Stallings Boyd (pinkthrill5)

We hypothesize that this pattern results from differences in cell size constraining diffusion and evaporation of water from the skin under warm conditions when cutaneous perfusion is reduced. Testing this hypothesis may confirm a previously unappreciated adaptive role for C-value variation in this group, and reveals the possibility that genome size influences physiological exchange across transport barriers more broadly.High-quality metadata annotations for data hosted in large public repositories are essential for research reproducibility and for conducting fast, powerful and scalable meta-analyses. Currently, a majority of sequencing samples in the National Center for Biotechnology Information's Sequence Read Archive (SRA) are missing metadata across several categories. In an effort to improve the metadata coverage of these samples, we leveraged almost 44 million attribute-value pairs from SRA BioSample to train a scalable, recurrent neural network that predicts missing metadata via named entity recognition (NER). The network was first trained to classify short text phrases according to 11 metadata categories and achieved an overall accuracy and area under the receiver operating characteristic curve of 85.2% and 0.977, respectively. We then applied our classifier to predict 11 metadata categories from the longer TITLE attribute of samples, evaluating performance on a set of samples withheld from model training. Prediction accuracies were high when extracting sample Genus/Species (94.85%), Condition/Disease (95.65%) and Strain (82.03%) from TITLEs, with lower accuracies and lack of predictions for other categories highlighting multiple issues with the current metadata annotations in BioSample. These results indicate the utility of recurrent neural networks for NER-based metadata prediction and the potential for models such as the one presented here to increase metadata coverage in BioSample while minimizing the need for manual curation. Database URL https//github.com/cartercompbio/PredictMEE.Activation of inflammation by lipopolysaccharide (LPS) is an important innate immune response. Here we investigated the contribution of caspases to the LPS-mediated inflammatory response and discovered distinctive temporal roles of RIPK1 in mediating proinflammatory cytokine production when caspases are inhibited. We propose a biphasic model that differentiates the role of RIPK1 in early versus late phase. The early production of proinflammation cytokines stimulated by LPS with caspase inhibition is mediated by the NF-κB pathway that requires the scaffold function of RIPK1 but is kinase independent. Autocrine production of TNFα in the late phase promotes the formation of a novel TNFR1-associated complex with activated RIPK1, FADD, caspase-8, and key mediators of NF-κB signaling. The production of proinflammatory cytokines in the late phase can be blocked by RIPK1 kinase inhibitor Nec-1s. Our study demonstrates a mechanism by which the activation of RIPK1 promotes its own scaffold function to regulate the NF-κB-mediated proinflammatory cytokine production that is negatively regulated by caspases to restrain inflammatory signaling.Rac1 GTPase is hyperactivated in tumors and contributes to malignancy. Rac1 disruption of junctions requires its effector PAK1, but the precise mechanisms are unknown. Here, we show that E-cadherin is internalized via micropinocytosis in a PAK1-dependent manner without catenin dissociation and degradation. In addition to internalization, PAK1 regulates E-cadherin transport by fine-tuning Rab small GTPase function. PAK1 phosphorylates a core Rab regulator, RabGDIβ, but not RabGDIα. Phosphorylated RabGDIβ preferentially associates with Rab5 and Rab11, which is predicted to promote Rab retrieval from membranes. Consistent with this hypothesis, Rab11 is activated by Rac1, and inhibition of Rab11 function partially rescues E-cadherin destabilization. Thus, Rac1 activation reduces surface cadherin levels as a net resu