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1. DQA: An Efficient Method for Deep Quantization of Deep Neural Network Activations

Author

Hu, Wenhao, Henderson, Paul, and Cano, José

Subjects
Computer Science - Machine Learning, Computer Science - Computer Vision and Pattern Recognition, Statistics - Machine Learning
Abstract

Quantization of Deep Neural Network (DNN) activations is a commonly used technique to reduce compute and memory demands during DNN inference, which can be particularly beneficial on resource-constrained devices. To achieve high accuracy, existing methods for quantizing activations rely on complex mathematical computations or perform extensive searches for the best hyper-parameters. However, these expensive operations are impractical on devices with limited computation capabilities, memory capacities, and energy budgets. Furthermore, many existing methods do not focus on sub-6-bit (or deep) quantization. To fill these gaps, in this paper we propose DQA (Deep Quantization of DNN Activations), a new method that focuses on sub-6-bit quantization of activations and leverages simple shifting-based operations and Huffman coding to be efficient and achieve high accuracy. We evaluate DQA with 3, 4, and 5-bit quantization levels and three different DNN models for two different tasks, image classification and image segmentation, on two different datasets. DQA shows significantly better accuracy (up to 29.28%) compared to the direct quantization method and the state-of-the-art NoisyQuant for sub-6-bit quantization., Comment: Accepted to Second Workshop on Machine Learning with New Compute Paradigms at NeurIPS 2024 (MLNCP 2024)

Published
2024

2. Unsupervised Segmentation by Diffusing, Walking and Cutting

Author

Ivanova, Daniela, Aversa, Marco, Henderson, Paul, and Williamson, John

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

We propose an unsupervised image segmentation method using features from pre-trained text-to-image diffusion models. Inspired by classic spectral clustering approaches, we construct adjacency matrices from self-attention layers between image patches and recursively partition using Normalised Cuts. A key insight is that self-attention probability distributions, which capture semantic relations between patches, can be interpreted as a transition matrix for random walks across the image. We leverage this by first using Random Walk Normalized Cuts directly on these self-attention activations to partition the image, minimizing transition probabilities between clusters while maximizing coherence within clusters. Applied recursively, this yields a hierarchical segmentation that reflects the rich semantics in the pre-trained attention layers, without any additional training. Next, we explore other ways to build the NCuts adjacency matrix from features, and how we can use the random walk interpretation of self-attention to capture long-range relationships. Finally, we propose an approach to automatically determine the NCut cost criterion, avoiding the need to tune this manually. We quantitatively analyse the effect incorporating different features, a constant versus dynamic NCut threshold, and incorporating multi-node paths when constructing the NCuts adjacency matrix. We show that our approach surpasses all existing methods for zero-shot unsupervised segmentation, achieving state-of-the-art results on COCO-Stuff-27 and Cityscapes.

Published
2024

3. ARTeFACT: Benchmarking Segmentation Models on Diverse Analogue Media Damage

Author

Ivanova, Daniela, Aversa, Marco, Henderson, Paul, and Williamson, John

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Accurately detecting and classifying damage in analogue media such as paintings, photographs, textiles, mosaics, and frescoes is essential for cultural heritage preservation. While machine learning models excel in correcting degradation if the damage operator is known a priori, we show that they fail to robustly predict where the damage is even after supervised training; thus, reliable damage detection remains a challenge. Motivated by this, we introduce ARTeFACT, a dataset for damage detection in diverse types analogue media, with over 11,000 annotations covering 15 kinds of damage across various subjects, media, and historical provenance. Furthermore, we contribute human-verified text prompts describing the semantic contents of the images, and derive additional textual descriptions of the annotated damage. We evaluate CNN, Transformer, diffusion-based segmentation models, and foundation vision models in zero-shot, supervised, unsupervised and text-guided settings, revealing their limitations in generalising across media types. Our dataset is available at $\href{https://daniela997.github.io/ARTeFACT/}{https://daniela997.github.io/ARTeFACT/}$ as the first-of-its-kind benchmark for analogue media damage detection and restoration., Comment: Accepted for publication at WACV 2025

Published
2024

4. Differentially Private Integrated Decision Gradients (IDG-DP) for Radar-based Human Activity Recognition

Author

Zakariyya, Idris, Tran, Linda, Sivangi, Kaushik Bhargav, Henderson, Paul, and Deligianni, Fani

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Computer Science - Cryptography and Security, Computer Science - Machine Learning
Abstract

Human motion analysis offers significant potential for healthcare monitoring and early detection of diseases. The advent of radar-based sensing systems has captured the spotlight for they are able to operate without physical contact and they can integrate with pre-existing Wi-Fi networks. They are also seen as less privacy-invasive compared to camera-based systems. However, recent research has shown high accuracy in recognizing subjects or gender from radar gait patterns, raising privacy concerns. This study addresses these issues by investigating privacy vulnerabilities in radar-based Human Activity Recognition (HAR) systems and proposing a novel method for privacy preservation using Differential Privacy (DP) driven by attributions derived with Integrated Decision Gradient (IDG) algorithm. We investigate Black-box Membership Inference Attack (MIA) Models in HAR settings across various levels of attacker-accessible information. We extensively evaluated the effectiveness of the proposed IDG-DP method by designing a CNN-based HAR model and rigorously assessing its resilience against MIAs. Experimental results demonstrate the potential of IDG-DP in mitigating privacy attacks while maintaining utility across all settings, particularly excelling against label-only and shadow model black-box MIA attacks. This work represents a crucial step towards balancing the need for effective radar-based HAR with robust privacy protection in healthcare environments., Comment: Accepted at WACV 2025. 12 pages, 7 figures

Published
2024

5. Flat'n'Fold: A Diverse Multi-Modal Dataset for Garment Perception and Manipulation

Author

Zhuang, Lipeng, Fan, Shiyu, Ru, Yingdong, Audonnet, Florent, Henderson, Paul, and Aragon-Camarasa, Gerardo

Subjects
Computer Science - Robotics, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition
Abstract

We present Flat'n'Fold, a novel large-scale dataset for garment manipulation that addresses critical gaps in existing datasets. Comprising 1,212 human and 887 robot demonstrations of flattening and folding 44 unique garments across 8 categories, Flat'n'Fold surpasses prior datasets in size, scope, and diversity. Our dataset uniquely captures the entire manipulation process from crumpled to folded states, providing synchronized multi-view RGB-D images, point clouds, and action data, including hand or gripper positions and rotations. We quantify the dataset's diversity and complexity compared to existing benchmarks and show that our dataset features natural and diverse manipulations of real-world demonstrations of human and robot demonstrations in terms of visual and action information. To showcase Flat'n'Fold's utility, we establish new benchmarks for grasping point prediction and subtask decomposition. Our evaluation of state-of-the-art models on these tasks reveals significant room for improvement. This underscores Flat'n'Fold's potential to drive advances in robotic perception and manipulation of deformable objects. Our dataset can be downloaded at https://cvas-ug.github.io/flat-n-fold

Published
2024

6. Learning Semi-Supervised Medical Image Segmentation from Spatial Registration

Author

Liu, Qianying, Henderson, Paul, Gu, Xiao, Dai, Hang, and Deligianni, Fani

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Semi-supervised medical image segmentation has shown promise in training models with limited labeled data and abundant unlabeled data. However, state-of-the-art methods ignore a potentially valuable source of unsupervised semantic information -- spatial registration transforms between image volumes. To address this, we propose CCT-R, a contrastive cross-teaching framework incorporating registration information. To leverage the semantic information available in registrations between volume pairs, CCT-R incorporates two proposed modules: Registration Supervision Loss (RSL) and Registration-Enhanced Positive Sampling (REPS). The RSL leverages segmentation knowledge derived from transforms between labeled and unlabeled volume pairs, providing an additional source of pseudo-labels. REPS enhances contrastive learning by identifying anatomically-corresponding positives across volumes using registration transforms. Experimental results on two challenging medical segmentation benchmarks demonstrate the effectiveness and superiority of CCT-R across various semi-supervised settings, with as few as one labeled case. Our code is available at https://github.com/kathyliu579/ContrastiveCross-teachingWithRegistration.

