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44,480 results on '"A. Redmond"'

152. A Prime Power Equation

Author

Redmond, Timothy and Ryavec, Charles

Subjects
Mathematics - Number Theory, 11 A 41, 11 M 06
Abstract

A real valued function, $G$, is provided whose Fourier transform, $\hat G$, is an entire function that satisfies, $E(s)\zeta(s) = \hat G(\frac{s -\frac{1}{2}}{i})$. Then $\hat G(\gamma) = 0$ for all nonreal zeros, $\rho = \frac{1}{2} + i \gamma$, of $\zeta(s)$. Combined with Guinand's explicit formula we obtain a prime power equation free of zeta zeros. Using infinitely many translates of G, an infinite system of equations, indexed on the natural numbers, is obtained. The solution vector of this system is the vector of values of von Mangoldt's function, $\Lambda(n), n = 1, 2 \cdots$. The entries of the matrix are special values of the fourth power of the Jacobi theta function, $\theta_2(\tau)$.

Published
2022

153. Towards advanced robotic manipulation

Author

Sanchez, Francisco Roldan, Redmond, Stephen, McGuinness, Kevin, and O'Connor, Noel

Subjects
Computer Science - Robotics
Abstract

Robotic manipulation and control has increased in importance in recent years. However, state of the art techniques still have limitations when required to operate in real world applications. This paper explores Hindsight Experience Replay both in simulated and real environments, highlighting its weaknesses and proposing reinforcement-learning based alternatives based on reward and goal shaping. Additionally, several research questions are identified along with potential research directions that could be explored to tackle those questions., Comment: 4 pages, 1 figure, Submitted to PhD Workshop submission at IRC 2022. Updated figure 1

Published
2022

154. Apiary: A DBMS-Integrated Transactional Function-as-a-Service Framework

Author

Kraft, Peter, Li, Qian, Kaffes, Kostis, Skiadopoulos, Athinagoras, Kumar, Deeptaanshu, Cho, Danny, Li, Jason, Redmond, Robert, Weckwerth, Nathan, Xia, Brian, Bailis, Peter, Cafarella, Michael, Graefe, Goetz, Kepner, Jeremy, Kozyrakis, Christos, Stonebraker, Michael, Suresh, Lalith, Yu, Xiangyao, and Zaharia, Matei

Subjects
Computer Science - Databases, Computer Science - Distributed, Parallel, and Cluster Computing
Abstract

Developers increasingly use function-as-a-service (FaaS) platforms for data-centric applications that perform low-latency and transactional operations on data, such as for microservices or web serving. Unfortunately, existing FaaS platforms support these applications poorly because they physically and logically separate application logic, executed in cloud functions, from data management, done in interactive transactions accessing remote storage. Physical separation harms performance while logical separation complicates efficiently providing transactional guarantees and fault tolerance. This paper introduces Apiary, a novel DBMS-integrated FaaS platform for deploying and composing fault-tolerant transactional functions. Apiary physically co-locates and logically integrates function execution and data management by wrapping a distributed DBMS engine and using it as a unified runtime for function execution, data management, and operational logging, thus providing similar or stronger transactional guarantees as comparable systems while greatly improving performance and observability. To allow developers to write complex stateful programs, we leverage this integration to enable efficient and fault-tolerant function composition, building a frontend for orchestrating workflows of functions with the guarantees that each workflow runs to completion and each function in a workflow executes exactly once. We evaluate Apiary against research and production FaaS platforms and show it outperforms them by 2--68x on microservice workloads by reducing communication overhead., Comment: 14 pages, 13 figures, 3 tables. Preprint

Published
2022

155. Machine Learning with DBOS

Author

Redmond, Robert, Weckwerth, Nathan W., Xia, Brian S., Li, Qian, Kraft, Peter, Kumar, Deeptaanshu, Demiralp, Çağatay, and Stonebraker, Michael

Subjects
Computer Science - Cryptography and Security, Computer Science - Databases, Computer Science - Distributed, Parallel, and Cluster Computing, Computer Science - Human-Computer Interaction, Computer Science - Machine Learning
Abstract

We recently proposed a new cluster operating system stack, DBOS, centered on a DBMS. DBOS enables unique support for ML applications by encapsulating ML code within stored procedures, centralizing ancillary ML data, providing security built into the underlying DBMS, co-locating ML code and data, and tracking data and workflow provenance. Here we demonstrate a subset of these benefits around two ML applications. We first show that image classification and object detection models using GPUs can be served as DBOS stored procedures with performance competitive to existing systems. We then present a 1D CNN trained to detect anomalies in HTTP requests on DBOS-backed web services, achieving SOTA results. We use this model to develop an interactive anomaly detection system and evaluate it through qualitative user feedback, demonstrating its usefulness as a proof of concept for future work to develop learned real-time security services on top of DBOS., Comment: AIDB@VLDB 2022

Published
2022

156. Adaptive Target-Condition Neural Network: DNN-Aided Load Balancing for Hybrid LiFi and WiFi Networks

Author

Ji, Han, Wang, Qiang, Redmond, Stephen J., Tavakkolnia, Iman, and Wu, Xiping

Subjects
Electrical Engineering and Systems Science - Signal Processing, Computer Science - Machine Learning, Computer Science - Networking and Internet Architecture
Abstract

Load balancing (LB) is a challenging issue in the hybrid light fidelity (LiFi) and wireless fidelity (WiFi) networks (HLWNets), due to the nature of heterogeneous access points (APs). Machine learning has the potential to provide a complexity-friendly LB solution with near-optimal network performance, at the cost of a training process. The state-of-the-art (SOTA) learning-aided LB methods, however, need retraining when the network environment (especially the number of users) changes, significantly limiting its practicability. In this paper, a novel deep neural network (DNN) structure named adaptive target-condition neural network (A-TCNN) is proposed, which conducts AP selection for one target user upon the condition of other users. Also, an adaptive mechanism is developed to map a smaller number of users to a larger number through splitting their data rate requirements, without affecting the AP selection result for the target user. This enables the proposed method to handle different numbers of users without the need for retraining. Results show that A-TCNN achieves a network throughput very close to that of the testing dataset, with a gap less than 3%. It is also proven that A-TCNN can obtain a network throughput comparable to two SOTA benchmarks, while reducing the runtime by up to three orders of magnitude., Comment: 13 pages, 9 figures, and 4 tables, submitted to IEEE JSAC SI-BeyondShannon

Published
2022

157. Radiotherapy and paclitaxel plus pazopanib or placebo in anaplastic thyroid cancer (NRG/RTOG 0912): a randomised, double-blind, placebo-controlled, multicentre, phase 2 trial.

Author

Sherman, Eric, Harris, Jonathan, Bible, Keith, Xia, Ping, Ghossein, Ronald, Chung, Christine, Riaz, Nadeem, Gunn, G, Foote, Robert, Wong, Stuart, Koyfman, Shlomo, Dzeda, Michael, Clump, David, Khan, Saad, Shah, Manisha, Redmond, Kevin, Torres-Saavedra, Pedro, Le, Quynh-Thu, Lee, Nancy, and Yom, Sue

Subjects
Female, Humans, Male, Antineoplastic Combined Chemotherapy Protocols, Double-Blind Method, Paclitaxel, Thyroid Carcinoma, Anaplastic, Thyroid Neoplasms, Chemoradiotherapy
Abstract

