Virus Identification in Unknown Tropical Febrile Illness Cases Using Deep Sequencing

Dengue virus is an emerging infectious agent that infects an estimated 50–100 million people annually worldwide, yet current diagnostic practices cannot detect an etiologic pathogen in ∼40% of dengue-like illnesses. Metagenomic approaches to pathogen detection, such as viral microarrays and deep sequencing, are promising tools to address emerging and non-diagnosable disease challenges. In this study, we used the Virochip microarray and deep sequencing to characterize the spectrum of viruses present in human sera from 123 Nicaraguan patients presenting with dengue-like symptoms but testing negative for dengue virus. We utilized a barcoding strategy to simultaneously deep sequence multiple serum specimens, generating on average over 1 million reads per sample. We then implemented a stepwise bioinformatic filtering pipeline to remove the majority of human and low-quality sequences to improve the speed and accuracy of subsequent unbiased database searches. By deep sequencing, we were able to detect virus sequence in 37% (45/123) of previously negative cases. These included 13 cases with Human Herpesvirus 6 sequences. Other samples contained sequences with similarity to sequences from viruses in the Herpesviridae, Flaviviridae, Circoviridae, Anelloviridae, Asfarviridae, and Parvoviridae families. In some cases, the putative viral sequences were virtually identical to known viruses, and in others they diverged, suggesting that they may derive from novel viruses. These results demonstrate the utility of unbiased metagenomic approaches in the detection of known and divergent viruses in the study of tropical febrile illness

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State TANF Spending: Predictors of State Tax Effort to Support Welfare Reform

State spending on Temporary Assistance to Needy Families (TANF) greatly varies. Combined federal and state spending by the states per TANF family or recipient reflects the historic level of state generosity for Aid to Families with Dependent Children (AFDC), the failure of the federal government to set any minimum spending standard for the states, and the failure of the federal government to adjust federal grants for huge changes in state TANF caseloads. Our multivariate analysis shows that state spending for TANF is greatly influenced by the percentage of the state population that is black, the percentage of the state population that is on TANF (especially if a significant percentage of the rolls consist of black recipients), and the economic conditions within the state. Some states spend as much as their economies will allow, while other states spend far below capacity. Despite the very different goals of TANF, state spending is still heavily influenced by their historic approach to AFDC. Copyright 2006 by The Policy Studies Organization.