bluuebunny/crossref_metadata_2025_split huggingface dataset:
Modeling the Impact of Secondary Ice Production on the Charge Structure of a Mesoscale Convective System
Authors: Huang Shiye , Jing Xiaoqin , Yang Jing , Zhang Qilin , Guo Fengxia , Wang Zhien , Chen Baojun 
Published: April 2024

AbstractThe charge structure in thunderstorms may be strongly affected by different secondary ice production (SIP) processes, but has not been well understood. In this study, the impacts of three SIP mechanisms on microphysics and electrification in a squall line are investigated using model simulation, including the rime‐splintering, ice‐ice collisional breakup, and shattering of freezing drops. The parameterization of the three SIP mechanisms, a noninductive and an inductive charging parameterization are implemented in the spectral bin microphysics. The results show that with SIP processes included, the modeled radar reflectivity is more consistent with observation. It is found that both the mass and concentrations of graupel/hail are enhanced by SIP processes, while the diameter decreases. The mixing ratio of ice/snow decreases due to the rime‐splintering, and increases in mixing ratio are due to the shattering of freezing drops. Particle charging is significantly affected by SIP, leading to a dipole structure of the total charge density, which includes a lower negative and an upper positive charge region. With both the noninductive and inductive charging considered, the charge carried by graupel/hail changes from negative to a bipolar structure, and the charge sign carried by ice/snow is inverted due to the SIP. The modeled lightning activity is enhanced by implementing all three SIP processes, while if only considering the rime‐splintering process, the flash rate would be suppressed. The insights obtained from this study highlight the importance of considering different mechanisms of SIP in modeling the charge structure and lightning activity in thunderstorms.
Surface and Atmospheric Heating Responses to Spectrally Resolved Albedo of Frozen and Liquid Water Surfaces
Authors: Tolento Juan P. , Zender Charles S. , Whicker‐Clarke Chloe A. 
Published: April 2024

AbstractMultiple Earth system models (ESMs) discretize surface albedo into two semi‐broadbands comprising the UV/visible and near‐infrared (NIR) wavelengths. Here, we use an offline single‐column radiative transfer model to investigate the radiative effects of spectrally resolving the surface albedo. We use the Snow, Ice, and Aerosol Radiative model, extended to simulate liquid water, to calculate snow, ice, and liquid water albedo. We flux‐weight the hyperspectral albedo into the coarser spectral bands used by the atmospheric shortwave radiative transfer model. We establish representative atmospheric profiles for the three surface types and compare their shortwave fluxes and atmospheric warming rates with the spectrally resolved albedo to those calculated with the semi‐broadband approximation. Spectrally resolved surface albedo over snow and ice reduces atmospheric warming by darkening the albedo of NIR bands, correcting the too‐strong surface absorption in visible bands, and too‐weak surface absorption in shortwave infrared bands caused by the semi‐broadband approximation. We explore the effects on surface and atmospheric warming rates of varying solar zenith angle, cloud cover, relative humidity, and snow grain/air bubble radii. The semi‐broadband albedo biases can exceed 10% and 2% for the surface and atmospheric net flux respectively, being particularly strong under conditions which alter the distributions of surface insolation (i.e., cloud cover or increased atmospheric water vapor). These results show that transmitting a higher resolution spectral radiation field between the atmosphere and surface reduces biases in surface absorption and atmospheric heating present in ESMs that currently use the semi‐broadband approximation.
Reconstructing Balloon‐Observed Gravity Wave Momentum Fluxes Using Machine Learning and Input From ERA5
Authors: Has Sothea , Plougonven Riwal , Fischer Aurélie , Rani Raj , Lott Francois , Hertzog Albert , Podglajen Aurélien , Corcos Milena 
Published: April 2024

