Use of high-content imaging to quantify transduction of AAV-PHP viruses in the brain following systemic delivery

The engineering of the AAV-PHP capsids was an important development for CNS research and the modulation of gene expression in the brain. They cross the blood brain barrier and transduce brain cells after intravenous systemic delivery, a property dependent on the genotype of Ly6a, the AAV-PHP capsid receptor. It is important to determine the transduction efficiency of a given viral preparation, as well as the comparative tropism for different brain cells; however, manual estimation of adeno-associated viral transduction efficiencies can be biased and time consuming. Therefore, we have used the Opera Phenix high-content screening system, equipped with the Harmony processing and analysis software, to reduce bias and develop an automated approach to determining transduction efficiency in the mouse brain. We used R Studio and ‘gatepoints’ to segment the data captured from coronal brain sections into brain regions of interest. C57BL/6J and CBA/Ca mice were injected with an AAV-PHP.B virus containing a green fluorescent protein reporter with a nuclear localization signal. Coronal sections at 600 μm intervals throughout the entire brain were stained with Hoechst dye, combined with immunofluorescence to NeuN and green fluorescent protein to identify all cell nuclei, neurons and transduced cells, respectively. Automated data analysis was applied to give an estimate of neuronal percentages and transduction efficiencies throughout the entire brain as well as for the cortex, striatum and hippocampus. The data from each coronal section from a given mouse were highly comparable. The percentage of neurons in the C57BL/6J and CBA/Ca brains was approximately 40% and this was higher in the cortex than striatum and hippocampus. The systemic injection of AAV-PHP.B resulted in similar transduction rates across the entire brain for C57BL/6J mice. Approximately 10–15% of all cells were transduced, with neuronal transduction efficiencies ranging from 5% to 15%, estimates that were similar across brain regions, and were in contrast to the much more localized transduction efficiencies achieved through intracerebral injection. We confirmed that the delivery of the AAV-PHP.B viruses to the brain from the vasculature resulted in widespread transduction. Our methodology allows the rapid comparison of transduction rates between brain regions producing comparable data to more time-consuming approaches. The methodology developed here can be applied to the automated quantification of any parameter of interest that can be captured as a fluorescent signal

Impairments in reinforcement learning do not explain enhanced habit formation in cocaine use disorder

Rationale Drug addiction has been suggested to develop through drug-induced changes in learning and memory processes. Whilst the initiation of drug use is typically goal-directed and hedonically motivated, over time, drug-taking may develop into a stimulus-driven habit, characterised by persistent use of the drug irrespective of the consequences. Converging lines of evidence suggest that stimulant drugs facilitate the transition of goal-directed into habitual drug-taking, but their contribution to goal-directed learning is less clear. Computational modelling may provide an elegant means for elucidating changes during instrumental learning that may explain enhanced habit formation. Objectives We used formal reinforcement learning algorithms to deconstruct the process of appetitive instrumental learning and to explore potential associations between goal-directed and habitual actions in patients with cocaine use disorder (CUD). Methods We re-analysed appetitive instrumental learning data in 55 healthy control volunteers and 70 CUD patients by applying a reinforcement learning model within a hierarchical Bayesian framework. We used a regression model to determine the influence of learning parameters and variations in brain structure on subsequent habit formation. Results Poor instrumental learning performance in CUD patients was largely determined by difficulties with learning from feedback, as reflected by a significantly reduced learning rate. Subsequent formation of habitual response patterns was partly explained by group status and individual variation in reinforcement sensitivity. White matter integrity within goal-directed networks was only associated with performance parameters in controls but not in CUD patients. Conclusions Our data indicate that impairments in reinforcement learning are insufficient to account for enhanced habitual responding in CUD

An Introduction to RNA Databases

We present an introduction to RNA databases. The history and technology behind RNA databases is briefly discussed. We examine differing methods of data collection and curation, and discuss their impact on both the scope and accuracy of the resulting databases. Finally, we demonstrate these principals through detailed examination of four leading RNA databases: Noncode, miRBase, Rfam, and SILVA.Comment: 27 pages, 10 figures, 1 tables. Submitted as a chapter for "An introduction to RNA bioinformatics" to be published by "Methods in Molecular Biology

Towards quantifying uncertainty in predictions of Amazon 'dieback'.

This is the final version of the article. It first appeared from The Royal Society via http://dx.doi.org/10.1098/rstb.2007.0028Simulations with the Hadley Centre general circulation model (HadCM3), including carbon cycle model and forced by a 'business-as-usual' emissions scenario, predict a rapid loss of Amazonian rainforest from the middle of this century onwards. The robustness of this projection to both uncertainty in physical climate drivers and the formulation of the land surface scheme is investigated. We analyse how the modelled vegetation cover in Amazonia responds to (i) uncertainty in the parameters specified in the atmosphere component of HadCM3 and their associated influence on predicted surface climate. We then enhance the land surface description and (ii) implement a multilayer canopy light interception model and compare with the simple 'big-leaf' approach used in the original simulations. Finally, (iii) we investigate the effect of changing the method of simulating vegetation dynamics from an area-based model (TRIFFID) to a more complex size- and age-structured approximation of an individual-based model (ecosystem demography). We find that the loss of Amazonian rainforest is robust across the climate uncertainty explored by perturbed physics simulations covering a wide range of global climate sensitivity. The introduction of the refined light interception model leads to an increase in simulated gross plant carbon uptake for the present day, but, with altered respiration, the net effect is a decrease in net primary productivity. However, this does not significantly affect the carbon loss from vegetation and soil as a consequence of future simulated depletion in soil moisture; the Amazon forest is still lost. The introduction of the more sophisticated dynamic vegetation model reduces but does not halt the rate of forest dieback. The potential for human-induced climate change to trigger the loss of Amazon rainforest appears robust within the context of the uncertainties explored in this paper. Some further uncertainties should be explored, particularly with respect to the representation of rooting depth

