A SEQUENTIAL CHOICE MODEL OF RECREATION BEHAVIOR

The travel cost model is the standard model used in the recreation demand literature. This model assumes that the decision on the number of trips to a particular site in a given period (a season, for example) is determined at the beginning of the period. For certain types of recreation activity, this decision may be more appropriately modeled as a sequential process, in which the decision of whether or not to take each additional trip is made after previous trips have occurred. This decision is dependent on the realization of random variables on previous trips as well as travel costs. A model is developed in which the choice of a discrete number of sequentially chosen trips to a given site is specified as function of site-specific variables and variables realized on previous trips. This models advantage over the traditional travel cost model is that it specifies discrete, nonnegative integer values for the number of trips and allows intraseasonal effects to determine the probability of taking each additional trip.Resource /Energy Economics and Policy,

High molecular weight adiponectin levels are inversely associated with adiposity in pediatric brain tumor survivors.

While children with brain tumors are surviving at record rates, survivors are at risk of cardiovascular disease and type 2 diabetes mellitus; these conditions may be driven by excess body fat. Adiponectin in an adipokine that is inversely associated with the fat mass, and has been linked to cardiometabolic risk stratification in the general population. However, adiponectin\u27s profile and determinants in SCBT have not been established. We tested the hypothesis that high molecular weight (HMW) adiponectin levels, the more biologically active form of adiponectin, were associated with adiposity in SCBT similarly to non-cancer controls. Seventy-four SCBT (n = 32 female) and 126 controls (n = 59 female) who were 5-17 years old were included. Partial correlations and multivariable regression analyses assessed the relationship between HMW adiponectin and adiposity. HMW adiponectin was inversely associated with total and central adiposity (FM%: β - 0.21, 95% CI - 0.15, - 0.08; p value \u3c 0.0001; WHR: β - 0.14, 95% CI - 0.02, - 0.01; p value \u3c 0.0001 ;WHtR: β - 0.21, 95% CI - 0.05, - 0.03; p value \u3c 0.0001). In conclusion, HMW adiponectin is inversely correlated with adiposity in SCBT. Adiponectin may serve as a biomarker of cardiometabolic risk and response to interventions to prevent and manage obesity and its comorbidities in SCBT

Circulating leptin levels are associated with adiposity in survivors of childhood brain tumors.

Survivors of Childhood Brain Tumors (SCBT) are at a higher risk of developing cardiovascular disease and type 2 diabetes compared to the general population. Adiposity is an important risk factor for the development of these outcomes, and identifying biomarkers of adiposity may help the stratification of survivors based on their cardiovascular risk or allow for early screening and interventions to improve cardiometabolic outcomes. Leptin is an adipokine that positively correlates with the adipose mass in the general population and is a predictor of adverse cardiometabolic outcomes, yet its association with adiposity in SCBT has not been studied. The aim of this study was to determine if leptin levels are associated with the adipose mass in SCBT, and to define its predictors. This cross-sectional study included 74 SCBT (n = 32 females) with 126 non-cancer controls (n = 59 females). Total adiposity was measured using Bioelectrical Impendence Analysis (BIA) and central adiposity was measured using waist-to-hip ratio (WHR) and waist-to-height ratio (WHtR). We used multivariable linear regression analysis to determine if leptin predicts adiposity in SCBT and adjusted for age, sex, puberty, and cancer status. Leptin correlated strongly with total (p \u3c 0.001) and central (WHR p = 0.001; WHtR p \u3c 0.001) adiposity in SCBT and non-cancer controls. In conclusion, leptin is a potential biomarker for adiposity in SCBT, and further investigation is needed to clarify if leptin is a predictor of future cardiometabolic risk in SCBT

Genome-wide association study of problematic opioid prescription use in 132,113 23andMe research participants of European ancestry

The growing prevalence of opioid use disorder (OUD) constitutes an urgent health crisis. Ample evidence indicates that risk for OUD is heritable. As a surrogate (or proxy) for OUD, we explored the genetic basis of using prescription opioids \u27not as prescribed\u27. We hypothesized that misuse of opiates might be a heritable risk factor for OUD. To test this hypothesis, we performed a genome-wide association study (GWAS) of problematic opioid use (POU) in 23andMe research participants of European ancestry (N = 132,113; 21% cases). We identified two genome-wide significant loci (rs3791033, an intronic variant of KDM4A; rs640561, an intergenic variant near LRRIQ3). POU showed positive genetic correlations with the two largest available GWAS of OUD and opioid dependence (

NAF-1 and mitoNEET are central to human breast cancer proliferation by maintaining mitochondrial homeostasis and promoting tumor growth

