bluuebunny/crossref_metadata_2025 huggingface dataset:
Comparative evaluation of intraoral scanners and a spectrophotometer for percent correct shade identification in clinical dentistry
Authors: Hein Sascha , Nold Julian , Masannek Matthias , Westland Stephen , Spies Benedikt C. , Wrbas Karl Thomas
Published: January 2025

Objectives

The study aimed to assess the percent correct shade identification of four intraoral scanners (IOS) and a spectrophotometer, focusing on how reliably each device selects the correct tooth shade compared to a visual observer’s selection. The research question addresses how much clinicians can trust the device-selected shade without visual verification.

Materials and methods

Sixteen participants with natural, unrestored teeth were included. The teeth evaluated were tooth 21 (left maxillary central incisor), tooth 23 (left maxillary canine), and tooth 26 (first left maxillary molar). Tooth color was measured using four IOS devices and the Vita Easyshade V in three regions: incisal, middle, and cervical. The nearest 3D Master shade selected by each device was compared to the visual observer’s selection. The percent exact match, acceptable match (> 1.2, ≤ 2.7 ∆E
ab), and mismatch type A (< 2.7, ≤ 5.4 ∆E
ab) were calculated. Statistical analysis was performed using a chi-square test with a 95% confidence level.

Results

The overall clinical pass rate was highest for Carestream (78.2%), followed by Easyshade (63.5%), Primescan (51.2%), Trios (39.5%), and Medit (31.3%). Carestream also recorded the highest rate of mismatch type A (47.7%). Significant differences between devices were observed for all categories (p < 0.05).

Conclusions

Carestream demonstrated the highest overall clinical pass rate, while Medit exhibited the lowest. The study highlights the variability between devices in shade matching performance.

Clinical relevance

This study highlights the importance of considering device performance when relying on IOS or spectrophotometers for shade selection without visual assessment, as the reliability can vary significantly across devices.
A taxonomy of virtual reality sports applications
Authors: Elsholz Sophia , Pham Kevin , Zarnekow Rüdiger
Published: January 2025

Although physical fitness is one of the most important factors regarding an individual’s health, many people perceive a lack of motivation. One promising solution is the usage of virtual reality (VR) sports applications. Studies showed that VR sport offers several advantages, ranging from a high enjoyment, the improvement of skill quality and motivation, to effectiveness in rehabilitation. The present study aims to address the growing need for a comprehensive understanding of the fast-evolving landscape of VR sports applications. By examining 59 research studies in a systematic literature review and 141 commercial applications, we developed a taxonomy that highlights key trends, gaps, and discrepancies between academic and commercial approaches within the VR sports domain. A comparison of both theoretical and commercial applications reveals that while research applications often include specific sports movements for a well-defined target group, most market applications are designed for a wider audience and emphasize gamification. Further, market applications often include social experiences and competition that can motivate users significantly. Additionally, we identified that current VR sports applications align with established learning theories and that their design can thereby facilitate sport skill learning. Our taxonomy offers valuable insights for both researchers and practitioners in the dynamic landscape of VR sports through describing trends and deriving gaps for future research.
What does being “research active” mean in public health practice? Exploring behavioural and motivational dimensions of research activity in English local government through perceptions from embedded researchers
Authors: Edwards Rachael C. , Kneale Dylan , Stansfield Claire , Lester Sarah
Published: January 2025

Aim

Developing policies and services to address health inequalities occurs within highly complex, political environments, and the literature points to an underutilisation of research. This ineffective mobilisation of evidence is a result of challenges emerging from both research and practice organisations. In response, many interventions have been funded to enhance “research activity” in public health decision-making, but we lack a holistic understanding of what characterises research activity in these settings. Addressing this gap, we explored behavioural and motivational dimensions that are viewed as comprising research activity in public health practice.

Subject and methods

We undertook seven semi-structured interviews with researchers embedded in local government settings in England. As change agents holding dual affiliations with research and practice organisations, embedded researchers are ideally placed to develop comparative insights on the processes through which research activity is manifested in these contexts and to identify disparities inhibiting the flow of research evidence into practice.

Results

We found that research activity is manifested through a variety of behaviours relating to accessing, conducting, and contributing to research, and evidence use. Motivational dimensions underlying engagement with research institutions include concerns around capability, capacity, and local value. Many of these concerns stem partly from narrow conceptualisations of the term “research” which are held by academics and decision-makers alike.

Conclusion

Interventions seeking to enhance research activity in public health decision-making could benefit from flexibility in their language and application to build cross-organisational understanding of pressures and priorities and to account for variation in research capacity, interest, and skillsets.
Quantifying the Diagnostic Odyssey Burden Among Persons with Inborn Errors of Immunity
Authors: Nikzad Sarina , Johnson Rebekah , Scalchunes Christopher , Rider Nicholas L.
Published: January 2025

Purpose

Patients with inborn errors of immunity (IEI) have lifelong health complications including severe infections and physical impairments. Previous studies show that a patient’s perception of their health is an important predictor of health outcomes. The purpose of this study was to understand factors related to patient reported health status.

