Regulatory Mechanisms and Information Processing in Uncertain Fisheries

We study the effects on fisherman decision processes of periodic (e.g., weekly) individual quotas. In the model, the fisherman must choose at the start of each week which of two grounds to fish on. The catch per week on each ground is a random variable and the fisherman does not know with certainty the parameters of the distribution of that variable. He does have estimates on each parameter and can improve these estimates by Bayesian updating. The choice of a fishing ground takes into account the expected catch on that ground and the expected improvement in information from fishing on that ground. Our study is concerned with the effect of weekly quotas on the joint production of information and fish. Various policy implications are discussed, and the results are compared with the policy analysis of Clark (1980) in the deterministic case. We show that the quota affects the value of Information and that if quotas are transferable, then the quota may limit its own value.Environmental Economics and Policy, Research Methods/ Statistical Methods, Resource /Energy Economics and Policy, Risk and Uncertainty,

Using size-selected gold clusters on graphene oxide films to aid cryo-transmission electron tomography alignment

A three-dimensional reconstruction of a nano-scale aqueous object can be achieved by taking a series of transmission electron micrographs tilted at different angles in vitreous ice: cryo-Transmission Electron Tomography. Presented here is a novel method of fine alignment for the tilt series. Size-selected gold clusters of ~2.7 nm (Au(561 ± 14)), ~3.2 nm (Au(923 ± 22)), and ~4.3 nm (Au(2057 ± 45)) in diameter were deposited onto separate graphene oxide films overlaying holes on amorphous carbon grids. After plunge freezing and subsequent transfer to cryo-Transmission Electron Tomography, the resulting tomograms have excellent (de-)focus and alignment properties during automatic acquisition. Fine alignment is accurate when the evenly distributed 3.2 nm gold particles are used as fiducial markers, demonstrated with a reconstruction of a tobacco mosaic virus. Using a graphene oxide film means the fiducial markers are not interfering with the ice bound sample and that automated collection is consistent. The use of pre-deposited size-selected clusters means there is no aggregation and a user defined concentration. The size-selected clusters are mono-dispersed and can be produced in a wide size range including 2–5 nm in diameter. The use of size-selected clusters on a graphene oxide films represents a significant technical advance for 3D cryo-electron microscopy

Object-Based Image Classification of Summer Crop with Machine Learning Methods

The strategic management of agricultural lands involves crop field monitoring each year. Crop discrimination via remote sensing is a complex task, especially if different crops have a similar spectral response and cropping pattern. In such cases, crop identification could be improved by combining object-based image analysis and advanced machine learning methods. In this investigation, we evaluated the C4.5 decision tree, logistic regression (LR), support vector machine (SVM) and multilayer perceptron (MLP) neural network methods, both as single classifiers and combined in a hierarchical classification, for the mapping of nine major summer crops (both woody and herbaceous) from ASTER satellite images captured in two different dates. Each method was built with different combinations of spectral and textural features obtained after the segmentation of the remote images in an object-based framework. As single classifiers, MLP and SVM obtained maximum overall accuracy of 88%, slightly higher than LR (86%) and notably higher than C4.5 (79%). The SVM+SVM classifier (best method) improved these results to 89%. In most cases, the hierarchical classifiers considerably increased the accuracy of the most poorly classified class (minimum sensitivity). The SVM+SVM method offered a significant improvement in classification accuracy for all of the studied crops compared to the conventional decision tree classifier, ranging between 4% for safflower and 29% for corn, which suggests the application of object-based image analysis and advanced machine learning methods in complex crop classification tasks.This research was partly financed by the TIN2011-22794 project of the Spanish Ministerial Commission of Science and Technology (MICYT), FEDER funds, the P2011-TIC-7508 project of the “Junta de Andalucía” (Spain) and the Kearney Foundation of Soil Science (USA). The research of Peña was co-financed by the Fulbright-MEC postdoctoral program, financed by the Spanish Ministry for Science and Innovation, and by the JAEDoc Program, supported by CSIC and FEDER funds. ASTER data were available to us through a NASA EOS scientific investigator affiliation.We acknowledge support by the CSIC Open Access Publication Initiative through its Unit of Information Resources for Research (URICI).Peer Reviewe

