bluuebunny/crossref_metadata_2025_split huggingface dataset:
Plasma viscosity, immunoglobulins and risk of cardiovascular disease and mortality: new data and meta-analyses
Authors: Lowe Gordon D O , Harris Katie , Koenig Wolfgang , Ben-Shlomo Yoav , Thorand Barbara , Peters Annette , Meisinger Christa , Imhof Armin , Tunstall-Pedoe Hugh , Peters Sanne A E , Woodward Mark 
Published: February 2023

AimsAssociations of plasma viscosity and plasma Ig levels (a determinant of viscosity) with incident coronary heart disease (CHD) events; and with CHD, cardiovascular disease (CVD: CHD and stroke) and all-cause mortalities.MethodsMeta-analysis of plasma viscosity levels from the MONitoring of trends and determinants of CArdiovascular (MONICA)/Cooperative Health Research in the Region of Augsburg, MONICA Glasgow and Speedwell Studies; and five other published studies. Meta-analysis of IgA, IgG and IgM levels from the Augsburg, Glasgow and Speedwell studies; and one other published study.ResultsOver median follow-up periods of 14–26 years, there were 2270 CHD events, and 4220 all cause deaths in 28 605 participants with baseline plasma viscosity measurements. After adjustment for major risk factors, (HRs; 95% CIs) for a 1 SD increase in viscosity were 1.14 (1.09 to 1.20) for CHD events; and 1.21 (1.17 to 1.25) for all-cause mortality. 821 CHD events and 2085 all-cause deaths occurred in 8218 participants with baseline Ig levels. For CHD events, adjusted HRs for 1 SD increases in IgA, IgG and IgM were, respectively, 0.97 (0.89 to 1.05); 0.95(0.76 to 1.17) and 0.90 (0.79 to 1.03). Corresponding adjusted HRs for all-cause mortality were 1.08 (95% CI 1.02 to 1.13), 1.03 (95% CI 0.94 to 1.14) and 1.01 (95% CI 0.96 to 1.06).ConclusionsAfter risk factor adjustment, plasma viscosity was significantly associated with risks of CHD events; and with CHD, CVD and all-cause mortalities. We found no significant association of IgA, IgG or IgM levels with incident CHD events or mortality, except for a borderline association of IgA with all-cause mortality.
QCD cusp anomalous dimension: Current status
Authors: Grozin Andrey 
Published: February 2023

Calculation results for the HQET field anomalous dimension and the QCD cusp anomalous dimension, as well as their properties, are reviewed. The HQET field anomalous dimension [Formula: see text] is known up to four loops. The cusp anomalous dimension [Formula: see text] is known up to three loops, and its small-angle and large-angle asymptotics up to four loops. Some (but not all) color structures at four loops are known with the full [Formula: see text]-dependence. Some simple contributions are known at higher loops. For the [Formula: see text] asymptotics of [Formula: see text] (the light-like cusp anomalous dimension) and the [Formula: see text]-term of the small-[Formula: see text] expansion (the Bremsstrahlung function), the [Formula: see text] SYM results are equal to the highest-weight parts of the QCD results. There is an interesting conjecture about the structure of [Formula: see text] which holds up to three loops; at four loops it holds for some color structures and breaks down for other ones. In the cases when it holds, it related highly nontrivial functions of [Formula: see text], and it cannot be accidental; however, the reasons of this conjecture and its failures are not understood. The cusp anomalous dimension at the Euclidean angle [Formula: see text] is related to the static quark–antiquark potential due to conformal symmetry; in QCD, this relation is broken by an anomalous term proportional to the [Formula: see text]-function. Some new results are also presented. Using the recent four-loop result for [Formula: see text], here we obtain analytical expressions for some terms in the four-loop on-shell renormalization constant of the massive quark field [Formula: see text] which were previously known only numerically. We also present two new contributions to [Formula: see text], [Formula: see text] at five loops and to the quark–antiquark potential at four loops.
High-resolution core data and machine learning schemes applied to rock facies classification
Authors: Germay Christophe , Lhomme Tanguy , Perneder Luc 
Published: February 2023