Methods

We used data from the Immune Deficiency Foundation (IDF) 2017 National Patient Survey and analyzed factors which correlated with the reported health status (RHS). Among a cohort of 1139 self-reported IEI patients, we identified age at the time of diagnosis, time gap between symptom onset and diagnosis, number of physicians seen, and whether the diagnosis was made in the first 5 years of life as significant. We used a two-tailed t-test, single-factor ANOVA, and Tukey-Kramer post-hoc test to assess statistical significance in the observed difference.

Results

Patients who received a diagnosis before the age of 12 had a significantly better mean RHS (n = 207 pre-12a vs. n = 900 post-12a; p < 0.0001). Patients who received a diagnosis within 10 years of symptom onset showed improved mean RHS (n = 413 pre-10 vs. n = 524 post-10; p < 0.0001). Among patients who had symptom onset within the first 5 years of life, those who received a diagnosis had a significantly improved RHS (3.5 ± 0.92, n = 275 undiagnosed vs. 2.8 ± 0.94, n = 108 diagnosed; p < 0.0001). Finally, RHS was significantly impacted by number of physicians(n ≥ 4) seen prior to diagnosis (3.1 ± 0.96 vs. 3.4 ± 0.80, p < 0.0001).

Conclusion

These findings shed light upon critical factors which impact IEI patient RHS. Specifically, we find that efficient, rapid and early-life IEI identification should improve patient reported health and relevant outcomes. These improvements appear to be independent of the clinician specialty ultimately making the IEI diagnosis.
Analysis of the Induced Mild Heating by Airborne Ultrasound Application on the Convective Drying of Pork Liver
Authors: Sánchez-Torres Eduardo A. , Giacomozzi Anabella S. , Abril Blanca , Benedito Jose , Bon Jose , García-Pérez Jose V.
Published: January 2025

Efficient use of meat by-products, such as pork liver, may entail a previous stage of dehydration for their stabilization, which involves significant energy and time investments. Airborne ultrasound application has been reported as a promising technique to accelerate the air drying of food materials. In this context, the present study addresses, for the first time, the thermal effect associated with ultrasound application on a meat by-product. For that purpose, drying experiments were conducted at 40 and 60 °C on pork liver cylinders at 2 m⋅s−1 with (US) and without (AIR) airborne ultrasound application. The modeling process was based on the principles of heat conduction and moisture diffusion, taking into account the external convection. The results showed that the use of ultrasound reduced the drying time by around 30% at 40 °C, although its impact was less pronounced at 60 °C. With the application of ultrasound, both the sample and air flow temperatures rose by about 4.5 and 2.5 °C, respectively, which partly explains the improvement of drying rate. Due to this low heating effect, airborne ultrasound application must be considered a non-thermal intensification strategy for convective drying of pork liver.
Widely targeted metabolomics analysis reveals differences in volatile metabolites among four Angelica species
Authors: Ji Jiaojiao , Zang Lanlan , Lu Tingting , Li Cheng , Han Xiaoxu , Lee Soo-Rang , Wang Li
Published: January 2025

Angelica L. has attracted global interest for its traditional medicinal uses and commercial values. However, few studies have focused on the metabolomic differences among the Angelica species. In this study, widely targeted metabolomics based on gas chromatography-tandem mass spectrometry was employed to analyze the metabolomes of four Angelica species (Angelica sinensis (Oliv.) Diels (A. sinensis), Angelica biserrata (R.H.Shan & Yuan) C.Q.Yuan & R.H.Shan (A. biserrata), Angelica dahurica (Hoffm.) Benth. & Hook.f. ex Franch. & Sav. (A. dahurica) and Angelica keiskei Koidz. (A. keiskei)). A total of 698 volatile metabolites were identified and classified into fifteen different categories. The metabolomic analysis indicated that 7-hydroxycoumarin and Z-ligustilide accumulated at significantly higher levels in A. sinensis, whereas bornyl acetate showed the opposite pattern. Furthermore, a high correspondence between the dendrogram of metabolite contents and phylogenetic positions of the four species. This study provides a comprehensive biochemical map for the exploitation, application and development of the Angelica species as medicinal plants or health-related dietary supplements.

Graphical Abstract
Improving Your British Sign Language
Authors: Lerose Luigi
Published: January 2025

This practical, illustrated guide is designed for students who want to improve their use of British Sign Language (BSL), helping them to manage some of the more challenging aspects of BSL learning in an accessible way. Written by a highly experienced sign language speaker, it contains around 750 photos of signs, including examples of common mistakes alongside the corresponding correct signs. Each chapter is accompanied by video demonstrations of all the signs it exemplifies, showing BSL in action. The book is based on the latest research on BSL within theoretical linguistics, since understanding the latest advances in this fast-moving field is known to help improve the skills of non-native speakers. It is intended primarily for self-study, allowing students to work at their own pace on articulation accuracy, recognise the kinds of errors they are likely to make, and gain a better understanding of the visual nature of BSL.
The Romanization of Britain
Authors: Millett Martin , Higgins Charlotte
Published: January 2025