Object-Based Image Classification of Summer Crops with Machine Learning Methods

The strategic management of agricultural lands involves crop field monitoring each year. Crop discrimination via remote sensing is a complex task, especially if different crops have a similar spectral response and cropping pattern. In such cases, crop identification could be improved by combining object-based image analysis and advanced machine learning methods. In this investigation, we evaluated the C4.5 decision tree, logistic regression (LR), support vector machine (SVM) and multilayer perceptron (MLP) neural network methods, both as single classifiers and combined in a hierarchical classification, for the mapping of nine major summer crops (both woody and herbaceous) from ASTER satellite images captured in two different dates. Each method was built with different combinations of spectral and textural features obtained after the segmentation of the remote images in an object-based framework. As single classifiers, MLP and SVM obtained maximum overall accuracy of 88%, slightly higher than LR (86%) and notably higher than C4.5 (79%). The SVM+SVM classifier (best method) improved these results to 89%. In most cases, the hierarchical classifiers considerably increased the accuracy of the most poorly classified class (minimum sensitivity). The SVM+SVM method offered a significant improvement in classification accuracy for all of the studied crops compared to the conventional decision tree classifier, ranging between 4% for safflower and 29% for corn, which suggests the application of object-based image analysis and advanced machine learning methods in complex crop classification task

Chromium inhibition and size-selected Au nanocluster catalysis for the solution growth of low-density ZnO nanowires

he wet chemical synthesis of nanostructures has many crucial advantages over high-temperature methods, including simplicity, low-cost, and deposition on almost arbitrary substrates. Nevertheless, the density-controlled solution growth of nanowires still remains a challenge, especially at the low densities (e.g. 1 to 10 nanowires/100\u2009\u3bcm2) required, as an example, for intracellular analyses. Here, we demonstrate the solution-growth of ZnO nanowires using a thin chromium film as a nucleation inhibitor and Au size-selected nanoclusters (SSNCs) as catalytic particles for which the density and, in contrast with previous reports, size can be accurately controlled. Our results also provide evidence that the enhanced ZnO hetero-nucleation is dominated by Au SSNCs catalysis rather than by layer adaptation. The proposed approach only uses low temperatures ( 6470\u2009\ub0C) and is therefore suitable for any substrate, including printed circuit boards (PCBs) and the plastic substrates which are routinely used for cell cultures. As a proof-of-concept we report the density-controlled synthesis of ZnO nanowires on flexible PCBs, thus opening the way to assembling compact intracellular-analysis systems, including nanowires, electronics, and microfluidics, on a single substrate

Detection of a glitch in the pulsar J1709-4429

We report the detection of a glitch event in the pulsar J1709$-$4429 (also known as B1706$-$44) during regular monitoring observations with the Molonglo Observatory Synthesis Telescope (UTMOST). The glitch was found during timing operations, in which we regularly observe over 400 pulsars with up to daily cadence, while commensally searching for Rotating Radio Transients, pulsars, and FRBs. With a fractional size of $\Delta\nu/\nu \approx 52.4 \times10^{-9}$, the glitch reported here is by far the smallest known for this pulsar, attesting to the efficacy of glitch searches with high cadence using UTMOST.Comment: 3 pages, 1 figur

Genome-Wide Association Study and Gene Expression Analysis Identifies CD84 as a Predictor of Response to Etanercept Therapy in Rheumatoid Arthritis

