Distinguishing between True and False Stories using various Linguistic Features

This paper analyzes what linguistic features differentiate true and false stories written in Hebrew. To do so, we have defined four feature sets containing 145 features: POS-tags, quantitative, repetition, and special expressions. The examined corpus contains stories that were composed by 48 native Hebrew speakers who were asked to tell both false and true stories. Classification experiments on all possible combinations of these four feature sets using five supervised machine learning methods have been applied. The Part of Speech (POS) set was superior to all others and has been found as a key component. The best accuracy result (89.6%) has been achieved by a combination of sixteen POS-tags and one quantitative feature.

Phenotypic screens for compounds that target the cellular pathologies underlying Parkinson's disease

Parkinson's disease (PD) is a devastating neurodegenerative disease that affects over one million patients in the US. Yet, no disease modifying drugs exist, only those that temporarily alleviate symptoms. Because of its poorly defined and highly complex disease etiology, it is essential to embrace unbiased and innovative approaches for identifying new chemical entities that target the underlying toxicities associated with PD. Traditional target-based drug discovery paradigm can suffer from a bias toward a small number of potential targets. Phenotypic screening of both genetic and pharmacological PD models offers an alternative approach to discover compounds that target the initiating causes and effectors of cellular toxicity. The relative paucity of reported phenotypic screens illustrates the intrinsic difficulty in establishing model systems that are both biologically meaningful and adaptable to high-throughput screening. Parallel advances in PD models and in vivo screening technologies will help create opportunities for identifying new therapeutic leads with unanticipated, breakthrough mechanisms of action