The Romanization of Britain was greeted, on first publication, as an innovative study of cultural change and interaction, offering a bold new perspective on Roman Britain based on archaeological evidence. It set out to explore the social dynamics of cultural change from a local perspective by looking at the patterns of interaction between provincial peoples and imperial power. Drawing together a wide range of excavated data as well as textual evidence, it provided a new synthesis of the province whilst offering an alternative way of understanding cultural change in the Roman Empire more widely. Its publication served to catalyse debate, stimulating very considerable discussion and generating a wide variety of responses in a range of publications. This revised edition adds a new introductory essay exploring the genesis of this classic work and reviewing the subsequent debate, while also recalibrating the author's perspective on cultural change within the wider Roman provinces.
Neural network approaches for sea surface height predictability using sea surface temperature
Authors: Ollier Luther , Thiria Sylvie , Mejia Carlos E. , Crépon Michel , Charantonis Anastase
Published: January 2025

Sea Surface Height Anomaly (SLA) is a signature of the mesoscale dynamics of the upper ocean. Sea surface temperature (SST) is driven by these dynamics and can be used to improve the spatial interpolation of SLA fields. In this study, we focused on the temporal evolution of SLA fields. We explored the capacity of deep learning (DL) methods to predict short-term SLA fields using SST fields. We used simulated daily SLA and SST data from the Mercator Global Analysis and Forecasting System, with a resolution of (1/12)° in the North Atlantic Ocean (26.5–44.42°N, −64.25–41.83°E), covering the period from 1993 to 2019. Using a slightly modified image-to-image convolutional DL architecture, we demonstrated that SST is a relevant variable for controlling the SLA prediction. With a learning process inspired by the teaching-forcing method, we managed to improve the SLA forecast at 5 days by using the SST fields as additional information. We obtained predictions of 12 cm (20 cm) error of SLA evolution for scales smaller than mesoscales and at time scales of 5 days (20 days) respectively. Moreover, the information provided by the SST allows us to limit the SLA error to 16 cm at 20 days when learning the trajectory.
Cloudy with a chance of uncertainty: autoconversion rates forecasting via evidential regression from satellite data
Authors: Novitasari Maria Carolina , Quaas Johannes , Rodrigues Miguel R. D.
Published: January 2025

High-resolution simulations such as the ICOsahedral Non-hydrostatic Large-Eddy Model (ICON-LEM) can be used to understand the interactions among aerosols, clouds, and precipitation processes that currently represent the largest source of uncertainty involved in determining the radiative forcing of climate change. Nevertheless, due to the exceptionally high computing cost required, this simulation-based approach can only be employed for a short period within a limited area. Despite the potential of machine learning to alleviate this issue, the associated model and data uncertainties may impact its reliability. To address this, we developed a neural network (NN) model powered by evidential learning, which is easy to implement, to assess both data (aleatoric) and model (epistemic) uncertainties applied to satellite observation data. By differentiating whether uncertainties stem from data or the model, we can adapt our strategies accordingly. Our study focuses on estimating the autoconversion rates, a process in which small droplets (cloud droplets) collide and coalesce to become larger droplets (raindrops). This process is one of the key contributors to the precipitation formation of liquid clouds, crucial for a better understanding of cloud responses to anthropogenic aerosols and, subsequently, climate change. We demonstrate that incorporating evidential regression enhances the model’s credibility by accounting for uncertainties without compromising performance or requiring additional training or inference. Additionally, the uncertainty estimation shows good calibration and provides valuable insights for future enhancements, potentially encouraging more open discussions and exploration, especially in the field of atmospheric science.
Deep learning based bias correction of TRMM precipitation estimates using IMD-gridded precipitation as ground observation
Authors: Mishra Sharma Sumanta Chandra , Mitra Adway
Published: January 2025

Bias correction is a critical aspect of data-centric climate studies, as it aims to improve the consistency between observational data and simulations by climate models or estimates by remote sensing. Satellite-based estimates of climatic variables like precipitation often exhibit systematic bias when compared to ground observations. To address this issue, the application of bias correction techniques becomes necessary. This research work examines the use of deep learning to reduce the systematic bias of satellite estimations at each grid location while maintaining the spatial dependency across grid points. More specifically, we try to calibrate daily precipitation values of tropical rainfall measuring mission based TRMM_3B42_Daily precipitation data over Indian landmass with ground observations recorded by India Meteorological Department (IMD). We have focused on the precipitation estimates of the Indian Summer Monsoon Rainfall (ISMR) period (June–September) since India gets more than 75% of its annual rainfall in this period. We have benchmarked these deep learning methods against standard statistical methods like quantile mapping and quantile delta mapping on the above datasets. The comparative analysis shows the effectiveness of the deep learning architecture in bias correction.
MLP-mixer-based deep learning network for pedestrian-level wind assessment
Authors: Clarke Adam , Giljarhus Knut Erik Teigen , Oggiano Luca , Saddington Alistair , Depuru-Mohan Karthik
Published: January 2025

This article addresses the challenges of assessing pedestrian-level wind conditions in urban environments using a deep learning approach. The influence of large buildings on urban wind patterns has significant implications for thermal comfort, pollutant transport, pedestrian safety, and energy usage. Traditional methods, such as wind tunnel testing, are time-consuming and costly, leading to a growing interest in computational methods like computational fluid dynamics (CFD) simulations. However, CFD still requires a significant time investment for such studies, limiting the available time for design modification prior to lockdown. This study proposes a deep learning surrogate model based on a MLP-mixer architecture to predict mean flow conditions for complex arrays of buildings. The model is trained on a diverse dataset of synthetic geometries and corresponding CFD simulations, demonstrating its effectiveness in capturing intricate wind dynamics. The article discusses the model architecture and data preparation and evaluates its performance qualitatively and quantitatively. Results show promising capabilities in replicating key wind features with a mean error of 0.3 m/s and rarely exceeding 0.75 m/s, making the proposed model a valuable tool for early-stage urban wind modelling.
Machine learning for stochastic parametrization
Authors: Christensen Hannah M. , Kouhen Salah , Miller Greta , Parthipan Raghul
Published: January 2025

