Individual Professional Practice in the Company

Import 23/08/2017Cílem této bakalářské práce je popsat absolvování odborné praxe ve firmě HS Interactive s.r.o. Praxe byla zaměřena na vývoj mobilní aplikace pro operační systém Android. Aplikace je mobilním klientem pro sociální síť MatchToMe. V úvodu popisuji důvody, které vedly k výběru odborné praxe. Dále se věnuji úkolům, které mi byly zadány s jejich implementací a postupem řešení problémů, které se objevily při vývoji. Závěr práce je věnován zhodnocení získaných zkušeností a dosažených výsledků.Purpose of this bachelor thesis is to describe a professional practice in company HS Interactive s.r.o. Practice was focused on the development of mobile application for the operating system Android. The application is a mobile client for social network MatchToMe. In the introduction I describe reasons that led to the selection of professional practice. Then I describe tasks that I have been awarded with their implementations and process of solution issues that have emerged during development. The conclusion of thesis is dedicated to the evaluation of the experience gained and the results achieved.440 - Katedra telekomunikační technikyvýborn

Batch Effects Correction with Unknown Subtypes

<p>High-throughput experimental data are accumulating exponentially in public databases. Unfortunately, however, mining valid scientific discoveries from these abundant resources is hampered by technical artifacts and inherent biological heterogeneity. The former are usually termed “batch effects,” and the latter is often modeled by subtypes. Existing methods either tackle batch effects provided that subtypes are known or cluster subtypes assuming that batch effects are absent. Consequently, there is a lack of research on the correction of batch effects with the presence of unknown subtypes. Here, we combine a location-and-scale adjustment model and model-based clustering into a novel hybrid one, the batch-effects-correction-with-unknown-subtypes model (BUS). BUS is capable of (a) correcting batch effects explicitly, (b) grouping samples that share similar characteristics into subtypes, (c) identifying features that distinguish subtypes, (d) allowing the number of subtypes to vary from batch to batch, (e) integrating batches from different platforms, and (f) enjoying a linear-order computation complexity. We prove the identifiability of BUS and provide conditions for study designs under which batch effects can be corrected. BUS is evaluated by simulation studies and a real breast cancer dataset combined from three batches measured on two platforms. Results from the breast cancer dataset offer much better biological insights than existing methods. We implement BUS as a free Bioconductor package BUScorrect. Supplementary materials for this article are available online.</p

The schematic diagram of hyperspectral imaging system.

<p>The schematic diagram of hyperspectral imaging system.</p

Average original spectra of fungi.

<p>Average original spectra of fungi.</p

Results of PCA for different fungi at 120 h after inoculation in peach samples.

<p>Results of PCA for different fungi at 120 h after inoculation in peach samples.</p

Typical hyperspectral RGB images of different fungi (R: 662 nm, G 554.5 nm, and B 450 nm).

<p>Typical hyperspectral RGB images of different fungi (R: 662 nm, G 554.5 nm, and B 450 nm).</p

Results of models for three fungi growth of <i>R</i>. <i>stoloifenr</i>, <i>B</i>. <i>cinerea</i> and <i>C</i>. <i>acutatum</i> in the peach samples^*.

<p>Results of models for three fungi growth of <i>R</i>. <i>stoloifenr</i>, <i>B</i>. <i>cinerea</i> and <i>C</i>. <i>acutatum</i> in the peach samples^{<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0143400#t004fn001" target="_blank">*</a>}.</p

Growth Simulation and Discrimination of <i>Botrytis cinerea</i>, <i>Rhizopus stolonifer</i> and <i>Colletotrichum acutatum</i> Using Hyperspectral Reflectance Imaging

<div><p>This research aimed to develop a rapid and nondestructive method to model the growth and discrimination of spoilage fungi, like <i>Botrytis cinerea</i>, <i>Rhizopus stolonifer</i> and <i>Colletotrichum acutatum</i>, based on hyperspectral imaging system (HIS). A hyperspectral imaging system was used to measure the spectral response of fungi inoculated on potato dextrose agar plates and stored at 28°C and 85% RH. The fungi were analyzed every 12 h over two days during growth, and optimal simulation models were built based on HIS parameters. The results showed that the coefficients of determination (R²) of simulation models for testing datasets were 0.7223 to 0.9914, and the sum square error (SSE) and root mean square error (RMSE) were in a range of 2.03–53.40×10⁻⁴ and 0.011–0.756, respectively. The correlation coefficients between the HIS parameters and colony forming units of fungi were high from 0.887 to 0.957. In addition, fungi species was discriminated by partial least squares discrimination analysis (PLSDA), with the classification accuracy of 97.5% for the test dataset at 36 h. The application of this method in real food has been addressed through the analysis of <i>Botrytis cinerea</i>, <i>Rhizopus stolonifer</i> and <i>Colletotrichum acutatum</i> inoculated in peaches, demonstrating that the HIS technique was effective for simulation of fungal infection in real food. This paper supplied a new technique and useful information for further study into modeling the growth of fungi and detecting fruit spoilage caused by fungi based on HIS.</p></div

Outlier Analysis Defines Zinc Finger Gene Family DNA Methylation in Tumors and Saliva of Head and Neck Cancer Patients

<div><p>Head and Neck Squamous Cell Carcinoma (HNSCC) is the fifth most common cancer, annually affecting over half a million people worldwide. Presently, there are no accepted biomarkers for clinical detection and surveillance of HNSCC. In this work, a comprehensive genome-wide analysis of epigenetic alterations in primary HNSCC tumors was employed in conjunction with cancer-specific outlier statistics to define novel biomarker genes which are differentially methylated in HNSCC. The 37 identified biomarker candidates were top-scoring outlier genes with prominent differential methylation in tumors, but with no signal in normal tissues. These putative candidates were validated in independent HNSCC cohorts from our institution and TCGA (The Cancer Genome Atlas). Using the top candidates, <i>ZNF14</i>, <i>ZNF160</i>, and <i>ZNF420</i>, an assay was developed for detection of HNSCC cancer in primary tissue and saliva samples with 100% specificity when compared to normal control samples. Given the high detection specificity, the analysis of ZNF DNA methylation in combination with other DNA methylation biomarkers may be useful in the clinical setting for HNSCC detection and surveillance, particularly in high-risk patients. Several additional candidates identified through this work can be further investigated toward future development of a multi-gene panel of biomarkers for the surveillance and detection of HNSCC.</p></div

Correlation, concordance and agreement of ZNF DNA methylation signal in salivary rinses and in primary tissues from the validation cohort.

<p>Significant values are in bold.</p><p>Correlation, concordance and agreement of ZNF DNA methylation signal in salivary rinses and in primary tissues from the validation cohort.</p