Signaling from blood vessels to CNS axons through nitric oxide

Brain function is usually perceived as being performed by neurons with the support of glial cells, the network of blood vessels situated nearby serving simply to provide nutrient and to dispose of metabolic waste. Revising this view, we find from experiments on a rodent central white matter tract (the optic nerve) in vitro that microvascular endothelial cells signal persistently to axons using nitric oxide (NO) derived from the endothelial NO synthase (eNOS). The endogenous NO acts to stimulate guanylyl cyclase-coupled NO receptors in the axons, leading to a raised cGMP level which then causes membrane depolarization, apparently by directly engaging hyperpolarization-activated cyclic nucleotide-gated ion channels. The tonic depolarization and associated endogenous NO-dependent cGMP generation was absent in optic nerves from mice lacking eNOS, although such nerves responded to exogenous NO, with raised cGMP generation in the axons and associated depolarization. In addition to the tonic activity, exposure of optic nerves to bradykinin, a classical stimulator of eNOS in endothelial cells, elicited reversible NO- and cGMP-dependent depolarization through activation of bradykinin B-2 receptors, to which eNOS is physically complexed. No contribution of other NO synthase isoforms to either the action of bradykinin or the continuous ambient NO level could be detected. The results suggest that microvascular endothelial cells participate in signal processing in the brain and can do so by generating both tonic and phasic NO signals

Modified confidence intervals for the Mahalanobis distance

Reiser (2001) proposes a method of forming confidence interval for a Mahalanobis distance that yields intervals which have exactly the nominal coverage, but sometimes the interval is View the MathML source (0,0). We consider the case where Mahalanobis distance quantifies the difference between an individual and a population mean, and suggest a modification that avoids implausible intervals

Arthropod pest control for UK oilseed rape – Comparing insecticide efficacies, side effects and alternatives

Oilseed rape (Brassica napus) is an important combinable break crop in the UK, which is largely protected from arthropod pests by insecticidal chemicals. Despite ongoing debate regarding the use of neonicotinoids, the dominant seed treatment ingredients used for this crop, there is little publicly available data comparing the efficacy of insecticides in controlling key arthropod pests or comparing the impacts on non-target species and the wider environment. To provide an insight into these matters, a UK-wide expert survey targeting agronomists and entomologists was conducted from March to June 2015. Based on the opinions of 90 respondents, an average of 20% yield loss caused by the key arthropod pests was expected to have occurred in the absence of insecticide treatments. Relatively older chemical groups were perceived to have lower efficacy for target pests than newer ones, partly due to the development of insecticide resistance. Without neonicotinoid seed treatments, a lack of good control for cabbage stem flea beetle was perceived. Wide spectrum foliar insecticide sprays were perceived to have significantly greater negative impacts than seed treatments on users' health, natural enemies, pollinators, soil and water, and many foliar active ingredients have had potential risks for non-target arthropod species in UK oilseed rape fields for the past 25 years. Overall, 72% of respondents opposed the neonicotinoid restriction, while 10% supported it. Opposition and support of the restriction were largely based on concerns for pollinators and the wider environment, highlighting the uncertainty over the side effects of neonicotinoid use. More people from the government and research institutes leaned towards neutrality over the issue, compared to those directly involved in growing the crop. Neonicotinoid restriction was expected to result in greater effort and expenditure on pest control and lower production (0–1 t/ha less). Alternatives for future oilseed rape protection were then discussed

Bayes linear kinematics in the analysis of failure rates and failure time distributions

Collections of related Poisson or binomial counts arise, for example, from a number of different failures in similar machines or neighbouring time periods. A conventional Bayesian analysis requires a rather indirect prior specification and intensive numerical methods for posterior evaluations. An alternative approach using Bayes linear kinematics in which simple conjugate specifications for individual counts are linked through a Bayes linear belief structure is presented. Intensive numerical methods are not required. The use of transformations of the binomial and Poisson parameters is proposed. The approach is illustrated in two examples, one involving a Poisson count of failures, the other involving a binomial count in an analysis of failure times