Published
2024

7. State-of-the-Art Fails in the Art of Damage Detection

Author

Ivanova, Daniela, Aversa, Marco, Henderson, Paul, and Williamson, John

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Accurately detecting and classifying damage in analogue media such as paintings, photographs, textiles, mosaics, and frescoes is essential for cultural heritage preservation. While machine learning models excel in correcting global degradation if the damage operator is known a priori, we show that they fail to predict where the damage is even after supervised training; thus, reliable damage detection remains a challenge. We introduce DamBench, a dataset for damage detection in diverse analogue media, with over 11,000 annotations covering 15 damage types across various subjects and media. We evaluate CNN, Transformer, and text-guided diffusion segmentation models, revealing their limitations in generalising across media types.

Published
2024

8. Sampling 3D Gaussian Scenes in Seconds with Latent Diffusion Models

Author

Henderson, Paul, de Almeida, Melonie, Ivanova, Daniela, and Anciukevičius, Titas

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

We present a latent diffusion model over 3D scenes, that can be trained using only 2D image data. To achieve this, we first design an autoencoder that maps multi-view images to 3D Gaussian splats, and simultaneously builds a compressed latent representation of these splats. Then, we train a multi-view diffusion model over the latent space to learn an efficient generative model. This pipeline does not require object masks nor depths, and is suitable for complex scenes with arbitrary camera positions. We conduct careful experiments on two large-scale datasets of complex real-world scenes -- MVImgNet and RealEstate10K. We show that our approach enables generating 3D scenes in as little as 0.2 seconds, either from scratch, from a single input view, or from sparse input views. It produces diverse and high-quality results while running an order of magnitude faster than non-latent diffusion models and earlier NeRF-based generative models

Published
2024

9. Detail-Enhanced Intra- and Inter-modal Interaction for Audio-Visual Emotion Recognition

Author

Shi, Tong, Ge, Xuri, Jose, Joemon M., Pugeault, Nicolas, and Henderson, Paul

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Capturing complex temporal relationships between video and audio modalities is vital for Audio-Visual Emotion Recognition (AVER). However, existing methods lack attention to local details, such as facial state changes between video frames, which can reduce the discriminability of features and thus lower recognition accuracy. In this paper, we propose a Detail-Enhanced Intra- and Inter-modal Interaction network(DE-III) for AVER, incorporating several novel aspects. We introduce optical flow information to enrich video representations with texture details that better capture facial state changes. A fusion module integrates the optical flow estimation with the corresponding video frames to enhance the representation of facial texture variations. We also design attentive intra- and inter-modal feature enhancement modules to further improve the richness and discriminability of video and audio representations. A detailed quantitative evaluation shows that our proposed model outperforms all existing methods on three benchmark datasets for both concrete and continuous emotion recognition. To encourage further research and ensure replicability, we will release our full code upon acceptance., Comment: Submitted to 27th International Conference of Pattern Recognition (ICPR 2024)

Published
2024

11. Detail-Enhanced Intra- and Inter-modal Interaction for Audio-Visual Emotion Recognition

Author

Shi, Tong, Ge, Xuri, Jose, Joemon M., Pugeault, Nicolas, Henderson, Paul, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Antonacopoulos, Apostolos, editor, Chaudhuri, Subhasis, editor, Chellappa, Rama, editor, Liu, Cheng-Lin, editor, Bhattacharya, Saumik, editor, and Pal, Umapada, editor

Published
2025
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12. Understanding and Mitigating Human-Labelling Errors in Supervised Contrastive Learning

Author

Long, Zijun, Zhuang, Lipeng, Killick, George, McCreadie, Richard, Camarasa, Gerardo Aragon, and Henderson, Paul

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

Human-annotated vision datasets inevitably contain a fraction of human mislabelled examples. While the detrimental effects of such mislabelling on supervised learning are well-researched, their influence on Supervised Contrastive Learning (SCL) remains largely unexplored. In this paper, we show that human-labelling errors not only differ significantly from synthetic label errors, but also pose unique challenges in SCL, different to those in traditional supervised learning methods. Specifically, our results indicate they adversely impact the learning process in the ~99% of cases when they occur as false positive samples. Existing noise-mitigating methods primarily focus on synthetic label errors and tackle the unrealistic setting of very high synthetic noise rates (40-80%), but they often underperform on common image datasets due to overfitting. To address this issue, we introduce a novel SCL objective with robustness to human-labelling errors, SCL-RHE. SCL-RHE is designed to mitigate the effects of real-world mislabelled examples, typically characterized by much lower noise rates (<5%). We demonstrate that SCL-RHE consistently outperforms state-of-the-art representation learning and noise-mitigating methods across various vision benchmarks, by offering improved resilience against human-labelling errors., Comment: arXiv admin note: substantial text overlap with arXiv:2311.16481

Published
2024

13. Denoising Diffusion via Image-Based Rendering

Author

Anciukevičius, Titas, Manhardt, Fabian, Tombari, Federico, and Henderson, Paul

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Graphics, Computer Science - Machine Learning
Abstract

Generating 3D scenes is a challenging open problem, which requires synthesizing plausible content that is fully consistent in 3D space. While recent methods such as neural radiance fields excel at view synthesis and 3D reconstruction, they cannot synthesize plausible details in unobserved regions since they lack a generative capability. Conversely, existing generative methods are typically not capable of reconstructing detailed, large-scale scenes in the wild, as they use limited-capacity 3D scene representations, require aligned camera poses, or rely on additional regularizers. In this work, we introduce the first diffusion model able to perform fast, detailed reconstruction and generation of real-world 3D scenes. To achieve this, we make three contributions. First, we introduce a new neural scene representation, IB-planes, that can efficiently and accurately represent large 3D scenes, dynamically allocating more capacity as needed to capture details visible in each image. Second, we propose a denoising-diffusion framework to learn a prior over this novel 3D scene representation, using only 2D images without the need for any additional supervision signal such as masks or depths. This supports 3D reconstruction and generation in a unified architecture. Third, we develop a principled approach to avoid trivial 3D solutions when integrating the image-based rendering with the diffusion model, by dropping out representations of some images. We evaluate the model on several challenging datasets of real and synthetic images, and demonstrate superior results on generation, novel view synthesis and 3D reconstruction., Comment: Accepted at ICLR 2024. Project page: https://anciukevicius.github.io/generative-image-based-rendering

Published
2024

14. Foveation in the Era of Deep Learning

Author

Killick, George, Henderson, Paul, Siebert, Paul, and Aragon-Camarasa, Gerardo

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Computer Science - Machine Learning, I.2.10, I.5.1, I.4.8
Abstract

In this paper, we tackle the challenge of actively attending to visual scenes using a foveated sensor. We introduce an end-to-end differentiable foveated active vision architecture that leverages a graph convolutional network to process foveated images, and a simple yet effective formulation for foveated image sampling. Our model learns to iteratively attend to regions of the image relevant for classification. We conduct detailed experiments on a variety of image datasets, comparing the performance of our method with previous approaches to foveated vision while measuring how the impact of different choices, such as the degree of foveation, and the number of fixations the network performs, affect object recognition performance. We find that our model outperforms a state-of-the-art CNN and foveated vision architectures of comparable parameters and a given pixel or computation budget, Comment: Accepted at BMVC2023

Published
2023

15. Multi-Scale Cross Contrastive Learning for Semi-Supervised Medical Image Segmentation

Author

Liu, Qianying, Gu, Xiao, Henderson, Paul, and Deligianni, Fani

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence
Abstract

Semi-supervised learning has demonstrated great potential in medical image segmentation by utilizing knowledge from unlabeled data. However, most existing approaches do not explicitly capture high-level semantic relations between distant regions, which limits their performance. In this paper, we focus on representation learning for semi-supervised learning, by developing a novel Multi-Scale Cross Supervised Contrastive Learning (MCSC) framework, to segment structures in medical images. We jointly train CNN and Transformer models, regularising their features to be semantically consistent across different scales. Our approach contrasts multi-scale features based on ground-truth and cross-predicted labels, in order to extract robust feature representations that reflect intra- and inter-slice relationships across the whole dataset. To tackle class imbalance, we take into account the prevalence of each class to guide contrastive learning and ensure that features adequately capture infrequent classes. Extensive experiments on two multi-structure medical segmentation datasets demonstrate the effectiveness of MCSC. It not only outperforms state-of-the-art semi-supervised methods by more than 3.0% in Dice, but also greatly reduces the performance gap with fully supervised methods.

