Washington: Round 1 - State-Level Field Network Study of the Implementation of the Affordable Care Act

This report is part of a series of 21 state and regional studies examining the rollout of the ACA. The national network -- with 36 states and 61 researchers -- is led by the Rockefeller Institute of Government, the public policy research arm of the State University of New York, the Brookings Institution, and the Fels Institute of Government at the University of Pennsylvania.The state of Washington is expanding its Medicaid program and operating its own health insurance marketplace, as authorized by the Patient Protection and Affordable Care Act (ACA). The state legislature made the decision to run an insurance exchange in 2011, ahead of the June 2012 Supreme Court decision on the ACA's constitutionality, and well in advance of the 2012 presidential election. On July 1, 2013, Governor Jay Inslee signed the state's biennial budget, which authorized Medicaid expansion. Thus began the formal action signaling Washington State's intent to fully implement the ACA

Not Alone

Undergraduate Performing Arts- Theatr

The environmental gradients and plant communities of Bergen Swamp, N.Y., U.S.A.

No survey of plant communities has been performed recently in the Bergen Swamp, NY, USA, a unique strongly minerotrophic mire with active marl deposition. In summer 2004, I established an array of randomly placed plots throughout Bergen Swamp survey plant communities. The plant survey included stem counts of herbaceous plant species and shrubs within 1m square quadrats. I performed a Raup and Crick clustering analysis at two different spatial scales to group plant communities and found that there were five communities at the subplot level, and three communities at the plot level. I then used detrended correspondence analysis (DCA), an indirect gradient analysis, to infer and predict important local and landscape environmental gradients associated with identified communities. Observed differences between spatial scales are possibly a result of micro-topological differences related to hummock and hollow formation. The major environmental gradients associated with plant communities were, in order of decreasing importance, depth to water table, hydrologic activity, and pH

Post-Election Audits: Restoring Trust in Elections

With the intention of assisting legislators, election officials and the public to make sense of recent literature on post-election audits and convert it into realistic audit practices, the Brennan Center and the Samuelson Law, Technology and Public Policy Clinic at Boalt Hall School of Law (University of California Berkeley) convened a blue ribbon panel (the "Audit Panel") of statisticians, voting experts, computer scientists and several of the nation's leading election officials. Following a review of the literature and extensive consultation with the Audit Panel, the Brennan Center and the Samuelson Clinic make several practical recommendations for improving post-election audits, regardless of the audit method that a jurisdiction ultimately decides to adopt

Genome-wide Protein-chemical Interaction Prediction

The analysis of protein-chemical reactions on a large scale is critical to understanding the complex interrelated mechanisms that govern biological life at the cellular level. Chemical proteomics is a new research area aimed at genome-wide screening of such chemical-protein interactions. Traditional approaches to such screening involve in vivo or in vitro experimentation, which while becoming faster with the application of high-throughput screening technologies, remains costly and time-consuming compared to in silico methods. Early in silico methods are dependant on knowing 3D protein structures (docking) or knowing binding information for many chemicals (ligand-based approaches). Typical machine learning approaches follow a global classification approach where a single predictive model is trained for an entire data set, but such an approach is unlikely to generalize well to the protein-chemical interaction space considering its diversity and heterogeneous distribution. In response to the global approach, work on local models has recently emerged to improve generalization across the interaction space by training a series of independant models localized to each predict a single interaction. This work examines current approaches to genome-wide protein-chemical interaction prediction and explores new computational methods based on modifications to the boosting framework for ensemble learning. The methods are described and compared to several competing classification methods. Genome-wide chemical-protein interaction data sets are acquired from publicly available resources, and a series of experimental studies are performed in order to compare the the performance of each method under a variety of conditions

Group H: Expandable Brush

This is an overview of our project to design an expandable brush. This brush is designed to be versatile and able to clean a variety of vessels with narrow necks, wide bases, and complex geometries. Our prototype consisted of two parts: an outer sleeve with a deflector and a handle with a head piece to hold our bristle filaments. This design allows the user to insert the brush into a vessel and then control how far outward the bristles expand for easy cleaning

Order from disorder: do soil organic matter composition and turnover co-vary with iron phase crystallinity?

Soil organic matter (SOM) often increases with the abundance of short-range-ordered iron (SRO Fe) mineral phases at local to global scales, implying a protective role for SRO Fe. However, less is known about how Fe phase composition and crystal order relate to SOM composition and turnover, which could be linked to redox alteration of Fe phases. We tested the hypothesis that the composition and turnover of mineral-associated SOM co-varied with Fe phase crystallinity and abundance across a well-characterized catena in the Luquillo Experimental Forest, Puerto Rico, using dense fractions from 30 A and B horizon soil samples. The 13C and 15N values of dense fractions were strongly and positively correlated (R2 = 0.75), indicating microbial transformation of plant residues with lower 13C and 15N values. However, comparisons of dense fraction isotope ratios with roots and particulate matter suggested a greater contribution of plant vs. microbial biomass to dense fraction SOM in valleys than ridges. Similarly, diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) indicated that SOM functional groups varied significantly along the catena. These trends in dense fraction SOM composition, as well as Δ14C values indicative of turnover rates, were significantly related to Fe phase crystallinity and abundance quantified with selective extractions. Mössbauer spectroscopy conducted on independent bulk soil samples indicated that nanoscale ordered Fe oxyhydroxide phases (nano-goethite, ferrihydrite, and/or very-SRO Fe with high substitutions) dominated (66 – 94%) total Fe at all positions and depths, with minor additional contributions from hematite, silicate and adsorbed FeII, and ilmenite. An additional phase that could represent organic-FeIII complexes or aluminosilicate-bearing FeIII was most abundant in valley soils (17 – 26% of total Fe). Overall, dense fraction samples with increasingly disordered Fe phases were significantly associated with increasingly plant-derived and faster-cycling SOM, while samples with relatively more-crystalline Fe phases tended towards slower-cycling SOM with a greater microbial component. Our data suggest that counter to prevailing thought, increased SRO Fe phase abundance in dynamic redox environments could facilitate transient accumulation of litter derivatives while not necessarily promoting long-term C stabilization

Macroeconomic Indicator Forecasting with Deep Neural Networks

Resumen de la comunicación[EN] Economic policymaking relies upon accurate forecasts of economic conditions. Current methods for unconditional forecasting are dominated by inherently linear models that exhibit model dependence and have high data demands. We explore deep neural networks as an opportunity to improve upon forecast accurac y with limited data and while remaining agnostic as to functional form. We focus on predicting civilian unemployment using models based on four different neural network architectures. Each of these models outperforms benchmark models at short time horizons. One model, based on an Encoder Decoder architecture outperforms benchmark models at every forecast horizon (up to four quarters).Cook, T.; Smalter Hall, A. (2018). Macroeconomic Indicator Forecasting with Deep Neural Networks. En 2nd International Conference on Advanced Reserach Methods and Analytics (CARMA 2018). Editorial Universitat Politècnica de València. 261-261. https://doi.org/10.4995/CARMA2018.2018.8571OCS26126