Atmospheric models used for weather and climate prediction are traditionally formulated in a deterministic manner. In other words, given a particular state of the resolved scale variables, the most likely forcing from the subgrid scale processes is estimated and used to predict the evolution of the large-scale flow. However, the lack of scale separation in the atmosphere means that this approach is a large source of error in forecasts. Over recent years, an alternative paradigm has developed: the use of stochastic techniques to characterize uncertainty in small-scale processes. These techniques are now widely used across weather, subseasonal, seasonal, and climate timescales. In parallel, recent years have also seen significant progress in replacing parametrization schemes using machine learning (ML). This has the potential to both speed up and improve our numerical models. However, the focus to date has largely been on deterministic approaches. In this position paper, we bring together these two key developments and discuss the potential for data-driven approaches for stochastic parametrization. We highlight early studies in this area and draw attention to the novel challenges that remain.
Adaptive decision-making: Bayesian Network Modeling for blue–green infrastructure selection in dynamic climate and land use context
Authors: Seker Dima , Orak Nur H.
Published: January 2025

Nature-based solutions are becoming increasingly recognized as effective tools for addressing various environmental problems. This study presents a novel approach to selecting optimal blue–green infrastructure (BGI) solutions tailored to the unique environmental and climatic challenges of Istanbul, Türkiye. The primary objective is to utilize a Bayesian Belief Network (BBN) model for assisting in the identification of the most effective BGI solutions, considering the city’s distinct environmental conditions and vulnerabilities to climate change. Our methodology integrates comprehensive data collection, including meteorological and land use data, and employs a BBN model to analyze and weigh the complex network of factors influencing BGI suitability. Key findings reveal the model’s capacity to effectively predict BGI applicability across diverse climate scenarios, with quantitative results demonstrating a notable enhancement in decision-making processes for urban sustainability. Quantitative results from our model reveal a significant improvement in decision-making accuracy, with a predictive accuracy rate of 82% in identifying suitable BGI solutions for various urban scenarios. This enhancement is particularly notable in densely populated districts, where our model predicted a 25% greater efficiency in stormwater management and urban heat island mitigation compared to traditional planning methods. The study also acknowledges the limitations, such as data scarcity and the need for further model refinement. The results highlight the model’s potential for application in other complex urban areas, making it a valuable tool for improving urban sustainability and climate change adaptation. This study shows the importance of incorporating detailed meteorological and local climate zones data into urban planning processes and suggests that similar methodologies could be beneficial for addressing environmental challenges in diverse urban settings.
TB or not TB? Development and validation of a clinical decision support system to inform airborne isolation requirements in the evaluation of suspected tuberculosis
Authors: Dugdale Caitlin M. , Zachary Kimon C. , Craig Rebecca L. , Doms Alexandra , Germaine Lindsay , Green Chloe V. , Gulbas Eren , Hurtado Rocio M. , Hyle Emily P. , Jerry Michelle S. , Lazarus Jacob E. , Maxfield Stephen , Paras Molly , Swanson Katherine , Shenoy Erica S.
Published: January 2025

Background:

The study objective was to develop and validate a clinical decision support system (CDSS) to guide clinicians through the diagnostic evaluation of hospitalized individuals with suspected pulmonary tuberculosis (TB) in low-prevalence settings.

Methods:

The “TBorNotTB” CDSS was developed using a modified Delphi method. The CDSS assigns points based on epidemiologic risk factors, TB history, symptoms, chest imaging, and sputum/bronchoscopy results. Below a set point threshold, airborne isolation precautions are automatically discontinued; otherwise, additional evaluation, including infection control review, is recommended. The model was validated through retrospective application of the CDSS to all individuals hospitalized in the Mass General Brigham system from July 2016 to December 2022 with culture-confirmed pulmonary TB (cases) and equal numbers of age and date of testing-matched controls with three negative respiratory mycobacterial cultures.

Results:

104 individuals with TB (cases) and 104 controls were identified. Prior residence in a highly endemic country, positive interferon release assay, weight loss, absence of symptom resolution with treatment for alternative diagnoses, and findings concerning for TB on chest imaging were significant predictors of TB (all P < 0.05). CDSS contents and scoring were refined based on the case–control analysis. The final CDSS demonstrated 100% sensitivity and 27% specificity for TB with an AUC of 0.87.

