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63,683 results on '"Arthur, J. A."'

52. A Fast-Binding, Functionally Reversible, COX‑2 Radiotracer for CNS PET Imaging

Author

Michael S. Placzek, Daniel K. Wilton, Michel Weïwer, Mariah A. Manter, Sarah E. Reid, Christopher J. Meyer, Arthur J. Campbell, Besnik Bajrami, Antoine Bigot, Sarah Bricault, Agathe Fayet, Arnaud Frouin, Frederick Gergits, Mehak Gupta, Wei Jiang, Michelle Melanson, Chiara D. Romano, Misha M. Riley, Jessica M. Wang, Hsiao-Ying Wey, Florence F. Wagner, Beth Stevens, and Jacob M. Hooker

Subjects
Chemistry, QD1-999
Published
2024
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53. Appropriateness of Ophthalmology Recommendations From an Online Chat-Based Artificial Intelligence Model

Author

Prashant D. Tailor, MD, Timothy T. Xu, MD, Blake H. Fortes, MD, Raymond Iezzi, MD, Timothy W. Olsen, MD, Matthew R. Starr, MD, Sophie J. Bakri, MD, Brittni A. Scruggs, MD, PhD, Andrew J. Barkmeier, MD, Sanjay V. Patel, MD, Keith H. Baratz, MD, Ashlie A. Bernhisel, MD, Lilly H. Wagner, MD, Andrea A. Tooley, MD, Gavin W. Roddy, MD, PhD, Arthur J. Sit, MD, Kristi Y. Wu, MD, Erick D. Bothun, MD, Sasha A. Mansukhani, MBBS, Brian G. Mohney, MD, John J. Chen, MD, PhD, Michael C. Brodsky, MD, Deena A. Tajfirouz, MD, Kevin D. Chodnicki, MD, Wendy M. Smith, MD, and Lauren A. Dalvin, MD

Subjects
Computer applications to medicine. Medical informatics, R858-859.7
Abstract

Objective: To determine the appropriateness of ophthalmology recommendations from an online chat-based artificial intelligence model to ophthalmology questions. Patients and Methods: Cross-sectional qualitative study from April 1, 2023, to April 30, 2023. A total of 192 questions were generated spanning all ophthalmic subspecialties. Each question was posed to a large language model (LLM) 3 times. The responses were graded by appropriate subspecialists as appropriate, inappropriate, or unreliable in 2 grading contexts. The first grading context was if the information was presented on a patient information site. The second was an LLM-generated draft response to patient queries sent by the electronic medical record (EMR). Appropriate was defined as accurate and specific enough to serve as a surrogate for physician-approved information. Main outcome measure was percentage of appropriate responses per subspecialty. Results: For patient information site-related questions, the LLM provided an overall average of 79% appropriate responses. Variable rates of average appropriateness were observed across ophthalmic subspecialties for patient information site information ranging from 56% to 100%: cataract or refractive (92%), cornea (56%), glaucoma (72%), neuro-ophthalmology (67%), oculoplastic or orbital surgery (80%), ocular oncology (100%), pediatrics (89%), vitreoretinal diseases (86%), and uveitis (65%). For draft responses to patient questions via EMR, the LLM provided an overall average of 74% appropriate responses and varied by subspecialty: cataract or refractive (85%), cornea (54%), glaucoma (77%), neuro-ophthalmology (63%), oculoplastic or orbital surgery (62%), ocular oncology (90%), pediatrics (94%), vitreoretinal diseases (88%), and uveitis (55%). Stratifying grades across health information categories (disease and condition, risk and prevention, surgery-related, and treatment and management) showed notable but insignificant variations, with disease and condition often rated highest (72% and 69%) for appropriateness and surgery-related (55% and 51%) lowest, in both contexts. Conclusion: This LLM reported mostly appropriate responses across multiple ophthalmology subspecialties in the context of both patient information sites and EMR-related responses to patient questions. Current LLM offerings require optimization and improvement before widespread clinical use.

Published
2024
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54. Silencing CA1 pyramidal cells output reveals the role of feedback inhibition in hippocampal oscillations

Author

Chinnakkaruppan Adaikkan, Justin Joseph, Georgios Foustoukos, Jun Wang, Denis Polygalov, Roman Boehringer, Steven J. Middleton, Arthur J. Y. Huang, Li-Huei Tsai, and Thomas J. McHugh

Subjects
Science
Abstract

Abstract The precise temporal coordination of neural activity is crucial for brain function. In the hippocampus, this precision is reflected in the oscillatory rhythms observed in CA1. While it is known that a balance between excitatory and inhibitory activity is necessary to generate and maintain these oscillations, the differential contribution of feedforward and feedback inhibition remains ambiguous. Here we use conditional genetics to chronically silence CA1 pyramidal cell transmission, ablating the ability of these neurons to recruit feedback inhibition in the local circuit, while recording physiological activity in mice. We find that this intervention leads to local pathophysiological events, with ripple amplitude and intrinsic frequency becoming significantly larger and spatially triggered local population spikes locked to the trough of the theta oscillation appearing during movement. These phenotypes demonstrate that feedback inhibition is crucial in maintaining local sparsity of activation and reveal the key role of lateral inhibition in CA1 in shaping circuit function.

Published
2024
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55. Differential functional consequences of GRIN2A mutations associated with schizophrenia and neurodevelopmental disorders

Author

Nate Shepard, David Baez-Nieto, Sumaiya Iqbal, Erkin Kurganov, Nikita Budnik, Arthur J. Campbell, Jen Q. Pan, Morgan Sheng, and Zohreh Farsi

Subjects
Medicine, Science
Abstract

Abstract Human genetic studies have revealed rare missense and protein-truncating variants in GRIN2A, encoding for the GluN2A subunit of the NMDA receptors, that confer significant risk for schizophrenia (SCZ). Mutations in GRIN2A are also associated with epilepsy and developmental delay/intellectual disability (DD/ID). However, it remains enigmatic how alterations to the same protein can result in diverse clinical phenotypes. Here, we performed functional characterization of human GluN1/GluN2A heteromeric NMDA receptors that contain SCZ-linked GluN2A variants, and compared them to NMDA receptors with GluN2A variants associated with epilepsy or DD/ID. Our findings demonstrate that SCZ-associated GRIN2A variants were predominantly loss-of-function (LoF), whereas epilepsy and DD/ID-associated variants resulted in both gain- and loss-of-function phenotypes. We additionally show that M653I and S809R, LoF GRIN2A variants associated with DD/ID, exert a dominant-negative effect when co-expressed with a wild-type GluN2A, whereas E58Ter and Y698C, SCZ-linked LoF variants, and A727T, an epilepsy-linked LoF variant, do not. These data offer a potential mechanism by which SCZ/epilepsy and DD/ID-linked variants can cause different effects on receptor function and therefore result in divergent pathological outcomes.

Published
2024
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58. Phase retrieval of programmable photonic integrated circuits based on an on-chip fractional-delay reference path

Author

Xu, Xingyuan, Ren, Guanghui, Dubey, Aditya, Feleppa, Tim, Liu, Xumeng, Boes, Andreas, Mitchell, Arnan, and Lowery, Arthur J.

Subjects
Physics - Optics, Electrical Engineering and Systems Science - Signal Processing
Abstract

Programmable photonic integrated circuits (PICs), offering diverse signal processing functions within a single chip, are promising solutions for applications ranging from optical communications to artificial intelligence. While the scale and complexity of programmable PICs is increasing, the characterization, and thus calibration, of them becomes increasingly challenging. Here we demonstrate a phase retrieval method for programmable PICs using an on-chip fractional-delay reference path. The impulse response of the chip can be uniquely and precisely identified from only the insertion loss using a standard complex Fourier transform. We demonstrate our approach experimentally with a 4-tap finite-impulse-response chip. The results match well with expectations and verifies our approach as effective for individually determining the taps' weights without the need for additional ports and photodiodes.