Abstract

This paper presents core analysis practices developed to increase the value of cores in reservoir characterization workflows. Core analysis traditionally implies extensive rock testing programmes that require large numbers of plug samples. Numerous stakeholders compete for intact core material and often do not base sample selection criteria on objective and reliable information. As a consequence, samples dedicated to core analysis programmes consume a significant fraction of the material available, yet they are selected on the basis of very little
a priori
information and therefore do not necessarily capture the complexity of the targeted formations. In an attempt to change this paradigm, we promote a more integrated core analysis solution combining transdisciplinary, high-resolution, non-destructive measurements on whole cores, for an early yet objective description of cores and the rapid estimation of formation properties. The first section describes multidisciplinary and non-destructive tests designed to increase the value of information from cores while keeping a minimal footprint. Core samples are scanned along a fit-for-purpose surface, with a collection of sensors interfaced to the same table-top equipment. Technologies including ultra-high-resolution pictures, elemental composition and the direct measurements of geomechanical properties provide continuous and high-resolution profiles with a unique depth reference. Panoramic pictures are processed to extract textural and colour features and grain size distributions. Grain size distributions are backed up by analysis of topographical maps created with a laser scan. The results are combined under one unique format, thereby easing interdisciplinary work from the verification of standard tests (routine core analysis, rock mechanical test) to the construction of robust predictive rock models. The paper describes machine learning schemes applied to core datasets. Unsupervised schemes are designed to identify rock facies, while supervised schemes are used to classify tested rocks into predefined rock facies with known petrophysical properties. Case studies highlight the benefits of this approach of core analysis in conjunction with artificial intelligence for the automated identification and classification of rock facies. This novel approach to core analysis leverages a detailed and comprehensive knowledge of the distributions of core properties, available under one unique format for all disciplines, which eases interdisciplinary work and significantly improves existing core analysis standards. It also provides a sound basis to train artificial intelligence rock reservoir property predictors linking well-log data to core-based lithofacies signatures.
Enabling secure subsurface storage in future energy systems: an introduction
Authors: Miocic Johannes M. , Heinemann Niklas , Alcalde Juan , Edlmann Katriona , Schultz Richard A. 
Published: February 2023

Abstract

Geological structures in the subsurface have been used for the storage of energy and waste products for over a century. Depleted oil and gas fields, saline aquifers or engineered caverns in salt or crystalline rocks are used worldwide to store energy fluids intended to provide demand buffers and sustained energy supply. The transition of our energy system into a clean, renewable-based system will most likely require an expansion of these subsurface storage activities, to host a wide variety of energy products (e.g. natural gas, hydrogen, heat or waste energy products, like CO
2
) to balance the inherent intermittence of the renewable energy supply. Ensuring the safety and effectiveness of these subsurface storage operations is therefore crucial to achieve the sought-after renewable energy transition while ensuring energy security.
Cancer Epidemiology in Hispanic Populations: An Analysis of Funded Observational Research at the National Cancer Institute
Authors: Elena Joanne W. , Gallicchio Lisa , Pottinger Camille A. , Akif Kaitlin E. , Hanisch Rachel , Kennedy Amy E. , Lai Gabriel Y. , Mahabir Somdat , Martin Damali N. , Srinivasan Shobha , Yu Catherine T. , Lam Tram K. 
Published: February 2023

Abstract

Background:
More than 62 million people self-identified as Hispanic/Latino (H/L) in the 2020 United States census. The U.S. H/L population has higher burden of certain cancers compared with their non-Hispanic White counterparts.

Methods:
Key term search using the NIH Query/View/Report (QVR) system, along with Research, Condition, and Disease Categorization codes identified cancer epidemiology research grants in H/L populations funded by the NCI as a primary or secondary funder from fiscal years 2016 through 2021. Three reviewers identified eligible grants based on specified inclusion/exclusion criteria and a codebook for consistency extracting key characteristics.

Results:
A total of 450 grants were identified through the QVR system using key words related to H/Ls; 41 cancer epidemiology grants remained after applying exclusion criteria. These grants contained specific aims focused on H/Ls (32%) or included H/Ls as part of a racial/ethnic comparison (68%). NCI was the primary funder of the majority of the grants (85%), and most of the research grants focused on cancer etiology (44%) and/or survivorship (49%). Few grants (10%) investigated environmental exposures.