Published
2023

16. Simulating analogue film damage to analyse and improve artefact restoration on high-resolution scans

Author

Ivanova, Daniela, Williamson, John, and Henderson, Paul

Subjects
Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

Digital scans of analogue photographic film typically contain artefacts such as dust and scratches. Automated removal of these is an important part of preservation and dissemination of photographs of historical and cultural importance. While state-of-the-art deep learning models have shown impressive results in general image inpainting and denoising, film artefact removal is an understudied problem. It has particularly challenging requirements, due to the complex nature of analogue damage, the high resolution of film scans, and potential ambiguities in the restoration. There are no publicly available high-quality datasets of real-world analogue film damage for training and evaluation, making quantitative studies impossible. We address the lack of ground-truth data for evaluation by collecting a dataset of 4K damaged analogue film scans paired with manually-restored versions produced by a human expert, allowing quantitative evaluation of restoration performance. We construct a larger synthetic dataset of damaged images with paired clean versions using a statistical model of artefact shape and occurrence learnt from real, heavily-damaged images. We carefully validate the realism of the simulated damage via a human perceptual study, showing that even expert users find our synthetic damage indistinguishable from real. In addition, we demonstrate that training with our synthetically damaged dataset leads to improved artefact segmentation performance when compared to previously proposed synthetic analogue damage. Finally, we use these datasets to train and analyse the performance of eight state-of-the-art image restoration methods on high-resolution scans. We compare both methods which directly perform the restoration task on scans with artefacts, and methods which require a damage mask to be provided for the inpainting of artefacts., Comment: Accepted as full paper at Eurographics 2023

Published
2023

17. RenderDiffusion: Image Diffusion for 3D Reconstruction, Inpainting and Generation

Author

Anciukevičius, Titas, Xu, Zexiang, Fisher, Matthew, Henderson, Paul, Bilen, Hakan, Mitra, Niloy J., and Guerrero, Paul

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Diffusion models currently achieve state-of-the-art performance for both conditional and unconditional image generation. However, so far, image diffusion models do not support tasks required for 3D understanding, such as view-consistent 3D generation or single-view object reconstruction. In this paper, we present RenderDiffusion, the first diffusion model for 3D generation and inference, trained using only monocular 2D supervision. Central to our method is a novel image denoising architecture that generates and renders an intermediate three-dimensional representation of a scene in each denoising step. This enforces a strong inductive structure within the diffusion process, providing a 3D consistent representation while only requiring 2D supervision. The resulting 3D representation can be rendered from any view. We evaluate RenderDiffusion on FFHQ, AFHQ, ShapeNet and CLEVR datasets, showing competitive performance for generation of 3D scenes and inference of 3D scenes from 2D images. Additionally, our diffusion-based approach allows us to use 2D inpainting to edit 3D scenes., Comment: Accepted at CVPR 2023. Project page: https://github.com/Anciukevicius/RenderDiffusion

Published
2022

18. Deep learning extraction of band structure parameters from density of states: a case study on trilayer graphene

Author

Henderson, Paul, Ghazaryan, Areg, Zibrov, Alexander A., Young, Andrea F., and Serbyn, Maksym

Subjects
Condensed Matter - Mesoscale and Nanoscale Physics, Condensed Matter - Strongly Correlated Electrons, Computer Science - Machine Learning
Abstract

The development of two-dimensional materials has resulted in a diverse range of novel, high-quality compounds with increasing complexity. A key requirement for a comprehensive quantitative theory is the accurate determination of these materials' band structure parameters. However, this task is challenging due to the intricate band structures and the indirect nature of experimental probes. In this work, we introduce a general framework to derive band structure parameters from experimental data using deep neural networks. We applied our method to the penetration field capacitance measurement of trilayer graphene, an effective probe of its density of states. First, we demonstrate that a trained deep network gives accurate predictions for the penetration field capacitance as a function of tight-binding parameters. Next, we use the fast and accurate predictions from the trained network to automatically determine tight-binding parameters directly from experimental data, with extracted parameters being in a good agreement with values in the literature. We conclude by discussing potential applications of our method to other materials and experimental techniques beyond penetration field capacitance., Comment: (v2): 12 pages, 6 figures, close to published version; (v1): 11 pages, 4 figures

Published
2022
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19. Report of the first seven agents in the I-SPY COVID trial: a phase 2, open label, adaptive platform randomised controlled trial

Author

Consortium, The I-SPY COVID, Files, D Clark, Aggarwal, Neil, Albertson, Timothy, Auld, Sara, Beitler, Jeremy R, Berger, Paul, Burnham, Ellen L, Calfee, Carolyn S, Cobb, Nathan, Crippa, Alessio, Discacciati, Andrea, Eklund, Martin, Esserman, Laura, Friedman, Eliot, Gandotra, Sheetal, Khan, Kashif, Koff, Jonathan, Kumar, Santhi, Liu, Kathleen D, Martin, Thomas R, Matthay, Michael A, Meyer, Nuala J, Obermiller, Timothy, Robinson, Philip, Russell, Derek, Thomas, Karl, Wong, Fum, Wunderink, Richard G, Wurfel, Mark M, Yen, Albert, Youssef, Fady A, Darmanian, Anita, Dzierba, Amy L, Garcia, Ivan, Gosek, Katarzyna, Madahar, Purnema, Mittel, Aaron M, Muir, Justin, Rosen, Amanda, Schicchi, John, Serra, Alexis L, Wahab, Romina, Gibbs, Kevin W, Landreth, Leigha, LaRose, Mary, Parks, Lisa, Wynn, Adina, Ittner, Caroline AG, Mangalmurti, Nilman S, Reilly, John P, Harris, Donna, Methukupally, Abhishek, Patel, Siddharth, Boerger, Lindsie, Kazianis, John, Higgins, Carrie, McKeehan, Jeff, Daniel, Brian, Fields, Scott, Hurst-Hopf, James, Jauregui, Alejandra, Swigart, Lamorna Brown, Blevins, Daniel, Nguyen, Catherine, Suarez, Alexis, Tanios, Maged A, Sarafian, Farjad, Shah, Usman, Adelman, Max, Creel-Bulos, Christina, Detelich, Joshua, Harris, Gavin, Nugent, Katherine, Spainhour, Christina, Yang, Philip, Haczku, Angela, Hardy, Erin, Harper, Richart, Morrissey, Brian, Sandrock, Christian, Budinger, GR Scott, Donnelly, Helen K, Singer, Benjamin D, Moskowitz, Ari, Coleman, Melissa, Levitt, Joseph, Lu, Ruixiao, Henderson, Paul, Asare, Adam, Dunn, Imogene, and Barragan, Alejandro Botello

Subjects
Health Services, Clinical Trials and Supportive Activities, Clinical Research, Good Health and Well Being, I-SPY COVID Consortium, Acute lung injury, Clinical trial, Respiratory insufficiency, SARS-CoV-2
Abstract