Published
2022

61. Assessing landscape N removal in coastal New England catchments using the N-Sink approach with the R Package, nsink [version 1; peer review: awaiting peer review]

Author

Dorothy Q Kellogg, Jeffrey W. Hollister, Chester L. Arnold, Arthur J. Gold, Emily H. Wilson, Cary B. Chadwick, David W. Dickson, Qian Lei-Parent, and Kenneth J. Forshay

Subjects
Software Tool Article, Articles, Nitrogen, R, watershed, catchment, GIS, landscape N sink, water resources, nsink
Abstract

Background Excess nitrogen (N) loading to coastal ecosystems impairs estuarine water quality. Land management decisions made within estuarine watersheds have a direct impact on downstream N delivery. Natural features within watersheds can act as landscape sinks for N, such as wetlands, streams and ponds that transform dissolved N into gaseous N, effectively removing it from the aquatic system. Identifying and evaluating these landscape sinks and their spatial relationship to N sources can help managers understand the effects of alternative decisions on downstream resources. Methods The N-Sink approach uses widely available GIS data to identify landscape sinks within HUC-12 (or larger) catchments, estimate their N removal potential and map the effect of those sinks on N movement through the catchment. Static maps are produced to visualize N removal efficiency, transport and delivery, the latter in the form of an index. The R package nsink was developed to facilitate data acquisition, processing and visualization. Results The R package creates static maps for a specific HUC-12, or users can visit the University of Connecticut website to explore previously mapped areas. Users can also investigate specific flowpaths interactively by clicking on any location within the catchment. A flowpath is generated with a table describing N removal along each segment. We describe the motivation behind developing nsink, discuss implementation in R, and present two use case examples. nsink is available from https://github.com/USEPA/nsink. Conclusions N-Sink is a decision support tool created for local decision-makers to facilitate better understanding of the relationship between land use and downstream N delivery. Local decision-makers that have prioritized N mitigation in their long-term planning can use nsink to better understand the potential impact of proposed development projects and zoning variances. Similarly, land trusts and other NGOs interested in N mitigation can use nsink to identify high priority areas for acquisition or restoration.

Published
2024
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62. Biomass-burning smoke's properties and its interactions with marine stratocumulus clouds in WRF-CAM5 and southeastern Atlantic field campaigns

Author

Howes, Calvin, Saide, Pablo E, Coe, Hugh, Dobracki, Amie, Freitag, Steffen, Haywood, Jim M, Howell, Steven G, Gupta, Siddhant, Uin, Janek, Kacarab, Mary, Kuang, Chongai, Leung, L Ruby, Nenes, Athanasios, McFarquhar, Greg M, Podolske, James, Redemann, Jens, Sedlacek, Arthur J, Thornhill, Kenneth L, Wong, Jenny PS, Wood, Robert, Wu, Huihui, Zhang, Yang, Zhang, Jianhao, and Zuidema, Paquita

Subjects
Earth Sciences, Atmospheric Sciences, Climate Action, Astronomical and Space Sciences, Meteorology & Atmospheric Sciences, Atmospheric sciences, Climate change science
Abstract

A large part of the uncertainty in climate projections comes from uncertain aerosol properties and aerosol-cloud interactions as well as the difficulty in remotely sensing them. The southeastern Atlantic functions as a natural laboratory to study biomass-burning smoke and to constrain this uncertainty. We address these gaps by comparing the Weather Research and Forecasting with Chemistry Community Atmosphere Model (WRF-CAM5) to the multi-campaign observations ORACLES (ObseRvations of Aerosols above CLouds and their intEractionS), CLARIFY (CLoud-Aerosol-Radiation Interaction and Forcing), and LASIC (Layered Atlantic Smoke Interactions with Clouds) in the southeastern Atlantic in August 2017 to evaluate a large range of the model's aerosol chemical properties, size distributions, processes, and transport, as well as aerosol-cloud interactions. Overall, while WRF-CAM5 is able to represent smoke properties and transport, some key discrepancies highlight the need for further analysis. Observations of smoke composition show an overall decrease in aerosol mean diameter as smoke ages over 4-12 d, while the model lacks this trend. A decrease in the mass ratio of organic aerosol (OA) to black carbon (BC), OA:BC, and the OA mass to carbon monoxide (CO) mixing ratio, OA:CO, suggests that the model is missing processes that selectively remove OA from the particle phase, such as photolysis and heterogeneous aerosol chemistry. A large (factor of ∼2.5) enhancement in sulfate from the free troposphere (FT) to the boundary layer (BL) in observations is not present in the model, pointing to the importance of properly representing secondary sulfate aerosol formation from marine dimethyl sulfide and gaseous SO2 smoke emissions. The model shows a persistent overprediction of aerosols in the marine boundary layer (MBL), especially for clean conditions, which multiple pieces of evidence link to weaker aerosol removal in the modeled MBL than reality. This evidence includes several model features, such as not representing observed shifts towards smaller aerosol diameters, inaccurate concentration ratios of carbon monoxide and black carbon, underprediction of heavy rain events, and little evidence of persistent biases in modeled entrainment. The average below-cloud aerosol activation fraction (NCLD/NAER) remains relatively constant in WRF-CAM5 between field campaigns (∼0.65), while it decreases substantially in observations from ORACLES (∼0.78) to CLARIFY (∼0.5), which could be due to the model misrepresentation of clean aerosol conditions. WRF-CAM5 also overshoots an observed upper limit on liquid cloud droplet concentration around NCLDCombining double low line 400-500 cm-3 and overpredicts the spread in NCLD. This could be related to the model often drastically overestimating the strength of boundary layer vertical turbulence by up to a factor of 10. We expect these results to motivate similar evaluations of other modeling systems and promote model development to reduce critical uncertainties in climate simulations. Copyright:

Published
2023

65. Synthesis of Red, Green, and Blue Carbon Quantum Dots and Construction of Multicolor Cellulose‐Based Light‐Emitting Diodes

Author

Xinrui Chen, Xing Han, Caixia Zhang, Xue Ou, Xiaoli Liu, Junhua Zhang, Wei Liu, Arthur J. Ragauskas, Xueping Song, and Zhanying Zhang

Subjects
carbon quantum dots, cellulose nanofibrils, light‐emitting diodes, thin‐film encapsulation, UV curable acrylic resins, Physics, QC1-999, Chemistry, QD1-999
Abstract

Light‐emitting diodes (LEDs) are widely used in lighting and display applications. Carbon quantum dots (CQDs), which have high biocompatibility, high resistance to photobleaching, and full‐spectrum luminescence, have inherent advantages as fluorescent materials for LED devices. Herein, multicolor CQDs are prepared by a new reagent engineering strategy due to the difference of effective conjugate length and the surface electron‐withdrawing groups of CQDs. White CQDs are realized by mixing blue, green, and red CQDs proportionally. Then, the aggregation‐caused quenching phenomenon of CQDs is suppressed through the hydrogen‐bonding network of cellulose nanofibrils (CNFs). Multicolor fluorescent films are prepared from CQDs and CNFs by simple mixing and casting methods. Finally, thin‐film encapsulation based on the photosensitive resin ABPE‐10 coating can be realized and rapidly assembles into fluorescent films with different light‐emitting colors into LED devices, leading to have superior thermal performance compared with conventional LEDs. White LEDs have excellent white‐light illumination performance, with Commission Internationale de L’Eclairage color coordinates of (0.33, 0.37), a correlated color temperature of 5688 K, and a color rendering index of 86. This strategy provides a convenient and scalable pathway for low‐cost, environmentally friendly, and high‐performance CQDs‐based LEDs.

Published
2024
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66. Factors influencing follow-on public offering of shippng companies from investor perspective – a hybrid multiple-criteria decision-making approach

Author

Arthur J. Lin, Sun-Weng Huang, Hai-Yen Chang, Jiuh-Biing Sheu, and Gwo-Hshiung Tzeng

Subjects
shipping industry, shipping finance, follow-on equity offering (FPO), share capital increase, multiple-criteria decision-making (MCDM), fuzzy DEMATEL, Economic growth, development, planning, HD72-88, Business, HF5001-6182
Abstract

The shipping industry transports nearly 80% of the goods worldwide and requires large funding. The shipping industry shifted from debt to equity as the source of funding in the last decade. Because most shipping companies already had their initial public offering before 2013, these companies tend to engage in follow-on equity offerings (FPO). However, the challenge faced by the shipping companies is the lack of knowledge on successful FPO. The purpose of this study is to identify the most influential factors affecting shipping companies’ FPO from the investor perspective. This research applies a hybrid multiple-criteria decision-making model integrating the fuzzy-Delphi method and Decision-Making Trial and Evaluation Laboratory, processing survey responses covering four dimensions and 16 criteria from 33 investment experts. The results show that financial indicator is the primary cause affecting offering condition, technical indicators. An increase in earnings per share would help the financial performance of the shipping companies to appear most attractive to investors.

