bluuebunny/crossref_metadata_2025_split huggingface dataset:
Hydrogen Diffusion in Garnet: Insights From Atomistic Simulations
Authors: Zhong Xin , Höfling Felix , John Timm 
Published: February 2025

AbstractGarnet has been widely used to decipher the pressure‐temperature‐time history of rocks, but its physical properties such as elasticity and diffusion are strongly affected by trace amounts of hydrogen. Experimental measurements of H diffusion in garnet are limited to room pressure. We use atomistic simulations to study H diffusion in perfect and defective garnet lattices, focusing on protonation defects at the Si and Mg sites, which are shown to be energetically favored. Transient trapping of H renders ab‐initio simulations of H diffusion computationally challenging, which is overcome with machine learning techniques by training a deep neural network that encodes the interatomic potential. Our results from such deep potential molecular dynamics (DeePMD) simulations show high mobility of hydrogen in defect‐free garnet lattices, whereas H diffusivity is significantly diminished in defective lattices. Tracer simulations focusing on H alone highlight the vital role of atomic vibrations of heavier atoms like Mg on the release of H atoms. Two regimes of H diffusion are identified: a diffuser‐dominated regime at high hydrogen content with low activation energies due to saturation of vacancies by hydrogen, and a vacancy‐dominated regime at low hydrogen content with high activation energies due to trapping of H atoms at vacancy sites. These regimes account for experimental observations, such as a H‐concentration dependent diffusivity and the discrepancy in activation energy between deprotonation and D‐H exchange experiments. This study underpins the crucial role of vacancies in H diffusion and demonstrates the utility of machine‐learned interatomic potentials in studying kinetic processes in the Earth's interior.
On the Definition of Extreme Evaporation Events
Authors: Markonis Yannis 
Published: February 2025

AbstractEven though evaporation is a crucial component of the energy and water cycles, its extremes remain largely unexplored. To address this gap, this study introduces a statistical framework defining Extreme Evaporation Events (ExEvEs). We investigate their characteristics over Czechia, a country in Central Europe with increasing evaporation and examine their seasonal correlation with radiation and/or precipitation—the main drivers of land evaporation. Despite their statistical definition, ExEvEs are shown to have a physical basis, also evident in their onset and termination. We also observe that ExEvEs fluctuate differently than the average evaporation resulting to significant implications for water availability and regional water cycle's acceleration. The proposed event‐based framework provides a systematic way to detect, characterize, and analyze evaporation extremes, which helps to improve our understanding of their drivers and impacts.
Simulation of Cloud Processes Over Offshore Coastal Antarctica Using the High‐Resolution Regional UK Met Office Unified Model With Interactive Aerosols
Authors: Price R. , Orr A. , Field P. F. , Mace G. , Protat A. 
Published: February 2025

AbstractThe Southern Ocean and offshore coastal Antarctica are key regions for global climate. Low level mixed‐phase clouds strongly control the surface radiation budget of this region but remain challenging for climate models because of the complex processes controlling the sources and sinks of cloud liquid water, including both cloud liquid water and ice crystals. Here, we examine these interactions using the Unified Model (UM) regional climate model, with the Cloud AeroSol Interacting Microphysics (CASIM) and UK Chemistry and Aerosol (UKCA) models included for interactive aerosol and cloud microphysics. We simulate two case studies from the second field campaign of Clouds Aerosols Precipitation Radiation and atmospheric Composition over the Southern Ocean Phase 2 (CAPRICORN‐2), which represent the open ocean and the offshore coastal region of Antarctica. Compared with these observations, we find that the UM underestimates surface aerosol concentration by up to an order of magnitude and investigate the effect of this bias on the simulated cloud microphysical and radiative properties. We find that the cloud liquid water path (LWP) and surface radiative fluxes are also biased in the offshore coastal Antarctic case study, with a 32% mean underestimation of LWP and 76% mean overestimation of downwelling surface shortwave flux. Sensitivity tests show that the cloud liquid water bias is largely caused by deficiencies in the representation of the meteorology, and less by aerosol or cloud microphysical properties. Our results provide key insights on the modeling of cloud processes in high southern latitudes.
On the Geoelectric Field Response to the SSC of the May 2024 Super Storm Over Europe
Authors: Piersanti M. , Oliveira D. M. , D’Angelo G. , Diego P. , Napoletano G. , Zesta E. 
Published: February 2025