BACKGROUND: Anaplastic thyroid cancer is a rare and aggressive cancer with no standard radiotherapy-based local treatment. Based on data suggesting synergy between pazopanib and paclitaxel in anaplastic thyroid cancer, NRG Oncology did a double-blind, placebo-controlled, randomised phase 2 clinical trial comparing concurrent paclitaxel and intensity-modulated radiotherapy (IMRT) with the addition of pazopanib or placebo with the aim of improving overall survival in this patient population. METHODS: Eligible patients were aged 18 years or older with a pathological diagnosis of anaplastic thyroid cancer, any TNM stage, Zubrod performance status of 0-2, no recent haemoptysis or bleeding, and no brain metastases. Patients were enrolled from 34 centres in the USA. Initially, a run-in was done to establish safety. In the randomised phase 2 trial, patients in the experimental group (pazopanib) received 2-3 weeks of weekly paclitaxel (80 mg/m2) intravenously and daily pazopanib suspension 400 mg orally followed by concurrent weekly paclitaxel (50 mg/m2), daily pazopanib (300 mg), and IMRT 66 Gy given in 33 daily fractions (2 Gy fractions). In the control group (placebo), pazopanib was replaced by matching placebo. Patients were randomly assigned (1:1) to the two treatment groups by permuted block randomisation by NRG Oncology with stratification by metastatic disease. All investigators, patients, and funders of the study were masked to group allocation. The primary endpoint was overall survival in the intention-to-treat population. Safety was assessed in all patients who received at least one dose of study treatment. This trial is registered with Clinicaltrials.gov, NCT01236547, and is complete. FINDINGS: The safety run-showed the final dosing regimen to be safe based on two out of nine participants having adverse events of predefined concern. Between June 23, 2014, and Dec 30, 2016, 89 patients were enrolled to the phase 2 trial, of whom 71 were eligible (36 in the pazopanib group and 35 in the placebo group; 34 [48%] males and 37 [52%] females). At the final analysis (data cutoff March 9, 2020), with a median follow-up of 2·9 years (IQR 0·002-4·0), 61 patients had died. Overall survival was not significantly improved with pazopanib versus placebo, with a median overall survival of 5·7 months (95% CI 4·0-12·8) in the pazopanib group versus 7·3 months (4·3-10·6) in the placebo group (hazard ratio 0·86, 95% CI 0·52-1·43; one-sided log-rank p=0·28). 1-year overall survival was 37·1% (95% CI 21·1-53·2) in the pazopanib group and 29·0% (13·2-44·8) in the placebo group. The incidence of grade 3-5 adverse events did not differ significantly between the treatment groups (pazopanib 88·9% [32 of 36 patients] and placebo 85·3% [29 of 34 patients]; p=0·73). The most common clinically significant grade 3-4 adverse events in the 70 eligible treated patients (36 in the pazopanib group and 34 in the placebo group) were dysphagia (13 [36%] vs 10 [29%]), radiation dermatitis (8 [22%] vs 13 [38%]), increased alanine aminotransferase (12 [33%] vs none), increased aspartate aminotransferase (eight [22%] vs none), and oral mucositis (five [14%] vs eight [24%]). Treatment-related serious adverse events were reported for 16 (44%) patients on pazopanib and 12 (35%) patients on placebo. The most common serious adverse events were dehydration and thromboembolic event (three [8%] each) in patients on pazopanib and oral mucositis (three [8%]) in those on placebo. There was one treatment-related death in each group (sepsis in the pazopanib group and pneumonitis in the placebo group). INTERPRETATION: To our knowledge, this study is the largest randomised anaplastic thyroid cancer study that has completed accrual showing feasibility in a multicenter NCI National Clinical Trials Network setting. Although no significant improvement in overall survival was recorded in the pazopanib group, the treatment combination was shown to be feasible and safe, and hypothesis-generating data that might warrant further investigation were generated. FUNDING: National Cancer Institute and Novartis.

Published
2023

158. Identification of circulating proteins associated with general cognitive function among middle-aged and older adults

Author

Tin, Adrienne, Fohner, Alison E, Yang, Qiong, Brody, Jennifer A, Davies, Gail, Yao, Jie, Liu, Dan, Caro, Ilana, Lindbohm, Joni V, Duggan, Michael R, Meirelles, Osorio, Harris, Sarah E, Gudmundsdottir, Valborg, Taylor, Adele M, Henry, Albert, Beiser, Alexa S, Shojaie, Ali, Coors, Annabell, Fitzpatrick, Annette L, Langenberg, Claudia, Satizabal, Claudia L, Sitlani, Colleen M, Wheeler, Eleanor, Tucker-Drob, Elliot M, Bressler, Jan, Coresh, Josef, Bis, Joshua C, Candia, Julián, Jennings, Lori L, Pietzner, Maik, Lathrop, Mark, Lopez, Oscar L, Redmond, Paul, Gerszten, Robert E, Rich, Stephen S, Heckbert, Susan R, Austin, Thomas R, Hughes, Timothy M, Tanaka, Toshiko, Emilsson, Valur, Vasan, Ramachandran S, Guo, Xiuqing, Zhu, Yineng, Tzourio, Christophe, Rotter, Jerome I, Walker, Keenan A, Ferrucci, Luigi, Kivimäki, Mika, Breteler, Monique MB, Cox, Simon R, Debette, Stephanie, Mosley, Thomas H, Gudnason, Vilmundur G, Launer, Lenore J, Psaty, Bruce M, Seshadri, Sudha, and Fornage, Myriam

Subjects
Biochemistry and Cell Biology, Biological Sciences, Prevention, Acquired Cognitive Impairment, Brain Disorders, Neurosciences, Aging, Alzheimer's Disease, Behavioral and Social Science, Dementia, Neurodegenerative, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), 2.1 Biological and endogenous factors, Neurological, Middle Aged, Humans, Aged, Alzheimer Disease, Cognition, Cognitive Dysfunction, Neurons, Biomarkers, Biological sciences, Biomedical and clinical sciences
Abstract

Identifying circulating proteins associated with cognitive function may point to biomarkers and molecular process of cognitive impairment. Few studies have investigated the association between circulating proteins and cognitive function. We identify 246 protein measures quantified by the SomaScan assay as associated with cognitive function (p

Published
2023

161. What outcomes are important to people with foot and ankle disorders in rheumatic and musculoskeletal diseases? An OMERACT qualitative interview study across four continents

Author

Chapman, Lara S., Flurey, Caroline A., Richards, Pamela, Redmond, Anthony C., Soliman, Eiman, Moshrif, Abdelhfeez, Malone, Lucy, Joyce, Christopher, Arnold, John B., Golightly, Yvonne M., Hofstetter, Catherine, Helliwell, Philip S., Menz, Hylton B., Hannan, Marian T., Rahman, Md Nazibur, Shea, Beverley J., Smith, Toby O., and Siddle, Heidi J.

Published
2025
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163. Pathway level subtyping identifies a slow-cycling biological phenotype associated with poor clinical outcomes in colorectal cancer

Author

Malla, Sudhir B., Byrne, Ryan M., Lafarge, Maxime W., Corry, Shania M., Fisher, Natalie C., Tsantoulis, Petros K., Mills, Megan L., Ridgway, Rachel A., Lannagan, Tamsin R. M., Najumudeen, Arafath K., Gilroy, Kathryn L., Amirkhah, Raheleh, Maguire, Sarah L., Mulholland, Eoghan J., Belnoue-Davis, Hayley L., Grassi, Elena, Viviani, Marco, Rogan, Emily, Redmond, Keara L., Sakhnevych, Svetlana, McCooey, Aoife J., Bull, Courtney, Hoey, Emily, Sinevici, Nicoleta, Hall, Holly, Ahmaderaghi, Baharak, Domingo, Enric, Blake, Andrew, Richman, Susan D., Isella, Claudio, Miller, Crispin, Bertotti, Andrea, Trusolino, Livio, Loughrey, Maurice B., Kerr, Emma M., Tejpar, Sabine, Maughan, Timothy S., Lawler, Mark, Campbell, Andrew D., Leedham, Simon J., Koelzer, Viktor H., Sansom, Owen J., and Dunne, Philip D.

Published
2024
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167. Verified Causal Broadcast with Liquid Haskell

Author

Redmond, Patrick, Shen, Gan, Vazou, Niki, and Kuper, Lindsey

Subjects
Computer Science - Programming Languages, Computer Science - Distributed, Parallel, and Cluster Computing
Abstract

Protocols to ensure that messages are delivered in causal order are a ubiquitous building block of distributed systems. For instance, distributed data storage systems can use causally ordered message delivery to ensure causal consistency, and CRDTs can rely on the existence of an underlying causally-ordered messaging layer to simplify their implementation. A causal delivery protocol ensures that when a message is delivered to a process, any causally preceding messages sent to the same process have already been delivered to it. While causal delivery protocols are widely used, verification of their correctness is less common, much less machine-checked proofs about executable implementations. We implemented a standard causal broadcast protocol in Haskell and used the Liquid Haskell solver-aided verification system to express and mechanically prove that messages will never be delivered to a process in an order that violates causality. We express this property using refinement types and prove that it holds of our implementation, taking advantage of Liquid Haskell's underlying SMT solver to automate parts of the proof and using its manual theorem-proving features for the rest. We then put our verified causal broadcast implementation to work as the foundation of a distributed key-value store., Comment: Appeared at IFL 2022

Published
2022
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168. Dexterous Robotic Manipulation using Deep Reinforcement Learning and Knowledge Transfer for Complex Sparse Reward-based Tasks

Author

Wang, Qiang, Sanchez, Francisco Roldan, McCarthy, Robert, Bulens, David Cordova, McGuinness, Kevin, O'Connor, Noel, Wüthrich, Manuel, Widmaier, Felix, Bauer, Stefan, and Redmond, Stephen J.

