Forecasting of commercial sales with large scale Gaussian Processes

This paper argues that there has not been enough discussion in the field of applications of Gaussian Process for the fast moving consumer goods industry. Yet, this technique can be important as it e.g., can provide automatic feature relevance determination and the posterior mean can unlock insights on the data. Significant challenges are the large size and high dimensionality of commercial data at a point of sale. The study reviews approaches in the Gaussian Processes modeling for large data sets, evaluates their performance on commercial sales and shows value of this type of models as a decision-making tool for management.Comment: 1o pages, 5 figure

Boxplots of the distribution of the expression of MT-ND2 and MT-CO2 across tertiles of MPSP and infant temperament indices.

<p>A-D. MPSP indices. E-F. Infant temperament indices. In each graph “low”, “average” and “high” represent the tertiles of MPSP and infant temperament indices. The star symbol and the bracketing lines represent the tertiles for which significant (p < 0.05) differences in gene expression have been detected.</p

Population demographics and variables statistics: continuous.

<p>Population demographics and variables statistics: continuous.</p

Mitochondrial Expression Clusters.

<p>The graph represents the five expression clusters identified for the expression of the 13 protein-coding mitochondrial-encoded genes. The expression clusters have been determined by Ward Linkage. The cutoff level used to generate the clusters in the clustering tree have been chosen accordingly to the multidimensional scaling analysis (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0138929#pone.0138929.s001" target="_blank">S1 Fig</a>) that showed that the expression data can efficiently be fit into a 5-dimensional space without imposing an excessive degree of stress to the dataset.</p

Population demographics and variables statistics: nominal.

<p>⁽¹⁾: Caucasian (white): N = 5 –% = 4.6.</p><p>Population demographics and variables statistics: nominal.</p

Multinomial regression statistics for the correlation between the PCA summary scores for the five mitochondrial gene expression clusters and the PCA summary scores for MPSP and infant temperament indices, maternal weight class and infant birth measures.

<p>Notes: </p><p></p><p></p><p>Regression p-values are reported as follows: 1) bold & underlined p < 0.01; 2) bold p < 0.05; 3) regular p ≥ 0.05</p><p></p><p></p><p>For the logistic regressions the Nagelkerke pseudo-r squared is reported</p><p></p><p></p><p>Standardized β values for the logistic regressions have been calculated by using the “Standardized Coefficients in Logistic Regression” method [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0138929#pone.0138929.ref053" target="_blank">53</a>].</p><p></p><p></p><p></p><p>Regression p-values are reported as follows: 1) bold & underlined p < 0.01; 2) bold p < 0.05; 3) regular p ≥ 0.05</p><p>For the logistic regressions the Nagelkerke pseudo-r squared is reported</p><p>Standardized β values for the logistic regressions have been calculated by using the “Standardized Coefficients in Logistic Regression” method [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0138929#pone.0138929.ref053" target="_blank">53</a>].</p><p>Multinomial regression statistics for the correlation between the PCA summary scores for the five mitochondrial gene expression clusters and the PCA summary scores for MPSP and infant temperament indices, maternal weight class and infant birth measures.</p

Boxplots representing the distribution of the expression clusters 2 and 3 across tertiles of PCA summary score for MPSP and infant temperament indices.

<p>A. PCA summary score for MPSP indices. B. PCA summary score for infant temperament indices. In each graph “low”, “average” and “high” represent the tertiles of the PCA summary score for MPSP and infant temperament indices. The star symbol and the bracketing lines represent the tertiles for which significant (p < 0.05) differences in gene expression have been detected.</p

Additional file 5: of Whole-transcriptome analysis delineates the human placenta gene network and its associations with fetal growth

Mapping of GWAS-linked genes in placental gene coexpression network. Genes linked to GWAS-associated traits that are enriched in the placental gene coexpression network are listed alongside assigned modules and GWAS-linked traits. (CSV 58 kb

First Experimental Demonstration of the Multipotential Carcinogenic Effects of Aspartame Administered in the Feed to Sprague-Dawley Rats-0

<p><b>Copyright information:</b></p><p>Taken from "First Experimental Demonstration of the Multipotential Carcinogenic Effects of Aspartame Administered in the Feed to Sprague-Dawley Rats"</p><p>Environmental Health Perspectives 2005;114(3):379-385.</p><p>Published online 17 Nov 2005</p><p>PMCID:PMC1392232.</p><p>This is an Open Access article: verbatim copying and redistribution of this article are permitted in all media for any purpose, provided this notice is preserved along with the article's original DOI.</p>les. () Mean body weights in males (M) and females (F). () Survival in males. () Survival in females. The arrow indicates the start of the experiment at 8 weeks of age

Placental imprinting variation associated with assisted reproductive technologies and subfertility

<p>Infertility affects one in 6 couples in developed nations, resulting in an increasing use of assisted reproductive technologies (ART). Both ART and subfertility appear to be linked to lower birth weight outcomes, setting infants up for poor long-term health. Prenatal growth is, in part, regulated via epigenetically-controlled imprinted genes in the placenta. Although differences in DNA methylation between ART and control infants have been found, it remains unclear whether these differences are due to the ART procedures or to the underlying parental subfertility and how these methylation differences affect imprinted gene expression. In this study, we examined the expression of 108 imprinted genes in placental tissues from infants born to subfertile parents (n = 79), matched naturally-conceived controls (n = 158), and infants conceived using <i>in vitro</i> fertilization (IVF, n = 18). Forty-five genes were identified as having significantly different expression between the subfertile infants and controls, whereas no significant differences were identified between the IVF and control groups. The expression of 4 genes—<i>IGF2, NAPIL5, PAX8-AS1</i>, and <i>TUBGCP5</i>—was significantly downregulated in the IVF compared with the subfertile group. Three of the 45 genes significantly dysregulated between subfertile and control placentae—<i>GRB10, NDN</i>, and <i>CD44</i> —were found to have a significant positive correlation between expression and birth weight. Methylation levels for these 3 genes and 4 others—<i>MKRN3, WRB, DHCR24</i>, and <i>CYR61</i>—were significantly correlated with expression. Our findings indicate that epigenetic differences in placentas resulting from IVF pregnancies may be related to the underlying subfertility in parents using IVF rather than the IVF procedure itself.</p