Conclusions:
This article provides an overview of NCI-funded cancer epidemiology research in H/L populations from 2016 to 2021. Future cancer epidemiology research should reflect the changing dynamics of the U.S. demography with diverse, representative populations and well-characterized ethnicity.

Impact:
Research that carefully measures the relevant biological, environmental, behavioral, psychologic, sociocultural, and clinical risk factors will be critical to better understanding the nuanced patterns influencing cancer-related outcomes in the heterogenous H/L population.
The Search for Genomic Biomarkers of Response to Immunotherapy in Ovarian Cancer
Authors: Al-Rawi Duaa H. , Rusk Nicole , Friedman Claire F. 
Published: February 2023

Summary
Immune checkpoint blockade has been ineffective in ovarian cancer, and there is an ongoing effort to identify biomarkers of therapeutic benefit. Despite promising preclinical data, a substudy of the IMagyn050 trial found that patients with homologous recombination deficient tumors did not have improved progression-free survival with the addition of the PD-L1 inhibitor atezolizumab.
See related article by Landen et al., p. 1698
Probabilistic Mixture Models Improve Calibration of Panel-derived Tumor Mutational Burden in the Context of both Tumor-normal and Tumor-only Sequencing
Authors: Anaya Jordan , Sidhom John-William , Cummings Craig A. , Baras Alexander S. ,   for AACR Project GENIE Consortium
Published: February 2023

Background:Tumor mutational burden (TMB) has been investigated as a biomarker for immune checkpoint blockade (ICB) therapy. Increasingly, TMB is being estimated with gene panel–based assays (as opposed to full exome sequencing) and different gene panels cover overlapping but distinct genomic coordinates, making comparisons across panels difficult. Previous studies have suggested that standardization and calibration to exome-derived TMB be done for each panel to ensure comparability. With TMB cutoffs being developed from panel-based assays, there is a need to understand how to properly estimate exomic TMB values from different panel-based assays.Design:Our approach to calibration of panel-derived TMB to exomic TMB proposes the use of probabilistic mixture models that allow for nonlinear relationships along with heteroscedastic error. We examined various inputs including nonsynonymous, synonymous, and hotspot counts along with genetic ancestry. Using The Cancer Genome Atlas cohort, we generated a tumor-only version of the panel-restricted data by reintroducing private germline variants.Results:We were able to model more accurately the distribution of both tumor-normal and tumor-only data using the proposed probabilistic mixture models as compared with linear regression. Applying a model trained on tumor-normal data to tumor-only input results in biased TMB predictions. Including synonymous mutations resulted in better regression metrics across both data types, but ultimately a model able to dynamically weight the various input mutation types exhibited optimal performance. Including genetic ancestry improved model performance only in the context of tumor-only data, wherein private germline variants are observed.Significance:A probabilistic mixture model better models the nonlinearity and heteroscedasticity of the data as compared with linear regression. Tumor-only panel data are needed to properly calibrate tumor-only panels to exomic TMB. Leveraging the uncertainty of point estimates from these models better informs cohort stratification in terms of TMB.
Strictly Enforcing Invertibility and Conservation in CNN-Based Super Resolution for Scientific Datasets
Authors: Geiss Andrew , Hardin Joseph C. 
Published: February 2023

Abstract
Recently, deep convolutional neural networks (CNNs) have revolutionized image “super resolution” (SR), dramatically outperforming past methods for enhancing image resolution. They could be a boon for the many scientific fields that involve imaging or any regularly gridded datasets: satellite remote sensing, radar meteorology, medical imaging, numerical modeling, and so on. Unfortunately, while SR-CNNs produce visually compelling results, they do not necessarily conserve physical quantities between their low-resolution inputs and high-resolution outputs when applied to scientific datasets. Here, a method for “downsampling enforcement” in SR-CNNs is proposed. A differentiable operator is derived that, when applied as the final transfer function of a CNN, ensures the high-resolution outputs exactly reproduce the low-resolution inputs under 2D-average downsampling while improving performance of the SR schemes. The method is demonstrated across seven modern CNN-based SR schemes on several benchmark image datasets, and applications to weather radar, satellite imager, and climate model data are shown. The approach improves training time and performance while ensuring physical consistency between the super-resolved and low-resolution data.

