Comparative muscle transcriptome of Mali and Hampshire breeds of pigs: a preliminary study.

Muscle development is an important priority of pig breeding programs. There is a considerable variation in muscularity between the breeds, but the regulation mechanisms of genes underlying myogenesis are still unclear. Transcriptome data from two breeds of pigs with divergent muscularity (Mali and Hampshire) were integrated with histology, immunofluorescence and meat yield to identify differences in myogenesis during the early growth phase. The muscle transcriptomics analysis revealed 17,721 common, 1413 and 1115 unique transcripts to Hampshire and Mali, respectively. This study identified 908 differentially expressed genes (p < 0.05; log2FC > ±1) in the muscle samples, of which 550 were upregulated and 358 were downregulated in Hampshire pigs, indicating differences in physiological process related to muscle function and development. Expression of genes related to myoblast fusion (MYMK), skeletal muscle satellite cell proliferation (ANGPT1, CDON) and growth factors (HGF, IGF1, IGF2) were higher in Hampshire than Mali, even though transcript levels of several other myogenesis-related genes (MYF6, MYOG, MSTN) were similar. The number of fibers per fascicle and the expression of myogenic marker proteins (MYOD1, MYOG and PAX7) were more in Hampshire as compared to Mali breed of pig, supporting results of transcriptome studies. The results suggest that differences in muscularity between breeds could be related to the regulation of myoblast fusion and myogenic activities. The present study will help to identify genes that could be explored for their utility in the selection of animals with different muscularities.

A sensor-enabled cloud-based computing platform for computational brain biomechanics.

BACKGROUND AND OBJECTIVES: Driven by the risk of repetitive head trauma, sensors have been integrated into mouthguards to measure head impacts in contact sports and military activities. These wearable devices, referred to as "instrumented" or "smart" mouthguards are being actively developed by various research groups and organizations. These instrumented mouthguards provide an opportunity to further study and understand the brain biomechanics due to impact. In this study, we present a brain modeling service that can use information from these sensors to predict brain injury metrics in an automated fashion. METHODS: We have built a brain modeling platform using several of Amazon's Web Services (AWS) to enable cloud computing and scalability. We use a custom-built cloud-based finite element modeling code to compute the physics-based nonlinear response of the intracranial brain tissue and provide a frontend web application and an application programming interface for groups working on head impact sensor technology to include simulated injury predictions into their research pipeline. RESULTS: The platform results have been validated against experimental data available in literature for brain-skull relative displacements, brain strains and intracranial pressure. The parallel processing capability of the platform has also been tested and verified. We also studied the accuracy of the custom head surfaces generated by Avatar 3D. CONCLUSION: We present a validated cloud-based computational brain modeling platform that uses sensor data as input for numerical brain models and outputs a quantitative description of brain tissue strains and injury metrics. The platform is expected to generate transparent, reproducible, and traceable brain computing results.