Managing the expatriate cycle

http://deepblue.lib.umich.edu/bitstream/2027.42/96945/1/MBA_StaffordW_1999Final.pd

An Analysis of State-Level Economic Impacts from the Development of Wind Power Plants in Box Elder County, Utah

This report provides an overview of the state of Utah’s development of its wind resources for the generation of electricity and an economic analysis of potential wind development in Box Elder County, Utah. This analysis draws on information from local wind developers and utilizes the Jobs and Economic Development Impact (JEDI) model (version W1.10.03) developed by the U.S. Department of Energy’s National Renewable Energy Laboratory (NREL) to estimate the total economic impacts (labor, supply chain, and induced) that could result from the development of a wind power plant in Box Elder County. Findings detail how a Box Elder County wind power plant could benefit the state in terms of job opportunities (during both construction and operations), lease payments to landowners, property tax revenues for local schools and communities, and overall economic output for the state

An Analysis of State-Level Economic Impacts from the Development of Wind Power Plants in Wayne County, Utah

This report provides an overview of the state of Utah’s development of its wind resources for the generation of electricity and an economic analysis of potential wind development in Wayne County, Utah. This analysis draws on information from local wind developers and utilizes the Jobs and Economic Development Impact (JEDI) model (version W1.10.03) developed by the U.S. Department of Energy’s National Renewable Energy Laboratory (NREL) to estimate the total economic impacts (labor, supply chain, and induced) that could result from the development of a wind power plant in Wayne County. Findings detail how a Wayne County wind power plant could benefit the state in terms of job opportunities (during both construction and operations), lease payments to landowners, property tax revenues for local schools and communities, and overall economic output for the state

A unified encyclopedia of human functional DNA elements through fully automated annotation of 164 human cell types [preprint]

Semi-automated genome annotation methods such as Segway enable understanding of chromatin activity. Here we present chromatin state annotations of 164 human cell types using 1,615 genomics data sets. To produce these annotations, we developed a fully-automated annotation strategy in which we train separate unsupervised annotation models on each cell type and use a machine learning classifier to automate the state interpretation step. Using these annotations, we developed a measure of the functional importance of each genomic position called the functionality score, which allows us to aggregate information across cell types into a multi-cell type view. This score provides a measure of importance directly attributable to a specific activity in a specific set of cell types. In contrast to evolutionary conservation, this measure is not biased to detect only elements shared with related species. Using the functionality score, we combined all our annotations into a single cell type-agnostic encyclopedia that catalogs all human functional regulatory elements, enabling easy and intuitive interpretation of the effect of genome variants on phenotype, such as in disease-associated, evolutionarily conserved or positively selected loci. These resources, including cell type-specific annotations, enyclopedia, and a visualization server, are available at http://noble.gs.washington.edu/proj/encyclopedia

Novel therapeutic targets in salivary duct carcinoma uncovered by comprehensive molecular profiling.

Salivary duct carcinoma (SDC) is a rare, aggressive salivary gland malignancy, which often presents at an advanced stage. A proportion of SDC are characterized by HER2 amplification and/or overexpression of androgen receptor (AR), which could be targeted in a subset of patients, but the presence of AR splice variant-7 (AR-V7) in some SDC cases could result in resistance to anti-androgen therapy. We evaluated a cohort of 28 cases of SDC for potentially targetable biomarkers and pathways using immunohistochemistry (IHC) and next-generation sequencing (DNA and RNA) assays. Pathogenic genetic aberrations were found in all but 1 case and affected TP53 (n = 19), HRAS (n = 7), PIK3CA, ERBB2 (HER2), and NF1 (n = 5 each); KMT2C (MLL3) and PTEN (n = 3 each); BRAF (p.V600E), KDM5C and NOTCH1 (n = 2 each). Androgen receptor was expressed in all cases and 13 of 27 harbored the AR-V7 splice variant (including a case without any other detectable genetic alteration). HER2 IHC was expressed in 11 of 28 cases. The majority of SDC cases had no biomarkers predictive of immunotherapy response: 5 cases exhibited low (1%-8%) programmed death ligand 1 (PD-L1) expression in tumor cells, 2 cases exhibited elevated TMB, and no samples exhibited microsatellite instability. Notably, the pre-treatment biopsies from 2 patients with metastatic disease, who demonstrated clinical responses to anti-androgen therapy, showed AR expression and no AR splice variants. We conclude that comprehensive molecular profiling of SDCs can guide the selection of patients for targeted therapies involving AR, HER2, PD-L1, mitogen-activated protein kinase, and PIK3CA pathways

Academic performance of children with sickle cell disease in the United States: A meta-analysis

