PGC1β activates an antiangiogenic program to repress neoangiogenesis in muscle ischemia

Revascularization of ischemic skeletalmuscle is governed by a balance between pro- and antiangiogenic factors in multiple cell types but particularly in myocytes and endothelial cells. Whereas the regulators of proangiogenic factors are well defined (e.g.,hypoxia-inducible factor [HIF]), the transcriptional pathways encoding antiangiogenic factors remain unknown. We report that the transcriptional cofactor PGC1β drives an antiangiogenic gene program in muscle and endothelial cells. PGC1β transcriptionally represses proangiogenic genes (e.g., Vegfc, Vegfd, Pdgfb, Angpt1, Angpt2, Fgf1, and Fgf2) and induces antiangiogenic genes (e.g., Thbs1, Thbs2, Angstat, Pedf, and Vash1). Consequently, musclespecific PGC1β overexpression impairs muscle revascularization in ischemia and PGC1β deletion enhances it. PGC1β overexpression or deletion in endothelial cells also blocks or stimulates angiogenesis, respectively. PGC1β stimulates the antiangiogenic genes partly by coactivating COUP-TFI. Furthermore, roangiogenic stimuli such as hypoxia, hypoxia-mimetic agents, and ischemia decrease PGC1β expression in a HIF-dependent manner. PGC1β is an antiangiogenic transcriptional switch that could be targeted for therapeutic angiogenesis

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Entrepreneurial Exits and Innovation

We examine how initial public offerings (IPOs) and acquisitions affect entrepreneurial innovation as measured by patent counts and forward patent citations. We construct a firm-year panel data set of all venture capital-backed biotechnology firms founded between 1980 and 2000, tracked yearly through 2006. We address the possibility of unobserved self-selection into exit mode by using coarsened exact matching, and in two additional ways: (1) comparing firms that filed for an IPO (or announced a merger) with those not completing the transaction for reasons unrelated to innovation, and (2) using an instrumental variables approach. We find that innovation quality is highest under private ownership and lowest under public ownership, with acquisition intermediate between the two. Together with a set of within-exit mode analyses, these results are consistent with the proposition that information confidentiality mechanisms shape innovation outcomes. The results are not explained by inventor-level turnover following exit events or by firms\u27 preexit window dressing behavior

DeepSolarEye: Power Loss Prediction and Weakly Supervised Soiling Localization via Fully Convolutional Networks for Solar Panels

The impact of soiling on solar panels is an important and well-studied problem in renewable energy sector. In this paper, we present the first convolutional neural network (CNN) based approach for solar panel soiling and defect analysis. Our approach takes an RGB image of solar panel and environmental factors as inputs to predict power loss, soiling localization, and soiling type. In computer vision, localization is a complex task which typically requires manually labeled training data such as bounding boxes or segmentation masks. Our proposed approach consists of specialized four stages which completely avoids localization ground truth and only needs panel images with power loss labels for training. The region of impact area obtained from the predicted localization masks are classified into soiling types using the webly supervised learning. For improving localization capabilities of CNNs, we introduce a novel bi-directional input-aware fusion (BiDIAF) block that reinforces the input at different levels of CNN to learn input-specific feature maps. Our empirical study shows that BiDIAF improves the power loss prediction accuracy by about 3% and localization accuracy by about 4%. Our end-to-end model yields further improvement of about 24% on localization when learned in a weakly supervised manner. Our approach is generalizable and showed promising results on web crawled solar panel images. Our system has a frame rate of 22 fps (including all steps) on a NVIDIA TitanX GPU. Additionally, we collected first of it's kind dataset for solar panel image analysis consisting 45,000+ images.Comment: Accepted for publication at WACV 201

How Does the Coupling of Secondary and Tertiary Interactions Control the Folding of Helical Macromolecules?

The authors study how the simultaneous presence of short-range secondary and long-range tertiary interactions controls the folding and collapse behavior of a helical macromolecule. The secondary interactions stabilize the helical conformation of the chain, while the tertiary interactions govern its overall three-dimensional shape. The authors have carried out Monte Carlo simulations to study the effect of chain length on the folding and collapse behavior of the chain. They have calculated state diagrams for four chain lengths and found that the physics is very rich with a plethora of stable conformational states. In addition to the helix-coil and coil-globule transitions, their model describes the coupling between them which takes place at low temperatures. Under these conditions, their model predicts a cascade of continuous, conformational transitions between states with an increase in the strength of the tertiary interactions. During each transition the chain shrinks, i.e., collapses, in a rapid and specific manner. In addition, the number of the transitions increases with increasing chain length. They have also found that the low-temperature regions of the state diagram between the transition lines cannot be associated with specific structures of the chain, but rather, with ensembles of various configurations of the chain with similar characteristics. Based on these results the authors propose a mechanism for the folding and collapse of helical macromolecules which is further supported by the analysis of configurational, configurational, and thermodynamic properties of the chain

Graduate Student Retention of Prerequisite Course Content

Although most graduate schools for Speech-Language Pathology require the same core prerequisite classes, there is still variation in class requirements as well as variation in course content. Sixty-one instructors completed a survey regarding what they felt was essential course content from their prerequisite communication sciences and disorders (CSD) classes. This information was used to create a student survey that consisted of 55 multiple choice questions chosen to assess knowledge from eight typically required prerequisite courses. Students preparing to enter their first year of their master’s program were asked to take the survey. Instructors that reported teaching a course in audiology agreed the most on course content. Regarding the student survey, students performed best in the areas of audiology and anatomy and physiology. Students that had a more than two-year gap between undergraduate coursework and their graduate program start date performed significantly worse than those who had a less than two-year gap. The variables of undergraduate major, age, and type of university did not prove significant. Implications for instructors and course content are discussed