Anti-tumor necrosis factor alpha (anti-TNF) biologic therapy is a widely used treatment for rheumatoid arthritis (RA). It is unknown why some RA patients fail to respond adequately to anti-TNF therapy, which limits the development of clinical biomarkers to predict response or new drugs to target refractory cases. To understand the biological basis of response to anti-TNF therapy, we conducted a genome-wide association study (GWAS) meta-analysis of more than 2 million common variants in 2,706 RA patients from 13 different collections. Patients were treated with one of three anti-TNF medications: etanercept (n = 733), infliximab (n = 894), or adalimumab (n = 1,071). We identified a SNP (rs6427528) at the 1q23 locus that was associated with change in disease activity score (ΔDAS) in the etanercept subset of patients (P = 8×10-8), but not in the infliximab or adalimumab subsets (P>0.05). The SNP is predicted to disrupt transcription factor binding site motifs in the 3′ UTR of an immune-related gene, CD84, and the allele associated with better response to etanercept was associated with higher CD84 gene expression in peripheral blood mononuclear cells (P = 1×10-11 in 228 non-RA patients and P = 0.004 in 132 RA patients). Consistent with the genetic findings, higher CD84 gene expression correlated with lower cross-sectional DAS (P = 0.02, n = 210) and showed a non-significant trend for better ΔDAS in a subset of RA patients with gene expression data (n = 31, etanercept-treated). A small, multi-ethnic replication showed a non-significant trend towards an association among etanercept-treated RA patients of Portuguese ancestry (n = 139, P = 0.4), but no association among patients of Japanese ancestry (n = 151, P = 0.8). Our study demonstrates that an allele associated with response to etanercept therapy is also associated with CD84 gene expression, and further that CD84 expression correlates with disease activity. These findings support a model in which CD84 genotypes and/or expression may serve as a useful biomarker for response to etanercept treatment in RA patients of European ancestry. © 2013 Cui et al

Lung cancer stage-shift following a symptom awareness campaign

Background: Lung cancer outcomes in the UK are worse than in many other developed nations. Symptom awareness campaigns aim to diagnose patients at an earlier stage to improve cancer outcomes. Methods: An early diagnosis campaign for lung cancer commenced in Leeds, UK in 2011 comprising public and primary-care facing components. Rates of community referral for chest X-ray and lung cancer stage (TNM seventh edition) at presentation were collected from 2008 to 2015. Linear trends were assessed by χ2 test for trend in proportions. Headline figures are presented for the 3 years pre-campaign (2008–2010) and the three most recent years for which data are available during the campaign (2013–2015). Findings: Community-ordered chest X-ray rates per year increased from 18 909 in 2008–2010 to 34 194 in 2013–2015 (80.8% increase). A significant stage shift towards earlier stage lung cancer was seen (χ2(1)=32.2, p<0.0001). There was an 8.8 percentage point increase in the proportion of patients diagnosed with stage I/II lung cancer (26.5% pre-campaign vs 35.3% during campaign) and a 9.3% reduction in the absolute number of patients diagnosed with stage III/IV disease (1254 pre-campaign vs 1137 during campaign). Interpretation: This is the largest described lung cancer stage-shift in association with a symptom awareness campaign. A causal link between the campaign and stage-shift cannot be proven but appears plausible. Limitations of the analysis include a lack of contemporary control population

Work and Welfare in the American States: Analyzing the Effects of the JOBS Program

This research seeks to determine whether the Job Opportunities and Basic Skills GOBS) program (established under the 1988 Family Support Act) was successful in reducing the number of welfare recipients among U.S. states for the period 1984 to 1996. Within the context of two theoretical perspectives-developmental and rational choice-we assess the impact of JOBS on AFDC participation rates using a pooled time-series design. At best, JOBS had a minimal effect. We estimate that states with higher proportions of their AFDC populations enrolled in JOBS programs had only slightly lower rates of participation in AFDC. Other forces were far more influential in reducing welfare participation. In particular, states with higher per capita income, lower female unemployment rates, lower poverty rates, and higher wages for low-paying jobs had the lowest welfare recipiency The AFDC participation rates of neighboring states had a significant effect, as well. The analysis showed that more generous AFDC benefits exerted strong upward pressure on a state's welfare rolls.Yeshttps://us.sagepub.com/en-us/nam/manuscript-submission-guideline