bluuebunny/crossref_metadata_2025_split huggingface dataset:
Impact of Victoria Mode on Late‐Season Tropical Cyclone Genesis Over the Western North Pacific
Authors: Dai Yifei , Wang Bin , Wei Na , Song Jinjie , Duan Yihong 
Published: January 2025

AbstractThe Victoria Mode (VM) plays a vital role in shaping Pacific Sea Surface Temperature (SST) variability. However, its impact on late‐season tropical cyclone genesis (TCG) over the western North Pacific (WNP) remains underexplored. This study investigated the influence of VM on WNP TCG from 1965 to 2020. Significant relationships emerge in interannual (6 year) and decadal (11 year) timescales. On the interannual timescale, VM correlates positively with TCG west of 145°E and negatively to the east. On the decadal timescale, the negative relationship spans the entire WNP. Victoria Mode directly modulates vertical motion and induces low‐latitude anticyclonic circulation across the low‐latitude North Pacific by altering surface wind convergence and diabatic heating. This, in turn, shapes environmental conditions, affecting TCG. Dynamic factors are more important in WNP TCG changes than thermodynamic factors. Critical environmental conditions in VM's impact on TCG include four dynamic factors: relative vorticity, vertical velocity, meridional gradient of zonal wind, and vertical shear of zonal winds. Vertical shear of zonal winds is particularly influential. Distinct correlation patterns and mechanisms are diagnosed between different timescales, attributed to anomalous SST observed over the WNP on the interannual timescale but not on the decadal timescale. Furthermore, this study illustrates the complexity of region‐ and time‐dependent relationships between environmental conditions and TCG. Such dependencies pose challenges for conventional genesis potential index models in accurately capturing the spatial characteristics of the VM‐TCG relationship. Overall, our findings uncover VM's substantial influence on WNP TCG, enhancing understanding of key environmental conditions and mechanisms driving interannual and decadal TCG variations.
Estimation of Fire Counts and Fire Radiative Power Using Satellite Optical and Microwave Vegetation Indices With Random Forest Method
Authors: Duan Jiawei , Hu Jiheng , Fu Yuyun , Liu Qingyang , Li Rui , Wang Yipu 
Published: January 2025

AbstractThe satellite microwave emissivity difference vegetation index (EDVI) has been used in previous studies to estimate FCs and FRP using traditional multivariate linear regression models. However, the nonlinear effects and contributions of numerous factors that affect forest fires cannot be disentangled by this model. Using the random forest (RF) model, this study utilized multiple EDVIs and the optical normalized difference vegetation index (NDVI) as key fuel properties to resolve the physical driving mechanisms of forest fires and to estimate the daily FCs and FRP over East Asia. The results showed that the estimated FCs and FRP were in good agreement with satellite observations, with a spatial R of 0.59 for FCs and 0.63 for FRP and a temporal R of 0.80 for FCs and 0.81 for FRP. The integration of EDVIs and NDVI into the RF model was found to improve model performance and generate overall lower systematic errors than the model without vegetation variables. Model performance was better than that in previous studies using multivariate linear regression models. In addition, EDVIs showed greater importance than NDVI. This was largely due to their daily temporal resolution that allowed EDVIs to capture forest fire dynamics in time. The combination of the RF model with satellite microwave and optical observations shows good performance and has great potential for FC and FRP estimations in global fire danger assessment.
How Well Do AMIP6 and CMIP6 Reproduce the Specific Extreme El Niño Teleconnections to North America?
Authors: Beniche Margot , Vialard Jérôme , Lengaigne Matthieu , Hall Nicholas M. J. 
Published: January 2025

