bluuebunny/crossref_metadata_2025_split huggingface dataset:
Modeling maize growth and nitrogen dynamics using CERES-Maize (DSSAT) under diverse nitrogen management options in a conservation agriculture-based maize-wheat system
Authors: Kumar Kamlesh , Parihar C. M. , Nayak H. S. , Sena D. R. , Godara Samarth , Dhakar Rajkumar , Patra Kiranmoy , Sarkar Ayan , Bharadwaj Sneha , Ghasal Prakash Chand , L.Meena A. , Reddy K. Srikanth , Das T. K. , Jat S. L. , Sharma D. K. , Saharawat Y. S. , Singh Upendra , Jat M. L. , Gathala M. K. 
Published: May 2024

AbstractAgricultural field experiments are costly and time-consuming, and often struggling to capture spatial and temporal variability. Mechanistic crop growth models offer a solution to understand intricate crop-soil-weather system, aiding farm-level management decisions throughout the growing season. The objective of this study was to calibrate and the Crop Environment Resource Synthesis CERES-Maize (DSSAT v 4.8) model to simulate crop growth, yield, and nitrogen dynamics in a long-term conservation agriculture (CA) based maize system. The model was also used to investigate the relationship between, temperature, nitrate and ammoniacal concentration in soil, and nitrogen uptake by the crop. Additionally, the study explored the impact of contrasting tillage practices and fertilizer nitrogen management options on maize yields. Using field data from 2019 and 2020, the DSSAT-CERES-Maize model was calibrated for plant growth stages, leaf area index-LAI, biomass, and yield. Data from 2021 were used to evaluate the model's performance. The treatments consisted of four nitrogen management options, viz., N0 (without nitrogen), N150 (150 kg N/ha through urea), GS (Green seeker-based urea application) and USG (urea super granules @150kg N/ha) in two contrasting tillage systems, i.e., CA-based zero tillage-ZT and conventional tillage-CT. The model accurately simulated maize cultivar’s anthesis and physiological maturity, with observed value falling within 5% of the model’s predictions range. LAI predictions by the model aligned well with measured values (RMSE 0.57 and nRMSE 10.33%), with a 14.6% prediction error at 60 days. The simulated grain yields generally matched with measured values (with prediction error ranging from 0 to 3%), except for plots without nitrogen application, where the model overestimated yields by 9–16%. The study also demonstrated the model's ability to accurately capture soil nitrate–N levels (RMSE 12.63 kg/ha and nRMSE 12.84%). The study concludes that the DSSAT-CERES-Maize model accurately assessed the impacts of tillage and nitrogen management practices on maize crop’s growth, yield, and soil nitrogen dynamics. By providing reliable simulations during the growing season, this modelling approach can facilitate better planning and more efficient resource management. Future research should focus on expanding the model's capabilities and improving its predictions further.
Understanding the impacts of coastal deoxygenation in nitrogen dynamics: an observational analysis
Authors: Farias Laura , de la Maza Lucas 
Published: May 2024

AbstractBiological production and outgassing of greenhouse gasses (GHG) in Eastern Boundary Upwelling Systems (EBUS) are vital for fishing productivity and climate regulation. This study examines temporal variability of biogeochemical and oceanographic variables, focusing on dissolved oxygen (DO), nitrate, nitrogen deficit (N deficit), nitrous oxide (N2O) and air-sea N2O flux. This analysis is based on monthly observations from 2000 to 2023 in a region of intense seasonal coastal upwelling off central Chile (36°S). Strong correlations are estimated among N2O concentrations and N deficit in the 30–80 m layer, and N2O air-sea fluxes with the proportion of hypoxic water (4 < DO < 89 µmol L−1) in the water column, suggesting that N2O accumulation and its exchange are mainly associated with partial denitrification. Furthermore, we observe interannual variability in concentrations and inventories in the water column of DO, nitrate, N deficit, as well as air-sea N2O fluxes in both downwelling and upwelling seasons. These variabilities are not associated with El Niño-Southern Oscillation (ENSO) indices but are related to interannual differences in upwelling intensity. The time series reveals significant nitrate removal and N2O accumulation in both mid and bottom layers, occurring at rates of 1.5 µmol L−1 and 2.9 nmol L−1 per decade, respectively. Particularly significant is the increase over the past two decades of air-sea N2O fluxes at a rate of 2.9 µmol m−2 d−1 per decade. These observations suggest that changes in the EBUS, such as intensification of upwelling and the prevalence of hypoxic waters may have implications for N2O emissions and fixed nitrogen loss, potentially influencing coastal productivity and climate.
Partial discharge localization in power transformer tanks using machine learning methods
Authors: Khodaveisi Farzin , Karami Hamidreza , Karimpour Matin Zarei , Rubinstein Marcos , Rachidi Farhad 
Published: May 2024