AbstractSolar variability can lead to significant disturbances, such as coronal mass ejections (CMEs). A CME impacting the Earth's magnetosphere often causes geomagnetic storms that affect not only the magnetosphere but also the ionosphere, the upper atmosphere, and even the ground. During extreme events, rapidly changing geomagnetic fields can create strong geomagnetically induced currents (GICs) at ground. These GICs can severely impact human technology, causing damage to high‐voltage power transformers and leading to power outages, as well as corrosion in oil and gas pipelines. On 10 May 2024, the most intense geomagnetic storm since the Halloween 2003 storm impacted Earth's environment, causing auroras to appear at much lower latitudes than usual in both the northern and southern hemispheres. This study investigates the effects of geomagnetically induced electric fields (GIEs), and hence GICs, during the sudden storm commencement (SSC) of the geomagnetic storm on 10 May 2024, over Europe, using the European quasi‐Meridional Magnetometer Array ground magnetometers. Despite the magnetometer array being placed in the late afternoon (18:00 LT), the combined influence of a strong solar wind dynamic pressure amplitude and a significant, long‐lasting southward interplanetary magnetic field (IMF) (nT) resulted in strong SSC amplitudes (nT) at mid‐low latitudes (N). Results suggest that the CME‐driven shock inclination in the meridional plane leads to high GIE driven only at high latitudes. In addition, the decomposition of the SSC disturbance field at ground into ionospheric (DP field) and magnetospheric (DL field) origin contribution should commend input to GIEs (and hence to GICs) from both DL and DP fields, rather than ionospheric current alone.
Observations of Unusual Postsunrise Interhemispheric Geomagnetic Conjugate Super Plasma Depletions at Midlatitudes During the Recovery Phase of the November 2003 Superstorm
Authors: Ye Hailun , Xue Xianghui , Sun Yang‐Yi , Yu Tao , Yi Wen , Wu Jianfei , Yu Bingkun , Wang Jianyuan , Long Chi , Zhou Baozhu , Li Zezhong , Wang Chong , Chen Tingdi , Dou Xiankang 
Published: February 2025

AbstractThis study reports an extreme ionospheric plasma depletion event over the Eastern Pacific from postmidnight to early morning during the superstorm's recovery phase on 21 November 2003 observed by the DMSP F13, ROCSAT‐1, GRACE, and CHAMP satellites, and two ground‐based GPS receivers. The depletion and its interhemispheric geomagnetic conjugate effect occurred between ∼40°S and 40°N geomagnetic latitudes. This suggests a possible extremely long‐range flux tube connection with an apex altitude exceeding 6,000 km over the geomagnetic equator. Notably, the observed altitude for midlatitude depletions of equatorial origin in the morning is the highest ever reported. Previous studies have primarily observed the extension of equatorial plasma bubbles to middle latitudes in the post‐sunset hours. The continuous upward vertical plasma drift, supported by ROCSAT‐1 and TIE‐GCM simulations, may drive the unprecedentedly strong latitudinal extension of these midlatitude plasma depletions in the midnight‐to‐morning sector.
Creatine transporter (SLC6A8) knockout mice exhibit reduced muscle performance, disrupted mitochondrial Ca2+ homeostasis, and severe muscle atrophy
Authors: Pertici Irene , D’Angelo Donato , Vecellio Reane Denis , Reconditi Massimo , Morotti Ilaria , Putignano Elena , Napoli Debora , Rastelli Giorgia , Gherardi Gaia , De Mario Agnese , Rizzuto Rosario , Boncompagni Simona , Baroncelli Laura , Linari Marco , Caremani Marco , Raffaello Anna 
Published: February 2025

Abstract
Creatine (Cr) is essential for cellular energy homeostasis, particularly in muscle and brain tissues. Creatine Transporter Deficiency (CTD), an X-linked disorder caused by mutations in the SLC6A8 gene, disrupts Cr transport, leading to intellectual disability, speech delay, autism, epilepsy, and various non-neurological symptoms. In addition to neurological alterations, Creatine Transporter knockout (CrT−/y) mice exhibit severe muscle atrophy and functional impairments. This study provides the first characterization of the skeletal muscle phenotype in CrT−/y mice, revealing profound ultrastructural abnormalities accompanied by reduced fiber cross-sectional area and muscle performance. Notably, mitochondria are involved, as evidenced by disrupted cristae, increased mitochondrial size, impaired Ca2+ uptake, reduced membrane potential and ATP production. Mechanistically, the expression of atrophy-specific E3 ubiquitin ligases and suppression of the IGF1-Akt/PKB pathway, regulated by mitochondrial Ca2+ levels, further support the atrophic phenotype. These findings highlight the profound impact of Cr deficiency on skeletal muscle, emphasizing the need for targeted therapeutic strategies to address both the neurological and peripheral manifestations of CTD. Understanding the underlying mechanisms, particularly mitochondrial dysfunction, could lead to novel interventions for this disorder.
The liver-specific long noncoding RNA FAM99B inhibits ribosome biogenesis and cancer progression through cleavage of dead-box Helicase 21
Authors: He Yifei , Li Hongquan , Shi Qili , Liu Yanfang , Pan Qiaochu , He Xianghuo 
Published: February 2025

Abstract
Emerging evidence has demonstrated that long noncoding RNAs (lncRNAs) are promising targets or agents for the treatment of human cancers. Most liver-specific lncRNAs exhibit loss of expression and act as tumor suppressors in liver cancer. Modulating the expression of these liver-specific lncRNAs is a potential approach for lncRNA-based gene therapy for hepatocellular carcinoma (HCC). Here, we report that the expression of the liver-specific lncRNA FAM99B is significantly decreased in HCC tissues and that FAM99B suppresses HCC cell proliferation and metastasis both in vitro and in vivo. FAM99B promotes the nuclear export of DDX21 through XPO1, leading to further cleavage of DDX21 by caspase3/6 in the cytoplasm. FAM99B inhibits ribosome biogenesis by inhibiting ribosomal RNA (rRNA) processing and RPS29/RPL38 transcription, thereby reducing global protein synthesis through downregulation of DDX21 in HCC cells. Interestingly, the FAM99B65-146 truncation exhibits tumor-suppressive effects in vivo and in vitro. Moreover, GalNAc-conjugated FAM99B65-146 inhibits the growth and metastasis of orthotopic HCC xenografts, providing a new strategy for the treatment of HCC. This is the first report of the use of a lncRNA as an agent rather than a target in tumor treatment.
The anti-GD2 monoclonal antibody naxitamab plus GM-CSF for relapsed or refractory high-risk neuroblastoma: a phase 2 clinical trial
Authors: Mora Jaume , Chan Godfrey C. F. , Morgenstern Daniel A. , Amoroso Loredana , Nysom Karsten , Faber Jörg , Wingerter Arthur , Bear Melissa K. , Rubio-San-Simon Alba , de Las Heras Blanca Martínez , Tornøe Karen , Düring Maria , Kushner Brian H. 
Published: February 2025

Abstract
In this single-arm, non-randomized, phase 2 trial (NCT03363373), 74 patients with relapsed/refractory high-risk neuroblastoma and residual disease in bone/bone marrow (BM) received naxitamab on Days 1, 3, and 5 (3 mg/kg/day) with granulocyte-macrophage colony-stimulating factor (Days -4 to 5) every 4 weeks, until complete response (CR) or partial response (PR) followed by 5 additional cycles every 4 weeks. Primary endpoint in the prespecified interim analysis was overall response (2017 International Neuroblastoma Response Criteria). Among 26 responders (CR + PR) in the efficacy population (N = 52), 58% had refractory disease, and 42% had relapsed disease. Overall response rate (ORR) was 50% (95% CI: 36-64%), and CR and PR were observed in 38% and 12%, respectively. With the 95% CI lower limit for ORR exceeding 20%, the primary endpoint of overall response was met. Patients with evaluable bone disease had a 58% (29/50) bone compartment response (CR, 40%; PR, 18%). BM compartment response was 74% (17/23; CR, 74%). One-year overall survival and progression-free survival (secondary endpoints) were 93% (95% CI: 80-98%) and 35% (95% CI: 16-54%), respectively. Naxitamab-related Grade 3 adverse events included hypotension (58%) and pain (54%). Overall, naxitamab demonstrated clinically meaningful efficacy with manageable safety in patients with residual neuroblastoma in bone/BM.
Robust multi-label surgical tool classification in noisy endoscopic videos
Authors: Qayyum Adnan , Ali Hassan , Caputo Massimo , Vohra Hunaid , Akinosho Taofeek , Abioye Sofiat , Berrou Ilhem , Capik Paweł , Qadir Junaid , Bilal Muhammad 
Published: February 2025

Abstract
Over the past few years, surgical data science has attracted substantial interest from the machine learning (ML) community. Various studies have demonstrated the efficacy of emerging ML techniques in analysing surgical data, particularly recordings of procedures, for digitising clinical and non-clinical functions like preoperative planning, context-aware decision-making, and operating skill assessment. However, this field is still in its infancy and lacks representative, well-annotated datasets for training robust models in intermediate ML tasks. Also, existing datasets suffer from inaccurate labels, hindering the development of reliable models. In this paper, we propose a systematic methodology for developing robust models for surgical tool classification using noisy endoscopic videos. Our methodology introduces two key innovations: (1) an intelligent active learning strategy for minimal dataset identification and label correction by human experts through collective intelligence; and (2) an assembling strategy for a student-teacher model-based self-training framework to achieve the robust classification of 14 surgical tools in a semi-supervised fashion. Furthermore, we employ strategies such as weighted data loaders and label smoothing to enable the models to learn difficult samples and address class imbalance issues. The proposed methodology achieves an average F1-score of 85.88% for the ensemble model-based self-training with class weights, and 80.88% without class weights for noisy tool labels. Also, our proposed method significantly outperforms existing approaches, which effectively demonstrates its effectiveness.
Reducing inference cost of Alzheimer’s disease identification using an uncertainty-aware ensemble of uni-modal and multi-modal learners
Authors: Hagos Misgina Tsighe , Curran Kathleen M. , Mac Namee Brian ,   for the Alzheimer’s Disease Neuroimaging Initiative
Published: February 2025

Abstract
While multi-modal deep learning approaches trained using magnetic resonance imaging (MRI) and fluorodeoxyglucose positron emission tomography (FDG PET) data have shown promise in the accurate identification of Alzheimer’s disease, their clinical applicability is hindered by the assumption that both modalities are always available during model inference. In practice, clinicians adjust diagnostic tests based on available information and specific clinical contexts. We propose a novel MRI- and FDG PET-based multi-modal deep learning approach that mimics clinical decision-making by incorporating uncertainty estimates of an MRI-based model (generated using Monte Carlo dropout and evidential deep learning) to determine the necessity of an FDG PET scan, and only inputting the FDG PET to a multi-modal model when required. This approach significantly reduces the reliance on FDG PET scans, which are costly and expose patients to radiation. Our approach reduces the need for FDG PET by up to 92% without compromising model performance, thus optimizing resource use and patient safety. Furthermore, using a global model explanation technique, we provide insights into how anatomical changes in brain regions—such as the entorhinal cortex, amygdala, and ventricles—can positively or negatively influence the need for FDG PET scans in alignment with clinical understanding of Alzheimer’s disease.
Deciphering the code of resistance: a genomic and transcriptomic exploration of the Cystoisospora suis Holland-I strain
Authors: Cruz-Bustos Teresa , Eder Thomas , Ruttkowski Baerbel , Joachim Anja 
Published: February 2025