Subjects
Computer Science - Robotics, Computer Science - Machine Learning
Abstract

This paper describes a deep reinforcement learning (DRL) approach that won Phase 1 of the Real Robot Challenge (RRC) 2021, and then extends this method to a more difficult manipulation task. The RRC consisted of using a TriFinger robot to manipulate a cube along a specified positional trajectory, but with no requirement for the cube to have any specific orientation. We used a relatively simple reward function, a combination of goal-based sparse reward and distance reward, in conjunction with Hindsight Experience Replay (HER) to guide the learning of the DRL agent (Deep Deterministic Policy Gradient (DDPG)). Our approach allowed our agents to acquire dexterous robotic manipulation strategies in simulation. These strategies were then applied to the real robot and outperformed all other competition submissions, including those using more traditional robotic control techniques, in the final evaluation stage of the RRC. Here we extend this method, by modifying the task of Phase 1 of the RRC to require the robot to maintain the cube in a particular orientation, while the cube is moved along the required positional trajectory. The requirement to also orient the cube makes the agent unable to learn the task through blind exploration due to increased problem complexity. To circumvent this issue, we make novel use of a Knowledge Transfer (KT) technique that allows the strategies learned by the agent in the original task (which was agnostic to cube orientation) to be transferred to this task (where orientation matters). KT allowed the agent to learn and perform the extended task in the simulator, which improved the average positional deviation from 0.134 m to 0.02 m, and average orientation deviation from 142{\deg} to 76{\deg} during evaluation. This KT concept shows good generalisation properties and could be applied to any actor-critic learning algorithm., Comment: This paper has been summited to Expert Systems: the Journal of Knowledge Engineering for reviewing. arXiv admin note: text overlap with arXiv:2109.15233

Published
2022
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169. Low-order wavefront control using a Zernike sensor through Lyot coronagraphs for exoplanet imaging

Author

Pourcelot, R., N'Diaye, M., Por, E. H., Laginja, I., Carbillet, M., Benard, H., Brady, G., Canas, L., Dohlen, K., Fowler, J., Lai, O., Maclay, M., McChesney, E., Noss, J., Perrin, M. D., Petrone, P., Pueyo, L., Redmond, S. F., Sahoo, A., Vigan, A., Will, S. D., and Soummer, R.

Subjects
Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - Earth and Planetary Astrophysics
Abstract

Combining large segmented space telescopes, coronagraphy and wavefront control methods is a promising solution to produce a dark hole (DH) region in the coronagraphic image of an observed star and study planetary companions. The thermal and mechanical evolution of such a high-contrast facility leads to wavefront drifts that degrade the DH contrast during the observing time, thus limiting the ability to retrieve planetary signals. Lyot-style coronagraphs are starlight suppression systems that remove the central part of the image for an unresolved observed star, the point spread function, with an opaque focal plane mask (FPM). When implemented with a flat mirror containing an etched pinhole, the mask rejects part of the starlight through the pinhole which can be used to retrieve information about low-order aberrations. We propose an active control scheme using a Zernike wavefront sensor (ZWFS) to analyze the light rejected by the FPM, control low-order aberrations, and stabilize the DH contrast. The concept formalism is first presented before characterizing the sensor behavior in simulations and in laboratory. We then perform experimental tests to validate a wavefront control loop using a ZWFS on the HiCAT testbed. By controlling the first 11 Zernike modes, we show a decrease in wavefront error standard deviation by a factor of up to 9 between open- and closed-loop operations using the ZWFS. In the presence of wavefront perturbations, we show the ability of this control loop to stabilize a DH contrast around 7x10^-8 with a standard deviation of 7x10^-9. Active control with a ZWFS proves a promising solution in Lyot coronagraphs with an FPM-filtered beam to control and stabilize low-order wavefront aberrations and DH contrast for exoplanet imaging with future space missions.

Published
2022
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171. Does Course Specific Nudging Enhance Student Engagement, Experience and Success?: A Data-Driven Longitudinal Tale

Author

Lawrence, Jill, Brown, Alice, Redmond, Petrea, Maloney, Suzanne, Basson, Marita, Galligan, Linda, and Turner, Joanna

Abstract

Low levels of online student engagement impact negatively on student success and adversely affect attrition. Course learning analytics data (CLAD), combined with nudging initiatives, have emerged as strategies for engaging online students. This article presents a mixed method case study involving a staged intervention strategy focussing on the employment of timely, strategic communication interventions conducted across 19 courses and six disciplines. The research methodology utilised CLAD, online surveys, student interviews and student evaluations of teaching. The findings substantiate benefits for both academics and students. Academics benefitted from the provision of a relatively simple, accessible and proactive intervention for increasing students' capacities to be more in control and engaged in their learning. Students benefitted as the intervention accentuated critical resources to assist them to better address assessment requirements, align their expectations more realistically with those of the course, and more readily demonstrate their learning obligations and responsibilities.

Published
2021

172. Introducing Design Thinking Online to Large Business Education Courses for Twenty-First Century Learning

Author

Vallis, Carmen and Redmond, Petrea

Abstract

Design thinking can be broadly defined as a set of creative skills to understand and problem-solve ambiguous and complex problems, and a practice that places humans at the heart of the design process. Such collaborative ways of design thinking and design-doing are much needed to address twenty-first century challenges such as climate change. Design thinking methodology is well known for teaching and learning in design disciplines, and to a lesser extent, as an innovative problem-solving framework for business education. Typically design thinking has been taught and practised in physical settings and to a lesser extent online. While design thinking is also increasingly practised online, this is challenging at scale in higher education contexts. This case study analyses design thinking activities with educational technologies in a large undergraduate cohort of first-year business students. Eleven students and three teachers were interviewed to ascertain their level of engagement with design thinking with digital tools and to identify common themes that enabled or inhibited such practice. Student artefacts of design thinking are explored and compared to the interview data. Findings indicate that students may develop novice design thinking skills, process knowledge and mindsets in online and remote delivery modes, despite limited experience, technical and time constraints. Broader learning design implications of design thinking constraints in digital practice are discussed to assist educators. It is suggested that higher education adopt and support design thinking, as a subject and practice, more widely. [Note: The page range (213-234) shown on the PDF is incorrect. The correct page range is 213-232.]

Published
2021

173. Creative Practice in Higher Education: Decentering Academic Experiences

Author

Luetkemeyer, Jennifer, Adams, Tempestt, Davis, Jewel, Redmond, Theresa, and Hash, Peaches

Abstract

This article reports on the preliminary results of a pedagogical experiment instituted by a community of practice (CoP) called the Creativity Collaborative, which is composed of scholars from varying disciplines, backgrounds, and scholarship interests: library and information science, technical education, academic librarianship, media literacy, and rhetoric and composition. In a desire to broaden approaches to teaching and research, the CoP came together around the shared interests of visual methodology and arts-based expression and embarked on a journey to infuse creativity in their professional practice. Through the methodology of self-study, including the CoP acting as a critical friends group, the Creativity Collaborative engaged in individual research activities and then brought data from those projects to the group for discussion and reflection. The results thus far indicate that providing students with opportunities for creativity and arts-based expression lead to a welcome and positive disruption of traditional teaching and learning, increased student engagement, meaning making and real-word connections, and collaborative risk taking that decenters traditional pedagogical systems. Together with students, the Creativity Collaborative discovered the freedom to depart from the normative traditions of higher education pedagogical practices.

