Quarterback Value Forecasting and Fixing the NFL Draft's Market Failure

The National Football League (NFL) is a business that is worth nearly $7 billion annually in revenue. That makes it the largest money making sport in the United States. The revenue earned by each franchise is dependent upon the repeated success of the team. A commonly held belief is that for a franchise to be successful you must have an elite Quarterback. This thesis uses NFL data and for the 2000-2008 seasons to determine the role that Quarterback performance plays in team success. With the determination that Quarterbacks are important to NFL team success the question becomes how does a franchise effectively obtain the best player. The NFL player draft is the most commonly used method for teams to find their Quarterback of the future. The problem is that the success rate for drafting Quarterbacks is very low. In this thesis I have determined a more statistical approach to determining whether a drafted Quarterback will be successful. The model shows that certain college statistics, such a passing completion percentage, are strong indicators of professional success at the Quarterback position. Use of the data may aid teams in effectively drafting Quarterbacks, thereby improving team winning percentage and profitability

Life cycle cost analysis of natural channel as a component of water sensitive urban design

The traditional method of urban drainage featured by a higher proportion of impervious area, concrete channels and pipes is being challenged by Water Sensitive Urban Design (WSUD), which is a more sustainable method of urban water cycle management. The history of WSUD method in Australia is about a decade old. Although this new method has significant advantages over the traditional method, the perception of greater maintenance needs of WSUD components have made many local authorities reluctant in adoption of the new technique. There has been limited research on the long-term viability of WSUD method, in particular on the life cycle cost analysis. This paper presents an investigation of life cycle cost analysis of a natural channel in an urban environment. The natural channel is an important component of WSUD and provides a number of advantages over the concrete channel; for example, reduced runoff at catchment outlet, improved stormwater quality, enhanced habitat value, and pleasant aesthetics. However, natural channels may require greater maintenance efforts, for example frequent clearing of vegetation and erosion control. The paper uses Cooks River Rehabilitation Works in South Strathfield in Sydney’s inner west in demonstrating the life cycle costs of natural and concrete channels. The traditional concrete channel was perceived as expensive in regards to the capital cost but showed cheaper maintenance and management costs. Its favourability was then weakened by the higher repair and replacement cost. The natural channel had a smaller capital cost at the beginning but increased maintenance and management costs over time. At lower interest rates, the life cycle cost of natural channel was higher. For example, at 5% interest rate, the natural channel showed life cycle cost about 6% higher than the concrete channel. At 17.5% interest rate, life cycle costs of the two methods became similar. This life cycle cost analysis did not include the cost of the extra benefits that would be provided by natural channels due to limited data availability. The paper shows that despite a little higher life cycle cost of natural channel, its adoption is justified when its relative advantages are considered

How Do COVID-19 Inpatients in the Denver Metropolitan Area Measure Up?

Background. Inpatient data for COVID-19 (SARS-CoV-2) afflicted inpatients remain sparse. Data are needed to create accurate projections for resource consumption as the pandemic continues. Published reports of inpatient data have come from China, Italy, Singapore, and both the East and West coasts of the United States. Objective. The objective is to present our inpatient experience with COVID-19. Design, Setting, and Participants. This is a retrospective study of 681 patients with laboratory-confirmed COVID-19 from six hospitals in the Denver metropolitan area admitted between February 18 and April 30, 2020. Clinical outcomes of patients discharged or expired by April 30, 2020, were analyzed. Main Outcomes. We compiled patient demographics, length of stay, number of patients transferred to or admitted to the ICU, ICU length of stay, mechanical ventilation requirements, and mortality rates. Results. Of the 890 patients with laboratory-confirmed COVID-19, 681 had discharged and were included in this analysis. We observed 100% survival of the 0–18 age group (n = 2), 97% survival of the 19–30 age group, 95% survival of the 31–64 age group, 79% survival of the 65–84 age group, and 75% survival of the 85 and older age group. Our total inpatient mortality was 13% (91 patients), rising to 29% (59 patients) for those requiring ICU care. Conclusions. Compared to similar reports from other metropolitan areas, our analysis of discharged or expired COVID-19 patients from six major hospitals in the Denver metropolitan area revealed a lower mortality. This includes the subset of patients admitted to the ICU regardless of the need for intubation. A lower ICU length of stay was also observed

Biochemical and behavioral effects of PDE10A inhibitors: Relationship to target site occupancy