Conclusions:

The TBorNotTB CDSS demonstrated modest specificity and high sensitivity to detect TB even when AFB smears were negative. This CDSS, embedded into the electronic medical record system, could help reduce risks of nosocomial TB transmission, patient-time in airborne isolation, and person-time spent reviewing individuals with suspected TB.
A sentinel survey in remote Western Thailand indicates that school-aged children and reproductive-aged women of the Indigenous Pwo Karen community are iodine sufficient
Authors: Joompa Pattamaporn , Sukboon Pornpan , Schultink Werner , Zimmermann Michael B. , Gowachirapant Sueppong
Published: January 2025

Indigenous peoples are often not routinely included in iodine programmes because of language barriers and remote access and may thus be at higher risk of iodine deficiency disorders, which could adversely impact their quality of life. We conducted this cross-sectional study in the remote Pwo Karen community of Thailand to determine the urinary iodine concentration of school-aged children and women of reproductive age and investigate the iodine content in household salt. We measured urinary iodine concentration in spot urine samples from healthy school-aged children and women of reproductive age, administered a questionnaire, estimated daily iodine intake and collected household salt samples to determine salt iodine concentration. The median urinary iodine concentration (range) of school-aged children (n 170) was 192 (136–263) µg/l, which was significantly higher than women of reproductive age (n 306) (147 (89–233) µg/l) (P < 0·001). The estimated daily iodine intake in school-aged children and women of reproductive age were 135 and 195 μg/d, respectively. The median (range) iodine concentration in rock and granulated salts consumed in the households were 2·32 (0·52–3·19) and 26·64 (20·86–31·01) ppm, respectively. Surprisingly, the use of iodised salt and the frequency of seafood consumption were NS predictors of urinary iodine concentration in these two groups. Our data suggest that school children and women of the Pwo Karen community have sufficient iodine intake, indicating the Thai salt iodisation programme is effectively reaching even this isolated Indigenous community. Sentinel surveys of remote vulnerable populations can be a useful tool in national iodine programmes to ensure that programme coverage is truly universal.
Ambient PM2.5 exposure and tuberculosis reactivation: a cross-sectional study in an intermediate burden city
Authors: Lau Leonia Hiu Wan , Wong Ngai Sze , Leung Chi Chiu , Chan Chi Kuen , Tai Lai-bun , Lau Alexis Kai Hon , Lin Changqing , Lee Shui Shan
Published: January 2025

Hong Kong is an intermediate tuberculosis (TB) endemicity city dominated by reactivation diseases. A cross-sectional study on the clinical and epidemiologic data of newly diagnosed TB cases was conducted in such a setting, to examine the association between ambient PM2.5 and TB reactivation. After the exclusion of cases most likely resulting from recent infection, four distinct TB population phenotypes were delineated by latent class analysis based on their reactivation risk and clinical profiles (N = 2,153): ‘Elderly male’ (26%), ‘Otherwise healthy younger adult’ (34%), ‘Older female’ (19%) and ‘Male smoker’ (21%). Overall, exposure to high concentrations of ambient PM2.5 6 and 12 months before the notification was significantly associated with ‘Otherwise healthy younger adults’ membership (OR = 1.07 and 1.11, respectively) compared with ‘Elderly male’. Such association was less evident for other phenotypes. The differential pattern of association between ambient PM2.5 exposure and TB population phenotypes suggested the role of ambient PM2.5 in TB reactivation.
Resolving Peer Disagreement about the Law
Authors: Houghton Alexander
Published: January 2025

Legal experts—lawyers, judges, and academics—typically resist changing their beliefs about what the law is or requires when they encounter disagreement from those committed to different jurisprudential or interpretive theories. William Baude and Ryan Doerfler are among the most prominent proponents of this view, holding it because fundamental differences in methodological commitments severs epistemic peerhood. This dominant approach to disagreement, and Baude and Doerfler’s rationale, are both wrong. The latter is committed to an overly stringent account of epistemic peerhood that dogmatically excludes opponents. The former violates the conjunction of three plausible epistemic principles: Complete Evidence, considering all epistemically permissible evidence; Independence, in which only dispute-independent evidence is epistemically permissible; and Peer Support, which involves epistemically permissible evidence. Instead, I argue for jurisprudential humility—we ought to be more willing to admit we do not know what the law is or requires, and take seriously conflicting views.
The Impacts of Erosion on the Carbon Cycle
Authors: Zheng Haiyan , Miao Chiyuan , Huntingford Chris , Tarolli Paolo , Li Dongfeng , Panagos Panos , Yue Yao , Borrelli Pasquale , Van Oost Kristof
Published: January 2025

Physical and chemical erosion associated with water both affect land–atmosphere carbon exchanges. However, previous studies have often addressed these processes separately or used oversimplified mechanisms, leading to ongoing debates and uncertainties about erosion‐induced carbon fluxes. We provide an overview of the on‐site carbon uptake fluxes induced by physical erosion (0.05–0.29 Pg C yr−1, globally) and chemical erosion (0.26–0.48 Pg C yr−1). Then, we discuss off‐site carbon dynamics (during transport, deposition, and burial). Soil organic carbon mineralization during transport is nearly 0.37–1.20 Pg C yr−1 on the globe. We also summarize the overall carbon fluxes into estuaries (0.71–1.06 Pg C yr−1) and identify the sources of different types of carbon within them, most of which are associated with land erosion. Current approaches for quantifying physical‐erosion‐induced vertical carbon fluxes focus on two distinct temporal scales: short‐term dynamics (ranging from minutes to decades), emphasizing net vertical carbon flux, and long‐term dynamics (spanning millennial to geological timescales), examining the fate of eroded carbon over extended periods. In addition to direct chemical measurement and modeling approaches, estimation using indicators of riverine material is popular for constraining chemical‐erosion‐driven carbon fluxes. Lastly, we highlight the key challenges for quantifying related fluxes. To overcome potential biases in future studies, we strongly recommend integrated research that addresses both physical and chemical erosion over a well‐defined timescale. A comprehensive understanding of the mechanisms driving erosion‐induced lateral and vertical carbon fluxes is crucial for closing the global carbon budget.
Future Extreme Climate Events Threaten Alpine and Subalpine Woody Plants in China
Authors: Wu Yongru , Shen Jian , Deane David C. , Yu Haibin , Yu Fangyuan , Wang Xuerong , Cao Zheng , Yu Rong , Xiao Fuan , Wang Tiejun , Wu Zhifeng
Published: January 2025

