An Efficient Search Strategy for Aggregation and Discretization of Attributes of Bayesian Networks Using Minimum Description Length

Bayesian networks are convenient graphical expressions for high dimensional probability distributions representing complex relationships between a large number of random variables. They have been employed extensively in areas such as bioinformatics, artificial intelligence, diagnosis, and risk management. The recovery of the structure of a network from data is of prime importance for the purposes of modeling, analysis, and prediction. Most recovery algorithms in the literature assume either discrete of continuous but Gaussian data. For general continuous data, discretization is usually employed but often destroys the very structure one is out to recover. Friedman and Goldszmidt suggest an approach based on the minimum description length principle that chooses a discretization which preserves the information in the original data set, however it is one which is difficult, if not impossible, to implement for even moderately sized networks. In this paper we provide an extremely efficient search strategy which allows one to use the Friedman and Goldszmidt discretization in practice

Unsupervised Neural Hidden Markov Models

In this work, we present the first results for neuralizing an Unsupervised Hidden Markov Model. We evaluate our approach on tag in- duction. Our approach outperforms existing generative models and is competitive with the state-of-the-art though with a simpler model easily extended to include additional context.Comment: accepted at EMNLP 2016, Workshop on Structured Prediction for NLP. Oral presentatio

Spearfishing-induced behavioral changes of an unharvested species inside and outside a marine protected area.

By prohibiting fishing, marine protected areas (MPAs) provide a refuge for harvested species. Humans are often perceived as predators by prey and therefore respond fearfully to humans. Thus, fish responses to humans inside and outside of an MPA can provide insights into their perception of humans as a predatory threat. Previous studies have found differences in the distance that harvested species of fish initiate flight (flight initiation distance-FID) from humans inside and outside an MPA, but less is known about unharvested species. We focused on whether the lined bristletooth Ctenochaetus striatus, an unharvested surgeonfish, can discriminate between a snorkeler and a snorkeler with a spear gun inside and outside of a no-take MPA in Mo'orea, French Polynesia. Additionally, we incorporated starting distance (the distance between the person and prey at the start of an experimental approach), a variable that has been found to be important in assessing prey escape decisions in terrestrial species, but that has not been extensively studied in aquatic systems. Lined bristletooth FID was significantly greater in the presence of a spear gun and varied depending on if the spear gun encounter was inside or outside of the MPA. These results imply a degree of sophistication of fish antipredator behavior, generate questions as to how a nontargeted species of fish could acquire fear of humans, and demonstrate that behavioral surveys can provide insights about antipredator behavior

Structure of the saxiphilin:saxitoxin (STX) complex reveals a convergent molecular recognition strategy for paralytic toxins.

Dinoflagelates and cyanobacteria produce saxitoxin (STX), a lethal bis-guanidinium neurotoxin causing paralytic shellfish poisoning. A number of metazoans have soluble STX-binding proteins that may prevent STX intoxication. However, their STX molecular recognition mechanisms remain unknown. Here, we present structures of saxiphilin (Sxph), a bullfrog high-affinity STX-binding protein, alone and bound to STX. The structures reveal a novel high-affinity STX-binding site built from a "proto-pocket" on a transferrin scaffold that also bears thyroglobulin domain protease inhibitor repeats. Comparison of Sxph and voltage-gated sodium channel STX-binding sites reveals a convergent toxin recognition strategy comprising a largely rigid binding site where acidic side chains and a cation-π interaction engage STX. These studies reveal molecular rules for STX recognition, outline how a toxin-binding site can be built on a naïve scaffold, and open a path to developing protein sensors for environmental STX monitoring and new biologics for STX intoxication mitigation

Droplet-Microfluidic Device for the Characterization of Perfluorinated Emulsions

Microfluidics is being used throughout academia and industry today to perform large numbers of experiments with extremely small volumes of fluids. By doing this, those that study microfluidics hope to raise through-put, lower cost and limit the environmental impact of scientific research.2 Complementing the increased use of microfluidics, the use of perfluorinated emulsions in the field of droplet-based microfluidics is also experiencing large growth.3 However, many of the products available today are either proprietary and/or poorly understood. While some chemical structures are known, some of the most scientifically intriguing perfluorinated oils and surfactants do not have their chemical structures or characteristics available to the public research community. During the course of our senior design project, we isolated one particular perfluorinated oil and surfactant mixture and performed a number of tests on the stability of the emulsions over a variety of different biologically relevant variables. To achieve this, we were required to design multiple iterations of droplet-microfluidic chips to create the simplest emulsions for study. The resulting designs gave us an excellent platform to study not only our emulsion system, but many future emulsions systems in a novel manner. We proved its efficacy by using the system to show great variance in the stability and surface energy of different pH droplets in QX100 perfluorinated oil with surfactant as well as a characterizable change in stability when changing the concentration of phosphate and media constituents in the droplets. Finally, we further analyzed the emulsion with the help of Dr. Gerald Fuller’s coalescence lab in Stanford University’s chemical engineering department and observed changes in the viscoelastic nature of aqueous-perfluorinated oil interfaces over a variety of biologically relevant conditions. Though much was accomplished, there is still a need to further characterize both more aqueous conditions and more perfluorinated systems to truly shed light on the field. Additionally, more detailed viscoelastic analysis needs to be performed on these new conditions and oil-surfactant mixtures as well as a thorough pendant-drop style surface tension experiment on all experiments performed