Phosphodiesterase 10A (PDE10A) inhibitors increase the functionality of striatal medium spiny neurons and produce antipsychotic-like effects in rodents by blocking PDE10A mediated hydrolysis of cAMP and/or cGMP. In the current study, we characterized a radiolabeled PDE10A inhibitor, [3H]BMS-843496, and developed an ex vivo PDE10 binding autoradiographic assay to explore the relationship between PDE10 binding site occupancy and the observed biochemical and behavioral effects of PDE10 inhibitors in mice. [3H]BMS-843496 is a potent PDE10A inhibitor with a binding affinity (KD) of 0.15 nM and a functional selectivity of \u3e100-fold over other PDE subtypes tested. Specific [3H]BMS-843496 binding sites were dominant in the basal ganglia, especially the striatum, with low to moderate binding in the cortical and hippocampal areas, of the mouse and monkey brain. Systemic administration of PDE10 inhibitors produced a dose- and plasma/brain concentration-dependent increase in PDE10A occupancy measured in the striatum. PDE10A occupancy was positively correlated with striatal pCREB expression levels. PDE10A occupancy was also correlated with antipsychotic-like effects measured using the conditioned avoidance response model; a minimum of ∼40% occupancy was typically required to achieve efficacy. In contrast, a clear relationship between PDE10A occupancy and catalepsy scores, a potential extrapyramidal symptom readout in rodent, was not evident

Association study in African-admixed populations across the Americas recapitulates asthma risk loci in non-African populations

Asthma is a complex disease with striking disparities across racial and ethnic groups. Despite its relatively high burden, representation of individuals of African ancestry in asthma genome-wide association studies (GWAS) has been inadequate, and true associations in these underrepresented minority groups have been inconclusive. We report the results of a genome-wide meta-analysis from the Consortium on Asthma among African Ancestry Populations (CAAPA; 7009 asthma cases, 7645 controls). We find strong evidence for association at four previously reported asthma loci whose discovery was driven largely by non-African populations, including the chromosome 17q12–q21 locus and the chr12q13 region, a novel (and not previously replicated) asthma locus recently identified by the Trans-National Asthma Genetic Consortium (TAGC). An additional seven loci reported by TAGC show marginal evidence for association in CAAPA. We also identify two novel loci (8p23 and 8q24) that may be specific to asthma risk in African ancestry populations

Author Correction: Association study in African-admixed populations across the Americas recapitulates asthma risk loci in non-African populations (Nature Communications, (2019), 10, 1, (880), 10.1038/s41467-019-08469-7)

The original version of this Article contained an error in the spelling of a member of the CAAPA Consortium, Hrafnhildur Bjarnadóttir which was incorrectly given as Hilda Bjarnadóttir. This has now been corrected in both the PDF and HTML versions of the Article.

Association study in African-admixed populations across the Americas recapitulates asthma risk loci in non-African populations

