The Vertical Distribution of some West African Mosquitoes (Diptera,Culicidae) Over Open Farmland in a Freshwater Area of the Gambia

Mosquitoes flying at low levels over open farmland were sampled by means of electrical suction traps. These were set up at nine levels from ground level up to 6 m. From the vertical profiles obtained it was possible to recognise three patterns of behaviour: (1) a low-flying group with relatively very high densities below 1 m, comprising Mansonia (Mansonioides) spp., Aedes spp. and some species of Anopheles; (2) an intermediate group with densities rather evenly distributed at the lower levels but declining above 2-4 m, comprising A. funestus Giles, A. gambiae Giles and Culex neavei Theo.; (3) a high-flying group with catches at 6 m greater, or much greater, than at 1 m, composed of C. antennatus (Becfcer),C. thalassius Theo. and C. poicilipes (Theo.). For all species, catches after 23.00 h showed an increase in the proportion of mosquitoes taken in traps at the lower levels, this being most marked at ground level and 0-5 m. No influence of either moonlight or wind speed could be detected to account for this. Biting catches on human baits showed a generally similar pattern to suction-trap catches, although differences between baits at 1-m intervals at the higher levels were less than with unbaited traps

The potential for dietary factors to prevent or treat osteoarthritis

Osteoarthritis (OA) is a degenerative joint disease for which there are no disease-modifying drugs. It is a leading cause of disability in the UK. Increasing age and obesity are both major risk factors for OA and the health and economic burden of this disease will increase in the future. Focusing on compounds from the habitual diet that may prevent the onset or slow the progression of OA is a strategy that has been under-investigated to date. An approach that relies on dietary modification is clearly attractive in terms of risk/benefit and more likely to be implementable at the population level. However, before undertaking a full clinical trial to examine potential efficacy, detailed molecular studies are required in order to optimise the design. This review focuses on potential dietary factors that may reduce the risk or progression of OA, including micronutrients, fatty acids, flavonoids and other phytochemicals. It therefore ignores data coming from classical inflammatory arthritides and nutraceuticals such as glucosamine and chondroitin. In conclusion, diet offers a route by which the health of the joint can be protected and OA incidence or progression decreased. In a chronic disease, with risk factors increasing in the population and with no pharmaceutical cure, an understanding of this will be crucial

The Sigma 13 (10-14) twin in alpha-Al2O3: A model for a general grain boundary

The atomistic structure and energetics of the Sigma 13 (10-14)[1-210] symmetrical tilt grain boundary in alpha-Al2O3 are studied by first-principles calculations based on the local-density-functional theory with a mixed-basis pseudopotential method. Three configurations, stable with respect to intergranular cleavage, are identified: one Al-terminated glide-mirror twin boundary, and two O-terminated twin boundaries, with glide-mirror and two-fold screw-rotation symmetries, respectively. Their relative energetics as a function of axial grain separation are described, and the local electronic structure and bonding are analysed. The Al-terminated variant is predicted to be the most stable one, confirming previous empirical calculations, but in contrast with high-resolution transmission electron microscopy observations on high-purity diffusion-bonded bicrystals, which resulted in an O-terminated structure. An explanation of this discrepancy is proposed, based on the different relative energetics of the internal interfaces with respect to the free surfaces

The cellular microscopy phenotype ontology

BACKGROUND: Phenotypic data derived from high content screening is currently annotated using free-text, thus preventing the integration of independent datasets, including those generated in different biological domains, such as cell lines, mouse and human tissues. DESCRIPTION: We present the Cellular Microscopy Phenotype Ontology (CMPO), a species neutral ontology for describing phenotypic observations relating to the whole cell, cellular components, cellular processes and cell populations. CMPO is compatible with related ontology efforts, allowing for future cross-species integration of phenotypic data. CMPO was developed following a curator-driven approach where phenotype data were annotated by expert biologists following the Entity-Quality (EQ) pattern. These EQs were subsequently transformed into new CMPO terms following an established post composition process. CONCLUSION: CMPO is currently being utilized to annotate phenotypes associated with high content screening datasets stored in several image repositories including the Image Data Repository (IDR), MitoSys project database and the Cellular Phenotype Database to facilitate data browsing and discoverability

Predictive use of the Maximum Entropy Production principle for Past and Present Climates

In this paper, we show how the MEP hypothesis may be used to build simple climate models without representing explicitly the energy transport by the atmosphere. The purpose is twofold. First, we assess the performance of the MEP hypothesis by comparing a simple model with minimal input data to a complex, state-of-the-art General Circulation Model. Next, we show how to improve the realism of MEP climate models by including climate feedbacks, focusing on the case of the water-vapour feedback. We also discuss the dependence of the entropy production rate and predicted surface temperature on the resolution of the model