Published
2024
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67. Preoperative Function Affects Ability to Achieve One-Year Minimum Clinically Important Difference for Patients Undergoing Total Knee Arthroplasty

Author

Patrick Albright, MD, MS, Fernando A. Huyke-Hernández, BS, Andrew Ormseth, MD, Stephen A. Doxey, DO, Evan Banks, BS, Arthur J. Only, MD, Patrick K. Horst, MD, and Brian P. Cunningham, MD

Subjects
Minimum clinically important difference, Total knee arthroplasty, Patient-reported outcomes, Preoperative function, Orthopedic surgery, RD701-811
Abstract

Background: Minimum clinically important difference (MCID) values are commonly used to measure treatment success for total knee arthroplasty (TKA). MCID values vary according to calculation methodology, and prior studies have shown that patient factors are associated with failure to achieve MCID thresholds. The purpose of this study was to determine if anchor-based 1-year Knee Injury and Osteoarthritis Outcome Score Joint Replacement (KOOS-JR) MCID values varied among patients undergoing TKA based on patient-specific factors. Methods: This was a retrospective review of patients undergoing TKA from 2017-2018. Patients without baseline or 1-year KOOS-JR or Patient-Reported Outcome Measurement Information System Global Health data or that underwent procedures other than primary TKA were excluded. MCIDs were calculated and compared between patient groups according to preoperative characteristics. Results: Among the included 976 patients, 1-year KOOS-JR MCIDs were 26.6 for men, 28.2 for women, 30.7 for patients with a diagnosis of anxiety and/or depression, and 26.7 for patients without a diagnosis. One-year MCID values did not differ significantly according to gender (P = .379) or mental health diagnosis (P = .066), nor did they correlate with body mass index (β = −0.034, P = .822). Preoperative KOOS-JR decile demonstrated an inverse relationship with 1-year MCID values and attainment of MCID. Conclusions: The proportion of patients attaining KOOS-JR MCID values demonstrated an inverse relationship with preoperative baseline function. Future investigation may identify patient factors that allow surgeons to better capture patient satisfaction with their procedure despite failure to attain a 1-year MCID.

Published
2024
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70. PANEL DISCUSSION

Author

Moss, Arthur J., primary, Assali, Nicholas S., additional, Braunwald, Eugene, additional, Downing, S. Evans, additional, Gessner, Ira H., additional, and Rudolph, Abraham M., additional

Published
2023
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72. On quantum states over time

Author

Fullwood, James and Parzygnat, Arthur J.

Subjects
Quantum Physics, Mathematics - Category Theory
Abstract

In 2017, D. Horsman, C. Heunen, M. Pusey, J. Barrett, and R. Spekkens proved that there is no physically reasonable assignment that takes a quantum channel and an initial state and produces a joint state on the tensor product of the input and output spaces. The interpretation was that there is a clear distinction between space and time in the quantum setting that is not visible classically, where in the latter, one can freely use Bayes' theorem to go between joint states and marginals with noisy channels. In this paper, we prove that there actually is such a physically reasonable assignment, bypassing the no-go result of Horsman et al., and we illustrate that this is achievable by restricting the domain of their assignment to a domain which represents the given data more faithfully., Comment: v3: minor clarifications added

Published
2022
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73. Testing a Theoretical Assumption of a Learning-Trajectories Approach in Teaching Length Measurement to Kindergartners

Author

Sarama, Julie, Clements, Douglas H., Baroody, Arthur J., Kutaka, Traci S., Chernyavskiy, Pavel, Shi, Jackie, and Cong, Menglong

Abstract

We tested a specific theoretical assumption of a learning trajectories (LTs) approach to curriculum and teaching in the domain of early length measurement. Participating kindergartners (n = 189) were assigned to one of three conditions: LT, reverse-order (REV), or business-as-usual (BAU). LT and REV students received one-on-one instruction using the same activities from a length LT, while the REV condition reversed the LT order. At posttest, LT and REV children exhibited significantly greater learning relative to BAU peers. But importantly, LT children outperformed their REV peers. We conclude that instruction following LTs (i.e., providing instruction just beyond a child's present level of thinking, progressing through the levels in order as the child advances) may promote more learning than an equivalent amount of instruction using the same activities but that are not theoretically sequenced.

Published
2021

74. Evaluating a Basic Assumption of Learning Trajectories: The Case of Early Patterning Learning

Author

Baroody, Arthur J., Yilmaz, Nursel, Clements, Douglas H., and Sarama, Julie

Abstract

Although hypothetical learning trajectories (HLTs) are often viewed as a valuable instructional tool, little research has directly evaluated their value. A basic assumption of HLTs is that ordering instructional activities by developmental difficulty enhances learning. A randomized control trial (RCT) served to evaluate this assumption with a hypothetical learning trajectory (HLT) for early patterning. The RCT (n = 16) entailed comparing two patterning interventions that involved the same activities, but only one ordered them based on an HLT. Each intervention involved twelve 15- to 20-minute sessions. Quantitative data were collected from testing before and 2 weeks after the intervention sessions; qualitative data were collected during the instruction sessions. The former indicated that both interventions were significantly and substantively successful in promoting transfer in identifying the core unit of repeating patterns and extending such patterns. As predicted, a small effect size favored the HLT-Like intervention for core identification but not extending patterns. Training on core identification or extending patterns was significantly and substantially more successful with 4- than with 3-year-olds, especially those less than 3.75-years of age. Moreover, qualitative analyses revealed that translating patterns into letters was much easier than doing so into concrete formats and that the former can be introduced early in patterning instruction while the later may need to be postponed. Reasons why instructional ordering did have a stronger impact and implications for further HLT research are discussed.

Published
2021
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75. Marine Pelagic Ecosystem Responses to Climate Variability and Change

Author

Ducklow, Hugh, Cimino, Megan, Dunton, Kenneth H, Fraser, William R, Hopcroft, Russell R, Ji, Rubao, Miller, Arthur J, Ohman, Mark D, and Sosik, Heidi M

Subjects
Life Below Water, Climate Action, oceanography, coastal ecosystems, climate change, ecology, marine biology, Environmental Sciences, Biological Sciences, Ecology
Abstract

abstract The marine coastal region makes up just 10% of the total area of the global ocean but contributes nearly 20% of its total primary production and over 80% of fisheries landings. Unicellular phytoplankton dominate primary production. Climate variability has had impacts on various marine ecosystems, but most sites are just approaching the age at which ecological responses to longer term, unidirectional climate trends might be distinguished. All five marine pelagic sites in the US Long Term Ecological Research (LTER) network are experiencing warming trends in surface air temperature. The marine physical system is responding at all sites with increasing mixed layer temperatures and decreasing depth and with declining sea ice cover at the two polar sites. Their ecological responses are more varied. Some sites show multiple population or ecosystem changes, whereas, at others, changes have not been detected, either because more time is needed or because they are not being measured.

Published
2022

82. Muscle weakness and mitochondrial stress occur before severe metastasis in a novel mouse model of ovarian cancer cachexia

Author

Delfinis, Luca J., Ogilvie, Leslie M., Khajehzadehshoushtar, Shahrzad, Gandhi, Shivam, Garibotti, Madison C., Thuhan, Arshdeep K., Matuszewska, Kathy, Pereira, Madison, Jones, Ronald G., III, Cheng, Arthur J., Hawke, Thomas J., Greene, Nicholas P., Murach, Kevin A., Simpson, Jeremy A., Petrik, Jim, and Perry, Christopher G.R.

