999 resultados para index fund


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State general fund revenue estimates are generated by the Iowa Revenue Estimating Conference (REC). The REC is comprised of the Governor or their designee, the Director of the Legislative Services Agency, and a third person agreed upon by the other two members. The REC meets periodically, generally in October, December, and March/April. The Governor and the Legislature are required to use the REC estimates in preparing the state budget.

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State general fund revenue estimates are generated by the Iowa Revenue Estimating Conference (REC). The REC is comprised of the Governor or their designee, the Director of the Legislative Services Agency, and a third person agreed upon by the other two members. The REC meets periodically, generally in October, December, and March/April. The Governor and the Legislature are required to use the REC estimates in preparing the state budget.

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State general fund revenue estimates are generated by the Iowa Revenue Estimating Conference (REC). The REC is comprised of the Governor or their designee, the Director of the Legislative Services Agency, and a third person agreed upon by the other two members. The REC meets periodically, generally in October, December, and March/April. The Governor and the Legislature are required to use the REC estimates in preparing the state budget.

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State general fund revenue estimates are generated by the Iowa Revenue Estimating Conference (REC). The REC is comprised of the Governor or their designee, the Director of the Legislative Services Agency, and a third person agreed upon by the other two members. The REC meets periodically, generally in October, December, and March/April. The Governor and the Legislature are required to use the REC estimates in preparing the state budget.

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State general fund revenue estimates are generated by the Iowa Revenue Estimating Conference (REC). The REC is comprised of the Governor or their designee, the Director of the Legislative Services Agency, and a third person agreed upon by the other two members. The REC meets periodically, generally in October, December, and March/April. The Governor and the Legislature are required to use the REC estimates in preparing the state budget.

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State general fund revenue estimates are generated by the Iowa Revenue Estimating Conference (REC). The REC is comprised of the Governor or their designee, the Director of the Legislative Services Agency, and a third person agreed upon by the other two members. The REC meets periodically, generally in October, December, and March/April. The Governor and the Legislature are required to use the REC estimates in preparing the state budget.

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State general fund revenue estimates are generated by the Iowa Revenue Estimating Conference (REC). The REC is comprised of the Governor or their designee, the Director of the Legislative Services Agency, and a third person agreed upon by the other two members. The REC meets periodically, generally in October, December, and March/April. The Governor and the Legislature are required to use the REC estimates in preparing the state budget.

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State general fund revenue estimates are generated by the Iowa Revenue Estimating Conference (REC). The REC is comprised of the Governor or their designee, the Director of the Legislative Services Agency, and a third person agreed upon by the other two members. The REC meets periodically, generally in October, December, and March/April. The Governor and the Legislature are required to use the REC estimates in preparing the state budget.

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State general fund revenue estimates are generated by the Iowa Revenue Estimating Conference (REC). The REC is comprised of the Governor or their designee, the Director of the Legislative Services Agency, and a third person agreed upon by the other two members. The REC meets periodically, generally in October, December, and March/April. The Governor and the Legislature are required to use the REC estimates in preparing the state budget.

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State general fund revenue estimates are generated by the Iowa Revenue Estimating Conference (REC). The REC is comprised of the Governor or their designee, the Director of the Legislative Services Agency, and a third person agreed upon by the other two members. The REC meets periodically, generally in October, December, and March/April. The Governor and the Legislature are required to use the REC estimates in preparing the state budget.

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State general fund revenue estimates are generated by the Iowa Revenue Estimating Conference (REC). The REC is comprised of the Governor or their designee, the Director of the Legislative Services Agency, and a third person agreed upon by the other two members. The REC meets periodically, generally in October, December, and March/April. The Governor and the Legislature are required to use the REC estimates in preparing the state budget.

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The Iowa Pore Index (IPI) measures the pore system of carbonate (limestone and dolomite) rocks using pressurized water to infiltrate the pore system. This technique provides quantitative results for the primary and capillary (secondary) pores in carbonate rocks. These results are used in conjunction with chemical and mineralogical test results to calculate a quality number, which is used as a predictor of aggregate performance in Portland cement concrete (PCC) leading to the durability classification of the aggregate. This study had two main objectives: to determine the effect different aggregate size has on IPI test results and to establish the precision of IPI test and test apparatus. It was found that smaller aggregate size fractions could be correlated to the standard 1/2”-3/4” size sample. Generally, a particle size decrease was accompanied by a slight decrease in IPI values. The IPI testing also showed fairly good agreement of the secondary pore index number between the 1/2”-3/4”and the 3/8”-1/2” fraction. The #4-3/8” showed a greater difference of the secondary number from the 1/2”-3/4” fraction. The precision of the IPI test was established as a standard deviation (Sr) of 2.85 (Primary) and 0.87 (Secondary) with a repeatability limit (%r) of 8.5% and 14.9% for the primary and secondary values, respectively.

