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CRISPR/Cas9 (Clustered Regularly Interspaced Short Palindromic Repeats and CRISPR-associated protein 9) is a popular and effective two-component technology used for targeted genetic manipulation. It is currently the most versatile and accurate method of gene and genome editing, which benefits from a large variety of practical applications. For example, in biomedicine, it has been used in research related to cancer, virus infections, pathogen detection, and genetic diseases. Current CRISPR/Cas9 research is based on data-driven models for on- and off-target prediction as a cleavage may occur at non-target sequence locations. Nowadays, conventional machine learning and deep learning methods are applied on a regular basis to accurately predict on-target knockout efficacy and off-target profile of given single-guide RNAs (sgRNAs). In this paper, we present an overview and a comparative analysis of traditional machine learning and deep learning models used in CRISPR/Cas9. We highlight the key research challenges and directions associated with target activity prediction. We discuss recent advances in the sgRNA–DNA sequence encoding used in state-of-the-art on- and off-target prediction models. Furthermore, we present the most popular deep learning neural network architectures used in CRISPR/Cas9 prediction models. Finally, we summarize the existing challenges and discuss possible future investigations in the field of on- and off-target prediction. Our paper provides valuable support for academic and industrial researchers interested in the application of machine learning methods in the field of CRISPR/Cas9 genome editing.

In the field, 29 collectible samples of legume feed plants from the gene pool of the VIK Williams FNC were studied. The data on chemical composition (dry matter, crude protein, crude fiber, crude ash, crude fat), precocity, height and discoloration of plants, grass strength and leaf size are presented. Promising forms for use in selection programs are highlighted.

It is shown that the disposal of spent complex chemical power sources (CPS) leads to severe environmental pollution. An environmentally safe technology for their disposal has been developed, including the stages of mechanical processing, leaching and extraction. The article discusses the combined processes of processing spent CPS with the use of mechanochemical destruction. The work of opening the CPS on a shredder in an argon atmosphere, knife and ball mills is analyzed. The grinding of model media with different morphology in a drum mill and a toroidal vibrating mill (TVM) was studied, and the operating parameters of the drum mill in a waterfall mode were determined. A narrowing of the granulometric composition from a particle diameter of 2–3 mm to a diameter of 10–40 microns was revealed. It is shown that when CPS particles are ground in a ball mill at a ratio of balls and ground phase of 5:1, the content of cobalt in the powder increases to 19.0 wt %. The time for the complete cycle of CPS processing, including the stages of opening in an argon atmosphere, grinding, grinding, leaching and extraction, was established to be 7 hours.

Electret effects of the selective electrostatic separation of crushed particles of recycled polymers were examined. Polymer electrets properties dependence on fissuring and chemical activation environments of synthetic polymeric wastes surfaces was revealed.

Sudden cardiac death from lethal arrhythmia is a preventable cause of death. Ventricular fibrillation and tachycardia are shockable electrocardiographic (ECG)rhythms that can respond to emergency electrical shock therapy and revert to normal sinus rhythm if diagnosed early upon cardiac arrest with the restoration of adequate cardiac pump function. However, manual inspection of ECG signals is a difficult task in the acute setting. Thus, computer-aided arrhythmia classification (CAAC) systems have been developed to detect shockable ECG rhythm. Traditional machine learning and deep learning methods are now progressively employed to enhance the diagnostic accuracy of CAAC systems. This paper reviews the state-of-the-art machine and deep learning based CAAC expert systems for shockable ECG signal recognition, discussing their strengths, advantages, and drawbacks. Moreover, unique bispectrum and recurrence plots are proposed to represent shockable and non-shockable ECG signals. Deep learning methods are usually more robust and accurate than standard machine learning methods but require big data of good quality for training. We recommend collecting large accessible ECG datasets with a meaningful proportion of abnormal cases for research and development of superior CAAC systems.