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1,772 results on '"Woodhead, P"'

1. DiVERT: Distractor Generation with Variational Errors Represented as Text for Math Multiple-choice Questions

Author

Fernandez, Nigel, Scarlatos, Alexander, Feng, Wanyong, Woodhead, Simon, and Lan, Andrew

Subjects
Computer Science - Computation and Language, Computer Science - Computers and Society, Computer Science - Machine Learning
Abstract

High-quality distractors are crucial to both the assessment and pedagogical value of multiple-choice questions (MCQs), where manually crafting ones that anticipate knowledge deficiencies or misconceptions among real students is difficult. Meanwhile, automated distractor generation, even with the help of large language models (LLMs), remains challenging for subjects like math. It is crucial to not only identify plausible distractors but also understand the error behind them. In this paper, we introduce DiVERT (Distractor Generation with Variational Errors Represented as Text), a novel variational approach that learns an interpretable representation of errors behind distractors in math MCQs. Through experiments on a real-world math MCQ dataset with 1,434 questions used by hundreds of thousands of students, we show that DiVERT, despite using a base open-source LLM with 7B parameters, outperforms state-of-the-art approaches using GPT-4o on downstream distractor generation. We also conduct a human evaluation with math educators and find that DiVERT leads to error labels that are of comparable quality to human-authored ones., Comment: EMNLP 2024: The 2024 Conference on Empirical Methods in Natural Language Processing

Published
2024

2. Math Multiple Choice Question Generation via Human-Large Language Model Collaboration

Author

Lee, Jaewook, Smith, Digory, Woodhead, Simon, and Lan, Andrew

Subjects
Computer Science - Computation and Language
Abstract

Multiple choice questions (MCQs) are a popular method for evaluating students' knowledge due to their efficiency in administration and grading. Crafting high-quality math MCQs is a labor-intensive process that requires educators to formulate precise stems and plausible distractors. Recent advances in large language models (LLMs) have sparked interest in automating MCQ creation, but challenges persist in ensuring mathematical accuracy and addressing student errors. This paper introduces a prototype tool designed to facilitate collaboration between LLMs and educators for streamlining the math MCQ generation process. We conduct a pilot study involving math educators to investigate how the tool can help them simplify the process of crafting high-quality math MCQs. We found that while LLMs can generate well-formulated question stems, their ability to generate distractors that capture common student errors and misconceptions is limited. Nevertheless, a human-AI collaboration has the potential to enhance the efficiency and effectiveness of MCQ generation., Comment: 17th International Conference on Educational Data Mining (EDM 2024)

Published
2024

3. Improving Automated Distractor Generation for Math Multiple-choice Questions with Overgenerate-and-rank

Author

Scarlatos, Alexander, Feng, Wanyong, Smith, Digory, Woodhead, Simon, and Lan, Andrew

Subjects
Computer Science - Computers and Society, Computer Science - Machine Learning
Abstract

Multiple-choice questions (MCQs) are commonly used across all levels of math education since they can be deployed and graded at a large scale. A critical component of MCQs is the distractors, i.e., incorrect answers crafted to reflect student errors or misconceptions. Automatically generating them in math MCQs, e.g., with large language models, has been challenging. In this work, we propose a novel method to enhance the quality of generated distractors through overgenerate-and-rank, training a ranking model to predict how likely distractors are to be selected by real students. Experimental results on a real-world dataset and human evaluation with math teachers show that our ranking model increases alignment with human-authored distractors, although human-authored ones are still preferred over generated ones., Comment: BEA workshop NAACL 2024

Published
2024

4. Exploring Automated Distractor Generation for Math Multiple-choice Questions via Large Language Models

Author

Feng, Wanyong, Lee, Jaewook, McNichols, Hunter, Scarlatos, Alexander, Smith, Digory, Woodhead, Simon, Ornelas, Nancy Otero, and Lan, Andrew

Subjects
Computer Science - Computation and Language
Abstract

Multiple-choice questions (MCQs) are ubiquitous in almost all levels of education since they are easy to administer, grade, and are a reliable format in assessments and practices. One of the most important aspects of MCQs is the distractors, i.e., incorrect options that are designed to target common errors or misconceptions among real students. To date, the task of crafting high-quality distractors largely remains a labor and time-intensive process for teachers and learning content designers, which has limited scalability. In this work, we study the task of automated distractor generation in the domain of math MCQs and explore a wide variety of large language model (LLM)-based approaches, from in-context learning to fine-tuning. We conduct extensive experiments using a real-world math MCQ dataset and find that although LLMs can generate some mathematically valid distractors, they are less adept at anticipating common errors or misconceptions among real students., Comment: NAACL 2024 findings

Published
2024

5. Improving the Validity of Automatically Generated Feedback via Reinforcement Learning

Author

Scarlatos, Alexander, Smith, Digory, Woodhead, Simon, and Lan, Andrew

Subjects
Computer Science - Computation and Language
Abstract

Automatically generating feedback via large language models (LLMs) in intelligent tutoring systems and online learning platforms has the potential to improve the learning outcomes of many students. However, both feedback generation and evaluation are challenging: feedback content has to be valid especially in subjects like math, which requires models to understand the problem, the solution, and where the student's error lies. Feedback also has to be pedagogically valid to reflect effective tutoring strategies, such as explaining possible misconceptions and encouraging the student, among other desirable features. In this work, we address both problems of automatically generating and evaluating feedback while considering both correctness and alignment. First, we propose a rubric for evaluating math feedback and show that GPT-4 is able to effectively use it to annotate human-written and LLM-generated feedback. Second, we propose a framework for feedback generation that optimizes both correctness and alignment using reinforcement learning (RL). Specifically, we use GPT-4's annotations to create preferences over feedback pairs in an augmented dataset for training via direct preference optimization (DPO). We show that our methods significantly increase the correctness and alignment of generated feedback with Llama 2, an open-source LLM, qualitatively analyze our generation and evaluation systems using case studies, and outline several areas for future work.

Published
2024

8. Novice Learner and Expert Tutor: Evaluating Math Reasoning Abilities of Large Language Models with Misconceptions

Author

Liu, Naiming, Sonkar, Shashank, Wang, Zichao, Woodhead, Simon, and Baraniuk, Richard G.

Subjects
Computer Science - Computation and Language
Abstract

We propose novel evaluations for mathematical reasoning capabilities of Large Language Models (LLMs) based on mathematical misconceptions. Our primary approach is to simulate LLMs as a novice learner and an expert tutor, aiming to identify the incorrect answer to math question resulted from a specific misconception and to recognize the misconception(s) behind an incorrect answer, respectively. Contrary to traditional LLMs-based mathematical evaluations that focus on answering math questions correctly, our approach takes inspirations from principles in educational learning sciences. We explicitly ask LLMs to mimic a novice learner by answering questions in a specific incorrect manner based on incomplete knowledge; and to mimic an expert tutor by identifying misconception(s) corresponding to an incorrect answer to a question. Using simple grade-school math problems, our experiments reveal that, while LLMs can easily answer these questions correctly, they struggle to identify 1) the incorrect answer corresponding to specific incomplete knowledge (misconceptions); 2) the misconceptions that explain particular incorrect answers. Our study indicates new opportunities for enhancing LLMs' math reasoning capabilities, especially on developing robust student simulation and expert tutoring models in the educational applications such as intelligent tutoring systems.

Published
2023

9. Automated Distractor and Feedback Generation for Math Multiple-choice Questions via In-context Learning

Author

McNichols, Hunter, Feng, Wanyong, Lee, Jaewook, Scarlatos, Alexander, Smith, Digory, Woodhead, Simon, and Lan, Andrew

Subjects
Computer Science - Computation and Language
Abstract

Multiple-choice questions (MCQs) are ubiquitous in almost all levels of education since they are easy to administer, grade, and are a reliable form of assessment. An important aspect of MCQs is the distractors, i.e., incorrect options that are designed to target specific misconceptions or insufficient knowledge among students. To date, the task of crafting high-quality distractors has largely remained a labor-intensive process for teachers and learning content designers, which has limited scalability. In this work, we explore the task of automated distractor and corresponding feedback message generation in math MCQs using large language models. We establish a formulation of these two tasks and propose a simple, in-context learning-based solution. Moreover, we propose generative AI-based metrics for evaluating the quality of the feedback messages. We conduct extensive experiments on these tasks using a real-world MCQ dataset. Our findings suggest that there is a lot of room for improvement in automated distractor and feedback generation; based on these findings, we outline several directions for future work., Comment: NeurlIPS 2023 GAIED Workshop Version

Published
2023

10. The heart is a resident tissue for hematopoietic stem and progenitor cells in zebrafish

Author

Dorothee Bornhorst, Amulya V. Hejjaji, Lena Steuter, Nicole M. Woodhead, Paul Maier, Alessandra Gentile, Alice Alhajkadour, Octavia Santis Larrain, Michael Weber, Khrievono Kikhi, Stefan Guenther, Jan Huisken, Owen J. Tamplin, Didier Y. R. Stainier, and Felix Gunawan

Subjects
Science
Abstract

Abstract The contribution of endocardial cells (EdCs) to the hematopoietic lineages has been strongly debated. Here, we provide evidence that in zebrafish, the endocardium gives rise to and maintains a stable population of hematopoietic cells. Using single-cell sequencing, we identify an endocardial subpopulation expressing enriched levels of hematopoietic-promoting genes. High-resolution microscopy and photoconversion tracing experiments uncover hematopoietic cells, mainly hematopoietic stem and progenitor cells (HSPCs)/megakaryocyte-erythroid precursors (MEPs), derived from EdCs as well as the dorsal aorta stably attached to the endocardium. Emergence of HSPCs/MEPs in hearts cultured ex vivo without external hematopoietic sources, as well as longitudinal imaging of the beating heart using light sheet microscopy, support endocardial contribution to hematopoiesis. Maintenance of these hematopoietic cells depends on the adhesion factors Integrin α4 and Vcam1 but is at least partly independent of cardiac trabeculation or shear stress. Finally, blocking primitive erythropoiesis increases cardiac-residing hematopoietic cells, suggesting that the endocardium is a hematopoietic reservoir. Altogether, these studies uncover the endocardium as a resident tissue for HSPCs/MEPs and a de novo source of hematopoietic cells.

Published
2024
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17. Acute hypoalgesic, neurophysiological and perceptual responses to low‐load blood flow restriction exercise and high‐load resistance exercise

Author

Ryan Norbury, Ian Grant, Alex Woodhead, Luke Hughes, Jamie Tallent, and Stephen D. Patterson

Subjects
blood flow restriction exercise, pain, transcranial magnetic stimulation, Physiology, QP1-981
Abstract

Abstract This study compared the acute hypoalgesic and neurophysiological responses to low‐load resistance exercise with and without blood flow restriction (BFR), and free‐flow, high‐load exercise. Participants performed four experimental conditions where they completed baseline measures of pain pressure threshold (PPT), maximum voluntary force (MVF) with peripheral nerve stimulation to determine central and peripheral fatigue. Corticospinal excitability (CSE), corticospinal inhibition and short interval intracortical inhibition (SICI) were estimated with transcranial magnetic stimulation. Participants then performed low‐load leg press exercise at 30% of one‐repetition maximum (LL); low‐load leg press with BFR at 40% (BFR40) or 80% (BFR80) of limb occlusion pressure; or high‐load leg press of four sets of 10 repetitions at 70% one‐repetition maximum (HL). Measurements were repeated at 5, 45 min and 24 h post‐exercise. There were no differences in CSE or SICI between conditions (all P > 0.05); however, corticospinal inhibition was reduced to a greater extent (11%–14%) in all low‐load conditions compared to HL (P 0.05). In summary, low‐load BFR resistance exercise does not induce different acute neurophysiological responses to low‐load, free‐flow exercise but it does promote a greater degree of hypoalgesia and reduces corticospinal inhibition more than high‐load exercise, making it a useful rehabilitation tool. The changes in neurophysiology following exercise were not related to changes in PPT.