Published
2021

174. Earlier treatment in adults with high lifetime risk of cardiovascular diseases: What prevention trials are feasible and could change clinical practice? Report of a National Heart, Lung, and Blood Institute (NHLBI) Workshop

Author

Navar, Ann Marie, Fine, Lawrence J, Ambrosius, Walter T, Brown, Arleen, Douglas, Pamela S, Johnson, Karen, Khera, Amit V, Lloyd-Jones, Donald, Michos, Erin D, Mujahid, Mahasin, Muñoz, Daniel, Nasir, Khurram, Redmond, Nicole, Ridker, Paul M, Robinson, Jennifer, Schopfer, David, Tate, Deborah F, and Lewis, Cora E

Subjects
Biomedical and Clinical Sciences, Cardiovascular Medicine and Haematology, Cardiovascular, Heart Disease - Coronary Heart Disease, Heart Disease, Clinical Trials and Supportive Activities, Prevention, Comparative Effectiveness Research, Atherosclerosis, Aging, Clinical Research, Health Services, Good Health and Well Being, Cardiovascular Disease, Young adults, Atherosclerotic cardiovascular disease, Risk assessment, Cardiovascular medicine and haematology
Abstract

More than half of U.S. young adults have low ten-year but high lifetime risk of cardiovascular disease (CVD). Improving primary prevention in young adulthood may help reduce persistent CVD disparities and overall CVD morbidity and mortality. The National Heart, Lung, and Blood Institute (NHLBI) convened a workshop in 2021 to identify potential trial opportunities in CVD prevention in young adults. The workshop identified promising interventions that could be tested, including interventions that focus on a single cardiovascular risk factor (e.g., lipids or inflammation) to multiple risk factor interventions (e.g., multicomponent lifestyle interventions or fixed-low dose combination of medications). Given the sample size and duration for a trial with hard endpoints, more research is needed on the utility of intermediate endpoints identified noninvasively such as subclinical coronary atherosclerosis as a surrogate endpoint. For now, clinical outcomes trials with hard endpoints will more likely change clinical practice. Trial efficiency depends on accurate identification of high-risk young adults, which can potentially be done using traditional risk equations, coronary artery calcium screening, computerized tomography coronary angiography, and polygenic risk scores. Trials in young adults should include enhanced recruitment strategies with intense community engagement to enroll a trial population that is racially, ethnically, geographically, and socially diverse. Despite the challenges in conducting large prevention trials in young adults, recent advances including innovation in clinical trial conduct, new therapies and successful interventions in older populations, and an increasing recognition of a lifespan approach to risk assessment have made such trials more feasible than ever.DisclosuresThe views expressed in this manuscript are those of the authors and do not necessarily represent the views of the National Heart, Lung, and Blood Institute; the National Institutes of Health; or the U.S. Department of Health and Human Services.

Published
2022

175. Positive Predictive Values Associated with Adapting the Redmond Sentence Recall Measure into a Kindergarten Screener for Developmental Language Disorder

Author

Christopulos, Tyler T. and Redmond, Sean M.

Abstract

Purpose: Researchers estimate the prevalence of developmental language disorder (DLD) in 5-year-olds to be between 7% and 12%. Current identification systems in public schools typically favor referral identification formats over targeted or universal screenings. Public schools face unique challenges when assessing the value of screening measures for DLD that include real-world considerations such as administration, time, and resource constraints. This study used the positive predictive value (PPV) of the Redmond Sentence Recall (RSR) to assess its fidelity when administered by special education paraprofessionals. Our obtained PPV was compared across three areas: (a) previous studies that have utilized the RSR, (b) rates extrapolated from the participating school district's preexisting referral system from a previous study, and (c) expectations based on DLD prevalence. Method: Language screenings were conducted in two elementary schools using the RSR administered by school-based paraprofessionals trained on the screener protocol. One hundred sixty-four kindergarten students (age range: 5-6 years) were screened. Confirmatory testing was completed on all students who failed the screener. Results: Of the 164 students screened, 19 failed the RSR (11.5%), and 14 met criteria (8.5%) for DLD (PPV = 0.74). Our PPV was similar to previously published studies that utilized the RSR using research assistants and was higher than the PPV associated with teacher-based referrals from the participating school district. Conclusion: The RSR represents a potentially useful screener for identifying children at risk for previously unidentified language disorders in public schools.

Published
2023
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176. 2 Play

Author

Redmond, Shana L.

Published
2020

180. Contents

Author

Redmond, Shana L.

Published
2020

182. 1 Hologram

Author

Redmond, Shana L.

Published
2020

187. Index

Author

Redmond, Shana L.

Published
2020

188. Notes

Author

Redmond, Shana L.

Published
2020

189. Dual CRALBP isoforms unveiled: iPSC-derived retinal modeling and AAV2/5-RLBP1 gene transfer raise considerations for effective therapy

Author

Damodar, Krishna, Dubois, Gregor, Guillou, Laurent, Mamaeva, Daria, Pequignot, Marie, Erkilic, Nejla, Sanjurjo-Soriano, Carla, Boukhaddaoui, Hassan, Bernex, Florence, Bocquet, Béatrice, Vialaret, Jérôme, Arsenijevic, Yvan, Redmond, T. Michael, Hirtz, Christopher, Meunier, Isabelle, Brabet, Philippe, and Kalatzis, Vasiliki

Published
2024
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193. A Case-based Guide for World Health Organization (WHO) Grade 2 Meningioma Radiosurgery and Radiation Therapy from The Radiosurgery Society

Author

Vassantachart, April K., Ehret, Felix, Chen, Eric, Kumar, Ritesh, Gogineni, Emile, Andraos, Therese Y., Sahgal, Arjun, Redmond, Kristin J., Lo, Simon S., Chang, Eric L., Sheehan, Jason, Chao, Samuel T., Kim, Grace Gwe-Ya, Kresl, John J., Schulder, Michael, Palmer, Joshua D., Gibbs, Iris C., Santacroce, Antonio, and Shih, Helen A.

Published
2024
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196. Stratified analyses refine association between TLR7 rare variants and severe COVID-19