Published
2024
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88. Arctic warming by abundant fine sea salt aerosols from blowing snow

Author

Gong, Xianda, Zhang, Jiaoshi, Croft, Betty, Yang, Xin, Frey, Markus M., Bergner, Nora, Chang, Rachel Y.-W., Creamean, Jessie M., Kuang, Chongai, Martin, Randall V., Ranjithkumar, Ananth, Sedlacek, Arthur J., Uin, Janek, Willmes, Sascha, Zawadowicz, Maria A., Pierce, Jeffrey R., Shupe, Matthew D., Schmale, Julia, and Wang, Jian

Published
2023
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90. Shortwave absorption by wildfire smoke dominated by dark brown carbon

Author

Chakrabarty, Rajan K., Shetty, Nishit J., Thind, Arashdeep S., Beeler, Payton, Sumlin, Benjamin J., Zhang, Chenchong, Liu, Pai, Idrobo, Juan C., Adachi, Kouji, Wagner, Nicholas L., Schwarz, Joshua P., Ahern, Adam, Sedlacek, III, Arthur J., Lambe, Andrew, Daube, Conner, Lyu, Ming, Liu, Chao, Herndon, Scott, Onasch, Timothy B., and Mishra, Rohan

Published
2023
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91. Automatic Quantification of Local Plaque Thickness Differences as Assessed by Serial Coronary Computed Tomography Angiography Using Scan-Quality-Based Vessel-Specific Thresholds

Author

Finn Y. van Driest, Alexander Broersen, Rob J. van der Geest, J. Wouter Jukema, Arthur J. H. A. Scholte, and Jouke Dijkstra

Subjects
Coronary computed tomography angiography, Contrast-to-noise ratio, Coronary CT, Serial CCTA, Diseases of the circulatory (Cardiovascular) system, RC666-701
Abstract

Abstract Introduction The use of serial coronary computed tomography angiography (CCTA) allows for the early assessment of coronary plaque progression, a crucial factor in averting major adverse cardiac events (MACEs). Traditionally, serial CCTA is assessed using anatomical landmarks to match baseline and follow-up scans. Recently, a tool has been developed that allows for the automatic quantification of local plaque thickness differences in serial CCTA utilizing plaque contour delineation. The aim of this study was to determine thresholds of plaque thickness differences that define whether there is plaque progression and/or regression. These thresholds depend on the contrast-to-noise ratio (CNR). Methods Plaque thickness differences between two scans acquired at the same moment in time should always be zero. The negative and positive differences in plaque contour delineation in these scans were used along with the CNR in order to create calibration graphs on which a linear regression analysis was performed. This analysis was conducted on a cohort of 50 patients referred for a CCTA due to chest complaints. A total of 300 coronary vessels were analyzed. First, plaque contours were semi-automatically determined for all major epicardial coronary vessels. Second, manual drawings of seven regions of interest (ROIs) per scan were used to quantify the scan quality based on the CNR for each vessel. Results A linear regression analysis was performed on the CNR and negative and positive plaque contour delineation differences. Accounting for the standard error of the estimate, the linear regression analysis revealed that above 1.009 − 0.002 × CNR there is an increase in plaque thickness (progression), and below − 1.638 + 0.012 × CNR there is a decrease in plaque thickness (regression). Conclusion This study demonstrates the feasibility of developing vessel-specific, quality-based thresholds for visualizing local plaque thickness differences evaluated by serial CCTA. These thresholds have the potential to facilitate the early detection of atherosclerosis progression.

Published
2023
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92. SVD entanglement entropy

Author

Arthur J. Parzygnat, Tadashi Takayanagi, Yusuke Taki, and Zixia Wei

Subjects
AdS-CFT Correspondence, Conformal and W Symmetry, Field Theories in Lower Dimensions, Nuclear and particle physics. Atomic energy. Radioactivity, QC770-798
Abstract

Abstract In this paper, we introduce a new quantity called SVD entanglement entropy. This is a generalization of entanglement entropy in that it depends on two different states, as in pre- and post-selection processes. This SVD entanglement entropy takes non-negative real values and is bounded by the logarithm of the Hilbert space dimensions. The SVD entanglement entropy can be interpreted as the average number of Bell pairs distillable from intermediates states. We observe that the SVD entanglement entropy gets enhanced when the two states are in the different quantum phases in an explicit example of the transverse-field Ising model. Moreover, we calculate the Rényi SVD entropy in various field theories and examine holographic calculations using the AdS/CFT correspondence.

Published
2023
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93. Bayesian inversion and the Tomita-Takesaki modular group

Author

Giorgetti, Luca, Parzygnat, Arthur J., Ranallo, Alessio, and Russo, Benjamin P.

Subjects
Mathematics - Operator Algebras, Quantum Physics, 47A05, 47D03, 81P47
Abstract

We show that conditional expectations, optimal hypotheses, disintegrations, and adjoints of unital completely positive maps, are all instances of Bayesian inverses. We study the existence of the latter by means of the Tomita-Takesaki modular group and we provide extensions of a theorem of Takesaki as well as a theorem of Accardi and Cecchini to the setting of not necessarily faithful states on finite-dimensional $C^*$-algebras.

Published
2021
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94. Gradient expansions for the large-coupling strength limit of the M{\o}ller-Plesset adiabatic connection

Author

Daas, Timothy J., Kooi, Derk P., Grooteman, Arthur J. A. F., Seidl, Michael, and Gori-Giorgi, Paola

Subjects
Physics - Chemical Physics
Abstract

The adiabatic connection that has as weak-interaction expansion the M{\o}ller-Plesset perturbation series has been recently shown to have a large coupling-strength expansion in terms of functionals of the Hartree-Fock density with a clear physical meaning. In this work we accurately evaluate these density functionals and we extract second-order gradient coefficients from the data for neutral atoms, following ideas similar to the ones used in the literature for exchange, with some modifications. These new gradient expansions will be the key ingredient for performing interpolations that have already been shown to reduce dramatically MP2 errors for large non-covalent complexes. As a byproduct, our investigation of neutral atoms with large number of electrons $N$ indicates that the second-order gradient expansion for exchange grows as $N\log(N)$ rather than as $N$ as often reported in the literature., Comment: 35 pages, 9 figures, 2 tables

Published
2021

95. Factors Associated With Coronary Angiography Performed Within 6 Months of Randomization to the Conservative Strategy in the ISCHEMIA Trial

