Browsing by Author "Samuel, Kadri"

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    High-throughput mRNA sequencing of stromal cells from endometriomas and endometrium
    (2017) Rekker, Kadri; Saare, Merli; Eriste, Elo; Tasa, Tõnis; Kukuškina, Viktorija; Roost, Anne Mari; Anderson, Kristi; Samuel, Kadri; Karro, Helle; Salumets, Andres; Peters, Maire
    The aetiology of endometriosis is still unclear and to find mechanisms behind the disease development, it is important to study each cell type from endometrium and ectopic lesions independently. The objective of this study was to uncover complete mRNA profiles in uncultured stromal cells from paired samples of endometriomas and eutopic endometrium. High-throughput mRNA sequencing revealed over 1300 dysregulated genes in stromal cells from ectopic lesions, including several novel genes in the context of endometriosis. Functional annotation analysis of differentially expressed genes highlighted pathways related to cell adhesion, extracellular matrix–receptor interaction and complement and coagulation cascade. Most importantly, we found a simultaneous upregulation of complement system components and inhibitors, indicating major imbalances in complement regulation in ectopic stromal cells. We also performed in vitro experiments to evaluate the effect of endometriosis patients’ peritoneal fluid (PF) on complement system gene expression levels, but no significant impact of PF on C3, CD55 and CFH levels was observed. In conclusion, the use of isolated stromal cells enables to determine gene expression levels without the background interference of other cell types. In the future, a new standard design studying all cell types from endometriotic lesions separately should be applied to reveal novel mechanisms behind endometriosis pathogenesis.
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    Real-Time Ensemble Based Face Recognition System for Humanoid Robots
    (Tartu Ülikool, 2016) Samuel, Kadri; Anbarjafari, Gholamreza; Bolotnikova, Anastasia; Tartu Ülikool. Loodus- ja täppisteaduste valdkond; Tartu Ülikool. Tehnoloogiainstituut
    Humanoid robots are being used in many industrial and domestic application in which human-robot interaction plays an important role. One of the important existing challenges is developing an accurate real-time face recognition system which is not required to be computationally expensive. In this research work a real-time face recognition system which requires low computational complexity is proposed. For this purpose, this thesis is investigating block processing of local binary patterns of the face images captured by NAO robot, a humanoid. For test purposes, the proposed method is adopted on NAO robot and tested under realworld conditions. The experimental results through this thesis are showing that the proposed face recognition algorithm compares favorably to the conventional and state-of-the-art techniques.