AbstractGlobal atmospheric models rely on parameterizations to capture the effects of gravity waves (GWs) on middle atmosphere circulation. As they propagate upwards from the troposphere, the momentum fluxes associated with these waves represent a crucial yet insufficiently constrained component. The present study employs three tree‐based ensemble machine learning (ML) techniques to probe the relationship between large‐scale flow and small‐scale GWs within the tropical lower stratosphere. The measurements collected by eight superpressure balloons from the Strateole 2 campaign, comprising a cumulative observation period of 680 days, provide valuable estimates of the gravity wave momentum fluxes (GWMFs). Multiple explanatory variables, including total precipitation, wind, and temperature, were interpolated from the ERA5 reanalysis at each balloon's location. The ML methods are trained on data from seven balloons and subsequently utilized to estimate reference GWMFs of the remaining balloon. We observed that parts of the GW signal are successfully reconstructed, with correlations typically around 0.54 and exceeding 0.70 for certain balloons. The models show significantly different performances from one balloon to another, whereas they show rather comparable performances for any given balloon. In other words, limitations from training data are a stronger constraint than the choice of the ML method. The most informative inputs generally include precipitation and winds near the balloons' level. However, different models highlight different informative variables, making physical interpretation uncertain. This study also discusses potential limitations, including the intermittent nature of GWMFs and data scarcity, providing insights into the challenges and opportunities for advancing our understanding of these atmospheric phenomena.
Solar Influences on Winter Atmospheric Circulation Over the Ural, North Atlantic and North Pacific Regions: Pitchfork‐Like Relationships
Authors: Li Dongdong , Zhang Renhe , Zhang Ruonan , Huang Jianping 
Published: April 2024

AbstractLi's previous study found a one‐to‐two correspondence relationship between the total solar irradiance (TSI) and monthly anomalies of the Ural atmospheric circulation during winter months. This relationship roughly meets the so‐called supercritical pitchfork bifurcation model, hence termed a pitchfork‐like (PL) relationship. Here, we extend this study and provide more evidence to support previous findings. By analyzing data from 192 winter months and estimating the joint probability density function, we find that over the Ural, North Atlantic, and North Pacific regions, there are all PL relationships between the TSI and geopotential height anomalies at pressure levels no higher than 700 hPa. When the TSI gradually decreases and passes a critical threshold of 1360.9 W m−2, the preferred circulation patterns undergo a regime transition from one regime to two others. When TSI < 1360.9 W m−2, positive and negative geopotential height anomalies occur at nearly equal frequencies within the same TSI range. This quantitative relation is met in almost the whole troposphere in each region. We suggest that the occurrence of PL relationships may be associated with the interaction between the westerly flow and topographic barriers; The decrease in TSI can regulate this interaction by reducing the stability of background westerly flow over all three regions, via increasing the land‐ocean thermal contrast and altering the planetary wavenumber‐2 wave at the mid‐high latitudes. Our work highlights the same quantitative relation between the TSI and the monthly mean geopotential height anomalies of three regions in winter, which has important implications for seasonal climate prediction.
Projections of Peak Water Timing From the East Rongbuk Glacier, Mt. Everest, Using a Higher‐Order Ice Flow Model
Authors: Zhang Tong , Wang Yuzhe , Leng Wei , Zhao Hongyu , Colgan Willliam , Wang Che , Ding Minghu , Sun Weijun , Yang Wei , Li Xin , Ren Jiawen , Xiao Cunde 
Published: April 2024

AbstractIn this study, we apply a three‐dimensional (3D) thermomechanically coupled higher‐order ice flow model to simulate the East Rongbuk Glacier (ERG), Mt. Everest. We first diagnostically investigate its present‐day ice dynamic features in 2009 and then prognostically simulate the glacier during the time period 2010–2100. The ice flow model is initialized based on a Robin‐type inversion method by conducting six sensitivity experiments relating to glacier thermal boundary conditions. We apply two different surface mass balance parameterizations in the model, and both of them can reproduce the observed ice volume loss (around 0.1 km3) during 2010–2020. We find that ERG is likely to experience maximum meltwater runoff at the year 2030 under the SSP‐126 scenario, while under SSP‐370 and ‐585 scenarios, the peak water will both likely occur at around 2060. The ice dynamics may contribute more to ice loss as climate warms in time.
Machine Learning Predicts Earthquakes in the Continuum Model of a Rate‐And‐State Fault With Frictional Heterogeneities
Authors: Norisugi Reiju , Kaneko Yoshihiro , Rouet‐Leduc Bertrand 
Published: April 2024