Author

Pracoń, Radosław, Spertus, John A., Broderick, Samuel, Bangalore, Sripal, Rockhold, Frank W., Ruzyllo, Witold, Demchenko, Elena, Nageh, Thuraia, Grossman, Gabriel Blacher, Mavromatis, Kreton, Manjunath, Cholenahally N., Smanio, Paola E.P., Stone, Gregg W., Mancini, G.B. John, Boden, William E., Newman, Jonathan D., Reynolds, Harmony R., Hochman, Judith S., Maron, David J., Doan, John, Linefsky, Jason, Lee, Raven, Patel, Risha, Miller, Todd, Yang Cho, So, Milbrandt, Susan, Shelstad, Dawn, Banerjee, Subhash, Kamath, Preeti, Tejani, Ishita, Cobos, Stanley E., Quiles, Kirsten J., Dwyer, Raven R., Donnino, Robert M., Espinosa, Dalisa, Phillips, Lawrence M., Saric, Muhamed, Abdul-Nour, Khaled, Schley, Allison, Golden, Heather, Stone, Peter H., Osseni, Hermine, Wiyarand, Charlene, Douglass, Peter, Pomeroy, Hayley, Craft, Alexandra, Harvey, Bethany, Jang, James J., Anaya, Olivia, Yee, Gennie, Goold, Phoebe, Weitz, Steven, Giovannone, Steven, Pritchard, Lori, Arnold, Suzanne, Gans, Rosann, Henry O’Keefe, Jr, James, Kennedy, Paul, Shapiro, Michael D., Ganesan, Shobana, Schlichting, David, Naher, Aynun, El-Hajjar, Mohammad, Sidhu, Mandeep S., Fein, Steven A., Stewart, Wendy L., Torosoff, Mikhail T., Salmi, Kristin M., Lyubarova, Radmila, Mookherjee, Sulagna, Drzymalski, Krzysztof, McFalls, Edward O., Garcia, Santiago A., Bertog, Stefan C., Johnson, Debra K., Siddiqui, Rizwan A., Herrmann, Rebekah R., Ishani, Areef, Hansen, Ronnell A., Georges Khouri, Michel, Arges, Kristine, LeFevre, Melissa, Tomfohr, Jennifer, Goldberg, Jonathan L., Ann Byrne, Kimberly, Zappernick, Taissa, Goldweit, Richard, Canada, Sallie, Kakade, Meghana, Mieses, Patricia, Cobos, Stanley E., Dwyer, Raven R., Cohen, Ronny A., Espinosa, Dalisa, Mirrer, Brooks, Quiles, Kirsten J., Navarro, Victor, Rantinella, Magdalena, Rodriguez, Jessica, Mancilla, Olivia, Winchester, David E., Stinson, Susan, Kronenberg, Marvin, Weyand, Terry, Rogal, Philip, Crook, Sherron C., McFarren, Christopher, Heitner, John F., Ho, Jean, Khan, Saadat, Mohamed, Mahmoud, Dauber, Ira M., Soltau, Mary R., Rose, Delsa K., Wimmer, Rebecca J., Siegel, Kathy E., Derbyshire, Susan, Cannan, Charles, Dixon, Michelle, Leonard, Gerald, Sudarshan, Sriram, Heard, Ciarra, Gabriel, Viviana, Desire, Sukie, Mehta, Puja K., McDaniel, Michael, Rashid, Fauzia, Lerakis, Stamatios, Asier, Senait, Quyyumi, Arshed, Patel, Keyur, Wenger, Nanette K., Hedgepeth, Chester M., Gillis, Jennifer, Hurlburt, Heather, Manocchia, Megan, Rosen, Alan, Moore, Susan, Congdon, Elizabeth, Sahul, Zakir, Brandt, Gail, Marchelletta, Nora, Wippler, Kristina, Booth, David, Taul, Yvonne, Leung, Steve, Isaacs, Jennifer, Abdel-Latif, Ahmed, Bulkley, Viktoria, Reda, Hassan, Rodgers, Caroline, Ziada, Khaled, Setty, Sampoornima, Halverson, Kimberly E., Roraff, Christine, Thorsen, Jonean, Barua, Rajat S., Ojajuni, Amarachi, Olurinde, Oni, Surineni, Kamalakar, Hage, Fadi, Valaiyapathi, Badhma, Caldeira, Christiano, Davies, James E., Leesar, Massoud, Heo, Jaekyeong, Iskandrian, Amy, Al Solaiman, Firas, Singh, Satinder, Dajani, Khaled, Kartje, Carol M., El-Hajjar, Mohammad, Mesropian, Paul Der, Sacco, Joseph, Rawlins, Michele, McCandless, Brian, Thomson, Jennifer, Orgera, Marisa, Sidhu, Mandeep S., Colleen Rogge, Mary, Arif, Imran, Bunke, Julie, Kerr, Hanan, Unterbrink, Kendra, Fannon, Jacqueline, Burman, Cynthia, Trejo, Jorge F., Dubin, Marcia F., Fletcher, Gerald, Lane, Gary E., Neeson, Lynn M., Parikh, Pragnesh P., Pollak, Peter M., Shapiro, Brian P., Landolfo, Kevin, Gemignani, Anthony, Beaudry, Sarah, O’Rourke, Daniel, Meadows, Judith L., Tirado, Stephanie A., Halliday, Janet, Julian, Pamela, Call, Jason T., Lane, Stephanie M., Stanford, Jennifer L., Hannan, Joseph, Bojar, Robert, Arsenault, Patricia, Kumar, Deepti, Sigel, Pamela, Mukai, John, Martin, Edward T., Brooks, Miriam, Vorobiof, Gabriel, Douangvila, Ladda, Gevorgyan, Rubine, Moorman, Alec, Ranjbaran, Fatima, Smith, Bryn, Ohmart, Carly, Kinlay, Scott, Hamburger, Robert J., Rocco, Thomas P., Ly, Samantha, Bhatt, Deepak L., Quinn, Margot C., Croce, Kevin, Temiyasathit, Sara, Quin, Jacquelyn A, Do, Jacquelyn, Anumpa, Jati, Tobin, Desiree, Zenati, Marco, Faxon, David P, Rayos, Glenn, Langdon, Jennifer, Werner Bayer, Marcia, Seedhom, Ashraf, O’Malley, Amanda, Sullenberger, Lance, Orvis, Erin, Kumkumian, Gregory, Murphy, Mandy, Greenberg, Ann, Iraola, Margaret, Sedlis, Steven P., Maranan, Leandro C., Donnino, Robert M., Lorin, Jeffrey, Tamis-Holland, Jacqueline E., Malinay, Ammy, Kornberg, Robert, Leber, Robert, Saba, Souheil, Edillo, Candice P., Lee, Michael W., Small, Delano R., Nona, Wassim, Alexander, Patrick B., Rehman, Iram, Badami, Umesh, Ostrander, Ann, Wasmiller, Stephanie, Marzo, Kevin, Drewes, Wendy, Patel, Dipti, Robbins, Inga H., Levite, Howard A., White, Jackie M, Shetty, Sanjay, Hallam, Alison, Patel, Mayuri, Hamroff, Glenn S., Spooner, Benjamin J, Hollenweger, Linda M, Little, Raymond W., Little, Holly, Zimbelman, Brandi D., Little, Tiffany, Lui, Charles Y., Eskelson, Nona A, Smith, Brigham R., Vezina, Daniel P., Khor, Lillian L., Abraham, Josephine D., Bull, David A., McKellar, Stephen H., Booth, David, Taul, Yvonne, Kotter, John, Rodgers, Caroline, Abdel-Latif, Ahmed, Isaacs, Jennifer, Bulkley, Viktoria, Hu, Bob, Kaneshiro, Renee, Labovitz, Arthur J., Berlowitz, Michael, Kirby, Bonnie J., Rogal, Philip, Tran, Nhi N., McFarren, Christopher, Jahrsdorfer, Catherine, Matar, Fadi, Caldeira, Christiano, Rodriguez, Fatima, Yunis, Reem, Schnittger, Ingela, Patro, Jhina, Fearon, William F., Deedwania, Prakash, Vega, Antonia, Reddy, Kiran, Sweeny, Joseph, Bloise-Adames, Hugo, Jimenez, Santa, Saint Vrestil, Nicole, Bhandari, Reyna, Spizzieri, Christopher, Schade, Danielle, Yost, Roxanne, Hochberg, Claudia P, Beardsley, Paula, Fine, Denise, Salerno, William D., Tancredi, Jana, Arakelian, Patricia, Mathus, Susan, O’Neill, Deborah, Wyman, Ray, Burkhardt, Joy, Hosino, Suellen, Lubyanaya, Oksana A., Salas, Jose D., Zarka, Amer, Aguirre, Maria, Shah, Anil V., Dhawan, Manu, Parra, Diana, Tran, Tri, Haldis, Thomas, Weick, Catherine, Fowler-Lehman, Katie, Spitzer, Natalie, Riedberger, Casey, Weick, Catherine, Kohn, Jeffrey A., Cobos, Stanley E., Dwyer, Raven R., Espinosa, Dalisa, Quiles, Kirsten J., Girotra, Saket, Drum, Carrie, Miller-Cox, Kimberly, Ollinger, Amy, Almousalli, Omar, Capasso-Gulve, Elizabeth, Melanie Loehr, Alaine, Mosley, Marlowe, Krishnam, Mayil S., Heydari, Shirin, Milliken, Jeffrey C., Lundeen, Andrea M., Patel, Pranav M., Karanjah, Edgar, Seto, Arnold H., Marfori, Wanda C., Harley, Kevin T., Hernandez-Rangel, Eduardo, Gibson, Michael A., Singh, Pam, Allen, Byron J., Coram, Rita, Marie Webb, Anne, Fridell, Ellie, Wilson, Heidi, Thomas, Sabu, Kim, Angela, Schwartz, Ronald G, Wilmot, Patrick, Chen, Wei, El Shahawy, Mahfouz, Stevens, Ramona, Stafford, James, Black, Loriane, Abernethy, William B., Hull, Amber B., Lim, Olivia J., Tucker, Helen C., Putnam, Natasha C., Hall, Linda L., Cauthren, Tia, Tucker, Trish, Zurick, Andrew, Horton, Hollie, Orga, Jan, Meyer, Thomas M., White, Joyce R., Morford, Ronald G., Baumann, Cynthia, Rutkin, Bruce, Seeratan, Vidya, Bokhari, Sabahat, Jimenez, Magnolia, Sokol, Seth I., Schultz, Cidney, Meisner, Jay, Russo, Jeanne, Hamzeh, Ihab, Misra, Arunima, Huda, Zohra, Wall, Matthew, Boan, Araceli, Lenges