Rank-based model selection for multiple ions quantum tomography

The statistical analysis of measurement data has become a key component of many quantum engineering experiments. As standard full state tomography becomes unfeasible for large dimensional quantum systems, one needs to exploit prior information and the "sparsity" properties of the experimental state in order to reduce the dimensionality of the estimation problem. In this paper we propose model selection as a general principle for finding the simplest, or most parsimonious explanation of the data, by fitting different models and choosing the estimator with the best trade-off between likelihood fit and model complexity. We apply two well established model selection methods -- the Akaike information criterion (AIC) and the Bayesian information criterion (BIC) -- to models consising of states of fixed rank and datasets such as are currently produced in multiple ions experiments. We test the performance of AIC and BIC on randomly chosen low rank states of 4 ions, and study the dependence of the selected rank with the number of measurement repetitions for one ion states. We then apply the methods to real data from a 4 ions experiment aimed at creating a Smolin state of rank 4. The two methods indicate that the optimal model for describing the data lies between ranks 6 and 9, and the Pearson $\chi^{2}$ test is applied to validate this conclusion. Additionally we find that the mean square error of the maximum likelihood estimator for pure states is close to that of the optimal over all possible measurements.Comment: 24 pages, 6 figures, 3 table

The rolling problem: overview and challenges

In the present paper we give a historical account -ranging from classical to modern results- of the problem of rolling two Riemannian manifolds one on the other, with the restrictions that they cannot instantaneously slip or spin one with respect to the other. On the way we show how this problem has profited from the development of intrinsic Riemannian geometry, from geometric control theory and sub-Riemannian geometry. We also mention how other areas -such as robotics and interpolation theory- have employed the rolling model.Comment: 20 page

Dissociable rate-dependent effects of oral methylphenidate on impulsivity and D2/3 receptor availability in the striatum.

We have previously shown that impulsivity in rats is linked to decreased dopamine D2/3 receptor availability in the ventral striatum. In the present study, we investigated, using longitudinal positron emission tomography (PET), the effects of orally administered methylphenidate (MPH), a first-line treatment for attention deficit hyperactivity disorder, on D2/3 receptor availability in the dorsal and ventral striatum and related these changes to impulsivity. Rats were screened for impulsive behavior on a five-choice serial reaction time task. After a baseline PET scan with the D2/3 ligand [(18)F]fallypride, rats received 6 mg/kg MPH, orally, twice each day for 28 d. Rats were then reassessed for impulsivity and underwent a second [(18)F]fallypride PET scan. Before MPH treatment, we found that D2/3 receptor availability was significantly decreased in the left but not the right ventral striatum of high-impulse (HI) rats compared with low-impulse (LI) rats. MPH treatment increased impulsivity in LI rats, and modulated impulsivity and D2/3 receptor availability in the dorsal and ventral striatum of HI rats through inverse relationships with baseline levels of impulsivity and D2/3 receptor availability, respectively. However, we found no relationship between the effects of MPH on impulsivity and D2/3 receptor availability in any of the striatal subregions investigated. These findings indicate that trait-like impulsivity is associated with decreased D2/3 receptor availability in the left ventral striatum, and that stimulant drugs modulate impulsivity and striatal D2/3 receptor availability through independent mechanisms.This work was funded by Medical Research Council Grant G0701500, and by a joint award from the Medical Research Council (Grant G1000183) and the Wellcome Trust (Grant 093875/Z/10/Z) in support of the Behavioural and Clinical Neuroscience Institute at the University of Cambridge. We also acknowledge funding from the Medical Research Council in support of the ICCAM addiction cluster in the United Kingdom (G1000018). B.J. is supported by grants from the AXA Research Fund and the Australian National Health and Medical Research Council (Grant 1016313).This is the author accepted manuscript. The final version is available from Society for Neuroscience via http://doi.org/10.1523/JNEUROSCI.3890-14.201

The BioDICE Taverna plugin for clustering and visualization of biological data: a workflow for molecular compounds exploration

Background: In many experimental pipelines, clustering of multidimensional biological datasets is used to detect hidden structures in unlabelled input data. Taverna is a popular workflow management system that is used to design and execute scientific workflows and aid in silico experimentation. The availability of fast unsupervised methods for clustering and visualization in the Taverna platform is important to support a data-driven scientific discovery in complex and explorative bioinformatics applications. Results: This work presents a Taverna plugin, the Biological Data Interactive Clustering Explorer (BioDICE), that performs clustering of high-dimensional biological data and provides a nonlinear, topology preserving projection for the visualization of the input data and their similarities. The core algorithm in the BioDICE plugin is Fast Learning Self Organizing Map (FLSOM), which is an improved variant of the Self Organizing Map (SOM) algorithm. The plugin generates an interactive 2D map that allows the visual exploration of multidimensional data and the identification of groups of similar objects. The effectiveness of the plugin is demonstrated on a case study related to chemical compounds. Conclusions: The number and variety of available tools and its extensibility have made Taverna a popular choice for the development of scientific data workflows. This work presents a novel plugin, BioDICE, which adds a data-driven knowledge discovery component to Taverna. BioDICE provides an effective and powerful clustering tool, which can be adopted for the explorative analysis of biological datasets