Author

Rimoldi, Valeria, Paraboschi, Elvezia M., Bandera, Alessandra, Peyvandi, Flora, Grasselli, Giacomo, Blasi, Francesco, Malvestiti, Francesco, Pelusi, Serena, Bianco, Cristiana, Miano, Lorenzo, Lombardi, Angela, Invernizzi, Pietro, Gerussi, Alessio, Citerio, Giuseppe, Biondi, Andrea, Valsecchi, Maria Grazia, Cazzaniga, Marina Elena, Foti, Giuseppe, Beretta, Ilaria, D'Angiò, Mariella, Bettini, Laura Rachele, Farré, Xavier, Iraola-Guzmán, Susana, Kogevinas, Manolis, Castaño-Vinyals, Gemma, Garcia-Etxebarria, Koldo, Nafria, Beatriz, D'Amato, Mauro, Palom, Adriana, Begg, Colin, Clohisey, Sara, Hinds, Charles, Horby, Peter, Knight, Julian, Ling, Lowell, Maslove, David, McAuley, Danny, Millar, Johnny, Montgomery, Hugh, Nichol, Alistair, Openshaw, Peter J.M., Pereira, Alexandre C., Ponting, Chris P., Rowan, Kathy, Semple, Malcolm G., Shankar-Hari, Manu, Summers, Charlotte, Walsh, Timothy, Baillie, J. Kenneth, Aravindan, Latha, Armstrong, Ruth, Biggs, Heather, Boz, Ceilia, Brown, Adam, Clark, Richard, Coutts, Audrey, Coyle, Judy, Cullum, Louise, Das, Sukamal, Day, Nicky, Donnelly, Lorna, Duncan, Esther, Fawkes, Angie, Fineran, Paul, Fourman, Max Head, Furlong, Anita, Furniss, James, Gallagher, Bernadette, Gilchrist, Tammy, Golightly, Ailsa, Griffiths, Fiona, Hafezi, Katarzyna, Hamilton, Debbie, Hendry, Ross, Law, Andy, Law, Dawn, Law, Rachel, Law, Sarah, Lidstone-Scott, Rebecca, Macgillivray, Louise, Maclean, Alan, Mal, Hanning, McCafferty, Sarah, Mcmaster, Ellie, Meikle, Jen, Moore, Shona C., Morrice, Kirstie, Murphy, Lee, Murphy, Sheena, Hellen, Mybaya, Oosthuyzen, Wilna, Zheng, Chenqing, Chen, Jiantao, Parkinson, Nick, Paterson, Trevor, Schon, Katherine, Stenhouse, Andrew, Das, Mihaela, Swets, Maaike, Szoor-McElhinney, Helen, Taneski, Filip, Turtle, Lance, Wackett, Tony, Ward, Mairi, Weaver, Jane, Wrobel, Nicola, Zechner, Marie, Arbane, Gill, Bociek, Aneta, Campos, Sara, Grau, Neus, Jones, Tim Owen, Lim, Rosario, Marotti, Martina, Ostermann, Marlies, Whitton, Christopher, Alldis, Zoe, Astin-Chamberlain, Raine, Bibi, Fatima, Biddle, Jack, Blow, Sarah, Bolton, Matthew, Borra, Catherine, Bowles, Ruth, Burton, Maudrian, Choudhury, Yasmin, Collier, David, Cox, Amber, Easthope, Amy, Ebano, Patrizia, Fotiadis, Stavros, Gurasashvili, Jana, Halls, Rosslyn, Hartridge, Pippa, Kallon, Delordson, Kassam, Jamila, Lancoma-Malcolm, Ivone, Matharu, Maninderpal, May, Peter, Mitchelmore, Oliver, Newman, Tabitha, Patel, Mital, Pheby, Jane, Pinzuti, Irene, Prime, Zoe, Prysyazhna, Oleksandra, Shiel, Julian, Taylor, Melanie, Tierney, Carey, Wood, Suzanne, Zak, Anne, Zongo, Olivier, Bonner, Stephen, Hugill, Keith, Jones, Jessica, Liggett, Steven, Headlam, Evie, Bandla, Nageswar, Gellamucho, Minnie, Davies, Michelle, Thompson, Christopher, Abdelrazik, Marwa, Bakthavatsalam, Dhanalakshmi, Elhassan, Munzir, Ganesan, Arunkumar, Haldeos, Anne, Moreno-Cuesta, Jeronimo, Purohit, Dharam, Vincent, Rachel, Xavier, Kugan, Rohit, Kumar, Alasdair, Frater, Saleem, Malik, David, Carter, Samuel, Jenkins, Lamond, Zoe, Alanna, Wall, Fernandez-Roman, Jaime, Hamilton, David O., Johnson, Emily, Johnston, Brian, Martinez, Maria Lopez, Mulla, Suleman, Shaw, David, Waite, Alicia A.C., Waugh, Victoria, Welters, Ingeborg D., Williams, Karen, Cavazza, Anna, Cockrell, Maeve, Corcoran, Eleanor, Depante, Maria, Finney, Clare, Jerome, Ellen, McPhail, Mark, Nayak, Monalisa, Noble, Harriet, O'Reilly, Kevin, Pappa, Evita, Saha, Rohit, Saha, Sian, Smith, John, Knighton, Abigail, Antcliffe, David, Banach, Dorota, Brett, Stephen, Coghlan, Phoebe, Fernandez, Ziortza, Gordon, Anthony, Rojo, Roceld, Arias, Sonia Sousa, Templeton, Maie, Meredith, Megan, Morris, Lucy, Ryan, Lucy, Clark, Amy, Sampson, Julia, Peters, Cecilia, Dent, Martin, Langley, Margaret, Ashraf, Saima, Wei, Shuying, Andrew, Angela, Bashyal, Archana, Davidson, Neil, Hutton, Paula, McKechnie, Stuart, Wilson, Jean, Baptista, David, Crowe, Rebecca, Fernandes, Rita, Herdman-Grant, Rosaleen, Joseph, Anna, O'Connor, Denise, Allen, Meryem, Loveridge, Adam, McKenley, India, Morino, Eriko, Naranjo, Andres, Simms, Richard, Sollesta, Kathryn, Swain, Andrew, Venkatesh, Harish, Khera, Jacyntha, Fox, Jonathan, Andrew, Gillian, Barclay, Lucy, Callaghan, Marie, Campbell, Rachael, Clark, Sarah, Hope, Dave, Marshall, Lucy, McCulloch, Corrienne, Briton, Kate, Singleton, Jo, Birch, Sohphie, Brimfield, Lutece, Daly, Zoe, Pogson, David, Rose, Steve, Battle, Ceri, Brinkworth, Elaine, Harford, Rachel, Murphy, Carl, Newey, Luke, Rees, Tabitha, Williams, Marie, Arnold, Sophie, Polgarova, Petra, Stroud, Katerina, Meaney, Eoghan, Jones, Megan, Ng, Anthony, Agrawal, Shruti, Pathan, Nazima, White, Deborah, Daubney, Esther, Elston, Kay, Grauslyte, Lina, Hussain, Musarat, Phull, Mandeep, Pogreban, Tatiana, Rosaroso, Lace, Salciute, Erika, Franke, George, Wong, Joanna, George, Aparna, Ortiz-Ruiz de Gordoa, Laura, Peasgood, Emily, Phillips, Claire, Bates, Michelle, Dasgin, Jo, Gill, Jaspret, Nilsson, Annette, Scriven, James, Delgado, Carlos Castro, Dawson, Deborah, Ding, Lijun, Durrant, Georgia, Ezeobu, Obiageri, Farnell-Ward, Sarah, Harrison, Abiola, Kanu, Rebecca, Leaver, Susannah, Maccacari, Elena, Manna, Soumendu, Saluzzio, Romina Pepermans, Queiroz, Joana, Samakomva, Tinashe, Sicat, Christine, Texeira, Joana, Da Gloria, Edna Fernandes, Lisboa, Ana, Rawlins, John, Mathew, Jisha, Kinch, Ashley, Hurt, William James, Shah, Nirav, Clark, Victoria, Thanasi, Maria, Yun, Nikki, Patel, Kamal, Bennett, Sara, Goodwin, Emma, Jackson, Matthew, Kent, Alissa, Tibke, Clare, Woodyatt, Wiesia, Zaki, Ahmed, Abraheem, Azmerelda, Bamford, Peter, Cawley, Kathryn, Dunmore, Charlie, Faulkner, Maria, Girach, Rumanah, Jeffrey, Helen, Jones, Rhianna, London, Emily, Nagra, Imrun, Nasir, Farah, Sainsbury, Hannah, Smedley, Clare, Patel, Tahera, Smith, Matthew, Chukkambotla, Srikanth, Kazi, Aayesha, Hartley, Janice, Dykes, Joseph, Hijazi, Muhammad, Keith, Sarah, Khan, Meherunnisa, Ryan-Smith, Janet, Springle, Philippa, Thomas, Jacqueline, Truman, Nick, Saad, Samuel, Coleman, Dabheoc, Fine, Christopher, Matt, Roseanna, Gay, Bethan, Dalziel, Jack, Ali, Syamlan, Goodchild, Drew, Harling, Rhiannan, Bhatterjee, Ravi, Goddard, Wendy, Davison, Chloe, Duberly, Stephen, Hargreaves, Jeanette, Bolton, Rachel, Davey, Miriam, Golden, David, Seaman, Rebecca, Cherian, Shiney, Cutler, Sean, Heron, Anne Emma, Roynon-Reed, Anna, Szakmany, Tamas, Williams, Gemma, Richards, Owen, Cheema, Yusuf, Brooke, Hollie, Buckley, Sarah, Suarez, Jose Cebrian, Charlesworth, Ruth, Hansson, Karen, Norris, John, Poole, Alice, Rose, Alastair, Sandhu, Rajdeep, Sloan, Brendan, Smithson, Elizabeth, Thirumaran, Muthu, Wagstaff, Veronica, Metcalfe, Alexandra, Brunton, Mark, Caterson, Jess, Coles, Holly, Frise, Matthew, Rai, Sabi Gurung, Jacques, Nicola, Keating, Liza, Tilney, Emma, Bartley, Shauna, Bhuie, Parminder, Gibson, Sian, Lyle, Amanda, McNeela, Fiona, Radhakrishnan, Jayachandran, Hughes, Alistair, Yates, Bryan, Reynolds, Jessica, Campbell, Helen, Thompsom, Maria, Dodds, Steve, Duffy, Stacey, Greer, Sandra, Shuker, Karen, Tridente, Ascanio, Khade, Reena, Sundar, Ashok, Tsinaslanidis, George, Birkinshaw, Isobel, Carter, Joseph, Howard, Kate, Ingham, Joanne, Joy, Rosie, Pearson, Harriet, Roche, Samantha, Scott, Zoe, Bancroft, Hollie, Bellamy, Mary, Carmody, Margaret, Daglish, Jacqueline, Moore, Faye, Rhodes, Joanne, Sangombe, Mirriam, Kadiri, Salma, Croft, Maria, White, Ian, Frost, Victoria, Aquino, Maia, Jha, Rajeev, Krishnamurthy, Vinodh, Lim, Lai, Lim, Li, Combes, Edward, Joefield, Teishel, Monnery, Sonja, Beech, Valerie, Trotman, Sallyanne, Almaden-Boyle, Christine, Austin, Pauline, Cabrelli, Louise, Cole, Stephen, Casey, Matt, Chapman, Susan, Whyte, Clare, Baird, Yolanda, Butler, Aaron, Chadbourn, Indra, Folkes, Linda, Fox, Heather, Gardner, Amy, Gomez, Raquel, Hobden, Gillian, Hodgson, Luke, King, Kirsten, Margarson, Michael, Martindale, Tim, Meadows, Emma, Raynard, Dana, Thirlwall, Yvette, Helm, David, Margalef, Jordi, Criste, Kristine, Cusack, Rebecca, Golder, Kim, Golding, Hannah, Jones, Oliver, Leggett, Samantha, Male, Michelle, Marani, Martyna, Prager, Kirsty, Williams, Toran, Roberts, Belinda, Salmon, Karen, Anderson, Peter, Archer, Katie, Austin, Karen, Davis, Caroline, Durie, Alison, Kelsall, Olivia, Thrush, Jessica, Vigurs, Charlie, Wild, Laura, Wood, Hannah-Louise, Tranter, Helen, Harrison, Alison, Cowley, Nicholas, McAlindon, Michael, Burtenshaw, Andrew, Digby, Stephen, Low, Emma, Morgan, Aled, Cother, Naiara, Rankin, Tobias, Clayton, Sarah, McCurdy, Alex, Ahmed, Cecilia, Baines, Balvinder, Clamp, Sarah, Colley, Julie, Haq, Risna, Hayes, Anne, Hulme, Jonathan, Hussain, Samia, Joseph, Sibet, Kumar, Rita, Maqsood, Zahira, Purewal, Manjit, Benham, Leonie, Bradshaw, Zena, Brown, Joanna, Caswell, Melanie, Cupitt, Jason, Melling, Sarah, Preston, Stephen, Slawson, Nicola, Stoddard, Emma, Warden, Scott, Deacon, Bethan, Lynch, Ceri, Pothecary, Carla, Roche, Lisa, Howe, Gwenllian Sera, Singh, Jayaprakash, Turner, Keri, Ellis, Hannah, Stroud, Natalie, Hunt, Jodie, Dearden, Joy, Dobson, Emma, Drummond, Andy, Mulcahy, Michelle, Munt, Sheila, O'Connor, Grainne, Philbin, Jennifer, Rishton, Chloe, Tully, Redmond, Winnard, Sarah, Cathcart, Susanne, Duffy, Katharine, Puxty, Alex, Puxty, Kathryn, Turner, Lynne, Ireland, Jane, Semple, Gary, Long, Kate, Whiteley, Simon, Wilby, Elizabeth, Ogg, Bethan, Cowton, Amanda, Kay, Andrea, Kent, Melanie, Potts, Kathryn, Wilkinson, Ami, Campbell, Suzanne, Brown, Ellen, Melville, Julie, Naisbitt, Jay, Joseph, Rosane, Lazo, Maria, Walton, Olivia, Neal, Alan, Alexander, Peter, Allen, Schvearn, Bradley-Potts, Joanne, Brantwood, Craig, Egan, Jasmine, Felton, Timothy, Padden, Grace, Ward, Luke, Moss, Stuart, Glasgow, Susannah, Abel, Lynn, Brett, Michael, Digby, Brian, Gemmell, Lisa, Hornsby, James, MacGoey, Patrick, O'Neil, Pauline, Price, Richard, Rodden, Natalie, Rooney, Kevin, Sundaram, Radha, Thomson, Nicola, Hopkins, Bridget, Thrasyvoulou, Laura, Willis, Heather, Clark, Martyn, Coulding, Martina, Jude, Edward, McCormick, Jacqueline, Mercer, Oliver, Potla, Darsh, Rehman, Hafiz, Savill, Heather, Turner, Victoria, Downes, Charlotte, Holding, Kathleen, Riches, Katie, Hilton, Mary, Hayman, Mel, Subramanian, Deepak, Daniel, Priya, Adanini, Oluronke, Bhatia, Nikhil, Msiska, Maines, Collins, Rebecca, Clement, Ian, Patel, Bijal, Gulati, A., Hays, Carole, Webster, K., Hudson, Anne, Webster, Andrea, Stephenson, Elaine, McCormack, Louise, Slater, Victoria, Nixon, Rachel, Hanson, Helen, Fearby, Maggie, Kelly, Sinead, Bridgett, Victoria, Robinson, Philip, Camsooksai, Julie, Humphrey, Charlotte, Jenkins, Sarah, Reschreiter, Henrik, Wadams, Beverley, Death, Yasmin, Bastion, Victoria, Clarke, Daphene, David, Beena, Kent, Harriet, Lorusso, Rachel, Lubimbi, Gamu, Murdoch, Sophie, Penacerrada, Melchizedek, Thomas, Alastair, Valentine, Jennifer, Vochin, Ana, Wulandari, Retno, Djeugam, Brice, Bell, Gillian, English, Katy, Katary, Amro, Wilcox, Louise, Bruce, Michelle, Connolly, Karen, Duncan, Tracy, T-Michael, Helen, Lindergard, Gabriella, Hey, Samuel, Fox, Claire, Alfonso, Jordan, Durrans, Laura Jayne, Guerin, Jacinta, Blackledge, Bethan, Harris, Jade, Hruska, Martin, Eltayeb, Ayaa, Lamb, Thomas, Hodgkiss, Tracey, Cooper, Lisa, Rothwell, Joanne, Allan, Angela, Anderson, Felicity, Kaye, Callum, Liew, Jade, Medhora, Jasmine, Scott, Teresa, Trumper, Erin, Botello, Adriana, Lankester, Liana, Nikitas, Nikitas, Wells, Colin, Stowe, Bethan, Spencer, Kayleigh, Brandwood, Craig, Smith, Lara, Birchall, Katie, Kolakaluri, Laurel, Baines, Deborah, Sukumaran, Anila, Apetri, Elena, Basikolo, Cathrine, Catlow, Laura, Charles, Bethan, Dark, Paul, Doonan, Reece, Harvey, Alice, Horner, Daniel, Knowles, Karen, Lee, Stephanie, Lomas, Diane, Lyons, Chloe, Marsden, Tracy, McLaughlan, Danielle, McMorrow, Liam, Pendlebury, Jessica, Perez, Jane, Poulaka, Maria, Proudfoot, Nicola, Slaughter, Melanie, Slevin, Kathryn, Thomas, Vicky, Walker, Danielle, Michael, Angiy, Collis, Matthew, Cosier, Tracey, Millen, Gemma, Richardson, Neil, Schumacher, Natasha, Weston, Heather, Rand, James, Baxter, Nicola, Henderson, Steven, Kennedy-Hay, Sophie, McParland, Christopher, Rooney, Laura, Sim, Malcolm, McCreath, Gordan, Akeroyd, Louise, Bano, Shereen, Bromley, Matt, Gurr, Lucy, Lawton, Tom, Morgan, James, Sellick, Kirsten, Warren, Deborah, Wilkinson, Brian, McGowan, Janet, Ledgard, Camilla, Stacey, Amelia, Pye, Kate, Bellwood, Ruth, Bentley, Michael, Bewley, Jeremy, Garland, Zoe, Grimmer, Lisa, Gumbrill, Bethany, Johnson, Rebekah, Sweet, Katie, Webster, Denise, Efford, Georgia, Convery, Karen, Fottrell-Gould, Deirdre, Hudig, Lisa, Keshet-Price, Jocelyn, Randell, Georgina, Stammers, Katie, Bokhari, Maria, Linnett, Vanessa, Lucas, Rachael, McCormick, Wendy, Ritzema, Jenny, Sanderson, Amanda, Wild, Helen, Rostron, Anthony, Roy, Alistair, Woods, Lindsey, Cornell, Sarah, Wakinshaw, Fiona, Rogerson, Kimberley, Jarmain, Jordan, Parker, Robert, Reddy, Amie, Turner-Bone, Ian, Wilding, Laura, Harding, Peter, Abernathy, Caroline, Foster, Louise, Gratrix, Andrew, Martinson, Vicky, Parkinson, Priyai, Stones, Elizabeth, Carbral-Ortega, Llucia, Bercades, Georgia, Brealey, David, Hass, Ingrid, MacCallum, Niall, Martir, Gladys, Raith, Eamon, Reyes, Anna, Smyth, Deborah, Zitter, Letizia, Benyon, Sarah, Marriott, Suzie, Park, Linda, Keenan, Samantha, Gordon, Elizabeth, Quinn, Helen, Baines, Kizzy, Cagova, Lenka, Fofano, Adama, Garner, Lucie, Holcombe, Helen, Mepham, Sue, Mitchell, Alice