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It is common practice in genome-wide association studies (GWAS) to focus on the relationship between disease risk and genetic variants one marker at a time. When relevant genes are identified it is often possible to implicate biological intermediates and pathways likely to be involved in disease aetiology. However, single genetic variants typically explain small amounts of disease risk. Our idea is to construct allelic scores that explain greater proportions of the variance in biological intermediates, and subsequently use these scores to data mine GWAS. To investigate the approach's properties, we indexed three biological intermediates where the results of large GWAS meta-analyses were available: body mass index, C-reactive protein and low density lipoprotein levels. We generated allelic scores in the Avon Longitudinal Study of Parents and Children, and in publicly available data from the first Wellcome Trust Case Control Consortium. We compared the explanatory ability of allelic scores in terms of their capacity to proxy for the intermediate of interest, and the extent to which they associated with disease. We found that allelic scores derived from known variants and allelic scores derived from hundreds of thousands of genetic markers explained significant portions of the variance in biological intermediates of interest, and many of these scores showed expected correlations with disease. Genome-wide allelic scores however tended to lack specificity suggesting that they should be used with caution and perhaps only to proxy biological intermediates for which there are no known individual variants. Power calculations confirm the feasibility of extending our strategy to the analysis of tens of thousands of molecular phenotypes in large genome-wide meta-analyses. We conclude that our method represents a simple way in which potentially tens of thousands of molecular phenotypes could be screened for causal relationships with disease without having to expensively measure these variables in individual disease collections.

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PURPOSE: To evaluate the prognostic and predictive value of Ki-67 labeling index (LI) in a trial comparing letrozole (Let) with tamoxifen (Tam) as adjuvant therapy in postmenopausal women with early breast cancer. PATIENTS AND METHODS: Breast International Group (BIG) trial 1-98 randomly assigned 8,010 patients to four treatment arms comparing Let and Tam with sequences of each agent. Of 4,922 patients randomly assigned to receive 5 years of monotherapy with either agent, 2,685 had primary tumor material available for central pathology assessment of Ki-67 LI by immunohistochemistry and had tumors confirmed to express estrogen receptors after central review. The prognostic and predictive value of centrally measured Ki-67 LI on disease-free survival (DFS) were assessed among these patients using proportional hazards modeling, with Ki-67 LI values dichotomized at the median value of 11%. RESULTS: Higher values of Ki-67 LI were associated with adverse prognostic factors and with worse DFS (hazard ratio [HR; high:low] = 1.8; 95% CI, 1.4 to 2.3). The magnitude of the treatment benefit for Let versus Tam was greater among patients with high tumor Ki-67 LI (HR [Let:Tam] = 0.53; 95% CI, 0.39 to 0.72) than among patients with low tumor Ki-67 LI (HR [Let:Tam] = 0.81; 95% CI, 0.57 to 1.15; interaction P = .09). CONCLUSION: Ki-67 LI is confirmed as a prognostic factor in this study. High Ki-67 LI levels may identify a patient group that particularly benefits from initial Let adjuvant therapy.

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PURPOSE: Phenotypic, genetic and molecular characterization of 69 index patients with retinitis pigmentosa (RP) and various inherited retinal diseases. PATIENTS AND METHOD: patients went through complete ocular examination and blood samples were drawn for mutational screening of three candidate genes: rhodopsin (RHO), peripherin/RDS, and ROM-1. RESULTS: the most frequent type of RP among our population was the autosomal dominant (43.6%). Three RHO mutations were found among the RP patients. A RDS mutation was detected in three unrelated families segregating dominant macular dystrophy. DISCUSSION AND CONCLUSIONS: 18% of the autosomal dominant RP patients presented a RHO mutation; RDS R172W mutation was present in 25% of the dominant macular dystrophies.