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

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

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

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

Study design schematic for discovery and replication of loci.

<p>QC, quality control; SBP, systolic blood pressure; DBP, diastolic blood pressure; PP, pulse pressure; HTN, hypertension; eQTL, expression quantitative loci.</p

Novel and previously identified BMI and WHR_adjBMI loci at <i>P</i> < 5×10⁻⁸ in African ancestry discovery and replication samples, and European ancestry replication samples.

<p>Novel and previously identified BMI and WHR_adjBMI loci at <i>P</i> < 5×10⁻⁸ in African ancestry discovery and replication samples, and European ancestry replication samples.</p

Additional novel BMI and WHR_adjBMI loci at <i>P</i> < 5×10⁻⁸ in sex-stratified analyses of African ancestry discovery and replication samples.

<p>Additional novel BMI and WHR_adjBMI loci at <i>P</i> < 5×10⁻⁸ in sex-stratified analyses of African ancestry discovery and replication samples.</p

Identification of four independent LD blocks in the 8p23.1 region <i>(~3</i>.<i>3 MBs</i>).

<p>Identification of four independent LD blocks in the 8p23.1 region <i>(~3</i>.<i>3 MBs</i>).</p

Novel SNVs/Genes associated with BP traits from correlated meta-analysis in European ancestry in Stage 1.

<p>Novel SNVs/Genes associated with BP traits from correlated meta-analysis in European ancestry in Stage 1.</p