bluuebunny/crossref_metadata_2025_split huggingface dataset:
The effects of surface fossil magnetic fields on massive star evolution: IV. Grids of models at Solar, LMC, and SMC metallicities
Authors: Keszthelyi Z , de Koter A , Götberg Y , Meynet G , Brands S A , Petit V , Carrington M , David-Uraz A , Geen S T , Georgy C , Hirschi R , Puls J , Ramalatswa K J , Shultz M E , ud-Doula A 
Published: September 2022

ABSTRACT
Magnetic fields can drastically change predictions of evolutionary models of massive stars via mass-loss quenching, magnetic braking, and efficient angular momentum transport, which we aim to quantify in this work. We use the mesa software instrument to compute an extensive main-sequence grid of stellar structure and evolution models, as well as isochrones, accounting for the effects attributed to a surface fossil magnetic field. The grid is densely populated in initial mass (3–60 M⊙), surface equatorial magnetic field strength (0–50 kG), and metallicity (representative of the Solar neighbourhood and the Magellanic Clouds). We use two magnetic braking and two chemical mixing schemes and compare the model predictions for slowly rotating, nitrogen-enriched (‘Group 2’) stars with observations in the Large Magellanic Cloud. We quantify a range of initial field strengths that allow for producing Group 2 stars and find that typical values (up to a few kG) lead to solutions. Between the subgrids, we find notable departures in surface abundances and evolutionary paths. In our magnetic models, chemical mixing is always less efficient compared to non-magnetic models due to the rapid spin-down. We identify that quasi-chemically homogeneous main sequence evolution by efficient mixing could be prevented by fossil magnetic fields. We recommend comparing this grid of evolutionary models with spectropolarimetric and spectroscopic observations with the goals of (i) revisiting the derived stellar parameters of known magnetic stars, and (ii) observationally constraining the uncertain magnetic braking and chemical mixing schemes.
Standardization of Epidemiological Surveillance of Group A Streptococcal Pharyngitis
Authors: Miller Kate M , Tanz Robert R , Shulman Stanford T , Carapetis Jonathan R , Cherian Thomas , Lamagni Theresa , Bowen Asha C , Pickering Janessa , Fulurija Alma , Moore Hannah C , Cannon Jeffrey W , Barnett Timothy C , Van Beneden Chris A ,   for the Strep A Vaccine Global Consortium (SAVAC) Burden of Disease Working Group, Carapetis Jonathan , Van Beneden Chris , Kaslow David C , Cherian Thomas , Lamagni Theresa , Engel Mark , Cannon Jeffrey , Moore Hannah C , Bowen Asha , Seale Anna , Kang Gagandeep , Watkins David , Kariuki Sam 
Published: September 2022

Abstract
Pharyngitis, more commonly known as sore throat, is caused by viral and/or bacterial infections. Group A Streptococcus (Strep A) is the most common bacterial cause of pharyngitis. Strep A pharyngitis is an acute, self-limiting disease but if undertreated can lead to suppurative complications, nonsuppurative poststreptococcal immune-mediated diseases, and toxigenic presentations. We present a standardized surveillance protocol, including case definitions for pharyngitis and Strep A pharyngitis, as well as case classifications that can be used to differentiate between suspected, probable, and confirmed cases. We discuss the current tests used to detect Strep A among persons with pharyngitis, including throat culture and point-of-care tests. The type of surveillance methodology depends on the resources available and the objectives of surveillance. Active surveillance and laboratory confirmation is the preferred method for case detection. Participant eligibility, the surveillance population and additional considerations for surveillance of pharyngitis are addressed, including baseline sampling, community engagement, frequency of screening and season. Finally, we discuss the core elements of case report forms for pharyngitis and provide guidance for the recording of severity and pain associated with the course of an episode.
Up and away? Inflation and debt consolidation in historical perspective
Authors: Eichengreen Barry , Esteves Rui 
Published: September 2022

