Water Integration for Squamscott Exeter (WISE): Preliminary Integrated Plan, Final Technical Report

This document introduces the goals, background and primary elements of an Integrated Plan for the Lower Exeter and Squamscott River in the Great Bay estuary in southern New Hampshire. This Plan will support management of point (wastewater treatment plant) and nonpoint sources in the communities of Exeter, Stratham and Newfields. The Plan also identifies and quantifies the advantages of the use of green infrastructure as a critical tool for nitrogen management and describes how collaboration between those communities could form the basis for an integrated plan. The Plan will help communities meet new wastewater and proposed stormwater permit requirements. Critical next steps are need before this Plan will fulfill the 2018 Nitrogen Control Plan requirements for Exeter and proposed draft MS4 requirements for both Stratham and Exeter. These next steps include conducting a financial capability assessment, development of an implementation schedule and development of a detailed implementation plan. The collaborative process used to develop this Plan was designed to provide decision makers at the local, state and federal levels with the knowledge they need to trust the Plan’s findings and recommendations, and to enable discussions between stakeholders to continue the collaborative process. This Plan includes the following information to guide local response to new federal permit requirements for treating and discharging stormwater and wastewater: Sources of annual pollutant load quantified by type and community; Assessment and evaluation of different treatment control strategies for each type of pollutant load; Assessment and evaluation of nutrient control strategies designed to reduce specific types of pollutants; Evaluation of a range of point source controls at the wastewater treatment facility based on regulatory requirements; Costs associated with a range of potential control strategies to achieve reduction of nitrogen and other pollutants of concern; and A preliminary implementation schedule with milestones for target load reductions using specific practices for specific land uses at points in time; Recommendations on how to implement a tracking and accounting program to document implementation; Design tools such as BMP performance curves for crediting the use of structural practices to support nitrogen accounting requirements; and Next Steps for how to complete this Plan

Data and code associated with “Supporting Adaptive Management with Ecological Forecasting: Chronic Wasting Disease in the Jackson Elk Herd”

Final_Data.zip contains several spreadsheets representing data collected by both the Wyoming Game and Fish Department and the US Fish and Wildlife Service for elk management: Jackson feedground census, 1998-2016; Harvest data, 1997-2015; Hunt area census, 1998-2016; Chronic wasting disease test results, 1998-2015. Final_Code.zip contains several Program R scripts written for data analysis and model fitting as described in the full associated article.Adaptive management has emerged as the prevailing approach for combining environmental research and management to advance science and policy. Adaptive management, as originally formulated by Carl Walters in 1986, depends on the use of Bayesian models to provide a framework to accumulate knowledge. The emergence of ecological forecasting using the Bayesian framework has provided robust tools and supports a new approach to informing adaptive management, which can be particularly useful in developing policy for managing infectious disease in wildlife. We used the potential infection of elk populations with chronic wasting disease in the Jackson Valley of Wyoming and the National Elk Refuge as a model system to show how Bayesian forecasting can support adaptive management in anticipation of management challenges. The core of our approach resembles the sex- and age-structured, discrete time models used to support management decisions on elk harvest throughout western North America. Our model differs by including stages for CWD infected and unaffected animals. We used data on population counts, sex and age classification, and CWD testing, as well as results from prior research, in a Bayesian statistical framework to predict model parameters and the number of animals in each age, sex, and disease stage over time. Initial forecasts suggested CWD may reach a mean prevalence in the population of 12%, but uncertainty in this forecast is large and we cannot rule out a mean forecasted prevalence as high as 20%. Using recruitment rates observed during the last two decades, the model predicted that a CWD prevalence of 7% in females would cause the population growth rate (l) to drop below 1, resulting in population declines even when female harvest was zero. The primary value of this ecological forecasting approach is to provide a framework to assimilate data with understanding of disease processes to enable continuous improvement in understanding the ecology of CWD and its management.Data collection was funded as part of management efforts by the Wyoming Game and Fish Department and the US Fish and Wildlife Service. Data analysis and work for publication was funded by the US Fish and Wildlife Service and the National Park Service

