Institution
Brunel University London
Education•London, United Kingdom•
About: Brunel University London is a education organization based out in London, United Kingdom. It is known for research contribution in the topics: Context (language use) & Large Hadron Collider. The organization has 10918 authors who have published 29515 publications receiving 893330 citations. The organization is also known as: Brunel & University of Brunel.
Papers published on a yearly basis
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Institute of Cancer Research1, Imperial College London2, University of Dundee3, King's College London4, University of Birmingham5, University of Southampton6, University of Manchester7, University of Sheffield8, University of Cambridge9, Cambridge University Hospitals NHS Foundation Trust10, Newcastle University11, University of Nottingham12, University of London13, Queen Mary University of London14, University of East Anglia15, University of Glasgow16, University College Dublin17, Cardiff University18, University of Leicester19, University College London20, Brunel University London21, Royal College of Surgeons in Ireland22, University of Stirling23, University of Chester24, University of Oxford25, University of Edinburgh26, National Cancer Research Institute27, Queen's University Belfast28, University College Cork29, University of Liverpool30, London Research Institute31, Brighton and Sussex Medical School32, University of Leeds33
TL;DR: With resources to conduct further high-quality targeted research focusing on the gaps identified, increased knowledge translating into improved clinical care should be achievable within five years.
Abstract: Introduction: Breast cancer remains a significant scientific, clinical and societal challenge. This gap analysis has reviewed and critically assessed enduring issues and new challenges emerging from recent research, and proposes strategies for translating solutions into practice. Methods: More than 100 internationally recognised specialist breast cancer scientists, clinicians and healthcare professionals collaborated to address nine thematic areas: genetics, epigenetics and epidemiology; molecular pathology and cell biology; hormonal influences and endocrine therapy; imaging, detection and screening; current/ novel therapies and biomarkers; drug resistance; metastasis, angiogenesis, circulating tumour cells, cancer ‘stem’ cells; risk and prevention; living with and managing breast cancer and its treatment. The groups developed summary papers through an iterative process which, following further appraisal from experts and patients, were melded into this summary account. (Continued on next page)
390 citations
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University of California, Davis1, University of California, Berkeley2, California Pacific Medical Center3, Organisation for Economic Co-operation and Development4, North Carolina State University5, International Agency for Research on Cancer6, Brunel University London7, United States Environmental Protection Agency8, University of California, San Francisco9, Maastricht University10, National Institute for Environmental Studies11, National Institutes of Health12, University of Texas at Austin13, California Environmental Protection Agency14
TL;DR: This Expert Consensus Statement reflects on how these ten KCs can be used to identify, organize and utilize mechanistic data when evaluating chemicals as EDCs, and uses diethylstilbestrol, bisphenol A and perchlorate as examples to illustrate this approach.
Abstract: Endocrine-disrupting chemicals (EDCs) are exogenous chemicals that interfere with hormone action, thereby increasing the risk of adverse health outcomes, including cancer, reproductive impairment, cognitive deficits and obesity. A complex literature of mechanistic studies provides evidence on the hazards of EDC exposure, yet there is no widely accepted systematic method to integrate these data to help identify EDC hazards. Inspired by work to improve hazard identification of carcinogens using key characteristics (KCs), we have developed ten KCs of EDCs based on our knowledge of hormone actions and EDC effects. In this Expert Consensus Statement, we describe the logic by which these KCs are identified and the assays that could be used to assess several of these KCs. We reflect on how these ten KCs can be used to identify, organize and utilize mechanistic data when evaluating chemicals as EDCs, and we use diethylstilbestrol, bisphenol A and perchlorate as examples to illustrate this approach.
390 citations
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TL;DR: Understanding how different cultures use the Net---as well as perceive the same Web sites---can translate to truly global e-commerce.
Abstract: Understanding how different cultures use the Net---as well as perceive the same Web sites---can translate to truly global e-commerce.
389 citations
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TL;DR: Using the Lyapunov method and stochastic analysis techniques, sufficient conditions are first derived to guarantee the existence of the desired controllers, and then the controller parameters are characterized in terms of linear matrix inequalities (LMIs).
387 citations
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TL;DR: An effective linear matrix inequality approach is developed to solve the neuron state estimation problem for neural networks with time-varying delays and can be easily extended to cope with the traditional stability analysis problem for delayed neural networks.
Abstract: In this letter, the state estimation problem is studied for neural networks with time-varying delays. The interconnection matrix and the activation functions are assumed to be norm-bounded. The problem addressed is to estimate the neuron states, through available output measurements, such that for all admissible time-delays, the dynamics of the estimation error is globally exponentially stable. An effective linear matrix inequality approach is developed to solve the neuron state estimation problem. In particular, we derive the conditions for the existence of the desired estimators for the delayed neural networks. We also parameterize the explicit expression of the set of desired estimators in terms of linear matrix inequalities (LMIs). Finally, it is shown that the main results can be easily extended to cope with the traditional stability analysis problem for delayed neural networks. Numerical examples are included to illustrate the applicability of the proposed design method.
385 citations
Authors
Showing all 11074 results
Name | H-index | Papers | Citations |
---|---|---|---|
Yang Yang | 171 | 2644 | 153049 |
Hongfang Liu | 166 | 2356 | 156290 |
Gavin Davies | 159 | 2036 | 149835 |
Marjo-Riitta Järvelin | 156 | 923 | 100939 |
Matt J. Jarvis | 144 | 1064 | 85559 |
Alexander Belyaev | 142 | 1895 | 100796 |
Louis Lyons | 138 | 1747 | 98864 |
Silvano Tosi | 135 | 1712 | 97559 |
John A Coughlan | 135 | 1312 | 96578 |
Kenichi Hatakeyama | 134 | 1731 | 102438 |
Kristian Harder | 134 | 1613 | 96571 |
Peter R Hobson | 133 | 1590 | 94257 |
Christopher Seez | 132 | 1256 | 89943 |
Liliana Teodorescu | 132 | 1471 | 90106 |
Umesh Joshi | 131 | 1249 | 90323 |