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Report on a review of selected general and application controls over the Iowa Department of Workforce Development (IWD) MyIowaUI and Unemployment Insurance Benefit systems for the period April 16, 2012 through May 4, 2012
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Crop and livestock summaries for the state of Iowa, produced by the Iowa Department of Agriculture. Previously Agri-News
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Weekly newsletter for Center For Acute Disease Epidemiology of Iowa Department of Public Health.
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The CREB-binding protein (CBP) is a large nuclear protein that regulates many signal transduction pathways and is involved in chromatin-mediated transcription. The translocation t(8;16)(p11;p13.3) consistently disrupts two genes: the CBP gene on chromosome band 16p13.3 and the MOZ gene on chromosome band 8p11. Although a fusion of these two genes as a result of the translocation is expected, attempts at detecting the fusion transcript by reverse transcriptase polymerase chain reaction (RT-PCR) have proven difficult; to date, only one in-frame CBP/MOZ fusion transcript has been reported. We therefore sought other reliable means of detecting CBP rearrangements. We applied fluorescence in situ hybridization (FISH) and Southern blot analyses to a series of AML patients with a t(8;16) and detected DNA rearrangements of both the CBP and the MOZ loci in all cases tested. All six cases examined for CBP rearrangements have breakpoints within a 13 kb breakpoint cluster region at the 5' end of the CBP gene. Additionally, we used a MOZ cDNA probe to construct a surrounding cosmid contig and detect DNA rearrangements in three t(8;16) cases, all of which display rearrangements within a 6 kb genomic fragment of the MOZ gene. We have thus developed a series of cosmid probes that consistently detect the disruption of the CBP gene in t(8;16) patients. These clones could potentially be used to screen other cancer-associated or congenital translocations involving chromosome band 16p13.3 as well.
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Purpose: Plasma adiponectin and serum uric acid (SUA) levels are negatively correlated. To better understand the possible mechanisms linking adiponectin and uric acid, we analyzed whether the association between adiponectin and SUA differed by hypertension status (or blood pressure level) and by sex. Methods and materials: We analyzed data from the populationbased CoLaus study (Switzerland). Fasting plasma adiponectin levels were assessed by ELISA and SUA by uricase-PAP. Blood pressure (BP) was measured using a validated automated device and hypertension was defined as having office BP 140/90 mm Hg or being on current antihypertensive treatment. Results: In the 2897 men and 3181 women, aged 35-74, BMI (mean ± SD) was 26.6 ± 4.0 and 25.1 ± 4.8 Kg/m2, systolic blood pressure (SBP) was 132.2 ± 16.6 and 124.8 ± 18.3 mm Hg, median (interquartile range) plasma adiponectin was 6.2 (4.1-9.2) and 10.6 (6.9-15.4) mg/dL, and hypertension prevalence was 42.0% and 30.2%, respectively. The age- and BMI- adjusted partial correlation coefficients between log-adiponectin and SUA were 0.09 and 0.06 in normotensive men and women (P <0.01), and 0.004 (P = 0.88) and 0.15 (P <0.001) in hypertensive men and women, respectively. In median regression adjusted for BMI, insulin, smoking, alcohol consumption, menopausal status and HDL-cholesterol, there was a significant three-way interaction between SUA, SBP and sex for their effect on adiponectin (dependent variable, P = 0.005), as well as interactions between SBP and sex (P = 0.014) and between SUA and sex (P = 0.033). Conclusion: Plasma adiponectin and SUA are negatively associated, independently of BMI and insulin, in a population-based study in Caucasians. However, BP modifies this inverse relationship, as it was significant mainly in women with elevated BP. This observation suggests that the link between adiponectin and SUA may be mediated by sex hormones and the hypertension status.
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IDPH Quick Reads is an electronic newsletter produced by the Director’s Office at the Iowa Department of Public Health. IDPH Quick Reads are published every three to four weeks.
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Report on a review of selected general and application controls over the University of Northern Iowa Student Information System for the period June 12, 2012 through July 16, 2012
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Report produced by the The Department of Agriculture and Land Stewardship, Climatology Bureau. Iowa Secretary of Agriculture Bill Northey today commented on the Iowa Crops and Weather report released by the USDA National Agricultural Statistical Service. The report is released weekly from April through October.
