Adverse reactions from consumption of oral rabies vaccine baits in dogs in Finland

Background Oral rabies vaccination of wildlife has effectively reduced the incidence of rabies in wildlife and has led to the elimination of rabies in large areas of Europe. The safety of oral rabies vaccines has been assessed in both target (red fox and raccoon dog) and several non-target species. Case presentation Since 2011, the competent authority in Finland has received a few reports of dogs experiencing adverse reactions that have been assumed to be caused by the consumption of baits containing oral rabies vaccine. The dogs usually exhibited gastrointestinal symptoms (vomiting, inappetence, constipation or diarrhoea) or behavioral symptoms (restlessness, listlessness and unwillingness to continue hunting). Conclusions Nevertheless, these adverse reactions are transient and non-life threatening. Even though the adverse reactions are unpleasant to individual dogs and their owners, the benefits of oral rabies vaccination clearly outweigh the risks

Competencies for bibliometrics

Universities are increasingly offering support services for bibliometrics, often based in the library. This paper describes work done to produce a competency model for those supporting bibliometrics. The results of a questionnaire in which current practitioners rated bibliometric tasks as entry level, core or specialist are reported. Entry level competencies identified were explaining bibliometric concepts, doing basic calculations and some professional skills. Activities identified by participants as core are outlined. Reflecting on items that were considered in scope but specialist there was less stress on evaluating scholars, work at a strategic level, working with data outside proprietary bibliometric tools and consultancy-type services as opposed to training for disintermediated use. A competency model is presented as an appendix

The economic geology of the Okiep copper deposits, Namaqualand, South Africa

The Okiep Copper District situated in the north-western Cape Province, covers some 3 000 km and is the oldest mining area in the Republic of South Africa. The O'okiep Copper Company Limited commenced production in 1940 with a proven ore reserve of 9 million tons at 2,45 % cu. Production since 1940 and present ore reserves total some 93 million tonnes at 1,08 % Cu. The rocks comprising the Okiep Copper District are of Proterozoic age and have been subdivided into a meta-volcanosedimentary succession, intruded by various sub-horizontally emplaced granitoid intrusions. The various intrusions occurred at different stages relative to the main structural and metamorphic events. The copper deposits are confined to basic rocks which are the youngest major group of intrusives in the District. They occur as swarms of generally easterly-trending, steep northdipping, irregular dyke-like bodies consisting of diorite, anorthosite and norite. The dominant silicate constituents are andesite ranging to labradorite, hypersthene, biotite and phlogopite. Copper sulphides are preferentially associated with the more basic varieties. The copper sulphides are mainly chalcopyrite, bornite and subsidiary chalcocite. The copper content of the basic rocks is erratic ranging over small distances from a mere trace to several percent. Emplacement of the cupriferous basic rocks is predisposed to a large extent by enigmatic structural features locally referred to as steep structures. The most common manifestation of steep structure deformation is typically a narrow antiformal linear feature along which continuity of the country rocks has been interrupted by piercement folding and shearing. In places, pipelike bodies of megabreccia occur along steep structures, and also act as hosts to the basic rock. Areas of steep structure are thus prime exploration targets, due to their close spatial association with the cupriferous basic rocks. Exploration techiques employed in the Okiep Copper District in~ elude regional and detailed geological mapping, geophysical surveys utilizing magnetic, gravimetric and electrical methods, as well as limited application of soil and stream-sediment geochemistry. Final evaluation is by surface and underground diamond drilling. Exploration has to date discovered 18 new mines with individual ore reserves ranging from 200 000 to 37 000 000 tonnes. All are underground operations, and the sub-level open stoping method of mining is standard

A surrogate modelling approach based on nonlinear dimension reduction for uncertainty quantification in groundwater flow models

In this paper, we develop a surrogate modelling approach for capturing the output field (e.g., the pressure head) from groundwater flow models involving a stochastic input field (e.g., the hy- draulic conductivity). We use a Karhunen-Lo`eve expansion for a log-normally distributed input field, and apply manifold learning (local tangent space alignment) to perform Gaussian process Bayesian inference using Hamiltonian Monte Carlo in an abstract feature space, yielding outputs for arbitrary unseen inputs. We also develop a framework for forward uncertainty quantification in such problems, including analytical approximations of the mean of the marginalized distri- bution (with respect to the inputs). To sample from the distribution we present Monte Carlo approach. Two examples are presented to demonstrate the accuracy of our approach: a Darcy flow model with contaminant transport in 2-d and a Richards equation model in 3-d

Second case of European bat lyssavirus type 2 detected in a Daubenton’s bat in Finland

European bat lyssavirus type 2 (EBLV-2) was detected in Finland in a Daubenton’s bat (Myotis daubentonii) found in the municipality of Inkoo (60°02′45″N, 024°00′20″E). The bat showed neurological signs and was later found dead. The laboratory analysis revealed the presence of lyssavirus, and the virus was characterized as EBLV-2. This isolation of EBLV-2 was the second time that the virus has been detected in a Daubenton’s bat in Finland. This provides additional proof that EBLV-2 is endemic in the Finnish Daubenton’s bat population

Probabilistic Modelling of Uncertainty with Bayesian nonparametric Machine Learning

This thesis addresses the use of probabilistic predictive modelling and machine learning for quantifying uncertainties. Predictive modelling makes inferences of a process from observations obtained using computational modelling, simulation, or experimentation. This is often achieved using statistical machine learning models which predict the outcome as a function of variable predictors and given process observations. Towards this end Bayesian nonparametric regression is used, which is a highly flexible and probabilistic type of statistical model and provides a natural framework in which uncertainties can be included. The contributions of this thesis are threefold. Firstly, a novel approach to quantify parametric uncertainty in the Gaussian process latent variable model is presented, which is shown to improve predictive performance when compared with the commonly used variational expectation maximisation approach. Secondly, an emulator using manifold learning (local tangent space alignment) is developed for the purpose of dealing with problems where outputs lie in a high dimensional manifold. Using this, a framework is proposed to solve the forward problem for uncertainty quantification and applied to two fluid dynamics simulations. Finally, an enriched clustering model for generalised mixtures of Gaussian process experts is presented, which improves clustering, scaling with the number of covariates, and prediction when compared with what is known as the alternative model. This is then applied to a study of Alzheimer’s disease, with the aim of improving prediction of disease progression

Science into policy: preparing for pandemic influenza

Authoratative government pandemic preparedness requires an evidence-based approach. The scientific advisory process that has informed the current UK pandemic preparedness plans is described. The final endorsed scientific papers are now publicly available