Tissue-specific gene expression of <i>RBFOX1</i> from GTEx database (http://www.gtexportal.org/home/gene/RBFOX1).

<p>Tissue-specific gene expression of <i>RBFOX1</i> from GTEx database (<a href="http://www.gtexportal.org/home/gene/RBFOX1" target="_blank">http://www.gtexportal.org/home/gene/RBFOX1</a>).</p

The CFS family carrying the protective rare variant rs149974858.

<p>A) The variant rs149974858 segregates with BP in a 17-member CFS family. Squares represent males and circles represent females. Half-filled subjects represent the carriers of the rare variant rs149974858. Grey subjects represent no information. Age of each subject is presented in parenthesis. B) The distribution of corresponding residuals of SBP, DBP, PP and BMI are presented.</p

Characteristics of white participants in the Cleveland Family Study.

<p>Characteristics of white participants in the Cleveland Family Study.</p

Linkage region on chromosome 16 of white participants in CFS.

<p>Linkage peak on chromosome 16 for SBP. The linkage curves are plotted with (red curve) and without (blue curve) adjusting for the risk score defined by the 13 coding variants as a covariate. The positions of the 13 coding variants are listed under the linkage peak and above the genes.</p

Results of gene-based analysis in discovery data and replication cohorts.

<p>Results of gene-based analysis in discovery data and replication cohorts.</p

Enrichment for functional annotations and cell-type groups using stratified LD score regression.

<p><b>A.</b> Enrichment estimates of 24 main annotations for each of four BP traits. Annotations are ordered by size. Error bars represent jackknife standard errors around the estimates of enrichment, and stars indicate significance at P < 0.05 after Bonferroni correction for 24 hypotheses tested and four BP traits. <b>B.</b> Significance of enrichment of 10 cell-type groups for four BP traits. Dotted line and stars indicate significance at P < 0.05 after Bonferroni correction for 10 hypotheses tested and four BP traits.</p

Intelligent Forecasting of Electricity Demand

In this paper, a number of approaches to the modelling of electricity demand, on a variety of time-scales, are considered. These approaches fall under the category of 'intelligent' systems engineering, where techniques such as neural networks, fuzzy logic and genetic algorithms are employed. The paper attempts to give some motivation for the employment of such techniques, while also making some effort to be realistic about the limitations of such methods, in particular a number of important caveats that should be borne in mind when utilising these techniques within the current application domain. In general, the electricity demand data is modelled as a time series, but one application considered involves application of linguistic modelling to capture operator expertise

Relative renal mRNA levels of genes identified at baseline and after 2 weeks of Ang II-induced hypertension.

<p><i>HPRT</i> gene was used for normalization. N ≥ 5 in each group. <b>A.</b> Genes that were differentially expressed between baseline and Ang II conditions. <b>B.</b> Genes that were not altered between the two conditions. * P < 0.05. ** P < 0.01. *** P < 0.001.</p

Loci identified in combined COGENT-BP African ancestry discovery samples and multi-ethnic replication samples.

<p>Loci identified in combined COGENT-BP African ancestry discovery samples and multi-ethnic replication samples.</p

Enrichment for functional annotations and cell-type groups using stratified LD score regression.

<p><b>A.</b> Enrichment estimates of 24 main annotations for each of four BP traits. Annotations are ordered by size. Error bars represent jackknife standard errors around the estimates of enrichment, and stars indicate significance at P < 0.05 after Bonferroni correction for 24 hypotheses tested and four BP traits. <b>B.</b> Significance of enrichment of 10 cell-type groups for four BP traits. Dotted line and stars indicate significance at P < 0.05 after Bonferroni correction for 10 hypotheses tested and four BP traits.</p