AbstractMachine learning (ML) has been used to study the predictability of laboratory earthquakes. However, the question remains whether or not this approach can be applied in a tectonic setting where one may have to rely on sparse earthquake catalogs, and where important timescales vary by orders of magnitude. Here, we apply ML to a synthetic seismicity catalog, generated by continuum models of a rate‐and‐state fault with frictional heterogeneities, which contains foreshocks, mainshocks, and aftershocks that nucleate in a similar manner. We develop a network representation of the seismicity catalog to calculate input features and find that the trained ML model can predict the time‐to‐mainshock with great accuracy, from the scale of decades to minutes. Our results offer clues as to why ML can predict laboratory earthquakes and how the developed approach could be applied to more complex problems where multiple timescales are at play.
In-silico prediction of dislodgeable foliar residues and regulatory implications for plant protection products
Authors: Shi Yi , Choudhury Kanak , Sopko Xiaoyi , Adham Sarah , Chikwana Edward 
Published: April 2024

Abstract
Background
When experimentally determined dislodgeable foliar residue (DFR) values are not available, regulatory agencies use conservative default DFR values as a first-tier approach to assess post-application dermal exposures to plant protection products (PPPs). These default values are based on a limited set of field studies, are very conservative, and potentially overestimate exposures from DFRs.

Objective
Use Random Forest to develop classification and regression-type ensemble models to predict DFR values after last application (DFR0) by considering experimentally-based variability due to differences in physical and chemical properties of PPPs, agronomic practices, crop type, and climatic conditions.

Methods
Random Forest algorithm was used to develop in-silico ensemble DFR0 prediction models using more than 100 DFR studies from Corteva AgriscienceTM. Several variables related to the active ingredient (a.i.) that was applied, crop, and climate conditions at the time of last application were considered as model parameters.

Results
The proposed ensemble models demonstrated 98% prediction accuracy that if a DFR0 is predicted to be less than the European Food Safety Authority (EFSA) default DFR0 value of 3 µg/cm2/kg a.i./ha, it is highly indicative that the measured DFR value will be less than the default if the study is conducted. If a value is predicted to be larger than or equal to the EFSA default, the model has an 83% prediction accuracy.

Impact statement
This manuscript is expected to have significant impact globally as it provides:

A framework for incorporating in silico DFR data into worker exposure assessment,

A roadmap for a tiered approach for conducting re-entry exposure assessment, and

A proof of concept for using existing DFR data to provide a read-across framework that can easily be harmonized across all regulatory agencies to provide more robust assessments for PPP exposures.
Direct breastfeeding frequency of late preterm and term infants in the neonatal intensive care unit and availability of mother’s own milk at six months of age. a retrospective cohort study
Authors: Smith Amanda E , Sweigart Erin , Falatic Kimberly , Stuart Dena , Szugye Heidi , Lam Suet Kam , Aly Hany , Das Anirudha 
Published: April 2024

Abstract
Background
Mother’s Own Milk (MOM) reduces the risk of complications in premature infants. Breastfeeding rates for late preterm and term infants in the neonatal intensive care unit (NICU) are significantly lower than that of breastfed healthy term newborns at 6 months of age.

Design
This was a retrospective cohort study of neonates born at 34 weeks 0 days or later. Infants who were directly breastfed in the NICU and were discharged on breast milk were included. Logistic regression modeling was used to determine the significance of association.

Results
171 mother-infant dyads were included. After adjusting for confounders, the number of breastfeeding attempts during the NICU stay was significantly associated with the availability of MOM at six months of age (p = 0.003, 95% CI 1.02 to 1.14).

