80 resultados para Image Processing in Molecular Biology Research
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The process of binding of small ligands to dihydrofolate reductase protein has been investigated using all-atom molecular dynamics simulations. The existence of a mechanism that facilitates the search of the binding site by the ligand is demonstrated. The mechanism consists of ligand diffusing on the protein’s surface. It has been discussed in the literature before, but has not been explicitly confirmed for realistic molecular systems. The strength of this nonspecific binding is roughly estimated and found to be essential for the binding kinetics.
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The relationship between research and learning and teaching represents what has been described as ‘amongst the most intellectually tangled, managerially complex and politically contentious issues in mass higher education’ (Scott, 2005, p 53). Despite this, arguments that in order to achieve high quality scholarly outcomes, university teachers need to adopt an approach to teaching similar to that of research (i.e. founded upon academic rigour and evidence), has long been discussed in the literature (see for example, Elton, 2005 & Healey, 2000). However, the practicalities of promoting an empirical and evidence-based approach to teaching within a research-led institution makes dealing with the research/learning and teaching nexus a somewhat challenging proposition. Drawing upon the findings of a mixed methodological study, this paper critically analyses the pedagogical, organisational and practical issues encountered by academics and support staff working within a newly established Centre for Learning Innovation and Professional Practice. Comprising an eclectic group of staff drawn from across the five Schools in the University, the Centre is dedicated to enhancing student learning through the development of evidence based teaching practice. Based upon the premise that the promotion of research-led teaching will act to bring teaching and research together, and in doing so enhance students learning experiences (Simmons & Elen 2007), the paper critically analyses the challenges encountered by staff responsible for developing and introducing a new learning & teaching focused organisational strategy (by reflecting on the previous 12 months work). In doing so it makes a significant contribution to current academic theory and debate in the areas of pedagogic practice and organisational management. Focusing specifically on the impact of the new policy on various aspects of university life including, pedagogic practice, student support, staff training, and organisational management, the paper critically addresses the cultural and attitudinal challenges of change management (Kotter, 1996) within a ‘grey-brick’ university. It concludes by arguing that the move towards becoming a more learning-focused university has started to develop an awareness of the positive impact the change initiative is having on the student experience and wider institution; whilst also drawing attention to the organisational challenges ahead.
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This paper offers a selected review of strategic group theory and seeks to explore the benefits and limitations of modern strategic group analysis within the context of the Pharmaceutical Industry. The rise and fall of strategic group research is reviewed and some suggestions advanced as to the reasons why strategic group research has often produced conflicting results, particularly with regard to the link between group membership and performance. The review concludes that strategic group research continues to offer a valuable way to classify firms by their strategy and provides some suggestions as to how future studies may avoid the pitfalls exposed by previous research.
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In response to the increasing interest in the growth and developments in the Indian economy, and the dynamic nature of the rapidly changing Indian business environment, this textbook is designed to provide a comprehensive guide to doing business in the Indian context. Written by academic experts in their respective fields, this book is divided into three parts: the Indian business context, conducting business in India, and India and the world. Key information is presented on a wide range of topics, including: •Both the shortcomings and opportunities associated with the Indian business environment •The economic development model in India •Critical skills for negotiation and incentives for foreign investors, including case studies of Italian companies that have entered the Indian market in different ways •Business culture in India, including particular customs and etiquette In addition to the pedagogical features, each chapter contains a set of key issues, and there is also a list of useful websites covering a wide range of business needs. This book introduces students to business in India, and will be also be of use to investors, organisations and managers who are already doing business, or intend to start one, in India.
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Background - The Met allele of the catechol-O-methyltransferase (COMT) valine-to-methionine (Val158Met) polymorphism is known to affect dopamine-dependent affective regulation within amygdala-prefrontal cortical (PFC) networks. It is also thought to increase the risk of a number of disorders characterized by affective morbidity including bipolar disorder (BD), major depressive disorder (MDD) and anxiety disorders. The disease risk conferred is small, suggesting that this polymorphism represents a modifier locus. Therefore our aim was to investigate how the COMT Val158Met may contribute to phenotypic variation in clinical diagnosis using sad facial affect processing as a probe for its neural action. Method - We employed functional magnetic resonance imaging to measure activation in the amygdala, ventromedial PFC (vmPFC) and ventrolateral PFC (vlPFC) during sad facial affect processing in family members with BD (n=40), MDD and anxiety disorders (n=22) or no psychiatric diagnosis (n=25) and 50 healthy controls. Results - Irrespective of clinical phenotype, the Val158 allele was associated with greater amygdala activation and the Met allele with greater signal change in the vmPFC and vlPFC. Signal changes in the amygdala and vmPFC were not associated with disease expression. However, in the right vlPFC the Met158 allele was associated with greater activation in all family members with affective morbidity compared with relatives without a psychiatric diagnosis and healthy controls. Conclusions - Our results suggest that the COMT Val158Met polymorphism has a pleiotropic effect within the neural networks subserving emotional processing. Furthermore the Met158 allele further reduces cortical efficiency in the vlPFC in individuals with affective morbidity. © 2010 Cambridge University Press.
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Scale-up from shake flasks to bioreactors allows for the more reproducible, high-yielding production of recombinant proteins in yeast. The ability to control growth conditions through real-time monitoring facilitates further optimization of the process. The setup of a 3-L stirred-tank bioreactor for such an application is described. © 2012 Springer Science+business Media, LLC.
