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5,010 results on '"Spencer, L."'

1. Sparse flow reconstruction methods to reduce the costs of analyzing large unsteady datasets

Author

Stahl, Spencer L. and Benton, Stuart I.

Subjects
Physics - Data Analysis, Statistics and Probability
Abstract

The cost of writing, transferring, and storing large data from unsteady simulations limits access to the entire solution, often leaving much of the flow under-sampled or unanalyzed. For example, modeling transient behavior of rare dynamic events requires 3D snapshots at high sampling rates over long periods, generating significant amounts of data and creating challenges for practical CFD workflows, especially with limited memory resources and costly GPU writing penalties. In this work, multiple sparse flow reconstruction (SFR) methods are developed to approximate a full unsteady solution using far fewer sparse measurements, thus reducing writing costs, data storage, and enabling higher sampling rates. SFR is motivated by a large-eddy simulation of rare inlet distortion events, demonstrating that down-sampling full snapshots and supplementing them with high-frequency sparse measurements can drastically cut writing time for GPU solvers and nearly eliminate this cost for CPU solvers. The simplest single-equation "snapshot" SFR method can be compressed further using Proper Orthogonal Decomposition (POD-SFR) or a more efficient double POD-SFR variant. A streaming SFR modification improves reconstruction efficiency when local memory is limited. A sensitivity study evaluates trade-offs between sparse sampling rates and reconstruction accuracy, offering best practices. To offset error of using random sparse measurements, SFR exactly preserves dynamics in key regions by prescribing sparse measurement locations, used here to capture distortion events. Distortion events are evaluated using the conditional space-time proper orthogonal decomposition (CST-POD) to pursue physical insights that characterize the upstream causality at full resolution. A validation study of CST-POD modes confirms SFR effectiveness at retaining the event dynamics with substantial computational and memory savings., Comment: 29 pages, 15 figures

Published
2024

2. Shear-Layer Perturbation Responses from Time-Resolved Schlieren Data

Author

Stahl, Spencer L., Kumar, Chandan, and Gaitonde, Datta V.

Subjects
Physics - Fluid Dynamics
Abstract

A novel combination of physics-based and data-driven post-processing techniques is proposed to extract acoustic-related shear-layer perturbation responses directly from spatio-temporally resolved schlieren video. The physics-based component is derived from a momentum potential theory extension that extracts irrotational (acoustic and thermal) information from density gradients embedded in schlieren pixel intensities. For the unheated shear layer, the method spotlights acoustic structures and tones otherwise hidden. The filtered data is then subjected to a data-driven Dynamic Mode Decomposition Reduced Order Model (DMD-ROM), which provides the response to forced perturbations. This method applies a learned linear model to isolate and quantify growth rates of acoustic phenomena suited for efficient parametric studies. A shear-layer comprised of two streams at Mach 2.461 and 0.175, corresponding to a convective Mach number 0.88 and containing shocks, is adopted for illustration. The overall perturbation response is first obtained using an impulse forcing in the wall normal direction of the splitter plate, extending in both subsonic and supersonic streams. Subsequently, impulse and harmonic forcings are independently applied in a local pixel-by-pixel manner for a precise receptivity study. The acoustic response shows a convective wavepacket and an acoustic burst from the splitter plate. The interaction with the primary shock and associated wave dispersion emits a second, slower, acoustic wave. Harmonic forcing indicates higher frequency-dependent sensitivity in the supersonic stream, with the most sensitive location near the outer boundary layer region. Excitation here yields an order of magnitude larger acoustic response compared to disturbances in the subsonic stream. Some receptive forcing inputs do not generate significant acoustic waves, which may guide excitation with low noise impact., Comment: 27 pages

Published
2024

5. Identifiability of Product of Experts Models

Author

Gordon, Spencer L., Kant, Manav, Ma, Eric, Schulman, Leonard J., and Staicu, Andrei

Subjects
Computer Science - Machine Learning, Mathematics - Algebraic Geometry, Mathematics - Statistics Theory, 62E10, 62F99, 68T05, I.2.6
Abstract

Product of experts (PoE) are layered networks in which the value at each node is an AND (or product) of the values (possibly negated) at its inputs. These were introduced as a neural network architecture that can efficiently learn to generate high-dimensional data which satisfy many low-dimensional constraints -- thereby allowing each individual expert to perform a simple task. PoEs have found a variety of applications in learning. We study the problem of identifiability of a product of experts model having a layer of binary latent variables, and a layer of binary observables that are iid conditional on the latents. The previous best upper bound on the number of observables needed to identify the model was exponential in the number of parameters. We show: (a) When the latents are uniformly distributed, the model is identifiable with a number of observables equal to the number of parameters (and hence best possible). (b) In the more general case of arbitrarily distributed latents, the model is identifiable for a number of observables that is still linear in the number of parameters (and within a factor of two of best-possible). The proofs rely on root interlacing phenomena for some special three-term recurrences., Comment: 24 pages, 2 figures

Published
2023

6. Identification of Mixtures of Discrete Product Distributions in Near-Optimal Sample and Time Complexity

Author

Gordon, Spencer L., Jahn, Erik, Mazaheri, Bijan, Rabani, Yuval, and Schulman, Leonard J.

Subjects
Computer Science - Machine Learning, Computer Science - Data Structures and Algorithms, Electrical Engineering and Systems Science - Signal Processing, Statistics - Machine Learning
Abstract

We consider the problem of identifying, from statistics, a distribution of discrete random variables $X_1,\ldots,X_n$ that is a mixture of $k$ product distributions. The best previous sample complexity for $n \in O(k)$ was $(1/\zeta)^{O(k^2 \log k)}$ (under a mild separation assumption parameterized by $\zeta$). The best known lower bound was $\exp(\Omega(k))$. It is known that $n\geq 2k-1$ is necessary and sufficient for identification. We show, for any $n\geq 2k-1$, how to achieve sample complexity and run-time complexity $(1/\zeta)^{O(k)}$. We also extend the known lower bound of $e^{\Omega(k)}$ to match our upper bound across a broad range of $\zeta$. Our results are obtained by combining (a) a classic method for robust tensor decomposition, (b) a novel way of bounding the condition number of key matrices called Hadamard extensions, by studying their action only on flattened rank-1 tensors.

Published
2023

7. A data-driven approach to guide supersonic impinging jet control

Author

Stahl, Spencer L. and Gaitonde, Datta V.

Subjects
Physics - Fluid Dynamics
Abstract

A data-driven framework using snapshots of an uncontrolled flow is proposed to identify, and subsequently demonstrate, effective control strategies for different objectives in supersonic impinging jets. The approach, based on a dynamic mode decomposition reduced order model (DMD-ROM), determines forcing receptivity in an economical manner by projecting flow and actuator-specific forcing snapshots onto a reduced subspace and then evolving the results forward in time. Since it effectively determines a linear response around the unsteady flow in the time-domain, the method differs materially from typical techniques that use steady basic states, such as stability or input-output approaches that employ linearized Navier-Stokes operators in the frequency-domain. The method naturally accounts for factors inherent to the snapshot basis, including configuration complexity and flow parameters such as Reynolds number. Furthermore, gain metrics calculated in the reduced subspace facilitate rapid assessments of flow sensitivities to a wide range of forcing parameters, from which optimal actuator inputs may be selected and results confirmed in scale-resolved simulations or experiments. The DMD-ROM approach is demonstrated from two different perspectives. The first concerns asymptotic feedback resonance, where the effects of harmonic pressure forcing are estimated and verified with nonlinear simulations using a blowing-suction actuator. The second examines time-local behavior within critical feedback events, where the phase of actuation becomes important. For this, a conditional space-time mode is used to identify the optimal forcing phase that minimizes convective instability initiation within the resonance cycle., Comment: 10 pages, 7 figures

Published
2023

8. Does Information Bias Teachers' Perceptions of Intervention Effectiveness?

Author

Spencer L. Massey, John T. Rapp, Helena Bush, and Adam J. Almanza

Abstract

In 2011, the United States Congress amended and passed laws requiring states to monitor and decrease psychotropic medication use in child welfare systems. In addition to prescribers, Luna et al. "Behavior and Social Issues," 27, AA16-AA20, (2018) identified case managers, caregivers, and teachers as potential contributors to the prescribing process. For example, due to the lower response effort, teachers may recommend caregivers seek a medical evaluation, which may give rise to pharmacological interventions for behavioral problems as opposed to behavioral interventions. To date, no study has evaluated the extent to which teachers display positive or negative bias for either behavioral or pharmacological intervention as treatment for behavior problems displayed by students. As a step toward addressing this issue, the current study randomly assigned 119 certified teachers to one of four groups: Medication information (Med info), Behavioral information (Beh info), No change (control), and Reverse video (also no change control). Teachers watched two 5-min videos of a child engaging in low to moderate levels of problem behavior. After watching each video, teachers rated seven questions on a 5-point Likert scale. Although teachers in the Med info and Beh info groups observed worsening of behavior in the second video, only the Beh info group provided more negative ratings than the control group regarding the child's ability to follow directions. In addition, Pre-K/elementary school teachers rated the child's behavior as "more normal" than did other teachers. Overall, findings suggest that teachers displayed (1) a slight negative bias toward the behavioral intervention; and (2) neither positive nor negative bias toward psychotropic medication.

Published
2024
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9. An unusual diarrheal outbreak in the community in Eastern Thailand caused by Norovirus GII.3[P25]

Author

Udompat, Patpong, Srimuang, Krongkan, Doungngern, Pawinee, Thippamom, Nattakarn, Petcharat, Sininat, Rattanatumhi, Khwankamon, Khiewbanyang, Sirorat, Taweewigyakarn, Pantila, Kripattanapong, Somkid, Ninwattana, Sasiprapa, Supataragul, Ananporn, Sterling, Spencer L., Klungthong, Chonticha, Joonlasak, Khajohn, Manasatienkij, Wudtichai, Cotrone, Thomas S., Fernandez, Stefan, Wacharapluesadee, Supaporn, and Putcharoen, Opass

Published
2024
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12. Supplemental Transmission Aided Attenuation Correction for Quantitative Cardiac PET/MR

Author

Park, Mi-Ae, Zaha, Vlad G., Badawi, Ramsey D., and Bowen, Spencer L.