Published
2024
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18. NeurIPS Competition Instructions and Guide: Causal Insights for Learning Paths in Education

Author

Gong, Wenbo, Smith, Digory, Wang, Zichao, Barton, Craig, Woodhead, Simon, Pawlowski, Nick, Jennings, Joel, and Zhang, Cheng

Subjects
Computer Science - Computers and Society, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

In this competition, participants will address two fundamental causal challenges in machine learning in the context of education using time-series data. The first is to identify the causal relationships between different constructs, where a construct is defined as the smallest element of learning. The second challenge is to predict the impact of learning one construct on the ability to answer questions on other constructs. Addressing these challenges will enable optimisation of students' knowledge acquisition, which can be deployed in a real edtech solution impacting millions of students. Participants will run these tasks in an idealised environment with synthetic data and a real-world scenario with evaluation data collected from a series of A/B tests., Comment: 19 pages, NeurIPS 2022 Competition Track

Published
2022

19. Almost qudits in the prepare-and-measure scenario

Author

Pauwels, Jef, Pironio, Stefano, Woodhead, Erik, and Tavakoli, Armin

Subjects
Quantum Physics
Abstract

Quantum communication is often investigated in scenarios where only the dimension of Hilbert space is known. However, assigning a precise dimension is often an approximation of what is actually a higher-dimensional process. Here, we introduce and investigate quantum information encoded in carriers that nearly, but not entirely, correspond to standard qudits. We demonstrate the relevance of this concept for semi-device-independent quantum information by showing how small higher-dimensional components can significantly compromise the conclusions of established protocols. Then we provide a general method, based on semidefinite relaxations, for bounding the set of almost qudit correlations, and apply it to remedy the demonstrated issues. This method also offers a novel systematic approach to the well-known task of device-independent tests of classical and quantum dimensions with unentangled devices. Finally, we also consider viewing almost qubit systems as a physical resource available to the experimenter and determine the optimal quantum protocol for the well-known Random Access Code., Comment: 5 + 5 pages, 6 figures. Changelog: v2 minor correction

Published
2022
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21. A regolith lead isoscape of Australia

Author

C. U. Desem, P. de Caritat, J. Woodhead, R. Maas, and G. Carr

Subjects
Environmental sciences, GE1-350, Geology, QE1-996.5
Abstract

We present the first national-scale lead (Pb) isotope maps of Australia based on surface regolith for five isotope ratios, 206Pb/204Pb, 207Pb/204Pb, 208Pb/204Pb, 207Pb/206Pb, and 208Pb/206Pb, determined by single-collector sector field inductively coupled plasma mass spectrometry after an ammonium acetate leach followed by aqua regia digestion. The dataset is underpinned principally by the National Geochemical Survey of Australia (NGSA) archived floodplain sediment samples. We analysed 1219 samples (0–10 cm depth, mm grain size), collected near the outlet of 1119 large catchments covering 5.647×106 km2 (∼75 % of Australia). The samples consist of mixtures of the dominant soils and rocks weathering in their respective catchments (and possibly those upstream) and are therefore assumed to form a reasonable representation of the average isotopic signature of those catchments. This assumption was tested in one of the NGSA catchments, within which 12 similar samples were also taken; results show that the Pb isotope ratios of the NGSA catchment outlet sediment sample are close to the average of the 12 upstream sub-catchment samples. National minimum, median, and maximum values were 15.56, 18.84, and 30.64 for 206Pb/204Pb; 14.36, 15.69, and 18.01 for 207Pb/204Pb; 33.56, 38.99, and 48.87 for 208Pb/204Pb; 0.5880, 0.8318, and 0.9847 for 207Pb/206Pb; and 1.4149, 2.0665, and 2.3002 for 208Pb/206Pb, respectively. The new dataset was compared with published bedrock and ore Pb isotope data, and it was found to dependably represent crustal elements of various ages from Archaean to Phanerozoic. This suggests that floodplain sediment samples are a suitable proxy for basement and basin geology at this scale, despite various degrees of transport, mixing, and weathering experienced in the regolith environment, locally over protracted periods of time. An example of atmospheric Pb contamination around Port Pirie, South Australia, where a Pb smelter has operated since the 1890s, is shown to illustrate potential environmental applications of this new dataset. Other applications may include elucidating details of Australian crustal evolution and mineralisation-related investigations. The new regolith Pb isotope dataset for Australia is publicly available (Desem et al., 2023; https://doi.org/10.26186/5ea8f6fd3de64).

Published
2024
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22. What factors influence pain scores following Corticosteroid injection in patients with Greater Trochanteric Pain Syndrome? A systematic review

Author

Ben Foxcroft, Gareth Stephens, Tim Woodhead, and Colin Ayre

Subjects
GTPS, Steroid, Injection, Factors, Outcome, Diseases of the musculoskeletal system, RC925-935
Abstract

Abstract Background Cortico-Steroid Injections (CSI) are commonly used to treat patients with Greater Trochanteric Pain Syndrome (GTPS) but it is unclear which patients will experience improvements in pain. Objectives To identify factors that influence improvements in pain for patients with GTPS treated with CSI. Design Systematic review. Methods A search was undertaken of AMED, CINAHL, Cochrane Library, EMBASE, Medline and PEDro databases. Studies were eligible for inclusion if they investigated factors that influenced changes in pain experienced by patients with GTPS who received a CSI. Studies needed to include relevant summary statistics and tests of clinical significance. Risk Of Bias in Non-randomised Trials Of Interventions (ROBINS-I) and Risk Of Bias 2 (ROB2) tools were used to assess bias. Results The search identified 466 studies, 8 were included in the final review with a total of 643 participants. There was no association between demographic variables such as age, sex, symptom duration or obesity and pain outcomes post-CSI. Having a co-existing musculoskeletal (MSK) condition such as knee osteoarthritis or sacroiliac/lumbar spine pain was associated with less pain reduction post-CSI. Injections into the Trochanteric Bursa were associated with longer lasting pain reduction than Gluteus Medius Bursa or extra-bursal injections. Image guidance of CSI maintained lower pain scores at six months but did not increase the duration of the therapeutic effect past six months. The presence of specific ultrasound scan features was not associated with differences in pain scores. Conclusions Patients with co-existing MSK conditions may not respond to CSI as well as those without. Injections into the Greater Trochanteric Bursa may have longer lasting benefit. Further research is needed on the use of USS imaging findings and image guidance.

Published
2024
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23. Developing Crowdfunding-Based Financing Scheme to Increase Potential of Micro, Small, and Medium Business in Urban Green Space

Author

Mohammed Ali Berawi, Mustika Sari, Vanana Lumbantobing, suci indah susilowati, Bambang Susantono, Roy Woodhead, and Perdana Miraj Sejatiguna

Subjects
crowdfunding, life cycle cost, msmes, urban green space, Technology, Technology (General), T1-995
Abstract

Micro, Small, and Medium Business (MSMEs) are vital to the Indonesian economic development, and the government is focusing on this sector for post-pandemic recovery. There is an emphasis on infrastructure such as urban green spaces, or Ruang Terbuka Hijau (RTH). RTHs have untapped potential as centers for agricultural and processing MSMEs. However, these businesses often face financing challenges, prompting the exploration of alternative scheme like crowdfunding. Therefore, this study aimed to (1) explore how RTH lands can be leveraged for local economic activities, and (2) devise a public fund-based crowdfunding financing model. Using RTH Kalijodo in Jakarta as a case study, the study included literature reviews, benchmarking, fieldwork, and Life Cycle Cost (LCC) analysis. The results show the need for the development of vertical hydroponics, tilapia farming, and tilapia fillet processing business in RTH Kalijodo. Financial analyses, including IRR calculations exceeding the 10.41% WACC, a positive NPV, and a payback period under five years, show the financial viability of these sectors.

Published
2024
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24. Impact of e-ASPECTS software on the performance of physicians compared to a consensus ground truth: a multi-reader, multi-case study.

Author

Kobeissi, Hassan, Kallmes, David, Benson, John, Nagelschneider, Alex, Madhavan, Ajay, Messina, Steven, Schwartz, Kara, Campeau, Norbert, Carr, Carrie, Nasr, Deena, Braksick, Sherri, Scharf, Eugene, Klaas, James, Woodhead, Zoe, Harston, George, Briggs, James, Joly, Olivier, Gerry, Stephen, Kuhn, Anna, Kostas, Angelos, AbdalKader, Mohamad, Kadirvel, Ramanathan, Brinjikji, Waleed, and Nael, Kambiz

Subjects
ASPECTS, imaging, neuroradiology, stroke, thrombectomy
Abstract

BACKGROUND: The Alberta Stroke Program Early CT Score (ASPECTS) is used to quantify the extent of injury to the brain following acute ischemic stroke (AIS) and to inform treatment decisions. The e-ASPECTS software uses artificial intelligence methods to automatically process non-contrast CT (NCCT) brain scans from patients with AIS affecting the middle cerebral artery (MCA) territory and generate an ASPECTS. This study aimed to evaluate the impact of e-ASPECTS (Brainomix, Oxford, UK) on the performance of US physicians compared to a consensus ground truth. METHODS: The study used a multi-reader, multi-case design. A total of 10 US board-certified physicians (neurologists and neuroradiologists) scored 54 NCCT brain scans of patients with AIS affecting the MCA territory. Each reader scored each scan on two occasions: once with and once without reference to the e-ASPECTS software, in random order. Agreement with a reference standard (expert consensus read with reference to follow-up imaging) was evaluated with and without software support. RESULTS: A comparison of the area under the curve (AUC) for each reader showed a significant improvement from 0.81 to 0.83 (p = 0.028) with the support of the e-ASPECTS tool. The agreement of reader ASPECTS scoring with the reference standard was improved with e-ASPECTS compared to unassisted reading of scans: Cohens kappa improved from 0.60 to 0.65, and the case-based weighted Kappa improved from 0.70 to 0.81. CONCLUSION: Decision support with the e-ASPECTS software significantly improves the accuracy of ASPECTS scoring, even by expert US neurologists and neuroradiologists.

Published
2023

25. Multispectral confocal endomicroscopy in lung biopsy guidance

Author

Li, Zhenye, Woodhead, Gregory J, Rouse, Andrew R, Klein, Robert, Larson, Michael C, and Gmitro, Arthur F

Subjects
Cancer, Lung, Lung Cancer, Bioengineering, 4.1 Discovery and preclinical testing of markers and technologies, Detection, screening and diagnosis, endomicroscopy, fluorescence imaging, fiber bundle, lung cancer, multispectral imaging
Published
2023

26. Tilsotolimod Exploits the TLR9 Pathway to Promote Antigen Presentation and Type 1 IFN Signaling in Solid Tumors: A Multicenter International Phase I/II Trial (ILLUMINATE-101).