AbstractThe Global Financial Crisis and the COVID-19 pandemic bequeathed large increases in public debt. At some point governments may seek to bring down these elevated debt-to-GDP ratios, including by inflating (by raising nominal GDP). We assemble a panel of debt consolidation episodes spanning 220 years and 183 nations. The evidence confirms that moderate inflation has been instrumental in facilitating large consolidations in the past. In fact, the frequency of successful debt consolidations was lower in periods of relatively high inflation, when interest rates show a tendency to quickly catch up. The largest concentration of debt consolidations in fact coincides with periods of relatively low and stable inflation in the context of credible monetary policies and sound fiscal policies.
Promoting recovery and resilience for internally displaced persons: lessons from Colombia
Authors: Ibáñez Ana María , Moya Andrés , Velásquez Andrea 
Published: September 2022

AbstractThe number of forcibly displaced persons has increased substantially since the early 2000s and has more than doubled in the last decade. Responding to the needs of forcibly displaced persons requires comprehensive legal and policy frameworks and evidence-based programmes that promote durable solutions, including sustainable movements out of poverty and their successful integration into hosting communities. In this paper, we review the dynamics of forced displacement in Colombia, the country with the largest number of internally displaced persons worldwide, and the progression of legal and policy frameworks that have been implemented since the late 1990s. We also review over two decades of research on the economic, social, and psychological consequences of forced displacement following an asset-based poverty trap framework that allows us to understand how forced displacement can alter poverty dynamics across time and generations. Throughout the review, we draw lessons for other contexts and countries affected by forced displacement and refugee flows.
IEnglish Language
Authors: Kostadinova Viktorija , Wiemann Marco , Dreschler Gea , Gregersen Sune , Gyuris Beáta , Zhong Ai , Anderwald Lieselotte , Hansen Beke , Leuckert Sven , Kraš Tihana , Ting Shawnea Sum Pok , Parise Ida , Cogo Alessia , Reber Elisabeth 
Published: September 2022

Abstract
This chapter has the following sections: 1. General; 2. History of English Linguistics; 3. Phonetics and Phonology; 4. Morphology; 5. Syntax; 6. Semantics; 7. Lexicography, Lexicology, and Lexical Semantics; 8. Onomastics (not present this year); 9. Dialectology and Sociolinguistics; 10. New Englishes and Creolistics; 11. Second Language Acquisition; 12. English as a Lingua Franca; 13. Pragmatics and Discourse Analysis; 14. Stylistics (not present this year). Sections 1 and 2 are by Viktorija Kostadinova; section 3 is by Marco Wiemann; sections 4 and 5 are by Gea Dreschler and Sune Gregersen; section 6 is by Beáta Gyuris; section 7 is by Ai Zhong; section 9 is by Lieselotte Anderwald; section 10 is by Beke Hansen and Sven Leuckert; section 11 is by Tihana Kraš; section 12 is by Shawnea Sum Pok Ting, Ida Parise and Alessia Cogo; section 13 is by Elisabeth Reber.
Interfacility Transport of Critically Ill Patients
Authors: Wilcox Susan R. , Wax Randy S. , Meyer Michael T. , Stocking Jacqueline C. , Baez Amado Alejandro , Cohen Jason , Moss M. Michele , Frakes Michael A. , Scruth Elizabeth A. , Weir William B. , Zonies David , Guyette Francis X. , Kaplan Lewis J. , Cannon Jeremy W. 
Published: September 2022

OBJECTIVES:
To assess recent advances in interfacility critical care transport.

DATA SOURCES:
PubMed English language publications plus chapters and professional organization publications.

STUDY SELECTION:
Manuscripts including practice manuals and standard (1990–2021) focused on interfacility transport of critically ill patients.

DATA EXTRACTION:
Review of society guidelines, legislative requirements, objective measures of outcomes, and transport practice standards occurred in work groups assessing definitions and foundations of interfacility transport, transport team composition, and transport specific considerations. Qualitative analysis was performed to characterize current science regarding interfacility transport.

DATA SYNTHESIS:
The Task Force conducted an integrative review of 496 manuscripts combined with 120 from the authors’ collections including nonpeer reviewed publications. After title and abstract screening, 40 underwent full-text review, of which 21 remained for qualitative synthesis.