Significance Statement
Recent advancements in using deep learning to increase the resolution of images have substantial potential across the many scientific fields that use images and image-like data. Most image super-resolution research has focused on the visual quality of outputs, however, and is not necessarily well suited for use with scientific data where known physics constraints may need to be enforced. Here, we introduce a method to modify existing deep neural network architectures so that they strictly conserve physical quantities in the input field when “super resolving” scientific data and find that the method can improve performance across a wide range of datasets and neural networks. Integration of known physics and adherence to established physical constraints into deep neural networks will be a critical step before their potential can be fully realized in the physical sciences.
Sea2Cloud: From Biogenic Emission Fluxes to Cloud Properties in the Southwest Pacific
Authors: Sellegri Karine , Harvey Mike , Peltola Maija , Saint-Macary Alexia , Barthelmeß Theresa , Rocco Manon , Moore Kathryn A. , Cristi Antonia , Peyrin Frederic , Barr Neill , Labonnote Laurent , Marriner Andrew , McGregor John , Safi Karl , Deppeler Stacy , Archer Stephen , Dunne Erin , Harnwell James , Delanoe Julien , Freney Evelyn , Rose Clémence , Bazantay Clément , Planche Céline , Saiz-Lopez Alfonso , Quintanilla-López Jesús E. , Lebrón-Aguilar Rosa , Rinaldi Matteo , Banson Sandra , Joseph Romain , Lupascu Aurelia , Jourdan Olivier , Mioche Guillaume , Colomb Aurélie , Olivares Gus , Querel Richard , McDonald Adrian , Plank Graeme , Bukosa Beata , Dillon Wayne , Pelon Jacques , Baray Jean-Luc , Tridon Frederic , Donnadieu Franck , Szczap Frédéric , Engel Anja , DeMott Paul J. , Law Cliff S. 
Published: February 2023

Abstract
The goal of the Sea2Cloud project is to study the interplay between surface ocean biogeochemical and physical properties, fluxes to the atmosphere, and ultimately their impact on cloud formation under minimal direct anthropogenic influence. Here we present an interdisciplinary approach, combining atmospheric physics and chemistry with marine biogeochemistry, during a voyage between 41° and 47°S in March 2020. In parallel to ambient measurements of atmospheric composition and seawater biogeochemical properties, we describe semicontrolled experiments to characterize nascent sea spray properties and nucleation from gas-phase biogenic emissions. The experimental framework for studying the impact of the predicted evolution of ozone concentration in the Southern Hemisphere is also detailed. After describing the experimental strategy, we present the oceanic and meteorological context including provisional results on atmospheric thermodynamics, composition, and flux measurements. In situ measurements and flux studies were carried out on different biological communities by sampling surface seawater from subantarctic, subtropical, and frontal water masses. Air–Sea-Interface Tanks (ASIT) were used to quantify biogenic emissions of trace gases under realistic environmental conditions, with nucleation observed in association with biogenic seawater emissions. Sea spray continuously generated produced sea spray fluxes of 34% of organic matter by mass, of which 4% particles had fluorescent properties, and which size distribution resembled the one found in clean sectors of the Southern Ocean. The goal of Sea2Cloud is to generate realistic parameterizations of emission flux dependences of trace gases and nucleation precursors, sea spray, cloud condensation nuclei, and ice nuclei using seawater biogeochemistry, for implementation in regional atmospheric models.
Flow Dependence of Medium-Range Precipitation Forecast Skill over California
Authors: Moore Benjamin J. 
Published: February 2023