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Introduction: Survival of children born prematurely or with very low birth weight has increased dramatically, but the long term developmental outcome remains unknown. Many children have deficits in cognitive capacities, in particular involving executive domains and those disabilities are likely to involve a central nervous system deficit. To understand their neurostructural origin, we use DTI. Structurally segregated and functionally regions of the cerebral cortex are interconnected by a dense network of axonal pathways. We noninvasively map these pathways across cortical hemispheres and construct normalized structural connection matrices derived from DTI MR tractography. Group comparisons of brain connectivity reveal significant changes in fiber density in case of children with poor intrauterine grown and extremely premature children (gestational age<28 weeks at birth) compared to control subjects. This changes suggest a link between cortico-axonal pathways and the central nervous system deficit. Methods: Sixty premature born infants (5-6 years old) were scanned on clinical 3T scanner (Magnetom Trio, Siemens Medical Solutions, Erlangen, Germany) at two hospitals (HUG, Geneva and CHUV, Lausanne). For each subject, T1-weighted MPRAGE images (TR/TE=2500/2.91,TI=1100, resolution=1x1x1mm, matrix=256x154) and DTI images (30 directions, TR/TE=10200/107, in-plane resolution=1.8x1.8x2mm, 64 axial, matrix=112x112) were acquired. Parent(s) provided written consent on prior ethical board approval. The extraction of the Whole Brain Structural Connectivity Matrix was performed following (Cammoun, 2009 and Hagmann, 2008). The MPARGE images were registered using an affine registration to the non-weighted-DTI and WM-GM segmentation performed on it. In order to have equal anatomical localization among subjects, 66 cortical regions with anatomical landmarks were created using the curvature information, i.e. sulcus and gyrus (Cammoun et al, 2007; Fischl et al, 2004; Desikan et al, 2006) with freesurfer software (http://surfer.nmr.mgh.harvard.edu/). Tractography was performed in WM using an algorithm especially designed for DTI/DSI data (Hagmann et al., 2007) and both information were then combined in a matrix. Each row and column of the matrix corresponds to a particular ROI. Each cell of index (i,j) represents the fiber density of the bundle connecting the ROIs i and j. Subdividing each cortical region, we obtained 4 Connectivity Matrices of different resolution (33, 66, 125 and 250 ROI/hemisphere) for each subject . Subjects were sorted in 3 different groups, namely (1) control, (2) Intrauterine Growth Restriction (IUGR), (3) Extreme Prematurity (EP), depending on their gestational age, weight and percentile-weight score at birth. Group-to-group comparisons were performed between groups (1)-(2) and (1)-(3). The mean age at examination of the three groups were similar. Results: Quantitative analysis were performed between groups to determine fibers density differences. For each group, a mean connectivity matrix with 33ROI/hemisphere resolution was computed. On the other hand, for all matrix resolutions (33,66,125,250 ROI/hemisphere), the number of bundles were computed and averaged. As seen in figure 1, EP and IUGR subjects present an overall reduction of fibers density in both interhemispherical and intrahemispherical connections. This is given quantitatively in table 1. IUGR subjects presents a higher percentage of missing fiber bundles than EP when compared to control subjects (~16% against 11%). When comparing both groups to control subjects, for the EP subjects, the occipito-parietal regions seem less interhemispherically connected whilst the intrahemispherical networks present lack of fiber density in the lymbic system. Children born with IUGR, have similar reductions in interhemispherical connections than the EP. However, the cuneus and precuneus connections with the precentral and paracentral lobe are even lower than in the case of the EP. For the intrahemispherical connections the IUGR group preset a loss of fiber density between the deep gray matter structures (striatum) and the frontal and middlefrontal poles, connections typically involved in the control of executive functions. For the qualitative analysis, a t-test comparing number of bundles (p-value<0.05) gave some preliminary significant results (figure 2). Again, even if both IUGR and EP appear to have significantly less connections comparing to the control subjects, the IUGR cohort seems to present a higher lack of fiber density specially relying the cuneus, precuneus and parietal areas. In terms of fiber density, preliminary Wilcoxon tests seem to validate the hypothesis set by the previous analysis. Conclusions: The goal of this study was to determine the effect of extreme prematurity and poor intrauterine growth on neurostructural development at the age of 6 years-old. This data indicates that differences in connectivity may well be the basis for the neurostructural and neuropsychological deficit described in these populations in the absence of overt brain lesions (Inder TE, 2005; Borradori-Tolsa, 2004; Dubois, 2008). Indeed, we suggest that IUGR and prematurity leads to alteration of connectivity between brain structures, especially in occipito-parietal and frontal lobes for EP and frontal and middletemporal poles for IUGR. Overall, IUGR children have a higher loss of connectivity in the overall connectivity matrix than EP children. In both cases, the localized alteration of connectivity suggests a direct link between cortico-axonal pathways and the central nervous system deficit. Our next step is to link these connectivity alterations to the performance in executive function tests.
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This order mandates that the State of Iowa, not the federal government or any other organization, shall determine the content of Iowa's state academic standards, which are known as the Iowa Core. The Iowa Department of Education shall develop a regular review cycle for the Iowa Core, including public comment, to determine the contents of and to continually improve state, academic standards.
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Crop and livestock summaries for the state of Iowa, produced by the Iowa Department of Agriculture. Previously Agri-News