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The objective of this research was to evaluate the effect of sperm on chromatin stability and its relationship with the membrane integrity structural – physiological and the rate of fertilization of female sheep. Ejaculates of sperm (2 × 109 sperm·mL-1) with 70% of motility were collected using an artificial vagina (n=5, 2 years old. For this, each ram was served with fifteen female sheep (n=75), generating thus five different Groups (A, B, C, D, and E). A control Group also was considered. Sperm nuclear chromatin stability (NCS) was evaluated using the Borate Buffer (BB), Sodium Dodecyl Sulfate (SDS), and the mixture of Ethylenediaminetetraacetic acid (EDTA) + SDS. The fertilization rate was evaluated after 16-18 hours post sperm injection. Sperm concentration showed a significant difference (P0.05). A high correlation (r2=0.52) was observed between morphology and motility, and the fertilization rate was 74.6% (n=56). It was concluded in general that techniques to evaluate nuclear condensation values do have a high likelihood to give a diagnosis about the future potential of sperm populations in Junín ram.

Obtaining good performance when programming heterogeneous computing platforms poses significant challenges. We present a program transformation environment, implemented in Haskell, where architecture-agnostic scientific C code with semantic annotations is transformed into functionally equivalent code better suited for a given platform. The transformation steps are represented as rules that can be fired when certain syntactic and semantic conditions are fulfilled. These rules are not hard-wired into the rewriting engine: they are written in a C-like language and are automatically processed and incorporated into the rewriting engine. That makes it possible for end-users to add their own rules or to provide sets of rules that are adapted to certain specific domains or purposes.

The current trends in next-generation exascale systems go towards integrating a wide range of specialized (co-)processors into traditional supercomputers. Due to the efficiency of heterogeneous systems in terms of Watts and FLOPS per surface unit, opening the access of heterogeneous platforms to a wider range of users is an important problem to be tackled. However, heterogeneous platforms limit the portability of the applications and increase development complexity due to the programming skills required. Program transformation can help make programming heterogeneous systems easier by defining a step-wise transformation process that translates a given initial code into a semantically equivalent final code, but adapted to a specific platform. Program transformation systems require the definition of efficient transformation strategies to tackle the combinatorial problem that emerges due to the large set of transformations applicable at each step of the process. In this paper we propose a machine learning-based approach to learn heuristics to define program transformation strategies. Our approach proposes a novel combination of reinforcement learning and classification methods to efficiently tackle the problems inherent to this type of systems. Preliminary results demonstrate the suitability of this approach.