AbstractExtreme El Niño events (e.g., 1982–1983, 1997–1998) are characterized by strong, eastward‐shifted warm Sea Surface Temperature anomalies, and a southward migration of the eastern Pacific Inter‐Tropical Convergence Zone (ITCZ) to the equator. Using an ensemble simulation with a single AMIP6 model, Beniche et al. (2024, https://doi.org/10.1038/s41598‐024‐52580‐9) suggested that such events uniquely yield an eastward shift of the Pacific‐North American (PNA) teleconnection pattern, with specific impacts over North America. Here, we first examine the robustness of these results in 135 ensemble members from 23 different AMIP6 models. The specific, eastward‐shifted extreme El Niño teleconnection pattern is robust in all models but one. It is also highly reproducible across years and ensemble members, due to stronger teleconnection amplitude than that of internal atmospheric noise. This yields specific, predictable impacts (defined as 0.5 STD) such as warm conditions over Northeast America (69% chances), and wet anomalies over California (77%) and Florida (97%). We then show that 26 out of the 42 CMIP6 models we examined reproduce extreme El Niño events, defined as El Niño events associated with large eastern Pacific rainfall anomalies. These models tend to have a weaker cold tongue bias than the rest of CMIP6. Despite a degradation in performance from AMIP6 to CMIP6, 18 out of the 26 selected models capture the specific extreme El Niño teleconnections, albeit with some underestimation of wet anomalies over California and Florida. We end by discussing implications for future North American climate projections based on CMIP6.
Biofilm Development, Senescence, and Benthic Substrate Influence Hyporheic Transport in Streams
Authors: Volponi Sabrina N. , Tank Jennifer L. , Vincent Anna E. S. , Snyder Elise D. , Pruitt Abagael N. , Bolster Diogo 
Published: January 2025

AbstractUnderstanding fate and transport within fluvial systems requires accurate modeling of breakthrough curve (BTC) tails, which often display non‐Fickian behaviors. However, it is unclear how anomalous processes relate to the physical and biological characteristics of the stream ecosystem. We use the Stochastic Mobile Immobile model (SMIM) to determine the impact of biofilm colonization among different substrate types on reach‐scale transport velocity (V) and dispersion (D), rate of delivery to the subsurface (Λ), and retention within the subsurface (reflected by power law slope; β). During the summers of 2020 and 2021, we conducted a total of n = 42 Rhodamine‐WT releases in four experimental streams lined with contrasting substrata (sand, pea gravel, cobble, and a three‐way mix) at the Notre Dame Linked Experimental Ecosystem Facility (ND‐LEEF) in Indiana (USA). To explore the effect of biofilm colonization, we conducted releases under artificially shaded, early and late biofilm development, and senescent biofilm conditions. We found that replicated releases under constant conditions consistently reproduced stream BTCs and modeled transport parameters. Biofilm abundance, biofilm status (living vs. dead), and substrate type produced significant variations in BTC shape and transport parameterizations. We found a non‐linear relationship between algal biomass and V, where increases in biomass produced decreases in V at low biomass and increases in V at high biomass. Substrate type also predicted patterns in transport, with sand producing higher V, Λ, and β than larger substrata. These results suggest that substrate type acts as the primary driver and biofilm development the secondary control on transport in these stream systems.
Neural signatures of risk-taking adaptions across health, bipolar disorder, and lithium treatment
Authors: Scholl Jacqueline , Panchal Priyanka , Nelissen Natalie , Atkinson Lauren Z. , Kolling Nils , Saunders Kate EA , Geddes John , Rushworth Matthew FS , Nobre Anna C. , Harrison Paul J. , Harmer Catherine J. 
Published: January 2025

