bluuebunny/crossref_metadata_2025_split huggingface dataset:
Exposure dynamics of Ross River virus in horses – Horses as potential sentinels (a One Health approach)
Authors: Yuen Nicholas K. Y. , Bielefeldt-Ohmann Helle , Coyle Mitchell P. , Henning Joerg 
Published: April 2024

Abstract
Ross River virus (RRV), the most medically and economically important arbovirus in Australia, has been the most prevalent arbovirus infections in humans for many years. Infected humans and horses often suffer similar clinical symptoms. We conducted a prospective longitudinal study over a 3.5-year period to investigate the exposure dynamics of RRV in three foal cohorts (n = 32) born in a subtropical region of South East Queensland, Australia, between 2020 and 2022. RRV-specific seroconversion was detected in 56% (n = 18) of foals with a median time to seroconversion, after waning of maternal antibodies, of 429 days (95% CI: 294–582). The median age at seroconversion was 69 weeks (95% CI: 53–57). Seroconversion events were only detected between December and March (Southern Hemisphere summer) over the entire study period. Cox proportion hazards regression analyses revealed that seroconversions were significantly (p < 0.05) associated with air temperature in the month of seroconversion. Time-lags in meteorological variables were not significantly (p > 0.05) associated with seroconversion, except for relative humidity (p = 0.036 at 2-month time-lag). This is in contrast to research results of RRV infection in humans, which peaked between March and May (Autumn) and with a 0–3 month time-lag for various meteorological risk factors. Therefore, horses may be suitable sentinels for monitoring active arbovirus circulation and could be used for early arbovirus outbreak detection in human populations.
Genetic dilated cardiomyopathy with inflammation in an infant that responded to immunosuppressive therapy evaluated using cardiovascular magnetic resonance
Authors: Shirozu Hiromitsu , Ishikawa Yuichi , Kan Nobuhiko 
Published: April 2024

AbstractCardiovascular magnetic resonance T1 and T2 mapping reflects inflammation, fibrosis, and myocardial oedema. However, its application in infants remains uncertain. Herein, we report a three-month-old boy with dilated cardiomyopathy successfully treated with steroids. Cardiovascular magnetic resonance was useful for diagnosis based on the elevated native T1, T2, and extracellular volume and evaluation of response to immunosuppressive therapy in infantile inflammatory dilated cardiomyopathy.
Cardiac MRI evaluation of aortic biophysical properties in paediatric Turner syndrome
Authors: Suntratonpipat Somjate , Pajunen Kiera , Rosolowsky Elizabeth , Escudero Carolina A. , Girgis Rose , Thompson Richard B. , Pagano Joseph J. , Tham Edythe B. 
Published: April 2024

AbstractBackground:Aortopathy in Turner syndrome is associated with aortic dilation, and the risk of dissection is increased when the aortic size index is ≥ 2–2.5 cm/m2. We evaluated the aortic biophysical properties in paediatric Turner syndrome using cardiac MRI to determine their relationship to aortic size index.Methods:Turner syndrome patients underwent cardiac MRI to evaluate ventricular function, aortic dimensions, and biophysical properties (aortic stiffness index, compliance, distensibility, pulse wave velocity, and aortic and left ventricular elastance). Spearman correlation examined correlations between these properties and aortic size index. Data was compared to 10 controls.Results:Of 25 Turner syndrome patients, median age 14.7 years (interquartile range: 11.0–16.8), height z score −2.7 (interquartile range: −2.92 – −1.54), 24% had a bicuspid aortic valve. Turner syndrome had increased diastolic blood pressure (p < 0.001) and decreased left ventricular end-diastolic (p < 0.001) and end-systolic (p = 0.002) volumes compared to controls. Median aortic size index was 1.81 cm/m2 (interquartile range: 1.45–2.1) and 7 had an aortic size index > 2 cm/m2. Aortic and left ventricular elastance were greater in Turner syndrome compared to controls (both p < 0.001). Increased aortic size index correlated with increased aortic elastance (r = 0.5, p = 0.01) and left ventricular elastance (r = 0.59, p = 0.002) but not aortic compliance. Higher ascending aortic areas were associated with increased aortic compliance (r = 0.44, p = 0.03) and left ventricular elastance (r = 0.49, p = 0.01).Conclusion:Paediatric Turner syndrome with similar aortic size index to controls showed MRI evidence of abnormal aortic biophysical properties. These findings point to an underlying aortopathy and provide additional parameters that may aid in determining risk factors for aortic dissection.
Food insecurity and perinatal depression among pregnant women in BUNMAP cohort in Ethiopia: a structural equation modelling
Authors: Biratu Abera , Alem Atalay , Medhin Girmay , Gebreyesus Seifu Hagos 
Published: April 2024