Conclusion
This study is the first to show an association between the number of direct breastfeeding attempts in the NICU and availability of MOM at six months of age.
Long COVID: plasma levels of neurofilament light chain in mild COVID-19 patients with neurocognitive symptoms
Authors: Gutman Elisa Gouvea , Salvio Andreza Lemos , Fernandes Renan Amphilophio , Duarte Larissa Araujo , Raposo-Vedovi Jessica Vasques , Alcaraz Helena França , Teixeira Milene Ataíde , Passos Giselle Fazzioni , de Medeiros Karoline Queiroz Muniz , Hammerle Mariana Beiral , Pires Karina Lebeis , Vasconcelos Claudia Cristina Ferreira , Leon Luciane Almeida Amado , Figueiredo Cláudia Pinto , Alves-Leon Soniza Vieira 
Published: April 2024

AbstractIt is well known the potential of severe acute respiratory coronavirus type 2 (SARS-CoV-2) infection to induce post-acute sequelae, a condition called Long COVID. This syndrome includes several symptoms, but the central nervous system (CNS) main one is neurocognitive dysfunction. Recently it has been demonstrated the relevance of plasma levels of neurofilament light chain (pNfL), as a biomarker of early involvement of the CNS in COVID-19. The aim of this study was to investigate the relationship between pNfL in patients with post-acute neurocognitive symptoms and the potential of NfL as a prognostic biomarker in these cases. A group of 63 long COVID patients ranging from 18 to 59 years-old were evaluated, submitted to a neurocognitive battery assessment, and subdivided in different groups, according to results. Plasma samples were collected during the long COVID assessment and used for measurement of pNfL with the Single molecule array (SIMOA) assays. Levels of pNfL were significantly higher in long COVID patients with neurocognitive symptoms when compared to HC (p = 0.0031). Long COVID patients with cognitive impairment and fatigue symptoms presented higher pNfL levels when compared to long COVID patients without these symptoms, individually and combined (p = 0.0263, p = 0.0480, and 0.0142, respectively). Correlation analysis showed that levels of cognitive lost and exacerbation of fatigue in the neurocognitive evaluation had a significative correlation with higher pNfL levels (p = 0.0219 and 0.0255, respectively). Previous reports suggested that pNfL levels are related with higher risk of severity and predict lethality of COVID-19. Our findings demonstrate that SARS-CoV-2 infection seems to have a long-term impact on the brain, even in patients who presented mild acute disease. NfL measurements might be useful to identify CNS involvement in long COVID associated with neurocognitive symptoms and to identify who will need continuous monitoring and treatment support.
Psilocybin restrains activity-based anorexia in female rats by enhancing cognitive flexibility: contributions from 5-HT1A and 5-HT2A receptor mechanisms
Authors: Conn K. , Milton L. K. , Huang K. , Munguba H. , Ruuska J. , Lemus M. B. , Greaves E. , Homman-Ludiye J. , Oldfield B. J. , Foldi C. J. 
Published: April 2024

AbstractPsilocybin has shown promise for alleviating symptoms of depression and is currently in clinical trials for the treatment of anorexia nervosa (AN), a condition that is characterised by persistent cognitive inflexibility. Considering that enhanced cognitive flexibility after psilocybin treatment is reported to occur in individuals with depression, it is plausible that psilocybin could improve symptoms of AN by breaking down cognitive inflexibility. A mechanistic understanding of the actions of psilocybin is required to tailor the clinical application of psilocybin to individuals most likely to respond with positive outcomes. This can only be achieved using incisive neurobiological approaches in animal models. Here, we use the activity-based anorexia (ABA) rat model and comprehensively assess aspects of reinforcement learning to show that psilocybin (post-acutely) improves body weight maintenance in female rats and facilitates cognitive flexibility, specifically via improved adaptation to the initial reversal of reward contingencies. Further, we reveal the involvement of signalling through the serotonin (5-HT) 1 A and 5-HT2A receptor subtypes in specific aspects of learning, demonstrating that 5-HT1A antagonism negates the cognitive enhancing effects of psilocybin. Moreover, we show that psilocybin elicits a transient increase and decrease in cortical transcription of these receptors (Htr2a and Htr1a, respectively), and a further reduction in the abundance of Htr2a transcripts in rats exposed to the ABA model. Together, these findings support the hypothesis that psilocybin could ameliorate cognitive inflexibility in the context of AN and highlight a need to better understand the therapeutic mechanisms independent of 5-HT2A receptor binding.
Turning anecdotal irradiation-induced anticancer immune responses into reproducible in situ cancer vaccines via disulfiram/copper-mediated enhanced immunogenic cell death of breast cancer cells
Authors: Guo Wei , Jia Lin , Xie Ling , Kiang Juliann G. , Wang Yangyang , Sun Fengfei , Lin Zunwen , Wang Enwen , Zhang Yida , Huang Peigen , Sun Ting , Zhang Xiao , Bian Zhengying , Tang Tiejun , Guo Jingtian , Ferrone Soldano , Wang Xinhui 
Published: April 2024

AbstractIrradiation (IR) induces immunogenic cell death (ICD) in tumors, but it rarely leads to the abscopal effect (AE); even combining IR with immune checkpoint inhibitors has shown only anecdotal success in inducing AEs. In this study, we aimed to enhance the IR-induced immune response and generate reproducible AEs using the anti-alcoholism drug, disulfiram (DSF), complexed with copper (DSF/Cu) to induce tumor ICD. We measured ICD in vitro and in vivo. In mouse tumor models, DSF/Cu was injected intratumorally followed by localized tumor IR, creating an in situ cancer vaccine. We determined the anticancer response by primary tumor rejection and assessed systemic immune responses by tumor rechallenge and the occurrence of AEs relative to spontaneous lung metastasis. In addition, we analyzed immune cell subsets and quantified proinflammatory and immunosuppressive chemokines/cytokines in the tumor microenvironment (TME) and blood of the vaccinated mice. Immune cell depletion was investigated for its effects on the vaccine-induced anticancer response. The results showed that DSF/Cu and IR induced more potent ICD under hypoxia than normoxia in vitro. Low-dose intratumoral (i.t.) injection of DSF/Cu and IR(12Gy) demonstrated strong anti-primary and -rechallenged tumor effects and robust AEs in mouse models. These vaccinations also increased CD8+ and CD4+ cell numbers while decreasing Tregs and myeloid-derived suppressor cells in the 4T1 model, and increased CD8+, dendritic cells (DC), and decreased Treg cell numbers in the MCa-M3C model. Depleting both CD8+ and CD4+ cells abolished the vaccine’s anticancer response. Moreover, vaccinated tumor-bearing mice exhibited increased TNFα levels and reduced levels of immunosuppressive chemokines/cytokines. In conclusion, our novel approach generated an anticancer immune response that results in a lack of or low tumor incidence post-rechallenge and robust AEs, i.e., absence of or decreased spontaneous lung metastasis in tumor-bearing mice. This approach is readily translatable to clinical settings and may increase IR-induced AEs in cancer patients.
Circadian rhythm regulates the function of immune cells and participates in the development of tumors
Authors: Zeng Yuen , Guo Zichan , Wu Mengqi , Chen Fulin , Chen Lihua 
Published: April 2024

AbstractCircadian rhythms are present in almost all cells and play a crucial role in regulating various biological processes. Maintaining a stable circadian rhythm is essential for overall health. Disruption of this rhythm can alter the expression of clock genes and cancer-related genes, and affect many metabolic pathways and factors, thereby affecting the function of the immune system and contributing to the occurrence and progression of tumors. This paper aims to elucidate the regulatory effects of BMAL1, clock and other clock genes on immune cells, and reveal the molecular mechanism of circadian rhythm’s involvement in tumor and its microenvironment regulation. A deeper understanding of circadian rhythms has the potential to provide new strategies for the treatment of cancer and other immune-related diseases.