BackgroundAn urgent need exists to rapidly screen potential therapeutics for severe COVID-19 or other emerging pathogens associated with high morbidity and mortality.MethodsUsing an adaptive platform design created to rapidly evaluate investigational agents, hospitalised patients with severe COVID-19 requiring ≥6 L/min oxygen were randomised to either a backbone regimen of dexamethasone and remdesivir alone (controls) or backbone plus one open-label investigational agent. Patients were enrolled to the arms described between July 30, 2020 and June 11, 2021 in 20 medical centres in the United States. The platform contained up to four potentially available investigational agents and controls available for randomisation during a single time-period. The two primary endpoints were time-to-recovery (

Published
2023

20. I-SPY COVID adaptive platform trial for COVID-19 acute respiratory failure: rationale, design and operations

Author

Files, Daniel Clark, Matthay, Michael A, Calfee, Carolyn S, Aggarwal, Neil R, Asare, Adam L, Beitler, Jeremy R, Berger, Paul A, Burnham, Ellen L, Cimino, George, Coleman, Melissa H, Crippa, Alessio, Discacciati, Andrea, Gandotra, Sheetal, Gibbs, Kevin W, Henderson, Paul T, Ittner, Caroline AG, Jauregui, Alejandra, Khan, Kashif T, Koff, Jonathan L, Lang, Julie, LaRose, Mary, Levitt, Joe, Lu, Ruixiao, McKeehan, Jeffrey D, Meyer, Nuala J, Russell, Derek W, Thomas, Karl W, Eklund, Martin, Esserman, Laura J, Liu, Kathleen D, Mittel, Aaron M, Yen, Albert F, Suarez, Alexis E, Serra, Alexis L, Amin, Alpesh N, Rosen, Amanda, Dzierba, Amy L, Haczku, Angela, Barker, Anna D, Weisman, Ariel R, Daniel, Brian M, Morrissey, Brian M, Jones, Chayse, Creel-Bulos, Christina, Angelucci, Christina M, Files, Daniel C, Ng, Diana, Youssef, Fady A, Chaparro-Rojas, Fredy, Harris, Gavin H, Barot, Harsh V, Su, Heny, Garcia, Ivan, Sutter, Jacqueline B, Dodin, Jamal, Lee, Jerry S, Kazianis, John, Reilly, John P, Detelich, Joshua F, Lang, Julie E, Muir, Justin, Gosek, Katarzyna, Nugent, Katherine L, Yee, Kimberly, Rodrigues, Laura G, Macias, Laura R, Orr, Lindsey A, Boerger, Lindsie L, Rosario-Remigio, Lissette, Kufa, Lucia, Huerta, Luis E, Tanios, Maged, Reyes, Maria B, Adelman, Max W, Juarez, Maya M, Jung, Michelle, Meyers, Michelle, Sternlieb, Mitchell P, Cobb, Nathan K, and Aggarwal, Neil

Subjects
Clinical Research, Clinical Trials and Supportive Activities, Good Health and Well Being, Bayes Theorem, COVID-19, Humans, Pandemics, Respiratory Distress Syndrome, Respiratory Insufficiency, SARS-CoV-2, Treatment Outcome, ISPY COVID Adaptive Platform Trial Network, undefined, adult intensive & critical care, clinical trials, Clinical Sciences, Public Health and Health Services, Other Medical and Health Sciences
Abstract

IntroductionThe COVID-19 pandemic brought an urgent need to discover novel effective therapeutics for patients hospitalised with severe COVID-19. The Investigation of Serial studies to Predict Your Therapeutic Response with Imaging And moLecular Analysis (ISPY COVID-19 trial) was designed and implemented in early 2020 to evaluate investigational agents rapidly and simultaneously on a phase 2 adaptive platform. This manuscript outlines the design, rationale, implementation and challenges of the ISPY COVID-19 trial during the first phase of trial activity from April 2020 until December 2021.Methods and analysisThe ISPY COVID-19 Trial is a multicentre open-label phase 2 platform trial in the USA designed to evaluate therapeutics that may have a large effect on improving outcomes from severe COVID-19. The ISPY COVID-19 Trial network includes academic and community hospitals with significant geographical diversity across the country. Enrolled patients are randomised to receive one of up to four investigational agents or a control and are evaluated for a family of two primary outcomes-time to recovery and mortality. The statistical design uses a Bayesian model with 'stopping' and 'graduation' criteria designed to efficiently discard ineffective therapies and graduate promising agents for definitive efficacy trials. Each investigational agent arm enrols to a maximum of 125 patients per arm and is compared with concurrent controls. As of December 2021, 11 investigational agent arms had been activated, and 8 arms were complete. Enrolment and adaptation of the trial design are ongoing.Ethics and disseminationISPY COVID-19 operates under a central institutional review board via Wake Forest School of Medicine IRB00066805. Data generated from this trial will be reported in peer-reviewed medical journals.Trial registration numberNCT04488081.

Published
2022

22. Adeno-associated virus 2 infection in children with non-A–E hepatitis

Author

Ho, Antonia, Orton, Richard, Tayler, Rachel, Asamaphan, Patawee, Herder, Vanessa, Davis, Chris, Tong, Lily, Smollett, Katherine, Manali, Maria, Allan, Jay, Rawlik, Konrad, McDonald, Sarah E., Vink, Elen, Pollock, Louisa, Gannon, Louise, Evans, Clair, McMenamin, Jim, Roy, Kirsty, Marsh, Kimberly, Divala, Titus, Holden, Matthew T. G., Lockhart, Michael, Yirrell, David, Currie, Sandra, O’Leary, Maureen, Henderson, David, Shepherd, Samantha J., Jackson, Celia, Gunson, Rory, MacLean, Alasdair, McInnes, Neil, Bradley-Stewart, Amanda, Battle, Richard, Hollenbach, Jill A., Henderson, Paul, Odam, Miranda, Chikowore, Primrose, Oosthuyzen, Wilna, Chand, Meera, Hamilton, Melissa Shea, Estrada-Rivadeneyra, Diego, Levin, Michael, Avramidis, Nikos, Pairo-Castineira, Erola, Vitart, Veronique, Wilkie, Craig, Palmarini, Massimo, Ray, Surajit, Robertson, David L., da Silva Filipe, Ana, Willett, Brian J., Breuer, Judith, Semple, Malcolm G., Turner, David, Baillie, J. Kenneth, and Thomson, Emma C.

Published
2023
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24. HLA-DP on Epithelial Cells Enables Tissue Damage by NKp44+ Natural Killer Cells in Ulcerative Colitis

