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

Introduction: Proteins 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.

Deep learning applications in protein crystallography.

Deep learning techniques can recognize complex patterns in noisy, multidimensional data. In recent years, researchers have started to explore the potential of deep learning in the field of structural biology, including protein crystallography. This field has some significant challenges, in particular producing high-quality and well ordered protein crystals.

TERSE/PROLIX (TRPX) - a new algorithm for fast and lossless compression and decompression of diffraction and cryo-EM data.

High-throughput data collection in crystallography poses significant challenges in handling massive amounts of data. Here, TERSE/PROLIX (or TRPX for short) is presented, a novel lossless compression algorithm specifically designed for diffraction data. The algorithm is compared with established lossless compression algorithms implemented in gzip, bzip2, CBF (crystallographic binary file), Zstandard(zstd), LZ4 and HDF5 with gzip, LZF and bitshuffle+LZ4 filters, in terms of compression efficiency and speed, using continuous-rotation electron diffraction data of an inorganic compound and raw cryo-EM data.

A review and meta-analysis of stem cell therapies in stroke patients: effectiveness and safety evaluation.

Purpose: Stem cells have been extensively used during the last decade to improve clinical outcomes after stroke. The dramatic increase in trials in this field has led us to perform a systematic review and meta-analysis to understand the safety, effectiveness, and relative limitations of this type of intervention.

Method: This review summarizes the current evidence pooled from PubMed (Medline), EMBASE, EBSCOhost, http://clinicaltrials.

Machine learning for classifying narrow-beam electron diffraction data.

As an alternative approach to X-ray crystallography and single-particle cryo-electron microscopy, single-molecule electron diffraction has a better signal-to-noise ratio and the potential to increase the resolution of protein models. This technology requires collection of numerous diffraction patterns, which can lead to congestion of data collection pipelines. However, only a minority of the diffraction data are useful for structure determination because the chances of hitting a protein of interest with a narrow electron beam may be small.