Striving for Resilience in Virginia\u27s Transportation Sector

To help address the need for increased resiliency in the Commonwealth’s transportation sector, and in furtherance of the goals set forth in the VDOT [Virginia Department of Transportation] Resilience Plan, this white paper highlights green infrastructure and natural and nature-based features as ways to increase resilience for transportation infrastructure and mitigating impacts from climate change. Additionally, this paper describes potential methods of incorporating resilient best practices with respect to Virginia’s transportation infrastructure and planning decisions. This abstract has been taken from the paper\u27s Section I, Background

Gravitational microlensing as a test of stellar model atmospheres

We present calculations illustrating the potential of gravitational microlensing to discriminate between classical models of stellar surface brightness profiles and the recently computed ``Next Generation'' models of Hauschildt et al. These spherically-symmetric models include a much improved treatment of molecular lines in the outer atmospheres of cool giants -- stars which are very typical sources in Galactic bulge microlensing events. We show that the microlensing signatures of intensively monitored point and fold caustic crossing events are readily able to distinguish between NextGen and the classical models, provided a photometric accuracy of 0.01 magnitudes is reached. This accuracy is now routinely achieved by alert networks, and hence current observations can discriminate between such model atmospheres, providing a unique insight on stellar photospheres.Comment: 4 pages, 4 figures, Astronomy & Astrophysics (Letters), vol. 388, L1 (2002

SFRP2 Regulates Cardiomyogenic Differentiation by Inhibiting a Positive Transcriptional Autofeedback Loop of Wnt3a

Wnts comprise a family of 20 lipid-modified glycoproteins in mammals and play critical roles during embryological development and organogenesis of several organ systems, including the heart. They are required for mesoderm formation and have been implicated in promoting cardiomyogenic differentiation of mammalian embryonic stem cells, but the underlying mechanisms regulating Wnt signaling during cardiomyogenesis remain poorly understood. In this report, we show that in a pluripotent mouse embryonal carcinoma stem cell line, SFRP2 inhibits cardiomyogenic differentiation by regulating Wnt3a transcription. SFRP2 inhibited early stages of cardiomyogenesis, preventing mesoderm specification and maintaining the cells in the undifferentiated state. Using a gain- and loss-of-function approach, we demonstrate that although addition of recombinant SFRP2 decreased Wnt3a transcription and cardiomyogenic differentiation, silencing of Sfrp2 led to enhanced Wnt3a transcription, mesoderm formation, and increased cardiomyogenesis. We show that the inhibitory effects of SFRP2 on Wnt transcription are secondary to interruption of a positive feedback effect of Wnt3a on its own transcription. Wnt3a increased its own transcription via the canonical pathway and TCF4 family of transcription factors, and the inhibitory effects of SFRP2 on Wnt3a transcription were associated with disruption of downstream canonical Wnt signaling. The inhibitory effects of Sfrp2 on Wnt3a expression identify Sfrp2 as a “checkpoint gene,” which exerts its control on cardiomyogenesis through regulation of Wnt3a transcription

Comparing the performance of FA, DFA and DMA using different synthetic long-range correlated time series

Notwithstanding the significant efforts to develop estimators of long-range correlations (LRC) and to compare their performance, no clear consensus exists on what is the best method and under which conditions. In addition, synthetic tests suggest that the performance of LRC estimators varies when using different generators of LRC time series. Here, we compare the performances of four estimators [Fluctuation Analysis (FA), Detrended Fluctuation Analysis (DFA), Backward Detrending Moving Average (BDMA), and centred Detrending Moving Average (CDMA)]. We use three different generators [Fractional Gaussian Noises, and two ways of generating Fractional Brownian Motions]. We find that CDMA has the best performance and DFA is only slightly worse in some situations, while FA performs the worst. In addition, CDMA and DFA are less sensitive to the scaling range than FA. Hence, CDMA and DFA remain "The Methods of Choice" in determining the Hurst index of time series.Comment: 6 pages (including 3 figures) + 3 supplementary figure

An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics

For a decade, The Cancer Genome Atlas (TCGA) program collected clinicopathologic annotation data along with multi-platform molecular profiles of more than 11,000 human tumors across 33 different cancer types. TCGA clinical data contain key features representing the democratized nature of the data collection process. To ensure proper use of this large clinical dataset associated with genomic features, we developed a standardized dataset named the TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR), which includes four major clinical outcome endpoints. In addition to detailing major challenges and statistical limitations encountered during the effort of integrating the acquired clinical data, we present a summary that includes endpoint usage recommendations for each cancer type. These TCGA-CDR findings appear to be consistent with cancer genomics studies independent of the TCGA effort and provide opportunities for investigating cancer biology using clinical correlates at an unprecedented scale. Analysis of clinicopathologic annotations for over 11,000 cancer patients in the TCGA program leads to the generation of TCGA Clinical Data Resource, which provides recommendations of clinical outcome endpoint usage for 33 cancer types

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