Author

Akar, Alaa, Flemming, Cornelius, Felix, Flomm, Flosbach, Markus, Jäger, Julia, Jeromin, Niklas, Jung, Johannes, Ohms, Mareike, Reinshagen, Konrad, Rische, Johann, Sagebiel, Adrian, Sandfort, Deborah, Steinert, Fenja, Tomuschat, Christian, Wesche, Jasmin, Shifteh Abedian, Abraham, Clara, Achkar, Jean-Paul, Ahmad, Tariq, Alberts, Rudi, Alizadeh, Behrooz, Amininejad, Leila, Ananthakrishnan, Ashwin N., Andersen, Vibeke, Anderson, Carl A., Andrews, Jane M., Annese, Vito, Aumais, Guy, Baidoo, Leonard, Baldassano, Robert N., Bampton, Peter A., Barclay, Murray, Barrett, Jeffrey C., Bethge, Johannes, Bewshea, Claire, Bis, Joshua C., Bitton, Alain, BK, Thelma, Boucher, Gabrielle, Brain, Oliver, Brand, Stephan, Brant, Steven R., Cheon, Jae Hee, Chew, Angela, Cho, Judy H., Cleynen, Isabelle, Cohain, Ariella, Cooney, Rachel, Croft, Anthony, Daly, Mark J., D'Amato, Mauro, Danese, Silvio, Daryani, Naser Ebrahim, Datta, Lisa Wu, Degenhardt, Frauke, Denapiene, Goda, Denson, Lee A., Devaney, Kathy L., Dewit, Olivier, D'Inca, Renata, Drummond, Hazel E., Dubinsky, Marla, Duerr, Richard H., Edwards, Cathryn, Ellinghaus, David, Ellul, Pierre, Esaki, Motohiro, Essers, Jonah, Ferguson, Lynnette R., Festen, Eleonora A., Fleshner, Philip, Florin, Tim, Franchimont, Denis, Franke, Andre, Fuyuno, Yuta, Gearry, Richard, Georges, Michel, Gieger, Christian, Glas, Jürgen, Goyette, Philippe, Green, Todd, Griffiths, Anne M., Guthery, Stephen L., Hakonarson, Hakon, Halfvarson, Jonas, Hanigan, Katherine, Haritunians, Talin, Hart, Ailsa, Hawkey, Chris, Hayward, Nicholas K., Hedl, Matija, Henderson, Paul, Hold, Georgina L., Hong, Myhunghee, Hu, Xinli, Huang, Hailiang, Hugot, Jean-Pierre, Hui, Ken Y., Imielinski, Marcin, Jazayeri, Omid, Jonaitis, Laimas, Jostins, Luke, Juyal, Garima, Chandra Juyal, Ramesh, Kalla, Rahul, Karlsen, Tom H., Kennedy, Nicholas A., Khan, Mohammed Azam, Kim, Won Ho, Kitazono, Takanari, Kiudelis, Gediminas, Kubo, Michiaki, Kugathasan, Subra, Kupcinskas, Limas, Lamb, Christopher A., de Lange, Katrina M., Latiano, Anna, Laukens, Debby, Lawrance, Ian C., Lee, James C., Lees, Charlie W., Leja, Marcis, Lewis, Nina, Van Limbergen, Johan, Lionetti, Paolo, Liu, Jimmy Z., Louis, Edouard, Luo, Yang, Mahy, Gillian, Malekzadeh, Masoud Mohammad, Malekzadeh, Reza, Mansfield, John, Marriott, Suzie, Massey, Dunecan, Mathew, Christopher G., Matsui, Toshiyuki, McGovern, Dermot P.B., van der Meulen, Andrea, Midha, Vandana, Milgrom, Raquel, Mirzaei, Samaneh, Mitrovic, Mitja, Montgomery, Grant W., Mowat, Craig, Müller, Christoph, Newman, William G., Ng, Aylwin, Ng, Siew C., Evelyn Ng, Sok Meng, Nikolaus, Susanna, Ning, Kaida, Nöthen, Markus, Oikonomou, Ioannis, Okou, David, Orchard, Timothy R., Palmieri, Orazio, Parkes, Miles, Phillips, Anne, Ponsioen, Cyriel Y., Potocnik, Urõs, Poustchi, Hossein, Prescott, Natalie J., Proctor, Deborah D., Radford-Smith, Graham, Rahier, Jean- Francois, Regueiro, Miguel, Reinisch, Walter, Rieder, Florian, Rioux, John D., Roberts, Rebecca, Rogler, Gerhard, Russell, Richard K., Sanderson, Jeremy D., Sans, Miquel, Satsangi, Jack, Schadt, Eric E., Scharl, Michael, Schembri, John, Schreiber, Stefan, Schumm, L. Philip, Scott, Regan, Seielstad, Mark, Shah, Tejas, Sharma, Yashoda, Silverberg, Mark S., Simmons, Alison, Simms, Lisa A., Singh, Abhey, Skieceviciene, Jurgita, van Sommeren, Suzanne, Song, Kyuyoung, Sood, Ajit, Spain, Sarah L., Steinhart, A. Hillary, Stempak, Joanne M., Stronati, Laura, Sung, Joseph J.Y., Targan, Stephan R., Taylor, Kirstin M., Theatre, Emilie, Torkvist, Leif, Torres, Esther A., Tremelling, Mark, Uhlig, Holm H., Umeno, Junji, Vahedi, Homayon, Vasiliauskas, Eric, Velde, Anje ter, Ventham, Nicholas T., Vermeire, Severine, Verspaget, Hein W., De Vos, Martine, Walters, Thomas, Wang, Kai, Wang, Ming-Hsi, Weersma, Rinse K., Wei, Zhi, Whiteman, David, Wijmenga, Cisca, Wilson, David C., Winkelmann, Juliane, Wong, Sunny H., Xavier, Ramnik J., Yamazaki, Keiko, Yang, Suk-Kyun, Ye, Byong Duk, Zeissig, Sebastian, Zhang, Bin, Zhang, Clarence K., Zhang, Hu, Zhang, Wei, Zhao, Hongyu, Zhao, Zhen Z., Baumdick, Martin E., Niehrs, Annika, Schwerk, Maria, Hinrichs, Ole, Jordan-Paiz, Ana, Padoan, Benedetta, Wegner, Lucy H.M., Schloer, Sebastian, Zecher, Britta F., Malsy, Jakob, Joshi, Vinita R., Illig, Christin, Schröder-Schwarz, Jennifer, Möller, Kimberly J., Martin, Maureen P., Yuki, Yuko, Ozawa, Mikki, Sauter, Jürgen, Schmidt, Alexander H., Perez, Daniel, Giannou, Anastasios D., Carrington, Mary, Davis, Randall S., Schumacher, Udo, Sauter, Guido, Huber, Samuel, Puelles, Victor G., Melling, Nathaniel, Altfeld, Marcus, and Bunders, Madeleine J.

Published
2023
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25. Report of the first seven agents in the I-SPY COVID trial: a phase 2, open label, adaptive platform randomised controlled trial

Author

Files, D. Clark, Aggarwal, Neil, Albertson, Timothy, Auld, Sara, Beitler, Jeremy R., Berger, Paul, Burnham, Ellen L., Calfee, Carolyn S., Cobb, Nathan, Crippa, Alessio, Discacciati, Andrea, Eklund, Martin, Esserman, Laura, Friedman, Eliot, Gandotra, Sheetal, Khan, Kashif, Koff, Jonathan, Kumar, Santhi, Liu, Kathleen D., Martin, Thomas R., Matthay, Michael A., Meyer, Nuala J., Obermiller, Timothy, Robinson, Philip, Russell, Derek, Thomas, Karl, Wong, Se Fum, Wunderink, Richard G., Wurfel, Mark M., Yen, Albert, Youssef, Fady A., Darmanian, Anita, Dzierba, Amy L., Garcia, Ivan, Gosek, Katarzyna, Madahar, Purnema, Mittel, Aaron M., Muir, Justin, Rosen, Amanda, Schicchi, John, Serra, Alexis L., Wahab, Romina, Gibbs, Kevin W., Landreth, Leigha, LaRose, Mary, Parks, Lisa, Wynn, Adina, Ittner, Caroline A.G., Mangalmurti, Nilman S., Reilly, John P., Harris, Donna, Methukupally, Abhishek, Patel, Siddharth, Boerger, Lindsie, Kazianis, John, Higgins, Carrie, McKeehan, Jeff, Daniel, Brian, Fields, Scott, Hurst-Hopf, James, Jauregui, Alejandra, Brown Swigart, Lamorna, Blevins, Daniel, Nguyen, Catherine, Suarez, Alexis, Tanios, Maged A., Sarafian, Farjad, Shah, Usman, Adelman, Max, Creel-Bulos, Christina, Detelich, Joshua, Harris, Gavin, Nugent, Katherine, Spainhour, Christina, Yang, Philip, Haczku, Angela, Hardy, Erin, Harper, Richart, Morrissey, Brian, Sandrock, Christian, Budinger, G. R. Scott, Donnelly, Helen K., Singer, Benjamin D., Moskowitz, Ari, Coleman, Melissa, Levitt, Joseph, Lu, Ruixiao, Henderson, Paul, Asare, Adam, Dunn, Imogene, and Botello Barragan, Alejandro

