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

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

Identification of single-site gold catalysis in acetylene hydrochlorination

There remains considerable debate over the active form of gold under operating conditions of a recently validated gold catalyst for acetylene hydrochlorination. We have performed an in situ x-ray absorption fine structure study of gold/carbon (Au/C) catalysts under acetylene hydrochlorination reaction conditions and show that highly active catalysts comprise single-site cationic Au entities whose activity correlates with the ratio of Au(I):Au(III) present. We demonstrate that these Au/C catalysts are supported analogs of single-site homogeneous Au catalysts and propose a mechanism, supported by computational modeling, based on a redox couple of Au(I)-Au(III) species

Risk factors for delayed presentation and referral of symptomatic cancer: Evidence for common cancers

Background:It has been suggested that the known poorer survival from cancer in the United Kingdom, compared with other European countries, can be attributed to more advanced cancer stage at presentation. There is, therefore, a need to understand the diagnostic process, and to ascertain the risk factors for increased time to presentation.Methods:We report the results from two worldwide systematic reviews of the literature on patient-mediated and practitioner-mediated delays, identifying the factors that may influence these.Results:Across cancer sites, non-recognition of symptom seriousness is the main patient-mediated factor resulting in increased time to presentation. There is strong evidence of an association between older age and patient delay for breast cancer, between lower socio-economic status and delay for upper gastrointestinal and urological cancers and between lower education level and delay for breast and colorectal cancers. Fear of cancer is a contributor to delayed presentation, while sanctioning of help seeking by others can be a powerful mediator of reduced time to presentation. For practitioner delay, ‘misdiagnosis’ occurring either through treating patients symptomatically or relating symptoms to a health problem other than cancer, was an important theme across cancer sites. For some cancers, this could also be linked to inadequate patient examination, use of inappropriate tests or failing to follow-up negative or inconclusive test results.Conclusion:Having sought help for potential cancer symptoms, it is therefore important that practitioners recognise these symptoms, and examine, investigate and refer appropriately. © 2009 Cancer Research UK All rights reserved

Clinical Implication of Targeting of Cancer Stem Cells

The existence of cancer stem cells (CSCs) is receiving increasing interest particularly due to its potential ability to enter clinical routine. Rapid advances in the CSC field have provided evidence for the development of more reliable anticancer therapies in the future. CSCs typically only constitute a small fraction of the total tumor burden; however, they harbor self-renewal capacity and appear to be relatively resistant to conventional therapies. Recent therapeutic approaches aim to eliminate or differentiate CSCs or to disrupt the niches in which they reside. Better understanding of the biological characteristics of CSCs as well as improved preclinical and clinical trials targeting CSCs may revolutionize the treatment of many cancers. Copyright (c) 2012 S. Karger AG, Base

In vitro comparison of the effects of rough and polished stem surface finish on pressure generation in cemented hip arthroplasty

Background and purpose High pressures around implants can cause bone lysis and loosening. We investigated how pressures are generated around cemented femoral stems

RNAstrand: reading direction of structured RNAs in multiple sequence alignments

<p>Abstract</p> <p>Motivation</p> <p>Genome-wide screens for structured ncRNA genes in mammals, urochordates, and nematodes have predicted thousands of putative ncRNA genes and other structured RNA motifs. A prerequisite for their functional annotation is to determine the reading direction with high precision.</p> <p>Results</p> <p>While folding energies of an RNA and its reverse complement are similar, the differences are sufficient at least in conjunction with substitution patterns to discriminate between structured RNAs and their complements. We present here a support vector machine that reliably classifies the reading direction of a structured RNA from a multiple sequence alignment and provides a considerable improvement in classification accuracy over previous approaches.</p> <p>Software</p> <p>RNAstrand is freely available as a stand-alone tool from <url>http://www.bioinf.uni-leipzig.de/Software/RNAstrand</url> and is also included in the latest release of RNAz, a part of the Vienna RNA Package.</p