Predicting how Congressional legislators will vote is important for understanding their past and future behavior. However, previous work on roll-call prediction has been limited to single session settings, thus did not consider generalization across sessions. In this paper, we show that metadata is crucial for modeling voting outcomes in new contexts, as changes between sessions lead to changes in the underlying data generation process. We show how augmenting bill text with the sponsors' ideologies in a neural network model can achieve an average of a 4% boost in accuracy over the previous stateof-the-art. 1 A session is a 2-year period of legislative business.

Modeling U.S. Congressional legislation and roll-call votes has received significant attention in previous literature. However, while legislators across 50 state governments and D.C. propose over 100,000 bills each year, and on average enact over 30% of them, state level analysis has received relatively less attention due in part to the difficulty in obtaining the necessary data. Since each state legislature is guided by their own procedures, politics and issues, however, it is difficult to qualitatively asses the factors that affect the likelihood of a legislative initiative succeeding. Herein, we present several methods for modeling the likelihood of a bill receiving floor action across all 50 states and D.C. We utilize the lexical content of over 1 million bills, along with contextual legislature and legislator derived features to build our predictive models, allowing a comparison of the factors that are important to the lawmaking process. Furthermore, we show that these signals hold complementary predictive power, together achieving an average improvement in accuracy of 18% over state specific baselines.

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Ideal point models have become a powerful tool for defining and measuring the ideology of many kinds of political actors, including legislators, judges, campaign donors, and members of the general public. We extend the application of ideal point models to the public using a novel data source: real-time reactions to statements by candidates in the 2012 presidential debates. Using these reactions as inputs to an ideal point model, we estimate individual-level ideology and evaluate the quality of the measure. Debate reaction ideal points provide a method for estimating a continuous, individual-level measure of ideology that avoids survey response biases, provides better estimates for moderates and the politically unengaged, and reflects the content of salient political discourse relevant to viewers’ attitudes and vote choices. As expected, we find that debate reaction ideal points are more extreme among respondents who strongly identify with a political party, but retain substantial within-party variation. Ideal points are also more extreme among respondents who are more politically interested. Using topical subsets of the debate statements, we find that ideal points in the sample are more moderate for foreign policy than for economic or domestic policy.

Introduction Approximately one quarter of all prescription drugs contain active ingredients of plant origins. Lichens have been historically used to treat a multitude of ailments, ranging from headaches during the Middle Ages to dressing wounds before colonial times and much of the potency of lichen relies on the secondary metabolites they produce. The purpose of this project is to analyze antiproliferative properties of the secondary metabolites of lichen Parmelia vagans. Methods Lichen extracts were prepared using different organic solvents and further fractionated either by preparative TLC or by reverse‐phase chromatography. We assessed the antiproliferative activity of the fraction using human cancer cell lines. The same number of cells of different cell lines were plated into a 96‐well plate and allowed to adhere overnight. Next day, different dilutions of the enriched fractions were added to the adhered cells and the plate was placed in CO2 incubator for 24 hours. By the end of the incubation time a picture of each well was taken to assess cell confluency and morphology followed by Resazurin assay to analyze the effect of the lichen extract on cell growth and proliferation. After Resazurin assay, the cells in each well were treated with trypsin, suspended in growth medium and counted using Bio‐Rad cell counter to evaluate a percent of live cells. Results Assessment of antiproliferative activity of the TLC and reverse‐phase chromatography fractions revealed two separate peaks of inhibitory activity against cancer cell. The fraction obtained from P.vagans extract significantly suppress (30% ‐ 80%) the growth of human lung carcinoma A549. We also found that cancer cells treated by the lichen extracts results in their morphological changes that resemble an activation of apoptosis. Discussion Our findings indicate that from P.vagans produces secondary metabolites that slow cancer cells growth and proliferation. Future experiments will be focused on isolation and identification of the compounds from P.vagans that are responsible for the inhibition of cancer cells growth. Another important aspect of the future research is to identify the cellular target(s) of the antiproliferative compound(s) and its molecular mechanism of action. I summary, the lichen’s secondary metabolites may hold a vast medicinal potential and could be a viable source of a novel of anticancer drugs.