Abstract

Objective:
To assess the effect of food insecurity on perinatal depression in rural Ethiopia.

Design:
We used a prospective cohort in which food insecurity was considered as primary exposure and perinatal depression as an outcome. Food insecurity at baseline (in the period of 8–24 weeks of pregnancy) was measured using the Household Food Insecurity Access Scale (HFIAS), and perinatal depression at follow-up (in 32–36 weeks of pregnancy) was measured using a Patient Health Questionnaire (PHQ-9). We used multivariable regression to assess the effect of food insecurity on the prevalence of perinatal depression. We explored food insecurity’s direct and indirect impacts on perinatal depression using structural equation modelling (SEM).

Setting:
This paper used data from the Butajira Nutrition, Mental Health and Pregnancy (BUNMAP) cohort established under the Butajira Health and Demographic Surveillance Site (BHDSS).

Participants:
Seven hundred and fifty-five pregnant women.

Results:
Among the study participants, 50 % were food-insecure, and about one-third were depressed at 32–36 follow-up. In SEM, higher values of baseline food insecurity, depressive symptoms and state–trait anxiety (STA) were positively and significantly associated with perinatal depression. The direct impact of food insecurity on perinatal depression accounts for 42 % of the total effect, and the rest accounted for the indirect effect through baseline depression (42 %) and STA (16 %).

Conclusion:
The significant effect of food insecurity at baseline on perinatal depression and the indirect effect of baseline food insecurity through baseline anxiety and depression in the current study implies the importance of tailored interventions for pregnant women that consider food insecurity and psychosocial problems.
Fractionation Mechanism and Flux Estimation of Strontium Isotopes During Basalt Weathering
Authors: Luo Kai , Ma Jinlong , Wang Zhibing , Zhu Guanhong , Zeng Ti , Wei Gangjian 
Published: April 2024

AbstractThe fluxes of metal cations and isotopes released by weathering of silicate rocks are crucial and a prerequisite for constraining geochemical fluxes to rivers and oceans. This study presents mineral and elemental compositions along with 87Sr/86Sr and δ88Sr data from a basaltic weathering regolith on Hainan Island, South China to elucidate Sr isotope fractionation and weathering fluxes. The 87Sr/86Sr ratios vary from 0.703936 to 0.706338 as a result of differential weathering of the minerals. δ88Sr values in the weathering regolith range from −0.29 to 0.37‰, with the majority of the weathering regolith having lower δ88Sr values than the parent rock. Sr is leached into the soil solution during plagioclase decomposition, while 86Sr is preferentially adsorbed on the surface of secondary minerals. As weathering progresses, smectite decomposes and kaolinite desorbs under weakly acidic conditions, releasing the previously adsorbed 86Sr into the soil solution. The differential weathering of kaolinite and smectite controls the δ88Sr values of the weathering regolith, with pH being an important determinant of isotope fractionation. Furthermore, Sr elemental fluxes (SrFlux) and Sr isotopic fluxes (δ88SrFlux) of this weathering regolith were calculated using a mass balance model, yielding mean values of 0.20 (mg cm−3 Myr−1) and 0.052 (‰ (mg cm−3 Myr−1)), respectively. The δ88SrFlux exhibits a nonlinear positive correlation with the Chemical Index of Alteration (CIA), indicating that enhanced weathering leads to significant stable Sr isotope fractionation at CIA values below 95%. Our research promotes the understanding of Sr recycling and the fractionation behavior of stable Sr isotopes during consecutive weathering.
Lawsonite and Garnet Oxygen Isotope Record of Fluid‐Rock Interaction During Subduction
Authors: Kang Patricia , Martin Laure A. J. , Vitale Brovarone Alberto , Whitney Donna L. 
Published: April 2024

