Evidence synthesis as the basis for decision analysis: a method of selecting the best agricultural practices for multiple ecosystem services

Agricultural management practices have impacts not only on crops and livestock, but also on soil, water, wildlife, and ecosystem services. Agricultural research provides evidence about these impacts, but it is unclear how this evidence should be used to make decisions. Two methods are widely used in decision making: evidence synthesis and decision analysis. However, a system of evidence-based decision making that integrates these two methods has not yet been established. Moreover, the standard methods of evidence synthesis have a narrow focus (e.g., the effects of one management practice), but the standard methods of decision analysis have a wide focus (e.g., the comparative effectiveness of multiple management practices). Thus, there is a mismatch between the outputs from evidence synthesis and the inputs that are needed for decision analysis. We show how evidence for a wide range of agricultural practices can be reviewed and summarized simultaneously (“subject-wide evidence synthesis”), and how this evidence can be assessed by experts and used for decision making (“multiple-criteria decision analysis”). We show how these methods could be used by The Nature Conservancy (TNC) in California to select the best management practices for multiple ecosystem services in Mediterranean-type farmland and rangeland, based on a subject-wide evidence synthesis that was published by Conservation Evidence (www.conservationevidence.com). This method of “evidence-based decision analysis” could be used at different scales, from the local scale (farmers deciding which practices to adopt) to the national or international scale (policy makers deciding which practices to support through agricultural subsidies or other payments for ecosystem services). We discuss the strengths and weaknesses of this method, and we suggest some general principles for improving evidence synthesis as the basis for multi-criteria decision analysis

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

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

Planck 2015 results. XIV. Dark energy and modified gravity

We study the implications of Planck data for models of dark energy (DE) and modified gravity (MG), beyond the cosmological constant scenario. We start with cases where the DE only directly affects the background evolution, considering Taylor expansions of the equation of state, principal component analysis and parameterizations related to the potential of a minimally coupled DE scalar field. When estimating the density of DE at early times, we significantly improve present constraints. We then move to general parameterizations of the DE or MG perturbations that encompass both effective field theories and the phenomenology of gravitational potentials in MG models. Lastly, we test a range of specific models, such as k-essence, f(R) theories and coupled DE. In addition to the latest Planck data, for our main analyses we use baryonic acoustic oscillations, type-Ia supernovae and local measurements of the Hubble constant. We further show the impact of measurements of the cosmological perturbations, such as redshift-space distortions and weak gravitational lensing. These additional probes are important tools for testing MG models and for breaking degeneracies that are still present in the combination of Planck and background data sets. All results that include only background parameterizations are in agreement with LCDM. When testing models that also change perturbations (even when the background is fixed to LCDM), some tensions appear in a few scenarios: the maximum one found is \sim 2 sigma for Planck TT+lowP when parameterizing observables related to the gravitational potentials with a chosen time dependence; the tension increases to at most 3 sigma when external data sets are included. It however disappears when including CMB lensing

Planck 2015 results. XX. Constraints on inflation

We present the implications for cosmic inflation of the Planck measurements of the cosmic microwave background (CMB) anisotropies in both temperature and polarization based on the full Planck survey. The Planck full mission temperature data and a first release of polarization data on large angular scales measure the spectral index of curvature perturbations to be n s = 0.968 ± 0.006 and tightly constrain its scale dependence to dn s /dlnk = −0.003 ± 0.007 when combined with the Planck lensing likelihood. When the high-ℓ polarization data is included, the results are consistent and uncertainties are reduced. The upper bound on the tensor-to-scalar ratio is r 0.002 <0.11 (95% CL), consistent with the B-mode polarization constraint r<0.12 (95% CL) obtained from a joint BICEP2/Keck Array and Planck analysis. These results imply that V(ϕ)∝ϕ 2 and natural inflation are now disfavoured compared to models predicting a smaller tensor-to-scalar ratio, such as R 2 inflation. Three independent methods reconstructing the primordial power spectrum are investigated. The Planck data are consistent with adiabatic primordial perturbations. We investigate inflationary models producing an anisotropic modulation of the primordial curvature power spectrum as well as generalized models of inflation not governed by a scalar field with a canonical kinetic term. The 2015 results are consistent with the 2013 analysis based on the nominal mission data

Методология синтеза архитектуры программно-технического комплекса автоматизированной системы мониторинга обстановки

Предложен подход к проектированию архитектуры программно-технического комплекса автоматизированной системы мониторинга обстановки в реальном времени, основанный на классификации решаемых функциональных задач на основе методов кластерного анализа и выбранного множества признаков подобия. Разработанный подход позволяет из множества функций системы выделить подобные (по определенным признакам) и объединить их в архитектурные компоненты (унифицированные функциональные модули).Запропоновано підхід до проектування архітектури центру обробки інформації автоматизованої системи моніторингу середовища в реальному часі, що заснований на класифікації функціональних задач на підставі методів кластерного аналізу і обраної множини ознак схожості. Розроблений підхід дозволяє вибрати із множини функцій системи схожі (за певними ознаками) і поєднати їх в архітектурні компоненти (уніфіковані функціональні модулі).The approach to designing architecture of the information processing complex of the automated real time conditions monitoring system based on classification of functional tasks on the basis of methods of cluster analysis and the chosen set of similarity attributes is offered. The developed approach allows to allocate from a set of functions the systems similar (on certain attributes) and to unite them in architectural components (unified functional modules)

Integrative Genomic Analysis of Cholangiocarcinoma Identifies Distinct IDH -Mutant Molecular Profiles

Cholangiocarcinoma (CCA) is an aggressive malignancy of the bile ducts, with poor prognosis and limited treatment options. Here, we describe the integrated analysis of somatic mutations, RNA expression, copy number, and DNA methylation by The Cancer Genome Atlas of a set of predominantly intrahepatic CCA cases and propose a molecular classification scheme. We identified an IDH mutant-enriched subtype with distinct molecular features including low expression of chromatin modifiers, elevated expression of mitochondrial genes, and increased mitochondrial DNA copy number. Leveraging the multi-platform data, we observed that ARID1A exhibited DNA hypermethylation and decreased expression in the IDH mutant subtype. More broadly, we found that IDH mutations are associated with an expanded histological spectrum of liver tumors with molecular features that stratify with CCA. Our studies reveal insights into the molecular pathogenesis and heterogeneity of cholangiocarcinoma and provide classification information of potential therapeutic significance