Improving Hurricane Power Outage Prediction Models Through the Inclusion of Local Environmental Factors

Tropical cyclones can significantly damage the electrical power system, so an accurate spatiotemporal forecast of outages prior to landfall can help utilities to optimize the power restoration process. The purpose of this article is to enhance the predictive accuracy of the Spatially Generalized Hurricane Outage Prediction Model (SGHOPM) developed by Guikema et al. (2014). In this version of the SGHOPM, we introduce a new two‐step prediction procedure and increase the number of predictor variables. The first model step predicts whether or not outages will occur in each location and the second step predicts the number of outages. The SGHOPM environmental variables of Guikema et al. (2014) were limited to the wind characteristics (speed and duration of strong winds) of the tropical cyclones. This version of the model adds elevation, land cover, soil, precipitation, and vegetation characteristics in each location. Our results demonstrate that the use of a new two‐step outage prediction model and the inclusion of these additional environmental variables increase the overall accuracy of the SGHOPM by approximately 17%.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/147200/1/risa12728_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/147200/2/risa12728.pd

Identification of a C/G polymorphism in the promoter region of the BRCA1 gene and its use as a marker for rapid detection of promoter deletions

Reduced expression of BRCA1 has been implicated in sporadic breast cancer, although the mechanisms underlying this phenomenon remain unclear. To determine whether regulatory mutations could account for the reduced expression, we screened the promoter region by sequencing in 20 patients with sporadic disease. No mutations were detected; however, a new polymorphism consisting of a C-to-G base change within the β-promoter was identified, with the frequency of the G allele being 0.34. Close to complete linkage disequilibrium was found between this marker and the Pro871Leu polymorphism, situated in exon 11, which has previously been shown not to be associated with breast or ovarian cancer. This indicates that the C/G polymorphism is also unlikely to play a role in either disease. However, the strength of linkage disequilibrium between these markers permitted their use for rapid screening for genomic deletions within BRCA1. A series of 214 cases with familial breast cancer were analysed using this approach; 88/214 were heterozygous for the promoter polymorphism, thereby excluding a deletion in this region. Among the remaining patients, one hemizygous case reflecting a promoter deletion was successfully identified. Therefore, this study indicates that deletions within the β-promoter region of BRCA1 are an uncommon event in familial breast cancer. Furthermore, it suggests that mutations within the BRCA1 promoter are unlikely to account for the reported decreased expression of BRCA1 in sporadic disease. 1999 Cancer Research Campaig

Detecting autozygosity through runs of homozygosity: A comparison of three autozygosity detection algorithms

<p>Abstract</p> <p>Background</p> <p>A central aim for studying runs of homozygosity (ROHs) in genome-wide SNP data is to detect the effects of autozygosity (stretches of the two homologous chromosomes within the same individual that are identical by descent) on phenotypes. However, it is unknown which current ROH detection program, and which set of parameters within a given program, is optimal for differentiating ROHs that are truly autozygous from ROHs that are homozygous at the marker level but vary at unmeasured variants between the markers.</p> <p>Method</p> <p>We simulated 120 Mb of sequence data in order to know the true state of autozygosity. We then extracted common variants from this sequence to mimic the properties of SNP platforms and performed ROH analyses using three popular ROH detection programs, PLINK, GERMLINE, and BEAGLE. We varied detection thresholds for each program (e.g., prior probabilities, lengths of ROHs) to understand their effects on detecting known autozygosity.</p> <p>Results</p> <p>Within the optimal thresholds for each program, PLINK outperformed GERMLINE and BEAGLE in detecting autozygosity from distant common ancestors. PLINK's sliding window algorithm worked best when using SNP data pruned for linkage disequilibrium (LD).</p> <p>Conclusion</p> <p>Our results provide both general and specific recommendations for maximizing autozygosity detection in genome-wide SNP data, and should apply equally well to research on whole-genome autozygosity burden or to research on whether specific autozygous regions are predictive using association mapping methods.</p

Spectrum of HLA associations: the case of medically refractory pediatric acute lymphoblastic leukemia

Although studies of HLA and disease now date back some 50 years, a principled understanding of that relationship has been slow to emerge. Here, we examine the associations of three HLA loci with medically refractory pediatric acute lymphoblastic leukemia (pALL) patients in a case–control study involving 2,438 cases and 41,750 controls. An analysis of alleles from the class I loci, HLA-A and HLA-B, and the class II locus DRB1 illuminates a spectrum of extremely significant allelic associations conferring both predisposition and protection. Genotypes constructed from predisposing, protective, and neutral allelic categories point to an additive mode of disease causation. For all three loci, genotypes homozygous for predisposing alleles are at highest disease risk while the favorable effect of homozygous protective genotypes is less striking. Analysis of A–B and B–DRB1 haplotypes reveals locus-specific differences in disease effects, while that all three loci influence pALL; the influence of HLA-B is greater than that of HLA-A, and the predisposing effect of DRB1 exceeds that of HLA-B. We propose that the continuum in disease susceptibility suggests a system in which many alleles take part in disease predisposition based on differences in binding affinity to one or a few peptides of exogenous origin. This work provides evidence that an immune response mediated by alleles from several HLA loci plays a critical role in the pathogenesis of pALL, adding to the numerous studies pointing to a role for an infectious origin in pALL

Measuring and Modeling Risk Using High-Frequency Data

Measuring and modeling financial volatility is the key to derivative pricing, asset allocation and risk management. The recent availability of high-frequency data allows for refined methods in this field. In particular, more precise measures for the daily or lower frequency volatility can be obtained by summing over squared high-frequency returns. In turn, this so-called realized volatility can be used for more accurate model evaluation and description of the dynamic and distributional structure of volatility. Moreover, non-parametric measures of systematic risk are attainable, that can straightforwardly be used to model the commonly observed time-variation in the betas. The discussion of these new measures and methods is accompanied by an empirical illustration using high-frequency data of the IBM incorporation and of the DJIA index

New Model of Macrophage Acquisition of the Lymphatic Endothelial Phenotype

Macrophage-derived lymphatic endothelial cell progenitors (M-LECPs) contribute to new lymphatic vessel formation, but the mechanisms regulating their differentiation, recruitment, and function are poorly understood. Detailed characterization of M-LECPs is limited by low frequency in vivo and lack of model systems allowing in-depth molecular analyses in vitro. Our goal was to establish a cell culture model to characterize inflammation-induced macrophage-to-LECP differentiation under controlled conditions.Time-course analysis of diaphragms from lipopolysaccharide (LPS)-treated mice revealed rapid mobilization of bone marrow-derived and peritoneal macrophages to the proximity of lymphatic vessels followed by widespread (∼50%) incorporation of M-LECPs into the inflamed lymphatic vasculature. A differentiation shift toward the lymphatic phenotype was found in three LPS-induced subsets of activated macrophages that were positive for VEGFR-3 and many other lymphatic-specific markers. VEGFR-3 was strongly elevated in the early stage of macrophage transition to LECPs but undetectable in M-LECPs prior to vascular integration. Similar transient pattern of VEGFR-3 expression was found in RAW264.7 macrophages activated by LPS in vitro. Activated RAW264.7 cells co-expressed VEGF-C that induced an autocrine signaling loop as indicated by VEGFR-3 phosphorylation inhibited by a soluble receptor. LPS-activated RAW264.7 macrophages also showed a 68% overlap with endogenous CD11b(+)/VEGFR-3(+) LECPs in the expression of lymphatic-specific genes. Moreover, when injected into LPS- but not saline-treated mice, GFP-tagged RAW264.7 cells massively infiltrated the inflamed diaphragm followed by integration into 18% of lymphatic vessels.We present a new model for macrophage-LECP differentiation based on LPS activation of cultured RAW264.7 cells. This system designated here as the "RAW model" mimics fundamental features of endogenous M-LECPs. Unlike native LECPs, this model is unrestricted by cell numbers, heterogeneity of population, and ability to change genetic composition for experimental purposes. As such, this model can provide a valuable tool for understanding the LECP and lymphatic biology

Vertebrate Paralogous MEF2 Genes: Origin, Conservation, and Evolution

BACKGROUND: The myocyte enhancer factor 2 (MEF2) gene family is broadly expressed during the development and maintenance of muscle cells. Although a great deal has been elucidated concerning MEF2 transcription factors' regulation of specific gene expression in diverse programs and adaptive responses, little is known about the origin and evolution of the four members of the MEF2 gene family in vertebrates. METHODOLOGY/PRINCIPAL FINDINGS: By phylogenetic analyses, we investigated the origin, conservation, and evolution of the four MEF2 genes. First, among the four MEF2 paralogous branches, MEF2B is clearly distant from the other three branches in vertebrates, mainly because it lacks the HJURP_C (Holliday junction recognition protein C-terminal) region. Second, three duplication events might have occurred to produce the four MEF2 paralogous genes and the latest duplication event occurred near the origin of vertebrates producing MEF2A and MEF2C. Third, the ratio (K(a)/K(s)) of non-synonymous to synonymous nucleotide substitution rates showed that MEF2B evolves faster than the other three MEF2 proteins despite purifying selection on all of the four MEF2 branches. Moreover, a pair model of M0 versus M3 showed that variable selection exists among MEF2 proteins, and branch-site analysis presented that sites 53 and 64 along the MEF2B branch are under positive selection. Finally, and interestingly, substitution rates showed that type II MADS genes (i.e., MEF2-like genes) evolve as slowly as type I MADS genes (i.e., SRF-like genes) in animals, which is inconsistent with the fact that type II MADS genes evolve much slower than type I MADS genes in plants. CONCLUSION: Our findings shed light on the relationship of MEF2A, B, C, and D with functional conservation and evolution in vertebrates. This study provides a rationale for future experimental design to investigate distinct but overlapping regulatory roles of the four MEF2 genes in various tissues

Comparative Linkage Meta-Analysis Reveals Regionally-Distinct, Disparate Genetic Architectures: Application to Bipolar Disorder and Schizophrenia

New high-throughput, population-based methods and next-generation sequencing capabilities hold great promise in the quest for common and rare variant discovery and in the search for ”missing heritability.” However, the optimal analytic strategies for approaching such data are still actively debated, representing the latest rate-limiting step in genetic progress. Since it is likely a majority of common variants of modest effect have been identified through the application of tagSNP-based microarray platforms (i.e., GWAS), alternative approaches robust to detection of low-frequency (1–5% MAF) and rare (<1%) variants are of great importance. Of direct relevance, we have available an accumulated wealth of linkage data collected through traditional genetic methods over several decades, the full value of which has not been exhausted. To that end, we compare results from two different linkage meta-analysis methods—GSMA and MSP—applied to the same set of 13 bipolar disorder and 16 schizophrenia GWLS datasets. Interestingly, we find that the two methods implicate distinct, largely non-overlapping, genomic regions. Furthermore, based on the statistical methods themselves and our contextualization of these results within the larger genetic literatures, our findings suggest, for each disorder, distinct genetic architectures may reside within disparate genomic regions. Thus, comparative linkage meta-analysis (CLMA) may be used to optimize low-frequency and rare variant discovery in the modern genomic era