Objectiveness in the Market for Third-Party Certification: Does market structure matter?

The globalization of trade in high quality foods is stimulating the development of international food standards and certification systems. Third-party certification as evolved as a means of ensuring that product information and signals on quality and safety attributes are sound and reliable. Certification can only provide credible market signals if it operates objectively and independently. This paper investigates the potential trade-off between certifiers objectivity and the level of competition in the rapidly expanding market for third-party certification of quality foods. Based on a theoretical supply chain framework a nested panel analysis is applied to a set of accredited certifiers for the EurepGAP fruits and vegetables standard. Our results indicate that increasing economies of scale and market share in certification do matter.Third-party certification, objectiveness, market structure, nested panel analysis, EurepGAP, Marketing,

The Effects of ITQ Management on Fishermen’s Welfare When the Processing Sector is Imperfectly Competitive

In this paper we use a general model of imperfect competition to predict welfare changes within an open-access fishery transitioning to individual transferable quota (ITQ) management. Although related research has explored the effects of market power in the harvesting sector on ITQ performance, none have considered the implications of an imperfectly competitive processing sector. This study addresses this question specifically in the context of the Atlantic herring fishery, although its implications are relevant to all fisheries with similar industry structure. Our results show that ITQs could have a negative impact on fishermen’s welfare when processors have market power and the cap on aggregate harvest is binding or becomes binding with the implementation of ITQs.ITQ, imperfect competition, welfare analysis, fisheries

A Multi-Factorial Risk Prioritization Framework for Food-Borne Pathogens

To lower the incidence of human food-borne disease, experts and stakeholders have urged the development of a science- and risk-based management system in which food-borne hazards are analyzed and prioritized. A literature review shows that most approaches to risk prioritization developed to date are based on measures of health outcomes and do not systematically account for other factors that may be important to decision making. The Multi-Factorial Risk Prioritization Framework developed here considers four factors that may be important to risk managers: public health, consumer risk perceptions and acceptance, market-level impacts, and social sensitivity. The framework is based on the systematic organization and analysis of data on these multiple factors. The basic building block of the information structure is a three-dimensional cube based on pathogen-food-factor relationships. Each cell of the cube has an information card associated with it and data from the cube can be aggregated along different dimensions. The framework is operationalized in three stages, with each stage adding another dimension to decision-making capacity. The first stage is the information cards themselves that provide systematic information that is not pre-processed or aggregated across factors. The second stage maps the information on the various information cards into cobweb diagrams that create a graphical profile of, for example, a food-pathogen combination with respect to each of the four risk prioritization factors. The third stage is formal multi-criteria decision analysis in which decision makers place explicit values on different criteria in order to develop risk priorities. The process outlined above produces a ‘List A’ of priority food-pathogen combinations according to some aggregate of the four risk prioritization factors. This list is further vetted to produce ‘List B’, which brings in feasibility analysis by ranking those combinations where practical actions that have a significant impact are feasible. Food-pathogen combinations where not enough is known to identify any or few feasible interventions are included in ‘List C’. ‘List C’ highlights areas with significant uncertainty where further research may be needed to enhance the precision of the risk prioritization process. The separation of feasibility and uncertainty issues through the use of ‘Lists A, B, and C’ allows risk managers to focus separately on distinct dimensions of the overall prioritization. The Multi-Factorial Risk Prioritization Framework provides a flexible instrument that compares and contrasts risks along four dimensions. Use of the framework is an iterative process. It can be used to establish priorities across pathogens for a particular food, across foods for a particular pathogen and/or across specific food-pathogen combinations. This report provides a comprehensive conceptual paper that forms the basis for a wider process of consultation and for case studies applying the framework.risk analysis, risk prioritization, food-borne pathogens, benefits and costs

Realtime implementation of a particle filter with integrated voice activity detector for acoustic speaker tracking

