Abstract: The systems and methods described herein utilize a probabilistic modeling framework for reverse engineering an ensemble of causal models, from data and then forward simulating the ensemble of models to analyze and predict the behavior of the network. In certain embodiments, the systems and methods described herein include data-driven techniques for developing causal models for biological networks. Causal network models include computational representations of the causal relationships between independent variables such as a compound of interest and dependent variables such as measured DNA alterations, changes in mRNA, protein, and metabolites to phenotypic readouts of efficacy and toxicity.

Abstract: The systems and methods described herein utilize a probabilistic modeling framework for reverse engineering an ensemble of causal models, from data and then forward simulating the ensemble of models to analyze and predict the behavior of the network. In certain embodiments, the systems and methods described herein include data-driven techniques for developing causal models for biological networks. Causal network models include computational representations of the causal relationships between independent variables such as a compound of interest and dependent variables such as measured DNA alterations, changes in mRNA, protein, and metabolites to phenotypic readouts of efficacy and toxicity.

Abstract: Direction of material emitted from orifices is directly related to the effectiveness of spray devices to deliver the material to an intended location area. Improper emission direction results in material, such as medication, paint, or fuel, not reaching its intended target and causes cascading effects, such as improper patient dosing, compromised patient relief, waste, poor target coverage, missed target penetration, poor engine performance, and other safety/performance problems. An apparatus for inspecting operation of an orifice according to an example embodiment measures actual material emission direction relative to an expected material emission direction determined as a function of position and orientation of an orifice. The apparatus may confirm whether the emission direction is within an expected emission direction and accepted tolerance range. Through use of the apparatus, accurate screening of production products can be performed efficiently and manufacturing defects can be diagnosed and eliminated.

Abstract: The systems and methods described herein utilize a probabilistic modeling framework for reverse engineering an ensemble of causal models, from data and then forward simulating the ensemble of models to analyze and predict the behavior of the network. In certain embodiments, the systems and methods described herein include data-driven techniques for developing causal models for biological networks. Causal network models include computational representations of the causal relationships between independent variables such as a compound of interest and dependent variables such as measured DNA alterations, changes in mRNA, protein, and metabolites to phenotypic readouts of efficacy and toxicity.