Selection of clonal preadipogenic cell line as a rapidly differentiating adipogenesis model.

<p>(A) Schematic display of robust OP9 adipogenesis demonstrating the desired morphological characteristics of a rounded cell, peripheral nucleus, lipid accumulation, and a coalesced lipid droplet. Triglyceride accumulation shown in red and nucleus shown in blue. OP9 cells differentiate over the course of 72 hours. After one day of insulin oleate (IO) media exposure, cells begin to accumulate small lipid droplets. After two days of IO media exposure, cells adopt a round morphology, the nucleus migrates to the periphery, and the cytoplasm is filled with lipid droplet. After three days of IO media exposure, cells resemble mature adipocytes. The nucleus is peripherally located and lipid droplets have fused into one large lipid droplet. OP9 cells are capable of differentiation at sub-confluent levels. (B) OP9 clonal populations were differentiated into adipocytes using insulin oleate (IO) media. Clone K (orange box) demonstrates desired adipogenesis morphology. Cells were fixed with paraformaldehyde and then stained with the lipophilic dye, Nile Red. Nile Red stain is shown in red. The arrow points to a mature adipocyte. Magnification is 40X. (C) Triglyceride (TG) content of clonal OP9 cell lines after 72 hours exposure to IO media, low serum media (lsm), or propagation media (pm), using TG assay. Assays performed in triplicate. Clone K (orange box) robustly accumulates triglyceride. Clone K consistently demonstrated adipocyte morphology of a rounded cell, coalesced lipid droplet, and triglyceride accumulation. Therefore, clone K OP9 cells were selected for further study.</p

OP9 cells are efficiently and effectively transfected.

<p>(A) OP9 cells uptake Alexa (green) tagged RNAi during transfection. Amanis imagestream images of OP9 clone K cells transfected with Alexa 647 tagged RNAi and sorted based on Alexa 647 fluorescence. (B) Quantification of cell populations as classified by Amanis imagestream. (C) Relative expression of GAPDH demonstrates gene knockdown in OP9 cells. qPCR analysis of GAPDH expression level in two GAPDH RNAi samples and two negative control RNAi samples. </p

Microarray analysis profile of probes for adipocyte marker genes.

<p>Expression values are RMA normalized and log transformed. Fold change is relative to Day 0.</p><p>Microarray analysis profile of probes for adipocyte marker genes.</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