Germline CRISPR/Cas9-mediated gene editing prevents vision loss in a novel mousemodel of Aniridia.

Aniridia is a rare eye disorder, which is caused by mutations in the paired box 6 (PAX6) gene and results in vision loss due to the lack of a long-term vision-saving therapy. One potential approach to treating aniridia is targeted CRISPR-based genome editing. To enable the Pax6 small eye (Sey) mouse model of aniridia, which carries the same mutation found in patients, for preclinical testing of CRISPR-based therapeutic approaches, we endogenously tagged the Sey allele, allowing for the differential detection of protein from each allele. We optimized a correction strategy in vitro then tested it in vivo in the germline of our new mouse to validate the causality of the Sey mutation. The genomic manipulations were analyzed by PCR, as well as by Sanger and next-generation sequencing. The mice were studied by slit lamp imaging, immunohistochemistry, and western blot analyses. We successfully achieved both in vitro and in vivo germline correction of the Sey mutation, with the former resulting in an average 34.8% ± 4.6% SD correction, and the latter in restoration of 3xFLAG-tagged PAX6 expression and normal eyes. Hence, in this study we have created a novel mouse model for aniridia, demonstrated that germline correction of the Sey mutation alone rescues the mutant phenotype, and developed an allele-distinguishing CRISPR-based strategy for aniridia

Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems

This work proposes two novel optimization algorithms called Salp Swarm Algorithm (SSA) and Multi-objective Salp Swarm Algorithm (MSSA) for solving optimization problems with single and multiple objectives. The main inspiration of SSA and MSSA is the swarming behaviour of salps when navigating and foraging in oceans. These two algorithms are tested on several mathematical optimization functions to observe and confirm their effective behaviours in finding the optimal solutions for optimization problems. The results on the mathematical functions show that the SSA algorithm is able to improve the initial random solutions effectively and converge towards the optimum. The results of MSSA show that this algorithm can approximate Pareto optimal solutions with high convergence and coverage. The paper also considers solving several challenging and computationally expensive engineering design problems (e.g. airfoil design and marine propeller design) using SSA and MSSA. The results of the real case studies demonstrate the merits of the algorithms proposed in solving real-world problems with difficult and unknown search spaces