How to Use the Changing Components of the Corporate Annual Report

The amount of information required in a corporate annual report continues to increase. Most recently, additional reporting requirements brought about by the Sarbanes-Oxley Act of 2002 (SOA), the Public Company Accounting Oversight Board (PCAOB) and the Securities and Exchange Commission (SEC) increase the number of component reports that must be included in the annual report package. Lenders need to be familiar with the additional information that these new components provide. Therefore, this article summarizes the required component reports, discusses the information conveyed in each report and gives some examples of the types of significant new information that can be obtained

The Changing Components of the Corporate Annual Report: An Update

Recent regulatory changes affect not only the content of annual reports but also the population of companies that are required to comply with these reporting regulations. Lenders need to stay abreast of the information provided in corporate reporting packages. This article provides an update on regulatory changes and discusses how these changes affect the information that can be found in corporate annual reports

The MicroArray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models

Gene expression data from microarrays are being applied to predict preclinical and clinical endpoints, but the reliability of these predictions has not been established. In the MAQC-II project, 36 independent teams analyzed six microarray data sets to generate predictive models for classifying a sample with respect to one of 13 endpoints indicative of lung or liver toxicity in rodents, or of breast cancer, multiple myeloma or neuroblastoma in humans. In total, >30,000 models were built using many combinations of analytical methods. The teams generated predictive models without knowing the biological meaning of some of the endpoints and, to mimic clinical reality, tested the models on data that had not been used for training. We found that model performance depended largely on the endpoint and team proficiency and that different approaches generated models of similar performance. The conclusions and recommendations from MAQC-II should be useful for regulatory agencies, study committees and independent investigators that evaluate methods for global gene expression analysis. © 2010 Nature America, Inc. All rights reserved.0SCOPUS: ar.jinfo:eu-repo/semantics/publishe