Subjects
Physics - Medical Physics
Abstract

Quantitative PET attenuation correction (AC) for combined cardiac PET/MR is a challenging problem. We propose and evaluate an AC approach that uses coincidences from a relatively weak and physically fixed sparse external source, in combination with that from the patient, to correct for PET attenuation based on physics principles alone. The low 30 ml volume of the source makes it easy to fill and place, and the method does not use prior image data or attenuation map assumptions. Our supplemental transmission aided maximum likelihood reconstruction of attenuation and activity (sTX-MLAA) algorithm contains an attenuation map update that maximizes the likelihood of terms representing coincidences originating from tracer in the patient and a weighted expression of counts segmented from the external source alone. Both external source and patient scatter and randoms are fully corrected. We evaluated performance of sTX-MLAA compared to reference standard CT-based AC with FDG PET/CT phantom studies; including modeling a patient with myocardial inflammation. Through an ROI analysis we measured less than 5% bias in activity concentrations for PET images generated with sTX-MLAA relative to CT-AC. PET background variability (from noise and sparse sampling) was substantially reduced with sTX-MLAA compared to using coincidences segmented from the transmission source alone for AC. The study suggests that sTX-MLAA will produce PET images on PET/MR with quantification comparable to PET/CT results during human cardiac exams., Comment: 10 pages, 8 figures

Published
2022

15. Avidity-Based Method for the Efficient Generation of Monoubiquitinated Recombinant Proteins

Author

Nelson, Spencer L, Li, Yunan, Chen, Yue, and Deshmukh, Lalit

Subjects
Ubiquitination, Nedd4 Ubiquitin Protein Ligases, Recombinant Proteins, Chemical Sciences, General Chemistry
Abstract

Monoubiquitination of proteins governs diverse physiological processes, and its dysregulation is implicated in multiple pathologies. The difficulty of preparing sufficient material often complicates the biophysical studies of monoubiquitinated recombinant proteins. Here we describe a robust avidity-based method that overcomes this problem. As a proof-of-concept, we produced milligram quantities of two monoubiquitinated targets, Parkinson's protein α-synuclein and ESCRT-protein ALIX, using NEDD4-family E3 ligases. Monoubiquitination hotspots were identified by quantitative chemical proteomics. Using FRAP and dye-binding assays, we uncovered strikingly opposite effects of monoubiquitination on the phase separation and fibrillization properties of these two amyloidogenic proteins, reflecting differences in their intermolecular interactions, thereby providing unique insights into the impact of monoubiquitination on protein aggregation.

Published
2023

19. An unusual diarrheal outbreak in the community in Eastern Thailand caused by Norovirus GII.3[P25]

Author

Patpong Udompat, Krongkan Srimuang, Pawinee Doungngern, Nattakarn Thippamom, Sininat Petcharat, Khwankamon Rattanatumhi, Sirorat Khiewbanyang, Pantila Taweewigyakarn, Somkid Kripattanapong, Sasiprapa Ninwattana, Ananporn Supataragul, Spencer L. Sterling, Chonticha Klungthong, Khajohn Joonlasak, Wudtichai Manasatienkij, Thomas S. Cotrone, Stefan Fernandez, Supaporn Wacharapluesadee, and Opass Putcharoen

Subjects
Acute gastroenteritis, Norovirus, Outbreak, Dual-typing, Next Generation Sequencing, Infectious and parasitic diseases, RC109-216
Abstract

Abstract Background Sentinel laboratory surveillance for diarrheal disease determined norovirus to be the most common cause of non-bacterial gastroenteritis in people during the COVID-19 pandemic in Thailand. An increase in patients presenting with diarrhea and vomiting in hospitals across Chanthaburi province between December 2021 and January 2022 led to the need for the identification of viral pathogens that may be responsible for the outbreak. Methods Fecal samples (rectal swabs or stool) from 93 patients, of which 65 patients were collected during the December 2021 to January 2022 outbreak, were collected and screened for viral infection by real-time RT-PCR. Positive samples for norovirus GII were then genotyped by targeted amplification and sequencing of partial polymerase and capsid genes. Full genome sequencing was performed from the predominant strain, GII.3[P25]. Results Norovirus was the most common virus detected in human fecal samples in this study. 39 of 65 outbreak samples (60%) and 3 of 28 (10%) non-outbreak samples were positive for norovirus genogroup II. One was positive for rotavirus, and one indicated co-infection with rotavirus and norovirus genogroups I and II. Nucleotide sequences of VP1 and RdRp gene were successfully obtained from 28 of 39 positive norovirus GII and used for dual-typing; 25/28 (89.3%) were GII.3, and 24/28 (85.7) were GII.P25, respectively. Norovirus GII.3[P25] was the predominant strain responsible for this outbreak. The full genome sequence of norovirus GII.3[P25] from our study is the first reported in Thailand and has 98.62% and 98.57% similarity to norovirus found in China in 2021 and the USA in 2022, respectively. We further demonstrate the presence of multiple co-circulating norovirus genotypes, including GII.21[P21], GII.17[P17], GII.3[P12] and GII.4[P31] in our study. Conclusions An unusual diarrhea outbreak was found in December 2021 in eastern Thailand. Norovirus strain GII.3[P25] was the cause of the outbreak and was first detected in Thailand. The positive rate during GII.3[P25] outbreak was six times higher than sporadic cases (GII.4), and, atypically, adults were the primary infected population rather than children.

Published
2024
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20. Causal Inference Despite Limited Global Confounding via Mixture Models

Author

Gordon, Spencer L., Mazaheri, Bijan, Rabani, Yuval, and Schulman, Leonard J.

Subjects
Computer Science - Machine Learning, Computer Science - Data Structures and Algorithms, Electrical Engineering and Systems Science - Signal Processing, Statistics - Machine Learning, 68W40, 62F99, 62-09, F.2, G.3
Abstract

A Bayesian Network is a directed acyclic graph (DAG) on a set of $n$ random variables (the vertices); a Bayesian Network Distribution (BND) is a probability distribution on the random variables that is Markovian on the graph. A finite $k$-mixture of such models is graphically represented by a larger graph which has an additional ``hidden'' (or ``latent'') random variable $U$, ranging in $\{1,\ldots,k\}$, and a directed edge from $U$ to every other vertex. Models of this type are fundamental to causal inference, where $U$ models an unobserved confounding effect of multiple populations, obscuring the causal relationships in the observable DAG. By solving the mixture problem and recovering the joint probability distribution with $U$, traditionally unidentifiable causal relationships become identifiable. Using a reduction to the more well-studied ``product'' case on empty graphs, we give the first algorithm to learn mixtures of non-empty DAGs., Comment: Published in CleaR 2023

Published
2021

21. Selective representations of texture and motion in mouse higher visual areas

Author

Yu, Yiyi, Stirman, Jeffrey N, Dorsett, Christopher R, and Smith, Spencer L

Subjects
Biomedical and Clinical Sciences, Neurosciences, Eye Disease and Disorders of Vision, Underpinning research, 1.1 Normal biological development and functioning, Neurological, Animals, Mice, Motion Perception, Neurons, Photic Stimulation, Visual Cortex, Visual Fields, Visual Pathways, Gabor filter model, calcium imaging, form-motion segregation, mouse higher visual areas, mutual information, naturalistic video, visual edge density, visual motion, visual neuron model, visual texture, Biological Sciences, Medical and Health Sciences, Psychology and Cognitive Sciences, Developmental Biology, Biological sciences, Biomedical and clinical sciences, Psychology
Abstract

The mouse visual cortex contains interconnected higher visual areas, but their functional specializations are unclear. Here, we used a data-driven approach to examine the representations of complex visual stimuli by L2/3 neurons across mouse higher visual areas, measured using large-field-of-view two-photon calcium imaging. Using specialized stimuli, we found higher fidelity representations of texture in area LM, compared to area AL. Complementarily, we found higher fidelity representations of motion in area AL, compared to area LM. We also observed this segregation of information in response to naturalistic videos. Finally, we explored how receptive field models of visual cortical neurons could produce the segregated representations of texture and motion we observed. These selective representations could aid in behaviors such as visually guided navigation.

Published
2022

26. Shark nanobodies with potent SARS-CoV-2 neutralizing activity and broad sarbecovirus reactivity

Author

Chen, Wei-Hung, Hajduczki, Agnes, Martinez, Elizabeth J., Bai, Hongjun, Matz, Hanover, Hill, Thomas M., Lewitus, Eric, Chang, William C., Dawit, Layla, Peterson, Caroline E., Rees, Phyllis A., Ajayi, Adelola B., Golub, Emily S., Swafford, Isabella, Dussupt, Vincent, David, Sapna, Mayer, Sandra V., Soman, Sandrine, Kuklis, Caitlin, Corbitt, Courtney, King, Jocelyn, Choe, Misook, Sankhala, Rajeshwer S., Thomas, Paul V., Zemil, Michelle, Wieczorek, Lindsay, Hart, Tricia, Duso, Debora, Kummer, Larry, Yan, Lianying, Sterling, Spencer L., Laing, Eric D., Broder, Christopher C., Williams, Jazmean K., Davidson, Edgar, Doranz, Benjamin J., Krebs, Shelly J., Polonis, Victoria R., Paquin-Proulx, Dominic, Rolland, Morgane, Reiley, William W., Gromowski, Gregory D., Modjarrad, Kayvon, Dooley, Helen, and Joyce, M. Gordon

Published
2023
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27. Neurophotonic tools for microscopic measurements and manipulation: status report

Author

Abdelfattah, Ahmed S, Ahuja, Sapna, Akkin, Taner, Allu, Srinivasa Rao, Brake, Joshua, Boas, David A, Buckley, Erin M, Campbell, Robert E, Chen, Anderson I, Cheng, Xiaojun, Čižmár, Tomáš, Costantini, Irene, De Vittorio, Massimo, Devor, Anna, Doran, Patrick R, Khatib, Mirna El, Emiliani, Valentina, Fomin-Thunemann, Natalie, Fainman, Yeshaiahu, Fernandez-Alfonso, Tomas, Ferri, Christopher GL, Gilad, Ariel, Han, Xue, Harris, Andrew, Hillman, Elizabeth MC, Hochgeschwender, Ute, Holt, Matthew G, Ji, Na, Kılıç, Kıvılcım, Lake, Evelyn MR, Li, Lei, Li, Tianqi, Mächler, Philipp, Miller, Evan W, Mesquita, Rickson C, Nadella, KM Naga Srinivas, Nägerl, U Valentin, Nasu, Yusuke, Nimmerjahn, Axel, Ondráčková, Petra, Pavone, Francesco S, Campos, Citlali Perez, Peterka, Darcy S, Pisano, Filippo, Pisanello, Ferruccio, Puppo, Francesca, Sabatini, Bernardo L, Sadegh, Sanaz, Sakadzic, Sava, Shoham, Shy, Shroff, Sanaya N, Silver, R Angus, Sims, Ruth R, Smith, Spencer L, Srinivasan, Vivek J, Thunemann, Martin, Tian, Lei, Tian, Lin, Troxler, Thomas, Valera, Antoine, Vaziri, Alipasha, Vinogradov, Sergei A, Vitale, Flavia, Wang, Lihong V, Uhlířová, Hana, Xu, Chris, Yang, Changhuei, Yang, Mu-Han, Yellen, Gary, Yizhar, Ofer, and Zhao, Yongxin

Subjects
Engineering, Biomedical and Clinical Sciences, Neurosciences, Biomedical Engineering, 1.1 Normal biological development and functioning, Underpinning research, Neurological, optical imaging, molecular sensors, optogenetics, fluorescence, label free, blood flow, multimodal, Medical Biotechnology, Biomedical engineering
Abstract

Neurophotonics was launched in 2014 coinciding with the launch of the BRAIN Initiative focused on development of technologies for advancement of neuroscience. For the last seven years, Neurophotonics' agenda has been well aligned with this focus on neurotechnologies featuring new optical methods and tools applicable to brain studies. While the BRAIN Initiative 2.0 is pivoting towards applications of these novel tools in the quest to understand the brain, this status report reviews an extensive and diverse toolkit of novel methods to explore brain function that have emerged from the BRAIN Initiative and related large-scale efforts for measurement and manipulation of brain structure and function. Here, we focus on neurophotonic tools mostly applicable to animal studies. A companion report, scheduled to appear later this year, will cover diffuse optical imaging methods applicable to noninvasive human studies. For each domain, we outline the current state-of-the-art of the respective technologies, identify the areas where innovation is needed, and provide an outlook for the future directions.

Published
2022

28. Hadamard Extensions and the Identification of Mixtures of Product Distributions

Author

Gordon, Spencer L. and Schulman, Leonard J.

Subjects
Computer Science - Machine Learning, Electrical Engineering and Systems Science - Signal Processing, Statistics - Machine Learning, 68W40, 62F99, F.2, G.3
Abstract

The Hadamard Extension of a matrix is the matrix consisting of all Hadamard products of subsets of its rows. This construction arises in the context of identifying a mixture of product distributions on binary random variables: full column rank of such extensions is a necessary ingredient of identification algorithms. We provide several results concerning when a Hadamard Extension has full column rank., Comment: V2: re-titled and slight edits

Published
2021

29. Source Identification for Mixtures of Product Distributions

Author

Gordon, Spencer L., Mazaheri, Bijan, Rabani, Yuval, and Schulman, Leonard J.

Subjects
Computer Science - Machine Learning, Computer Science - Data Structures and Algorithms, Electrical Engineering and Systems Science - Signal Processing, Statistics - Machine Learning, 68W40, 62F99, F.2, G.3
Abstract

We give an algorithm for source identification of a mixture of $k$ product distributions on $n$ bits. This is a fundamental problem in machine learning with many applications. Our algorithm identifies the source parameters of an identifiable mixture, given, as input, approximate values of multilinear moments (derived, for instance, from a sufficiently large sample), using $2^{O(k^2)} n^{O(k)}$ arithmetic operations. Our result is the first explicit bound on the computational complexity of source identification of such mixtures. The running time improves previous results by Feldman, O'Donnell, and Servedio (FOCS 2005) and Chen and Moitra (STOC 2019) that guaranteed only learning the mixture (without parametric identification of the source). Our analysis gives a quantitative version of a qualitative characterization of identifiable sources that is due to Tahmasebi, Motahari, and Maddah-Ali (ISIT 2018).

Published
2020

31. Antibody targeting of conserved sites of vulnerability on the SARS-CoV-2 spike receptor-binding domain

Author

Sankhala, Rajeshwer S., Dussupt, Vincent, Chen, Wei-Hung, Bai, Hongjun, Martinez, Elizabeth J., Jensen, Jaime L., Rees, Phyllis A., Hajduczki, Agnes, Chang, William C., Choe, Misook, Yan, Lianying, Sterling, Spencer L., Swafford, Isabella, Kuklis, Caitlin, Soman, Sandrine, King, Jocelyn, Corbitt, Courtney, Zemil, Michelle, Peterson, Caroline E., Mendez-Rivera, Letzibeth, Townsley, Samantha M., Donofrio, Gina C., Lal, Kerri G., Tran, Ursula, Green, Ethan C., Smith, Clayton, de Val, Natalia, Laing, Eric D., Broder, Christopher C., Currier, Jeffrey R., Gromowski, Gregory D., Wieczorek, Lindsay, Rolland, Morgane, Paquin-Proulx, Dominic, van Dyk, Dewald, Britton, Zachary, Rajan, Saravanan, Loo, Yueh Ming, McTamney, Patrick M., Esser, Mark T., Polonis, Victoria R., Michael, Nelson L., Krebs, Shelly J., Modjarrad, Kayvon, and Joyce, M. Gordon

Published
2024
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32. MuscleJ2: a rebuilding of MuscleJ with new features for high-content analysis of skeletal muscle immunofluorescence slides

Author

Anne Danckaert, Aurélie Trignol, Guillaume Le Loher, Sébastien Loubens, Bart Staels, Hélène Duez, Spencer L. Shorte, and Alicia Mayeuf-Louchart

Subjects
Histology, Muscle fiber morphology, Centro- and perinuclei, Fiber typing, Vascularization, Phenotype cartography, Diseases of the musculoskeletal system, RC925-935
Abstract

Abstract Histological analysis of skeletal muscle is of major interest for understanding its behavior in different pathophysiological conditions, such as the response to different environments or myopathies. In this context, many software programs have been developed to perform automated high-content analysis. We created MuscleJ, a macro that runs in ImageJ/Fiji on batches of images. MuscleJ is a multianalysis tool that initially allows the analysis of muscle fibers, capillaries, and satellite cells. Since its creation, it has been used in many studies, and we have further developed the software and added new features, which are presented in this article. We converted the macro into a Java-language plugin with an improved user interface. MuscleJ2 provides quantitative analysis of fibrosis, vascularization, and cell phenotype in whole muscle sections. It also performs analysis of the peri-myonuclei, the individual capillaries, and any staining in the muscle fibers, providing accurate quantification within regional sublocalizations of the fiber. A multicartography option allows users to visualize multiple results simultaneously. The plugin is freely available to the muscle science community.

Published
2023
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33. Non-invasive monitoring of pulmonary blood flow, functional residual capacity, and shunt index in a porcine model

Author

Maximilian Edlinger-Stanger, Caroline Fritz, Hanna C. McGregor, Spencer L. Bustin, Nathan Ayoubi, Harpreet K. Bath, Johannes Müller, Sebastian Kühnl-Brady, Thomas Schweiger, Julia Jedamzik, Marita Windpassinger, Michael Hiesmayr, Bernard P. Cholley, and Neal W. Fleming

Subjects
Pulmonary physiology, Pulmonary blood flow, Functional residual capacity, Intrapulmonary shunt, sequential gas delivery, Medicine
Abstract

Abstract Background Management of mechanically ventilated patients can be improved with monitoring of key pulmonary function parameters that facilitate individualization and optimization. The VQm Pulmonary Health Monitor™ (PHM) (Rostrum Medical Innovations Inc., Vancouver, Canada) is a new monitor that continuously measures pulmonary blood flow (PBF), functional residual capacity (FRC) and a novel parameter: shunt index (Qsi). The goal of this study was to provide an initial assessment of the performance of the VQm PHM™ when compared with reference measures of PBF, FRC and intra-pulmonary shunt. Methods This was a prospective, experimental, large animal (porcine) study. After baseline measurements, three interventions were performed: increased cardiac output (CO) using dobutamine infusion, increased PEEP (from 4 to 12 cmH2O), and experimental shunt induced by an extracorporeal circuit. PBF, FRC and shunt were measured by the VQm Pulmonary Health Monitor™ (PHM) before and after each intervention. The PHM™ uses sequential gas delivery to deliver targeted alveolar concentrations of CO2 or N2O. PBF and FRC were calculated using CO2 bolus delivery and the modified differential Fick equation. Shunt was estimated from the number of breaths required to eliminate N2O after a 25-breath N2O bolus and expressed as Qsi. PHM™-derived PBF and Qsi were compared to thermodilution CO and calculated Berggren shunt, respectively. Results Studies were completed in 19 animals. Measurements of PBF, FRC and Qsi obtained from the PHM™ trended as expected following each intervention. The mean difference between paired values of PBF was − 0.2 ± 0.9 L/min and the 95% limits of agreement were 1.5 and − 1.9 L/min. Concordance was 94.1%. The mean baseline FRC was 1.7 $$\pm$$ 0.4 L and increased to 2.0 $$\pm$$ 0.6 L, following the increase in PEEP (p = 0.0078). For shunt (Qsi), the mean values during low (1 L/min) and high (50% of baseline CO) shunt value were 40 $$\pm$$ 4 and 27 $$\pm$$ 5, respectively, p = 0.002. Conclusions PBF obtained through the modified differential Fick equation and Qsi obtained through N2O uptake and decay dynamics by the VQm PHM™ provide comparable results to reference standards. FRC measurements trended as expected following interventions.

Published
2023
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34. The Herschel SPIRE Fourier Transform Spectrometer Spectral Feature Finder V. Rotational measurements of NGC 891

Author

Benson, Chris S., Spencer, L. D., Valtchanov, I., Scott, J., and Hładczuk, N.

Subjects
Astrophysics - Astrophysics of Galaxies, Astrophysics - Instrumentation and Methods for Astrophysics
Abstract

The ESA Herschel Spectral and Photometric Imaging Receiver (SPIRE) Fourier Transform Spectrometer (FTS) Spectral Feature Finder (FF) project is an automated spectral feature fitting routine developed within the SPIRE instrument team to extract all prominent spectral features from all publicly available SPIRE FTS observations. In this work, we demonstrate the use of the FF information extracted from three observations of the edge-on spiral galaxy NGC 891 to measure the rotation of NII and CI gas at Far-infrared frequencies in complement to radio observations of the HI 21cm line and the CO(1-0) transition as well as optical measurements of Halpha. We find that measurements of both NII and CI gas follow a similar velocity profile to that of HI and Halpha showing a correlation between neutral and ionized regions of the interstellar medium (ISM) in the disk of NGC 891.

Published
2020
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35. Planck intermediate results. LV. Reliability and thermal properties of high-frequency sources in the Second Planck Catalogue of Compact Sources

Author

Planck Collaboration, Akrami, Y., Ashdown, M., Aumont, J., Baccigalupi, C., Ballardini, M., Banday, A. J., Barreiro, R. B., Bartolo, N., Basak, S., Benabed, K., Bernard, J. -P., Bersanelli, M., Bielewicz, P., Bond, J. R., Borrill, J., Bouchet, F. R., Burigana, C., Calabrese, E., Carvalho, P., Chiang, H. C., Crill, B. P., Cuttaia, F., de Rosa, A., de Zotti, G., Delabrouille, J., Delouis, J. -M., Di Valentino, E., Diego, J. M., Dupac, X., Dusini, S., Efstathiou, G., Elsner, F., Enßlin, T. A., Eriksen, H. K., Fernandez-Cobos, R., Finelli, F., Fraisse, A. A., Franceschi, E., Frolov, A., Galeotta, S., Ganga, K., Gerbino, M., González-Nuevo, J., Górski, K. M., Gratton, S., Gruppuso, A., Gudmundsson, J. E., Handley, W., Hansen, F. K., Herranz, D., Hivon, E., Hobson, M., Huang, Z., Jones, W. C., Keihänen, E., Keskitalo, R., Kim, J., Kisner, T. S., Krachmalnicoff, N., Kunz, M., Kurki-Suonio, H., Lamarre, J. -M., Lasenby, A., Lattanzi, M., Lawrence, C. R., Jeune, M. Le, Levrier, F., Lilje, P. B., Lindholm, V., López-Caniego, M., Ma, Y. -Z., Macías-Pérez, J. F., Maggio, G., Mandolesi, N., Marcos-Caballero, A., Maris, M., Martin, P. G., Martínez-González, E., Matarrese, S., Mauri, N., McEwen, J. D., Migliaccio, M., Molinari, D., Moneti, A., Montier, L., Morgante, G., Natoli, P., Paoletti, D., Partridge, B., Perrotta, F., Pettorino, V., Piacentini, F., Polenta, G., Puget, J. -L., Rachen, J. P., Reinecke, M., Remazeilles, M., Renzi, A., Rocha, G., Roudier, G., Ruiz-Granados, B., Savelainen, M., Scott, D., Sirri, G., Spencer, L. D., Suur-Uski, A. -S., Tauber, J. A., Tavagnacco, D., Tenti, M., Toffolatti, L., Tomasi, M., Trombetti, T., Valiviita, J., Van Tent, B., Vielva, P., Villa, F., Wehus, I. K., Zacchei, A., and Zonca, A.

Subjects
Astrophysics - Astrophysics of Galaxies, Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - Instrumentation and Methods for Astrophysics
Abstract

We describe an extension of the most recent version of the Planck Catalogue of Compact Sources (PCCS2), produced using a new multi-band Bayesian Extraction and Estimation Package (BeeP). BeeP assumes that the compact sources present in PCCS2 at 857 GHz have a dust-like spectral energy distribution, which leads to emission at both lower and higher frequencies, and adjusts the parameters of the source and its SED to fit the emission observed in Planck's three highest frequency channels at 353, 545, and 857 GHz, as well as the IRIS map at 3000 GHz. In order to reduce confusion regarding diffuse cirrus emission, BeeP's data model includes a description of the background emission surrounding each source, and it adjusts the confidence in the source parameter extraction based on the statistical properties of the spatial distribution of the background emission. BeeP produces the following three new sets of parameters for each source: (a) fits to a modified blackbody (MBB) thermal emission model of the source; (b) SED-independent source flux densities at each frequency considered; and (c) fits to an MBB model of the background in which the source is embedded. BeeP also calculates, for each source, a reliability parameter, which takes into account confusion due to the surrounding cirrus. We define a high-reliability subset (BeeP/base), containing 26 083 sources (54.1 per cent of the total PCCS2 catalogue), the majority of which have no information on reliability in the PCCS2. The results of the BeeP extension of PCCS2, which are made publicly available via the PLA, will enable the study of the thermal properties of well-defined samples of compact Galactic and extra-galactic dusty sources., Comment: 55 pages. Accepted for publication in A&A. The BeeP catalogue will be published in the Planck Legacy Archive (https://pla.esac.esa.int/pla)

Published
2020
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36. Planck intermediate results. LVII. Joint Planck LFI and HFI data processing

Author

Planck Collaboration, Akrami, Y., Andersen, K. J., Ashdown, M., Baccigalupi, C., Ballardini, M., Banday, A. J., Barreiro, R. B., Bartolo, N., Basak, S., Benabed, K., Bernard, J. -P., Bersanelli, M., Bielewicz, P., Bond, J. R., Borrill, J., Burigana, C., Butler, R. C., Calabrese, E., Casaponsa, B., Chiang, H. C., Colombo, L. P. L., Combet, C., Crill, B. P., Cuttaia, F., de Bernardis, P., de Rosa, A., de Zotti, G., Delabrouille, J., Di Valentino, E., Diego, J. M., Doré, O., Douspis, M., Dupac, X., Eriksen, H. K., Fernandez-Cobos, R., Finelli, F., Frailis, M., Fraisse, A. A., Franceschi, E., Frolov, A., Galeotta, S., Galli, S., Ganga, K., Gerbino, M., Ghosh, T., González-Nuevo, J., Górski, K. M., Gruppuso, A., Gudmundsson, J. E., Handley, W., Helou, G., Herranz, D., Hildebrandt, S. R., Hivon, E., Huang, Z., Jaffe, A. H., Jones, W. C., Keihänen, E., Keskitalo, R., Kiiveri, K., Kim, J., Kisner, T. S., Krachmalnicoff, N., Kunz, M., Kurki-Suonio, H., Lasenby, A., Lattanzi, M., Lawrence, C. R., Jeune, M. Le, Levrier, F., Liguori, M., Lilje, P. B., Lilley, M., Lindholm, V., López-Caniego, M., Lubin, P. M., Macías-Pérez, J. F., Maino, D., Mandolesi, N., Marcos-Caballero, A., Maris, M., Martin, P. G., Martínez-González, E., Matarrese, S., Mauri, N., McEwen, J. D., Meinhold, P. R., Mennella, A., Migliaccio, M., Mitra, S., Molinari, D., Montier, L., Morgante, G., Moss, A., Natoli, P., Paoletti, D., Partridge, B., Patanchon, G., Pearson, D., Pearson, T. J., Perrotta, F., Piacentini, F., Polenta, G., Rachen, J. P., Reinecke, M., Remazeilles, M., Renzi, A., Rocha, G., Rosset, C., Roudier, G., Rubiño-Martín, J. A., Ruiz-Granados, B., Salvati, L., Savelainen, M., Scott, D., Sirignano, C., Sirri, G., Spencer, L. D., Suur-Uski, A. -S., Svalheim, T. L., Tauber, J. A., Tavagnacco, D., Tenti, M., Terenzi, L., Thommesen, H., Toffolatti, L., Tomasi, M., Tristram, M., Trombetti, T., Valiviita, J., Van Tent, B., Vielva, P., Villa, F., Vittorio, N., Wandelt, B. D., Wehus, I. K., Zacchei, A., and Zonca, A.

Subjects
Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract

We present the NPIPE processing pipeline, which produces calibrated frequency maps in temperature and polarization from data from the Planck Low Frequency Instrument (LFI) and High Frequency Instrument (HFI) using high-performance computers. NPIPE represents a natural evolution of previous Planck analysis efforts, and combines some of the most powerful features of the separate LFI and HFI analysis pipelines. The net effect of the improvements is lower levels of noise and systematics in both frequency and component maps at essentially all angular scales, as well as notably improved internal consistency between the various frequency channels. Based on the NPIPE maps, we present the first estimate of the Solar dipole determined through component separation across all nine Planck frequencies. The amplitude is ($3366.6 \pm 2.7$)$\mu$K, consistent with, albeit slightly higher than, earlier estimates. From the large-scale polarization data, we derive an updated estimate of the optical depth of reionization of $\tau = 0.051 \pm 0.006$, which appears robust with respect to data and sky cuts. There are 600 complete signal, noise and systematics simulations of the full-frequency and detector-set maps. As a Planck first, these simulations include full time-domain processing of the beam-convolved CMB anisotropies. The release of NPIPE maps and simulations is accompanied with a complete suite of raw and processed time-ordered data and the software, scripts, auxiliary data, and parameter files needed to improve further on the analysis and to run matching simulations., Comment: 97 pages, 93 figures and 16 tables, abstract abridged for arXiv submission, accepted for publication in A&A

Published
2020
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37. Planck intermediate results. LVI. Detection of the CMB dipole through modulation of the thermal Sunyaev-Zeldovich effect: Eppur si muove II

Author

Planck Collaboration, Akrami, Y., Ashdown, M., Aumont, J., Baccigalupi, C., Ballardini, M., Banday, A. J., Barreiro, R. B., Bartolo, N., Basak, S., Benabed, K., Bernard, J. -P., Bersanelli, M., Bielewicz, P., Bond, J. R., Borrill, J., Bouchet, F. R., Burigana, C., Calabrese, E., Cardoso, J. -F., Casaponsa, B., Chiang, H. C., Combet, C., Contreras, D., Crill, B. P., Cuttaia, F., de Bernardis, P., de Rosa, A., de Zotti, G., Delabrouille, J., Di Valentino, E., Diego, J. M., Doré, O., Douspis, M., Dupac, X., Enßlin, T. A., Eriksen, H. K., Fernandez-Cobos, R., Finelli, F., Frailis, M., Franceschi, E., Frolov, A., Galeotta, S., Galli, S., Ganga, K., Génova-Santos, R. T., Gerbino, M., González-Nuevo, J., Górski, K. M., Gruppuso, A., Gudmundsson, J. E., Handley, W., Herranz, D., Hivon, E., Huang, Z., Jaffe, A. H., Jones, W. C., Keihänen, E., Keskitalo, R., Kiiveri, K., Kim, J., Kisner, T. S., Krachmalnicoff, N., Kunz, M., Kurki-Suonio, H., Lamarre, J. -M., Lattanzi, M., Lawrence, C. R., Jeune, M. Le, Levrier, F., Liguori, M., Lilje, P. B., Lindholm, V., López-Caniego, M., Macías-Pérez, J. F., Maino, D., Mandolesi, N., Marcos-Caballero, A., Maris, M., Martin, P. G., Martínez-González, E., Matarrese, S., Mauri, N., McEwen, J. D., Mennella, A., Migliaccio, M., Molinari, D., Moneti, A., Montier, L., Morgante, G., Moss, A., Natoli, P., Pagano, L., Paoletti, D., Perrotta, F., Pettorino, V., Piacentini, F., Polenta, G., Rachen, J. P., Reinecke, M., Remazeilles, M., Renzi, A., Rocha, G., Rosset, C., Rubiño-Martín, J. A., Ruiz-Granados, B., Salvati, L., Savelainen, M., Scott, D., Sirignano, C., Sirri, G., Spencer, L. D., Sullivan, R. M., Sunyaev, R., Suur-Uski, A. -S., Tauber, J. A., Tavagnacco, D., Tenti, M., Toffolatti, L., Tomasi, M., Trombetti, T., Valiviita, J., Van Tent, B., Vielva, P., Villa, F., Vittorio, N., Wehus, I. K., Zacchei, A., and Zonca, A.