Mitochondria are emerging as important players in the transformation process of cells, maintaining the biosynthetic and energetic capacities of cancer cells and serving as one of the primary sites of apoptosis and autophagy regulation. Although several avenues of cancer therapy have focused on mitochondria, progress in developing mitochondria-targeting anticancer drugs nonetheless has been slow, owing to the limited number of known mitochondrial target proteins that link metabolism with autophagy or cell death. Recent studies have demonstrated that two members of the newly discovered family of NEET proteins, NAF-1 (CISD2) and mitoNEET (mNT; CISD1), could play such a role in cancer cells. NAF-1 was shown to be a key player in regulating autophagy, and mNT was proposed to mediate iron and reactive oxygen homeostasis in mitochondria. Here we show that the protein levels of NAF-1 and mNT are elevated in human epithelial breast cancer cells, and that suppressing the level of these proteins using shRNA results in significantly reduced cell proliferation and tumor growth, decreased mitochondrial performance, uncontrolled accumulation of iron and reactive oxygen in mitochondria, and activation of autophagy. Our findings highlight NEET proteins as promising mitochondrial targets for cancer therapy

Multi-Messenger Gravitational Wave Searches with Pulsar Timing Arrays: Application to 3C66B Using the NANOGrav 11-year Data Set

When galaxies merge, the supermassive black holes in their centers may form binaries and, during the process of merger, emit low-frequency gravitational radiation in the process. In this paper we consider the galaxy 3C66B, which was used as the target of the first multi-messenger search for gravitational waves. Due to the observed periodicities present in the photometric and astrometric data of the source of the source, it has been theorized to contain a supermassive black hole binary. Its apparent 1.05-year orbital period would place the gravitational wave emission directly in the pulsar timing band. Since the first pulsar timing array study of 3C66B, revised models of the source have been published, and timing array sensitivities and techniques have improved dramatically. With these advances, we further constrain the chirp mass of the potential supermassive black hole binary in 3C66B to less than $(1.65\pm0.02) \times 10^9~{M_\odot}$ using data from the NANOGrav 11-year data set. This upper limit provides a factor of 1.6 improvement over previous limits, and a factor of 4.3 over the first search done. Nevertheless, the most recent orbital model for the source is still consistent with our limit from pulsar timing array data. In addition, we are able to quantify the improvement made by the inclusion of source properties gleaned from electromagnetic data to `blind' pulsar timing array searches. With these methods, it is apparent that it is not necessary to obtain exact a priori knowledge of the period of a binary to gain meaningful astrophysical inferences.Comment: 14 pages, 6 figures. Accepted by Ap

Recovery of a temperate reef assemblage in a marine protected area following the exclusion of towed demersal fishing.

Marine Protected Areas MPA have been widely used over the last 2 decades to address human impacts on marine habitats within an ecosystem management context. Few studies have quantified recovery of temperate rocky reef communities following the cessation of scallop dredging or demersal trawling. This is critical information for the future management of these habitats to contribute towards conservation and fisheries targets. The Lyme Bay MPA, in south west UK, has excluded towed demersal fishing gear from 206 km(2) of sensitive reef habitat using a Statutory Instrument since July 2008. To assess benthic recovery in this MPA we used a flying video array to survey macro epi-benthos annually from 2008 to 2011. 4 treatments (the New Closure, previously voluntarily Closed Controls and Near or Far Open to fishing Controls) were sampled to test a recovery hypothesis that was defined as 'the New Closure becoming more similar to the Closed Controls and less similar to the Open Controls'. Following the cessation of towed demersal fishing, within three years positive responses were observed for species richness, total abundance, assemblage composition and seven of 13 indicator taxa. Definitive evidence of recovery was noted for species richness and three of the indicator taxa (Pentapora fascialis, Phallusia mammillata and Pecten maximus). While it is hoped that MPAs, which exclude anthropogenic disturbance, will allow functional restoration of goods and services provided by benthic communities, it is an unknown for temperate reef systems. Establishing the likely timescales for restoration is key to future marine management. We demonstrate the early stages of successful recruitment and link these to the potential wider ecosystem benefits including those to commercial fisheries

Aptamer-based multiplexed proteomic technology for biomarker discovery

Interrogation of the human proteome in a highly multiplexed and efficient manner remains a coveted and challenging goal in biology. We present a new aptamer-based proteomic technology for biomarker discovery capable of simultaneously measuring thousands of proteins from small sample volumes (15 [mu]L of serum or plasma). Our current assay allows us to measure ~800 proteins with very low limits of detection (1 pM average), 7 logs of overall dynamic range, and 5% average coefficient of variation. This technology is enabled by a new generation of aptamers that contain chemically modified nucleotides, which greatly expand the physicochemical diversity of the large randomized nucleic acid libraries from which the aptamers are selected. Proteins in complex matrices such as plasma are measured with a process that transforms a signature of protein concentrations into a corresponding DNA aptamer concentration signature, which is then quantified with a DNA microarray. In essence, our assay takes advantage of the dual nature of aptamers as both folded binding entities with defined shapes and unique sequences recognizable by specific hybridization probes. To demonstrate the utility of our proteomics biomarker discovery technology, we applied it to a clinical study of chronic kidney disease (CKD). We identified two well known CKD biomarkers as well as an additional 58 potential CKD biomarkers. These results demonstrate the potential utility of our technology to discover unique protein signatures characteristic of various disease states. More generally, we describe a versatile and powerful tool that allows large-scale comparison of proteome profiles among discrete populations. This unbiased and highly multiplexed search engine will enable the discovery of novel biomarkers in a manner that is unencumbered by our incomplete knowledge of biology, thereby helping to advance the next generation of evidence-based medicine