Author

Babiker, Hani, Borazanci, Erkut, Subbiah, Vivek, Agarwala, Sanjiv, Algazi, Alain, Schachter, Jacob, Lotem, Michael, Maurice-Dror, Corinne, Hendler, Daniel, Rahimian, Shah, Minderman, Hans, Haymaker, Cara, Mahadevan, Daruka, Bernatchez, Chantale, Murthy, Ravi, Hultsch, Rolf, Kaplan, Nadia, Woodhead, Gregory, Hennemeyer, Charles, Chunduru, Srinivas, Anderson, Peter, Diab, Adi, and Puzanov, Igor

Subjects
Humans, Toll-Like Receptor 9, Antigen Presentation, Neoplasms, Melanoma, Cohort Studies, Skin Neoplasms, Tumor Microenvironment
Abstract

PURPOSE: Tilsotolimod is an investigational synthetic Toll-like receptor 9 (TLR9) agonist that has demonstrated antitumor activity in preclinical models. The ILLUMINATE-101 phase I study explored the safety, dose, efficacy, and immune effects of intratumoral (it) tilsotolimod monotherapy in multiple solid tumors. PATIENTS AND METHODS: Patients with a diagnosis of refractory cancer not amenable to curative therapies received tilsotolimod in doses escalating from 8 to 32 mg into a single lesion at weeks 1, 2, 3, 5, 8, and 11. Additional patients with advanced malignant melanoma were enrolled into an expansion cohort at the 8 mg dose. Objectives included characterizing the safety, establishing the dose, efficacy, and immunologic assessment. Blood samples and tumor biopsies of injected and noninjected lesions were obtained at baseline and 24 hours after treatment for immune analyses. RESULTS: Thirty-eight and 16 patients were enrolled into the dose escalation and melanoma expansion cohorts, respectively. Deep visceral injections were conducted in 91% of patients. No dose-limiting toxicities (DLT) or grade 4 treatment-related adverse events were observed. Biopsies 24 hours after treatment demonstrated an increased IFN pathway signature and dendritic cell maturation. Immunologic profiling revealed upregulation of IFN-signaling genes and modulation of genes for checkpoint proteins. In the dose escalation cohort, 12 (34%) of 35 evaluable patients achieved a best overall response rate (ORR) of stable disease (SD), whereas 3 (19%) of 16 evaluable patients in the melanoma cohort achieved stable disease. CONCLUSIONS: Overall, tilsotolimod monotherapy was generally well tolerated and induced rapid, robust alterations in the tumor microenvironment. See related commentary by Punekar and Weber, p. 5007.

Published
2022

27. Long-term impact of COVID-19 hospitalisation among individuals with pre-existing airway diseases in the UK: a multicentre, longitudinal cohort study – PHOSP-COVID