De Rosen, Veronica, Alam, Mahboob, Turner, Michael C., Hinton, Christine R, Mulhearn, Thomas J., Good, Arnold P., Archer, Beth A., Dionne, Julia S., Allardyce, Cheryl A., Sikora, Lindsey N., Czerniak, Jennifer H., Mull, Jennifer A., Ferguson, Elizabeth, Laube, Frances, Shammas, Nicolas W., Shammas, Gail A, Christensen, Lori, Park, Holly, Chilton, Robert, Hecht, Joan, Nguyen, Patricia K., Vo, Davis, Hirsch, James, Jezior, Matthew, Bindeman, Jody, Salkind, Sara, Espinosa, Dalisa, Desimone, Lori-Ann, Gordon, Paul C., Felix-Stern, Lina, Crain, Thomas, Gomes, Jassira, Gordon, Catherine, Stenberg, Robert, Mann, Aimee, McCreary, Theresa, Pedalino, Ronald P., Cobos, Stanley E., Dwyer, Raven R., Espinosa, Dalisa, Quiles, Kirsten J., Wiesel, Joseph, Cobos, Stanley E., Dwyer, Raven R., Espinosa, Dalisa, Quiles, Kirsten J., Juang, George J., Gopaul, Candace, Hultberg, Karen, Huk, Tauqir, Hussain, Afshan, Al-Amoodi, Mohammed, Zambrano, Yesenia, Medina Rodriguez, Sarah, Milner, Trudie, Wohns, David, Mulder, Abbey, Van Oosterhout, Stacie, Lader, Ellis W., Meyer, Martha, Mumma, Michael, Clapp, Nancy L., Barrentine, Heather, Dharmarajan, Lekshmi, Jose, Jenne M., Cobos, Stanley E., Dwyer, Raven R., Espinosa, Dalisa, Quiles, Kirsten J., Manchery, Jenne, McGarvey Jr, Joseph F.X., McKinney, Vera, Schwarz, Linda, Downes, Thomas R., Kaczkowski, Scott M., Luckasen, Gary J., Jaskowiak, Adam J., Klitch, Joel, Cheong, Benjamin, Dees, Debra, Potluri, Srinivasa, Vasquez, Precilia, Mastouri, Ronald A., Breall, Jeffery A., Hannemann, Elise L., Revtyak, George E., Mae Foltz, Judy, Bazeley, Jonathan W., Li, Dayuan, DeRosa, Emily, Jorgenson, Beth, Riestenberg-Smith, Joyce, Giedd, Kenneth, Old, Wayne, Bariciano, Rebecca, Burt, Francis, Sokhon, Kozhaya, Waldron, Jessica, Mayon, Michelle, Gopal, Deepika, Valeti, Uma S., Ann Peichel, Gretchen, Kobashigawa, Jon, Starks, Brandy, Garcia, Lucilla, Thottam, Maria, Bhargava, Balram, Anand, Anjali, Chakanalil Govindan, Sajeev, Raj, Janitha, Gopalan Nair, Rajesh, Ravindran, Reshma, Rajalekshmi, VS, Nataraj, Nandita, Moorthy, Nagaraja, Nayak, Soundarya, Mylarappa, Mahevamma, Narayanappa, Suryaprakash, Pandit, Neeraj, Bajaj, Sheromani, Kumar Nath, Ranjit, Yadav, Vandana, Mishra, Girish, Dwivedi, S.K., Tewari, Roma, Narain, V.S., Mishra, Meenakshi, Chandra, Sharad, Patel, Shivali, Singh, Suman, Wander, Gurpreet S., Tandon, Rohit, Ralhan, Sarju, Kaur, Baljeet, Aslam, Naved, Gupta, Sonika, Goyal, Abhishek, Bhargava, Balram, Suvarna, Chandini, Karthikeyan, G., Ramakrishnan, S., Seth, Sandeep, Yadav, Rakesh, Singh, Sandeep, Roy, Ambuj, Parakh, Neeraj, Kumar Verma, Sunil, Narang, Rajiv, Mishra, Sundeep, Naik, Nitish, Sharma, Gautam, Kumar Choudhary, Shiv, Patel, Chetan, Gulati, Gurpreet, Sharma, Sanjeev, Bahl, V K, Mathew, Anoop, Mannekkattukudy Kurian, Binoy, Punnoose, Eapen, Avdhoot Gadkari, Milind, Rupesh Karwa, Sheetal, Gadage, Siddharth, Kolhe, Suvarna, Umesh Pillay, Tapan, Satheesh, Santhosh, Vindhya, R. J., Jain, Peeyush, Seth, Ashok, Singh Meharwal, Zile, Mathur, Atul, Verma, Atul, Kaul, Upendra, Bhatia, Mona, Sachdeva, Ankush, Indira Devi, Thounaojam, Jungla, Nungshi, Christopher, Johann, Manjula Rani, K., Menon, Rajeev, Sowjanya Reddy, M., Kumar, Nirmal, Preethi, K., Oomman, Abraham, sidh, Rinu R, Mao, Robert, Ramakrishnan, T., Solomon, Hilda, Francis, Rajesh, Naik, Sudhir, Vamshi, Priya P., Parveen Khan, Sajeeda, Christopher, Johann, Preethi, Kotiboinna, Kumar, Nirmal, Grant, Purvez, Hande, Shweta, Sonawane, Poonam, Kachru, Ranjan, Dubey, Abhishek, Rawat, Kavita, Kumar, Ajit, Ganapathi, Sanjay, K, Jayakumar, CP, Vineeth, Sivadasanpillai, Harikrishnan, Chacko, Manas, Sasidharan, Bijulal, Babu, Suresh, TR, Kapilamoorthy, Christopher, Johann, Reddy, Sowjanya, Polamuri, Praneeth, Rani, Manjula, Kaul, Upendra, Arambam, Priyadarshani, Singh, Bebek, Senior, Roxy, Fox, Keith AA, Young, Grace M., Carruthers, Kathryn, Senior, Roxy, Elghamaz, Ahmed, Gurunathan, Sothinathan, Karogiannis, Nikolaos, Young, Grace M., Shah, Benoy N, Kinsey, Christopher, Trimlett, Richard HJ, Kavalakkat, Raisa, Rubens, Michael B, Evans, Jo, Nicol, Edward D, Hassan, Ikraam, Mittal, Tarun K, Hampson, Reinette, Andreas Gamma, Reto, Williams, Sarah, Holland, Kim, Swan, Karen, de Belder, Mark A, Atkinson, Bev, Thambyrajah, Jeet, Kunhunny, Swapna, Davies, John R, Lindsay, Steven J., Atkinson, Craig, Kurian, John, Krannila, Carita, Jamil, Haqeel, Vinod, Manitha, Raheem, Osama, Hoye, Angela, Chaytor, Lisa, Cox, Leanne, Morrow, Julie, Rowe, Kay, Donnelly, Patrick, Kelly, Stephanie, Valecka, Bernardas, Regan, Susan, Turnbull, Dawn, Chauhan, Anoop, Fleming, Catherine, Ghosh, Arijit, Gratrix, Karen, Preston, Stephen, Barr, Craig, Cartwright, Anne, Alfakih, Khaled, Knighton, Abigail, Byrne, Jonathan, Martin, Katherine, Webb, Ian, Henriksen, Peter, Flint, Laura, Harrison, James, OKane, Peter, Lakeman, Nicki, Ljubez, Anja, de Silva, Ramesh, Conway, Dwayne S. G., Wright, Judith, Exley, Donna, Sirker, Alexander A, Andiapen, Mervyn, Richards, Amy J., Hoole, Stephen P, Wong, Lisa, Witherow, Fraser N., Munro, Melanie J., Johnston, Nicola, Harbinson, Mark, McEvoy, Michelle, Walsh, Simon, Brown, Caroline, Douglas, Hanna, Luckie, Matthew, Charles, Thabitha, Kolakaluri, Laurel, Phillips, Hannah, Sobolewska, Jolanta, Morby, Louise, Hallett, Karen, Corbett, Carolyn, Winstanley, Lynne, Jeetley, Paramjit, Smit, Angelique, Patel, Niket, Kotecha, Tushar, Travill, Christopher, Gent, Susan, Karimullah, Iqbal, Hussain, Nafisa, Al-Bustami, Mahmud, Braganza, Denise, Haines, Fiona, Taaffe, Joanne, Henderson, Robert, Burton, Jane, Pointon, Kate, Colton, Maria, Naik, Surendra, King, Rachel, Mathew, Thomas, Brown, Ammani, Docherty, Andrew, Berry, Colin, McCloy, Lisa, Collison, Damien, Robb, Kate, Roditi, Giles, Paterson, Craig, Crawford, Wenda, Kelly, Joanne, McGregor, Lorraine, Moriarty, Andrew J, Mackin, Anne, Glover, Jason D., Knight, Janet P, Pradhan, Jiwan, Mikhail, Ghada, Bose, Tuhina, Francis, Darrel P., Dzavik, Vladimir, Goodman, Shaun, Gosselin, Gilbert, Gosselin, Gilbert, Proietti, Anna, Brousseau, Myriam, Corfias, Magalie, Blaise, Patricia, Harvey, Luc, Diaz, Ariel, Rheault, Philippe, Barrero, Miguel, Gagné, Carl-Éric, Alarie, Patricia, Pépin-Dubois, Yanek, Arcand, Linda, Costa, Ricardo, Roy, Isabelle, Tung Sia, Ying, Montpetit, Estelle, Lemay, Catherine, Gisbert, Alejandro, Gervais, Pierre, Rheault, Alain, Drouin, Katia, Carl Phaneuf, Denis, Bergeron, Christine, Gosselin, Gilbert, Shelley, Christine, Masson, Christine, Garg, Pallav, Carr, Sandy, Bone, Catherine, Chow, Benjamin J.W., Moga, Ermina, Hessian, Renee C., Kourzenkova, Janetta, Beanlands, Rob S., Walter, Olga, Davies, Richard F., Bainey, Kevin R., Hogg, Norma, Welsh, Suzanne, Cheema, Asim N., Bagai, Akshay, Wald, Ron, Goodman, Shaun, Kushniriuk, Khrystyna, Joseph Graham, John, Hussain, Mohammed, Peterson, Mark, Bello, Olugbenga, Chow, Chi-Ming, Abramson, Beth, Nazir Cheema, Asim, Syed, Ishba, Hussain, Mohammed, Kushniriuk, Khrystyna, Cha, James, Otis, Judy, Otis, Rebecca, Howarth, Andrew G, Seib, Michelle M, Rivest, Sandra