHe) for animal space-use studies

O_LIVideo recordings of animals are used for many areas of research such as collective movement, animal space-use, animal censuses and behavioural neuroscience. They provide us with behavioural data at scales and resolutions not possible with manual observations. Many automated methods are being developed to extract data from these high-resolution videos. However, the task of animal detection and tracking for videos taken in natural settings remains challenging due to heterogeneous environments. C_LIO_LIWe present an open-source end-to-end pipeline called Multi-Object Tracking in Heterogenous environments (MOTHe), a python-based application that uses a basic convolutional neural network for object detection. MOTHe allows researchers with minimal coding experience to track multiple animals in their natural habitats. It identifies animals even when individuals are stationary or partially camouflaged. C_LIO_LIMOTHe has a command-line-based interface with one command for each action, for example, finding animals in an image and tracking each individual. Parameters used by the algorithm are well described in a configuration file along with example values for different types of tracking scenario. MOTHe doesnt require any sophisticated infrastructure and can be run on basic desktop computing units. C_LIO_LIWe demonstrate MOTHe on six video clips from two species in their natural habitat - wasp colonies on their nests (up to 12 individuals per colony) and antelope herds in four different types of habitats (up to 156 individuals in a herd). Using MOTHe, we are able to detect and track all individuals in these animal group videos. MOTHes computing time on a personal computer with 4 GB RAM and i5 processor is 5 minutes for a 30-second long ultra-HD (4K resolution) video recorded at 30 frames per second. C_LIO_LIMOTHe is available as an open-source repository with a detailed user guide and demonstrations at Github (https://github.com/tee-lab/MOTHe). C_LI

ecology↗

bioRxiv2019

A sorghum Practical Haplotype Graph facilitates genome-wide imputation and cost-effective genomic prediction

Successful management and utilization of increasingly large genomic datasets is essential for breeding programs to increase genetic gain and accelerate cultivar development. To help with data management and storage, we developed a sorghum Practical Haplotype Graph (PHG) pangenome database that stores all identified haplotypes and variant information for a given set of individuals. We developed two PHGs in sorghum, one with 24 individuals and another with 398 individuals, that reflect the diversity across genic regions of the sorghum genome. 24 founders of the Chibas sorghum breeding program were sequenced at low coverage (0.01x) and processed through the PHG to identify genome-wide variants. The PHG called SNPs with only 5.9% error at 0.01x coverage - only 3% lower than its accuracy when calling SNPs from 8x coverage sequence. Additionally, 207 progeny from the Chibas genomic selection (GS) training population were sequenced and processed through the PHG. Missing genotypes in the progeny were imputed from the parental haplotypes available in the PHG and used for genomic prediction. Mean prediction accuracies with PHG SNP calls range from 0.57-0.73 for different traits, and are similar to prediction accuracies obtained with genotyping-by-sequencing (GBS) or markers from sequencing targeted amplicons (rhAmpSeq). This study provides a proof of concept for using a sorghum PHG to call and impute SNPs from low-coverage sequence data and also shows that the PHG can unify genotype calls from different sequencing platforms. By reducing the amount of input sequence needed, the PHG has the potential to decrease the cost of genotyping for genomic selection, making GS more feasible and facilitating larger breeding populations that can capture maximum recombination. Our results demonstrate that the PHG is a useful research and breeding tool that can maintain variant information from a diverse group of taxa, store sequence data in a condensed but readily accessible format, unify genotypes from different genotyping methods, and provide a cost-effective option for genomic selection for any species.

genomics↗

bioRxiv2019

The pneumococcal two-component system VisRH is linked to enhanced intracellular survival of Streptococcus pneumoniae in influenza-infected pneumocytes

The virus-bacterial synergism implicated in secondary bacterial infections caused by Streptococcus pneumoniae following infection with epidemic or pandemic influenza A virus (IAV) is well documented. However, the molecular mechanisms behind such synergism remain largely ill-defined. In pneumocytes infected with influenza A virus, subsequent infection with S. pneumoniae leads to enhanced pneumococcal intracellular survival. The pneumococcal two-component system VisRH appears essential for such enhanced survival. Through comparative transcriptomic analysis between the {Delta}visR and wt strains, a list of 179 differentially expressed genes was defined. Among those, the clpL protein chaperone gene and the psaB Mn+2 transporter gene, which are involved in the stress response, are important in enhancing S. pneumoniae survival in influenza-infected cells. The {Delta}visR, {Delta}clpL and {Delta}psaB deletion mutants display increased susceptibility to acidic and oxidative stress and no enhancement of intracellular survival in IAV-infected pneumocyte cells. These results suggest that the VisRH two-component system senses IAV-induced stress conditions and controls adaptive responses that allow survival of S. pneumoniae in IAV-infected pneumocytes.\n\nAuthor summaryS. pneumoniae is an inhabitant of the human nasopharynx that is capable of causing a variety of infections contributing to an estimated 1.6 million deaths each year. Many of these deaths occur as result of secondary S. pneumoniae infections following seasonal or pandemic influenza. Although S. pneumoniae is considered a typical extracellular pathogen, an intracellular survival mechanism has been more recently recognized as significant in bacterial pathogenesis. The synergistic effects between influenza A and S. pneumoniae in secondary bacterial infection are well documented; however, the effects of influenza infections on intracellular survival of S. pneumoniae are ill-defined. Here, we provide evidence that influenza infection increases S. pneumoniae intracellular survival in pneumocytes. We demonstrate that the poorly understood VisRH signal transduction system in pneumococcus controls the expression of genes involved in the stress response that S. pneumoniae needs to increase intracellular survival in influenza A-infected pneumocytes. These findings have important implications for understanding secondary bacterial pathogenesis following influenza and for the treatment of such infections in influenza-stricken patients.

microbiology↗