Subjects
Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract

The largest temperature anisotropy in the cosmic microwave background (CMB) is the dipole, which has been measured with increasing accuracy for more than three decades, particularly with the Planck satellite. The simplest interpretation of the dipole is that it is due to our motion with respect to the rest frame of the CMB. Since current CMB experiments infer temperature anisotropies from angular intensity variations, the dipole modulates the temperature anisotropies with the same frequency dependence as the thermal Sunyaev-Zeldovich (tSZ) effect. We present the first, and significant, detection of this signal in the tSZ maps and find that it is consistent with direct measurements of the CMB dipole, as expected. The signal contributes power in the tSZ maps, which is modulated in a quadrupolar pattern, and we estimate its contribution to the tSZ bispectrum, noting that it contributes negligible noise to the bispectrum at relevant scales., Comment: 15 pages, 8 figures. Added references, small clarifying and language edits. All results remain the same

Published
2020
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38. Vacancy diffusion in multi-principal element alloys: the role of chemical disorder in the ordered lattice

Author

Thomas, Spencer L. and Patala, Srikanth

Subjects
Condensed Matter - Materials Science
Abstract

Many of the purported virtues of Multi-Principal Element Alloys (MPEAs), such as corrosion, high-temperature oxidation and irradiation resistance, are highly sensitive to vacancy diffusivity. Similarly, solute interdiffusion is governed by vacancy diffusion -- it is often unclear whether MPEAs are truly stable, or effectively stabilized by slow interdiffusion. The considerable composition space afforded to these alloys makes optimizing for desired properties a daunting task; theoretical and computational tools are necessary to guide alloy development. For diffusion, such tools depend on both a knowledge of the vacancy migration barriers within a given alloy and an understanding of how these barriers influence vacancy diffusivity. We present a generalized theory of vacancy diffusion in rugged energy landscapes, paired with Kinetic Monte Carlo simulations of MPEA vacancy diffusion. The barrier energy statistics are informed by nudged elastic band calculations in the equiatomic CoNiCrFeMn alloy. Theory and simulations show that vacancy diffusion in solid-solution MPEAs is not necessarily sluggish, but can potentially be tuned, and that trap models are an insufficient explanation for sluggish diffusion in the CoNiCrFeMn HEA. These results also show that any model that endeavors to faithfully represent diffusion-related phenomena must account for the full nature of the energy landscape, not just the migration barriers.

Published
2020

39. Dynamic Graph Learning based on Graph Laplacian

Author

Jiang, Bo, Panahi, Ashkan, Krim, Hamid, Yu, Yiyi, and Smith, Spencer L.

Subjects
Electrical Engineering and Systems Science - Signal Processing
Abstract

The purpose of this paper is to infer a global (collective) model of time-varying responses of a set of nodes as a dynamic graph, where the individual time series are respectively observed at each of the nodes. The motivation of this work lies in the search for a connectome model which properly captures brain functionality upon observing activities in different regions of the brain and possibly of individual neurons. We formulate the problem as a quadratic objective functional of observed node signals over short time intervals, subjected to the proper regularization reflecting the graph smoothness and other dynamics involving the underlying graph's Laplacian, as well as the time evolution smoothness of the underlying graph. The resulting joint optimization is solved by a continuous relaxation and an introduced novel gradient-projection scheme. We apply our algorithm to a real-world dataset comprising recorded activities of individual brain cells. The resulting model is shown to not only be viable but also efficiently computable.

Published
2020

40. Innovation, Flexibility, and Adaptation: Keys to Patton's Information Dominance

Author

French, Spencer L.

Subjects
United States. Army -- Innovations, Information services industry -- Innovations, Information services -- Innovations, Information management -- Military aspects, Information accessibility, Information services industry, Military and naval science
Abstract

Upon arriving in Greenock, Scotland, in January 1944, Lieutenant General George S. Patton Jr. met the first elements of Third Army (call sign Lucky). He greeted them by saying, 'I [...]

Published
2023

42. Multiorgan MRI findings after hospitalisation with COVID-19 in the UK (C-MORE): a prospective, multicentre, observational cohort study