M, Sandonato, Rosa, Wong, Graham, Chow, Jackie, Starovoytov, Andrew, Uchida, Naomi, Meadows, Ngaire, Uxa, Amar, Asif, Nadia, Tavares, Suzana, Galiwango, Paul, Bozek, Bev, Kassam, Saleem, Shier, Maria, Mukherjee, Ashok, Larmand, Lori-Ann, Ricci, A. Joseph, Janmohamed, Amir, Hart, Brenda, Lam, Andy, Marucci, Jane, Tai, Sharon, Mehta, Shamir, Brons, Sonya, Beck, Chris, Wong, Glenda, Etherington, Krystal, Arumairajah, Thippeekaa, Udell, Jacob, Aprile, Maria, Karlsson, Sara, Webber, Susan, Généreux, Philippe, Mercure, Chantale, Hameed, Adnan, Aedy, Nancy, Daba, Ledjalem, Farquharson, Fran, Siddiqui, Anam, Carlos Carvalho, Antonio, Lopes, Renato D., Hueb, Whady, Emy Takiuti, Myrthes, Cury Rezende, Paulo, Eustáquio Ribeiro Silva, Expedito, Ciappina Hueb, Alexandre, Pizzol Caetano, Leonardo, Schaan de Quadros, Alexandre, Abdala Karam Kalil, Renato, Peixoto Deiro, Aline, Luiz da Costa Vieira, José, Manica Muller, Alice, Antonieta Pereira de Moraes, Maria, Píccaro de Oliveira, Pedro, Maria Ascoli, Bruna, Bridi, Leonardo, Zottis Poletti, Sílvia, Savaris, Simone, Vitola, João V, Cerci, Rodrigo J, Zier, Sandra S., Farias, Fabio R, Veiga Jr, Vilmar, Fernandes, Miguel M, Antonio Marin-Neto, José, Schmidt, André, de Oliveira Lima Filho, Moysés, Franca da Cunha, Diego, Mendes Oliveira, Ricardo, Reynaldo Abbud Chierice, João, Polanczyk, Carísi A., Rucatti, Guilherme G, Furtado, Mariana V., Igansi, Fernanda, Smidt, Luis F., Haeffner, Mauren P, Carlos Carvalho, Antonio, Almeida, Viviane, Pucci, Gustavo, Sanchez de Souza, Gabriela, Lyra, Flavio, Rabelo Alves Junior, Alvaro, Almeida, Mayana, dos Santos, Viviane, Dracoulakis, Marianna D. A., Oliveira, Natalia S, Lima, Rodolfo G. S. D, Figueiredo, Estevao, Edilena Paulino Azevedo, Bruna, Ricardo Caramori, Paulo, Bizzaro Santos, Marco, Germann, Amanda, Gomes, Vitor, Homem, Rosa, Magedanz, Ellen, Tumelero, Rogerio, Laimer, Rosane, Tognon, Alexandre, Dall’Orto, Frederico, Mesquita, Claudio T., Santos, Roberta P, Colafranseschi, Alexandre S., Oliveira, Amarino C., Carvalho, Luiz A., Palazzo, Isabella C., Sousa, Andre S., Eustáquio Ribeiro da Silva, Expedito, Gabriel Melo de Barros e Silva, Pedro, Yumi Okada, Mariana, de Pádua Silva Baptista, Luciana, Paula Batista, Ana, Jamus Rodrigues, Marcelo, Nogueira Rabaça, Aline, Valério Coimbra de Resende, Marcos, Francisco Saraiva, Jose, Miranda Trama, Larissa, Silva, Talita, Thais de Souza Ormundo, Camila, Vicente, Carla, Costantini, Costantino, Pinheiro, Caroline, Komar, Daniele, Szwed, Hanna, Demkow, Marcin, Kepka, Cezary, Teresinska, Anna, Walesiak, Olga, Kryczka, Karolina, Malinowska, Katarzyna, Henzel, Jan, Solecki, Mateusz, Kaczmarska, Edyta, Mazurek, Tomasz, Maksym, Jakub, Wojtera, Karolina, Fojt, Anna, Szczerba, Ewa, Drozdz, Jaroslaw, Czarniak, Bartosz, Frach, Malgorzata, Szymczyk, Konrad, Niedzwiecka, Iwona, Sobczak, Sebastian, Ciurus, Tomasz, Jakubowski, Piotr, Misztal-Teodorczyk, Magdalena, Teodorczyk, Dawid, Swiderek, Marta, Fratczak, Aleksandra, Wojtala, Ewelina, Szkopiak, Marcin, Lebioda, Patrycja, Wlodarczyk, Michal, Plachcinska, Anna, Kusmierek, Jacek, Miller, Magdalena, Marciniak, Halina, Wojtczak-Soska, Karolina, Łuczak, Katarzyna, Tarchalski, Tomasz, Cichocka-Radwan, Anna, Szwed, Hanna, Karwowski, Jaroslaw, Anna Szulczyk, Grazyna, Witkowski, Adam, Kukuła, Krzysztof, Celińska-Spodar, Małgorzta, Zalewska, Joanna, Gajos, Grzegorz, Bury, Krzysztof, Pruszczyk, Piotr, Łabyk, Andrzej, Roik, Marek, Szramowska, Agnieszka, Zdończyk, Olga, Łoboz-Grudzień, Krystyna, Jaroch, Joanna, Sokalski, Leszek, Brzezińska, Barbara, Lesiak, Maciej, Łanocha, Magdalena, Reczuch, Krzysztof W., Kolodziej, Adam, Kalarus, Zbigniew, Swiatkowski, Andrzej, Szulik, Mariola, Musial, Wlodzimierz J., Marcinkiewicz-Siemion, Marta, Bockeria, Olga, Bockeria, Leo, Bockeria, Olga, Petrosyan, Karen, Kudzoeva, Zalina, Trifonova, Tatiana, Aripova, Nodira, Chernyavskiy, Alexander M., Naryshkin, Ivan A., Kretov, Evgeniy I., Kuleshova, Alena, Grazhdankin, Igor O., Malaev, Dastan, Bershtein, Leonid L., Sayganov, Sergey A., Subbotina, Irina, Kuzmina-Krutetskaya, Anastasia M., Gumerova, Victoria, Zbyshevskaya, Elizaveta V., Katamadze, Nana O., Nikolaeva, Olga B., Kozlov, Pavel S., Kozulin, Vikentiy Y., Lubinskaya, Ekaterina I., Luis Lopez-Sendon, Jose, Castro, Almudena, Lopez-Sendon, Jose, Fernández-Figares, Virginia, Castro, Almudena, Refoyo Salicio, Elena, Guzman, Gabriela, Galeote, Gabriel, Valbuena, Silvia, Peteiro, Jesús, Dolores Martínez-Ruíz, María, Pérez-Fernández, Ruth, Blanco-Calvo, Moisés, Cuenca-Castillo, José J, Alonso-Álvarez, Encarnación, Flores-Ríos, Xacobe, García-González, Paula, Prada-Delgado, Óscar, Barge-Caballero, Gonzalo, Ramon Gonzalez Juanatey, Jose, Seijas Amigo, Jose, Souto Bayarri, Miguel, Pubull Nuñez, Virginia, Ocaranza Sanchez, Raymundo, Cid Alvarez, Belen, Peña Gil, Carlos, Martinez Monzonis, Amparo, Sionis, Alessandro, Fernández Martínez, Ana, Vila Perales, Montserrat, Maria Padró, Josep, Serra Peñaranda, Antonio, García Picart, Joan, Ginel Iglesias, Antonino, Garcia-Moll Marimon, Xavier, Pons Lladó, Guillem, Carreras Costa, Francesc, Miro, Vicente, Igual, Begoña, Diez, Jose L, Calvillo, Pilar, Ortuño, F. Marin, Valdés Chávarri, M., Quintana Giner, M., Tello Montolliu, A., Romero Aniorte, A.I., Pinar Bermudez, E., Rivera Caravaca, JM., De La Morena, G., Gracida Blancas, Montserrat, Cañavate, Olga, Guerrero, Sonia, Riera, Silvia, Enrique Castillo Luena, Jose, Enrique Castillo Luena, Jose, Lasala, Maria, Fernandez-Aviles, Francisco, Lorenzo, Maria, Sobrino, Olga, Vazquez, Alexandra, Jiang, Lixin, Chen, Jiyan, Dong, Haojian, He, Peiyu, Xia, Chunli, Yang, Junqing, Zhong, Qi, Wu, Yongjian, Tian, Yanmeng, Li, Dongze, Ma, Yitong, Li, Xiaomei, Yang, Yining, Ma, Xiang, Yu, Zixiang, Zhao, Qian, Ji, Zheng, Li, Chunguang, Zhang, Lei, Zhao, Yu, Zhu, Bolin, Yang, Xinchun, Chen, Mulei, Chi, Hongjie, Wang, Yang, Zhang, Jing, Lin, Wenhua, Jing, Rui, Liu, Jingjing, Zeng, Hesong, Zhou, Qiang, Xu, Chang, Li, Zhuxi, Li, Junhua, Xiong, Luyang, Fu, Xin, Gao, Dan, Jiang, Dengke, Leng, Ran, Wang, Xutong, Yuan, Qianqian, Zhang, Lili, Yang, Bin, Bai, Ziliang, Li, Jianhua, Qi, Jie, Wang, Fei, Wang, Haitao, Yang, Bin, Yue, Zhou, Zhang, Zhulin, Wang, Songtao, Dong, Yumei, Mao, Jiajia, Zhang, Bin, Cheng, Gong, Li, Xiuhong, Yao, Xiaowei, Zhong, Nier, Zhou, Ning, Zhao, Yulan, Huang, Yaping, Zhou, Panpan, Fang, Xuehua, Su, Wei, Zeng, Qiutang, Kunwu, Yu, Peng, Yudong, Su, Xin, Su, Xi, Wang, Chen, Zhao, Yunhai, Li, Qingxian, Geng, Yaming, Wang, Yanfu, Nie, Shao-ping, Fan, Jing-yao, Feng, Si-ting, Wang, Xiao, Yan, Yan, Zhang, Hui-min, Yu, Qin, Chi, Lingping, Liu, Fang, Wang, Jian’an, Chen, Han, Jiang, Jun, Li, Huajun, Wang, Jian’an, Han, Yechen, Xu, Lihong, Zhang, Shuyang, Liu, Zhenyu, Liu, Zhenyu, Chen, Gang, Hu, Rongrong, Maggioni, Aldo P., Piero Perna, Gian, Pietrucci, Francesca, Marini, Marco, Gabrielli, Gabriele, Provasoli, Stefano, Di Donato, Anna, Verna, Edoardo, Monti, Lorenzo, Nardi, Barbara, Di Chiara, Antonio, Pezzetta, Francesca, Mortara, Andrea, Casali, Valentina, Galvani, Marcello, Attanasio, Chiara, Ottani, Filippo, Sicuro, Marco, Leone, Gianpiero, Pisano, Francesco, Bare, Cristina, 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Published
2024
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96. Improving a WRF-Based High-Impact Weather Forecast System for a Northern California Power Utility