AbstractThis paper presents a comparison of machine learning (ML) methods used for three-dimensional localization of partial discharges (PD) in a power transformer tank. The study examines ML and deep learning (DL) methods, ranging from support vector machines (SVM) to more complex approaches like convolutional neural networks (CNN). Multiple case studies are considered, each with different attributes, including sensor position, frequency content of the PD signal, and size of the transformer tank. The paper focuses on predicting the PD location in three-dimensional space using single-sensor electric field measurements. Various aspects of each method are analyzed, such as the input signal, core methodology, correlation coefficient between the predicted location and the actual location, and root mean square error (RMSE). These features are discussed and compared across the different methods. The results indicate that the CNN model exhibits superior performance in terms of location accuracy among the methods considered.
Efficacy and safety of vitamin C supplementation in the treatment of community-acquired pneumonia: a systematic review and meta-analysis with trial sequential analysis
Authors: Sharma Yogesh , Sumanadasa Subodha , Shahi Rashmi , Woodman Richard , Mangoni Arduino A. , Bihari Shailesh , Thompson Campbell 
Published: May 2024

AbstractCommunity-acquired pneumonia (CAP) poses a significant global health challenge, prompting exploration of innovative treatments. This systematic review and meta-analysis aimed to evaluate the efficacy and safety of vitamin C supplementation in adults undergoing treatment for CAP. A comprehensive search of the MEDLINE, Embase, CINAHL, the Cochrane Central Register of Controlled Trials, and Clinical Trials.gov databases from inception to 17 November 2023 identified six randomized-controlled-trials (RCTs) meeting inclusion criteria. The primary outcome analysis revealed a non-significant trend towards reduced overall mortality in the vitamin C group compared to controls (RR 0.51; 95% CI 0.24 to 1.09; p = 0.052; I2 = 0; p = 0.65). Sensitivity analysis, excluding corona-virus-disease 2019 (COVID-19) studies and considering the route of vitamin C administration, confirmed this trend. Secondary outcomes, including hospital length-of-stay (LOS), intensive-care-unit (ICU) LOS, and mechanical ventilation, exhibited mixed results. Notably, heterogeneity and publication bias were observed in hospital LOS analysis, necessitating cautious interpretation. Adverse effects were minimal, with isolated incidents of nausea, vomiting, hypotension, and tachycardia reported. This meta-analysis suggests potential benefits of vitamin C supplementation in CAP treatment. However, inconclusive findings and methodological limitations warrants cautious interpretation, emphasising the urgency for high-quality trials to elucidate the true impact of vitamin C supplementation in CAP management.
Absence of sympathetic innervation hampers the generation of tertiary lymphoid structures upon acute lung inflammation
Authors: Riffard Clémence , Letaïef Laïla , Azar Safa , Casrouge Armanda , Brunet Isabelle , Teillaud Jean-Luc , Dieu-Nosjean Marie-Caroline 
Published: May 2024

AbstractTertiary lymphoid structures (TLS) are lymphoid organs present in inflammatory non-lymphoid tissues. Studies have linked TLS to favorable outcomes for patients with cancers or infectious diseases, but the mechanisms underlying their formation are not fully understood. In particular, secondary lymphoid organs innervation raises the question of sympathetic nerve fibers involvement in TLS organogenesis. We established a model of pulmonary inflammation based on 5 daily intranasal instillations of lipopolysaccharide (LPS) in immunocompetent mice. In this setting, lung lymphoid aggregates formed transiently, evolving toward mature TLS and disappearing when inflammation resolved. Sympathetic nerve fibers were then depleted using 6-hydroxydopamine. TLS quantification by immunohistochemistry showed a decrease in LPS-induced TLS number and surface in denervated mouse lungs. Although a reduction in alveolar space was observed, it did not impair overall pulmonary content of transcripts encoding TNF-α, IL-1β and IFN-γ inflammation molecules whose expression was induced by LPS instillations. Immunofluorescence analysis of immune infiltrates in lungs of LPS-treated mice showed a drop in the proportion of CD23+ naive cells among CD19+ B220+ B cells in denervated mice whereas the proportion of other cell subsets remained unchanged. These data support the existence of neuroimmune crosstalk impacting lung TLS neogenesis and local naive B cell pool.
The association between three prevalent autoimmune disorders and the likelihood of developing prostate cancer: a Mendelian randomization study
Authors: Deng Xiaoqian , Sun Shiwei , Yao Wei , Yue Peng , Guo Fuyu , Wang Yue , Zhang Yangang 
Published: May 2024