OLIVEIRA, Ricardo Riccio. Fundação Oswaldo Cruz. Instituto Gonçalo Moniz. Salvador, BA, Brasil. Michelle Daya1, Nicholas Rafaels1, Tonya M. Brunetti1, Sameer Chavan1, Albert M. Levin2, Aniket Shetty1, Christopher R. Gignoux1, Meher Preethi Boorgula1, Genevieve Wojcik 3, Monica Campbell1, Candelaria Vergara 4, Dara G. Torgerson5, Victor E. Ortega6, Ayo Doumatey7, Henry Richard Johnston8, Nathalie Acevedo9, Maria Ilma Araujo10, Pedro C. Avila 11, Gillian Belbin12, Eugene Bleecker13, Carlos Bustamante3, Luis Caraballo9, Alvaro Cruz14, Georgia M. Dunston15, Celeste Eng5, Mezbah U. Faruque16, Trevor S. Ferguson 17, Camila Figueiredo18, Jean G. Ford19, Weiniu Gan20, Pierre-Antoine Gourraud21, Nadia N. Hansel4, Ryan D. Hernandez22, Edwin Francisco Herrera-Paz 23,24, Silvia Jiménez9, Eimear E. Kenny12, Jennifer Knight-Madden17, Rajesh Kumar25, Leslie A. Lange1, Ethan M. Lange1, Antoine Lizee21, Pissamai Maul26, Trevor Maul26, Alvaro Mayorga27, Deborah Meyers13, Dan L. Nicolae28, Timothy D. O’Connor29, Ricardo Riccio Oliveira30, Christopher O. Olopade31, Olufunmilayo Olopade28, Zhaohui S. Qin 32, Charles Rotimi 7, Nicolas Vince 21, Harold Watson33, Rainford J. Wilks17, James G. Wilson34, Steven Salzberg 35, Carole Ober36, Esteban G. Burchard22, L. Keoki Williams37, Terri H. Beaty 38, Margaret A. Taub39, Ingo Ruczinski39, CAAPA, Rasika A. Mathias4 & Kathleen C. Barnes1, Ayola Akim Adegnika40, Ganiyu Arinola41, Ulysse Ateba-Ngoa40, Gerardo Ayestas23, Hilda Bjarnadóttir42, Adolfo Correa 43, Said Omar Leiva Erazo23, Marilyn G. Foreman44, Cassandra Foster4, Li Gao4, Jingjing Gao45, Leslie Grammer11, Mark Hansen46, Tina Hartert47, Yijuan Hu32, Iain Königsberg1, Kwang-Youn A. Kim 48, Pamela Landaverde-Torres23, Javier Marrugo49, Beatriz Martinez49, Rosella Martinez23, Luis F. Mayorga23, Delmy-Aracely Mejia-Mejia50, Catherine Meza49, Solomon Musani43, Shaila Musharoff3, Oluwafemi Oluwole28, Maria Pino-Yanes 5, Hector Ramos23, Allan Saenz23, Maureen Samms-Vaughan51, Robert Schleimer11, Alan F. Scott52, Suyash S. Shringarpure3, Wei Song29, Zachary A. Szpiech 22, Raul Torres 53, Gloria Varela23, Olga Marina Vasquez54, Francisco M. De La Vega3, Lorraine B. Ware47 & Maria Yazdanbakhsh 5. 1Department of Medicine, University of Colorado Denver, Aurora, CO 80045, USA. 2Department of Public Health Sciences, Henry Ford Health System, Detroit, MI 48202, USA. 3Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA. 4Department of Medicine, Johns Hopkins University, Baltimore, MD 21224, USA. 5Department of Medicine, University of California San Francisco, San Francisco, CA 94143, USA. 6Center for Human Genomics and Personalized Medicine, Wake Forest School of Medicine, Winston-Salem 27157, USA. 7Center for Research on Genomics & Global Health, National Institutes of Health, Bethesda, MD 20892, USA. 8Department of Human Genetics, Emory University, Atlanta, GA 30322, USA. 9Institute for Immunological Research, Universidad de Cartagena, Cartagena 130000, Colombia 10Immunology Service, Universidade Federal da Bahia, Salvador 401110170, Brazil. 11Department of Medicine, Northwestern University, Chicago, IL 60611, USA. 12Department of Genetics and Genomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA. 13Department of Medicine, University of Arizona College of Medicine, Tucson, AZ 85724, USA. 14Universidade Federal da Bahia, Salvador 401110170, Brazil. 15Department of Microbiology, Howard University College of Medicine, Washington, DC 20059, USA. 16National Human Genome Center, Howard University College of Medicine, Washington, DC 20059, USA. 17Caribbean Institute for Health Research, The University of the West Indies, Kingston 00007, Jamaica. 18Departamento de Biorregulacao, Universidade Federal da Bahia, Salvador 401110170, Brazil. 19Department of Medicine, Einstein Medical Center, Philadelphia, PA 19141, USA. 20National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD 20892, USA. 21Université de Nantes, INSERM, Centre de Recherche en Transplantation et Immunologie, UMR, 1064ATIP-Avenir, Equipe 5, Nantes, France. 22Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA 94143, USA. 23Facultad de Medicina, Universidad Católica de Honduras, San Pedro Sula 21102, Honduras. 24Universidad Tecnológica Centroamericana (UNITEC), Facultad de Ciencias Médicas, Tegucigalpa, Honduras. 25Department of Pediatrics, Northwestern University, Chicago, IL 60611, USA. 26Genetics and Epidemiology of Asthma in Barbados, The University of the West Indies, Chronic Disease Research Centre, Jemmots Lane, St. Michael BB11115, Barbados. 27Centro de Neumologia y Alergias, San Pedro Sula 21102, Honduras. 28Department of Medicine, University of Chicago, Chicago, IL 60637, USA. 29Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD 21201, USA. 30Laboratório de Patologia Experimental, Centro de Pesquisas Gonçalo Moniz, Salvador 40296-710, Brazil. 31Department of Medicine and Center for Global Health, University of Chicago, Chicago, IL 60637, USA. 32Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA 30322, USA. 33Faculty of Medical Sciences, The University of the West Indies, Queen Elizabeth Hospital, Bridgetown, St. Michael BB11000, Barbados. 34Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, MS 39216, USA. 35Departments of Biomedical Engineering and Biostatistics, Johns Hopkins University, Baltimore, MD 21205, USA. 36Department of Human Genetics, University of Chicago, Chicago, IL 60637, USA. 37Center for Individualized and Genomic Medicine Research, Henry Ford Health System, Detroit, MI 48202, USA. 38Department of Epidemiology, Bloomberg School of Public Health, JHU, Baltimore, MD 21205, USA. 39Department of Biostatistics, Bloomberg School of Public Health, JHU, Baltimore, MD 21205, USA. These authors contributed equally: Rasika A. Mathias, Kathleen C. Barnes.40Centre de Recherches Médicales de Lambaréné, BP:242, Lambaréné 13901, Gabon. 41Department of Chemical Pathology, University of Ibadan, Ibadan 900001, Nigeria. 42Faculty of Medicine, University of Iceland, 101 Reykjavík, Iceland. 43Department of Medicine, University of Mississippi Medical Center, Jackson, MS 39216, USA. 44Pulmonary and Critical Care Medicine, Morehouse School of Medicine, Atlanta, GA 30310, USA. 45Data and Statistical Sciences, AbbVie, North Chicago, IL 60064, USA. 46Illumina, Inc., San Diego, CA 92122, USA. 47Department of Medicine, Vanderbilt University, Nashville, TN 37232, USA. 48Department of Preventive Medicine, Northwestern University, Chicago, IL 60611, USA. 49Instituto de Investigaciones Immunologicas, Universidad de Cartagena, Cartagena 130000, Colombia. 50Facultad de Ciencias de la Salud, Universidad Tecnológica Centroamericana (UNITEC), San Pedro Sula 21102, Honduras. 51Department of Child Health, The University of the West Indies, Kingston 00007, Jamaica. 52Department of Medicine, Johns Hopkins University, Baltimore, MD 21287, USA. 53Biomedical Sciences Graduate Program, University of California San Francisco, San Francisco, CA 94158, USA. 54Centro Medico de la Familia, San Pedro Sula 21102, Honduras. 55Department of Parasitology, Leiden University Medical Center, Leiden 02333, NetherlandsSubmitted by Ana Maria Fiscina Sampaio ([email protected]) on 2019-03-25T16:18:22Z No. of bitstreams: 1 Daya M Association study in African-admixed...2019.pdf: 1446713 bytes, checksum: ec386d63089da2ac2c2f15c4ef98f264 (MD5)Approved for entry into archive by Ana Maria Fiscina Sampaio ([email protected]) on 2019-03-25T16:36:07Z (GMT) No. of bitstreams: 1 Daya M Association study in African-admixed...2019.pdf: 1446713 bytes, checksum: ec386d63089da2ac2c2f15c4ef98f264 (MD5)Made available in DSpace on 2019-03-25T16:36:07Z (GMT). No. of bitstreams: 1 Daya M Association study in African-admixed...2019.pdf: 1446713 bytes, checksum: ec386d63089da2ac2c2f15c4ef98f264 (MD5) Previous issue date: 2019Múltipla - ver em NotasAsthma is a complex disease with striking disparities across racial and ethnic groups. Despite its relatively high burden, representation of individuals of African ancestry in asthma genome-wide association studies (GWAS) has been inadequate, and true associations in these underrepresented minority groups have been inconclusive. We report the results of a genome-wide meta-analysis from the Consortium on Asthma among African Ancestry Populations (CAAPA; 7009 asthma cases, 7645 controls). We find strong evidence for association at four previously reported asthma loci whose discovery was driven largely by non-African populations, including the chromosome 17q12-q21 locus and the chr12q13 region, a novel (and not previously replicated) asthma locus recently identified by the Trans-National Asthma Genetic Consortium (TAGC). An additional seven loci reported by TAGC show marginal evidence for association in CAAPA. We also identify two novel loci (8p23 and 8q24) that may be specific to asthma risk in African ancestry populations