Increases in the frequency, intensity, and duration of extreme climate events (ECEs) are already impacting ecosystems, with many of the strongest effects associated with high‐elevation areas. Most research on the ecological impacts of climate change has focused on climatic averages, which might differ from ECEs. Rhododendron, a diverse genus of alpine and subalpine woody plant, plays a crucial role in ecosystem stability and biodiversity in the biodiversity hotspots of the Himalayas and Hengduan Mountains. Here, we compared the predicted impacts of average climate with those including ECEs on 189 Rhododendron species in China for the historical period (1981–2010) and the future period (2071–2100) under two emissions scenarios (SSP2‐4.5 and SSP5‐8.5). We analyzed changes in suitable habitat and patterns of species richness, weighted endemism, and phylogenetic diversity, identifying areas of coinciding high‐risk as priority conservation areas. Inclusion of ECEs altered the projected areas of suitable habitat across all species from an increase of over 3% to a decrease exceeding 10%, with the distribution of most Rhododendron species strongly influenced by extremes of drought and high temperatures. We found fewer than 18% of high‐risk areas of diversity loss were currently protected, with priority conservation areas mainly located in the Daxue, Daliang, Wumeng, and Jade Dragon Snow Mountains, as well as in the Nyingchi. We suggest inclusion of ECEs is critical when projecting changes in alpine and subalpine species distributions for effective conservation planning for climate change.
Inference of Wildfire Causes From Their Physical, Biological, Social and Management Attributes
Authors: Pourmohamad Yavar , Abatzoglou John T. , Fleishman Erica , Short Karen C. , Shuman Jacquelyn , AghaKouchak Amir , Williamson Matthew , Seydi Seyd Teymoor , Sadegh Mojtaba
Published: January 2025

Effective wildfire prevention includes actions to deliberately target different wildfire causes. However, the cause of an increasing number of wildfires is unknown, hindering targeted prevention efforts. We developed a machine learning model of wildfire ignition cause across the western United States on the basis of physical, biological, social, and management attributes associated with wildfires. Trained on wildfires from 1992 to 2020 with 12 known causes, the overall accuracy of our model exceeded 70% when applied to out‐of‐sample test data. Our model more accurately separated wildfires ignited by natural versus human causes (93% accuracy), and discriminated among the 11 classes of human‐ignited wildfires with 55% accuracy. Our model attributed the greatest percentage of 150,247 wildfires from 1992 to 2020 for which the ignition source was unknown to equipment and vehicle use (21%), lightning (20%), and arson and incendiarism (18%).
Tidal Conversion Into Vertical Normal Modes by Continental Margins
Authors: Geoffroy Gaspard , Kelly Samuel M. , Nycander Jonas
Published: January 2025

We construct a computationally inexpensive semi‐analytical method to compute the tidal conversion into vertical modes by continental slopes and shelves, and apply it at the global scale. It relies on a vertically two‐dimensional reduced‐physics numerical model and uses the observed bottom topography, ocean stratification, and tidal currents as inputs. The method is applicable no matter how steep the slope is and it resolves the onshore and offshore baroclinic tidal energy fluxes. The output is validated with the conversion diagnosed from a global general circulation model simulation.
The System for Classification of Low‐Pressure Systems (SyCLoPS): An All‐In‐One Objective Framework for Large‐Scale Data Sets
Authors: Han Yushan , Ullrich Paul A.
Published: January 2025

We propose the first unified objective framework (SyCLoPS) for detecting and classifying all types of low‐pressure systems (LPSs) in a given data set. We use the state‐of‐the‐art automated feature tracking software TempestExtremes (TE) to detect and track LPS features globally in ERA5 and compute 16 parameters from commonly found atmospheric variables for classification. A Python classifier is implemented to classify all LPSs at once. The framework assigns 16 different labels (classes) to each LPS data point and designates four different types of high‐impact LPS tracks, including tracks of tropical cyclone (TC), monsoonal system, subtropical storm and polar low. The classification process involves disentangling high‐altitude and drier LPSs, differentiating tropical and non‐tropical LPSs using novel criteria, and optimizing for the detection of the four types of high‐impact LPS. A comparison of our labels with those in the International Best Track Archive for Climate Stewardship (IBTrACS) revealed an overall accuracy of 95% in distinguishing between tropical systems, extratropical cyclones, and disturbances. SyCLoPS produces a better TC detection skill compared to the previous algorithms, highlighted by an approximately 6% reduction in the false alarm rate compared to the previous TE algorithm. The vertical cross section composite of the four types of high‐impact LPS we detect each shows distinct structural characteristics. Finally, we demonstrate that SyCLoPS is valuable for investigating various aspects of LPSs in climate data, such as the evolution of a single LPS track, patterns of LPS frequencies, and precipitation or wind influence associated with a particular LPS class.
Response and Feedback of Mesoscale Eddies to Tropical Cyclones Over the South China Sea
Authors: Liu Ping , Guan Shoude , Lin I.‐I. , Huang Mengya , Jin Fei‐Fei , Wang Qian , Lu Zhumin , Zhao Wei , Tian Jiwei
Published: January 2025