Author

Omer Elneima, John R. Hurst, Carlos Echevarria, Jennifer K. Quint, Samantha Walker, Salman Siddiqui, Petr Novotny, Paul E. Pfeffer, Jeremy S. Brown, Manu Shankar-Hari, Hamish J.C. McAuley, Olivia C. Leavy, Aarti Shikotra, Amisha Singapuri, Marco Sereno, Matthew Richardson, Ruth M. Saunders, Victoria C. Harris, Linzy Houchen-Wolloff, Neil J. Greening, Ewen M. Harrison, Annemarie B. Docherty, Nazir I. Lone, James D. Chalmers, Ling-Pei Ho, Alex Horsley, Michael Marks, Krisnah Poinasamy, Betty Raman, Rachael A. Evans, Louise V. Wain, Aziz Sheikh, Chris E. Brightling, Anthony De Soyza, Liam G. Heaney, J.K. Baillie, N.I. Lone, E. Pairo-Castineira, N. Avramidis, K. Rawlik, S Jones, L. Armstrong, B. Hairsine, H. Henson, C. Kurasz, A. Shaw, L. Shenton, H. Dobson, A. Dell, S. Fairbairn, N. Hawkings, J. Haworth, M. Hoare, V. Lewis, A. Lucey, G. Mallison, H. Nassa, C. Pennington, A. Price, C. Price, A. Storrie, G. Willis, S. Young, K. Poinasamy, S. Walker, I. Jarrold, A. Sanderson, K. Chong-James, C. David, W.Y. James, P. Pfeffer, O. Zongo, A. Martineau, C. Manisty, C. Armour, V. Brown, J. Busby, B. Connolly, T. Craig, S. Drain, L.G. Heaney, B. King, N. Magee, E. Major, D. McAulay, L. McGarvey, J. McGinness, T. Peto, R. Stone, A. Bolger, F. Davies, A. Haggar, J. Lewis, A. Lloyd, R. Manley, E. McIvor, D. Menzies, K. Roberts, W. Saxon, D. Southern, C. Subbe, V. Whitehead, A. Bularga, N.L. Mills, J. Dawson, H. El-Taweel, L. Robinson, L. Brear, K. Regan, D. Saralaya, K. Storton, S. Amoils, A. Bermperi, I. Cruz, K. Dempsey, A. Elmer, J. Fuld, H. Jones, S. Jose, S. Marciniak, M. Parkes, C. Ribeiro, J. Taylor, M. Toshner, L. Watson, J. Worsley, L. Broad, T. Evans, M. Haynes, L. Jones, L. Knibbs, A. McQueen, C. Oliver, K. Paradowski, R. Sabit, J. Williams, I. Jones, L. Milligan, E. Harris, C. Sampson, E. Davies, C. Evenden, A. Hancock, K. Hancock, C. Lynch, M. Rees, L. Roche, N. Stroud, T. Thomas-Woods, S. Heller, T. Chalder, K. Shah, E. Robertson, B. Young, M. Babores, M. Holland, N. Keenan, S. Shashaa, H. Wassall, L. Austin, E. Beranova, T. Cosier, J. Deery, T. Hazelton, H. Ramos, R. Solly, S. Turney, H. Weston, M. Ralser, L. Pearce, S. Pugmire, W. Stoker, A. Wilson, W. McCormick, E. Fraile, J. Ugoji, L. Aguilar Jimenez, G. Arbane, S. Betts, K. Bisnauthsing, A. Dewar, N. Hart, G. Kaltsakas, H. Kerslake, M.M. Magtoto, P. Marino, L.M. Martinez, M. Ostermann, J. Rossdale, T.S. Solano, M. Alvarez Corral, A. Arias, E. Bevan, D. Griffin, J. Martin, J. Owen, S. Payne, A. Prabhu, A. Reed, W. Storrar, N. Williams, C. Wrey Brown, T. Burdett, J. Featherstone, C. Lawson, A. Layton, C. Mills, L. Stephenson, Y. Ellis, P. Atkin, K. Brindle, M.G. Crooks, K. Drury, N. Easom, R. Flockton, L. Holdsworth, A. Richards, D.L. Sykes, S. Thackray-Nocera, C. Wright, S. Coetzee, K. Davies, R. Hughes, R. Loosley, H. McGuinness, A. Mohamed, L. O'Brien, Z. Omar, E. Perkins, J. Phipps, G. Ross, A. Taylor, H. Tench, R. Wolf-Roberts, L. Burden, E. Calvelo, B. Card, C. Carr, E.R. Chilvers, D. Copeland, P. Cullinan, P. Daly, L. Evison, T. Fayzan, H. Gordon, S. Haq, R.G. Jenkins, C. King, O. Kon, K. March, M. Mariveles, L. McLeavey, N. Mohamed, S. Moriera, U. Munawar, J. Nunag, U. Nwanguma, L. Orriss-Dib, A. Ross, M. Roy, E. Russell, K. Samuel, J. Schronce, N. Simpson, L. Tarusan, D.C. Thomas, C. Wood, N. Yasmin, D. Altmann, L.S. Howard, D. Johnston, A. Lingford-Hughes, W.D-C. Man, J. Mitchell, P.L. Molyneaux, C. Nicolaou, D.P. O'Regan, L. Price, J. Quint, D. Smith, R.S. Thwaites, J. Valabhji, S. Walsh, C.M. Efstathiou, F. Liew, A. Frankel, L. Lightstone, S. McAdoo, M. Wilkins, M. Willicombe, R. Touyz, A-M. Guerdette, M. Hewitt, R. Reddy, K. Warwick, S. White, A. McMahon, M. Malim, K. Bramham, M. Brown, K. Ismail, T. Nicholson, C. Pariante, C. Sharpe, S. Wessely, J. Whitney, O. Adeyemi, R. Adrego, H. Assefa-Kebede, J. Breeze, S. Byrne, P. Dulawan, A. Hoare, C.J. Jolley, A. Knighton, S. Patale, I. Peralta, N. Powell, A. Ramos, K. Shevket, F. Speranza, A. Te, A. Shah, A. Chiribiri, C. O'Brien, A. Hayday, A. Ashworth, P. Beirne, J. Clarke, C. Coupland, M. Dalton, C. Favager, J. Glossop, J. Greenwood, L. Hall, T. Hardy, A. Humphries, J. Murira, D. Peckham, S. Plein, J. Rangeley, G. Saalmink, A.L. Tan, E. Wade, B. Whittam, N. Window, J. Woods, G. Coakley, L. Turtle, L. Allerton, A.M. Allt, M. Beadsworth, A. Berridge, J. Brown, S. Cooper, A. Cross, S. Defres, S.L. Dobson, J. Earley, N. French, W. Greenhalf, K. Hainey, H.E. Hardwick, J. Hawkes, V. Highett, S. Kaprowska, A.L. Key, L. Lavelle-Langham, N. Lewis-Burke, G. Madzamba, F. Malein, S. Marsh, C. Mears, L. Melling, M.J. Noonan, L. Poll, J. Pratt, E. Richardson, A. Rowe, M.G. Semple, V. Shaw, K.A. Tripp, L.O. Wajero, S.A. Williams-Howard, D.G. Wootton, J. Wyles, S.N. Diwanji, S. Gurram, P. Papineni, S. Quaid, G.F. Tiongson, E. Watson, A. Briggs, M. Marks, C. Hastie, N. Rogers, N. Smith, D. Stensel, L. Bishop, K. McIvor, P. Rivera-Ortega, B. Al-Sheklly, C. Avram, J. Blaikely, M. Buch, N. Choudhury, D. Faluyi, T. Felton, T. Gorsuch, N.A. Hanley, A. Horsley, T. Hussell, Z. Kausar, N. Odell, R. Osbourne, K. Piper Hanley, K. Radhakrishnan, S. Stockdale, T. Kabir, J.T. Scott, P.J.M. Openshaw, I.D. Stewart, D. Burn, A. Ayoub, G. Burns, G. Davies, A. De Soyza, C. Echevarria, H. Fisher, C. Francis, A. Greenhalgh, P. Hogarth, J. Hughes, K. Jiwa, G. Jones, G. MacGowan, D. Price, A. Sayer, J. Simpson, H. Tedd, S. Thomas, S. West, M. Witham, S. Wright, A. Young, M.J. McMahon, P. Neill, D. Anderson, N. Basu, H. Bayes, A. Brown, A. Dougherty, K. Fallon, L. Gilmour, D. Grieve, K. Mangion, A. Morrow, R. Sykes, C. Berry, I.B. McInnes, K. Scott, F. Barrett, A. Donaldson, E.K. Sage, D. Bell, R. Hamil, K. Leitch, L. Macliver, M. Patel, J. Quigley, A. Smith, B. Welsh, G. Choudhury, S. Clohisey, A. Deans, A.B. Docherty, J. Furniss, E.M. Harrison, S. Kelly, A. Sheikh, J.D. Chalmers, D. Connell, C. Deas, A. Elliott, J. George, S. Mohammed, J. Rowland, A.R. Solstice, D. Sutherland, C.J. Tee, J. Bunker, R. Gill, R. Nathu, K. Holmes, H. Adamali, D. Arnold, S. Barratt, A. Dipper, S. Dunn, N. Maskell, A. Morley, L. Morrison, L. Stadon, S. Waterson, H. Welch, B. Jayaraman, T. Light, I. Vogiatzis, P. Almeida, C.E. Bolton, A. Hosseini, L. Matthews, R. Needham, K. Shaw, A.K. Thomas, J. Bonnington, M. Chrystal, C. Dupont, P.L. Greenhaff, A. Gupta, W. Jang, S. Linford, A. Nikolaidis, S. Prosper, A. Burns, N. Kanellakis, V.M. Ferreira, C. Nikolaidou, C. Xie, M. Ainsworth, A. Alamoudi, A. Bloss, P. Carter, M. Cassar, J. Chen, F. Conneh, T. Dong, R.I. Evans, E. Fraser, J.R. Geddes, F. Gleeson, P. Harrison, M. Havinden-Williams, L.P. Ho, P. Jezzard, I. Koychev, P. Kurupati, H. McShane, C. Megson, S. Neubauer, D. Nicoll, G. Ogg, E. Pacpaco, M. Pavlides, Y. Peng, N. Petousi, J. Pimm, N.M. Rahman, B. Raman, M.J. Rowland, K. Saunders, M. Sharpe, N. Talbot, E.M. Tunnicliffe, A. Korszun, S. Kerr, R.E. Barker, D. Cristiano, N. Dormand, P. George, M. Gummadi, S. Kon, K. Liyanage, C.M. Nolan, B. Patel, S. Patel, O. Polgar, P. Shah, S. Singh, J.A. Walsh, M. Gibbons, S. Ahmad, S. Brill, J. Hurst, H. Jarvis, L. Lim, S. Mandal, D. Matila, O. Olaosebikan, C. Singh, C. Laing, H. Baxendale, L. Garner, C. Johnson, J. Mackie, A. Michael, J. Newman, J. Pack, K. Paques, H. Parfrey, J. Parmar, A. Reddy, M. Halling-Brown, P. Dark, N. Diar-Bakerly, D. Evans, E. Hardy, A. Harvey, D. Holgate, S. Knight, N. Mairs, N. Majeed, L. McMorrow, J. Oxton, J. Pendlebury, C. Summersgill, R. Ugwuoke, S. Whittaker, W. Matimba-Mupaya, S. Strong-Sheldrake, P. Chowienczyk, J. Bagshaw, M. Begum, K. Birchall, R. Butcher, H. Carborn, F. Chan, K. Chapman, Y. Cheng, L. Chetham, C. Clark, Z. Coburn, J. Cole, M. Dixon, A. Fairman, J. Finnigan, H. Foot, D. Foote, A. Ford, R. Gregory, K. Harrington, L. Haslam, L. Hesselden, J. Hockridge, A. Holbourn, B. Holroyd-Hind, L. Holt, A. Howell, E. Hurditch, F. Ilyas, C. Jarman, A. Lawrie, J-H. Lee, E. Lee, R. Lenagh, A. Lye, I. Macharia, M. Marshall, A. Mbuyisa, J. McNeill, S. Megson, J. Meiring, L. Milner, S. Misra, H. Newell, T. Newman, C. Norman, L. Nwafor, D. Pattenadk, M. Plowright, J. Porter, P. Ravencroft, C. Roddis, J. Rodger, S.L. Rowland-Jones, P. Saunders, J. Sidebottom, J. Smith, L. Smith, N. Steele, G. Stephens, R. Stimpson, B. Thamu, A.A.R. Thompson, N. Tinker, K. Turner, H. Turton, P. Wade, J. Watson, I. Wilson, A. Zawia, L. Allsop, K. Bennett, P. Buckley, M. Flynn, M. Gill, C. Goodwin, M. Greatorex, H. Gregory, C. Heeley, L. Holloway, M. Holmes, J. Hutchinson, J. Kirk, W. Lovegrove, T.A. Sewell, S. Shelton, D. Sissons, K. Slack, S. Smith, D. Sowter, S. Turner, V. Whitworth, I. Wynter, J. Tomlinson, L. Warburton, S. Painter, S. Palmer, D. Redwood, J. Tilley, C. Vickers, T. Wainwright, G. Breen, M. Hotopf, R. Aul, D. Forton, M. Ali, A. Dunleavy, M. Mencias, N. Msimanga, T. Samakomva, S. Siddique, V. Tavoukjian, J. Teixeira, R. Ahmed, R. Francis, L. Connor, A. Cook, G.A. Davies, T. Rees, F. Thaivalappil, C. Thomas, M. McNarry, K.E. Lewis, M. Coulding, S. Kilroy, J. McCormick, J. McIntosh, V. Turner, J. Vere, A. Butt, H. Savill, S.S. Kon, G. Landers, H. Lota, S. Portukhay, M. Nasseri, A. Daniels, A. Hormis, J. Ingham, L. Zeidan, M. Chablani, L. Osborne, S. Aslani, A. Banerjee, R. Batterham, G. Baxter, R. Bell, A. David, E. Denneny, A.D. Hughes, W. Lilaonitkul, P. Mehta, A. Pakzad, B. Rangelov, B. Williams, J. Willoughby, M. Xu, N. Ahwireng, D. Bang, D. Basire, J.S. Brown, R.C. Chambers, A. Checkley, R. Evans, M. Heightman, T. Hillman, J. Jacob, R. Jastrub, M. Lipman, S. Logan, D. Lomas, M. Merida Morillas, H. Plant, J.C. Porter, K. Roy, E. Wall, T. Treibel, N. Ahmad Haider, C. Atkin, R. Baggott, M. Bates, A. Botkai, A. Casey, B. Cooper, J. Dasgin, C. Dawson, K. Draxlbauer, N. Gautam, J. Hazeldine, T. Hiwot, S. Holden, K. Isaacs, T. Jackson, V. Kamwa, D. Lewis, J.M. Lord, S. Madathil, C. McGhee, K. McGee, A. Neal, A. Newton-Cox, J. Nyaboko, D. Parekh, Z. Peterkin, H. Qureshi, L. Ratcliffe, E. Sapey, J. Short, T. Soulsby, J. Stockley, Z. Suleiman, T. Thompson, M. Ventura, S. Walder, C. Welch, D. Wilson, S. Yasmin, K.P. Yip, N. Chaudhuri, C. Childs, R. Djukanovic, S. Fletcher, M. Harvey, M.G. Jones, E. Marouzet, B. Marshall, R. Samuel, T. Sass, T. Wallis, H. Wheeler, R. Steeds, P. Beckett, C. Dickens, U. Nanda, M. Aljaroof, N. Armstrong, H. Arnold, H. Aung, M. Bakali, M. Bakau, E. Baldry, M. Baldwin, C. Bourne, M. Bourne, C.E. Brightling, N. Brunskill, P. Cairns, L. Carr, A. Charalambou, C. Christie, M.J. Davies, E. Daynes, S. Diver, R. Dowling, S. Edwards, C. Edwardson, O. Elneima, H. Evans, R.A. Evans, J. Finch, S. Finney, S. Glover, N. Goodman, B. Gooptu, N.J. Greening, K. Hadley, P. Haldar, B. Hargadon, V.C. Harris, L. Houchen-Wolloff, W. Ibrahim, L. Ingram, K. Khunti, A. Lea, D. Lee, H.J.C. McAuley, G.P. McCann, P. McCourt, T. McNally, G. Mills, W. Monteiro, M. Pareek, S. Parker, A. Prickett, I.N. Qureshi, A. Rowland, R. Russell, M. Sereno, A. Shikotra, S. Siddiqui, A. Singapuri, S.J. Singh, J. Skeemer, M. Soares, E. Stringer, S. Terry, T. Thornton, M. Tobin, T.J.C. Ward, F. Woodhead, T. Yates, A.J. Yousuf, B. Guillen Guiio, O.C. Leavy, L.V. Wain, M. Broome, P. McArdle, D. Thickett, R. Upthegrove, D. Wilkinson, P. Moss, D. Wraith, J. Evans, E. Bullmore, J.L. Heeney, C. Langenberg, W. Schwaeble, C. Summers, J. Weir McCall, D. Adeloye, D.E. Newby, R. Pius, I. Rudan, M. Shankar-Hari, C.L. Sudlow, M. Thorpe, S. Walmsley, B. Zheng, L. Allan, C. Ballard, A. McGovern, J. Dennis, J. Cavanagh, S. MacDonald, K. O'Donnell, J. Petrie, N. Sattar, M. Spears, E. Guthrie, M. Henderson, R.J. Allen, M. Bingham, T. Brugha, R. Free, D. Jones, L. Gardiner, A.J. Moss, E. Mukaetova-Ladinska, P. Novotny, C. Overton, J.E. Pearl, T. Plekhanova, M. Richardson, N. Samani, J. Sargent, M. Sharma, M. Steiner, C. Taylor, C. Tong, E. Turner, J. Wormleighton, B. Zhao, K. Ntotsis, R.M. Saunders, D. Lozano-Rojas, D. Cuthbertson, G. Kemp, A. McArdle, B. Michael, W. Reynolds, L.G. Spencer, B. Vinson, M. Ashworth, K. Abel, H. Chinoy, B. Deakin, M. Harvie, C.A. Miller, S. Stanel, P. Barran, D. Trivedi, H. McAllister-Williams, S. Paddick, A. Rostron, J.P. Taylor, D. Baguley, C. Coleman, E. Cox, L. Fabbri, S. Francis, I. Hall, E. Hufton, S. Johnson, F. Khan, P. Kitterick, R. Morriss, N. Selby, L. Wright, C. Antoniades, A. Bates, M. Beggs, K. Bhui, K. Breeze, K.M. Channon, D. Clark, X. Fu, M. Husain, X. Li, E. Lukaschuk, C. McCracken, K. McGlynn, R. Menke, K. Motohashi, T.E. Nichols, G. Ogbole, S. Piechnik, I. Propescu, J. Propescu, A.A. Samat, Z.B. Sanders, L. Sigfrid, M. Webster, L. Kingham, P. Klenerman, H. Lamlum, G. Carson, M. Taquet, L. Finnigan, L.C. Saunders, J.M. Wild, P.C. Calder, N. Huneke, G. Simons, D. Baldwin, S. Bain, L. Daines, E. Bright, P. Crisp, R. Dharmagunawardena, M. Stern, L. Bailey, A. Reddington, A. Wight, A. Ashish, J. Cooper, E. Robinson, A. Broadley, L. Barman, C. Brookes, K. Elliott, L. Griffiths, Z. Guy, K. Howard, D. Ionita, H. Redfearn, C. Sarginson, and A. Turnbull

Subjects
Medicine
Abstract

Background The long-term outcomes of COVID-19 hospitalisation in individuals with pre-existing airway diseases are unknown. Methods Adult participants hospitalised for confirmed or clinically suspected COVID-19 and discharged between 5 March 2020 and 31 March 2021 were recruited to the Post-hospitalisation COVID-19 (PHOSP-COVID) study. Participants attended research visits at 5 months and 1 year post discharge. Clinical characteristics, perceived recovery, burden of symptoms and health-related quality of life (HRQoL) of individuals with pre-existing airway disease (i.e., asthma, COPD or bronchiectasis) were compared to the non-airways group. Results A total of 615 out of 2697 (22.8%) participants had a history of pre-existing airway diseases (72.0% diagnosed with asthma, 22.9% COPD and 5.1% bronchiectasis). At 1 year, the airways group participants were less likely to feel fully recovered (20.4% versus 33.2%, p

Published
2024
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28. Effectiveness of a nonweight‐based daily dosage of ready‐to‐use therapeutic food in children suffering from uncomplicated severe acute malnutrition: A nonrandomized, noninferiority analysis of programme data in Afghanistan

Author

Paluku Bahwere, Grace Funnell, Ahmad Nawid Qarizada, Sophie Woodhead, Wilfred Bengnwi, and Minh Tram Le

Subjects
Afghanistan, community‐based, effectiveness, management of acute malnutrition, noninferiority, ready‐to‐use therapeutic food, Pediatrics, RJ1-570, Gynecology and obstetrics, RG1-991, Nutritional diseases. Deficiency diseases, RC620-627
Abstract

Abstract Severe acute malnutrition (SAM) remains a major global public health problem. SAM cases are treated using ready‐to‐use therapeutic food (RUTF) at a dosage of ∼200 kcal/kg/day per the standard treatment protocol (STD). Emerging evidence on simplifications to the standard protocol, which among other adaptations, includes reducing the daily RUTF dosage, indicates that it is effective and safe for treating children with SAM. In response to a foreseen stock shortage of RUTF, the government of Afghanistan endorsed the temporary use of a modified treatment protocol in which the daily RUTF dosage was prescribed at 1000 kcal/day (irrespective of body weight) until the child achieved moderate acute malnutrition status (weight‐for‐height z‐score ≥ −3 or mid‐upper arm circumference [MUAC] ≥ 115 mm), at which point 500 kcal/day was prescribed until cured (modified treatment protocol [MTP]). In this paper, we report the results of this nonweight‐based daily RUTF dosage experience. Data of 2042 children with SAM, treated using either the STD protocol (n = 269) or the MTP protocol (n = 1773) from August 2019 to March 2021 in five provinces, were analyzed. The per‐protocol analyses confirmed noninferiority of MTP protocol when compared to STD protocol for recovery rate [93.3% vs. 90.2%; ∆ (95% confidence interval, CI) = 3.1 (−0.9; 7.2) %] and length‐of‐stay [82.6 vs. 75.6 days; ∆ (95% CI) = 6.9 (3.3; 10.5) days], considering the margin of noninferiority of −10% and +14 days, respectively. Weight gain velocity was smaller in the MTP protocol group than in the STD protocol group [3.7 (1.7) vs. 5.2 (2.9) g/kg/day; ∆ (95% CI) = −1.5 (−1.8, −1.2); p

Published
2024
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29. Long COVID and cardiovascular disease: a prospective cohort study

Author

Amitava Banerjee, Jennifer Kathleen Quint, Linzy Houchen-Wolloff, S Thomas, Kamlesh Khunti, Naveed Sattar, J Breeze, Michael Marks, S Johnson, D Smith, C Wright, Colin Berry, Matthew Richardson, Ling-Pei Ho, C Tong, Amisha Singapuri, J Chen, Gerry P McCann, J Cole, X Li, J Greenwood, S Plein, A Brown, J Smith, J Brown, M Brown, J Lewis, A Young, Nicholas L Mills, A Banerjee, R Hughes, C King, L Osborne, S Jones, A Wilson, R Francis, Stefan Neubauer, D Wilkinson, P Marino, N Hart, G Kaltsakas, Alastair James Moss, Betty Raman, John Greenwood, F Khan, J Martin, S Smith, A Casey, A Sheikh, P Carter, T Thompson, B Patel, N Rahman, C Coleman, N Smith, B Williams, K Turner, D Lee, S Barratt, J Williams, L Jones, A Smith, A Gupta, R Reddy, S White, N Williams, A Michael, V Turner, H Evans, L Hall, C Lawson, J Hughes, H Gordon, C Dawson, A Ford, J Simpson, C Bloomfield, E Lee, A Taylor, D Anderson, J Clarke, S Turner, K Shaw, P Shah, S Misra, J Evans, H Jones, M Ali, A Arias, C Dupont, A Harvey, J Wormleighton, A Reed, L Pearce, P Harrison, M Marks, K Shah, J Cooper, C Berry, C David, J Parmar, R Ahmed, P Almeida, M Holland, L Lim, J Mitchell, K Bennett, S Walker, S Ahmad, M Begum, B Young, L Wright, M Holmes, N Sattar, D Clark, Ewen Harrison, M Sharma, J Teixeira, S Patel, D Thomas, I B McInnes, Nazir I Lone, D Grieve, D Griffin, S Siddiqui, E Turner, K McGlynn, C Mills, N Mohamed, A Hosseini, S Knight, K Samuel, L Smith, Chris Brightling, B Guillen-Guio, A Dewar, C Bourne, SJ Singh, RA Evans, I Vogiatzis, D Parekh, S Mandal, H Adamali, M Heightman, P Rivera-Ortega, S Stanel, N Chaudhuri, Y Cheng, L Bishop, F Gleeson, S Janes, D Baldwin, D Arnold, N Maskell, T Nicholson, L Howard, M Toshner, M Steiner, A Price, D Price, M Lipman, A Shaw, J Busby, M Patel, L McGarvey, R Evans, S West, N Petousi, D Thickett, T Gorsuch, J Fuld, P Cullinan, L Houchen-Wolloff, R Free, E Daynes, A De Soyza, E Harris, H Parfrey, F Woodhead, L Watson, K Jiwa, G Davies, G Jones, J Hurst, M Spears, J Finch, A Dipper, C Echevarria, G Jenkins, I Stewart, E Sapey, N Talbot, B Gooptu, M Richardson, P Greenhaff, K Roy, S Holden, R Russell, M Gibbons, A Morley, J Porter, R Djukanovic, V Lewis, T Shaw, Jayanth Ranjit Arnold, K Elliott, S Young, A David, C Armour, S Edwards, H Henson, P Atkin, A Daniels, L Zeidan, M Broome, M Gill, A Broadley, L Matthews, H Redfearn, S Kelly, C Thomas, D Evans, Z Omar, E Perkins, Annemarie B Docherty, J George, S Wessely, R Upthegrove, L Lavelle-Langham, D Bell, James Chalmers, Alun D Hughes, Victoria Harris, B Cooper, S Byrne, P Moss, C Singh, S Painter, A McMahon, M Ainsworth, K Scott, G Mills, C Carr, D Jones, D Faluyi, S Kerr, A Richards, S Parker, P Dark, T Jackson, L Carr, C Taylor, E Watson, C Vickers, L Armstrong, B Hairsine, L Allsop, L Stephenson, E Beranova, M Bates, C McGhee, M Harvey, A Cook, S Dunn, I Wynter, H Tench, R Loosley, J Featherstone, L Bailey, D Wilson, N Gautam, A Burns, Neil J Greening, B Card, N Powell, T Craig, L Daines, CM Nolan, RE Barker, JA Walsh, O Polgar, S Diver, J Quint, A Dunleavy, C Avram, C Francis, R Aul, J Rossdale, G Burns, H Tedd, T Felton, L Morrison, C Xie, D Menzies, A Haggar, S Marciniak, S Francis, T Dong, H Jarvis, S Brill, A Martineau, F Liew, P Haldar, C Price, A Butt, T Kabir, N Armstrong, P Beirne, E Cox, W Storrar, P Beckett, W Ibrahim, S Cooper, D Lewis, E Robinson, L Allan, C Antoniades, J T Scott, K Radhakrishnan, N Bishop, J Taylor, J Kirk, C Heeley, M Hewitt, J Watson, J Hutchinson, L Finnigan, D Lomas, S Macdonald, H Chinoy, A Ross, A Mohamed, M Soares, C Oliver, A Lucey, N Simpson, N Basu, S Logan, M J Davies, P C Calder, L Griffiths, K Davies, J McNeill, X Fu, P Cairns, F Davies, M Xu, J Quigley, A Ramos, R Stone, K Roberts, A Prabhu, L Robinson, C Wood, M Baldwin, S Wright, M G Jones, K Saunders, C O’Brien, N Rogers, S Heller, K Chapman, C O'Brien, J M Wild, A L Tan, J McCormick, C Childs, C Coupland, M Buch, J Dennis, G Baxter, H Welch, A D Hughes, M J McMahon, A Howell, J Kwan, A Rowland, A Ashworth, S Walsh, J Owen, I Jones, E McIvor, D Connell, R Thwaites, A McGovern, J Petrie, G Arbane, R Butcher, C Brookes, K Khunti, T Yates, P Chowienczyk, M Witham, M Stern, M Marshall, S Payne, L S Howard, J Woods, A Hormis, C Johnson, J Jacob, P McArdle, T Chalder, K Holmes, M Sharpe, D Stensel, T Peto, F Chan, H Ramos, C E Bolton, J-H Lee, P Mehta, M Ashworth, M Dalton, A Lloyd, L Austin, C Sampson, S Palmer, P Klenerman, K Howard, I Rudan, A McQueen, K Fallon, Catherine Bagot, M Webster, E Davies, S Jose, A McArdle, D Johnston, H Fisher, C Lynch, T Hardy, S Mohammed, V C Harris, B Elliott, G Coakley, J Stockley, S Barrett, E Guthrie, Y Peng, M Ventura, N Selby, A Briggs, G Stephens, E Richardson, K Bhui, J McIntosh, K Lewis, N French, H Qureshi, M Henderson, A Elliott, N I Lone, C Clark, K Ismail, C Summers, S Fletcher, J Rowland, M Hotopf, A Korszun, S Shashaa, H Gregory, P Daly, E Robertson, J S Brown, A Bates, P Saunders, B Marshall, A Cross, A Donaldson, B Zhao, H Lamlum, I Wilson, P Buckley, J Dawson, S Glover, C Christie, B Connolly, M Parkes, L Holloway, B King, F Speranza, M Haynes, T Rees, I Cruz, T McNally, G Ross, G Carson, M Dixon, H Arnold, P M George, K Harrington, M Rees, R Morriss, C Dickens, C Laing, E Hardy, L P Ho, P Chowdhury, M Roy, J Glossop, J Pratt, R A Evans, P Wade, Rachael Evans, S Defres, J Short, S Neubauer, R Batterham, E Wall, T Newman, G J Kemp, J R Geddes, E Russell, C Langenberg, N A Hanley, R Samuel, S Haq, D Trivedi, J Willoughby, E Stringer, S Marsh, K Bramham, L Lightstone, A Hancock, S Shelton, J P Greenwood, N Brunskill, K Munro, T Soulsby, U Nanda, A Ashish, K Liyanage, L Holt, E R Chilvers, D E Newby, L Ingram, A Bolger, J Tomlinson, C Ballard, A Humphries, V Brown, C Sharpe, D Forton, P Kar, R Gregory, D Redwood, R Steeds, K Mangion, A Chiribiri, L Ratcliffe, G P McCann, K M Channon, A M Shah, N L Mills, A Lawrie, A Greenhalgh, K O’Donnell, T Evans, K Drury, D Sutherland, A A R Thompson, J K Baillie, K Hancock, M Hoare, J Valabhji, V Shaw, K SLACK, N M Rahman, C J Jolley, S J SINGH, J D Chalmers, C E Brightling, L G Heaney, D F McAuley, D Peckham, R C Chambers, R G Jenkins, P J M Openshaw, P Neill, H Wheeler, A Moss, C Overton, D Altmann, Alex Horsley, J Blaikley, M Ostermann, L G Spencer, A Horsley, A Singapuri, B Hargadon, K E Lewis, I Jarrold, A Shikotra, S Terry, S S Kon, M Pareek, G Choudhury, W Monteiro, M Bourne, D Nicoll, A Morrow, L Roche, D G Wootton, E K Sage, N J Greening, J Hazeldine, J M Lord, A Zawia, WDC Man, D C Thomas, H Baxendale, J Rodger, D Saralaya, T Hussell, A Lea, M McNarry, B Al-Sheklly, S Thackray-Nocera, T Thornton, J Skeemer, S Greenwood, E Fraser, L Stadon, N Kanellakis, N Magee, S Kon, A Hayday, A J Moss, A Yousuf, N Lewis-Burke, S Finney, T Hillman, H McShane, C Pennington, L Gardiner, R Dharmagunawardena, G MacGowan, L Fabbri, C Subbe, L Burden, P Jezzard, N Samani, C Manisty, P Novotny, D J Cuthbertson, G A Davies, M G Semple, J Murira, W Greenhalf, A Hoare, Louise V Wain, L V Wain, I Hall, G Willis, O Adeyemi, H McGuinness, F Thaivalappil, M Babores, B Michael, D Burn, B Zheng, M Husain, J Hawkes, N Goodman, L Broad, L Turtle, R Gill, J Haworth, J Cavanagh, S Piechnik, C A Miller, S Whittaker, C Ribeiro, R Touyz, P L Molyneaux, J C Porter, R Solly, A Dougherty, E Bullmore, A Sayer, C Kurasz, S Walmsley, D Southern, K Brindle, T Wallis, L O’Brien, S Madathil, A Wight, B Jayaraman, M Flynn, A Checkley, M Plowright, E Major, K Isaacs, M Pavlides, W Schwaeble, E M Harrison, A Ayoub, N Stroud, E Lukaschuk, D P O'Regan, E Wade, V M Ferreira, R I Evans, S Siddique, A Lingford-Hughes, C Nicolaou, B Deakin, H Dobson, A Layton, C Atkin, R Flockton, I Peralta, T Brugha, C Pariante, C Welch, A Frankel, M Tobin, S Fairbairn, A Rowe, A K Thomas, R Sykes, F Barrett, H Atkins, C Norman, L Milner, K Abel, P Crisp, C Nolan, J Mackie, Marco Sereno, Krisnah Poinasamy, S Gurram, G Saalmink, H Bayes, H Aung, P Pfeffer, H Nassa, W McCormick, Claire Alexandra Lawson, R J Allen, Omer Elneima, J Hockridge, B Raman, A Fairman, H Turton, N Majeed, J Bonnington, M Bakali, M Shankar-Hari, L Holdsworth, A Buttress, R Sabit, A Rostron, K Piper Hanley, Olivia C Leavy, Aarti Shikotra, D Wraith, J P Taylor, A Alamoudi, O Elneima, E Denneny, L Saunders, J Earley, M Ralser, O Kon, D Basire, G Simons, Hamish JC McAuley, Ruth Saunders, K Poinasamy, R Dowling, C Edwardson, L Houchen--Wolloff, O C Leavy, H J C McAuley, T Plekhanova, R M Saunders, M Sereno, Y Ellis, H E Hardwick, W Reynolds, B Venson, A B Docherty, D Lozano-Rojas, K Ntotsis, R Pius, M Halling-Brown, S Aslani, M Beggs, M P Cassar, C McCracken, R Menke, T E Nichols, C Nikolaidou, G Ogbole, B Rangelov, D P O’Regan, A Pakzad, I Propescu, A A Samat, Z B Sanders, T Treibel, E M Tunnicliffe, J Weir McCall, I Koychev, J Pearl, D Adeloye, D Baguley, G Breen, K Breeze, F Callard, N Huneke, P Kitterick, P Mansoori, H McAllister-Williams, K McIvor, L Milligan, E Mukaetova-Ladinska, A Nevado-Holgado, S Paddick, J Pimm, S Amoils, A Bularga, A N Sattar, C L Sudlow, C M Efstathiou, J L Heeney, S L Rowland-Jones, R S Thwaites, M J Rowland, E Hufton, J E Pearl, L C Saunders, S Bain, Man W D-C, E Baldry, M Beadsworth, M Harvie, J Sargent Pimm, L Sigfrid, J Whitney, S McAdoo, K McCafferty, M Willicombe, J Bunker, C Hastie, R Nathu, L Shenton, A Dell, N Hawkings, G Mallison, A Storrie, K Chong-James, W Y James, O Zongo, A Sanderson, S Drain, D McAulay, J McGinness, R Manley, W Saxon, V Whitehead, H El-Taweel, L Brear, K Regan, K Storton, A Bermperi, K Dempsey, A Elmer, J Worsley, L Knibbs, K Paradowski, C Evenden, T Thomas-Woods, J Bradley-Potts, N Keenan, H Wassall, H Weston, T Cosier, J Deery, T Hazelton, S Turney, S Pugmire, W Stoker, LA Aguilar Jimenez, S Betts, K Bisnauthsing, H Kerslake, MM Magtoto, LM Martinez, TS Solano, E Wynn, M Alvarez Corral, E Bevan, C Wrey Brown, T Burdett, N Easom, M G Crooks, D L Sykes, S Coetzee, J Phipps, R Wolf-Roberts, S Anifowose, E Calvelo, D Copeland, L Evison, T Fayzan, K March, M Mariveles, L McLeavey, S Moriera, U Munawar, J Nunag, U Nwanguma, L Orriss- Dib, J Schronce, L Tarusan, N Yasmin, A-M Guerdette, K Warwick, R Adrego, H Assefa-Kebede, P Dulawan, A Knighton, M Malim, S Patale, K Shevket, A Te, C Favager, J Rangeley, B Whittam, N Window, L Allerton, AM All, A Berridge, S L Dobson, K Hainey, V Highett, S Kaprowska, AL Key, S Koprowska, G Madzamba, F Malein, C Mears, L Melling, M J Noonan, L Poll, K A Tripp, B Vinson, L O Wajero, S A Williams-Howard, J Wyles, S N Diwanji, P Papineni, S Quaid, G F Tiongson, P Barran, J Blaikely, N Choudhury, Z Kausar, N Odell, R Osbourne, S Stockdale, P Hogarth, L Gilmour, R Hamil, K Leitch, L Macliver, B Welsh, S Clohisey, A Deans, J Furniss, C Deas, A R Solstice, C J Tee, S Waterson, T Light, M Chrystal, W Jang, S Linford, R Needham, A Nikolaidis, S Prosper, A Bloss, M Cassar, F Conneh, M Havinden-Williams, P Kurupati, C Megson, K Motohashi, G Ogg, E Pacpaco, J Propescu, E Tunnicliffe, D Cristiano, N Dormand, M Gummadi, D Matila, O Olaosebikan, L Garner, J Pack, K Paques, NDiar Bakerly, D Holgate, N Mairs, L McMorrow, J Oxton, J Pendlebury, C Summersgill, R Ugwuoke, W Matimba-Mupaya, S Strong-Sheldrake, J Bagshaw, K Birchall, H Carborn, L Chetham, Z Coburn, J Finnigan, H Foot, D Foote, L Haslam, L Hesselden, A Holbourn, B Holroyd- Hind, E Hurditch, F Ilyas, C Jarman, R Lenagh, A Lye, I Macharia, A Mbuyisa, S Megson, J Meiring, H Newell, L Nwafor, D Pattenadk, P Ravencroft, C Roddis, J Sidebottom, N Steele, R Stimpson, B Thamu, N Tinker, N Msimanga, M Mencias, T Samakomva, V Tavoukjian, C Goodwin, M Greatorex, W Lovegrove, TA Sewell, D Sissons, D Sowter, V Whitworth, L Warburton, T Wainwright, J Tilley, L Connor, M Coulding, S Kilroy, H Savill, J Vere, E Fraile, J Ugoji, H Lota, G Landers, M Nasseri, S Portukhay, J Ingham, M Chablani, N Ahwireng, B Bang, R Jastrub, M Merida Morillas, H Plant, N Ahmad Haider, R Baggott, A Botkai, J Dasgin, K Draxlbauer, T Hiwot, V Kamwa, K Mcgee, A Neal, A Newton Cox, J Nyaboko, Z Peterkin, Z Suleiman, S Walder, S Yasmin, K P Yip, M Aljaroof, M Bakau, M Bingham, A Charalambou, B Gootpu, K Hadley, P McCourt, A Prickett, I N Qureshi, T J C Ward, E Marouzet, T Sass, E Bright, A Reddington, L Barman, Z Guy, and D Ionita

