Path planning in three dimensional live environment with randomly moving obstacles for viscoelastic bio-particle.

Significant capabilities of atomic force microscopy (AFM) such as operating in various environments and scales made it a useful device in different operations. According to AFM abilities and applications, in this work, the path through the live environment with fixed and moving obstacles that are distributed all over the space randomly has been provided. The optimized path has been discovered in this article based on the applications mentioned above. Since for biological applications, the tool's accuracy plays an important role in success and reliability of the operation, in this article, the cost function is defined as combination of the tool's error, the maximum applied force on the tool, and the maximum deformation of the particle to be minimized. In this regard, constraints which limit the particle's motion and speed such as critical force and time and the maximum applied force have been considered. While in living environment obstacle existence is possible, fixed and moving obstacles with random profile and distribution will be considered. Routing of viscoelastic particle considering above conditions has been performed and comparison with the previous works proved the correctness of the path. The effects of different constraints have been compared using path optimization in different situations. The time of path planning for critical force and time was about 117.657, for the maximum applied force 118.240, and for all constraints together was 120.540 s which shows that the applied force constraint has been more effective than others and increases path planning time.

OVAS: an open-source variant analysis suite with inheritance modelling.

BACKGROUND: The advent of modern high-throughput genetics continually broadens the gap between the rising volume of sequencing data, and the tools required to process them. The need to pinpoint a small subset of functionally important variants has now shifted towards identifying the critical differences between normal variants and disease-causing ones. The ever-increasing reliance on cloud-based services for sequence analysis and the non-transparent methods they utilize has prompted the need for more in-situ services that can provide a safer and more accessible environment to process patient data, especially in circumstances where continuous internet usage is limited. RESULTS: To address these issues, we herein propose our standalone Open-source Variant Analysis Sequencing (OVAS) pipeline; consisting of three key stages of processing that pertain to the separate modes of annotation, filtering, and interpretation. Core annotation performs variant-mapping to gene-isoforms at the exon/intron level, append functional data pertaining the type of variant mutation, and determine hetero/homozygosity. An extensive inheritance-modelling module in conjunction with 11 other filtering components can be used in sequence ranging from single quality control to multi-file penetrance model specifics such as X-linked recessive or mosaicism. Depending on the type of interpretation required, additional annotation is performed to identify organ specificity through gene expression and protein domains. In the course of this paper we analysed an autosomal recessive case study. OVAS made effective use of the filtering modules to recapitulate the results of the study by identifying the prescribed compound-heterozygous disease pattern from exome-capture sequence input samples. CONCLUSION: OVAS is an offline open-source modular-driven analysis environment designed to annotate and extract useful variants from Variant Call Format (VCF) files, and process them under an inheritance context through a top-down filtering schema of swappable modules, run entirely off a live bootable medium and accessed locally through a web-browser.