Author

Raman, Betty, McCracken, Celeste, Cassar, Mark P, Moss, Alastair J, Finnigan, Lucy, Samat, Azlan Helmy A, Ogbole, Godwin, Tunnicliffe, Elizabeth M, Alfaro-Almagro, Fidel, Menke, Ricarda, Xie, Cheng, Gleeson, Fergus, Lukaschuk, Elena, Lamlum, Hanan, McGlynn, Kevin, Popescu, Iulia A, Sanders, Zeena-Britt, Saunders, Laura C, Piechnik, Stefan K, Ferreira, Vanessa M, Nikolaidou, Chrysovalantou, Rahman, Najib M, Ho, Ling-Pei, Harris, Victoria C, Shikotra, Aarti, Singapuri, Amisha, Pfeffer, Paul, Manisty, Charlotte, Kon, Onn M, Beggs, Mark, O'Regan, Declan P, Fuld, Jonathan, Weir-McCall, Jonathan R, Parekh, Dhruv, Steeds, Rick, Poinasamy, Krisnah, Cuthbertson, Dan J, Kemp, Graham J, Semple, Malcolm G, Horsley, Alexander, Miller, Christopher A, O'Brien, Caitlin, Shah, Ajay M, Chiribiri, Amedeo, Leavy, Olivia C, Richardson, Matthew, Elneima, Omer, McAuley, Hamish J C, Sereno, Marco, Saunders, Ruth M, Houchen-Wolloff, Linzy, Greening, Neil J, Bolton, Charlotte E, Brown, Jeremy S, Choudhury, Gourab, Diar Bakerly, Nawar, Easom, Nicholas, Echevarria, Carlos, Marks, Michael, Hurst, John R, Jones, Mark G, Wootton, Daniel G, Chalder, Trudie, Davies, Melanie J, De Soyza, Anthony, Geddes, John R, Greenhalf, William, Howard, Luke S, Jacob, Joseph, Man, William D-C, Openshaw, Peter J M, Porter, Joanna C, Rowland, Matthew J, Scott, Janet T, Singh, Sally J, Thomas, David C, Toshner, Mark, Lewis, Keir E, Heaney, Liam G, Harrison, Ewen M, Kerr, Steven, Docherty, Annemarie B, Lone, Nazir I, Quint, Jennifer, Sheikh, Aziz, Zheng, Bang, Jenkins, R Gisli, Cox, Eleanor, Francis, Susan, Halling-Brown, Mark, Chalmers, James D, Greenwood, John P, Plein, Sven, Hughes, Paul J C, Thompson, A A Roger, Rowland-Jones, Sarah L, Wild, James M, Kelly, Matthew, Treibel, Thomas A, Bandula, Steven, Aul, Raminder, Miller, Karla, Jezzard, Peter, Smith, Stephen, Nichols, Thomas E, McCann, Gerry P, Evans, Rachael A, Wain, Louise V, Brightling, Christopher E, Neubauer, Stefan, Baillie, J K, Shaw, Alison, Hairsine, Brigid, Kurasz, Claire, Henson, Helen, Armstrong, Lisa, Shenton, Liz, Dobson, H, Dell, Amanda, Lucey, Alice, Price, Andrea, Storrie, Andrew, Pennington, Chris, Price, Claire, Mallison, Georgia, Willis, Gemma, Nassa, Heeah, Haworth, Jill, Hoare, Michaela, Hawkings, Nancy, Fairbairn, Sara, Young, Susan, Walker, S, Jarrold, I, Sanderson, Amy, David, C, Chong-James, K, Zongo, O, James, W Y, Martineau, A, King, Bernie, Armour, C, McAulay, D, Major, E, McGinness, Jade, McGarvey, L, Magee, N, Stone, Roisin, Drain, S, Craig, T, Bolger, A, Haggar, Ahmed, Lloyd, Arwel, Subbe, Christian, Menzies, Daniel, Southern, David, McIvor, Emma, Roberts, K, Manley, R, Whitehead, Victoria, Saxon, W, Bularga, A, Mills, N L, El-Taweel, Hosni, Dawson, Joy, Robinson, Leanne, Saralaya, Dinesh, Regan, Karen, Storton, Kim, Brear, Lucy, Amoils, S, Bermperi, Areti, Elmer, Anne, Ribeiro, Carla, Cruz, Isabel, Taylor, Jessica, Worsley, J, Dempsey, K, Watson, L, Jose, Sherly, Marciniak, S, Parkes, M, McQueen, Alison, Oliver, Catherine, Williams, Jenny, Paradowski, Kerry, Broad, Lauren, Knibbs, Lucy, Haynes, Matthew, Sabit, Ramsey, Milligan, L, Sampson, Claire, Hancock, Alyson, Evenden, Cerys, Lynch, Ceri, Hancock, Kia, Roche, Lisa, Rees, Meryl, Stroud, Natalie, Thomas-Woods, T, Heller, S, Robertson, E, Young, B, Wassall, Helen, Babores, M, Holland, Maureen, Keenan, Natalie, Shashaa, Sharlene, Price, Carly, Beranova, Eva, Ramos, Hazel, Weston, Heather, Deery, Joanne, Austin, Liam, Solly, Reanne, Turney, Sharon, Cosier, Tracey, Hazelton, Tracy, Ralser, M, Wilson, Ann, Pearce, Lorraine, Pugmire, S, Stoker, Wendy, McCormick, W, Dewar, A, Arbane, Gill, Kaltsakas, G, Kerslake, Helen, Rossdale, J, Bisnauthsing, Karen, Aguilar Jimenez, Laura A, Martinez, L M, Ostermann, Marlies, Magtoto, Murphy M, Hart, Nicholas, Marino, Philip, Betts, Sarah, Solano, Teresa S, Arias, Ava Maria, Prabhu, A, Reed, Annabel, Wrey Brown, Caroline, Griffin, Denise, Bevan, Emily, Martin, Jane, Owen, J, Alvarez Corral, Maria, Williams, Nick, Payne, Sheila, Storrar, Will, Layton, Alison, Lawson, Cathy, Mills, Clare, Featherstone, James, Stephenson, Lorraine, Burdett, Tracy, Ellis, Y, Richards, A, Wright, C, Sykes, D L, Brindle, K, Drury, Katie, Holdsworth, L, Crooks, M G, Atkin, Paul, Flockton, Rachel, Thackray-Nocera, Susannah, Mohamed, Abdelrahman, Taylor, Abigail, Perkins, Emma, Ross, Gavin, McGuinness, Heather, Tench, Helen, Phipps, Janet, Loosley, Ronda, Wolf-Roberts, Rebecca, Coetzee, S, Omar, Zohra, Ross, Alexandra, Card, Bethany, Carr, Caitlin, King, Clara, Wood, Chloe, Copeland, D, Calvelo, Ellen, Chilvers, Edwin R, Russell, Emily, Gordon, Hussain, Nunag, Jose Lloyd, Schronce, J, March, Katherine, Samuel, Katherine, Burden, L, Evison, Lynsey, McLeavey, Laura, Orriss-Dib, Lorna, Tarusan, Lawrence, Mariveles, Myril, Roy, Maura, Mohamed, Noura, Simpson, Neil, Yasmin, Najira, Cullinan, P, Daly, Patrick, Haq, Sulaimaan, Moriera, Silvia, Fayzan, Tamanah, Munawar, Unber, Nwanguma, Uchechi, Lingford-Hughes, A, Altmann, Danny, Johnston, D, Mitchell, J, Valabhji, J, Price, L, Molyneaux, P L, Thwaites, Ryan S, Walsh, S, Frankel, A, Lightstone, L, Wilkins, M, Willicombe, M, McAdoo, S, Touyz, R, Guerdette, Anne-Marie, Warwick, Katie, Hewitt, Melanie, Reddy, R, White, Sonia, McMahon, A, Hoare, Amy, Knighton, Abigail, Ramos, Albert, Te, Amelie, Jolley, Caroline J, Speranza, Fabio, Assefa-Kebede, Hosanna, Peralta, Ida, Breeze, Jonathon, Shevket, K, Powell, Natassia, Adeyemi, Oluwaseun, Dulawan, Pearl, Adrego, Rita, Byrne, S, Patale, Sheetal, Hayday, A, Malim, M, Pariante, C, Sharpe, C, Whitney, J, Bramham, K, Ismail, K, Wessely, S, Nicholson, T, Ashworth, Andrew, Humphries, Amy, Tan, Ai Lyn, Whittam, Beverley, Coupland, C, Favager, Clair, Peckham, D, Wade, Elaine, Saalmink, Gwen, Clarke, Jude, Glossop, Jodie, Murira, Jennifer, Rangeley, Jade, Woods, Janet, Hall, Lucy, Dalton, Matthhew, Window, Nicola, Beirne, Paul, Hardy, Tim, Coakley, G, Turtle, Lance, Berridge, Anthony, Cross, Andy, Key, Angela L, Rowe, Anna, Allt, Ann Marie, Mears, Chloe, Malein, Flora, Madzamba, Gladys, Hardwick, H E, Earley, Joanne, Hawkes, Jenny, Pratt, James, Wyles, J, Tripp, K A, Hainey, Kera, Allerton, Lisa, Lavelle-Langham, L, Melling, Lucy, Wajero, Lilian O, Poll, L, Noonan, Matthew J, French, N, Lewis-Burke, N, Williams-Howard, S A, Cooper, Shirley, Kaprowska, Sabina, Dobson, S L, Marsh, Sophie, Highett, Victoria, Shaw, V, Beadsworth, M, Defres, S, Watson, Ekaterina, Tiongson, Gerlynn F, Papineni, Padmasayee, Gurram, Sambasivarao, Diwanji, Shalin N, Quaid, Sheena, Briggs, A, Hastie, Claire, Rogers, Natalie, Stensel, D, Bishop, L, McIvor, K, Rivera-Ortega, P, Al-Sheklly, B, Avram, Cristina, Faluyi, David, Blaikely, J, Piper Hanley, K, Radhakrishnan, K, Buch, M, Hanley, N A, Odell, Natasha, Osbourne, Rebecca, Stockdale, Sue, Felton, T, Gorsuch, T, Hussell, T, Kausar, Zunaira, Kabir, T, McAllister-Williams, H, Paddick, S, Burn, D, Ayoub, A, Greenhalgh, Alan, Sayer, A, Young, A, Price, D, Burns, G, MacGowan, G, Fisher, Helen, Tedd, H, Simpson, J, Jiwa, Kasim, Witham, M, Hogarth, Philip, West, Sophie, Wright, S, McMahon, Michael J, Neill, Paula, Dougherty, Andrew, Morrow, A, Anderson, David, Grieve, D, Bayes, Hannah, Fallon, K, Mangion, K, Gilmour, L, Basu, N, Sykes, R, Berry, C, McInnes, I B, Donaldson, A, Sage, E K, Barrett, Fiona, Welsh, B, Bell, Murdina, Quigley, Jackie, Leitch, Karen, Macliver, L, Patel, Manish, Hamil, R, Deans, Andrew, Furniss, J, Clohisey, S, Elliott, Anne, Solstice, A R, Deas, C, Tee, Caroline, Connell, David, Sutherland, Debbie, George, J, Mohammed, S, Bunker, Jenny, Holmes, Katie, Dipper, A, Morley, Anna, Arnold, David, Adamali, H, Welch, H, Morrison, Leigh, Stadon, Louise, Maskell, Nick, Barratt, Shaney, Dunn, Sarah, Waterson, Samuel, Jayaraman, Bhagy, Light, Tessa, Selby, N, Hosseini, A, Shaw, Karen, Almeida, Paula, Needham, Robert, Thomas, Andrew K, Matthews, Laura, Gupta, Ayushman, Nikolaidis, Athanasios, Dupont, Catherine, Bonnington, J, Chrystal, Melanie, Greenhaff, P L, Linford, S, Prosper, Sabrina, Jang, W, Alamoudi, Asma, Bloss, Angela, Megson, Clare, Nicoll, Debby, Fraser, Emily, Pacpaco, Edmund, Conneh, Florence, Ogg, G, McShane, H, Koychev, Ivan, Chen, Jin, Pimm, John, Ainsworth, Mark, Pavlides, M, Sharpe, M, Havinden-Williams, May, Petousi, Nayia, Talbot, Nick, Carter, Penny, Kurupati, Prathiba, Dong, T, Peng, Yanchun, Burns, A, Kanellakis, N, Korszun, A, Connolly, B, Busby, J, Peto, T, Patel, B, Nolan, C M, Cristiano, Daniele, Walsh, J A, Liyanage, Kamal, Gummadi, Mahitha, Dormand, N, Polgar, Oliver, George, P, Barker, R E, Patel, Suhani, Gibbons, M, Matila, Darwin, Jarvis, Hannah, Lim, Lai, Olaosebikan, Olaoluwa, Ahmad, Shanaz, Brill, Simon, Mandal, S, Laing, C, Michael, Alice, Reddy, A, Johnson, C, Baxendale, H, Parfrey, H, Mackie, J, Newman, J, Pack, Jamie, Parmar, J, Paques, K, Garner, Lucie, Harvey, Alice, Summersgill, C, Holgate, D, Hardy, E, Oxton, J, Pendlebury, Jessica, McMorrow, L, Mairs, N, Majeed, N, Dark, P, Ugwuoke, R, Knight, Sean, Whittaker, S, Strong-Sheldrake, Sophia, Matimba-Mupaya, Wadzanai, Chowienczyk, P, Pattenadk, Dibya, Hurditch, E, Chan, Flora, Carborn, H, Foot, H, Bagshaw, J, Hockridge, J, Sidebottom, J, Lee, Ju Hee, Birchall, K, Turner, Kim, Haslam, L, Holt, L, Milner, L, Begum, M, Marshall, M, Steele, N, Tinker, N, Ravencroft, Phillip, Butcher, Robyn, Misra, S, Coburn, Zach, Fairman, Alexandra, Ford, Amber, Holbourn, Ailsa, Howell, Alice, Lawrie, Allan, Lye, Alison, Mbuyisa, Angeline, Zawia, Amira, Holroyd-Hind, B, Thamu, B, Clark, Cameron, Jarman, Claire, Norman, C, Roddis, C, Foote, David, Lee, Elvina, Ilyas, F, Stephens, G, Newell, Helen, Turton, Helena, Macharia, Irene, Wilson, Imogen, Cole, Joby, McNeill, J, Meiring, J, Rodger, J, Watson, James, Chapman, Kerry, Harrington, Kate, Chetham, Luke, Hesselden, L, Nwafor, Lorenza, Dixon, Myles, Plowright, Megan, Wade, Phillip, Gregory, Rebecca, Lenagh, Rebecca, Stimpson, R, Megson, Sharon, Newman, Tom, Cheng, Yutung, Goodwin, Camelia, Heeley, Cheryl, Sissons, D, Sowter, D, Gregory, Heidi, Wynter, Inez, Hutchinson, John, Kirk, Jill, Bennett, Kaytie, Slack, Katie, Allsop, Lynne, Holloway, Leah, Flynn, Margaret, Gill, Mandy, Greatorex, M, Holmes, Megan, Buckley, Phil, Shelton, Sarah, Turner, Sarah, Sewell, Terri Ann, Whitworth, V, Lovegrove, Wayne, Tomlinson, Johanne, Warburton, Louise, Painter, Sharon, Vickers, Carinna, Redwood, Dawn, Tilley, Jo, Palmer, Sue, Wainwright, Tania, Breen, G, Hotopf, M, Dunleavy, A, Teixeira, J, Ali, Mariam, Mencias, Mark, Msimanga, N, Siddique, Sulman, Samakomva, T, Tavoukjian, Vera, Forton, D, Ahmed, R, Cook, Amanda, Thaivalappil, Favas, Connor, Lynda, Rees, Tabitha, McNarry, M, Williams, N, McCormick, Jacqueline, McIntosh, Jerome, Vere, Joanne, Coulding, Martina, Kilroy, Susan, Turner, Victoria, Butt, Al-Tahoor, Savill, Heather, Fraile, Eva, Ugoji, Jacinta, Landers, G, Lota, Harpreet, Portukhay, Sofiya, Nasseri, Mariam, Daniels, Alison, Hormis, Anil, Ingham, Julie, Zeidan, Lisa, Osborne, Lynn, Chablani, Manish, Banerjee, A, David, A, Pakzad, A, Rangelov, B, Williams, B, Denneny, E, Willoughby, J, Xu, M, Mehta, P, Batterham, R, Bell, R, Aslani, S, Lilaonitkul, W, Checkley, A, Bang, Dongchun, Basire, Donna, Lomas, D, Wall, E, Plant, Hannah, Roy, K, Heightman, M, Lipman, M, Merida Morillas, Marta, Ahwireng, Nyarko, Chambers, R C, Jastrub, Roman, Logan, S, Hillman, T, Botkai, A, Casey, A, Neal, A, Newton-Cox, A, Cooper, B, Atkin, C, McGee, C, Welch, C, Wilson, D, Sapey, E, Qureshi, H, Hazeldine, J, Lord, J M, Nyaboko, J, Short, J, Stockley, J, Dasgin, J, Draxlbauer, K, Isaacs, K, Mcgee, K, Yip, K P, Ratcliffe, L, Bates, M, Ventura, M, Ahmad Haider, N, Gautam, N, Baggott, R, Holden, S, Madathil, S, Walder, S, Yasmin, S, Hiwot, T, Jackson, T, Soulsby, T, Kamwa, V, Peterkin, Z, Suleiman, Z, Chaudhuri, N, Wheeler, H, Djukanovic, R, Samuel, R, Sass, T, Wallis, T, Marshall, B, Childs, C, Marouzet, E, Harvey, M, Fletcher, S, Dickens, C, Beckett, P, Nanda, U, Daynes, E, Charalambou, A, Yousuf, A J, Lea, A, Prickett, A, Gooptu, Bibek, Hargadon, Beverley, Bourne, Charlotte, Christie, C, Edwardson, C, Lee, D, Baldry, E, Stringer, E, Woodhead, F, Mills, G, Arnold, H, Aung, H, Qureshi, I N, Finch, J, Skeemer, J, Hadley, K, Khunti, Kamlesh, Carr, Liesel, Ingram, L, Aljaroof, M, Bakali, M, Bakau, M, Baldwin, M, Bourne, Michelle, Pareek, Manish, Soares, M, Tobin, Martin, Armstrong, Natalie, Brunskill, Nigel, Goodman, N, Cairns, P, Haldar, Pranab, McCourt, P, Dowling, R, Russell, Richard, Diver, Sarah, Edwards, Sarah, Glover, Sarah, Parker, S, Siddiqui, Salman, Ward, T J C, Mcnally, T, Thornton, T, Yates, Tom, Ibrahim, W, Monteiro, Will, Thickett, D, Wilkinson, D, Broome, M, McArdle, P, Upthegrove, R, Wraith, D, Langenberg, C, Summers, C, Bullmore, E, Heeney, J L, Schwaeble, W, Sudlow, C L, Adeloye, D, Newby, D E, Rudan, I, Shankar-Hari, M, Thorpe, M, Pius, R, Walmsley, S, McGovern, A, Ballard, C, Allan, L, Dennis, J, Cavanagh, J, Petrie, J, O'Donnell, K, Spears, M, Sattar, N, MacDonald, S, Guthrie, E, Henderson, M, Guillen Guio, Beatriz, Zhao, Bang, Lawson, C, Overton, Charlotte, Taylor, Chris, Tong, C, Mukaetova-Ladinska, Elizabeta, Turner, E, Pearl, John E, Sargant, J, Wormleighton, J, Bingham, Michelle, Sharma, M, Steiner, Mike, Samani, Nilesh, Novotny, Petr, Free, Rob, Allen, R J, Finney, Selina, Terry, Sarah, Brugha, Terry, Plekhanova, Tatiana, McArdle, A, Vinson, B, Spencer, L G, Reynolds, W, Ashworth, M, Deakin, B, Chinoy, H, Abel, K, Harvie, M, Stanel, S, Rostron, A, Coleman, C, Baguley, D, Hufton, E, Khan, F, Hall, I, Stewart, I, Fabbri, L, Wright, L, Kitterick, P, Morriss, R, Johnson, S, Bates, A, Antoniades, C, Clark, D, Bhui, K, Channon, K M, Motohashi, K, Sigfrid, L, Husain, M, Webster, M, Fu, X, Li, X, Kingham, L, Klenerman, P, Miiler, K, Carson, G, Simons, G, Huneke, N, Calder, P C, Baldwin, D, Bain, S, Lasserson, D, Daines, L, Bright, E, Stern, M, Crisp, P, Dharmagunawardena, R, Reddington, A, Wight, A, Bailey, L, Ashish, A, Robinson, E, Cooper, J, Broadley, A, Turnbull, A, Brookes, C, Sarginson, C, Ionita, D, Redfearn, H, Elliott, K, Barman, L, Griffiths, L, Guy, Z, Gill, Rhyan, Nathu, Rashmita, Harris, Edward, Moss, P, Finnigan, J, Saunders, Kathryn, Saunders, Peter, Kon, S, Kon, Samantha S, O'Brien, Linda, Shah, K, Shah, P, Richardson, Emma, Brown, V, Brown, M, Brown, Jo, Brown, J, Brown, Ammani, Brown, Angela, Choudhury, N, Jones, S, Jones, H, Jones, L, Jones, I, Jones, G, Jones, Heather, Jones, Don, Davies, Ffyon, Davies, Ellie, Davies, Kim, Davies, Gareth, Davies, Gwyneth A, Howard, K, Porter, Julie, Rowland, J, Rowland, A, Scott, Kathryn, Singh, Suver, Singh, Claire, Thomas, S, Thomas, Caradog, Lewis, Victoria, Lewis, J, Lewis, D, Harrison, P, Francis, C, Francis, R, Hughes, Rachel Ann, Hughes, Joan, Hughes, A D, Thompson, T, Kelly, S, Smith, D, Smith, Nikki, Smith, Andrew, Smith, Jacqui, Smith, Laurie, Smith, Susan, Evans, Teriann, Evans, Ranuromanana I, Evans, D, Evans, R, Evans, H, and Evans, J