Published
2023
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27. Neutralising antibody potency against SARS-CoV-2 wild-type and omicron BA.1 and BA.4/5 variants in patients with inflammatory bowel disease treated with infliximab and vedolizumab after three doses of COVID-19 vaccine (CLARITY IBD): an analysis of a prospective multicentre cohort study

Author

Islam, Madiha, Croft, Nick, Cipriano, Bessie, Francia, Caroline, Khalid, Nosheen, Kingston, Ashley, Lee, Irish, Lehmann, Anouk, Naik, Kinnari, Samuels, Kevin, Plaatjies, Nicolene, Khatun, Hafiza, Bokth, Farjana, Pabriaga, Elise, Saich, Rebecca, Cousins, Hayley, Fraser, Wendy, Thomas, Rachel, Brown, Matthew, White, Benjamin, Kirkineziadis, Nikolaos, Tilley, Bernadette, Porter, Pennie, Bryant, Rachel, Robaczewska, Natalia, Muhammed, Rafeeq, Bi, Rehana, Cotter, Catherine, Grove, Jayne, Hong, Kate, Howman, Ruth, Mitchell, Monica, Clayton, Sophie, Rogers, Louise, Sultan, Sugrah, Rooney, Melanie, Cottrill, Charlotte, Singh, Salil, Dawe, Chris, Hull, Robert, Silva, Natalie, Chadwick, Julie, Robertson, Laura, Manning, Jonathan, Finlayson, Lauren, Roebuck, Allison, Dawson, Joy, Sonwalkar, Sunil, Chambers, Naomi, Robinson, Matthew, Haigh, Andrew, Matapure, Lear, Raine, Tim, George, Varun, Kapizioni, Christina, Strongili, Konstantina, Thompson, Tina, Ahmed, Mohamed, Kontos, Christos, Dawson, Claire, Bourges, Christophe, Barbutti, Isabella, Gozzard, Megan E, Hendy, Philip, Bull, Rhian, Costa, Patricia, Davey, Lisa, Hannington, Hayley, Nundlall, Kribashnie, Martins, Catarina, Avanzi, Laura, Carungcong, Jaime, Barr, Sabrina, Appleby, Richard, Johnson, Emma, Shakweh, Eathar, Phillis, Kath, Gascoyne, Rachel, Crowder, Amanda, Whileman, Amanda, London, Ian, Grounds, Jenny, Martin, Emmeline, Pajak, Susie, Price, Jude, Cawley, Kathryn, Powell, Sandra, Kearsley, Nichola, Dhar, Anjan, Brown, Ellen, Cowton, Amanda, Stamp, Kimberley, Warner, Ben, Stuart, Carmel, Lacey, Louise, de Silva, Shanika, Allcock, Clare, Harvey, Philip, Jones, Lesley, Cooke, Elise, Slater, Jayne, King, Dominic, Brooks, Johanne, Baker, Pearl, Beadle, Hannah, Cruz, Carina, Potter, Debbie, Collum, Joe, Masters, Farzana, Kumar, Aashish, Coetzee, Samantha, Peiu, Mihaela, Icke, Becky, Williams, Jill, Raj, Meena, Gaynor, Edward, Chadokufa, Sibongile, Huggett, Bonita, Meghari, Hamza, El-Khouly, Sara, Kiparissi, Fevronia, Girshab, Waffa, Russell-Walker, Lynda, Jackson, Christopher, Sidler, Sara, Claridge, Andrew, Fowler, Emily, McCafferty, Laura, Haxton, Lesley, Irving, Peter, Christodoulides, Karolina, Clifford, Angela, Dawson, Patrick, Honap, Sailish, Lim, Samuel, Luber, Raphael, Mahiouz, Karina, Meade, Susanna, Raymode, Parizade, Reynolds, Rebecca, Stanton, Anna, Tripoli, Sherill, Hare, Naomi, Odukwe, Sopuluchukwu, Balachandran, Senthuran, North, Emma, North, Jessica, Browne, Bria, Cordle, Jessica, Jameson, Ella, Siaw, Yih Harn, Manzano, Lane, Segal, Jonathan, Al-Bakir, Ibrahim, Khakoo, Imran, Portukhay, Sofiya, Thoua, Nora, Davidson, Katherine, Miah, Jagrul, Canclini, Lisa, Hall, Alex, Furreed, Hassina, Mitchell-Inwang, Christine, Hayes, Melony, Myers, Sally, Talbot, Alison, Turnbull, Jack, Whitehead, Emma, Stamp, Katie, Pattinson, Alison, Mathew, Verghese, Sherris, Leanne, Wilcox, Julie, Ramachandran, Sankaranarayanan, Robertson, Hayley, Harvey, Angela, Hicks, Lucy, Byrne, Tara-Marie, Cabreros, Leilani, Downing-Wood, Hannah, Hunter, Sophie, Saifuddin, Mohammad Aamir, Prabhudev, Hemanth, Balarajah, Sharmili, Krucznski, Jan, Driva, Kalliopi, D'Mello, Andrea, Shah, Parith, Castro-Seoane, Rocio, Ibraheim, Hajir, Constable, Laura E, Lo, Jonathan W, Torkizadeh, Melissa, Hermangild, Sherine K, Sutherland, Helen, Wilhelmsen, Elva, Mackintosh, Katherine, Verma, Ajay M, Sebastian, Juliemol, Peerally, Mohammad Farhad, Guerdette, Anne-marie, Coburn, Susan, Novem lam, Ching Yee, Durrant, Donna, Schaefer, Belinda, Serna, Solange, Shahzad, Muhammad, Kent, Alexandra, Choong, Lee Meng, Pantaloni, Benedetta, Ravdas, Pantelis, Vadamalayan, Babu, Foley, Stephen, Arnold, Becky, Heeley, Cheryl, Lovegrove, Wayne, Sowton, Donna, Allsop, Lynne, Gregory, Heidi, Gill, Mandy, Holmes, Megan, Balan, Valeria, Smith, Susan, Turner, Sarah, Smith, Philip J, Steel, Alan, Bretland, Giovanna, King, Sarah, Lofthouse, Martina, Rigby, Lindsey, Subramanian, Sreedhar, Tyrer, David, Martin, Kate, Probert, Christopher, Kamperidis, Nikolaos, Adedoyin, Temi, Baden, Manisha, Brown, Jeannette, Chacko, Feba, Young, Lisa, Cicchetti, Michela, Saifuddin, Mohammad, Yesupatham, Priya, Gowda, Rohit, Williams, Maureen, Kemp, Karen, Akhand, Rima, Gray, Glaxy, John, Anu, John, Maya, Mohammed, Tasnim, Sathe, Diamond, Jones, Natasha, Soren, Jennifer, Sprakes, Michael, Burton, Julie, Kane, Patricia, Lupton, Stephanie, Bartholomew, Jacqueline, Denis, Elizabeth, Daniels, Alison, MacFaul, George, Scaletta, Diane, Siamia, Loria, Williams, Felicity, Green, Chloe, Ver, Zeljka, Lamb, Chris, Doona, Mary, Hogg, Ashleigh, Jeffrey, Lesley, King, Andrew, Speight, R Alexander, Doyle, Jennifer, Owen, Ruth, Haworth, Jenny, Patterson, Linda, Varnaulasingam, Vithusa, Mowat, Craig, Rice, Debbie, MacFarlane, Susan, MacLeod, Anne, Mohammed, Samera, Murray, Shona, Elliott, Anne, Anne Morris, Mary, Coke, Louise, Hindle, Grace, Kolokouri, Eirini, Wright, Catherine, Lee, Claire, Ward, Nicola, Dann, Adele, Lockett, Melanie, Cranfield, Charlotte, Jennings, Louise, Srivastava, Ankur, Ward, Lana, Jeynes, Nouf, Ranga, Poonam, Rajasekhar, Praveen, Gallagher, Lisa, Ward, Jill, Basnett, Rae, Murphy, Judy, Parking, Lauren, Lawson, Emma, Short, Stacey, Devadason, David, Moran, Gordon, Khan, Neelam, Tarr, Lauren, Olivia, Charmaine, Warbarton, Samantha, Kelly, Sian, Limdi, Jimmy, Goulden, Kay, Javed, Asad, McKenzie, Lauren, Melville, Julie, Liu, Eleanor, Sabine, Joseph, Jacob, Patricia, McSorland, Denise, Schofield, Nick, Cornwall, Lisa, Quirke, James, Crook, Emma, Turner, Anne, Bhandari, Pradeep, Baker-Moffatt, Michelle, Dash, Joanne, Le