Tropical cyclones (TCs) frequently encounter and interact with mesoscale eddies when moving over the ocean, affecting both of their subsequent evolutions. The South China Sea (SCS) suffers the most frequent TCs among global oceans and contains active eddies. On average, one TC encounters and interacts with at least one eddy during its lifespan in the SCS. Using 27‐year satellite data and numerical simulations, we examined the response and feedback of mesoscale eddies to TCs over the SCS, based on TCs' interaction with 183 cyclonic ocean eddies (COEs) and 152 anticyclonic ocean eddies (AOEs). TCs induce a symmetric sea surface height reduction with maximum reduction (∼5 cm on average) appearing at the TC center. Pre‐existing COEs (AOEs) are enhanced (weakened) by TCs with an average 24% increase (17% decrease) in amplitude and enlarged (shrunk) with an average 19% increase (13% decrease) in radius. Stronger or slower‐moving TCs enhance COEs more significantly. Pre‐TC existing COEs (AOEs) enhance (suppress) TC‐induced sea surface cooling, and thus suppress (fuel) TC intensification, with the effect of COEs statistically stronger than AOEs. This modulating effect is more pronounced under stronger or slower‐moving TCs. Eddy's location also influences the modulating effect, with pre‐TC COEs located to the left (right) of TC track shifting the largest cooling leftwards (rightwards). Furthermore, the left‐located COEs increase cooling amplitude by 61%, stronger than right‐located COEs (26%). These results suggest that the combination of mesoscale eddies and TC attributes complicates TC‐induced sea surface cooling, which potentially affects TC intensification, wind structure, and rainfall.
A Case Study of an Exceptional Atmospheric River and Explosively Deepening Cyclone Over the US Central Plains in March 2019
Authors: Zou Xun , Cordeira Jason M. , Bartlett Samuel M. , Ralph F. Martin
Published: January 2025

In March 2019, a strong atmospheric river (AR) originating from the Gulf of Mexico transported abundant moisture inland and fueled a record‐breaking bomb cyclone in Colorado, resulting in widespread winter weather hazards across several states. Experimental model simulations and trajectory analysis indicate that mid‐tropospheric latent heat release (LHR) coincided with the strength of the warm conveyor belt and played a key role in the deepening of the cyclone. The LHR promoted the generation of a lower tropospheric positive potential vorticity (PV) anomaly and a stronger low‐level cyclonic circulation, enhancing the cyclone, low‐level jet stream, and associated water vapor transport. Additionally, it generated an upper tropospheric negative PV anomaly and strong upper‐level anticyclonic circulation, influencing the structure of the trough‐ridge couplet and the associated Rossby wave. Reductions in the initial intensity of the AR and disallowing LHR both weakened the cyclone. However, disallowing LHR significantly disturbed the synoptic‐scale structure of the storm and embedded Rossby wave, resulting in a stronger impact. Thus, the reduction of diabatic PV generation, under the influence of AR activities, was crucial in the explosive intensification of the cyclone. Additionally, the reductions in AR intensity and the magnitude of cyclone weakening did not show a linear relationship. Few studies have explored interactions between ARs and continental cyclones, and this paper highlights the need for further research on AR‐associated extreme weather events inland.
Lunar Crustal KREEP Distribution
Authors: Levin J. N. , Evans A. J. , Andrews‐Hanna J. C. , Daubar I. J.
Published: January 2025

The distribution of KREEP—potassium (K), rare earth elements (REE), and phosphorus (P)—in the lunar crust is an important clue to deciphering the geochemical and thermal evolution of the Moon. Surface measurements of thorium abundance taken by the Lunar Prospector Gamma Ray Spectrometer (LP GRS) instrument have shown that KREEP is concentrated on the lunar nearside surface, mirroring the hemispheric asymmetry observed in the distribution of maria, crustal thickness, and topography. However, the overall lateral and vertical distribution of KREEP within the crust is poorly constrained, leaving uncertainty in estimates of bulk crustal thorium abundance and in the history and evolution of KREEP. In this study, we compared the overall lateral and vertical distribution of lunar KREEP in the upper crust by determining the thorium abundance of material excavated by complex impact craters. We find that the distribution of KREEP on the nearside is consistent with a layer of high‐Thorium ejecta from the Imbrium impact mixing with underlying low‐Th (<1 ppm) crustal material, suggesting the excavation of a sub‐crustal KREEP reservoir with thorium abundances as high as 45–120 ppm by the Imbrium‐forming impact. Imbrium ejecta alone does not explain the distribution of thorium on the lunar farside, particularly around the South Pole Aitken basin, suggesting other sources for farside thorium enrichments. Furthermore, our results refute the existence of a large‐scale Thorium‐enriched layer in the upper 16 km of the farside crust.
Simple‐To‐Complex Crater Transition for the Uranian Satellites Ariel and Miranda
Authors: Borrelli M. E. , Bierson C. J. , O’Rourke J. G.
Published: January 2025