CONCLUSIONS:
Since 2004, there have been numerous advances in critical care interfacility transport. Clinical deterioration may be mitigated by appropriate patient selection, pretransport optimization, and transport by a well-resourced team and vehicle. There remains a dearth of high-quality controlled studies, but notable advances in monitoring, en route management, transport modality (air vs ground), as well as team composition and training serve as foundations for future inquiry. Guidance from professional organizations remains uncoupled from enforceable regulations, impeding standardization of transport program quality assessment and verification.
Combatting seasonal malaria transmission using a highly potent Plasmodium falciparum transmission-blocking monoclonal antibody
Authors: Challenger Joseph D , van Beek Stijn W , Heine Rob ter , van der Boor Saskia C , Charles Giovanni D , Smit Merel J , Ockenhouse Chris , Aponte John J , McCall Matthew BB , Jore Matthijs M , Churcher Thomas S , Bousema Teun 
Published: September 2022

AbstractTransmission-blocking interventions can play an important role in combatting malaria worldwide. Recently, a highly potent Plasmodium falciparum transmission-blocking monoclonal antibody (TB31F) was demonstrated to be safe and efficacious in malaria-naïve volunteers. Here we determine what dose would be required to obtain effective transmission reduction throughout the malaria season and predict the potential public health impact of large-scale implementation of TB31F alongside existing interventions. To this purpose, we developed a pharmaco-epidemiological model, tailored to two settings of differing transmission intensity with already established insecticide-treated nets and seasonal malaria chemoprevention interventions. We found that a simple weight-based TB31F dosing strategy achieved >80% transmission-reducing activity for over 5 months. With this approach, community-wide annual administration (at 80% coverage) of TB31F over a three-year period was predicted to reduce clinical incidence by 54% (381 cases averted per 1000 people per year) in a high-transmission seasonal setting, and 74% (157 cases averted per 1000 people per year) in a low-transmission seasonal setting. Targeting school-aged children gave the largest reduction in terms of cases averted per dose. We conclude that annual administration of transmission-blocking mAb TB31F may be an effective intervention against malaria in seasonal malaria settings.Key QuestionsWhat is already known on this topic-Naturally acquired transmission reducing antibodies can prevent malaria transmission to mosquitoes-The humanized transmission-blocking monoclonal antibody TB31F is safe and single dose administration can achieve antibody concentrations that prevent malaria transmission for at least 84 daysWhat this study adds-A simple dosing regimen of TB31F in three weight-bands allows for single dose administration to sustain >80% transmission reducing activity for >5 months-Community administration of TB31F can prevent a similar number of clinical malaria episodes compared to the highly efficacious seasonal malaria chemoprevention-Community impact can be maximized when TB31F is combined with seasonal malaria chemoprevention-School age children are the most effective part of the population to be targeted for maximum impactHow this study might affect research, practice or policy-Transmission blocking monoclonal antibodies can have a profound effect on malaria burden and can be combined with current interventions for maximum impact-The predicted community impact of TB31F supports further clinical development of transmission-blocking monoclonal antibodies and exploration of use scenarios
A simple computational model of population substance use
Authors: Borodovsky Jacob T. 
Published: September 2022

BackgroundSubstance use behaviors and their etiologies are complex and often not amenable to traditional statistical analysis. Computational models are an increasingly popular alternative approach for investigating substance use. However, cumulative progress has been difficult because of a lack of standardization. This study aims to develop and evaluate a simple computational model that could serve as a common starting point for future computation-based investigations of substance use.MethodsA two-state (“Using” a substance or “Not using” a substance) stochastic model with three manipulable parameters is used to reproduce the distributions of past 30-day alcohol, cannabis, and tobacco cigarette consumption frequencies (e.g., used on 5 days within the past 30 days) observed in the U.S. National Survey on Drug Use and Health (NSDUH) (years 2002-2019 combined). The model employs a path-dependent process: during each iteration (i.e., each “day”) of the simulation, each computational object chooses to use or not use a substance based on probabilities that are contingent on choices made in prior iterations. The Lempel-Ziv complexity measure was used to examine the resulting sequences of binary decisions (use ordon’t use) made by each computational object.ResultsThe model accurately reproduces the population-level “U-shaped” distributions of past 30-day alcohol, cannabis, and cigarette use in the U.S. The path dependence function was required for reproducing these distributions. The model also suggests an “arc” of behavioral complexity stages: as the frequency of use increases, the complexity of decision sequences increases, peaks, and then decreases. However, decision sequence complexity still varied considerably among objects with similar frequencies of use.ConclusionA simple computational model that simulates individual-level sequences of substance use can reproduce the population-level distributions of substance use observed in national survey data. The model also suggests that complexity measures are a potentially helpful tool for examining substance use behaviors.
Trends in Low-Value Cancer Care During the COVID-19 Pandemic
Authors: Parikh Ravi B. , Civelek Yasin , Ozluk Pelin , Drell Helayne A. , DeBono David , Fisch Michael J. , Sylwestrzak Gosia , Bekelman Justin E. , Schwartz Aaron L. 
Published: September 2022