Poidevin, Alison, Downe, Hayley, Bombeo, Lucille, Blackman, Helen, Smith, Rebecca, Wiles, Alan, Bloxham, Hannah, Dias, Jose, Nadar, Evelyn, Curgenven, Hollie, Gilham, Ellie, Macdonald, Jonathan, Finan, Shona, McMeeken, Faye, Mahmood, Misbah, Shields, Stephanie, Seenan, John Paul, DeSilva, Des, Malkakorpi, Susanna, Carson, Rachel, Lawrence, Holly, Boateng, Ofori, Kpodo, Felix, Whiteoak, Simon, Edger-Earley, Kelli, Vamplew, Luke, Samways, Joanna, Roffe, Sue, Ingram, Sarah, James, Joel, Botfield, Sharon, Hammonds, Fiona, James, Clare, Berry, Zoe, Aspinall, Gemma, Hawkins, Sarah, Parkinson, Marian, Gardner-Thorpe, Helen, Marriott, Suzie, Redstone, Clare, Windak, Halina, Adam, Ana-Marie, Mabb, Hannah, Stevenson, Emma, Record, Jessica, Murray, Charles, Diaba, Cynthia, Joseph, Fexy, Pakou, Glykeria, Gleeson, Yvonne, Nunag, Annalyn, Berrill, James, Stroud, Natalie, Pothecary, Carla, Roche, Lisa, Turner, Keri, Deering, Lisa, Israel, Lynda, Baker, Evelyn, Nash, Maxine, Fagbemi, Andrew, Jennings, Felicia, Mayor, Imelda, Wilson, Jill, Wheeler, Alice, Phillips, Nicola, Gordon, John, Levell, Emma, Zagalo, Silvia, Hoad, Ina, Anil, Bindu, Russell, Richard, Henderson, Paul, Millar, Margaret, Alexakis, Christopher, Michalak, Natalia, Marriott, Cheryl, Stone, Sarah, Pristopan, Veronika, Saunders, John, Burton, Helen, Cambridge, Vanessa, Clark, Tonia, Ekblad, Charlotte, Hierons, Sarah, Katebe, Joyce, Saunsbury, Emma, Perry, Rachel, Brookes, Matthew, Davies, Kathryn, Green, Marie, Plumbe, Ann, Ormerod, Clare, Christensen, Helen, Howlett, Hannah, Keen, Anne, Ogor, Jonathan, Greenhaigh, Marie, Knowles, Karen, Yin, Shanzi, Poulaka, Maria, Anthony, Alpha, Newitt, Emily, Trim, Fiona, Casey, Ruth, Seymour, Katherine, Reed, Catherine, Joy, Lijo, Fogden, Edward, Russell, Kalisha, Hussain, Samia, Phillips, Anne, Abdulla, Muaad, Butterworth, Jeff, Adams, Colene, Carnahan, Mandy, Buckingham, Elizabeth, Childs, Danielle, Magness, Alison, Stickley, Jo, Motherwell, Nichola, Tonks, Louise, Gibson, Hannah, Wistance, Kate, Thomas, Caradog, Brinkworth, Elaine, Connor, Lynda, Cook, Amanda, Rees, Tabitha, Harford, Rachel, Farley, Sean, Jones, Marie, Wesley, Emma, Moss, Alison, Lucas, Jacob, Lorimer, Claire, Oleary, Maria, Dixon, Maxine, Goodchild, Fiona, Twenlow, Rebecca, Pawley, Corinne, Ramadas, Arvind, Tregonning, Julie, Okeke, Olaku, Jackson, Wendy, Koumoutsos, Ioannis, George, Viji, Kunhunny, Swapna, Laverick, Sophie, Anderson, Isla, Smith, Sophie, Joyce, Joan, Janarthan, Sarala, Patel, Kamal, Ali, Mariam, Mhandu, Hilda, Rana, Aleem, Spears, Katherine, Teixeira, Joana, Mencias, Mark, Seaward, Abigail, Sousa, Jessica, Said, Nooria, Soomaroo, Mark, Raspa, Valentina, Tacouri, Asha, Reps, Nicholas, Martin, Rebecca, Samakomva, Tinashe, Selinger, Christian, Carbonell, Jenelyn, Onovira, Felicia, Quartey, Doris, L'Anson, Alice, Ashworth, Andrew, Bailey, Jessica, Dunn, Angie, Bespaloi, Gjuzel, Rasalan, Harold, Mahmood, Zahid, Campbell, Racheal, Marsh, Liane, Coughlan, Tricia, Jafar, Wisam, Marrs, Janet, McPheat, Christopher, Rahman, Monira, Davies, Sarah, Habibi, Ruth, Jessup-Dunton, Ellen, Joefield, Teishel, Layug, Reina, Patel, Vinod, Vere, Joanne, Turner, Victoria, Kilroy, Susan, Coulding, Martina, Clark, Martyn, McCormick, Jacqueline, Nisar, Attiya, Walker, Gareth, Atkins, Stacey, Growdon, Jasmine, George, Becky, McNeill, Charlotte, Reed, Bryony, Foulds, Angela, Marshall, Catherine, Allison, Michele, Dillon, Briony, Cooney, Rachel, Bennett, Lillie, Bowlas, Louise, Shariff, Sharafaath, Fraser, Aileen, Punnette, Dwayne, Lambert, Rebecca, Bishop-Hurman, Charlotte, Undrell, Elizabeth, Belfield, Katherine, Din, Said, Addleton, Catherine, Appleby, Marie, Brown, Johanna, Holding, Kathleen, Fraser, Catherine, Birkenshaw, Janice, Williams, Jodie, Maulion, Kamille, Lane, Meg, Kravale, Arita, Smith, Claud, Hooper, Patricia, deCaestecker, John, Watchorn, Olivia, Clarke, Ellie, Hayward, Chris, Inniss, Susan, Pritchard, Lucy, Rudge, Karen, Carney, Amanda, Griffee, Sarah, Andreyev, Jervoise, Babu, Sathish, Hayhurst, Caroline, Lockwood, Carol, Osborne, Lynn, Roper, Amanda, Warner, Karen, Hindle, Julia, Lawrence, Tara, Netherton, Kimberley, Watt, Caroline, Szymiczek, Kinga, Mehta, Shameer, Bell, James, Blad, William, Whitley, Lisa, Jastrub, Roman, Durai, Dhamaraj, Baker, Mark, John Sivamurugan, Elizabeth, Evans, Mim, Cummings, Fraser, Harris, Clare, Jones, Amy, Krauze, Liga, Rahmany, Sohail, Earl, Michelle, Vowles, Jenny, Torokwa, Audrey, Petrova, Mirela, Procter, Andrew, Stanley, Jo, Silvamoniz, Claudia, Bettey, Marion, Wahid, Amar, Morrison, Zoe, Thomas-Turner, Rhian, Yendle, Louise, Muller, Jennifer, Mitchell, Marcus, Kirkwood, John, Barnes, Anna, Chaudhary, Rakesh, Claridge, Melanie, Ellis, Chiara, Kemp, Cheryl, Tobi, Ogwa, Milton, Jentus, Johnston, Emma, Oblak, Metod, Winpenny, Carmen, Svensson, Marie-Louise, Godden, Jo, Ashhar, Marium, Alexander, Debbie, Covil, Kate, Derikx, Lauranne, Siakavellas, Sryros, Baxter, Helen, Robertson, Scott, Smith, Linda, Poulose, Beena, Colemam, Anne, Balint, Margareta, Rhys-Jones, Gareth, Watters, Helen, Begg, Susan, Grosalcalde, Beatriz, Coyle, Judy, Johns, Kerrie, Hughes, Rachel, Phipps, Janet, Taylor, Abigail, MacPhee, Catherine, Brooks, Suzanne, John, Jolene, Edwards, Michelle, Smith, Katie, Howard, Linda, Wood, Dianne, Muddu, Ajay, Barman, Laura, Mallinson, Janine, Neale, Tania, Ionita, Diana, Elliot, Kerry, Turnball, Alison, Thomas, Iola, Thomas, Alice, Andrews, Kelly, Sutton, Jonathon, Mulvaney Jones, Caroline, Roberts, Julia, Bishop, Jeannie, Liu, Zhigang, Le, Kaixing, Zhou, Xin, Alexander, James L, Lin, Simeng, Bewshea, Claire, Chanchlani, Neil, Nice, Rachel, McDonald, Timothy J, Lamb, Christopher A, Sebastian, Shaji, Kok, Klaartje, Lees, Charlie W, Hart, Ailsa L, Pollok, Richard C, Boyton, Rosemary J, Altmann, Daniel M, Pollock, Katrina M, Goodhand, James R, Kennedy, Nicholas A, Ahmad, Tariq, and Powell, Nick

