Scarcely understood defects lead to asthenozoospermia, which results in poor fertility outcomes. Incomplete knowledge of these defects hinders the development of new therapies and reliance on interventional therapies, such as in vitro fertilization, increases. Sperm cells, being transcriptionally and translationally silent, necessitate the proteomic approach to study the sperm function. We have performed a differential proteomics analysis of human sperm and seminal plasma and identified and quantified 667 proteins in sperm and 429 proteins in seminal plasma data set, which were used for further analysis. Statistical and mathematical analysis combined with pathway analysis and self-organizing maps clustering and correlation was performed on the data set.It was found that sperm proteomic signature combined with statistical analysis as opposed to the seminal plasma proteomic signature can differentiate the normozoospermic versus the asthenozoospermic sperm samples. This is despite the results that some of the seminal plasma proteins have big fold changes among classes but they fall short of statistical significance. S-Plot of the sperm proteomic data set generated some high confidence targets, which might be implicated in sperm motility pathways. These proteins also had the area under the curve value of 0.9 or 1 in ROC curve analysis.Various pathways were either enriched in these proteomic data sets by pathway analysis or they were searched by their constituent proteins. Some of these pathways were axoneme activation and focal adhesion assembly, glycolysis, gluconeogenesis, cellular response to stress and nucleosome assembly among others. The mass spectrometric data is available via ProteomeXchange with identifier PXD004098. Molecular & Cellular

Learning when to communicate and doing that effectively is essential in multi-agent tasks. Recent works show that continuous communication allows efficient training with back-propagation in multiagent scenarios, but have been restricted to fullycooperative tasks. In this paper, we present Individualized Controlled Continuous Communication Model (IC3Net) which has better training efficiency than simple continuous communication model, and can be applied to semi-cooperative and competitive settings along with the cooperative settings. IC3Net controls continuous communication with a gating mechanism and uses individualized rewards for each agent to gain better performance and scalability while fixing credit assignment issues. Using variety of tasks including StarCraft BroodWars TM explore and combat scenarios, we show that our network yields improved performance and convergence rates than the baselines as the scale increases. Our results convey that IC3Net agents learn when to communicate based on the scenario and profitability.

No abstract

The problem of fault tolerant control is studied from the behavioral point of view. In this mathematical framework, the concept of interconnection among the variables describing the system is a key point. The problem is that the behavior we intend to control is not known. Therefore, we are interested in designing a fault accommodation scheme for an unknown behavior through an appropriate behavioral interconnection. Here we deal simply with the trajectories that are generated by the system in real time. These trajectories determine the behavior of a system in various (faulty/healthy) modes. Based on the desired interconnected behavior, only the trajectories that obey certain laws are selected. These laws, representing the desired behavior, can indeed be achieved by a regular interconnection. Thus, when the trajectories do not belong to a certain desired behavior, it is considered to be due to the occurrence of a fault in the system. The vantage point is that the fault tolerant control problem now becomes completely a model-free scheme. Moreover, no explicit fault diagnosis module is required in our approach. The proposed fault tolerance mechanism is illustrated on an aircraft during the landing phase.

No abstract