Subjects
Diseases of the circulatory (Cardiovascular) system, RC666-701
Abstract

Background Pre-existing cardiovascular disease (CVD) or cardiovascular risk factors have been associated with an increased risk of complications following hospitalisation with COVID-19, but their impact on the rate of recovery following discharge is not known.Objectives To determine whether the rate of patient-perceived recovery following hospitalisation with COVID-19 was affected by the presence of CVD or cardiovascular risk factors.Methods In a multicentre prospective cohort study, patients were recruited following discharge from the hospital with COVID-19 undertaking two comprehensive assessments at 5 months and 12 months. Patients were stratified by the presence of either CVD or cardiovascular risk factors prior to hospitalisation with COVID-19 and compared with controls with neither. Full recovery was determined by the response to a patient-perceived evaluation of full recovery from COVID-19 in the context of physical, physiological and cognitive determinants of health.Results From a total population of 2545 patients (38.8% women), 472 (18.5%) and 1355 (53.2%) had CVD or cardiovascular risk factors, respectively. Compared with controls (n=718), patients with CVD and cardiovascular risk factors were older and more likely to have had severe COVID-19. Full recovery was significantly lower at 12 months in patients with CVD (adjusted OR (aOR) 0.62, 95% CI 0.43 to 0.89) and cardiovascular risk factors (aOR 0.66, 95% CI 0.50 to 0.86).Conclusion Patients with CVD or cardiovascular risk factors had a delayed recovery at 12 months following hospitalisation with COVID-19. Targeted interventions to reduce the impact of COVID-19 in patients with cardiovascular disease remain an unmet need.Trail registration number ISRCTN10980107.

Published
2024
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30. Activity provider-facilitated patient and public involvement with care home residents

Author

Kerry Micklewright, Anne Killett, Gizdem Akdur, Priti Biswas, Pamela Blades, Lisa Irvine, Liz Jones, Julienne Meyer, Natalie Ravenscroft, Hilary Woodhead, and Claire Goodman

Subjects
Patient and public involvement and engagement (PPIE), Aged, Care home, Activity provider, Older people, Participatory action research, Medicine, Medicine (General), R5-920
Abstract

Abstract Background In care home research, residents are rarely included in patient and public involvement and engagement (PPIE) despite their lived experiences of day-to-day care. This paper reports on a novel approach to PPIE, developed in response to Covid-19, and utilised in a large UK-based study focused on care homes. PPIE sessions were facilitated on behalf of the research team by Activity Providers (APs) already working within the care homes. This paper provides an account of how PPIE with care home residents can be achieved. Methods An exploratory design was used to see if it was possible to support “in-house” PPIE, with researchers working at a distance in partnership with care home staff. The National Activity Providers Association recruited five APs working in care homes. A series of optional discussion or activity sessions were developed by the research team in partnership with APs, tailored to reflect the research topics of interest and to make sessions accessible to residents with differing needs. Results APs facilitated four rounds of PPIE with up to 56 residents per topic, including individuals living with cognitive and communication impairments. Topics discussed included residents’ views on data use, measuring quality of life and the prioritisation of care-related data for study collection. Feedback from the residents was observed to have unexpected and positive changes to participating care homes’ practice. APs valued participation and working with researchers. They identified acquisition of new skills and insights into residents’ thoughts and preferences as direct benefits. Challenges included time pressures on APs and managing emotive feedback. APs were able to approach residents at times convenient to them and in ways that best suited their individual needs. PPIE with residents provided different perspectives, particularly with respect to the importance of different types of data, and constructive challenge about some of the research team’s assumptions. Conclusions PPIE with APs as research partners is a promising approach to working in an inclusive and participatory way with care home residents. The voices of older care home residents, including those living with cognitive or communicative impairments, are important for the successful and meaningful completion of research.