AbstractBackgroundLow-value services are common in cancer care. The onset of the COVID-19 pandemic caused a dramatic decrease in health care utilization, leading many to suspect that low-value cancer services may decrease.MethodsIn this retrospective cohort study, we used administrative claims from the HealthCore Integrated Research Environment, a repository of medical and pharmacy data from US health plans representing over 80 million members, to identify 204,581 patients diagnosed with breast, colorectal, and/or lung cancer between January 1, 2015, and March 31, 2021. We used linear probability models to investigate the relation between the onset of COVID-19 pandemic and 5 guideline-based metrics of low-value cancer care: 1) Positron Emission Tomography/Computed Tomography (PET/CT) instead of conventional CT imaging for initial staging; 2) conventional fractionation instead of hypofractionation for early-stage breast cancer; 3) non-guideline-based antiemetic use for minimal-, low-, or moderate-to-high-risk chemotherapies; 4) off-pathway systemic therapy; and 5) aggressive end-of-life care.ResultsAmong 204,581 patients, the mean [SD] age was 63.1 [13.2], 68.1% were female, 83,593 (40.8%) had breast cancer, 56,373 (27.5%) had colon cancer, and 64,615 (31.5%) had lung cancer. Rates of low-value cancer services did not exhibit meaningful declines during the pandemic: PET/CT imaging, adjusted percentage point difference 1.87 (95% CI −0.13 to 3.87); conventional radiotherapy, adjusted percentage point difference 3.93 (95% CI 1.50 to 6.36); off-pathway systemic therapy, adjusted percentage point difference 0.82 (95% CI −0.62 to 2.25); non-guideline-based antiemetics, adjusted percentage point difference −3.62 (95% CI −4.97 to −2.27); aggressive end-of-life care, adjusted percentage point difference 2.71 (95% CI −0.59 to 6.02).DiscussionLow-value cancer care remained prevalent through the pandemic. Policymakers should consider changes to payment and incentive design to turn the tide toward higher-value cancer care.
Leveraging deep-learning on raw spirograms to improve genetic understanding and risk scoring of COPD despite noisy labels
Authors: Cosentino Justin , Behsaz Babak , Alipanahi Babak , McCaw Zachary R. , Hill Davin , Schwantes-An Tae-Hwi , Lai Dongbing , Carroll Andrew , Hobbs Brian D. , Cho Michael H. , McLean Cory Y. , Hormozdiari Farhad 
Published: September 2022

AbstractChronic obstructive pulmonary disease (COPD), the third leading cause of death worldwide, is highly heritable. While COPD is clinically defined by applying thresholds to summary measures of lung function, a quantitative liability score has more power to identify new genetic signals. Here we train a deep convolutional neural network on noisy self-reported and ICD-based labels to predict COPD case/control status from high-dimensional raw spirograms and use the model predictions as a liability score. The machine-learning-based (ML-based) liability score accurately discriminates COPD cases and controls (AUROC = 0.82 ± 0.01) and COPD-related hospitalization (AUROC = 0.89 ± 0.01) without any domain-specific knowledge. Moreover, the ML-based liability score is associated with overall survival (Hazard ratio = 1.22 ± 0.01; P ≤ 2 × 10−16) and exacerbation events (R2 = 0.10 ± 0.01; P ≤ 4 × 10−101). A genome-wide association study on the ML-based liability score replicates existing COPD and lung function loci, but also identifies 67 new loci. Thirty-eight of these have supportive evidence in independent datasets, including a locus near LTBR. We demonstrate the biological plausibility of the novel variants through enrichment analyses, phenome-wide association studies, and generalizability of COPD prediction in multiple datasets. These results provide an example of the potential to improve genetic discovery of disease-relevant variants by training deep neural networks to predict noisy labels from high-dimensional raw data.
Style Transfer Generative Adversarial Networks to Harmonize Multi-Site MRI to a Single Reference Image to Avoid Over-Correction
Authors: Liu Mengting , Zhu Alyssa H. , Maiti Piyush , Thomopoulos Sophia I. , Gadewar Shruti , Chai Yaqiong , Kim Hosung , Jahanshad Neda ,   the Alzheimer’s Disease Neuroimaging Initiative
Published: September 2022