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In the last few years, significant advances have been made in understanding how a yeast cell responds to the stress of producing a recombinant protein, and how this information can be used to engineer improved host strains. The molecular biology of the expression vector, through the choice of promoter, tag and codon optimization of the target gene, is also a key determinant of a high-yielding protein production experiment. Recombinant Protein Production in Yeast: Methods and Protocols examines the process of preparation of expression vectors, transformation to generate high-yielding clones, optimization of experimental conditions to maximize yields, scale-up to bioreactor formats and disruption of yeast cells to enable the isolation of the recombinant protein prior to purification. Written in the highly successful Methods in Molecular Biology™ series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and key tips on troubleshooting and avoiding known pitfalls.
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Improving bit error rates in optical communication systems is a difficult and important problem. The error correction must take place at high speed and be extremely accurate. We show the feasibility of using hardware implementable machine learning techniques. This may enable some error correction at the speed required.
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Sensory processing is a crucial underpinning of the development of social cognition, a function which is compromised in variable degree in patients with pervasive developmental disorders (PDD). In this manuscript, we review some of the most recent and relevant contributions, which have looked at auditory sensory processing derangement in PDD. The variability in the clinical characteristics of the samples studied so far, in terms of severity of the associated cognitive deficits and associated limited compliance, underlying aetiology and demographic features makes a univocal interpretation arduous. We hypothesise that, in patients with severe mental deficits, the presence of impaired auditory sensory memory as expressed by the mismatch negativity could be a non-specific indicator of more diffuse cortical deficits rather than causally related to the clinical symptomatology. More consistent findings seem to emerge from studies on less severely impaired patients, in whom increased pitch perception has been interpreted as an indicator of increased local processing, probably as compensatory mechanism for the lack of global processing (central coherence). This latter hypothesis seems extremely attractive and future trials in larger cohorts of patients, possibly standardising the characteristics of the stimuli are a much-needed development. Finally, specificity of the role of the auditory derangement as opposed to other sensory channels needs to be assessed more systematically using multimodal stimuli in the same patient group. (c) 2006 Elsevier B.V. All rights reserved.
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Motivated by policy goals to develop international research capability and our experiences of collaborative research, we wanted to learn more about the factors that influence success in collaborative research. This article presents a review of the academic literature on collaborative research, focusing on multinational teams doing international comparative research. We address the question ‘what accounts for variation in process and performance of collaborative research projects?’, through 11 themes: context; vision; reward and commitment; leadership; structure; contract; task capability; sociability; communication; finance; rhythm and pace. We then propose an agenda for future research with an analytic framework and, finally, our conclusions.
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Immunoinformatics is the application of informatics techniques to molecules of the immune system. One of its principal goals is the effective prediction of immunogenicity, be that at the level of epitope, subunit vaccine, or attenuated pathogen. Immunogenicity is the ability of a pathogen or component thereof to induce a specific immune response when first exposed to surveillance by the immune system, whereas antigenicity is the capacity for recognition by the extant machinery of the adaptive immune response in a recall response. In thisbook, we introduce these subjects and explore the current state of play in immunoinformatics and the in silico prediction of immunogenicity.
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Quantitative structure-activity relationship (QSAR) analysis is a cornerstone of modern informatics. Predictive computational models of peptide-major histocompatibility complex (MHC)-binding affinity based on QSAR technology have now become important components of modern computational immunovaccinology. Historically, such approaches have been built around semiqualitative, classification methods, but these are now giving way to quantitative regression methods. We review three methods--a 2D-QSAR additive-partial least squares (PLS) and a 3D-QSAR comparative molecular similarity index analysis (CoMSIA) method--which can identify the sequence dependence of peptide-binding specificity for various class I MHC alleles from the reported binding affinities (IC50) of peptide sets. The third method is an iterative self-consistent (ISC) PLS-based additive method, which is a recently developed extension to the additive method for the affinity prediction of class II peptides. The QSAR methods presented here have established themselves as immunoinformatic techniques complementary to existing methodology, useful in the quantitative prediction of binding affinity: current methods for the in silico identification of T-cell epitopes (which form the basis of many vaccines, diagnostics, and reagents) rely on the accurate computational prediction of peptide-MHC affinity. We have reviewed various human and mouse class I and class II allele models. Studied alleles comprise HLA-A*0101, HLA-A*0201, HLA-A*0202, HLA-A*0203, HLA-A*0206, HLA-A*0301, HLA-A*1101, HLA-A*3101, HLA-A*6801, HLA-A*6802, HLA-B*3501, H2-K(k), H2-K(b), H2-D(b) HLA-DRB1*0101, HLA-DRB1*0401, HLA-DRB1*0701, I-A(b), I-A(d), I-A(k), I-A(S), I-E(d), and I-E(k). In this chapter we show a step-by-step guide into predicting the reliability and the resulting models to represent an advance on existing methods. The peptides used in this study are available from the AntiJen database (http://www.jenner.ac.uk/AntiJen). The PLS method is available commercially in the SYBYL molecular modeling software package. The resulting models, which can be used for accurate T-cell epitope prediction, will be made are freely available online at the URL http://www.jenner.ac.uk/MHCPred.
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The binding between peptide epitopes and major histocompatibility complex (MHC) proteins is a major event in the cellular immune response. Accurate prediction of the binding between short peptides and class I or class II MHC molecules is an important task in immunoinformatics. SVRMHC which is a novel method to model peptide-MHC binding affinities based on support rector machine regression (SVR) is described in this chapter. SVRMHC is among a small handful of quantitative modeling methods that make predictions about precise binding affinities between a peptide and an MHC molecule. As a kernel-based learning method, SVRMHC has rendered models with demonstrated appealing performance in the practice of modeling peptide-MHC binding.