Published
2024
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31. Developing Machine Learning Model to Predict HVAC System of Healthy Building: A Case Study in Indonesia

Author

Mustika Sari, Mohammed Ali Berawi, Sylvia Putri Larasati, Suci Indah Susilowati, Bambang Susantono, and Roy Woodhead

Subjects
healthy building, indoor air quality, machine learning, Technology, Technology (General), T1-995
Abstract

Sick Building Syndrome (SBS) is the health and comfort issues experienced by people during the time indoor. As urban dwellers typically spend 90% of the time indoor, Indoor Air Quality (IAQ) becomes essential. Consequently, ensuring appropriate air exchange in building is essential, with Heating, Ventilation, and Air-Conditioning (HVAC) system playing a crucial ole in maintaining indoor comfort. Therefore, this study aimed to develop a predictive machine learning (ML) model using Industry 4.0 technological advancements to optimize HVAC system design that meets IAQ parameters in Indonesia healthy building (HB). An extensive literature review was carried out to identify IAQ parameters specific to Indonesia HB. Furthermore, four ML models were developed using the RapidMiner Studio application, validated with the Mean Absolute Error (MAE), and confusion matrix methods. The results showed that the cooling load and the chiller-type prediction models had a relative error of 1.11% and 3.33%. Meanwhile, Air Handling Unit (AHU) type and filter area predictive model had a relative error of 10% and 1.22%, respectively. These errors showed the accuracy of ML model in predicting HVAC system of HB.

Published
2023
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32. Simultaneous Missing Value Imputation and Structure Learning with Groups

Author

Morales-Alvarez, Pablo, Gong, Wenbo, Lamb, Angus, Woodhead, Simon, Jones, Simon Peyton, Pawlowski, Nick, Allamanis, Miltiadis, and Zhang, Cheng

Subjects
Computer Science - Machine Learning
Abstract

Learning structures between groups of variables from data with missing values is an important task in the real world, yet difficult to solve. One typical scenario is discovering the structure among topics in the education domain to identify learning pathways. Here, the observations are student performances for questions under each topic which contain missing values. However, most existing methods focus on learning structures between a few individual variables from the complete data. In this work, we propose VISL, a novel scalable structure learning approach that can simultaneously infer structures between groups of variables under missing data and perform missing value imputations with deep learning. Particularly, we propose a generative model with a structured latent space and a graph neural network-based architecture, scaling to a large number of variables. Empirically, we conduct extensive experiments on synthetic, semi-synthetic, and real-world education data sets. We show improved performances on both imputation and structure learning accuracy compared to popular and recent approaches.

Published
2021

33. CoRGi: Content-Rich Graph Neural Networks with Attention

Author

Kim, Jooyeon, Lamb, Angus, Woodhead, Simon, Jones, Simon Peyton, Zheng, Cheng, and Allamanis, Miltiadis

Subjects
Computer Science - Machine Learning
Abstract

Graph representations of a target domain often project it to a set of entities (nodes) and their relations (edges). However, such projections often miss important and rich information. For example, in graph representations used in missing value imputation, items - represented as nodes - may contain rich textual information. However, when processing graphs with graph neural networks (GNN), such information is either ignored or summarized into a single vector representation used to initialize the GNN. Towards addressing this, we present CoRGi, a GNN that considers the rich data within nodes in the context of their neighbors. This is achieved by endowing CoRGi's message passing with a personalized attention mechanism over the content of each node. This way, CoRGi assigns user-item-specific attention scores with respect to the words that appear in an item's content. We evaluate CoRGi on two edge-value prediction tasks and show that CoRGi is better at making edge-value predictions over existing methods, especially on sparse regions of the graph.

Published
2021

34. Entanglement in prepare-and-measure scenarios: many questions, a few answers

Author

Pauwels, Jef, Tavakoli, Armin, Woodhead, Erik, and Pironio, Stefano

Subjects
Quantum Physics
Abstract

Entanglement and quantum communication are paradigmatic resources in quantum information science leading to correlations between systems that have no classical analogue. Correlations due to entanglement when communication is absent have for long been studied in Bell scenarios. Correlations due to quantum communication when entanglement is absent have been studied extensively in prepare-and-measure scenarios in the last decade. Here, we set out to understand and investigate correlations in scenarios that involve both entanglement and communication, focusing on entanglement-assisted prepare-and-measure scenarios. In a recent companion paper [arXiv:2103.10748], we investigated correlations based on unrestricted entanglement. Here, our focus is on scenarios with restricted entanglement. We establish several elementary relations between standard classical and quantum communication and their entanglement-assisted counterparts. In particular, while it was already known that bits or qubits assisted by two-qubit entanglement between the sender and receiver constitute a stronger resource than bare bits or qubits, we show that higher-dimensional entanglement further enhance the power of bits or qubits. We also provide a characterisation of generalised dense coding protocols, a natural subset of entanglement-assisted quantum communication protocols, finding that they can be understood as standard quantum communication protocols in real-valued Hilbert space. Though such dense coding protocols can convey up to two bits of information, we provide evidence, perhaps counter-intuitively, that resources with a small information capacity, such as a bare qutrits, can sometimes produce stronger correlations. Along the way we leave several conjectures and conclude with a list of interesting open problems., Comment: Minor corrections

Published
2021
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36. Insights Into Changing Interglacial Conditions in Subarctic Canada From MIS 11 Through MIS 5e From Seasonally Resolved Speleothem Records

Author

Cameron J. Batchelor, David McGee, Jeremy D. Shakun, Jon Woodhead, Adam B. Jost, Sarah Arnold, Greg Horne, Christopher W. Kinsley, and Markey Freudenburg‐Puricelli

Subjects
paleoclimatology, subarctic, speleothems, interglacials, geochemistry, terrestrial, Geophysics. Cosmic physics, QC801-809
Abstract

Abstract High‐resolution records from past interglacial climates help constrain future responses to global warming, yet are rare. Here, we produce seasonally resolved climate records from subarctic‐Canada using micron‐scale measurements of oxygen isotopes (δ18O) in speleothems with apparent annual growth bands from three interglacial periods—Marine Isotope Stages (MIS) 11, 9, and 5e. We find 3‰ lower δ18O values during MIS 11 than MIS 5e, despite MIS 11 likely being warmer. We explore controls on high‐latitude speleothem δ18O and suggest low MIS 11 δ18O values reflect greater contribution of cold‐season precipitation to dripwater from longer annual ground thaw durations. Other potential influences include changes in precipitation source and/or increased fraction of cold‐season precipitation from diminished sea ice in MIS 11. Our study highlights the potential for high‐latitude speleothems to yield detailed isotopic records of Northern Hemisphere interglacial climates beyond the reach of Greenland ice cores and offers a framework for interpreting them.

Published
2024
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37. Simple and practical DIQKD security analysis via BB84-type uncertainty relations and Pauli correlation constraints

Author

Masini, Michele, Pironio, Stefano, and Woodhead, Erik

Subjects
Quantum Physics
Abstract

According to the entropy accumulation theorem, proving the unconditional security of a device-independent quantum key distribution protocol reduces to deriving tradeoff functions, i.e., bounds on the single-round von Neumann entropy of the raw key as a function of Bell linear functionals, conditioned on an eavesdropper's quantum side information. In this work, we describe how the conditional entropy can be bounded in the 2-input/2-output setting, where the analysis can be reduced to qubit systems, by combining entropy bounds for variants of the well-known BB84 protocol with quantum constraints on qubit operators on the bipartite system shared by Alice and Bob. The approach gives analytic bounds on the entropy, or semi-analytic ones in reasonable computation time, which are typically close to optimal. We illustrate the approach on a variant of the device-independent CHSH QKD protocol where both bases are used to generate the key as well as on a more refined analysis of the original single-basis variant with respect to losses. We obtain in particular a detection efficiency threshold slightly below 80.26%, within reach of current experimental capabilities.

Published
2021
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38. Contextual HyperNetworks for Novel Feature Adaptation

Author

Lamb, Angus, Saveliev, Evgeny, Li, Yingzhen, Tschiatschek, Sebastian, Longden, Camilla, Woodhead, Simon, Hernández-Lobato, José Miguel, Turner, Richard E., Cameron, Pashmina, and Zhang, Cheng

Subjects
Computer Science - Machine Learning
Abstract

While deep learning has obtained state-of-the-art results in many applications, the adaptation of neural network architectures to incorporate new output features remains a challenge, as neural networks are commonly trained to produce a fixed output dimension. This issue is particularly severe in online learning settings, where new output features, such as items in a recommender system, are added continually with few or no associated observations. As such, methods for adapting neural networks to novel features which are both time and data-efficient are desired. To address this, we propose the Contextual HyperNetwork (CHN), an auxiliary model which generates parameters for extending the base model to a new feature, by utilizing both existing data as well as any observations and/or metadata associated with the new feature. At prediction time, the CHN requires only a single forward pass through a neural network, yielding a significant speed-up when compared to re-training and fine-tuning approaches. To assess the performance of CHNs, we use a CHN to augment a partial variational autoencoder (P-VAE), a deep generative model which can impute the values of missing features in sparsely-observed data. We show that this system obtains improved few-shot learning performance for novel features over existing imputation and meta-learning baselines across recommender systems, e-learning, and healthcare tasks., Comment: 17 pages, 9 Figures, workshop paper at NeurIPS 2020 Meta-Learning Workshop

Published
2021

39. Results and Insights from Diagnostic Questions: The NeurIPS 2020 Education Challenge

Author

Wang, Zichao, Lamb, Angus, Saveliev, Evgeny, Cameron, Pashmina, Zaykov, Yordan, Hernandez-Lobato, Jose Miguel, Turner, Richard E., Baraniuk, Richard G., Barton, Craig, Jones, Simon Peyton, Woodhead, Simon, and Zhang, Cheng

Subjects
Computer Science - Computers and Society, Computer Science - Human-Computer Interaction
Abstract

This competition concerns educational diagnostic questions, which are pedagogically effective, multiple-choice questions (MCQs) whose distractors embody misconceptions. With a large and ever-increasing number of such questions, it becomes overwhelming for teachers to know which questions are the best ones to use for their students. We thus seek to answer the following question: how can we use data on hundreds of millions of answers to MCQs to drive automatic personalized learning in large-scale learning scenarios where manual personalization is infeasible? Success in using MCQ data at scale helps build more intelligent, personalized learning platforms that ultimately improve the quality of education en masse. To this end, we introduce a new, large-scale, real-world dataset and formulate 4 data mining tasks on MCQs that mimic real learning scenarios and target various aspects of the above question in a competition setting at NeurIPS 2020. We report on our NeurIPS competition in which nearly 400 teams submitted approximately 4000 submissions, with encouragingly diverse and effective approaches to each of our tasks., Comment: arXiv admin note: text overlap with arXiv:2007.12061

Published
2021

40. Correlations in entanglement-assisted prepare-and-measure scenarios

Author

Tavakoli, Armin, Pauwels, Jef, Woodhead, Erik, and Pironio, Stefano

Subjects
Quantum Physics
Abstract

We investigate the correlations that can arise between Alice and Bob in prepare-and-measure communication scenarios where the source (Alice) and the measurement device (Bob) can share prior entanglement. The paradigmatic example of such a scenario is the quantum dense coding protocol, where the communication capacity of a qudit can be doubled if a two-qudit entangled state is shared between Alice and Bob. We provide examples of correlations that actually require more general protocols based on higher-dimensional entangled states. This motivates us to investigate the set of correlations that can be obtained from communicating either a classical or a quantum $d$-dimensional system in the presence of an unlimited amount of entanglement. We show how such correlations can be characterized by a hierarchy of semidefinite programming relaxations by reducing the problem to a non-commutative polynomial optimization problem. We also introduce an alternative relaxation hierarchy based on the notion of informationally-restricted quantum correlations, which, though it represents a strict (non-converging) relaxation scheme, is less computationally demanding. As an application, we introduce device-independent tests of the dimension of classical and quantum systems that, in contrast to previous results, do not make the implicit assumption that Alice and Bob share no entanglement. We also establish several relations between communication with and without entanglement as resources for creating correlations., Comment: 14 pages, 2 figures

Published
2021
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43. Promoting physical activity to patients: a scoping review of the perceptions of doctors in the United Kingdom

Author

Gemma Woodhead, Divya Sivaramakrishnan, and Graham Baker

Subjects
Medicine
Abstract

Abstract Background The physician–patient encounter presents an ideal opportunity for physical activity (PA) promotion. This review aims to (i) explore the breadth and depth of existing literature investigating doctors’ perceptions of PA promotion in the United Kingdom (UK) and (ii) identify factors influencing the extent to which doctors engage in PA promotion during patient interactions. Methods A five-stage scoping review methodology and the PRISMA-ScR guidance were followed: Stage 1—research questions specified; Stage 2—relevant studies identified by searching five electronic databases and manual screening of references; Stage 3—studies screened using Covidence™; Stage 4—study data extracted and charted; and Stage 5—findings from included studies were analysed, summarised and reported using (i) descriptive numerical analysis to provide insight into study characteristics and (ii) narrative summary of the evidence categorised by factors that influence doctors’ engagement with PA promotion. Results In total, 16,961 studies were screened. Nineteen studies were included in the review with most conducted in primary care focusing on the perceptions of general practitioners. Seven influencing factors were identified: knowledge and training, personal interest and PA level, time, resources, confidence, the perceived role of the doctor and patient relevance. Conclusion This review provides new evidence that historical barriers and influencing factors have a persisting impact on the ability and willingness of UK doctors to engage with PA promotion with patients. Previous efforts to address these factors would appear to have had limited success. Further intervention efforts are required to ensure more widespread and effective PA promotion to patients.

