Activation of the Wnt pathway leads to increased FOXQ1 expression.

<p>(<b>A</b>) qRT-PCR analysis of FOXQ1 expression in HEK293 and HCT116 cell lines after Wnt activation. Cell were treated with 20 mM LiCl for 24 hours or transfected with a constitutively active form of ß-catenin (S33Y). Data are mean ± SD n = 3, **P<0.01 using Mann-Whitney test. (<b>B</b>) Western blot of FOXQ1 in HEK293 and HCT116 after Wnt activation as described above. GAPDH was used a loading control. (<b>C</b>) HEK293 and HCT116 cells stably expressing the Wnt sensitive reporter 7TGC <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0060051#pone.0060051-Fuerer1" target="_blank">[25]</a> were treated as described above and imaged. GFP (green) is under the control of a Wnt sensitive promoter and mCherry (red) is constitutively expressed to identify infected cells, white bar indicates 20 µm.</p

ß-catenin binds to the promoter region of FOXQ1 and increase transcription.

<p>(<b>A</b>) ChIP assay was performed on SW480 cells with antibodies against ß-catenin and IgG (control). Input and immunoprecipitated DNA was measured by qRT-PCR using primers amplifying the promoter region of FOXQ1 and the −100/0 promoter region of GAPDH. Data are mean ± SD n = 3, **P<0.01 using Mann-Whitney test. (<b>B</b>) Graphical depiction of the promoter region of FOXQ1, and the generated promoter reporter vectors. (<b>C</b>) Luciferase assay showing the effect of Wnt activation in HEK293 and HCT116 cells (as described in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0060051#pone-0060051-g005" target="_blank">figure 5</a>) transfected with the wild type promoter region of FOXQ1 (pGL3_FOXQ1_WT), a mutated TCF4 binding site (pLG3_FOXQ1_Mut) or an empty vector (pGL3_Emtpy). Data are mean ± SD n = 4–7, *P<0.05, ***P<0.001 by the Kruskal-Wallis Conover test.</p

FOXQ1 expression is not influenced by stage, grade and localization of the tumor.

<p>Boxplots of FOXQ1 mRNA expression levels (i.e. normalized, log2 transformed fluorescence signals) in the GSE2109 data set. Dukes Stages: stage A, B, C and D; Grades: grades 1, 2, 3 and 4; Localization: C = colon, A = ascending colon, S = sigmoid colon, RS = recto-sigmoid, R = rectum and CE = cecum. Metastasis: non-metastatic = no metastatic site observed, metastatic = metastatic site observed.</p

FOXQ1 expression in tumor samples.

<p>FOXQ1 belongs to the most overexpressed genes in human colon adenoma and carcinoma as compared to normal tissue. Rank orders, fold changes (FC) and average intensity signals (AS) of FOXQ1 expression in various solid tumors generated with GeneChip® Human Genome U133 Plus 2.0. A = adenoma, T = tumor carcinoma, N = normal, B = basal, NB = non-basal, D = ductal, L = lobular. Fold changes and rank orders in CRC were corroborated by data mining of TCGA dataset (<a href="http://cancergenome.nih.gov" target="_blank">http://cancergenome.nih.gov</a>) and others in Oncomine (<a href="http://www.oncomine.org" target="_blank">www.oncomine.org</a>).</p

Correlation of FOXQ1 with the Wnt pathway.

<p>Gene set enrichment analysis of FOXQ1 with a selection of gene set related to Wnt signalling. GSEA preformed in various solid tumors showed a significant enrichment when using direct Wnt targets and gene sets related to Wnt activation. SIZE = size of tested gene set, ES = enrichment score, NOM p-val = Nominal p-value, FDR q-val = false discovery rate q-value.</p

FOXQ1 is expressed in tumor cells and reactive stroma.

<p>Boxplot of FOXQ1 mRNA levels in laser dissected human CRC samples measured by microarray n = 3 and qRT-PCR n = 3–5. TC = tumor cells, NEC = normal epithelial cells, NS = normal stroma, i.e. stromal tissue surrounding normal colonocytes, RS = reactive stroma, i.e. stromal tissue with increased infiltration of inflammatory cells surrounding tumor cells, F = primary cells cultures of fibroblasts and CAF = cancer-associated fibroblasts.</p

FOXQ1 is induced on protein level in human colon carcinoma as compared to normal tissue.

<p>Western blot analysis of FOXQ1 in human CRC biopsy samples from three different patients. GAPDH was used as loading control. T = tumor tissue, N = normal colon tissue.</p

FOXQ1 expression correlates with the average Wnt signature strength in a wide panel of cell lines.

<p>Heatmap showing the correlation coefficients between single genes and the mean expression of all 24 direct Wnt targets in various cancer cell lines (data set GSE36133). Colon n = 57, breast n = 58, lung = 174, pancreas n = 44, stomach n = 38, all n = 967.</p

Regulation of FOXQ1 expression upon loss of CDX2 expression.

<p>Relative expression levels (fold changes) of CDX2, FOXQ1 and AXIN2 in shCDX2 Caco-2 cells grown for 5 days and 3 weeks as compared to control transfected cells, measured by qRT-PCR.</p

Collision detection on transmission lines with optical interferometer

V diplomski nalogi skušamo ugotoviti, v kolikšni meri je možno zaznavati in klasificirati trke na jeklenicah daljnovodov z optičnim interferometrom. Na začetku predstavimo osnovne pojme interferometrije in opišemo uporabljen optični interferometer. V jedru diplomske naloge natančneje opišemo eksperimentalni protokol in obdelavo signalov. Nadaljujemo z implementacijo algoritmov za segmentacijo in klasifikacijo zajetih signalov ter predstavimo dobljene rezultate. Segmentacijo izvedemo v domeni števila prehodov signala skozi ničlo, za klasifikacijo pa uporabimo večplastno nevronsko mrežo z algoritmom vzvratnega učenja. Rezultati študije nakazujejo, da sta implementirani segmentacija in klasifikacija uspešni v 77 % izvedenih trkov različnih predmetov.We analyse feasibility of collision detection on transmission lines with optical interferometer. We first provide a brief introduction into interferometry, along with a description of the optical interferometer used for measurements in this study. Afterwards, we describe the conducted experimental protocol and signal processing methodology. The focus is on implementation of algorithms for signal segmentation and collision classification. We used zero-crossing algorithm to transform signals into segmentation domain. Classification of collisions is done with a multilayer neural network trained by the backpropagation algorithm. The results demonstrate an average success rate of 77% for segmentation and classification of collision with five different objects