Michael, Mwaura, Lucy, Praman, Krithivasan, Vuylsteke, Alain, Zamikula, Julie, Purewal, Bally, Rivers, Vanessa, Bell, Stephanie, Blakemore, Hayley, Borislavova, Borislava, Faulkner, Beverley, Gendall, Emma, Goff, Elizabeth, Hayes, Kati, Thomas, Matt, Worner, Ruth, Smith, Kerry, Stephens, Deanna, Mew, Louise, Mwaura, Esther, Stewart, Richard, Williams, Felicity, Wren, Lynn, Sutherland, Sara-Beth, Bevan, Emily, Martin, Jane, Trodd, Dawn, Watson, Geoff, Brown, Caroline Wrey, Collins, Amy, Khaliq, Waqas, Gude, Estefania Treus, Akinkugbe, Olugbenga, Bamford, Alasdair, Beech, Emily, Belfield, Holly, Bell, Michael, Davies, Charlene, Jones, Gareth A.L., McHugh, Tara, Meghari, Hamza, O'Neill, Lauran, Peters, Mark J., Ray, Samiran, Tomas, Ana Luisa, Burn, Iona, Hambrook, Geraldine, Manso, Katarina, Penn, Ruth, Shanmugasundaram, Pradeep, Tebbutt, Julie, Thornton, Danielle, Cole, Jade, Davies, Rhys, Duffin, Donna, Hill, Helen, Player, Ben, Thomas, Emma, Williams, Angharad, Griffin, Denise, Muchenje, Nycola, Mupudzi, Mcdonald, Partridge, Richard, Conyngham, Jo-Anna, Thomas, Rachel, Wright, Mary, Corral, Maria Alvarez, Jacob, Reni, Jones, Cathy, Denmade, Craig, Beavis, Sarah, Dale, Katie, Gascoyne, Rachel, Hawes, Joanne, Pritchard, Kelly, Stevenson, Lesley, Whileman, Amanda, Doble, Patricia, Hutter, Joanne, Pawley, Corinne, Shovelton, Charmaine, Vaida, Marius, Butcher, Deborah, O'Sullivan, Susie, Butterworth-Cowin, Nicola, Ahmad, Norfaizan, Barker, Joann, Bauchmuller, Kris, Bird, Sarah, Cawthron, Kay, Harrington, Kate, Jackson, Yvonne, Kibutu, Faith, Lenagh, Becky, Masuko, Shamiso, Mills, Gary H., Raithatha, Ajay, Wiles, Matthew, Willson, Jayne, Newell, Helen, Lye, Alison, Nwafor, Lorenza, Jarman, Claire, Rowland-Jones, Sarah, Foote, David, Cole, Joby, Thompson, Roger, Watson, James, Hesseldon, Lisa, Macharia, Irene, Chetam, Luke, Smith, Jacqui, Ford, Amber, Anderson, Samantha, Birchall, Kathryn, Housley, Kay, Walker, Sara, Milner, Leanne, Hanratty, Helena, Trower, Helen, Phillips, Patrick, Oxspring, Simon, Donne, Ben, Jardine, Catherine, Williams, Dewi, Hay, Alasdair, Flanagan, Rebecca, Hughes, Gareth, Latham, Scott, McKenna, Emma, Anderson, Jennifer, Hull, Robert, Rhead, Kat, Cruz, Carina, Pattison, Natalie, Charnock, Rob, McFarland, Denise, Cosgrove, Denise, Ahmed, Ashar, Morris, Anna, Jakkula, Srinivas, Ali, Asifa, Brady, Megan, Dale, Sam, Dance, Annalisa, Gledhill, Lisa, Greig, Jill, Hanson, Kathryn, Holdroyd, Kelly, Home, Marie, Kelly, Diane, Kitson, Ross, Matapure, Lear, Melia, Deborah, Mellor, Samantha, Nortcliffe, Tonicha, Pinnell, Jez, Robinson, Matthew, Shaw, Lisa, Shaw, Ryan, Thomis, Lesley, Wilson, Alison, Wood, Tracy, Bayo, Lee-Ann, Merwaha, Ekta, Ishaq, Tahira, Hanley, Sarah, Hibbert, Meg, Tetla, Dariusz, Woodford, Chrsitopher, Durga, Latha, Kennard-Holden, Gareth, Branney, Debbie, Frankham, Jordan, Pitts, Sally, White, Nigel, Laha, Shondipon, Verlander, Mark, Williams, Alexandra, Altabaibeh, Abdelhakim, Alvaro, Ana, Gilbert, Kayleigh, Ma, Louise, Mostoles, Loreta, Parmar, Chetan, Simpson, Kathryn, Jetha, Champa, Booker, Lauren, Pratley, Anezka, Adams, Colene, Agasou, Anita, Arden, Tracie, Bowes, Amy, Boyle, Pauline, Beekes, Mandy, Button, Heather, Capps, Nigel, Carnahan, Mandy, Carter, Anne, Childs, Danielle, Donaldson, Denise, Hard, Kelly, Hurford, Fran, Hussain, Yasmin, Javaid, Ayesha, Jones, James, Jose, Sanal, Leigh, Michael, Martin, Terry, Millward, Helen, Motherwell, Nichola, Rikunenko, Rachel, Stickley, Jo, Summers, Julie, Ting, Louise, Tivenan, Helen, Tonks, Louise, Wilcox, Rebecca, Holland, Maureen, Keenan, Natalie, Lyons, Marc, Wassall, Helen, Marsh, Chris, Mahenthran, Mervin, Carter, Emma, Kong, Thomas, Blackman, Helen, Creagh-Brown, Ben, Donlon, Sinead, Michalak-Glinska, Natalia, Mtuwa, Sheila, Pristopan, Veronika, Salberg, Armorel, Smith, Eleanor, Stone, Sarah, Piercy, Charles, Verula, Jerik, Burda, Dorota, Montaser, Rugia, Harden, Lesley, Mayangao, Irving, Marriott, Cheryl, Bradley, Paul, Harris, Celia, Anderson, Susan, Andrews, Eleanor, Birch, Janine, Collins, Emma, Hammerton, Kate, O'Leary, Ryan, Clark, Michele, Purvis, Sarah, Barber, Russell, Hewitt, Claire, Hilldrith, Annette, Jackson-Lawrence, Karen, Shepardson, Sarah, Wills, Maryanne, Butler, Susan, Tavares, Silvia, Cunningham, Amy, Hindale, Julia, Arif, Sarwat, Bean, Sarah, Burt, Karen, Spivey, Michael, Demetriou, Carrie, Eckbad, Charlotte, Hierons, Sarah, Howie, Lucy, Mitchard, Sarah, Ramos, Lidia, Serrano-Ruiz, Alfredo, White, Katie, Kelly, Fiona, Cristiano, Daniele, Dormand, Natalie, Farzad, Zohreh, Gummadi, Mahitha, Liyanage, Kamal, Patel, Brijesh, Salmi, Sara, Sloane, Geraldine, Thwaites, Vicky, Varghese, Mathew, Zborowski, Anelise C., Allan, John, Geary, Tim, Houston, Gordon, Meikle, Alistair, O'Brien, Peter, Forsey, Miranda, Kaliappan, Agilan, Nicholson, Anne, Riches, Joanne, Vertue, Mark, Allan, Elizabeth, Darlington, Kate, Davies, Ffyon, Easton, Jack, Kumar, Sumit, Lean, Richard, Menzies, Daniel, Pugh, Richard, Qiu, Xinyi, Davies, Llinos, Williams, Hannah, Scanlon, Jeremy, Davies, Gwyneth, Mackay, Callum, Lewis, Joannne, Rees, Stephanie, Oblak, Metod, Popescu, Monica, Thankachen, Mini, Higham, Andrew, Simpson, Kerry, Craig, Jayne, Baruah, Rosie, Morris, Sheila, Ferguson, Susie, Shepherd, Amy, Prockter Moore, Luke Stephen, Vizcaychipi, Marcela Paola, Gomes de Almeida Martins, Laura, Carungcong, Jaime, Mohamed Ali, Inthakab Ali, Beaumont, Karen, Blunt, Mark, Coton, Zoe, Curgenven, Hollie, Elsaadany, Mohamed, Fernandes, Kay, Ally, Sameena Mohamed, Rangarajan, Harini, Sarathy, Varun, Selvanayagam, Sivarupan, Vedage, Dave, White, Matthew, Gill, Mandy, Paul, Paul, Ratnam, Valli, Shelton, Sarah, Wynter, Inez, Carmody, Siobhain, Page, Valerie