AbstractDuring the subduction of an oceanic plate, fluids are released from metabasaltic crust, metasediment, and serpentinite under high‐pressure/low‐temperature conditions. Although some fluids may eventually leave the slab, some participate in metamorphic reactions within the slab during subduction and exhumation. To identify fluid sources and other controls influencing mineral composition, we report the in situ‐measured δ18O of lawsonite and garnet in blueschist‐ to eclogite‐facies rocks from 10 subduction zones that represent various field settings, including mélanges, structurally coherent terranes, and an eclogite xenolith derived from a subducted plate. Lawsonite records distinct δ18O depending on the host rock type and other rock types that were fluid sources during lawsonite growth. In general, lawsonite in metabasalt (7.6 ± 0.2–14.8 ± 1.1‰) is isotopically lighter than in metasediment (20.6 ± 1.4–24.1 ± 1.3‰) but heavier than in metagabbro (4.0 ± 0.4–7.9 ± 0.3‰). The extent of δ18O fractionation was evaluated for lawsonite–fluid and lawsonite–garnet pairs as a function of temperature (T). Results demonstrate that variations of >1.7‰ in lawsonite and >0.9‰ in garnet are not related to changing T. More likely, the relative contributions of fluids derived from isotopically heavier lithologies (e.g., sediments) versus lighter lithologies (e.g., ultramafic rocks) are the major control. Monte Carlo simulations were performed to investigate the sources of metasomatic fluids and the water/rock ratio that formed lawsonite‐bearing metasomatite. Results indicate that δ18OLws and δ18OGrt record interactions with fluids sourced from diverse lithologies (sediment, serpentinite), further supporting that δ18OLws is a useful indicator of subduction fluid‐rock interactions.
Why Moist Dynamic Processes Matter for the Sub‐Seasonal Prediction of Atmospheric Blocking Over Europe
Authors: Wandel Jan , Büeler Dominik , Knippertz Peter , Quinting Julian F. , Grams Christian M. 
Published: April 2024

AbstractIn recent years, there has been growing evidence that latent heat release in midlatitude weather systems such as warm conveyor belts (WCBs) contributes significantly to the onset and maintenance of blocking anticyclones (blocked weather regimes). Still, numerical weather prediction (NWP) and climate models struggle to correctly predict and represent atmospheric blocking in particular over Europe. Here, we elucidate the representation of WCB activity in 20 years of extended winter (1997–2017) of European Centre for Medium‐Range Weather Forecast's IFS reforecasts around the onset of blocking over Europe (EuBL) employing different perspectives. First, we show that the model struggles to predict EuBL onsets already at 10–14 days lead time in line with a misrepresentation of WCB activity in the ensemble mean. However, we also find cases with accurate EuBL forecasts even in pentad 4 (15–19 days). This subset of successful forecasts at extended‐range lead times goes in line with accurate WCB forecasts over the North Atlantic several days prior to the blocking onset. Second, investigating the time‐lagged relationship of blocking onset and WCB activity, we find that WCB activity over the North Atlantic emerges well prior to the onset of the block and that different pathways into EuBL exist in the reforecasts compared to reanalysis. Finally, we find indication of predictability associated with a Rossby wave train emerging from the North Pacific. Although our study can not disentangle the roles of intrinsic predictability limits and model deficiencies, we show that correct predictions of EuBL go along with distinct patterns of WCB activity.
Turbulence Closure With Small, Local Neural Networks: Forced Two‐Dimensional and β‐Plane Flows
Authors: Srinivasan Kaushik , Chekroun Mickaël D. , McWilliams James C. 
Published: April 2024

