In this study, changes in neural activation during joint action were investigated with hyperscanning, using functional near infrared spectroscopy (fNIRS) and electroencephalography (EEG) as participants performed the same task individually and then collaborated as a dyad. 62 participants were tested in dyads with a dual n-back task. Results are in support of the social facilitation model, and neural measures including hyperscanning were successful to differentiate between individual and social task settings. Overall, this study is one of the first studies to conduct EEG and fNIRS-hyperscanning during a complex collaboration task that brought together temporal constraints and elements of visuospatial reasoning.

Date: 05.09.2023 / 11:00 Place: A-212

This study aims to provide a bibliometric map of Functional Near Infrared Spectroscopy (fNIRS), a growing research area worldwide, in terms of interdisciplinary and institutional collaboration. Interdisciplinary and institutional collaborations are increasingly encouraged by universities as they have a high academic citation impact. In this study, the number of articles, number of citations, CNCI and IREW percentage in the field of fNIRS from 1980 to 2020 were analysed. Interdisciplinary and university & country collaborations that can increase these academic indicators are analysed from a bibliometric perspective.

Date: 11.09.2023 / 10:30 Place: -

This PhD thesis delves into the impact of selective directional auditory attention on eye movements and pupil responses. Concentrating on single-ear stimuli is shown to enhance attentiveness and task performance. The research explores effects of multi-talker speech interference, age, gender, and audio direction on eye movements and pupil dilation. Notably, interference influences pupils, prompting gaze shifts toward sound sources and larger pupils during competing speakers. This underscores the study's significance. These findings highlight the value of eye movement patterns and pupillometry in understanding auditory attention nuances. The implications are profound, benefiting hearing aid technology, communication for autism spectrum disorder and hearing-impaired individuals. The study also uncovers links between speech quality, word familiarity, and emotional connotations on pupil responses, contributing to a comprehensive comprehension of auditory processes.

Date: 06.09.2023 / 10:00 Place: A-212

This study aims for detection and DOA estimation of gunshot sound onboard a drone, despite the excessive ego-noise. Array Correlation Map concept is introduced for improved detection through unanimity among sensors of an array. Also, adaptive auto-tuning to advantegous CWT scales brings adaptive denoising for transient events of varying frequency. Although studied specifically for the processing of gunshot sounds on drones, the novelties this study offers is expected to generalize to other array processing applications. Results reveal signal-to-noise-ratio enhancement, successful muzzle and shock wave signal detection, and DOA estimation performance improvement.

Date: 04.09.2023 / 10:00 Place: A-212

A new method has been developed to study allosteric protein regulation, which is important for understanding how proteins function. The method represents proteins as networks and identifies allosteric pathways, or sites where molecules bind and regulate protein activity, in order to predict the presence and location of allosteric regions. This could potentially aid in the development of targeted therapies.

Date: 28.07.2023 / 10:00 Place: A-212

In Türkiye, Social Security Institution is the primary healthcare insurer. Turkish citizens are registered under General Medicare Insurance coverage. In 2003, Ministry of Health (MoH) has initiated the “Health Transformation Program”, and implemented the interoperable health data exchange standards. The MoH is focusing on collecting medical data in a coded, structured, and electronic format, generated at all healthcare providers. Contrarily, genetic test results are exchanged in narrative, unstructured form among governmental and private health care providers. In this dissertation, we lay out the bottlenecks and put forward a conceptual model for meaningful genomic data exchange for Turkish Electronic Health Records.

Date: 24.07.2023 / 17:00 Place: A-108

Building on an extensive review of the psycholinguistics literature and Turkish Derivational Morphology (DM), we propose a novel structure for representing DM in three hierarchical layers: segmentation, lexical selection and derivation. This proposal involves laying a conventionalized structure over the traditional morphological structure of DM. We develop a computational model of morphology processing based on this structure using Bayesian Belief Networks (BBN). We present an algorithmic implementation for this model that learns and accurately represents new lexical items, recognizes affixes and tracks the salience of each item probabilistically. We carry out trials on this model with realistic observation lists and observe that model predictions are in line with the findings in studies in psycholinguistics.

Date: 21.07.2023 / 11:30 Place: A-212

This thesis introduces a novel loss function, the Class Distance Weighted Cross Entropy (CDW-CE) loss, for automated severity assessment of Ulcerative colitis (UC) using Convolutional Neural Networks (CNN) on endoscopic images. CDW-CE considers ordinal relationships between classes and enhances prediction accuracy, outperforming other loss functions across different metrics and architectures. It also improves class activation maps' precision, aiding explanation of model predictions. The approach's broad applicability is confirmed by successful testing on a diabetic retinopathy dataset. The study also created the largest public UC image dataset.

Date: 17.07.2023 / 12:30 Place: A-212

The study introduces a new method to classify Hepatocellular Carcinoma (HCC) using a hyperspectral imaging system (HSI) combined with a light microscope. This method leverages 3D convolutions in Convolutional Neural Networks (CNNs) to train a robust classifier, capturing unique spectral and spatial features automatically. The approach also addresses class imbalance in the dataset by employing a focal loss function, preventing overfitting. The results show that hyperspectral data surpasses RGB data in liver cancer tissue classification, and enhanced spectral resolution improves accuracy, highlighting the importance of both spectral and spatial features for effective cancer tissue classification.

Date: 19.06.2023 / 15:45 Place: B-116

Hepatocellular carcinoma (HCC) is a type of primary liver cancer that is highly lethal and needs better treatment options. The thesis identifies several drugs and drug combinations that show promise in targeting drug-resistant HCC cells and cancer stem cells based on in silico modeling and in vivo experiments. It found that a combination of Sunitinib and Chloroquine Phosphate is synergistically cytotoxic on HCC cells, while novel isoxazole-piperazine compounds also show bioactivities against HCC cells and cancer stem cells. The study presents significant findings that highlight the potential of repurposed drugs and novel compounds as drug candidates for HCC.

Date: 24.04.2023 / 10:30 Place: A-212