Association study in African-admixed populations across the Americas recapitulates asthma risk loci in non-African populations

Rationale: Asthma is a complex disease with striking disparities across racial and ethnic groups. Despite its high burden, representation of African ancestry individuals in asthma genome-wide association studies (GWAS) has been inadequate to date, and true associations in these underrepresented minority groups may have been missed. Here, we report the largest asthma GWAS to date from the Consortium on Asthma among African Ancestry Populations (CAAPA). Methods: CAAPA participants (7009 asthmatics, 7645 controls) were genotyped using the African Diaspora Power Chip (ADPC), an array designed to complement existing genome-wide array data, as well as Illumina’s Multi-Ethnic Genotyping array. Genotypes were imputed using the CAAPA whole genome-sequence reference panel. Logistic mixed effects models were used to test for association between allelic dosage and asthma, separately for each study. Results were meta-analyzed using a meta-regression approach that accounts for heterogeneity in allelic effects among ethnic groups. Results: We identified two novel loci that may be specific to asthma risk in African ancestry populations (lead SNP rs13277810, intronic to LOC101927815, p=3E-8; lead SNP rs114647118, intronic to TATDN1, p=3E-7). We found strong evidence for association at four previously reported asthma loci whose discovery was driven largely by non-African populations (p\u3c0.05/810 candidate SNPs investigated), including the chr12q13 region, a novel locus identified by the Trans-National Asthma Genetic Consortium (TAGC) that has previously not been replicated. Conclusions: We report two associations that may bespecific to asthma risk in African ancestry populations. Our results also suggest some asthma risk loci discovered in non-African populations are relevant in African ancestry populations