Setting an open-access computer assisted sperm analysis (CASA) may benefit the evaluation of motility in mammalian sperm, especially when economic constraints do not allow the use of a commercial system. There have been successful attempts to develop such a device in Zebra fish sperm and the system has been used in very few studies on mammalian spermatozoa. Against this background, the present study aimed at developing an open-access CASA system for mammalian sperm using the horse as a model and based upon the Image J software previously established for Zebra fish sperm. Along with determining the sperm progressive motility and other kinetic parameters (such as amplitude of lateral head displacement), the "results" window was adjusted to simplify subsequent statistical analyses. The path window was enriched with colored sperm trajectories on the basis of the subpopulation they belong to and a number that allowed the sperm track to be associated to the sperm motility data shown in the "results" window. Data obtained from the novel plugin (named as CASA_bgm) were compared with those of the commercial CASA Hamilton-Thorn IVOS Vers.12, through Bland Altman's plots. While the percentage of total and progressive motile sperm, VCL, VAP, VSL, LIN and STR and ALH were in agreement with those obtained with the commercial system, BCF significantly differed between the two systems probably due to their settings. Interestingly, a positive and significant correlation between the percentages of total motile sperm evaluated through CASA_bgm and those showing high mitochondrial membrane potential evaluated by JC-1 staining was found. In conclusion, CASA_bgm ImageJ plugin could be useful and reliable for stallion sperm motility analysis and it is our aim to apply this system to other mammalian species.

Abstract. One of the major tasks of structural engineering design optimization is the handling of uncertainties (such as variations in material properties, loading conditions, unknown environmental conditions or even uncertainties in modeling assumptions), which affect system performance in terms of robustness and reliability (or, in other words, the ability to respond to input variations with minimal alteration, loss of functionality or damage). This task is usually tackled with Optimization Under Uncertainty (OUU) methods[1], like robust design optimization and reliability-based design optimization. In most cases, the optimization has to deal with multi-objective problems (such as maximizing the performance while minimizing costs, system response variations, etc). These problems do not have a unique solution, but a set of tradeoff optimal solutions (the so-called Pareto front). The action of a decision maker (DM) is necessary for choosing the final optimal design according to some (pre-defined) preferences or criteria. Multi-Criteria Decision Making (MCDM) techniques [2] have been developed over the past years to try to make these choices objective and rational. In most MCDM methods, the preferences are usually taken into account during some a-posteriori analyses of the optimization outcomes.Here we address both OUU and MCDM problems with an approach that integrates directly the action of the DM with the optimization process. The DM is asked to express their preferences (based on their previous experience) to drive the optimization towards the most preferred regions of the Pareto front. This can lead to a more efficient exploration of specific regions of the Pareto front and reduce the computational cost of finding desirable solutions. Interactive MCDM approaches have been recently given more attention in the multi-objective optimization community [3,4,5]. A validation of this approach on simple test-cases is shown as well as its application to the design of a simple building structure under uncertainties with seismic hazard and snow loads.