Context-dependent combination of sensor information in Dempster–Shafer theory for BDI

© 2016, The Author(s). There has been much interest in the belief–desire–intention (BDI) agent-based model for developing scalable intelligent systems, e.g. using the AgentSpeak framework. However, reasoning from sensor information in these large-scale systems remains a significant challenge. For example, agents may be faced with information from heterogeneous sources which is uncertain and incomplete, while the sources themselves may be unreliable or conflicting. In order to derive meaningful conclusions, it is important that such information be correctly modelled and combined. In this paper, we choose to model uncertain sensor information in Dempster–Shafer (DS) theory. Unfortunately, as in other uncertainty theories, simple combination strategies in DS theory are often too restrictive (losing valuable information) or too permissive (resulting in ignorance). For this reason, we investigate how a context-dependent strategy originally defined for possibility theory can be adapted to DS theory. In particular, we use the notion of largely partially maximal consistent subsets (LPMCSes) to characterise the context for when to use Dempster’s original rule of combination and for when to resort to an alternative. To guide this process, we identify existing measures of similarity and conflict for finding LPMCSes along with quality of information heuristics to ensure that LPMCSes are formed around high-quality information. We then propose an intelligent sensor model for integrating this information into the AgentSpeak framework which is responsible for applying evidence propagation to construct compatible information, for performing context-dependent combination and for deriving beliefs for revising an agent’s belief base. Finally, we present a power grid scenario inspired by a real-world case study to demonstrate our work

Relations between lipoprotein(a) concentrations, LPA genetic variants, and the risk of mortality in patients with established coronary heart disease: a molecular and genetic association study

Background: Lipoprotein(a) concentrations in plasma are associated with cardiovascular risk in the general population. Whether lipoprotein(a) concentrations or LPA genetic variants predict long-term mortality in patients with established coronary heart disease remains less clear. Methods: We obtained data from 3313 patients with established coronary heart disease in the Ludwigshafen Risk and Cardiovascular Health (LURIC) study. We tested associations of tertiles of lipoprotein(a) concentration in plasma and two LPA single-nucleotide polymorphisms ([SNPs] rs10455872 and rs3798220) with all-cause mortality and cardiovascular mortality by Cox regression analysis and with severity of disease by generalised linear modelling, with and without adjustment for age, sex, diabetes diagnosis, systolic blood pressure, BMI, smoking status, estimated glomerular filtration rate, LDL-cholesterol concentration, and use of lipid-lowering therapy. Results for plasma lipoprotein(a) concentrations were validated in five independent studies involving 10 195 patients with established coronary heart disease. Results for genetic associations were replicated through large-scale collaborative analysis in the GENIUS-CHD consortium, comprising 106 353 patients with established coronary heart disease and 19 332 deaths in 22 studies or cohorts. Findings: The median follow-up was 9·9 years. Increased severity of coronary heart disease was associated with lipoprotein(a) concentrations in plasma in the highest tertile (adjusted hazard radio [HR] 1·44, 95% CI 1·14–1·83) and the presence of either LPA SNP (1·88, 1·40–2·53). No associations were found in LURIC with all-cause mortality (highest tertile of lipoprotein(a) concentration in plasma 0·95, 0·81–1·11 and either LPA SNP 1·10, 0·92–1·31) or cardiovascular mortality (0·99, 0·81–1·2 and 1·13, 0·90–1·40, respectively) or in the validation studies. Interpretation: In patients with prevalent coronary heart disease, lipoprotein(a) concentrations and genetic variants showed no associations with mortality. We conclude that these variables are not useful risk factors to measure to predict progression to death after coronary heart disease is established. Funding: Seventh Framework Programme for Research and Technical Development (AtheroRemo and RiskyCAD), INTERREG IV Oberrhein Programme, Deutsche Nierenstiftung, Else-Kroener Fresenius Foundation, Deutsche Stiftung für Herzforschung, Deutsche Forschungsgemeinschaft, Saarland University, German Federal Ministry of Education and Research, Willy Robert Pitzer Foundation, and Waldburg-Zeil Clinics Isny