Abstract-In noisy and reverberant environments, the problem of acoustic source localisation and tracking (ASLT) using an array of microphones presents a number of challenging difficulties. One of the main issues when considering real-world situations involving human speakers is the temporally discontinuous nature of speech signals: the presence of silence gaps in the speech can easily misguide the tracking algorithm, even in practical environments with low to moderate noise and reverberation levels. This work focuses on a realtime implementation of the ASLT algorithm proposed in [1], which circumvents this problem by integrating measurements from a voice activity detector (VAD) within the tracking algorithm framework. The algorithm is here optimized for low computational complexity, and is implemented on a PC based real-time system. The resulting computational load is calculated and is presented along with real measurements of the true execution speed for the considered algorithm implementation. The results show that the algorithm is suitable for implementation in currently existing low-power embedded systems

IL-7 receptor α expressing B cells act proinflammatory in collagen-induced arthritis and are inhibited by sympathetic neurotransmitters

Objectives: The sympathetic nervous system (SNS) as well as the interleukin (IL)-7/IL-7 receptor (IL-7R) system play a role in the pathogenesis of arthritis. However, the target cells and mechanisms involved are not fully resolved. The goal of this study was to determine if B cells are influenced by IL-7 and to investigate the possible interplay between the SNS and the IL-7/IL-7R system on B cells in arthritis. Methods: Collagen type II-induced arthritis (CIA) in DBA1 mice. ELISA to determine specific anti-CII antibodies. Fluorescence activated cell sorting (FACS) analysis to determine IL-7R+ cells and intracellular phosphorylated signal transducer and activator of transcription 5 (pSTAT5). Immunohistochemistry to show IL-7R+ B cells in rheumatoid arthritis (RA) and osteoarthritis (OA) synovial tissue. Results: IL-7 stimulated IL-7R+ mature B cells act proinflammatory (increased clinical score, increased anticollagen type II antibodies) after cell transfer in CIA. The sympathetic neurotransmitter norepinephrine abrogates this effect. Expression of IL-7Rα is increased when B cells are activated (anti-CD40 or lipopolysaccharide) in vitro and stimulating the IL-7R induces intracellular accumulation of pSTAT5. α- And β-adrenergic agonists show no influence on expression levels of IL-7R on activated B cells; however, intracellular IL-7R downstream signalling is abrogated via the β2-adreonceptor (β2AR) agonist terbutaline. IL-7R and β2AR are also expressed on B cells in synovial tissue from RA and OA patients. Conclusions: These data indicate that IL7R+ B cells have a proinflammatory role in arthritis which can be inhibited by the sympathetic neurotransmitter norepinephrine via inhibition of IL-7R signalling

Agents intervening against delirium in the intensive care unit trial-Protocol for a secondary Bayesian analysis

Background Delirium is highly prevalent in the intensive care unit (ICU) and is associated with high morbidity and mortality. The antipsychotic haloperidol is the most frequently used agent to treat delirium although this is not supported by solid evidence. The agents intervening against delirium in the intensive care unit (AID-ICU) trial investigates the effects of haloperidol versus placebo for the treatment of delirium in adult ICU patients. Methods This protocol describes the secondary, pre-planned Bayesian analyses of the primary and secondary outcomes up to day 90 of the AID-ICU trial. We will use Bayesian linear regression models for all count outcomes and Bayesian logistic regression models for all dichotomous outcomes. We will adjust for stratification variables (site and delirium subtype) and use weakly informative priors supplemented with sensitivity analyses using sceptical priors. We will present results as absolute differences (mean differences and risk differences) and relative differences (ratios of means and relative risks). Posteriors will be summarised using median values as point estimates and percentile-based 95% credibility intervals. Probabilities of any benefit/harm, clinically important benefit/harm and clinically unimportant differences will be presented for all outcomes. Discussion The results of this secondary, pre-planned Bayesian analysis will complement the primary frequentist analysis of the AID-ICU trial and facilitate a nuanced and probabilistic interpretation of the trial results.Peer reviewe

Leveraging genomic annotations and pleiotropic enrichment for improved replication rates in schizophrenia GWAS

