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Araçsallaştırmadan İttifaka: Suriye-Hizbullah İlişkilerinde Stratejik Bağımlılığın İnşası (1985-2023)
No full textNerve tissue model on a micropatterned surface: Axon guidance and neural regeneration
No full textThis study focuses on the design, production and testing of a micropatterned PDMS surface, featuring micropillars and microchannels to study the regeneration of individual axons of PC12 nerve cells after injury. Micropillar organization on the surface was designed to restrict the PC12 cell bodies while axons were guided into microchannels, allowing observation of individual axons. Surfaces were coated with poly(L-lysine) to improve cell attachment and proliferation. Netrin-1, a chemoattractant molecule and axonal elongation enhancer, was introduced in a gelatin methacrylate (GelMA) hydrogel carrier at the opposite end of the channels. Schwann cells (SC) were co-cultured with PC12 cells to enhance axon extension. MTT and Live-Dead assays showed 90% viability of the PC12 and Schwann cells on surfaces. The average PC12 axon length in the channels was 51 +/- 19 mu m; which increased to 75 +/- 16 mu m and 177 +/- 31 mu m upon co-culture with Schwann cells and Netrin-1 incorporation along with co-culturing, respectively, showing their synergistic effect on axon elongation. To study axon damage and regeneration processes, PC12 axons extended into the microchannels were cut using a microtome blade. An increase in the expression of injury markers ATF3, GFAP and S100 beta was observed after the injury with confocal microscopy, and their decrease from days 14 to 21 indicated the initiation of axon regeneration. The platform consisting of patterned PDMS surface, Schwann cells and Netrin-1 holds potential as a valuable tool for nerve damage and repair studies, and for in vitro testing of novel nerve tissue engineering strategies
How pond characteristics affect macrophyte diversity andcoverage in rural ponds of Ankara, Türkiye
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A robust approach for predicting mutation effects on transcription factor binding: insights from mutational signatures in 560 breast cancer samples
No full textRadar Target Detection Under Correlated Non-Gaussian Clutter Using Transfer Learning
No full textIn radar signal processing, accurate detection of targets in the presence of noise and clutter is critical for systems like autonomous vehicles and military defense. This is due to the degradation of reflected signals by noise and clutter, which complicates target detection for radars. Recent advancements in deep learning, particularly convolutional neural networks and transfer learning, offer promising solutions for enhancing radar signal classification. This paper explores the integration of ResNet models for multi-target detection, comparing their performance with traditional constant false alarm rate algorithms and adaptive normalized matched filters in addressing correlated nonGaussian clutter and low signal-to-clutter-plus-noise ratios. Our method outperforms conventional and state-of-the-art techniques using pre-trained models with high nonlinear representation capability in terms of probability of detection, offering a robust solution for radar signal processing