When less is more: Experimental Bishop-Koop technique for reduction in the use of laboratory animals for intestinal pathophysiological studies.

The use of animals to gain knowledge and understanding of diseases needs to be reduced and refined. In the field of intestinal research, because of the complexity of the gut immune system, living models testing is mandatory. Based on the 3Rs (replacement, reduction and refinement) principles, we aimed to developed and apply the derived-intestinal surgical procedure described by Bishop and Koop (BK) in rats to refine experimental gastrointestinal procedures and reduce the number of animals used for research employing two models of intestinal inflammation: intestinal ischemia-reperfusion injury and chemical-induced colitis. Our results show the feasibility of the application of the BK technique in rodents, with good success after surgical procedure in both small and large intestine (100% survival, clinical recovery and weight regain). A considerable reduction in the use of the number of rats in both intestinal inflammation models (80% in case of intestinal ischemia-reperfusion damage and 66.6% in chemical-induced colitis in our experimental design) was achieved. Compared with conventional experimental models described by various research groups, we report excellent reproducibility of intestinal damage and functionality, survival rate and clinical status of the animals when BK is applied.

Optimizing parameter sensitivity analysis of large-scale microscopy image analysis workflows with multilevel computation reuse.

Parameter sensitivity analysis (SA) is an effective tool to gain knowledge about complex analysis applications and assess the variability in their analysis results. However, it is an expensive process as it requires the execution of the target application multiple times with a large number of different input parameter values. In this work, we propose optimizations to reduce the overall computation cost of SA in the context of analysis applications that segment high-resolution slide tissue images, ie, images with resolutions of 100k × 100k pixels. Two cost-cutting techniques are combined to efficiently execute SA: use of distributed hybrid systems for parallel execution and computation reuse at multiple levels of an analysis pipeline to reduce the amount of computation. These techniques were evaluated using a cancer image analysis workflow on a hybrid cluster with 256 nodes, each with an Intel Phi and a dual socket CPU. Our parallel execution method attained an efficiency of over 90% on 256 nodes. The hybrid execution on the CPU and Intel Phi improved the performance by 2×. Multilevel computation reuse led to performance gains of over 2.9×.