Automated phenotyping of mouse social behavior

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (p. 66-68).Inspired by the connections between social behavior and intelligence, I have developed a trainable system to phenotype mouse social behavior. This system is of immediate interest to researchers studying mouse models of social disorders such as depression or autism. Mice studies provide a controlled environment to begin exploring the questions of how to best quantify social behavior. For the purposes of evaluating this system and to encourage further research, I introduce a new video dataset annotated with five social behaviors: nose-to-nose sniffing, nose-to-head sniffing, nose-to-anogenital sniffing, crawl under / crawl over, and upright head contact. These four behaviors are of particular importance to researchers characterizing mouse social avoidance [9]. To effectively phenotype mouse social behavior, the system incorporates a novel mice tracker, and modules to represent and to classify social behavior. The mice tracker addresses the challenging computer vision problem of tracking two identical, highly deformable mice through complex occlusions. The tracker maintains an ellipse model of both mice and leverages motion cues and shape priors to maintain tracks during occlusions. Using these tracks, the classification system represents behavior with 14 spatial features characterizing relative position, relative motion, and shape. A regularized least squares (RLS) classifier, trained over representative instances of each behavior, classifies the behavior present in each frame. This system demonstrates the enormous potential for building automated systems to quantitatively study mouse social behavior.by Nicholas Edelman.M.Eng

Trainable, vision-based automated home cage behavioral phenotyping

We describe a fully trainable computer vision system enabling the automated analysis of complex mouse behaviors. Our system computes a sequence of feature descriptors for each video sequence and a classifier is used to learn a mapping from these features to behaviors of interest. We collected a very large manually annotated video database of mouse behaviors for training and testing the system. Our system performs on par with human scoring, as measured from the ground-truth manual annotations of thousands of clips of freely behaving mice. As a validation of the system, we characterized the home cage behaviors of two standard inbred and two nonstandard mouse strains. From this data, we were able to predict the strain identity of individual mice with high accuracy.California Institute of Technology. Broad Fellows Program in Brain CircuitryNational Science Council of Taiwan (TMS-094-1-A032

Phenotypic Consequences of Copy Number Variation: Insights from Smith-Magenis and Potocki-Lupski Syndrome Mouse Models

The characterization of mice with different number of copies of the same genomic segment shows that structural changes influence the phenotypic outcome independently of gene dosage

AI is a viable alternative to high throughput screening: a 318-target study

: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery

DEBUNKING THE NCAA\u27S MYTH THAT AMATEURISM CONFORMS WITH ANTITRUST LAW: A LEGAL AND STATISTICAL ANALYSIS

This article provides the first detailed study to show that paying college football players does not decrease fan interest in watching college football-substantially debunking the NCAA\u27s myth that amateurism conforms to the requirements of antitrust law. Part I of this article details the history of collegiate sports in the United States and the NCAA\u27s amateurism rules. Part II examines the origins and evolution of the NCAA\u27s procompetitive presumption defense of amateurism; a legal fiction that presumes consumer interest in amateurism justifies a quasi-antitrust exemption for the NCAA\u27s no pay rules. Part III sets the framework for our empirical study by describing how the Ninth Circuit\u27s reasoning in O\u27Bannon v. NCAA established the need for an economic investigation into the influence of amateurism on consumer demand for the NCAA\u27s most popular product, college football. Part IV describes the methods used for the empirical examination in this study and analyzes the results. Finally, Part V concludes with a discussion of the implications drawn from the results of our investigation and explains why the findings in our study disprove the presumption that the consumer demand for college football depends on preservation of regulations that limit athlete compensation