Published
2023
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43. Diesel2p mesoscope with dual independent scan engines for flexible capture of dynamics in distributed neural circuitry

Author

Yu, Che-Hang, Stirman, Jeffrey N, Yu, Yiyi, Hira, Riichiro, and Smith, Spencer L

Subjects
Biomedical and Clinical Sciences, Neurosciences, Physical Sciences, Biomedical Imaging, Bioengineering, Neurological, Brain, Calcium, Equipment Design, Microscopy, Fluorescence, Multiphoton, Nerve Net, Neurons, Optical Imaging
Abstract

Imaging the activity of neurons that are widely distributed across brain regions deep in scattering tissue at high speed remains challenging. Here, we introduce an open-source system with Dual Independent Enhanced Scan Engines for Large field-of-view Two-Photon imaging (Diesel2p). Combining optical design, adaptive optics, and temporal multiplexing, the system offers subcellular resolution over a large field-of-view of ~25 mm2, encompassing distances up to 7 mm, with independent scan engines. We demonstrate the flexibility and various use cases of this system for calcium imaging of neurons in the living brain.

Published
2021

44. Deficits in higher visual area representations in a mouse model of Angelman syndrome

Author

Townsend, Leah B, Jones, Kelly A, Dorsett, Christopher R, Philpot, Benjamin D, and Smith, Spencer L

Subjects
Neurosciences, Intellectual and Developmental Disabilities (IDD), Brain Disorders, Eye Disease and Disorders of Vision, Rare Diseases, Aetiology, 1.1 Normal biological development and functioning, Underpinning research, 2.1 Biological and endogenous factors, Neurological, Mental health, Angelman Syndrome, Animals, Disease Models, Animal, Mice, Neurons, Visual Cortex, Psychology
Abstract

BackgroundSensory processing deficits are common in individuals with neurodevelopmental disorders. One hypothesis is that deficits may be more detectable in downstream, "higher" sensory areas. A mouse model of Angelman syndrome (AS), which lacks expression of the maternally inherited Ube3a allele, has deficits in synaptic function and experience-dependent plasticity in the primary visual cortex. Thus, we hypothesized that AS model mice have deficits in visually driven neuronal responsiveness in downstream higher visual areas (HVAs).MethodsHere, we used intrinsic signal optical imaging and two-photon calcium imaging to map visually evoked neuronal activity in the primary visual cortex and HVAs in response to an array of stimuli.ResultsWe found a highly specific deficit in HVAs. Drifting gratings that changed speed caused a strong response in HVAs in wildtype mice, but this was not observed in littermate AS model mice. Further investigation with two-photon calcium imaging revealed the effect to be largely driven by aberrant responses of inhibitory interneurons, suggesting a cellular basis for higher level, stimulus-selective cortical dysfunction in AS.ConclusionAssaying downstream, or "higher" circuitry may provide a more sensitive measure for circuit dysfunction in mouse models of neurodevelopmental disorders.Trial registrationNot applicable.

