Performance Motion Analysis Unable to Predict Running-Related Injury in Collegiate Distance Runners

ABSTRACT Purpose: Running-related injury (RRI) is common among competitive collegiate distance runners who participate in the sport of cross country and long distance track and field. Many factors contribute to RRI. Therefore, the purpose of this study was to determine if a 3D motion capture system’s performance motion analysis (PMA) report is capable of identifying factors predictive of RRI among collegiate distance runners during a cross country season. Methods: Thirty-one collegiate cross country runners (17 male, 14 female, mean age = 20.5 ± 1.4 years) gave their consent to participate in the investigation. Subjects were screened in the motion capture system and provided with PMA reports assessing their movement quality using several variables (composite score, power, strength, dysfunction, and vulnerability, based on measurements of 192 kinetic and kinematic variables). The athletes were then monitored throughout their 13-week competitive season for incidence of RRI. At the end of the season, participants were sorted into injured (n=17) and uninjured (n=14) groups. Injury was defined as appearing on the team injury report as missing or being limited in practice or competition for a week or more, in accordance with prior RRI research. Each sex was also separated into groups based on injury status. Results: Independent samples t-tests (pConclusion: The findings identified in this prospective study suggest that the movement screen was unable to identify runners at risk of injury. Future investigations isolating lower extremity movement characteristics in runners may prove more effective at predicting RRI

Thermal stress induces glycolytic beige fat formation via a myogenic state.

Environmental cues profoundly affect cellular plasticity in multicellular organisms. For instance, exercise promotes a glycolytic-to-oxidative fibre-type switch in skeletal muscle, and cold acclimation induces beige adipocyte biogenesis in adipose tissue. However, the molecular mechanisms by which physiological or pathological cues evoke developmental plasticity remain incompletely understood. Here we report a type of beige adipocyte that has a critical role in chronic cold adaptation in the absence of β-adrenergic receptor signalling. This beige fat is distinct from conventional beige fat with respect to developmental origin and regulation, and displays enhanced glucose oxidation. We therefore refer to it as glycolytic beige fat. Mechanistically, we identify GA-binding protein α as a regulator of glycolytic beige adipocyte differentiation through a myogenic intermediate. Our study reveals a non-canonical adaptive mechanism by which thermal stress induces progenitor cell plasticity and recruits a distinct form of thermogenic cell that is required for energy homeostasis and survival

Spatiotemporal expression and transcriptional perturbations by long noncoding RNAs in the mouse brain

Long noncoding RNAs (lncRNAs) have been implicated in numerous cellular processes including brain development. However, the in vivo expression dynamics and molecular pathways regulated by these loci are not well understood. Here, we leveraged a cohort of 13 lncRNA-null mutant mouse models to investigate the spatiotemporal expression of lncRNAs in the developing and adult brain and the transcriptome alterations resulting from the loss of these lncRNA loci. We show that several lncRNAs are differentially expressed both in time and space, with some presenting highly restricted expression in only selected brain regions. We further demonstrate altered regulation of genes for a large variety of cellular pathways and processes upon deletion of the lncRNA loci. Finally, we found that 4 of the 13 lncRNAs significantly affect the expression of several neighboring protein-coding genes in a cis-like manner. By providing insight into the endogenous expression patterns and the transcriptional perturbations caused by deletion of the lncRNA locus in the developing and postnatal mammalian brain, these data provide a resource to facilitate future examination of the specific functional relevance of these genes in neural development, brain function, and disease.National Science Foundation (U.S.) (Postdoctoral Research Fellowship in Biology DBI-0905973

Sparse Gamma Rhythms Arising through Clustering in Adapting Neuronal Networks

Gamma rhythms (30–100 Hz) are an extensively studied synchronous brain state responsible for a number of sensory, memory, and motor processes. Experimental evidence suggests that fast-spiking interneurons are responsible for carrying the high frequency components of the rhythm, while regular-spiking pyramidal neurons fire sparsely. We propose that a combination of spike frequency adaptation and global inhibition may be responsible for this behavior. Excitatory neurons form several clusters that fire every few cycles of the fast oscillation. This is first shown in a detailed biophysical network model and then analyzed thoroughly in an idealized model. We exploit the fact that the timescale of adaptation is much slower than that of the other variables. Singular perturbation theory is used to derive an approximate periodic solution for a single spiking unit. This is then used to predict the relationship between the number of clusters arising spontaneously in the network as it relates to the adaptation time constant. We compare this to a complementary analysis that employs a weak coupling assumption to predict the first Fourier mode to destabilize from the incoherent state of an associated phase model as the external noise is reduced. Both approaches predict the same scaling of cluster number with respect to the adaptation time constant, which is corroborated in numerical simulations of the full system. Thus, we develop several testable predictions regarding the formation and characteristics of gamma rhythms with sparsely firing excitatory neurons

Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas

Although theMYConcogene has been implicated incancer, a systematic assessment of alterations ofMYC, related transcription factors, and co-regulatoryproteins, forming the proximal MYC network (PMN),across human cancers is lacking. Using computa-tional approaches, we define genomic and proteo-mic features associated with MYC and the PMNacross the 33 cancers of The Cancer Genome Atlas.Pan-cancer, 28% of all samples had at least one ofthe MYC paralogs amplified. In contrast, the MYCantagonists MGA and MNT were the most frequentlymutated or deleted members, proposing a roleas tumor suppressors.MYCalterations were mutu-ally exclusive withPIK3CA,PTEN,APC,orBRAFalterations, suggesting that MYC is a distinct onco-genic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such asimmune response and growth factor signaling; chro-matin, translation, and DNA replication/repair wereconserved pan-cancer. This analysis reveals insightsinto MYC biology and is a reference for biomarkersand therapeutics for cancers with alterations ofMYC or the PMN

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment

Genotype, haplotype and copy-number variation in worldwide human populations

Genome-wide patterns of variation across individuals provide a powerful source of data for uncovering the history of migration, range expansion, and adaptation of the human species. However, high-resolution surveys of variation in genotype, haplotype and copy number have generally focused on a small number of population groups(1-3). Here we report the analysis of high-quality genotypes at 525,910 single-nucleotide polymorphisms ( SNPs) and 396 copy-number-variable loci in a worldwide sample of 29 populations. Analysis of SNP genotypes yields strongly supported fine-scale inferences about population structure. Increasing linkage disequilibrium is observed with increasing geographic distance from Africa, as expected under a serial founder effect for the out-of-Africa spread of human populations. New approaches for haplotype analysis produce inferences about population structure that complement results based on unphased SNPs. Despite a difference from SNPs in the frequency spectrum of the copy-number variants (CNVs) detected-including a comparatively large number of CNVs in previously unexamined populations from Oceania and the Americas-the global distribution of CNVs largely accords with population structure analyses for SNP data sets of similar size. Our results produce new inferences about inter-population variation, support the utility of CNVs in human population-genetic research, and serve as a genomic resource for human-genetic studies in diverse worldwide populations.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/62552/1/nature06742.pd

Optical variability of quasars with 20-yr photometric light curves

We study the optical gri photometric variability of a sample of 190 quasars within the SDSS Stripe 82 region that have long-term photometric coverage during ∼1998−2020 with SDSS, PanSTARRS-1, the Dark Energy Survey, and dedicated follow-up monitoring with Blanco 4m/DECam. With on average ∼200 nightly epochs per quasar per filter band, we improve the parameter constraints from a Damped Random Walk (DRW) model fit to the light curves over previous studies with 10–15 yr baselines and ≲ 100 epochs. We find that the average damping time-scale τDRW continues to rise with increased baseline, reaching a median value of ∼750 d (g band) in the rest frame of these quasars using the 20-yr light curves. Some quasars may have gradual, long-term trends in their light curves, suggesting that either the DRW fit requires very long baselines to converge, or that the underlying variability is more complex than a single DRW process for these quasars. Using a subset of quasars with better-constrained τDRW (less than 20 per cent of the baseline), we confirm a weak wavelength dependence of τDRW∝λ0.51 ± 0.20. We further quantify optical variability of these quasars over days to decades time-scales using structure function (SF) and power spectrum density (PSD) analyses. The SF and PSD measurements qualitatively confirm the measured (hundreds of days) damping time-scales from the DRW fits. However, the ensemble PSD is steeper than that of a DRW on time-scales less than ∼ a month for these luminous quasars, and this second break point correlates with the longer DRW damping time-scale