Abstract
This study employs a long time series (1997–2017) of reforecasts based on a version of the ECMWF Integrated Forecast System to evaluate the dependence of medium-range (i.e., 3–15 days) precipitation forecast skill over California on the state of the large-scale atmospheric flow. As a basis for this evaluation, four recurrent large-scale flow regimes over the North Pacific and western North America associated with precipitation in a domain encompassing northern and central California were objectively identified in ECMWF ERA5 reanalysis data for November–March 1981–2017. Two of the regimes are characterized by zonal upper-level flow across the North Pacific, and the other two are characterized by wavy, blocked flow. Forecast verification statistics conditioned on regime occurrence indicate considerably lower medium-range precipitation skill over California in blocking regimes than in zonal regimes. Moreover, forecasts of blocking regimes tend to exhibit larger errors and uncertainty in the synoptic-scale flow over the eastern North Pacific and western North America compared with forecasts of zonal regimes. Composite analyses for blocking forecasts reveal a tendency for errors to develop in conjunction with the amplification of a ridge over the western and central North Pacific. The errors in the ridge tend to be communicated through the large-scale Rossby wave pattern, resulting in misforecasting of downstream trough amplification and, thereby, moisture flux and precipitation over California. The composites additionally indicate that error growth in the blocking ridge can be linked to misrepresentation of baroclinic development as well as upper-level divergent outflow associated with latent heat release.

Significance Statement
This study examines the degree to which the medium-range (out to ∼2-week lead time) precipitation forecast skill over California depends on the large-scale atmospheric flow regime over the North Pacific. An evaluation of retrospective model forecasts from ECMWF for 1997–2017 reveals that the skill tends to be considerably lower in regimes featuring a wavy, “blocked” North Pacific jet stream than in regimes featuring a west–east-oriented jet stream. This difference in skill relates to a tendency for forecasts of blocked regimes to exhibit significantly larger errors than forecasts of zonal regimes. The results could aid forecasters by increasing situational awareness and informing the interpretation and application of model forecasts for precipitation affecting California.
Outcomes of a Large Series of Laparoscopic Ventral Hernia Repairs after Liver Transplantation
Authors: Han Ga-ram , Johnson Eleanor R. , Jogerst Kristen M. , Calderon Esteban , Hewitt Winston R. , Pearson David G. , Harold Kristi L. 
Published: February 2023

Background The hernia defects that develop in liver transplant recipients tend to be complex. Unfortunately, there is a paucity of data to guide post-transplant hernia management. Our goal was to evaluate the outcomes following laparoscopic ventral hernia repair (LVHR) in liver transplant recipients. Methods A retrospective review of a prospectively kept database of liver transplant patients at a single tertiary healthcare facility was completed. All patients between 2007 and 2020 who underwent LVHR for a hernia at their transplant incision site were included. The primary outcome studied was hernia recurrence. Secondary outcomes included time-to-hernia repair, complications, and length of stay (LOS). Results There were 89 patients who met inclusion criteria. 82% were male, mean age was 60 years, and mean body mass index was 30.2 kg/m2. 94.4% were on tacrolimus and 36% on mycophenolate mofetil. Median time-to-hernia repair was 16 months with a mean mesh size of 743 cm2 and length of stay of 3.7 days. None required conversion to an open operation. Postoperative complications included ileus (20.2%), acute kidney injury (11.2%), pneumonia (6.7%), and bleeding requiring re-operation (1.1%). Hernia-related complications included chronic suture site pain (1.1%), seroma requiring intervention (3.3%), surgical site infection (3.3%), nonoperative mesh infection (1.1%), and mesh infection requiring explantation (1.1%). Median follow-up was 23 months. Hernia recurrence occurred in 4.5% and no predictive variables for recurrence were identified. Conclusions Although the hernia defects that develop in liver transplant recipients are complex and their comorbidities significant, LVHR can safely and effectively repair these defects with low rates of recurrence and complications.
Inflation Adjustment, Endogenous Risk Premium and Exchange Rate: A Theoretical Analysis
Authors: Basu Moumita , Basu Rilina , Nag Ranjanendra Narayan 
Published: February 2023

This article develops a full employment monetary framework that deals with the interaction between exchange rate and inflation rate dynamics, emphasising the existence of risk premium. The economy consists of internal and foreign bonds. These are close substitutes since there exists a risk premium that depends on inflation rate, budget deficit and net exports. According to the monetary policy rule, both inflation rate and exchange rate negatively influence money supply. Overtime, changes in inflation rate are proportional to the excess supply in the money market. The dynamic adjustment of exchange rate arises due to discrepancy between home interest rate and world rate of interest and risk premium. Based on this framework, we investigate the implications of increase in exports, technological innovation and policy mix for the interaction between exchange rate and inflation rate. JEL Codes: E31, E63, F32, F41