Published
2023
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42. Twenty-first Century Trends in the Global Epidemiology of Pediatric-Onset Inflammatory Bowel Disease: Systematic Review

Author

Bhatti, Jasmine, Gerber, Samantha, Guay, Evelyne, Jayawarden, Supun K., Kadota, Leo, Maldonado, Fernando, Maltus, Emma, Bhattacharya, Shoumak, Osei, Jessica, Sandarage, Ryan, Stanton, Amanda, Wan, Melissa, Kuenzig, M. Ellen, Fung, Stephen G., Marderfeld, Luba, Mak, Joyce W.Y., Kaplan, Gilaad G., Ng, Siew C., Wilson, David C., Cameron, Fiona, Henderson, Paul, Kotze, Paulo G., Fang, Vixey, Kotteduwa Jayawarden, Supun, Maldonado D., Fernando, Osei, Jessica Amankwah, and Benchimol, Eric I.

Published
2022
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43. Trend in Clinical Trial Participation During COVID-19: A Secondary Analysis of the I-SPY COVID Clinical Trial

Author

Yang, Philip, Dickert, Neal W., Haczku, Angela, Spainhour, Christine, Auld, Sara C., Aggarwal, Neil R., Albertson, Timothy, Auld, Sara, Beitler, Jeremy R., Berger, Paul, Burnham, Ellen L., Calfee, Carolyn S., Cobb, Nathan, Crippa, Alessio, Discacciati, Andrea, Eklund, Martin, Esserman, Laura, Files, D. Clark, Friedman, Eliot, Gandotra, Sheetal, Khan, Kashif, Koff, Jonathan, Kumar, Santhi, Liu, Kathleen D., Martin, Thomas R., Matthay, Michael A., Meyer, Nuala J., Obermiller, Timothy, Robinson, Philip, Russell, Derek, Thomas, Karl, Wong, Se Fum, Wunderink, Richard G., Wurfel, Mark M., Yen, Albert, Youssef, Fady A., Darmanian, Anita, Dzierba, Amy L., Garcia, Ivan, Gosek, Katarzyna, Madahar, Purnema, Mittel, Aaron M., Muir, Justin, Roden, Amanda, Schicchi, John, Serra, Alexis L., Wahab, Romina, Gibbs, Kevin W., Landreth, Leigha, LaRose, Mary, Parks, Lisa, Wynn, Adina, Ittner, Caroline A. G., Mangalmurti, Nilam S., Reilly, John P., Harris, Donna, Methukupally, Abhishek, Patel, Siddharth, Boerger, Lindsie, Kazianis, John, Higgins, Carrie, McKeehan, Jeff, Daniel, Brian, Fields, Scott, Hurst-Hopf, James, Jauregui, Alejandra, Swigart, Lamorna Brown, Belvins, Daniel, Nguyen, Catherine, Suarez, Alexis, Tanios, Maged A., Sarafian, Farjad, Shah, Usman, Adelman, Max, Creel-Bulos, Christina, Detelich, Joshua, Harris, Gavin, Nugent, Katherine, Spainhour, Christine, Yang, Philip, Haczku, Angela, Hardy, Erin, Harper, Richart, Morrissey, Brian, Sandrock, Christian, Budinger, G. R. Scott, Donnelly, Helen K., Singer, Benjamin D., Moskowitz, Ari, Coleman, Melissa, Levitt, Joseph, Lu, Ruixiao, Henderson, Paul, Asare, Adam, Dunn, Imogene, and Barragan, Alejandro Botello

Published
2023
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47. Infantile and Very Early Onset Inflammatory Bowel Disease: A Multicenter Study.

Author

Guz-Mark, Anat, Aloi, Marina, Scarallo, Luca, Bramuzzo, Matteo, Escher, Johanna C., Alvisi, Patrizia, Henderson, Paul, Hojsak, Iva, Lev-Tzion, Raffi, El-Matary, Wael, Schwerd, Tobias, Granot, Maya, Sladek, l. Malgorzata, Strisciuglio, Caterina, Müller, Katalin E., Olbjørn, Christine, Tzivinikos, Christos, Yerushalmy-Feler, Anat, Huysentruyt, Koen, and Norsa, Lorenzo

Published
2024
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49. Wishbone Ash

Author

Henderson, Paul

Subjects
Music
Abstract

Live Dates Live 1973 - 2023 STEAMHAMMER/SPV Fiftieth-anniversary new live version of their celebrated live album. Playing one of your albums live in full and in sequence is a thing [...]

Published
2023

50. Enriched Regions of 228Ra Along the U.S. GEOTRACES Pacific Meridional Transect (GP15)

Author

Moore, Willard S., Charette, Matthew A., Henderson, Paul B., Hammond, Douglas E., Kemnitz, Nathaniel, Le Roy, Emilie, Kwon, Eun Young, Hult, Mikael, Moore, Willard S., Charette, Matthew A., Henderson, Paul B., Hammond, Douglas E., Kemnitz, Nathaniel, Le Roy, Emilie, Kwon, Eun Young, and Hult, Mikael

Abstract

The half‐life of 228Ra (5.7 years) aligns well with near‐surface and near‐bottom ocean mixing timescales. Because 228Ra is sourced from sediments, regions of enhanced activity represent water that has recently interacted with sediments on the continental margin or seabed. The GP15 meridional transect from Alaska to Tahiti along152°W encountered several regions in the upper ocean where 228Ra was enriched. These enrichments follow surface and subsurface ocean current patterns and pair with earlier measurements of 228Ra and transient radionuclides to reveal the origins of these enriched regions. An enriched region at Alaska margin stations 1–3 was sourced locally but did not extend to the Alaskan trench at station 4. A large shallow region between 47° and 32°N. was sourced from the west by the North Pacific Current; another shallow enriched region between 11° and 5° N was also sourced from the west by the North Equatorial Countercurrent. Subsurface enrichments (100–400 m) between 18 and 47°N were associated with Central Mode Water and North Pacific Intermediate Water. The 228Ra activities in the upper Pacific were six times lower than activities in the Atlantic. In deep waters the primary enrichment was 27°–47°N. Two stations (32° and 37°N) were especially enriched, having near‐bottom inventories several times greater than other stations. With these two exceptions the remaining Pacific stations exhibited averaged inventories lower than those in the Atlantic. There was one region of enriched 223Ra (half‐life = 11 days) above the Puna Ridge near Hawaii.

Published
2024
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