Background: Students with sickle cell disease are at risk for poor academic performance due to the combined and/or interactive effects of environmental, psychosocial, and disease-specific factors. Poor academic performance has significant social and health consequences. Objective: To study academic achievement and attainment in children with sickle cell disease in the United States. Design: Medline, Embase, SCOPUS, CINAHL, ERIC, and PsycINFO were searched for peer-reviewed articles. Studies of children (ages 5–18) diagnosed with sickle cell disease of any genotype reporting academic achievement (standardized tests of reading, math, and spelling) or attainment (grade retention or special education) outcomes were included. Outcomes were analyzed using a random effects model. Achievement scores were compared to within study controls or normative expectations. Prevalence of grade retention and special education services were compared to national (United States) estimates for Black students. Age at assessment and overall IQ were evaluated separately for association with reading and mathematics scores. Subgroup analyses of reading and math scores were analyzed by cerebral infarct status (no cerebrovascular accident, silent infarct, stroke). Results: There were 44 eligible studies. Students with sickle cell disease scored 0.70, 0.87, and 0.80 (p < 0.001) SD below normative expectations on measures of reading, mathematics, and spelling, respectively. Compared to unaffected sibling and/or healthy controls (k = 8, n = 508), reading and math scores were 0.40 (p = 0.017) and 0.36 (p = 0.033) SD below expectations. Grade retention was approximately 10 times higher in students with sickle cell disease than Black students nationally. Intellectual functioning explained 97.3 and 85.8% of the variance in reading and mathematics performance, respectively (p < 0.001). Subgroup analyses revealed significant differences in reading (p = 0.034) and mathematics (p < 0.001) based on infarct status, with lower performance associated with presence of a silent infarct or stroke. Conclusion: Students with sickle cell disease demonstrate notable academic difficulties and are at high risk for grade retainment. Development of academic interventions and increased access to school support services are needed for this vulnerable population. Systematic Review Registration: https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42020179062

The Ursinus Weekly, April 7, 1977

Ursinus news in brief: New CCC organized; Class elections to be held; Volunteers needed at Norristown; Exec. Comm. meets with Pres.; Late examination fee cancelled; Night school requirement changed • WCC meets food service rep. • New asst. to Harris chosen • Cub and Key inducts new members • Comment: A fond farewell to a close friend; Initial optimism of a new editor • Weekly special: FBI warns of more terrorist attacks • Letters to the editor • Movie attack: Bomb renamed • A low-scale of relief • Alumni assoc. gives gift • Dog Day afternoon • Forum review: Horrors recalled • Meisters prepare tour • Women\u27s basketball reaches nationals • USGA survey • Senior dance • 1977 baseball: title bound? • Bears begin seasonhttps://digitalcommons.ursinus.edu/weekly/1069/thumbnail.jp

Genomic data analysis workflows for tumors from patient-derived xenografts (PDXs): challenges and guidelines.

BACKGROUND: Patient-derived xenograft (PDX) models are in vivo models of human cancer that have been used for translational cancer research and therapy selection for individual patients. The Jackson Laboratory (JAX) PDX resource comprises 455 models originating from 34 different primary sites (as of 05/08/2019). The models undergo rigorous quality control and are genomically characterized to identify somatic mutations, copy number alterations, and transcriptional profiles. Bioinformatics workflows for analyzing genomic data obtained from human tumors engrafted in a mouse host (i.e., Patient-Derived Xenografts; PDXs) must address challenges such as discriminating between mouse and human sequence reads and accurately identifying somatic mutations and copy number alterations when paired non-tumor DNA from the patient is not available for comparison. RESULTS: We report here data analysis workflows and guidelines that address these challenges and achieve reliable identification of somatic mutations, copy number alterations, and transcriptomic profiles of tumors from PDX models that lack genomic data from paired non-tumor tissue for comparison. Our workflows incorporate commonly used software and public databases but are tailored to address the specific challenges of PDX genomics data analysis through parameter tuning and customized data filters and result in improved accuracy for the detection of somatic alterations in PDX models. We also report a gene expression-based classifier that can identify EBV-transformed tumors. We validated our analytical approaches using data simulations and demonstrated the overall concordance of the genomic properties of xenograft tumors with data from primary human tumors in The Cancer Genome Atlas (TCGA). CONCLUSIONS: The analysis workflows that we have developed to accurately predict somatic profiles of tumors from PDX models that lack normal tissue for comparison enable the identification of the key oncogenic genomic and expression signatures to support model selection and/or biomarker development in therapeutic studies. A reference implementation of our analysis recommendations is available at https://github.com/TheJacksonLaboratory/PDX-Analysis-Workflows