AbstractWe parameterize sub‐grid scale (SGS) fluxes in sinusoidally forced two‐dimensional turbulence on the β‐plane at high Reynolds numbers (Re ∼25,000) using simple 2‐layer convolutional neural networks (CNN) having only O(1000) parameters, two orders of magnitude smaller than recent studies employing deeper CNNs with 8–10 layers; we obtain stable, accurate, and long‐term online or a posteriori solutions at 16× downscaling factors. Our methodology significantly improves training efficiency and speed of online large eddy simulations runs, while offering insights into the physics of closure in such turbulent flows. Our approach benefits from extensive hyperparameter searching in learning rate and weight decay coefficient space, as well as the use of cyclical learning rate annealing, which leads to more robust and accurate online solutions compared to fixed learning rates. Our CNNs use either the coarse velocity or the vorticity and strain fields as inputs, and output the two components of the deviatoric stress tensor, Sd. We minimize a loss between the SGS vorticity flux divergence (computed from the high‐resolution solver) and that obtained from the CNN‐modeled Sd, without requiring energy or enstrophy preserving constraints. The success of shallow CNNs in accurately parameterizing this class of turbulent flows implies that the SGS stresses have a weak non‐local dependence on coarse fields; it also aligns with our physical conception that small‐scales are locally controlled by larger scales such as vortices and their strained filaments. Furthermore, 2‐layer CNN‐parameterizations are more likely to be interpretable.
Spatial Dispersion and Statistical Description of Organized Cumulus Cloud Ensembles in Radiative Convective Equilibrium
Authors: Savre J. 
Published: April 2024

AbstractDescriptions of cloud ensembles in radiative convective equilibrium (RCE) rely mostly on the assumption that clouds are randomly distributed in space, a hypothesis that obviously fails in presence of strong organization. In this work, idealized RCE simulations at horizontal grid spacings ranging from 2 km to 125 m are analyzed, displaying a transition between complete randomness at coarse resolution to a high degree of cold pool driven organization at high resolution. The objective is then twofold: (a) characterizing the spatial cloud patterns focusing on correlation scales and association with covariates; (b) providing a statistical description of organized cloud ensembles to generalize existing theories based on complete randomness. It is demonstrated that organized cloud ensembles may be treated as heterogeneous point processes where individual clouds do not interact substantially with their neighbors. In other words, clouds may be considered as randomly distributed if we restrict the space to regions of high cloud density. In addition, it is shown that in highly organized situations local cloud counts may be modeled using negative binomial distributions, while cloud mass fluxes follow lognormal distributions. The total mass flux within regions of varying sizes can then be predicted by compounding the two aforementioned distributions. The total mass flux variability at scales ≲5 km is dominated by the heavy‐tails of the mass flux distributions, whereas at scales ≳5 km, it is entirely controlled by the spatial cloud count variability (the spatial cloud pattern). These results have important implications for the parameterization of organized convection in quasi‐equilibrium.
Enhancing Urban Climate‐Energy Modeling in the Community Earth System Model (CESM) Through Explicit Representation of Urban Air‐Conditioning Adoption
Authors: Li Xinchang “Cathy” , Zhao Lei , Oleson Keith , Zhou Yuyu , Qin Yue , Zhang Keer , Fang Bowen 
Published: April 2024

AbstractImproved representation of urban processes in Earth System Models (ESMs) is a pressing need for climate modeling and climate‐driven urban energy studies. Despite recent improvements to its fully coupled Building Energy Model (BEM), the current Community Land Model Urban (CLMU) in the Community Earth System Model (CESM) lacks the infrastructure to model air‐conditioning (AC) adoption explicitly. This undermines CESM's fidelity in modeling urban climate and energy use, and limits its use in climate and energy risk assessments. Here, we establish a new parameterization scheme in CESM that represents AC adoption explicitly through an AC adoption rate parameter in the BEM of CLMU, and build a present‐day, global, survey‐based, and spatially explicit AC adoption rate data set at country and sub‐country level that is integrated within CESM. The new data set can be leveraged for other ESMs or global‐scale models and analyses. The explicit AC adoption scheme and the AC adoption rate data set significantly improve the accuracy of anthropogenic heat modeling due to AC in CESM. The new parameterization scheme makes it possible to evaluate the effects of changing AC adoption on global urban energy and climate using CESM. These developments enhance CESM in its use for climate impact assessments under future climate and socioeconomic development scenarios, and represent continued efforts in better representing urban processes and coupled human‐urban‐Earth dynamics in ESMs.
Seasonal and Species‐Level Water‐Use Strategies and Groundwater Dependence in Dryland Riparian Woodlands During Extreme Drought
Authors: Williams Jared , Stella John C. , Singer Michael Bliss , Lambert Adam M. , Voelker Steven L. , Drake John E. , Friedman Jonathan M. , Pelletier Lissa , Kui Li , Roberts Dar A. 
Published: April 2024

