Presentation and Examination of the Integration of Unlawful Discrimination Practices in the Private Business Sector with Artificial Intelligence

Business Administratio

How The Mountain West States Voted In 2016: A Post-Election Analysis of Trends, Demographics, and Politics in America’s New Swing Region

Brookings Mountain West and the Greenspun College of Urban Affairs hosted a panel of experts in state and regional politics and history to examine election returns and exit polling and provide a first-read of the 2016 election. The Mountain West is now one of the nation’s most contested political regions. Its population growth and ever-shifting demographics make the region harder to predict and most susceptible to political swings. Five states in the Southern Mountain West – Arizona, Colorado, Nevada, New Mexico, and Utah – now hold more electoral votes than all individual states except Texas and California. In our current political climate these 37 electoral votes can determine the majority in the U.S. Senate and the presidency of the United States

Transcriptome analysis reveals manifold mechanisms of cyst development in ADPKD

BACKGROUND: Autosomal dominant polycystic kidney disease (ADPKD) causes progressive loss of renal function in adults as a consequence of the accumulation of cysts. ADPKD is the most common genetic cause of end-stage renal disease. Mutations in polycystin-1 occur in 87% of cases of ADPKD and mutations in polycystin-2 are found in 12% of ADPKD patients. The complexity of ADPKD has hampered efforts to identify the mechanisms underlying its pathogenesis. No current FDA (Federal Drug Administration)-approved therapies ameliorate ADPKD progression. RESULTS: We used the de Almeida laboratory's sensitive new transcriptogram method for whole-genome gene expression data analysis to analyze microarray data from cell lines developed from cell isolates of normal kidney and of both non-cystic nephrons and cysts from the kidney of a patient with ADPKD. We compared results obtained using standard Ingenuity Volcano plot analysis, Gene Set Enrichment Analysis (GSEA) and transcriptogram analysis. Transcriptogram analysis confirmed the findings of Ingenuity, GSEA, and published analysis of ADPKD kidney data and also identified multiple new expression changes in KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways related to cell growth, cell death, genetic information processing, nucleotide metabolism, signal transduction, immune response, response to stimulus, cellular processes, ion homeostasis and transport and cofactors, vitamins, amino acids, energy, carbohydrates, drugs, lipids, and glycans. Transcriptogram analysis also provides significance metrics which allow us to prioritize further study of these pathways. CONCLUSIONS: Transcriptogram analysis identifies novel pathways altered in ADPKD, providing new avenues to identify both ADPKD's mechanisms of pathogenesis and pharmaceutical targets to ameliorate the progression of the disease

Use of a mixed tissue RNA design for performance assessments on multiple microarray formats

The comparability and reliability of data generated using microarray technology would be enhanced by use of a common set of standards that allow accuracy, reproducibility and dynamic range assessments on multiple formats. We designed and tested a complex biological reagent for performance measurements on three commercial oligonucleotide array formats that differ in probe design and signal measurement methodology. The reagent is a set of two mixtures with different proportions of RNA for each of four rat tissues (brain, liver, kidney and testes). The design provides four known ratio measurements of >200 reference probes, which were chosen for their tissue-selectivity, dynamic range coverage and alignment to the same exemplar transcript sequence across all three platforms. The data generated from testing three biological replicates of the reagent at eight laboratories on three array formats provides a benchmark set for both laboratory and data processing performance assessments. Close agreement with target ratios adjusted for sample complexity was achieved on all platforms and low variance was observed among platforms, replicates and sites. The mixed tissue design produces a reagent with known gene expression changes within a complex sample and can serve as a paradigm for performance standards for microarrays that target other species

HER-2 overexpression differentially alters transforming growth factor-β responses in luminal versus mesenchymal human breast cancer cells

INTRODUCTION: Amplification of the HER-2 receptor tyrosine kinase has been implicated in the pathogenesis and aggressive behavior of approximately 25% of invasive human breast cancers. Clinical and experimental evidence suggest that aberrant HER-2 signaling contributes to tumor initiation and disease progression. Transforming growth factor beta (TGF-β) is the dominant factor opposing growth stimulatory factors and early oncogene activation in many tissues, including the mammary gland. Thus, to better understand the mechanisms by which HER-2 overexpression promotes the early stages of breast cancer, we directly assayed the cellular and molecular effects of TGF-β1 on breast cancer cells in the presence or absence of overexpressed HER-2. METHODS: Cell proliferation assays were used to determine the effect of TGF-β on the growth of breast cancer cells with normal or high level expression of HER-2. Affymetrix microarrays combined with Northern and western blot analysis were used to monitor the transcriptional responses to exogenous TGF-β1 in luminal and mesenchymal-like breast cancer cells. The activity of the core TGF-β signaling pathway was assessed using TGF-β1 binding assays, phospho-specific Smad antibodies, immunofluorescent staining of Smad and Smad DNA binding assays. RESULTS: We demonstrate that cells engineered to over-express HER-2 are resistant to the anti-proliferative effect of TGF-β1. HER-2 overexpression profoundly diminishes the transcriptional responses induced by TGF-β in the luminal MCF-7 breast cancer cell line and prevents target gene induction by a novel mechanism that does not involve the abrogation of Smad nuclear accumulation, DNA binding or changes in c-myc repression. Conversely, HER-2 overexpression in the context of the mesenchymal MDA-MB-231 breast cell line potentiated the TGF-β induced pro-invasive and pro-metastatic gene signature. CONCLUSION: HER-2 overexpression promotes the growth and malignancy of mammary epithelial cells, in part, by conferring resistance to the growth inhibitory effects of TGF-β. In contrast, HER-2 and TGF-β signaling pathways can cooperate to promote especially aggressive disease behavior in the context of a highly invasive breast tumor model

Sweet Land of Liberty: The Gay Marriage Amendment in Nevada

This volume investigates some of the most visible issues in American politics today, including gay marriage and race, along with ongoing concerns that often fly below the radar of the mass media, such as healthcare and homelessness. The book uncovers explores the political motivations, effectiveness, and interplay of organized religious interests as they confront public problems in their local communities

Gene Expression Profiles Can Predict Panitumumab Monotherapy Responsiveness in Human Tumor Xenograft Models

Background Epidermal growth factor receptor (EGFR)-targeted agents have demonstrated clinical benefit in patients with cancer. Identifying tissue-of-origin-independent predictive biomarkers is important to optimally treat patients. We sought to identify a gene array profile that could predict responsiveness to panitumumab, a fully human EGFR-binding antibody, using preclinical models of human cancer. Methods Mice bearing 25 different xenograft models were treated twice weekly with panitumumab or immunoglobulin G2 control to determine their responsiveness to panitumumab. Samples from these xenografts and untreated xenografts were arrayed on the Affymetrix human U133A gene chip to identify gene sets predicting responsiveness to panitumumab using univariate and multivariate analyses. The predictive models were validated using the leave-one-group-out (LOO) method. Results Of the 25 xenograft models tested, 12 were responsive and 13 were resistant to panitumumab. Unsupervised analysis demonstrated that the xenograft models clustered by tissue type rather than responsiveness to panitumumab. After normalizing for tissue effects, samples clustered by responsiveness using an unsupervised multidimensional scaling. A multivariate selection algorithm was used to select 13 genes that could stratify xenograft models based on responsiveness after adjustment for tissue effects. The method was validated using the LOO method on a training set of 22 models and confirmed independently on three new models. In contrast, a univariate gene selection method resulted in higher misclassification rates. Conclusion A model was constructed from microarray data that prospectively predict responsiveness to panitumumab in xenograft models. This approach may help identify patients, independent of disease origin, likely to benefit from panitumumab