Sensitivity Analyses of Exoplanet Occurrence Rates from Kepler and Gaia

We infer the number of planets per star as a function of orbital period and planet size using Kepler archival data products with updated stellar properties from the Gaia Data Release 2. Using hierarchical Bayesian modeling and Hamiltonian Monte Carlo, we incorporate planet radius uncertainties into an inhomogeneous Poisson point process model. We demonstrate that this model captures the general features of the outcome of the planet formation and evolution around GK stars and provides an infrastructure to use the Kepler results to constrain analytic planet distribution models. We report an increased mean and variance in the marginal posterior distributions for the number of planets per GK star when including planet radius measurement uncertainties. We estimate the number of planets per GK star between 0.75 and 2.5 R⊕ and with orbital periods of 50–300 days to have a 68% credible interval of 0.49–0.77 and a posterior mean of 0.63. This posterior has a smaller mean and a larger variance than the occurrence rate calculated in this work and in Burke et al. for the same parameter space using the Q1−Q16 (previous Kepler planet candidate and stellar catalog). We attribute the smaller mean to many of the instrumental false positives at longer orbital periods being removed from the DR25 catalog. We find that the accuracy and precision of our hierarchical Bayesian model posterior distributions are less sensitive to the total number of planets in the sample, and more so for the characteristics of the catalog completeness and reliability and the span of the planet parameter space

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts

Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas

Although theMYConcogene has been implicated incancer, a systematic assessment of alterations ofMYC, related transcription factors, and co-regulatoryproteins, forming the proximal MYC network (PMN),across human cancers is lacking. Using computa-tional approaches, we define genomic and proteo-mic features associated with MYC and the PMNacross the 33 cancers of The Cancer Genome Atlas.Pan-cancer, 28% of all samples had at least one ofthe MYC paralogs amplified. In contrast, the MYCantagonists MGA and MNT were the most frequentlymutated or deleted members, proposing a roleas tumor suppressors.MYCalterations were mutu-ally exclusive withPIK3CA,PTEN,APC,orBRAFalterations, suggesting that MYC is a distinct onco-genic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such asimmune response and growth factor signaling; chro-matin, translation, and DNA replication/repair wereconserved pan-cancer. This analysis reveals insightsinto MYC biology and is a reference for biomarkersand therapeutics for cancers with alterations ofMYC or the PMN

The Long-Baseline Neutrino Experiment: Exploring Fundamental Symmetries of the Universe

The preponderance of matter over antimatter in the early Universe, the dynamics of the supernova bursts that produced the heavy elements necessary for life and whether protons eventually decay --- these mysteries at the forefront of particle physics and astrophysics are key to understanding the early evolution of our Universe, its current state and its eventual fate. The Long-Baseline Neutrino Experiment (LBNE) represents an extensively developed plan for a world-class experiment dedicated to addressing these questions. LBNE is conceived around three central components: (1) a new, high-intensity neutrino source generated from a megawatt-class proton accelerator at Fermi National Accelerator Laboratory, (2) a near neutrino detector just downstream of the source, and (3) a massive liquid argon time-projection chamber deployed as a far detector deep underground at the Sanford Underground Research Facility. This facility, located at the site of the former Homestake Mine in Lead, South Dakota, is approximately 1,300 km from the neutrino source at Fermilab -- a distance (baseline) that delivers optimal sensitivity to neutrino charge-parity symmetry violation and mass ordering effects. This ambitious yet cost-effective design incorporates scalability and flexibility and can accommodate a variety of upgrades and contributions. With its exceptional combination of experimental configuration, technical capabilities, and potential for transformative discoveries, LBNE promises to be a vital facility for the field of particle physics worldwide, providing physicists from around the globe with opportunities to collaborate in a twenty to thirty year program of exciting science. In this document we provide a comprehensive overview of LBNE's scientific objectives, its place in the landscape of neutrino physics worldwide, the technologies it will incorporate and the capabilities it will possess.Comment: Major update of previous version. This is the reference document for LBNE science program and current status. Chapters 1, 3, and 9 provide a comprehensive overview of LBNE's scientific objectives, its place in the landscape of neutrino physics worldwide, the technologies it will incorporate and the capabilities it will possess. 288 pages, 116 figure

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment