The Evolution of the SPRITE Detector and Related Components for Improved Performance of the UK Thermal Imaging Common Modules Class II

1990 ◽  
Author(s):  
Ann P. Davis
Keyword(s):  
Thermal Imaging ◽  
Class Ii ◽  
Improved Performance ◽  
The Uk

scholarly journals Multilayer modelling of the human transcriptome and biological mechanisms of complex diseases and traits

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Tiago Azevedo ◽  
Giovanna Maria Dimitri ◽  
Pietro Lió ◽  
Eric R. Gamazon
Keyword(s):  
Gene Expression ◽  
The Cancer Genome Atlas ◽  
Human Transcriptome ◽  
Reactive Protein ◽  
Cancer Genome Atlas ◽  
New Gene ◽  
Improved Performance ◽  
Genomic Resource ◽  
The Uk ◽  
Genetically Determined

AbstractHere, we performed a comprehensive intra-tissue and inter-tissue multilayer network analysis of the human transcriptome. We generated an atlas of communities in gene co-expression networks in 49 tissues (GTEx v8), evaluated their tissue specificity, and investigated their methodological implications. UMAP embeddings of gene expression from the communities (representing nearly 18% of all genes) robustly identified biologically-meaningful clusters. Notably, new gene expression data can be embedded into our algorithmically derived models to accelerate discoveries in high-dimensional molecular datasets and downstream diagnostic or prognostic applications. We demonstrate the generalisability of our approach through systematic testing in external genomic and transcriptomic datasets. Methodologically, prioritisation of the communities in a transcriptome-wide association study of the biomarker C-reactive protein (CRP) in 361,194 individuals in the UK Biobank identified genetically-determined expression changes associated with CRP and led to considerably improved performance. Furthermore, a deep learning framework applied to the communities in nearly 11,000 tumors profiled by The Cancer Genome Atlas across 33 different cancer types learned biologically-meaningful latent spaces, representing metastasis (p < 2.2 × 10−16) and stemness (p < 2.2 × 10−16). Our study provides a rich genomic resource to catalyse research into inter-tissue regulatory mechanisms, and their downstream consequences on human disease.

scholarly journals AI-MHC: an allele-integrated deep learning framework for improving Class I & Class II HLA-binding predictions

2018 ◽  
Author(s):  
John-William Sidhom ◽  
Drew Pardoll ◽  
Alexander Baras
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Immune System ◽  
State Of The Art ◽  
Class Ii ◽  
Class I ◽  
Binding Prediction ◽  
Translational Invariance ◽  
Mhc Molecules ◽  
Improved Performance

AbstractMotivationThe immune system has potential to present a wide variety of peptides to itself as a means of surveillance for pathogenic invaders. This means of surveillances allows the immune system to detect peptides derives from bacterial, viral, and even oncologic sources. However, given the breadth of the epitope repertoire, in order to study immune responses to these epitopes, investigators have relied on in-silico prediction algorithms to help narrow down the list of candidate epitopes, and current methods still have much in the way of improvement.ResultsWe present Allele-Integrated MHC (AI-MHC), a deep learning architecture with improved performance over the current state-of-the-art algorithms in human Class I and Class II MHC binding prediction. Our architecture utilizes a convolutional neural network that improves prediction accuracy by 1) allowing one neural network to be trained on all peptides for all alleles of a given class of MHC molecules by making the allele an input to the net and 2) introducing a global max pooling operation with an optimized kernel size that allows the architecture to achieve translational invariance in MHC-peptide binding analysis, making it suitable for sequence analytics where a frame of interest needs to be learned in a longer, variable length sequence. We assess AI-MHC against internal independent test sets and compare against all algorithms in the IEDB automated server benchmarks, demonstrating our algorithm achieves state-of-the-art for both Class I and Class II prediction.Availability and ImplementationAI-MHC can be used via web interface at baras.pathology.jhu.edu/[email protected]

Outcomes-based approaches and the devolved administrations

2018 ◽  
pp. 67-86
Author(s):  
Derek Birrell ◽  
Ann Marie Gray
Keyword(s):  
Northern Ireland ◽  
Policy Making ◽  
Improved Performance ◽  
The Uk ◽  
The Relationship

Governments in all three devolved administrations of the UK (Scotland, Wales and Northern Ireland) have been adopting what they define as an outcome based approach to aspects of their policy making. This chapter examines the attraction of outcome based approaches and the outcome based accountability (OBA) model in particular. It assesses the conceptual issues arising from the OBA methodology and its application and examines arguments about the value of the OBA approach. The relationship between outcomes and indicators and the use of OBA for monitoring and evaluating performance is discussed. While there has been substantial buy-in to the principle of an outcomes based approach in the three administrations the paper questions whether there is evidence that these approaches have improved performance and policy.

Albion: the UK 3rd generation high-performance thermal imaging programme

2007 ◽  
Author(s):  
R. K. McEwen ◽  
M. Lupton ◽  
M. Lawrence ◽  
P. Knowles ◽  
M. Wilson ◽  
...  
Keyword(s):  
Thermal Imaging ◽  
High Performance ◽  
The Uk

scholarly journals Understanding ‘The Essential Fact about Capitalism’: Markets, Competition and Creative Destruction

2001 ◽  
Vol 175 ◽  
pp. 67-84 ◽  
Author(s):  
Wendy Carlin ◽  
Jonathan Haskel ◽  
Paul Seabright
Keyword(s):  
Low Income ◽  
Productivity Growth ◽  
Selection Process ◽  
Low Income Countries ◽  
Poor Countries ◽  
Market Shares ◽  
Improved Performance ◽  
The Business Cycle ◽  
The Baltic ◽  
The Uk

This paper examines two ways in which competition works in modern capitalist economies to improve productivity. The first is through incentives: encouraging improvements in technology, organisation and effort on the part of existing establishments and firms. The second is through selection: replacing less-productive with more productive establishments and firms, whether smoothly via the transfer of market shares from less to more productive firms, or roughly through the exit of some firms and the entry of others. We report evidence from the UK suggesting that selection is responsible for a large proportion of aggregate productivity growth in manufacturing, and that much of this is due in turn to selection between plants belonging to multi-plant firms. We also investigate whether the nature of the selection process varies across the business cycle and report evidence suggesting that it is less effective in booms and recessions. Finally, although in principle productivity catch-up by low-income countries ought to be easier than innovation at the frontier, in the absence of a well functioning competitive infrastructure (a predicament that characterises many poor countries), selection may be associated with much more turbulence and a lower rate of productivity growth than in relatively prosperous societies. We report results of a survey of firms in transition economies suggesting that, particularly in the former Soviet states (excluding the Baltic states), poor output and productivity performance has not been due to an unwillingness on the part of firms to change and adapt. On the contrary, there has been a great deal of restructuring, much new entry and large reallocations of output between firms; but such activity has been much more weakly associated with improved performance than we would expect in established market economies.

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