Venture Capital Funding for Information Technology Businesses

The success of new ventures can hinge on obtaining venture capital (VC) funding. Virtually every successful IT venture has depended on VC funding early in its history. However, obtaining venture capital is difficult. Unlike earlier studies on VC funding that consider new ventures to be homogeneous, this study seeks to identify factors that VCs consider when they make funding decisions for IT ventures. Building on prior research in the area of agency and business risk, we develop a theoretical model that draws on work in finance and entrepreneurship. The model suggests that VCs consider two types of risk: business risk and agency risk. The relative importance of these two types of risk may be different across industries. We test this model using data from 139 business plans for IT startups that were considered for funding by VCs. Traditional structural equation modeling (SEM) does not accommodate non-normal data or dichotomous outcome variables. Using the Robust Weighted Least Squares approach, we test our model with non-normal data and dichotomous outcomes. In addition, we use Tetrad analysis to check model fit against alternative models, floor and ceiling analysis to test sample frame validity, relative effect size comparison to test relative elasticity of effects, and a Monte Carlo estimation approach to test overall model power and power of individual paths. We find that business risk is an important factor in start-up funding for IT ventures. We do not find agency risk to be an important consideration in start-up funding for IT ventures

Public Health Response to Imported Case of Poliomyelitis, Australia, 2007

Inactivated polio vaccine was offered, and the index case-patient and household contacts were quarantined

Extensive identification of genes involved in congenital and structural heart disorders and cardiomyopathy

Clinical presentation of congenital heart disease is heterogeneous, making identification of the disease-causing genes and their genetic pathways and mechanisms of action challenging. By using in vivo electrocardiography, transthoracic echocardiography and microcomputed tomography imaging to screen 3,894 single-gene-null mouse lines for structural and functional cardiac abnormalities, here we identify 705 lines with cardiac arrhythmia, myocardial hypertrophy and/or ventricular dilation. Among these 705 genes, 486 have not been previously associated with cardiac dysfunction in humans, and some of them represent variants of unknown relevance (VUR). Mice with mutations in Casz1, Dnajc18, Pde4dip, Rnf38 or Tmem161b genes show developmental cardiac structural abnormalities, with their human orthologs being categorized as VUR. Using UK Biobank data, we validate the importance of the DNAJC18 gene for cardiac homeostasis by showing that its loss of function is associated with altered left ventricular systolic function. Our results identify hundreds of previously unappreciated genes with potential function in congenital heart disease and suggest causal function of five VUR in congenital heart disease