Regulatory relationship of monocyte's core TRN elements.

<p>(A) Relative expressions to monocyte are represented in colors. Higher relative expression is depicted as intensifying green color. The value of each relative expression is the average of biological replicates (n = 3). (B) Illustration of hierarchical network of monocyte core TRN elements. Each node (circle) indicates monocyte core TRN elements and green nodes represent the identified TRN inducers. When the TRN inducers or NR4A2 upregulate the gene expression to more than 5% of that of monocyte, an edge was drawn. An edge from an upper node to lower node indicates positive regulation.</p

Motif activity revealed reconstructed monocyte TRN.

<p>(A) The activity of 10 monocyte-associated motifs were shown as a line chart in FIB-mock, FIB-SPI1, and FIB-4Fs. Vertical axis represents the motif activity. (B) SPI1 motif activity was shown as bar plot. Each bar represent an average of biological replicates and error bar represent s.d. (n = 3).</p

Cell feature assessments in reconstructed fibroblasts.

<p>(A) Morphological changes were visualized by microscopy. The cells were stained with Pallodin-Rhodamin (Yellow), Hoechst33342 (Blue), and Whole Cell Stains (Red). (B–D) Phagocytic latex beads were visualized (B) and the mean intensity of the ingested beads were confirmed by flow cytometry (C–D). (B) Beads-ingested cells are indicated by white arrows. Red color represents DID cell membrane staining, blue color represents nuclei, and green color represents the latex beads. (C) Beads ingested cells was quantified based on the beads fluorescence by flow cytometry in FIB-mock, FIB-SPI1 and DIB-4Fs 2 houes after beads addition.. The cells were cultured with 0.002 v/v%. Vertical and horizontal axes represent cell count and fluorescent intensity, respectively. Beads ingested cells were gated. The analysis was performed in triplicates, showing the similar results. (D) The flow cytometric analysis of the phagocytosis was summarized. Black, blue, and red lines represent FIB-mock, FIB-SPI1, and FIB-4Fs, respectively. The mean fluorescent intensity of ingested beads was measured by flow cytometry. The vertical axis and horizontal axis represent mean beads fluorescent intensity and incubation time, respectively. Error bars represent standard deviation (s.d.) (E) The expression change of <i>TNF</i>, <i>IL6</i>, <i>IL1A</i>, <i>IL1B</i>, <i>IL8</i>, <i>CCL2</i>, <i>CXCL10</i> and <i>IFNB1</i> induced by LPS treatment were measured by qRT-PCR in FIB-mock, FIB-SPI1, and FIB-4Fs. The cells were treated with LPS for 24 hours at the final concentration of 10 µg/µl. The bar represents the relative expression of LPS-treated cells as compared to untreated cells and error bar represents s.d. These experiments were repeated three times. * represents <i>P</i>-value≤0.05, ** represents <i>P</i>-value≤0.01 (t-test). The scale bar is 50 µm.</p

Core TRN elements isolation workflow.

<p>Key TFs were selected based on differential gene expression analysis and text mining. Left flow represents steps of the differential gene expression analysis. Gene expression profiling of primary human monocytes and that of human dermal fibroblasts were archived by using Human WG-6 v3.0 Expression beadschips (n = 3). Right flow represents the text mining. The text mining ranked the co-occurrence of “Monocyte” and “TF Name”. Finally, those two methods were integrated and isolated top 20 TFs as core monocyte TRN elements.</p

The monocyte core TRN elements.

<p>Rfc = differential expression fold change rank.</p><p>Rco = co-occurrence rank.</p><p>IS = Importance Score (1/Rfc+1/Rco).</p

Expression of monocyte markers.

<p>Expression of monocyte markers.</p

Reconstruction of Monocyte Transcriptional Regulatory Network Accompanies Monocytic Functions in Human Fibroblasts

<div><p>Transcriptional regulatory networks (TRN) control the underlying mechanisms behind cellular functions and they are defined by a set of core transcription factors regulating cascades of peripheral genes. Here we report SPI1, CEBPA, MNDA and IRF8 as core transcription factors of monocyte TRN and demonstrate functional inductions of phagocytosis, inflammatory response and chemotaxis activities in human dermal fibroblasts. The Gene Ontology and KEGG pathway analyses also revealed notable representation of genes involved in immune response and endocytosis in fibroblasts. Moreover, monocyte TRN-inducers triggered multiple monocyte-specific genes based on the transcription factor motif response analysis and suggest that complex cellular TRNs are uniquely amenable to elicit cell-specific functions in unrelated cell types.</p> </div

Four TRN-inducers adopted inflammatory cytokine secretion in response to LPS.

<p>FIB-mock and FIB-4Fs were treated or untreated with 10 µg/ml LPS. The cytokine levels of supernatant medium were assessed using the Proteome Profiler Human Cytokine Array, Pannel A. Array images were collected by LAS-3000 imaging system (A). The intensity of each spot was determined by Multi Gauge softwere (B).</p

Four TRN-inducers adopted chemotaxis activity towards CCL2.

<p>Chemotaxis activity was measured by performing a transwell assay. FIB-mock and FIB-4Fs were cultured in transwells and incubated in the lower-chamber containing 5 µM of CCL2. The cells that migrated to the bottom of transwell were stained with Calcein-AM. Relative signal intensities were calculated by comparing fluorescent intensities of CCL2 treated to untreated cells (n = 3). ** represents <i>P-value</i>≤0.01 (t-test).</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