Biology subjects

Matinyan, S.

Publications and source records attributed to Matinyan, S..

3 recordsLinked to original sources

bioRxiv2024

DiffGAN: a conditional generative adversarial network for phasing single molecule diffraction data to atomic resolution

IntroductionProteins that adopt multiple conformations pose significant challenges in structural biology research and pharmaceutical development, as structure determination via single particle cryo-electron microscopy (cryo-EM) is often impeded by data heterogeneity. In this context, the enhanced signal-to-noise ratio of single molecule cryo-electron diffraction (simED) offers a promising alternative. However, a significant challenge in diffraction methods is the loss of phase information, which is crucial for accurate structure determination. MethodsHere, we present DiffGAN, a conditional generative adversarial network (cGAN) that estimates the missing phases at high resolution from a combination of high-resolution single particle diffraction data and low-resolution image data. ResultsFor simulated datasets, DiffGAN allows effectively determine protein structures at atomic resolution from diffraction patterns and noisy low-resolution images. DiscussionOur findings suggest that combining single particle cryo-electron diffraction with advanced generative modeling, as in DiffGAN, could revolutionize the way protein structures are determined, offering a more accurate and efficient alternative to existing methods.

biophysics↗

bioRxiv2023

Characterization of predictive power of extracellular signal recordings in a cerebral ischemia animal model

IntroductionGlobal cerebral ischemia leads to substantial and irreversible damage of brain tissue. As it progresses in a less severe course, certain strategies should be implemented to screen this condition on its early onset. The research aimed to characterize the local field potential (LFP) alterations recorded during the subacute phase of the cerebral ischemia animal model and provide their predictive power. MethodsThe extracellular signal recordings from the parietal cortex of animals were registered with a neural probe. The signal was amplified, filtered, digitized, and acquired with Intan amplifier and USB interface boards. The recordings were obtained both in normal conditions and after implementation of unilateral common carotid artery occlusion. The data analysis and classification were performed using NI Diadem software and custom-written code in IPython environment. The respective morphological changes were screened in cerebral cortex and hippocampus. The whole-brain slicing and TTC staining were used for infarct size evaluation. ResultsIn Fourier spectrograms of intact brain recordings, a peak at 14.4-15 Hz frequencies was detected, whereas this phenomenon was absent in cerebral ischemia model recordings. In channels cross-correlograms for intact and ischemic brain recordings, there was a clear difference detected in the maximum peak power. With autocorrelation analysis, the long lag rhythmicity was detected in normal brain recordings, while no rhythmicity was observed in ischemic brains. The morphological and behavioral analyses did not result in any significant changes and neural loss. The TTC staining failed to show any damaged area ipsilateral to the occluded common carotid artery. ConclusionWe have analyzed and described the major characteristics of the electrical activity that vary between neural populations of the parietal cortex of normal and ischemic brains. This data proves that LFP recordings can be used for further investigation of changes occurring in the subacute phase after unilateral common carotid artery occlusion.

neuroscience↗

bioRxiv2023

TERSE: Efficient compression of the diffraction data

High-throughput data collection in crystallography poses significant challenges in handling massive amounts of data. Here, we present TERSE, a novel lossless compression algorithm specifically designed for diffraction data. We compare TERSE with the established lossless compression algorithms implemented in gzip, CBF, and HDF5, in terms of compression efficiency and speed, using continuous rotation electron diffraction data of an inorganic compound. Our results show that TERSE outperforms these algorithms by achieving a higher data compression at a speed that is at least an order of magnitude faster. TERSE files are byte-order independent and the algorithm can be readily implemented in hardware. By providing a tailored solution for diffraction data, TERSE facilitates more efficient data analysis and interpretation while mitigating storage and transmission concerns. TERSE C++20 compression/decompression code and an ImageJ/Fiji java plugin for reading TERSE files are open-sourced on GitHub under the permissive MIT license. SynopsisWe present a fast and lossless algorithm for compressing diffraction data, achieving up to 85% reduction in file size while processing up to 2000 512x512 frames per second. This breakthrough in compression technology is a significant step towards more efficient analysis and storage of large diffraction datasets.

biophysics↗