Joan, Beith, Claire Marie, Black, Karen, Clements, Suzanne, Morrison, Alan, Strachan, Dominic, Taylor, Margaret, Clarkson, Michelle, D'Sylva, Stuart, Norman, Kathryn, Auld, Fiona, Donnachie, Joanne, Edmond, Ian, Prentice, Lynn, Runciman, Nikole, Salutous, Dario, Symon, Lesley, Todd, Anne, Turner, Patricia, Short, Abigail, Sweeney, Laura, Murdoch, Euan, Senaratne, Dhaneesha, Hill, Michaela, Kannan, Thogulava, Laura, Wild, Crawley, Rikki, Crew, Abigail, Cunningham, Mishell, Daniels, Allison, Harrison, Laura, Hope, Susan, Inweregbu, Ken, Jones, Sian, Lancaster, Nicola, Matthews, Jamie, Nicholson, Alice, Wray, Gemma, Langton, Helen, Prout, Rachel, Watters, Malcolm, Novis, Catherine, Barron, Anthony, Collins, Ciara, Kaul, Sundeep, Passmore, Heather, Prendergast, Claire, Reed, Anna, Rogers, Paula, Shokkar, Rajvinder, Woodruff, Meriel, Middleton, Hayley, Polgar, Oliver, Nolan, Claire, Mahay, Kanta, Collier, Dawn, Hormis, Anil, Maynard, Victoria, Graham, Cheryl, Walker, Rachel, Knights, Ellen, Price, Alicia, Thomas, Alice, Thorpe, Chris, Behan, Teresa, Burnett, Caroline, Hatton, Jonathan, Heeney, Elaine, Mitra, Atideb, Newton, Maria, Pollard, Rachel, Stead, Rachael, Amin, Vishal, Anastasescu, Elena, Anumakonda, Vikram, Karthik, Komala, Kausar, Rizwana, Reid, Karen, Smith, Jacqueline, Imeson-Wood, Janet, Skinner, Denise, Gaylard, Jane, Mullan, Dee, Newman, Julie, Brown, Alison, Crickmore, Vikki, Debreceni, Gabor, Wilkins, Joy, Nicol, Liz, Reece-Anthony, Rosie, Birt, Mark, Ghosh, Alison, Williams, Emma, Allen, Louise, Beranova, Eva, Crisp, Nikki, Deery, Joanne, Hazelton, Tracy, Knight, Alicia, Price, Carly, Tilbey, Sorrell, Turki, Salah, Turney, Sharon, Cooper, Joshua, Finch, Cheryl, Liderth, Sarah, Quinn, Alison, Waddington, Natalia, Coventry, Tina, Fowler, Susan, MacMahon, Michael, McGregor, Amanda, Cowley, Anne, Highgate, Judith, Gregory, Jane, O'Connell, Susan, Smith, Tim, Barberis, Luigi, Gopal, Shameer, Harris, Nichola, Lake, Victoria, Metherell, Stella, Radford, Elizabeth, Daniel, Amelia, Finn, Joanne, Saha, Rajnish, White, Nikki, Donnison, Phil, Trim, Fiona, Eapen, Beena, Birch, Jenny, Bough, Laura, Goodsell, Josie, Tutton, Rebecca, Williams, Patricia, Williams, Sarah, Winter-Goodwin, Barbara, Nichol, Ailstair, Brickell, Kathy, Smyth, Michelle, Murphy, Lorna, Coetzee, Samantha, Gales, Alistair, Otahal, Igor, Raj, Meena, Sell, Craig, Hilltout, Paula, Evitts, Jayne, Tyler, Amanda, Waldron, Joanne, Beesley, Kate, Board, Sarah, Kubisz-Pudelko, Agnieszka, Lewis, Alison, Perry, Jess, Pippard, Lucy, Wood, Di, Buckley, Clare, Barry, Peter, Flint, Neil, Rekha, Patel, Hales, Dawn, Bunni, Lara, Jennings, Claire, Latif, Monica, Marshall, Rebecca, Subramanian, Gayathri, McGuigan, Peter J., Wasson, Christopher, Finn, Stephanie, Green, Jackie, Collins, Erin, King, Bernadette, Campbell, Andy, Smuts, Sara, Duffield, Joseph, Smith, Oliver, Mallon, Lewis, Claire, Watkins, Botfield, Liam, Butler, Joanna, Dexter, Catherine, Fletcher, Jo, Garg, Atul, Kuravi, Aditya, Ranga, Poonam, Virgilio, Emma, Belagodu, Zakaula, Fuller, Bridget, Gherman, Anca, Olufuwa, Olumide, Paramsothy, Remi, Stuart, Carmel, Oakley, Naomi, Kamundi, Charlotte, Tyl, David, Collins, Katy, Silva, Pedro, Taylor, June, King, Laura, Coates, Charlotte, Crowley, Maria, Wakefield, Phillipa, Beadle, Jane, Johnson, Laura, Sargeant, Janet, Anderson, Madeleine, Brady, Ailbhe, Chan, Rebekah, Little, Jeff, McIvor, Shane, Prady, Helena, Whittle, Helen, Mathew, Bijoy, Attwood, Ben, Parsons, Penny, Ward, Geraldine, Bremmer, Pamela, Joe, West, Tracy, Baird, Jim, Ruddy, Davies, Ellie, Sathe, Sonia, Dennis, Catherine, McGregor, 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Bernard, Kayani, Abdul, Masunda, Bridgett, Anderson, Taya, Hawcutt, Dan, O'Malley, Laura, Rad, Laura, Rogers, Naomi, Saunderson, Paula, Allison, Kathryn Sian, Afolabi, Deborah, Whitbread, Jennifer, Jones, Dawn, Dore, Rachael, Halkes, Matthew, Mercer, Pauline, Thornton, Lorraine, Dawson, Joy, Garrioch, Sweyn, Tolson, Melanie, Aldridge, Jonathan, Kapoor, Ritoo, Loader, David, Castle, Karen, Humphreys, Sally, Tampsett, Ruth, Mackintosh, Katherine, Ayers, Amanda, Harrison, Wendy, North, Julie, Allibone, Suzanne, Genetu, Roman, Kasipandian, Vidya, Patel, Amit, Mac, Ainhi, Murphy, Anthony, Mahjoob, Parisa, Nazari, Roonak, Worsley, Lucy, Fagan, Andrew, Bemand, Thomas, Black, Ethel, Dela Rosa, Arnold, Howle, Ryan, Jhanji, Shaman, Baikady, Ravishankar Rao, Tatham, Kate Colette, Thomas, Benjamin, Bell, Dina, Boyle, Rosalind, Douglas, Katie, Glass, Lynn, Lee, Emma, Lennon, Liz, Rattray, Austin, Taylor, Abigail, Hughes, Rachel Anne, Thomas, Helen, Rees, Alun, Duskova, Michaela, Phipps, Janet, Brooks, Suzanne, Edwards, Michelle, Quaid, Sheena, Watson, Ekaterina, Brayne, Adam, Fisher, Emma, Hunt, Jane, Jackson, Peter, Kaye, Duncan, Love, Nicholas, Parkin, Juliet, Tuckey, Victoria, Van Koutrik, Lynne, Carter, Sasha, Andrew, Benedict, Findlay, Louise, Adams, Katie, Service, Jen, Williams, Alison, Cheyne, Claire, Saunderson, Anne, Moultrie, Sam, Odam, Miranda, Hall, Kathryn, Mapfunde, Isheunesu, Willis, Charlotte, Lyon, Alex, Sri-Chandana, Chunda, Scherewode, Joslan, Stephenson, Lorraine, Marsh, Sarah, Hardy, John, Houlden, Henry, Moncur, Eleanor, Tariq, Ambreen, Tucci, Arianna, Hobrok, Maria, Loosley, Ronda, McGuinness, Heather, Tench, Helen, Wolf-Roberts, Rebecca, Irvine, Val, Shelley, Benjamin, Gorman, Claire, Gupta, Abhinav, Timlick, Elizabeth, Brady, Rebecca, Milligan, Barry, Bellini, Arianna, Bryant, Jade, Mayer, Anton, Pickard, Amy, Roe, Nicholas, Sowter, Jason, Howlett, Alex, Fidler, Katy, Tagliavini, Emma, Donnelly, Kevin, Boos, Jannik, van der Made, Caspar I., Ramakrishnan, Gayatri, Coughlan, Eamon, Asselta, Rosanna, Löscher, Britt-Sabina, Valenti, Luca V.C., de Cid, Rafael, Bujanda, Luis, Julià, Antonio, Pairo-Castineira, Erola, May, Sandra, Zametica, Berina, Heggemann, Julia, Albillos, Agustín, Banales, Jesus M., Barretina, Jordi, Blay, Natalia, Bonfanti, Paolo, Buti, Maria, Fernandez, Javier, Marsal, Sara, Prati, Daniele, Ronzoni, Luisa, Sacchi, Nicoletta, Schultze, Joachim L., Riess, Olaf, Franke, Andre, Rawlik, Konrad, Ellinghaus, David, Hoischen, Alexander, Schmidt, Axel, and Ludwig, Kerstin U.

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2024
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