Physical Therapy Management Of A Patient After Hemorrhagic Stroke Using A Task-Oriented Approach In A Skilled Nursing Facility: A Case Report

Stroke is the leading cause of long term disability in the U.S.; nearly 800,000 Americans have a stroke each year. Subarachnoid hemorrhagic stroke occurs when one of the blood vessels in the brain bursts causing a release of blood which increases intracranial pressure. There is a lack of rehabilitation research in the skilled nursing setting for hemorrhagic stroke. The purpose of this case report is to describe the PT management, using a task-oriented approach, of a patient with a subarachnoid hemorrhagic stroke being treated in a skilled nursing setting.https://dune.une.edu/pt_studcrposter/1104/thumbnail.jp

EJC-dependent nonsense-mediated decay (NMD) [4].

<p>After splicing, exonic-junction complexes (EJCs) remain 20–24 nucleotides upstream of every exon junction. These EJCs are then bound by UPF2, one of the core proteins of NMD. When the first ribosome translates the mRNA, it displaces the EJCs. However, if the ribosome encounters a premature stop codon and stalls, it forms a complex with a downstream EJC, mediated by UPF2 and another complex called SURF. (The SURF complex is named after its constituent proteins <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1002296#pgen.1002296-Kashima1" target="_blank">[5]</a>.) This complex then initiates mRNA decay. Because EJC-dependent NMD requires a downstream EJC, it is only effective in the coding regions upstream of the last EJC (indicated in blue). It cannot detect any premature stop codons downstream of the last EJC (indicated in red). Note that an alternative, less potent mode of NMD takes place in the absence of the EJC <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1002296#pgen.1002296-Bhler1" target="_blank">[6]</a>.</p

Geometric Constraints Dominate the Antigenic Evolution of Influenza H3N2 Hemagglutinin

<div><p>We have carried out a comprehensive analysis of the determinants of human influenza A H3 hemagglutinin evolution. We consider three distinct predictors of evolutionary variation at individual sites: solvent accessibility (as a proxy for protein fold stability and/or conservation), Immune Epitope Database (IEDB) epitope sites (as a proxy for host immune bias), and proximity to the receptor-binding region (as a proxy for one of the functions of hemagglutinin-to bind sialic acid). Individually, these quantities explain approximately 15% of the variation in site-wise <i>dN/dS</i>. In combination, solvent accessibility and proximity explain 32% of the variation in <i>dN/dS</i>; incorporating IEDB epitope sites into the model adds only an additional 2 percentage points. Thus, while solvent accessibility and proximity perform largely as independent predictors of evolutionary variation, they each overlap with the epitope-sites predictor. Furthermore, we find that the historical H3 epitope sites, which date back to the 1980s and 1990s, only partially overlap with the experimental sites from the IEDB, and display similar overlap in predictive power when combined with solvent accessibility and proximity. We also find that sites with <i>dN/dS</i> > 1, i.e., the sites most likely driving seasonal immune escape, are not correctly predicted by either historical or IEDB epitope sites, but only by proximity to the receptor-binding region. In summary, a simple geometric model of HA evolution outperforms a model based on epitope sites. These results suggest that either the available epitope sites do not accurately represent the true influenza antigenic sites or that host immune bias may be less important for influenza evolution than commonly thought.</p></div

np.tar

Alignments simulated using nucleoprotein amino acid fitness values and nucleoprotein mutation rates

polio.tar

Contains alignments simulated using nucleoprotein amino acid fitness values and polio mutation rates

synsel.tar

Contains simulated alignments and associated state codon frequencies for datasets with synonymous codon selection

no_synsel.tar

Contains simulated alignments and associated state codon frequencies for datasets without synonymous codon selection

Pyvolve: A Flexible Python Module for Simulating Sequences along Phylogenies

<div><p>We introduce Pyvolve, a flexible Python module for simulating genetic data along a phylogeny using continuous-time Markov models of sequence evolution. Easily incorporated into Python bioinformatics pipelines, Pyvolve can simulate sequences according to most standard models of nucleotide, amino-acid, and codon sequence evolution. All model parameters are fully customizable. Users can additionally specify custom evolutionary models, with custom rate matrices and/or states to evolve. This flexibility makes Pyvolve a convenient framework not only for simulating sequences under a wide variety of conditions, but also for developing and testing new evolutionary models. Pyvolve is an open-source project under a FreeBSD license, and it is available for download, along with a detailed user-manual and example scripts, from <a href="http://github.com/sjspielman/pyvolve" target="_blank">http://github.com/sjspielman/pyvolve</a>.</p></div

Sites identified by Koel et al. 2013 and those predicted to have <i>dN</i>/<i>dS</i> > 1.

<p>The sites shown in purple are those identified by Koel et al. 2013 [<a href="http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1004940#ppat.1004940.ref021" target="_blank">21</a>] to be critical for antigenic cluster transitions. Only one of these sites has a <i>dN</i>/<i>dS</i> significantly above one, site 145. The sites shown in red are those that our geometrical model predicts to have <i>dN</i>/<i>dS</i> > 1. (Half of those sites have observed <i>dN</i>/<i>dS</i> > 1.) Note that our model predicts only sites on the basal side of sialic acid to be under positive selection, since our reference point for proximity is site 224. Site 145, the only purple site under positive selection, is also the only purple site on the basal side of sialic acid.</p

conv.tar

Contains simulated alignments and associated state codon frequencies used to assess convergence