Plausibility functions and exact frequentist inference

In the frequentist program, inferential methods with exact control on error rates are a primary focus. The standard approach, however, is to rely on asymptotic approximations, which may not be suitable. This paper presents a general framework for the construction of exact frequentist procedures based on plausibility functions. It is shown that the plausibility function-based tests and confidence regions have the desired frequentist properties in finite samples---no large-sample justification needed. An extension of the proposed method is also given for problems involving nuisance parameters. Examples demonstrate that the plausibility function-based method is both exact and efficient in a wide variety of problems.Comment: 21 pages, 5 figures, 3 table

Impacts of neonicotinoid use on long-term population changes in wild bees in England

Wild bee declines have been ascribed in part to neonicotinoid insecticides. While short-term laboratory studies on commercially-bred species (principally honeybees and bumblebees) have identified sub-lethal effects, there is no strong evidence linking these insecticides to losses of the majority of wild bee species. We relate 18 years of UK national wild bee distribution data for 62 species to amounts of neonicotinoid use in oilseed rape. Using a multi-species dynamic Bayesian occupancy analysis, we find evidence of increased population extinction rates in response to neonicotinoid seed treatment use on oilseed rape. Species foraging on oilseed rape benefit from the cover of this crop, but were on average three times more negatively affected by exposure to neonicotinoids than non-crop foragers. Our results suggest that sub-lethal effects of neonicotinoids could scale-up to cause losses of bee biodiversity. Restrictions on neonicotinoid use may reduce population declines

Detection of Infectious Disease Outbreaks From Laboratory Data With Reporting Delays

Many statistical surveillance systems for the timely detection of outbreaks of infectious disease operate on laboratory data. Such data typically incur reporting delays between the time at which a specimen is collected for diagnostic purposes, and the time at which the results of the laboratory analysis become available. Statistical surveillance systems currently in use usually make some ad hoc adjustment for such delays, or use counts by time of report. We propose a new statistical approach that takes account of the delays explicitly, by monitoring the number of specimens identified in the current and past m time units, where m is a tuning parameter. Values expected in the absence of an outbreak are estimated from counts observed in recent years (typically 5 years). We study the method in the context of an outbreak detection system used in the United Kingdom and several other European countries. We propose a suitable test statistic for the null hypothesis that no outbreak is currently occurring. We derive its null variance, incorporating uncertainty about the estimated delay distribution. Simulations and applications to some test datasets suggest the method works well, and can improve performance over ad hoc methods in current use. Supplementary materials for this article are available online

Assessment of occupational exposure to pesticide mixtures with endocrine disrupting activity

Occupational exposure to pesticide mixtures comprising active substance(s) and/or co-formulant(s) with known/possible endocrine disrupting activity was assessed using long-term activity records for 50 professional operators representing arable and orchard cropping systems in Greece, Lithuania, and the UK. Exposure was estimated using the harmonised Agricultural Operator Exposure Model, and risk was quantified as a point of departure index (PODI) using the lowest no observed (adverse) effect level. Use of substances with known/possible endocrine activity was common, with 43 of the 50 operators applying at least one such active substance on more than 50% of spray days; at maximum, one UK operator sprayed five such active substances and ten such co-formulants in a single day. At 95th percentile, total exposure was largest in the UK orchard system (4.1x10-2 mg kg bw-1 day-1) whereas risk was largest in the Greek cropping systems (PODI 5.3x10-1). All five cropping systems had instances indicating potential for risk when expressed at a daily resolution (maximum PODI 1.2-10.7). Toxicological data are sparse for co-formulants so combined risk from complex mixtures of active substances and co-formulants may be larger in reality

Eliciting Dirichlet and Gaussian copula prior distributions for multinomial models

In this paper, we propose novel methods of quantifying expert opinion about prior distributions for multinomial models. Two different multivariate priors are elicited using median and quartile assessments of the multinomial probabilities. First, we start by eliciting a univariate beta distribution for the probability of each category. Then we elicit the hyperparameters of the Dirichlet distribution, as a tractable conjugate prior, from those of the univariate betas through various forms of reconciliation using least-squares techniques. However, a multivariate copula function will give a more flexible correlation structure between multinomial parameters if it is used as their multivariate prior distribution. So, second, we use beta marginal distributions to construct a Gaussian copula as a multivariate normal distribution function that binds these marginals and expresses the dependence structure between them. The proposed method elicits a positive-definite correlation matrix of this Gaussian copula. The two proposed methods are designed to be used through interactive graphical software written in Java