The latest decadal survey identified the Uranus system as the highest‐priority new target for a NASA Flagship mission. Ariel and Miranda are potential ocean worlds with evidence of resurfacing potentially due to past elevated heat flow. Learning about the geologic histories of these icy moons is important for understanding the potential for life in the outer solar system. Using limited data acquired by the Voyager 2 spacecraft, we explore open questions about the surfaces of Uranian satellites to gain a better understanding of their evolutionary histories. In this work, we update the estimates of Ariel and Miranda's simple‐to‐complex transition diameters, which have not yet been measured using modern GIS techniques and reprocessed data. The simple‐to‐complex transition diameter is a value used on many worlds to infer the composition of the surface. For the Uranian satellites, this value was last estimated shortly after the Voyager 2 flyby with a data set of 18 craters. We use reprocessed topography from more than 100 craters to estimate a simple‐to‐complex transition diameter on Ariel of ∼26 km, consistent with an icy surface composition. We place a lower limit of ∼49 km on the transition diameter for Miranda, where we cannot identify any complex craters. We also estimate the relative and absolute ages of terrains on Ariel and Miranda. Our results agree with recent studies showing that they likely experienced relatively recent (≤1 Gya) resurfacing. Finally, we suggest imaging requirements for the future missions to Uranus to answer outstanding questions about Ariel and Miranda.
Martian Atmospheric Disturbances From Orbital Images and Surface Pressure at Jezero Crater, Mars, During Martian Year 36
Authors: Sánchez‐Lavega A. , Larsen E. , del Rio‐Gaztelurrrutia T. , Hernández‐Bernal J. , Ordóñez‐Etxebarría I. , Hueso R. , Bertrand T. , Lemmon M. , Juarez M. de la Torre , Martínez G. M. , Munguira A. , Rodríguez‐Manfredi J. A. , Harri A.‐M. , Pla‐García J. , Toledo D. , Newman C.
Published: January 2025

We present a study of atmospheric disturbances at Jezero Crater, Mars, using ground‐based measurements of surface pressure by the Perseverance rover in combination with orbital images from the Mars Express and Mars Reconnaissance Orbiter missions. The study starts at Ls ∼ 13.3° in MY36 (6 March 2021) and extends up to Ls ∼ 30.3° in MY37 (28 February 2023). We focus on the characterization of the major atmospheric phenomena at synoptic and planetary‐scales. These are the thermal tides (measured up to the sixth component), long‐period pressure oscillations (periods >1 sol), the Aphelion Cloud Belt, and the occasional development of regional dust storms over Jezero. We present the seasonal evolution of the amplitudes and phases of the thermal tides and their relation with the atmospheric dust content (optical depth). Three regional dust storms and one polar storm extending over Jezero produced an increase in the diurnal and semidiurnal amplitudes but resulted in inverse responses in their phases. We show that the primary regular wave activity is due to baroclinic disturbances with periods of 2–4 sols and amplitudes ∼ 1–15 Pa increasing with dust content, in good agreement with theoretical predictions by model calculations. The spacecraft images show a number of arc‐shaped, spiral and irregular cyclonic vortices, traced by dust and clouds at the edge of the North Polar Cap, that could be behind some of the pressure oscillations measured at Jezero.
Terrestrial Organic Matter Contributes to CO2 Production From Siberian Shelf Sediments
Authors: Sauerland Lewis , Ray Nicholas , Martens Jannik , Tesi Tommaso , Dudarev Oleg , Gustafsson Örjan , Semiletov Igor , Wild Birgit
Published: January 2025

Arctic climate warming is causing permafrost thaw and erosion, which may lead to enhanced inputs of terrestrial organic matter into Arctic Ocean shelf sediments. Degradation of terrestrial organic matter in sediments might contribute to carbon dioxide production and bottom water acidification. Yet, the degradability of organic matter in shallow Arctic Ocean sediments, as well as the contribution of terrestrial input, is poorly quantified. Here, potential organic matter degradation rates were investigated for 16 surface sediments from the Kara Sea, Laptev Sea, and the western East Siberian Sea and compared with physicochemical sediment properties including molecular biomarkers, stable and radioactive carbon isotopes, and grain size. Aerobic oxygen and carbon dioxide fluxes, measured in laboratory incubations of sediment slurry, showed high spatial variability and correlated significantly with organic carbon content as well as with the amount and degradation state of terrestrial organic matter. The dependency on terrestrial organic matter declined with increasing distance from land, indicating that the presence of terrestrial organic matter is likely a constraining factor for organic matter degradation in shallow shelf seas. However, sediment oxygen consumption rates, measured in incubations of intact sediment cores, also exhibited substantial spatial variability but were not related to organic carbon content or terrestrial influence. Oxygen consumption of intact sediments may be more strongly influenced by in situ redox conditions. Together with previous observations, our findings support that terrestrial organic matter is easily degradable in shelf sea sediments and might substantially contribute to aerobic carbon dioxide production and oxygen consumption.