AbstractNumerous studies establish a significant correlation between autoimmune disorders (AIDs) and prostate cancer (PCa). Our Mendelian randomization (MR) analysis investigates the potential connection between rheumatoid arthritis (RA) and PCa, aiming to confirm causal links between systemic lupus erythematosus (SLE), hyperthyroidism, and PCa. Summary statistics from genome-wide association studies provided data on PCa and three AIDs. MR analysis, using IVW as the main approach, assessed causal relationships, validated by sensitivity analysis. IVW revealed a correlation between genetically anticipated RA and PCa, notably in Europeans (OR = 1.03; 95% CI 1.01–1.04, p = 2*10−5). Evidence supported a lower PCa risk in individuals with SLE (OR = 0.94; 95% CI 0.91–0.97, p = 2*10−4) and hyperthyroidism (OR = 0.02; 95% CI 0.001–0.2, p = 2*10−3). Weighted mode and median confirmed these findings. No pleiotropic effects were observed, and MR heterogeneity tests indicated dataset homogeneity. Our study establishes a causal link between RA, SLE, hyperthyroidism, and PCa.
Association between life’s essential 8 and overactive bladder
Authors: Feng Guoliang , Huang Shaoqun , Zhao Weimin , Gong Hongyang 
Published: May 2024

AbstractLimited research has explored the relationship between overactive bladder (OAB) and cardiovascular health (CVH). We aim to examine the correlation between OAB and CVH in American adults, utilizing the Life’s Essential 8 (LE8). We included 70,190 individuals from the National Health and Nutrition Examination Survey spanning from 2005 to 2018. In our study, the independent variable is LE8 score, where higher scores denote better CVH. The dependent variable is the presence of OAB. We employed multivariable logistic regression along with restricted cubic splines to evaluate the association between LE8 and OAB. Additionally, we performed interaction analyzes on subgroups to validate the findings. There is a significant negative correlation between LE8 scores and OAB. Upon adjusting for all covariates, a 10-point increase in LE8 total score correlated with a 17% decrease in the odds of OAB [0.83 (95% CI 0.78, 0.89)]. Compared to participants with lower LE8 scores, those with higher LE8 scores had a 46% lower probability of developing OAB. Consistent results were also observed in the association between scores of four health behaviors and four health factors and OAB. Furthermore, a notable interaction was observed between LE8 scores and smoking status. This study showed a significant negative correlation between LE8 scores and OAB prevalence.
MieAI: a neural network for calculating optical properties of internally mixed aerosol in atmospheric models
Authors: Kumar Pankaj , Vogel Heike , Bruckert Julia , Muth Lisa Janina , Hoshyaripour Gholam Ali 
Published: May 2024

AbstractAerosols influence weather and climate by interacting with radiation through absorption and scattering. These effects heavily rely on the optical properties of aerosols, which are mainly governed by attributes such as morphology, size distribution, and chemical composition. These attributes undergo continuous changes due to chemical reactions and aerosol micro-physics, resulting in significant spatio-temporal variations. Most atmospheric models struggle to incorporate this variability because they use pre-calculated tables to handle aerosol optics. This offline approach often leads to substantial errors in estimating the radiative impacts of aerosols along with posing significant computational burdens. To address this challenge, we introduce a computationally efficient and robust machine learning approach called MieAI. It allows for relatively inexpensive calculation of the optical properties of internally mixed aerosols with a log-normal size distribution. Importantly, MieAI fully incorporates the variability in aerosol chemistry and microphysics. Our evaluation of MieAI against traditional Mie calculations, using number concentrations from the ICOsahedral Nonhydrostatic model with Aerosol and Reactive Trace gases (ICON-ART) simulations, demonstrates that MieAI exhibits excellent predictive accuracy for aerosol optical properties. MieAI achieves this with errors well within 10%, and it operates more than 1000 times faster than the benchmark approach of Mie calculations. Due to its generalized nature, the MieAI approach can be implemented in any chemistry transport model which represents aerosol size distribution in the form of log-normally distributed internally mixed modes. This advancement has the potential to replace frequently employed look-up tables and plays a substantial role in the ongoing attempts to reduce uncertainties in estimating aerosol radiative forcing.
Basin-dependent response of Northern Hemisphere winter blocking frequency to CO2 removal
Authors: Hwang Jaeyoung , Son Seok-Woo , Martineau Patrick , Sung Mi-Kyung , Barriopedro David , An Soon-Il , Yeh Sang-Wook , Min Seung-Ki , Kug Jong-Seong , Shin Jongsoo 
Published: May 2024

