Name: Naor Balas Advisor: Prof. Gil Shalev Chay Aflalo Supervisor: Prof. Stanley Rotman name: Shadad Watad Advisor: Prof. Alina Karabchevsky

Seminar Abstract Naor Balas Protein–DNA interactions play a pivotal role in essential biological processes such as replication, repair, and recombination. Monitoring these interactions, and their disruption by small molecules, is vital for advancing molecular biology and developing innovative therapeutic strategies. However, conventional techniques often rely on labeling, indirect readouts, or slow and complex protocols, limiting their real-time applicability and sensitivity. This seminar presents a novel approach that employs a Meta-Nano-Channel biological Field-Effect Transistor (MNC bioFET) for real-time, label-free, and highly specific detection of DNA–primase interactions. The CMOS-compatible silicon-on-insulator (SOI) device, functionalized with DNA probes or primase, enables direct electrical readout of binding and dissociation events through modulation of the source–drain current. By introducing indole-derivative molecules, the platform further demonstrates quantitative analysis of small-molecule-induced disruption of primase–DNA binding. Extensive control experiments confirm the specificity and robustness of the method. This is the first reported use of MNC bioFET technology to simultaneously detect protein–DNA interactions and their disruption by small molecules in real time. The findings highlight the potential of this platform as a tool for molecular interaction studies, drug screening, and the development of next-generation label-free biosensing technologies. Keywords: Protein–DNA interactions, Primase, BioFET, Label-free biosensor, Real-time detection, Small molecule disruption Seminar Abstract: Chay Aflalo This seminar presents research on improving anomaly detection in hyperspectral images using a refined implementation of the RX algorithm. Hyperspectral images provide rich spectral information for each pixel, enabling the detection of subtle targets that are not visible in standard imaging. However, the high dimensionality and redundancy of the data pose major challenges for efficient and reliable analysis. The work investigates how different data manipulation and processing strategies such as dividing the hyperspectral cube into sub-cubes, applying noise filtering, and using alternative background estimation methods affect the performance of the RX algorithm. The study includes experiments on real hyperspectral datasets, such as the Viareggio and RIT datasets, and evaluates how segmentation and covariance modeling influence anomaly scores. The goal of the research is to enhance both the accuracy and robustness of anomaly detection, while also reducing computational complexity. The results contribute to improved hyperspectral image processing methods with applications in environmental monitoring, agriculture, and defense. Abstract: Shadad Watad Characterisation of nonlinear optical properties of MXene for photonic application, and implementing all optical activation function of neural networks based on waveguide MXene. Different geomtries of MXenes are investigated to study how the activation function changes.