Author

Richard L. Carpenter, Taylor A. Gowan, Samuel P. Lillo, Scott J. Strenfel, Arthur. J. Eiserloh, Evan J. Duffey, Xin Qu, Scott B. Capps, Rui Liu, and Wei Zhuang

Subjects
Weather Research and Forecasting (WRF) model, wildfire weather, Diablo winds, ensemble weather forecasting, power utility, Meteorology. Climatology, QC851-999
Abstract

We describe enhancements to an operational forecast system based on the Weather Research and Forecasting (WRF) model for the prediction of high-impact weather events affecting power utilities, particularly conditions conducive to wildfires. The system was developed for Pacific Gas and Electric Corporation (PG&E) to forecast conditions in Northern and Central California for critical decision-making such as proactively de-energizing selected circuits within the power grid. WRF forecasts are routinely produced on a 2 km grid, and the results are used as input to wildfire fuel moisture, fire probability, wildfire spread, and outage probability models. This forecast system produces skillful real-time forecasts while achieving an optimal blend of model resolution and ensemble size appropriate for today’s computational resources afforded to utilities. Numerous experiments were performed with different model settings, grid spacing, and ensemble configuration to develop an operational forecast system optimized for skill and cost. Dry biases were reduced by leveraging a new irrigation scheme, while wind skill was improved through a novel approach involving the selection of Global Ensemble Forecast System (GEFS) members used to drive WRF. We hope that findings in this study can help other utilities (especially those with similar weather impacts) improve their own forecast system.

Published
2024
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