AbstractRecent work within neuroimaging consortia have aimed to identify reproducible, and often subtle, brain signatures of psychiatric or neurological conditions. To allow for high-powered brain imaging analyses, it is often necessary to pool MR images that were acquired with different protocols across multiple scanners. Current retrospective harmonization techniques have shown promise in removing cross-site image variation. However, most statistical approaches may over-correct for technical, scanning-related, variation as they cannot distinguish between confounded image-acquisition based variability and cross-site population variability. Such statistical methods often require that datasets contain subjects or patient groups with similar clinical or demographic information to isolate the acquisition-based variability. To overcome this limitation, we consider cross-site MRI image harmonization as a style transfer problem rather than a domain transfer problem. Using a fully unsupervised deep-learning framework based on a generative adversarial network (GAN), we show that MR images can be harmonized by inserting the style information encoded from a single reference image, without knowing their site/scanner labelsa priori. We trained our model using data from five large-scale multi-site datasets with varied demographics. Results demonstrated that our style-encoding model can harmonize MR images, and match intensity profiles, without relying on traveling subjects. This model also avoids the need to control for clinical, diagnostic, or demographic information. We highlight the effectiveness of our method for clinical research by comparing extracted cortical and subcortical features, brain-age estimates, and case-control effect sizes before and after the harmonization. We showed that our harmonization removed the cross-site variances, while preserving the anatomical information and clinical meaningful patterns. We further demonstrated that with a diverse training set, our method successfully harmonized MR images collected from unseen scanners and protocols, suggesting a promising novel tool for ongoing collaborative studies. Source code is released inUSC-IGC/style_transfer_harmonization (github.com).
B/T cell crosstalk and aberrant inflammatory IgG exacerbate autoimmune intestinal inflammation
Authors: Gadjalova Iana , Heinze Julia M. , Goess Marie Christine , Hofmann Julian , Albers Julian J. , Spallek Ria , Blissenbach Birgit , Buck Annalisa , Weber Marie-Christin , Scherer Emely , Kampick Maximilian , Öllinger Rupert , Krut Oleg , Rad Roland , Steiger Katja , Winter Christof , Janssen Klaus-Peter , Neumann Philipp-Alexander , Geha Raif S. , Ruland Jürgen , Keppler Selina J. 
Published: September 2022

ABSTRACTDysregulated B cell responses have been described in inflammatory-bowel disease (IBD) patients; however, the role of B cells in IBD pathology remained incompletely understood. We here described Wiskott-Aldrich Syndrome interacting protein deficient (Wipf1-/-) mice as novel mouse model of spontaneous, chronic colitis modelling human IBD. Concomitant with aberrant IgG production in colonic tissue of Wipf1-/- mice, we identified systemic, hypo-sialylated IgG as drivers of IL-1β production in monocytes. Pathological antibody production was promoted by the hyper-reactivity of Wipf1-/- B cells in response to LPS stimulation, resulting in efficient activation of the MAPK/Erk and mTOR/Akt/4E-BP1 pathways and heightened metabolic activity. In addition to abundant inflammatory IgG, we found that B cells directly promoted the production of pro-inflammatory cytokines by intestinal CD4+ T cells. B/T co-culture assays defined the co-stimulatory molecule CD86 as driver of IFN-γ and GM-CSF production by CD4+ T cells. CD86 expression was further enhanced by the presence of sCD40L, which was elevated in sera of Wipf1-/- mice. Similarly, colonic B cells of IBD patients expressed increased mRNA levels of CD86 correlating with enhanced levels of systemic sCD40L. Together, B cell-mediated pro-inflammatory cytokine secretion and B cell-derived inflammatory antibody production contributed to exacerbated pathogenesis during intestinal inflammation.
One Sentence SummaryB cells fuel intestinal inflammation