Bovine Genome Analysis to Unravel the Location and Feature of Target Sites of RNA-Guided Hyperactivated Recombinase Gin with Spacer Length Six

Background: Programmable nucleases are very promising tools of genome editing (GE), but they suffer from limitations including potential risk of genotoxicity which led to the exploration of safer approach of GE based on RNA-guided recombinase (RGR) platform. RNA-guided recombinase (RGR) platform operates on a typical recognition or target site comprised of the minimal pseudo-core recombinase site, a 5 to 6-base pair spacer flanking it and whole this central region is flanked by two guide RNA-specified DNA sequences or Cas9 binding sites followed by protospacer adjacent motifs (PAMs). Methods: The current study focuses on analysis of entire cattle genome to prepare a detailed map of target sites for RNA-guided hyperactivated recombinase Gin with spacer length six. For this, chromosome wise whole genomic sequence data was retrieved from Ensembl. After that search pattern for recombinase Gin with spacer length six was designed. By using this search pattern, RGR target sites were located by using dreg program of Emboss package. Result: Total number of RGR target sites identified in bovine genome for recombinase Gin was 677 with spacer length six. It was also investigated that whether these RGR target sites are present with in any gene or not and it was found that RGR target sites lies in both genic and intergenic region. Besides this, description of genes in context with these target sites was identified.

A Motion Estimation based Algorithm for Encoding Time Reduction in HEVC

High Efficiency Video Coding (HEVC) is a video compression standard that offers 50% more efficiency at the expense of high encoding time contrasted with the H.264 Advanced Video Coding (AVC) standard. The encoding time must be reduced to satisfy the needs of real-time applications. This paper has proposed the Multi- Level Resolution Vertical Subsampling (MLRVS) algorithm to reduce the encoding time. The vertical subsampling minimizes the number of Sum of Absolute Difference (SAD) computations during the motion estimation process. The complexity reduction algorithm is also used for fast coding the coefficients of the quantised block using a flag decision. Two distinct search patterns are suggested: New Cross Diamond Diamond (NCDD) and New Cross Diamond Hexagonal (NCDH) search patterns, which reduce the time needed to locate the motion vectors. In this paper, the MLRVS algorithm with NCDD and MLRVS algorithm with NCDH search patterns are simulated separately and analyzed. The results show that the encoding time of the encoder is decreased by 55% with MLRVS algorithm using NCDD search pattern and 56% with MLRVS using NCDH search pattern compared to HM16.5 with Test Zone (TZ) search algorithm. These results are achieved with a slight increase in bit rate and negligible deterioration in output video quality.

A New Method for Identifying Kinetic Parameters of Industrial Robots

Identifying the kinetic parameters of an industrial robot is the basis for designing a controller for it. To solve the problems of the poor accuracy and easy premature convergence of common bionic algorithms for identifying the dynamic parameters of such robots, this study proposed simulated annealing with similar exponential changes based on the beetle swarm optimization (SEDSABSO) algorithm. Expressions for the dynamics of the industrial robot were first obtained through the SymPyBotics toolkit in Python, and the required trajectories of excitation were then designed to identify its dynamic parameters. Following this, the search pattern of the global optimal solution for the beetle swarm optimization algorithm was improved in the context of solving for these parameters. The global convergence of the algorithm was improved by improving the iterative form of the number N of skinks in it by considering random perturbations and the simulated annealing algorithm, whereas its accuracy of convergence was improved through the class exponential change model. The improved beetle swarm optimization algorithm was used to identify the kinetic parameters of the Zhichang Kawasaki RS010N industrial robot. The results of experiments showed that the proposed algorithm was fast and highly accurate in identifying the kinetic parameters of the industrial robot.

Macroscopic limit of a kinetic model describing the switch in T cell migration modes via binary interactions

Experimental results on the immune response to cancer indicate that activation of cytotoxic T lymphocytes (CTLs) through interactions with dendritic cells (DCs) can trigger a change in CTL migration patterns. In particular, while CTLs in the pre-activation state move in a non-local search pattern, the search pattern of activated CTLs is more localised. In this paper, we develop a kinetic model for such a switch in CTL migration modes. The model is formulated as a coupled system of balance equations for the one-particle distribution functions of CTLs in the pre-activation state, activated CTLs and DCs. CTL activation is modelled via binary interactions between CTLs in the pre-activation state and DCs. Moreover, cell motion is represented as a velocity-jump process, with the running time of CTLs in the pre-activation state following a long-tailed distribution, which is consistent with a Lévy walk, and the running time of activated CTLs following a Poisson distribution, which corresponds to Brownian motion. We formally show that the macroscopic limit of the model comprises a coupled system of balance equations for the cell densities, whereby activated CTL movement is described via a classical diffusion term, whilst a fractional diffusion term describes the movement of CTLs in the pre-activation state. The modelling approach presented here and its possible generalisations are expected to find applications in the study of the immune response to cancer and in other biological contexts in which switch from non-local to localised migration patterns occurs.

A Cooperative Coevolution Wingsuit Flying Search Algorithm with Spherical Evolution

The algorithm wingsuit flying search (WFS) mimics the procedure of landing the vehicle. The outstanding feature of WFS is parameterless and of rapid convergence. However, WFS also has its shortcomings, sometimes it will inevitably be trapped into local optima, thereby yield inferior solutions owing to its relatively weak exploration ability. Spherical evolution (SE) adopts a novel spherical search pattern that takes aim at splendid search ability. Cooperative coevolution is a useful parallel structure for reconciling algorithmic performance. Considering the complementary strengths of both algorithms, we herein propose a new hybrid algorithm that is comprised of SE and WFS using cooperative coevolution. During the search for optimal solutions in WFS, we replaced the original search matrix and introduced the spherical mechanism of SE, in parallel with coevolution to enhance the competitiveness of the population. The two distinct search dynamics were combined in a parallel and coevolutionary way, thereby getting a good search performance. The resultant hybrid algorithm, CCWFSSE, was tested on the CEC2017 benchmark set and 22 CEC 2011 real-world problems. The experimental data obtained can verify that CCWFSSE outperforms other algorithms in aspects of effectiveness and robustness.

Novel measures of Morris water maze performance that use vector field maps to assess accuracy, uncertainty, and intention of navigational searches

Most commonly used behavioural measures for testing learning and memory in the Morris water maze (MWM) involve comparisons of an animal s residence time in different quadrants of the pool. Such measures are limited in their ability to test different aspects of the animal s performance. Here, we describe novel measures of performance in the MWM that use vector fields to capture the motion of mice as well as their search pattern in the maze. Using these vector fields, we develop quantitative measures of performance that are intuitive and more sensitive than classical measures. First, we describe search patterns in terms of vector field properties and use these properties to define three metrics of spatial memory namely Spatial Accuracy, Uncertainty and, Intensity of Search. We demonstrate the usefulness of these measures using four different data sets including comparisons between different strains of mice, an analysis of two mouse models of Noonan syndrome (Ptpn11 D61G and Ptpn11 N308D/+), and a study of goal reversal training. Importantly, besides highlighting novel aspects of performance in this widely used spatial task, our measures were able to uncover previously undetected differences, including in an animal model of Noonan syndrome, which we rescued with the mitogen activated protein kinase kinase (MEK) inhibitor SL327. Thus, our results show that our approach breaks down performance in the Morris water maze into sensitive measurable independent components that highlight differences in spatial learning and memory in the MWM that were undetected by conventional measures.