A comparison of course-related stressors in undergraduate problem-based learning (PBL) versus non-PBL medical programmes

Background: Medical students report high levels of stress related to their medical training as well as to other personal and financial factors. The aim of this study is to investigate whether there are differences in course-related stressors reported by medical students on undergraduate problem-based learning (PBL) and non-PBL programmes in the UK. Method: A cross-sectional study of second-year medical students in two UK medical schools (one PBL and one non-PBL programme) was conducted. A 16-question self-report questionnaire, derived from the Perceived Medical Student Stress Scale and the Higher Education Stress Inventory, was used to measure course-related stressors. Following univariate analysis of each stressor between groups, multivariate logistic regression was used to determine which stressors were the best predictors of each course type, while controlling for socio-demographic differences between the groups. Results: A total of 280 students responded. Compared to the non-PBL students (N = 197), the PBL students (N = 83) were significantly more likely to agree that: they did not know what the faculty expected of them (Odds Ratio (OR) = 0.38, p = 0.03); there were too many small group sessions facilitated only by students resulting in an unclear curriculum (OR = 0.04, p < 0.0001); and that there was a lack of opportunity to explore academic subjects of interest (OR = 0.40, p = 0.02). They were significantly more likely to disagree that: there was a lack of encouragement from teachers (OR = 3.11, p = 0.02); and that the medical course fostered a sense of anonymity and feelings of isolation amongst students (OR = 3.42, p = 0.008). Conclusion: There are significant differences in the perceived course-related stressors affecting medical students on PBL and non-PBL programmes. Course designers and student support services should therefore tailor their work to minimise, or help students cope with, the specific stressors on each course type to ensure optimum learning and wellbeing among our future doctors

Does the initiation of urate lowering treatment during an acute gout attack prolong the current episode and precipitate recurrent attacks: a systematic literature review

Objectives: To systematically review the literature on effect of initiating urate lowering treatment (ULT) during an acute attack of gout on duration of index attack and persistence on ULT. Methods: OVID (MEDLINE), EMBASE and AMED were searched to identify randomized controlled trials (RCTs) of ULT initiation during acute gout attack published in English language. Two reviewers appraised the study quality and extracted data independently. Standardised mean difference (SMD) and relative risk (RR) were used to pool continuous and categorical data. Meta-analysis was carried out using STATA v14. Results: 537 studies were selected. 487 titles and abstracts were reviewed after removing duplicates. Three RCTs were identified. There was evidence from two high quality studies that early initiation of allopurinol did not increase pain severity at days 10 to 15 (SMDpooled (95%CI) 0.18(-0.58, 0.93)). Data from three studies suggested that initiation of ULT during an acute attack of gout did not associate with drop-outs (RRpooled (95%CI) 1.16(0.58, 2.31)). Conclusion: There is moderate-quality evidence that the initiation of ULT during an acute attack of gout does not increase pain severity and risk of ULT discontinuation. Larger studies are required to confirm these findings so that patients with acute gout can be initiated on ULT with confidence

International Glossina Genome Initiative 2004-2014: a driver for post-genomic era research on the African continent

Human African trypanosomiasis (HAT), also known as sleeping sickness, is a neglected disease that impacts 70 million people distributed over 1.55 million km2 in sub- Saharan Africa and includes at least 50% of the population of theDemocratic Republic of the Congo [1]. Trypanosoma brucei gambiense accounts for more than 98% of the infections in central and West Africa, the remaining infections being from Trypanosoma brucei rhodesiense in East Africa [2]. The parasites are transmitted to the hosts through the bite of an infected tsetse fly. Disease control is challenging as there are no vaccines, and effective, easily delivered drugs are still lacking. Treatment invariably involves lengthy hospitalization, with both medical and socioeconomic consequences.Web of Scienc

Optimal Conservation of Migratory Species

Background. Migratory animals comprise a significant portion of biodiversity worldwide with annual investment for their conservation exceeding several billion dollars. Designing effective conservation plans presents enormous challenges. Migratory species are influenced by multiple events across land and sea-regions that are often separated by thousands of kilometres and span international borders. To date, conservation strategies for migratory species fail to take into account how migratory animals are spatially connected between different periods of the annual cycle (i.e. migratory connectivity) bringing into question the utility and efficiency of current conservation efforts. Methodology/Principal Findings. Here, we report the first framework for determining an optimal conservation strategy for a migratory species. Employing a decision theoretic approach using dynamic optimization, we address the problem of how to allocate resources for habitat conservation for a Neotropical-Nearctic migratory bird, the American redstart Setophaga ruticilla, whose winter habitat is under threat. Our first conservation strategy used the acquisition of winter habitat based on land cost, relative bird density, and the rate of habitat loss to maximize the abundance of birds on the wintering grounds. Our second strategy maximized bird abundance across the entire range of the species by adding the constraint of maintaining a minimum percentage of birds within each breeding region in North America using information on migratory connectivity as estimated from stable-hydrogen isotopes in feathers. We show that failure to take into account migratory connectivity may doom some regional populations to extinction, whereas including information on migratory connectivity results in the protection of the species across its entire range. Conclusions/Significance. We demonstrate that conservation strategies for migratory animals depend critically upon two factors: knowledge of migratory connectivity and the correct statement of the conservation problem. Our framework can be used to identify efficient conservation strategies for migratory taxa worldwide, including insects, birds, mammals, and marine organisms