AbstractAtmospheric blocking has been identified as one of the key elements of the extratropical atmospheric variabilities, controlling extreme weather events in mid-latitudes. Future projections indicate that Northern Hemisphere winter blocking frequency may decrease as CO2 concentrations increase. Here, we show that such changes may not be reversed when CO2 concentrations return to the current levels. Blocking frequency instead exhibits basin-dependent changes in response to CO2 removal. While the North Atlantic blocking frequency recovers gradually from the CO2-induced eastward shift, the North Pacific blocking frequency under the CO2 removal remains lower than its initial state. These basin-dependent blocking frequency changes result from background flow changes and their interactions with high-frequency eddies. Both high-frequency eddy and background flow changes determine North Atlantic blocking changes, whereas high-frequency eddy changes dominate the slow recovery of North Pacific blocking. Our results indicate that blocking-related extreme events in the Northern Hemisphere winter may not monotonically respond to CO2 removal.
Early adverse physiological event detection using commercial wearables: challenges and opportunities
Authors: Phipps Jesse , Passage Bryant , Sel Kaan , Martinez Jonathan , Saadat Milad , Koker Teddy , Damaso Natalie , Davis Shakti , Palmer Jeffrey , Claypool Kajal , Kiley Christopher , Pettigrew Roderic I. , Jafari Roozbeh 
Published: May 2024

AbstractData from commercial off-the-shelf (COTS) wearables leveraged with machine learning algorithms provide an unprecedented potential for the early detection of adverse physiological events. However, several challenges inhibit this potential, including (1) heterogeneity among and within participants that make scaling detection algorithms to a general population less precise, (2) confounders that lead to incorrect assumptions regarding a participant’s healthy state, (3) noise in the data at the sensor level that limits the sensitivity of detection algorithms, and (4) imprecision in self-reported labels that misrepresent the true data values associated with a given physiological event. The goal of this study was two-fold: (1) to characterize the performance of such algorithms in the presence of these challenges and provide insights to researchers on limitations and opportunities, and (2) to subsequently devise algorithms to address each challenge and offer insights on future opportunities for advancement. Our proposed algorithms include techniques that build on determining suitable baselines for each participant to capture important physiological changes and label correction techniques as it pertains to participant-reported identifiers. Our work is validated on potentially one of the largest datasets available, obtained with 8000+ participants and 1.3+ million hours of wearable data captured from Oura smart rings. Leveraging this extensive dataset, we achieve pre-symptomatic detection of COVID-19 with a performance receiver operator characteristic (ROC) area under the curve (AUC) of 0.725 without correction techniques, 0.739 with baseline correction, 0.740 with baseline correction and label correction on the training set, and 0.777 with baseline correction and label correction on both the training and the test set. Using the same respective paradigms, we achieve ROC AUCs of 0.919, 0.938, 0.943 and 0.994 for the detection of self-reported fever, and 0.574, 0.611, 0.601, and 0.635 for detection of self-reported shortness of breath. These techniques offer improvements across almost all metrics and events, including PR AUC, sensitivity at 75% specificity, and precision at 75% recall. The ring allows continuous monitoring for detection of event onset, and we further demonstrate an improvement in the early detection of COVID-19 from an average of 3.5 days to an average of 4.1 days before a reported positive test result.
The integration of metabolic and proteomic data uncovers an augmentation of the sphingolipid biosynthesis pathway during T-cell differentiation
Authors: Kanno Toshio , Konno Ryo , Sato Masaru , Kurabayashi Atsushi , Miyako Keisuke , Nakajima Takahiro , Yokoyama Satoru , Sasamoto Shigemi , Asou Hikari K. , Ohzeki Junichiro , Hasegawa Yoshinori , Ikeda Kazutaka , Kawashima Yusuke , Ohara Osamu , Endo Yusuke 
Published: May 2024

