995 resultados para Spring Membership Program


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The Food Assistance Monthly Participation Report is a monthly summary of Food Assistance program participation, Statewide and for each Iowa county. Breakouts are reported for participants also in the FIP program, those only receiving Food Assistance, and those that are receiving economic assistance under other programs (primarily Medicaid). This report may also be known as the F-1 Report.

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Other Audit Reports - Performance Investigation

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Monthly Public Assistance Statistical Report Family Investment Program

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The Food Assistance Monthly Participation Report is a monthly summary of Food Assistance program participation, Statewide and for each Iowa county. Breakouts are reported for participants also in the FIP program, those only receiving Food Assistance, and those that are receiving economic assistance under other programs (primarily Medicaid). This report may also be known as the F-1 Report.

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International Events, Education Events, and Exporting Opportunities

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Supplemental security income program

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SUPPLEMENTAL SECURITY INCOME PROGRAM

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A-1A Supplemental Security Income Program, January 2005

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Newsletter for the Iowa Commission on the Status of African-Americans

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A-1A SUPPLEMENTAL SECURITY INCOME PROGRAM

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The Food Assistance Monthly Participation Report is a monthly summary of Food Assistance program participation, Statewide and for each Iowa county. Breakouts are reported for participants also in the FIP program, those only receiving Food Assistance, and those that are receiving economic assistance under other programs (primarily Medicaid). This report may also be known as the F-1 Report.

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Quarterly newsletter published by the Iowa Department of Transportation's Office of Aviation that contains information of interest to aviation-related persons and organizations.

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A-1 Monthly Public Assistance Statistical Report Family Investment Program

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Conventional methods of gene prediction rely on the recognition of DNA-sequence signals, the coding potential or the comparison of a genomic sequence with a cDNA, EST, or protein database. Reasons for limited accuracy in many circumstances are species-specific training and the incompleteness of reference databases. Lately, comparative genome analysis has attracted increasing attention. Several analysis tools that are based on human/mouse comparisons are already available. Here, we present a program for the prediction of protein-coding genes, termed SGP-1 (Syntenic Gene Prediction), which is based on the similarity of homologous genomic sequences. In contrast to most existing tools, the accuracy of SGP-1 depends little on species-specific properties such as codon usage or the nucleotide distribution. SGP-1 may therefore be applied to nonstandard model organisms in vertebrates as well as in plants, without the need for extensive parameter training. In addition to predicting genes in large-scale genomic sequences, the program may be useful to validate gene structure annotations from databases. To this end, SGP-1 output also contains comparisons between predicted and annotated gene structures in HTML format. The program can be accessed via a Web server at http://soft.ice.mpg.de/sgp-1. The source code, written in ANSI C, is available on request from the authors.

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GeneID is a program to predict genes in anonymous genomic sequences designed with a hierarchical structure. In the first step, splice sites, and start and stop codons are predicted and scored along the sequence using position weight matrices (PWMs). In the second step, exons are built from the sites. Exons are scored as the sum of the scores of the defining sites, plus the log-likelihood ratio of a Markov model for coding DNA. In the last step, from the set of predicted exons, the gene structure is assembled, maximizing the sum of the scores of the assembled exons. In this paper we describe the obtention of PWMs for sites, and the Markov model of coding DNA in Drosophila melanogaster. We also compare other models of coding DNA with the Markov model. Finally, we present and discuss the results obtained when GeneID is used to predict genes in the Adh region. These results show that the accuracy of GeneID predictions compares currently with that of other existing tools but that GeneID is likely to be more efficient in terms of speed and memory usage.