AbstractDrought‐induced groundwater decline and warming associated with climate change are primary threats to dryland riparian woodlands. We used the extreme 2012–2019 drought in southern California as a natural experiment to assess how differences in water‐use strategies and groundwater dependence may influence the drought susceptibility of dryland riparian tree species with overlapping distributions. We analyzed tree‐ring stable carbon and oxygen isotopes collected from two cottonwood species (Populus trichocarpa and P. fremontii) along the semi‐arid Santa Clara River. We also modeled tree source water δ18O composition to compare with observed source water δ18O within the floodplain to infer patterns of groundwater reliance. Our results suggest that both species functioned as facultative phreatophytes that used shallow soil moisture when available but ultimately relied on groundwater to maintain physiological function during drought. We also observed apparent species differences in water‐use strategies and groundwater dependence related to their regional distributions. P. fremontii was constrained to more arid river segments and ostensibly used a greater proportion of groundwater to satisfy higher evaporative demand. P. fremontii maintained ∆13C at pre‐drought levels up until the peak of the drought, when trees experienced a precipitous decline in ∆13C. This response pattern suggests that trees prioritized maintaining photosynthetic processes over hydraulic safety, until a critical point. In contrast, P. trichocarpa showed a more gradual and sustained reduction in ∆13C, indicating that drought conditions induced stomatal closure and higher water use efficiency. This strategy may confer drought avoidance for P. trichocarpa while increasing its susceptibility to anticipated climate warming.
Distributed Hydrological Modeling With Physics‐Encoded Deep Learning: A General Framework and Its Application in the Amazon
Authors: Wang Chao , Jiang Shijie , Zheng Yi , Han Feng , Kumar Rohini , Rakovec Oldrich , Li Siqi 
Published: April 2024

AbstractWhile deep learning (DL) models exhibit superior simulation accuracy over traditional distributed hydrological models (DHMs), their main limitations lie in opacity and the absence of underlying physical mechanisms. The pursuit of synergies between DL and DHMs is an engaging research domain, yet a definitive roadmap remains elusive. In this study, a novel framework that seamlessly integrates a process‐based hydrological model encoded as a neural network (NN), an additional NN for mapping spatially distributed and physically meaningful parameters from watershed attributes, and NN‐based replacement models representing inadequately understood processes is developed. Multi‐source observations are used as training data, and the framework is fully differentiable, enabling fast parameter tuning by backpropagation. A hybrid DL model of the Amazon Basin (∼6 × 106 km2) was established based on the framework, and HydroPy, a global‐scale DHM, was encoded as its physical backbone. Trained simultaneously with streamflow observations and Gravity Recovery and Climate Experiment satellite data, the hybrid model yielded median Nash‐Sutcliffe efficiencies of 0.83 and 0.77 for dynamic and distributed simulations of streamflow and total water storage, respectively, 41% and 35% higher than those of the original HydroPy model. Replacing the original Penman‒Monteith formulation in HydroPy with a replacement NN produces more plausible potential evapotranspiration (PET) estimates, and unravels the spatial pattern of PET in this giant basin. The NN used for parameterization was interpreted to identify the factors controlling the spatial variability in key parameters. Overall, this study lays out a feasible technical roadmap for distributed hydrological modeling in the big data era.