Published
2023
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44. Glycophagy is involved in cardiac glycogen regulation in response to exercise

Author

Samuel L. James, Parisa Koutsifeli, Randall F. D'Souza, Stewart WC. Masson, Jonathan ST. Woodhead, Troy L. Merry, Lea MD. Delbridge, and Kimberley M. Mellor

Subjects
Glycogen-autophagy, GABARAPL1, STBD1, GAA, Glycogen synthase, Glycogen phosphorylase, Physiology, QP1-981, Specialties of internal medicine, RC581-951
Abstract

Cardiac glycogen-autophagy (‘glycophagy’) is disturbed in cardiometabolic pathologies. The physiological role of cardiac glycophagy is unclear. Exercise induces transient cardiac glycogen accumulation. Thus, this study experimentally examined glycophagy involvement during recovery from an exhaustive exercise protocol. Peak myocardial glycogen accumulation in mice was evident at 2 h post-exercise, preceded by transient activation of glycogen synthase. At 4 and 16 h post-exercise, glycogen degradation was associated with decreased STBD1 (glycophagy tagging protein) and increased GABARAPL1 (Atg8 protein), suggesting that glycophagy activity was increased. These findings provide the first evidence that glycophagy is involved in cardiac glycogen physiologic homeostasis post-exercise.

Published
2024
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45. Intersectionality informed and narrative-shifting whole school approaches for LGBTQ+ secondary school student mental health: A UK qualitative study.

Author

Amy Morgan, Emily Cunningham, Juliet Dyrud, Liberty Elliott, Lauren Ige, Gemma Knowles, Lukasz Konieczka, Angela Mascolo, Ibrahim Sabra, Sara Sabra, E Singh, Katharine A Rimes, and Charlotte Woodhead

Subjects
Medicine, Science
Abstract

School is a key site for prevention and early intervention in public mental health, with sexual and gender minority students being a priority group for action. Context is important in understanding how school inclusion of sexual and gender minorities shapes mental health and well-being, with rapidly changing social and political forces necessitating ongoing research. This coproduced UK secondary school-based study aimed to understand (a) key components of mentally, socially and emotionally healthy school environments for LGBTQ+ students considerate of intersecting minoritised identities; (b) staff information, skills and capacity needs and (c) factors influencing uptake and implementation. Online interviews and focus groups were conducted with 63 participants (22 staff, 32 students (aged 13-19 years), and 9 training providers), diverse in relation to gender and sexual identity, ethnicity, religious and social context. Data were analysed thematically. One overarching theme captured the need for an intersectionality-informed, contextually adaptable, whole school approach which 'shifts the narrative' away from deficit thinking, challenging prevailing cis/heteronormative and White norms. This underpinned four themes: (1) 'Feeling safe, seen and celebrated: embedding intersectional signs, signals and symbols', (2) 'Everyone's business: the need for collaboration', (3) 'Embedding a culture of change', and (4) 'Re-locating the problem: challenging deficit thinking'. Contextually diverse research is needed which critically addresses ways in which social power enacted interpersonally and structurally serves to hinder schools from enacting LGBTQ+ inclusivity. Evidence to inform and develop implementation strategies for institutional changes and to advocate for wider socio-political support is also key to mitigate the potential for widening inequities linked to inequitable school environments.

Published
2024
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46. Artificial intelligence-based decision support software to improve the efficacy of acute stroke pathway in the NHS: an observational study

Author

Kiruba Nagaratnam, Ain Neuhaus, James H. Briggs, Gary A. Ford, Zoe V. J. Woodhead, Dibyaa Maharjan, and George Harston

Subjects
stroke, artificial intelligence, endovascular thrombectomy, clinical outcome analysis, door-in-door-out time, Neurology. Diseases of the nervous system, RC346-429
Abstract

IntroductionIn a drip-and-ship model for endovascular thrombectomy (EVT), early identification of large vessel occlusion (LVO) and timely referral to a comprehensive center (CSC) are crucial when patients are admitted to an acute stroke center (ASC). Several artificial intelligence (AI) decision-aid tools are increasingly being used to facilitate the rapid identification of LVO. This retrospective cohort study aimed to evaluate the impact of deploying e-Stroke AI decision support software in the hyperacute stroke pathway on process metrics and patient outcomes at an ASC in the United Kingdom.MethodsExcept for the deployment of e-Stroke on 01 March 2020, there were no significant changes made to the stroke pathway at the ASC. The data were obtained from a prospective stroke registry between 01 January 2019 and 31 March 2021. The outcomes were compared between the 14 months before and 12 months after the deployment of AI (pre-e-Stroke cohort vs. post-e-Stroke cohort) on 01 March 2020. Time window analyses were performed using Welch’s t-test. Cochran–Mantel–Haenszel test was used to compare changes in disability at 3 months assessed by modified Rankin Score (mRS) ordinal shift analysis, and Fisher’s exact test was used for dichotomised mRS analysis.ResultsIn the pre-e-Stroke cohort, 19 of 22 patients referred received EVT. In the post-e-Stroke cohort, 21 of the 25 patients referred were treated. The mean door-in-door-out (DIDO) and door-to-referral times in pre-e-Stroke vs. post-e-Stroke cohorts were 141 vs. 79 min (difference 62 min, 95% CI 96.9–26.8 min, p

Published
2024
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47. Informationally restricted correlations: a general framework for classical and quantum systems

Author

Tavakoli, Armin, Cruzeiro, Emmanuel Zambrini, Woodhead, Erik, and Pironio, Stefano

Subjects
Quantum Physics
Abstract

We introduce new methods and tools to study and characterise classical and quantum correlations emerging from prepare-and-measure experiments with informationally restricted communication. We consider the most general kind of informationally restricted correlations, namely the ones formed when the sender is allowed to prepare statistical mixtures of mixed states, showing that contrary to what happens in Bell nonlocality, mixed states can outperform pure ones. We then leverage these tools to derive device-independent witnesses of the information content of quantum communication, witnesses for different quantum information resources, and demonstrate that these methods can be used to develop a new avenue for semi-device independent random number generators.

Published
2020
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48. Device-independent quantum key distribution with asymmetric CHSH inequalities

Author

Woodhead, Erik, Acín, Antonio, and Pironio, Stefano

Subjects
Quantum Physics
Abstract

The simplest device-independent quantum key distribution protocol is based on the Clauser-Horne-Shimony-Holt (CHSH) Bell inequality and allows two users, Alice and Bob, to generate a secret key if they observe sufficiently strong correlations. There is, however, a mismatch between the protocol, in which only one of Alice's measurements is used to generate the key, and the CHSH expression, which is symmetric with respect to Alice's two measurements. We therefore investigate the impact of using an extended family of Bell expressions where we give different weights to Alice's measurements. Using this family of asymmetric Bell expressions improves the robustness of the key distribution protocol for certain experimentally-relevant correlations. As an example, the tolerable error rate improves from 7.15% to about 7.42% for the depolarising channel. Adding random noise to Alice's key before the postprocessing pushes the threshold further to more than 8.34%. The main technical result of our work is a tight bound on the von Neumann entropy of one of Alice's measurement outcomes conditioned on a quantum eavesdropper for the family of asymmetric CHSH expressions we consider and allowing for an arbitrary amount of noise preprocessing.

Published
2020
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49. Instructions and Guide for Diagnostic Questions: The NeurIPS 2020 Education Challenge

Author

Wang, Zichao, Lamb, Angus, Saveliev, Evgeny, Cameron, Pashmina, Zaykov, Yordan, Hernández-Lobato, José Miguel, Turner, Richard E., Baraniuk, Richard G., Barton, Craig, Jones, Simon Peyton, Woodhead, Simon, and Zhang, Cheng

Subjects
Computer Science - Computers and Society, Computer Science - Human-Computer Interaction, Computer Science - Machine Learning
Abstract

Digital technologies are becoming increasingly prevalent in education, enabling personalized, high quality education resources to be accessible by students across the world. Importantly, among these resources are diagnostic questions: the answers that the students give to these questions reveal key information about the specific nature of misconceptions that the students may hold. Analyzing the massive quantities of data stemming from students' interactions with these diagnostic questions can help us more accurately understand the students' learning status and thus allow us to automate learning curriculum recommendations. In this competition, participants will focus on the students' answer records to these multiple-choice diagnostic questions, with the aim of 1) accurately predicting which answers the students provide; 2) accurately predicting which questions have high quality; and 3) determining a personalized sequence of questions for each student that best predicts the student's answers. These tasks closely mimic the goals of a real-world educational platform and are highly representative of the educational challenges faced today. We provide over 20 million examples of students' answers to mathematics questions from Eedi, a leading educational platform which thousands of students interact with daily around the globe. Participants to this competition have a chance to make a lasting, real-world impact on the quality of personalized education for millions of students across the world., Comment: 28 pages, 6 figures, NeurIPS 2020 Competition Track

Published
2020

50. Educational Question Mining At Scale: Prediction, Analysis and Personalization

Author

Wang, Zichao, Tschiatschek, Sebastian, Woodhead, Simon, Hernandez-Lobato, Jose Miguel, Jones, Simon Peyton, Baraniuk, Richard G., and Zhang, Cheng

Subjects
Computer Science - Computers and Society, Computer Science - Machine Learning, Statistics - Applications
Abstract

Online education platforms enable teachers to share a large number of educational resources such as questions to form exercises and quizzes for students. With large volumes of available questions, it is important to have an automated way to quantify their properties and intelligently select them for students, enabling effective and personalized learning experiences. In this work, we propose a framework for mining insights from educational questions at scale. We utilize the state-of-the-art Bayesian deep learning method, in particular partial variational auto-encoders (p-VAE), to analyze real students' answers to a large collection of questions. Based on p-VAE, we propose two novel metrics that quantify question quality and difficulty, respectively, and a personalized strategy to adaptively select questions for students. We apply our proposed framework to a real-world dataset with tens of thousands of questions and tens of millions of answers from an online education platform. Our framework not only demonstrates promising results in terms of statistical metrics but also obtains highly consistent results with domain experts' evaluation., Comment: Accepted at AAAI-EAAI 2021

Published
2020

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