Most of the genetic architecture of schizophrenia (SCZ) has not yet been identified. Here, we apply a novel statistical algorithm called Covariate-Modulated Mixture Modeling (CM3), which incorporates auxiliary information (heterozygosity, total linkage disequilibrium, genomic annotations, pleiotropy) for each single nucleotide polymorphism (SNP) to enable more accurate estimation of replication probabilities, conditional on the observed test statistic (“z-score”) of the SNP. We use a multiple logistic regression on z-scores to combine information from auxiliary information to derive a “relative enrichment score” for each SNP. For each stratum of these relative enrichment scores, we obtain nonparametric estimates of posterior expected test statistics and replication probabilities as a function of discovery z-scores, using a resampling-based approach that repeatedly and randomly partitions meta-analysis sub-studies into training and replication samples. We fit a scale mixture of two Gaussians model to each stratum, obtaining parameter estimates that minimize the sum of squared differences of the scale-mixture model with the stratified nonparametric estimates. We apply this approach to the recent genome-wide association study (GWAS) of SCZ (n = 82,315), obtaining a good fit between the model-based and observed effect sizes and replication probabilities. We observed that SNPs with low enrichment scores replicate with a lower probability than SNPs with high enrichment scores even when both they are genome-wide significant (p < 5x10-8). There were 693 and 219 independent loci with model-based replication rates ≥80% and ≥90%, respectively. Compared to analyses not incorporating relative enrichment scores, CM3 increased out-of-sample yield for SNPs that replicate at a given rate. This demonstrates that replication probabilities can be more accurately estimated using prior enrichment information with CM3

Systematic Integration of Brain eQTL and GWAS Identifies ZNF323 as a Novel Schizophrenia Risk Gene and Suggests Recent Positive Selection Based on Compensatory Advantage on Pulmonary Function

Genome-wide association studies have identified multiple risk variants and loci that show robust association with schizophrenia. Nevertheless, it remains unclear how these variants confer risk to schizophrenia. In addition, the driving force that maintains the schizophrenia risk variants in human gene pool is poorly understood. To investigate whether expression-associated genetic variants contribute to schizophrenia susceptibility, we systematically integrated brain expression quantitative trait loci and genome-wide association data of schizophrenia using Sherlock, a Bayesian statistical framework. Our analyses identified ZNF323 as a schizophrenia risk gene (P = 2.22×10-6). Subsequent analyses confirmed the association of the ZNF323 and its expression-associated single nucleotide polymorphism rs1150711 in independent samples (gene-expression: P = 1.40×10-6; single-marker meta-analysis in the combined discovery and replication sample comprising 44123 individuals: P = 6.85×10−10). We found that the ZNF323 was significantly downregulated in hippocampus and frontal cortex of schizophrenia patients (P = .0038 and P = .0233, respectively). Evidence for pleiotropic effects was detected (association of rs1150711 with lung function and gene expression of ZNF323 in lung: P = 6.62×10-5 and P = 9.00×10-5, respectively) with the risk allele (T allele) for schizophrenia acting as protective allele for lung function. Subsequent population genetics analyses suggest that the risk allele (T) of rs1150711 might have undergone recent positive selection in human population. Our findings suggest that the ZNF323 is a schizophrenia susceptibility gene whose expression may influence schizophrenia risk. Our study also illustrates a possible mechanism for maintaining schizophrenia risk variants in the human gene poo

LPA5 Is Abundantly Expressed by Human Mast Cells and Important for Lysophosphatidic Acid Induced MIP-1β Release

Background: Lysophosphatidic acid (LPA) is a bioactive lipid inducing proliferation, differentiation as well as cytokine release by mast cells through G-protein coupled receptors. Recently GPR92/LPA5 was identified as an LPA receptor highly expressed by cells of the immune system, which prompted us to investigate its presence and influence on mast cells. Principal Findings: Transcript analysis using quantitative real-time PCR revealed that LPA5 is the most prevalent LPA-receptor in human mast cells. Reduction of LPA5 levels using shRNA reduced calcium flux and abolished MIP-1β release in response to LPA. Conclusions: LPA5 is a bona fide LPA receptor on human mast cells responsible for the majority of LPA induced MIP-1β release