Published
2020

45. Comparative Performance in the Detection of Four Coronavirus Genera from Human, Animal, and Environmental Specimens

Author

Supaporn Wacharapluesadee, Nattakarn Thippamom, Piyapha Hirunpatrawong, Khwankamon Rattanatumhi, Spencer L. Sterling, Wiparat Khunnawutmanotham, Kirana Noradechanon, Patarapol Maneeorn, Rome Buathong, Leilani Paitoonpong, and Opass Putcharoen

Subjects
coronaviruses, PCR, pan-CoV PCR, surveillance, reservoir host, Microbiology, QR1-502
Abstract

Emerging coronaviruses (CoVs) are understood to cause critical human and domestic animal diseases; the spillover from wildlife reservoirs can result in mild and severe respiratory illness in humans and domestic animals and can spread more readily in these naïve hosts. A low-cost CoV molecular method that can detect a variety of CoVs from humans, animals, and environmental specimens is an initial step to ensure the early identification of known and new viruses. We examine a collection of 50 human, 46 wastewater, 28 bat, and 17 avian archived specimens using 3 published pan-CoV PCR assays called Q-, W-, and X-CoV PCR, to compare the performance of each assay against four CoV genera. X-CoV PCR can detect all four CoV genera, but Q- and W-CoV PCR failed to detect δ-CoV. In total, 21 (42.0%), 9 (18.0%), and 21 (42.0%) of 50 human specimens and 30 (65.22%), 6 (13.04%), and 27 (58.70%) of 46 wastewater specimens were detected using Q-, W-, and X-CoV PCR assays, respectively. The X-CoV PCR assay has a comparable sensitivity to Q-CoV PCR in bat CoV detection. Combining Q- and X-CoV PCR assays can increase sensitivity and avoid false negative results in the early detection of novel CoVs.

Published
2024
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49. LRP2020: Machine Learning Advantages in Canadian Astrophysics

Author

Venn, K. A., Fabbro, S., Liu, A, Hezaveh, Y., Perreault-Levasseur, L., Eadie, G., Ellison, S., Woo, J., Kavelaars, JJ., Yi, K. M., Hlozek, R., Bovy, J., Teimoorinia, H., Ravanbakhsh, S., and Spencer, L.

Subjects
Astrophysics - Instrumentation and Methods for Astrophysics
Abstract

The application of machine learning (ML) methods to the analysis of astrophysical datasets is on the rise, particularly as the computing power and complex algorithms become more powerful and accessible. As the field of ML enjoys a continuous stream of breakthroughs, its applications demonstrate the great potential of ML, ranging from achieving tens of millions of times increase in analysis speed (e.g., modeling of gravitational lenses or analysing spectroscopic surveys) to solutions of previously unsolved problems (e.g., foreground subtraction or efficient telescope operations). The number of astronomical publications that include ML has been steadily increasing since 2010. With the advent of extremely large datasets from a new generation of surveys in the 2020s, ML methods will become an indispensable tool in astrophysics. Canada is an unambiguous world leader in the development of the field of machine learning, attracting large investments and skilled researchers to its prestigious AI Research Institutions. This provides a unique opportunity for Canada to also be a world leader in the application of machine learning in the field of astrophysics, and foster the training of a new generation of highly skilled researchers., Comment: White paper E015 submitted to the Canadian Long Range Plan LRP2020

Published
2019
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50. Effects of sleep disturbance on dyspnoea and impaired lung function following hospital admission due to COVID-19 in the UK: a prospective multicentre cohort study

Author

Jackson, C, Stewart, I D, Plekhanova, T, Cunningham, P S, Hazel, A L, Al-Sheklly, B, Aul, R, Bolton, C E, Chalder, T, Chalmers, J D, Chaudhuri, N, Docherty, A B, Donaldson, G, Edwardson, C L, Elneima, O, Greening, N J, Hanley, N A, Harris, V C, Harrison, E M, Ho, L-P, Houchen-Wolloff, L, Howard, L S, Jolley, C J, Jones, M G, Leavy, O C, Lewis, K E, Lone, N I, Marks, M, McAuley, H J C, McNarry, M A, Patel, B, Piper-Hanley, K, Poinasamy, K, Raman, B, Richardson, M, Rivera-Ortega, P, Rowland-Jones, S L, Rowlands, A V, Saunders, R M, Scott, J T, Sereno, M, Shah, A M, Shikotra, A, Singapuri, A, Stanel, S C, Thorpe, M, Wootton, D G, Yates, T, Jenkins, G, Singh, S J, Man, W D-C, Brightling, C E, Wain, L V, Porter, J C, Thompson, R, Horsley, A, Molyneaux, P L, Evans, R A, Jones, S E, Rutter, M K, Blaikley, J F, Abel, K, Adamali, H, Adeloye, D, Adeyemi, O, Adrego, R, Aguilar Jimenez, L A, Ahmad, S, Ahmad Haider, N, Ahmed, R, Ahwireng, N, Ainsworth, M, Alamoudi, A, Ali, M, Aljaroof, M, All, AM, Allan, L, Allen, R J, Allerton, L, Allsop, L, Almeida, P, Altmann, D, Alvarez Corral, M, Amoils, S, Anderson, D, Antoniades, C, Arbane, G, Arias, A, Armour, C, Armstrong, L, Armstrong, N, Arnold, D, Arnold, H, Ashish, A, Ashworth, A, Ashworth, M, Aslani, S, Assefa-Kebede, H, Atkin, C, Atkin, P, Aung, H, Austin, L, Avram, C, Ayoub, A, Babores, M, Baggott, R, Bagshaw, J, Baguley, D, Bailey, L, Baillie, J K, Bain, S, Bakali, M, Bakau, M, Baldry, E, Baldwin, D, Baldwin, M, Ballard, C, Banerjee, A, Bang, B, Barker, R E, Barman, L, Barratt, S, Barrett, F, Basire, D, Basu, N, Bates, M, Bates, A, Batterham, R, Baxendale, H, Bayes, H, Beadsworth, M, Beckett, P, Beggs, M, Begum, M, Beirne, P, Bell, D, Bell, R, Bennett, K, Beranova, E, Bermperi, A, Berridge, A, Berry, C, Betts, S, Bevan, E, Bhui, K, Bingham, M, Birchall, K, Bishop, L, Bisnauthsing, K, Blaikely, J, Bloss, A, Bolger, A, Bonnington, J, Botkai, A, Bourne, C, Bourne, M, Bramham, K, Brear, L, Breen, G, Breeze, J, Briggs, A, Bright, E, Brill, S, Brindle, K, Broad, L, Broadley, A, Brookes, C, Broome, M, Brown, A, Brown, J, Brown, J S, Brown, M, Brown, V, Brugha, T, Brunskill, N, Buch, M, Buckley, P, Bularga, A, Bullmore, E, Burden, L, Burdett, T, Burn, D, Burns, G, Burns, A, Busby, J, Butcher, R, Butt, A, Byrne, S, Cairns, P, Calder, P C, Calvelo, E, Carborn, H, Card, B, Carr, C, Carr, L, Carson, G, Carter, P, Casey, A, Cassar, M, Cavanagh, J, Chablani, M, Chambers, R C, Chan, F, Channon, K M, Chapman, K, Charalambou, A, Checkley, A, Chen, J, Cheng, Y, Chetham, L, Childs, C, Chilvers, E R, Chinoy, H, Chiribiri, A, Chong-James, K, Choudhury, G, Choudhury, N, Chowienczyk, P, Christie, C, Chrystal, M, Clark, D, Clark, C, Clarke, J, Clohisey, S, Coakley, G, Coburn, Z, Coetzee, S, Cole, J, Coleman, C, Conneh, F, Connell, D, Connolly, B, Connor, L, Cook, A, Cooper, B, Cooper, J, Cooper, S, Copeland, D, Cosier, T, Coulding, M, Coupland, C, Cox, E, Craig, T, Crisp, P, Cristiano, D, Crooks, M G, Cross, A, Cruz, I, Cullinan, P, Cuthbertson, D, Daines, L, Dalton, M, Daly, P, Daniels, A, Dark, P, Dasgin, J, David, A, David, C, Davies, E, Davies, F, Davies, G, Davies, G A, Davies, K, Davies, M J, Dawson, J, Daynes, E, De Soyza, A, Deakin, B, Deans, A, Deas, C, Deery, J, Defres, S, Dell, A, Dempsey, K, Denneny, E, Dennis, J, Dewar, A, Dharmagunawardena, R, Diar-Bakerly, N, Dickens, C, Dipper, A, Diver, S, Diwanji, S N, Dixon, M, Djukanovic, R, Dobson, H, Dobson, S L, Donaldson, A, Dong, T, Dormand, N, Dougherty, A, Dowling, R, Drain, S, Draxlbauer, K, Drury, K, Dulawan, P, Dunleavy, A, Dunn, S, Dupont, C, Earley, J, Easom, N, Echevarria, C, Edwards, S, Edwardson, C, El-Taweel, H, Elliott, A, Elliott, K, Ellis, Y, Elmer, A, Evans, D, Evans, H, Evans, J, Evans, R, Evans, R I, Evans, T, Evenden, C, Evison, L, Fabbri, L, Fairbairn, S, Fairman, A, Fallon, K, Faluyi, D, Favager, C, Fayzan, T, Featherstone, J, Felton, T, Finch, J, Finney, S, Finnigan, J, Finnigan, L, Fisher, H, Fletcher, S, Flockton, R, Flynn, M, Foot, H, Foote, D, Ford, A, Forton, D, Fraile, E, Francis, C, Francis, R, Francis, S, Frankel, A, Fraser, E, Free, R, French, N, Fu, X, Fuld, J, Furniss, J, Garner, L, Gautam, N, Geddes, J R, George, J, George, P M, Gibbons, M, Gill, M, Gilmour, L, Gleeson, F, Glossop, J, Glover, S, Goodman, N, Goodwin, C, Gooptu, B, Gordon, H, Gorsuch, T, Greatorex, M, Greenhaff, P L, Greenhalf, W, Greenhalgh, A, Greenwood, J, Gregory, H, Gregory, R, Grieve, D, Griffin, D, Griffiths, L, Guerdette, A-M, Guillen Guio, B, Gummadi, M, Gupta, A, Gurram, S, Guthrie, E, Guy, Z, Henson, H, Hadley, K, Haggar, A, Hainey, K, Hairsine, B, Haldar, P, Hall, I, Hall, L, Halling-Brown, M, Hamil, R, Hancock, A, Hancock, K, Haq, S, Hardwick, H E, Hardy, E, Hardy, T, Hargadon, B, Harrington, K, Harris, E, Harrison, P, Hart, N, Harvey, A, Harvey, M, Harvie, M, Haslam, L, Havinden-Williams, M, Hawkes, J, Hawkings, N, Haworth, J, Hayday, A, Haynes, M, Hazeldine, J, Hazelton, T, Heaney, L G, Heeley, C, Heeney, J L, Heightman, M, 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