Electoral Incentives and Firm Behavior: Evidence from U.S. Power Plant Pollution Abatement

Researchers have utilized the fact that many states have term limits (as opposed to being eligible for re-election) for governors to determine how changes in electoral incentives alter state regulatory agency behavior. This paper asks whether these impacts spill over into private sector decision-making. Using data from gubernatorial elections in the U.S., we find strong evidence that power plants spend less in water pollution abatement if the governor of the state where the plant is located is a term-limited democrat. We show that this evidence is consistent with compliance cost minimization by power plants reacting to changes in the regulatory enforcement. Finally, we show that the decrease in spending has environmental impacts as it leads to increased pollution

Drug target prediction and prioritization: using orthology to predict essentiality in parasite genomes

<p>Abstract</p> <p>Background</p> <p>New drug targets are urgently needed for parasites of socio-economic importance. Genes that are essential for parasite survival are highly desirable targets, but information on these genes is lacking, as gene knockouts or knockdowns are difficult to perform in many species of parasites. We examined the applicability of large-scale essentiality information from four model eukaryotes, <it>Caenorhabditis elegans, Drosophila melanogaster, Mus musculus </it>and <it>Saccharomyces cerevisiae</it>, to discover essential genes in each of their genomes. Parasite genes that lack orthologues in their host are desirable as selective targets, so we also examined prediction of essential genes within this subset.</p> <p>Results</p> <p>Cross-species analyses showed that the evolutionary conservation of genes and the presence of essential orthologues are each strong predictors of essentiality in eukaryotes. Absence of paralogues was also found to be a general predictor of increased relative essentiality. By combining several orthology and essentiality criteria one can select gene sets with up to a five-fold enrichment in essential genes compared with a random selection. We show how quantitative application of such criteria can be used to predict a ranked list of potential drug targets from <it>Ancylostoma caninum </it>and <it>Haemonchus contortus </it>- two blood-feeding strongylid nematodes, for which there are presently limited sequence data but no functional genomic tools.</p> <p>Conclusions</p> <p>The present study demonstrates the utility of using orthology information from multiple, diverse eukaryotes to predict essential genes. The data also emphasize the challenge of identifying essential genes among those in a parasite that are absent from its host.</p

Preclinical/subclinical rheumatoid arthritis-associated interstitial lung disease: misleading terms with potentially deleterious consequences

Interstitial lung disease (ILD) is a leading cause of mortality in patients with rheumatic diseases, including rheumatoid arthritis. The 5-year mortality rate is twice as high in patients with rheumatoid arthritis-associated ILD than in patients with rheumatoid arthritis without ILD. Moreover, a report showed that mortality rates in patients with disease codes for rheumatoid arthritis-associated ILD remained unchanged from 2005–18, even though the overall rheumatoid arthritis mortality rate declined during this time period. Despite the evidence that ILD contributes to premature death in rheumatoid arthritis, screening for ILD in patients with rheumatoid arthritis is not routinely performed in clinical practice and numerous questions remain regarding the management of rheumatoid arthritis-associated ILD

Role of yttrium-90 selective internal radiation therapy in the treatment of liver-dominant metastatic colorectal cancer: An evidence-based expert consensus algorithm

Surgical resection of colorectal liver metastases is associated with greater survival compared with non-surgical treatment, and a meaningful possibility of cure. However, the majority of patients are not eligible for resection and may require other non-surgical interventions, such as liver-directed therapies, to be converted to surgical eligibility. Given the number of available therapies, a general framework is needed that outlines the specific roles of chemotherapy, surgery, and locoregional treatments [including selective internal radiation therapy (SIRT) with Y-90 microspheres]. Using a data-driven, modified Delphi process, an expert panel of surgical oncologists, transplant surgeons, and hepatopancreatobiliary (HPB) surgeons convened to create a comprehensive, evidence-based treatment algorithm that includes appropriate treatment options for patients stratified by their eligibility for surgical treatment. The group coined a novel, more inclusive phrase for targeted locoregional tumor treatment (a blanket term for resection, ablation, and other emerging locoregional treatments)

Estimating multivariate similarity between neuroimaging datasets with sparse canonical correlation analysis:an application to perfusion imaging

An increasing number of neuroimaging studies are based on either combining more than one data modality (inter-modal) or combining more than one measurement from the same modality (intra-modal). To date, most intra-modal studies using multivariate statistics have focused on differences between datasets, for instance relying on classifiers to differentiate between effects in the data. However, to fully characterize these effects, multivariate methods able to measure similarities between datasets are needed. One classical technique for estimating the relationship between two datasets is canonical correlation analysis (CCA). However, in the context of high-dimensional data the application of CCA is extremely challenging. A recent extension of CCA, sparse CCA (SCCA), overcomes this limitation, by regularizing the model parameters while yielding a sparse solution. In this work, we modify SCCA with the aim of facilitating its application to high-dimensional neuroimaging data and finding meaningful multivariate image-to-image correspondences in intra-modal studies. In particular, we show how the optimal subset of variables can be estimated independently and we look at the information encoded in more than one set of SCCA transformations. We illustrate our framework using Arterial Spin Labeling data to investigate multivariate similarities between the effects of two antipsychotic drugs on cerebral blood flow

One of the closest exoplanet pairs to the 3:2 Mean Motion Resonance: K2-19b \& c

The K2 mission has recently begun to discover new and diverse planetary systems. In December 2014 Campaign 1 data from the mission was released, providing high-precision photometry for ~22000 objects over an 80 day timespan. We searched these data with the aim of detecting further important new objects. Our search through two separate pipelines led to the independent discovery of K2-19b \& c, a two-planet system of Neptune sized objects (4.2 and 7.2 $R_\oplus$), orbiting a K dwarf extremely close to the 3:2 mean motion resonance. The two planets each show transits, sometimes simultaneously due to their proximity to resonance and alignment of conjunctions. We obtain further ground based photometry of the larger planet with the NITES telescope, demonstrating the presence of large transit timing variations (TTVs), and use the observed TTVs to place mass constraints on the transiting objects under the hypothesis that the objects are near but not in resonance. We then statistically validate the planets through the \texttt{PASTIS} tool, independently of the TTV analysis.Comment: 18 pages, 10 figures, accepted to A&A, updated to match published versio