AbstractRecent studies have highlighted the significance of cellular metabolism in the initiation of clonal expansion and effector differentiation of T cells. Upon exposure to antigens, naïve CD4+ T cells undergo metabolic reprogramming to meet their metabolic requirements. However, only few studies have simultaneously evaluated the changes in protein and metabolite levels during T cell differentiation. Our research seeks to fill the gap by conducting a comprehensive analysis of changes in levels of metabolites, including sugars, amino acids, intermediates of the TCA cycle, fatty acids, and lipids. By integrating metabolomics and proteomics data, we discovered that the quantity and composition of cellular lipids underwent significant changes in different effector Th cell subsets. Especially, we found that the sphingolipid biosynthesis pathway was commonly activated in Th1, Th2, Th17, and iTreg cells and that inhibition of this pathway led to the suppression of Th17 and iTreg cells differentiation. Additionally, we discovered that Th17 and iTreg cells enhance glycosphingolipid metabolism, and inhibition of this pathway also results in the suppression of Th17 and iTreg cell generation. These findings demonstrate that the utility of our combined metabolomics and proteomics analysis in furthering the understanding of metabolic transition during Th cell differentiation.
Potential roles of sex-linked differences in obesity and cancer immunotherapy: revisiting the obesity paradox
Authors: Vick Logan V. , Rosario Spencer , Riess Jonathan W. , Canter Robert J. , Mukherjee Sarbajit , Monjazeb Arta M. , Murphy William J. 
Published: May 2024

AbstractObesity, a condition of excess adiposity usually defined by a BMI > 30, can have profound effects on both metabolism and immunity, connecting the condition with a broad range of diseases, including cancer and negative outcomes. Obesity and cancer have been associated with increased incidence, progression, and poorer outcomes of multiple cancer types in part due to the pro-inflammatory state that arises. Surprisingly, obesity has also recently been demonstrated in both preclinical models and clinical outcomes to be associated with improved response to immune checkpoint inhibition (ICI). These observations have laid the foundation for what has been termed the “obesity paradox”. The mechanisms underlying these augmented immunotherapy responses are still unclear given the pleiotropic effects obesity exerts on cells and tissues. Other important variables such as age and sex are being examined as further affecting the obesity effect. Sex-linked factors exert significant influences on obesity biology, metabolism as well as differential effects of different immune cell-types. Age can be another confounding factor contributing to the effects on both sex-linked changes, immune status, and obesity. This review aims to revisit the current body of literature describing the immune and metabolic changes mediated by obesity, the role of obesity on cancer immunotherapy, and to highlight questions on how sex-linked differences may influence obesity and immunotherapy outcome.
Distributed angle‐only orbit determination algorithm for non‐cooperative spacecraft based on factor graph
Authors: Zhang Zhixun , Zhang Keke , Shu Leizheng , Zhu Zhencai , Zhou Meijiang 
Published: May 2024

AbstractBayesian filtering provides an effective approach for the orbit determination of a non‐cooperative target using angle measurements from multiple CubeSats. However, existing methods face challenges such as low reliability and limited estimation accuracy. Two distributed filtering algorithms based on factor graphs employed in the sub‐parent and distributed cluster spacecraft architectures are proposed. Two appropriate factor graphs representing different cluster spacecraft structures are designed and implement distributed Bayesian filtering within these models. The Gaussian messages transmitted between nodes and the probability distributions of variable nodes are calculated using the derived non‐linear Gaussian belief propagation algorithm. Gaussian messages propagate from the deputy spacecraft to the chief spacecraft in the sub‐parent spacecraft architecture, demonstrating that the estimation accuracy converges to the centralised extended Kalman filter (EKF). Simulation results indicate that the algorithm enhances system robustness in observation node failures without compromising accuracy. In the distributed spacecraft architecture, neighbouring spacecraft iteratively exchanges Gaussian messages. The accuracy of the algorithm can rapidly approach the centralised EKF, benefiting from the efficient and unbiased transmission of observational information. Compared to existing distributed consensus filtering algorithms, the proposed algorithm improves estimation accuracy and reduces the number of iterations needed to achieve consensus.