Abstract

Cystoisospora suis, a member of the apicomplexan order Coccidia and causative agent of neonatal porcine coccidiosis, poses a challenge to pig production due to the emergence of reduced efficacy of toltrazuril, the only EU-approved treatment. To address the critical gaps in understanding toltrazuril resistance and possibilities of early diagnostics, our study investigated the genetic basis of resistance through whole-genome DNA sequencing and transcriptome analysis of two C. suis strains, the toltrazuril-susceptible Wien-I and the resistant Holland-I. Additionally, we studied the mitochondrial genome and analysed mitochondrial gene expression in both strains. Our results show that genes encoding proteins involved in host-cell invasion displayed variable expression patterns and genetic mutations, suggesting adaptive changes in invasion mechanisms. Moreover, substantial fluctuations in the expression of genes linked to retrotransposons, accompanied by genetic alterations, were observed, highlighting their potential involvement in genomic rearrangements. Finally, our mitochondrial genome analyses revealed important insights into its genetic organization and conservation. Notably, the marked downregulation of CoI, CoIII and Cytb mRNA levels in the resistant strain Holland-I upon toltrazuril exposure highlights the dynamic response of mitochondrial genes to toltrazuril. These mitochondrial adaptations appear to be closely linked to the parasite drug resistance mechanism, potentially facilitating its survival under pharmacological stress. These findings enhance our knowledge of drug resistance mechanisms in Coccidia and highlight the need for novel management strategies, leading to the development of targeted treatments and controls.
Effects of Nanoscale precipitates on mechanical properties, corrosion resistance, and biocompatibility in Zn-Mn alloy
Authors: Dong Cuilan , Liao Zikun , Yin Yanyi , Yi Yinzhi , Zhu Guanghui , Zheng Tuquan , Tan Qian , Xie Yonghong 
Published: February 2025

Abstract
Controlling degradation rate is essential for the biomedical application of biodegradable Zn alloys. Alloying with soluble elements is an effective way to regulate formation of second phases, which differ in potential from the Zn matrix. The potential difference exhibits positive or negative effects on corrosion resistance. This study successfully forms MnZn13 phase with nano size by altering ECAP temperature. Subsequently, MnZn13 phase promotes grain refinement, improvement of elongation, and corrosion resistance. Higher elongation in Zn-Mn alloy with MnZn13 phase is attributed to the grain boundary sliding, deformation twins in MnZn13 phase. Meanwhile, grain boundary corrosion in Zn-Mn alloy with MnZn13 phase is incomplete. Corrosion mode of Zn-Mn alloys without MnZn13 phase is dominated by grain boundary corrosion, accompanied by pitting corrosion. The increased corrosion resistance from MnZn13 phase stems from its higher potential than Zn matrix. Zn-Mn alloys with and without MnZn13 phase show a comparable cytocompatibility and osteogenic properties. Our findings provide an effective way to regulating mechanical properties and corrosion resistance of Zn alloys via controlling precipitation.
Multiple long-term conditions as the next transition in the global diabetes epidemic
Authors: Gregg Edward W. , Holman Naomi , Sophiea Marisa , Misra Shivani , Pearson-Stuttard Jonathan , Valabhji Jonathan , Khunti Kamlesh 
Published: February 2025

Abstract
Several transitions, or new patterns and dynamics in the contributors and health outcomes, have altered the character and burden of the multi-decade, worldwide growth in prevalence of type 2 diabetes (T2DM). These changes have led to different needs for prevention and care. These dynamics have been driven by diverse demographic, socio-economic, behavioural, and health system response factors. In this Perspective, we describe these transitions and how their attributes have set the stage for multimorbidity, or multiple long-term conditions (MLTCs), to be the next major challenge in the diabetes epidemic. We also describe how the timing and character of these stages differ in high-, middle-, and low-income countries. These challenges call for innovation and a stronger focus on MLTCs across the spectrum of cause, effectiveness, and implementation studies to guide prevention and treatment priorities.
Political enablers of ambitious climate policies: a framework and thematic review
Authors: Montfort Simon , Fesenfeld Lukas , Ingold Karin , Lamb William F. , Andrijevic Marina 
Published: February 2025

Abstract
Currently, most research explaining why countries lead or lag in climate policy assumes a problem-oriented perspective, focusing on barriers to climate policy adoption. Here, we argue that correcting for past failures, solving problems, and bringing climate policies back on track for the Paris Agreement requires a solution-oriented perspective on the political enablers of ambitious climate policies. We unite a growing research community that has previously been scattered across disciplinary subfields with various ontological and epistemological assumptions. Rooted in a thematic review of the scientific literature, we introduce a framework with a typology of six political enablers for ambitious climate policy at its core. For each enabler, we summarize key policy implications. We illustrate our framework with a case study on the adoption of emission trading systems in the transport and building sectors in Germany and the European Union (EU) allowing future solution-oriented research to build on our effort.