Abstract
Cognitive and neural mechanisms underlying bipolar disorder (BD) and its treatment are still poorly understood. Here we examined the role of adaptations in risk-taking using a reward-guided decision-making task. We recruited volunteers with high (n = 40) scores on the Mood Disorder Questionnaire, MDQ, suspected of high risk for bipolar disorder and those with low-risk scores (n = 37). We also recruited patients diagnosed with BD who were assigned (randomized, double-blind) to six weeks of lithium (n = 19) or placebo (n = 16) after a two-week baseline period (n = 22 for FMRI). Participants completed mood ratings daily over 50 (MDQ study) or 42 (BD study) days, as well as a risky decision-making task and functional magnetic resonance imaging. The task measured adaptation of risk taking to past outcomes (increased risk aversion after a previous win vs. loss, ‘outcome history’). While the low MDQ group was risk averse after a win, this was less evident in the high MDQ group and least so in the patients with BD. During fMRI, ‘outcome history’ was linked to medial frontal pole activation at the time of the decision and this activation was reduced in the high risk MDQ vs. the low risk MDQ group. While lithium did not reverse the pattern of BD in the task, nor changed clinical symptoms of mania or depression, it changed reward processing in the dorsolateral prefrontal cortex. Participants’ modulation of risk-taking in response to reward outcomes was reduced as a function of risk for BD and diagnosed BD. These results provide a model for how reward may prime escalation of risk-related behaviours in bipolar disorder and how mood stabilising treatments may work.
Raptin, a sleep-induced hypothalamic hormone, suppresses appetite and obesity
Authors: Xie Ling-Qi , Hu Biao , Lu Ren-Bin , Cheng Ya-Lun , Chen Xin , Wen Jie , Xiao Yao , An Yu-Ze , Peng Ning , Dai Yu , Xie Genqing , Guo Qi , Peng Hui , Luo Xiang-Hang 
Published: January 2025

Abstract
Sleep deficiency is associated with obesity, but the mechanisms underlying this connection remain unclear. Here, we identify a sleep-inducible hypothalamic protein hormone in humans and mice that suppresses obesity. This hormone is cleaved from reticulocalbin-2 (RCN2), and we name it Raptin. Raptin release is timed by the circuit from vasopressin-expressing neurons in the suprachiasmatic nucleus to RCN2-positive neurons in the paraventricular nucleus. Raptin levels peak during sleep, which is blunted by sleep deficiency. Raptin binds to glutamate metabotropic receptor 3 (GRM3) in neurons of the hypothalamus and stomach to inhibit appetite and gastric emptying, respectively. Raptin-GRM3 signaling mediates anorexigenic effects via PI3K-AKT signaling. Of note, we verify the connections between deficiencies in the sleeping state, impaired Raptin release, and obesity in patients with sleep deficiency. Moreover, humans carrying an RCN2 nonsense variant present with night eating syndrome and obesity. These data define a unique hormone that suppresses food intake and prevents obesity.
Intricacies of human–AI interaction in dynamic decision-making for precision oncology
Authors: Niraula Dipesh , Cuneo Kyle C. , Dinov Ivo D. , Gonzalez Brian D. , Jamaluddin Jamalina B. , Jin Jionghua Judy , Luo Yi , Matuszak Martha M. , Ten Haken Randall K. , Bryant Alex K. , Dilling Thomas J. , Dykstra Michael P. , Frakes Jessica M. , Liveringhouse Casey L. , Miller Sean R. , Mills Matthew N. , Palm Russell F. , Regan Samuel N. , Rishi Anupam , Torres-Roca Javier F. , Yu Hsiang-Hsuan Michael , El Naqa Issam 
Published: January 2025

Abstract
AI decision support systems can assist clinicians in planning adaptive treatment strategies that can dynamically react to individuals’ cancer progression for effective personalized care. However, AI’s imperfections can lead to suboptimal therapeutics if clinicians over or under rely on AI. To investigate such collaborative decision-making process, we conducted a Human–AI interaction study on response-adaptive radiotherapy for non-small cell lung cancer and hepatocellular carcinoma. We investigated two levels of collaborative behavior: model-agnostic and model-specific; and found that Human–AI interaction is multifactorial and depends on the complex interrelationship between prior knowledge and preferences, patient’s state, disease site, treatment modality, model transparency, and AI’s learned behavior and biases. In summary, some clinicians may disregard AI recommendations due to skepticism; others will critically analyze AI recommendations on a case-by-case basis; clinicians will adjust their decisions if they find AI recommendations beneficial to patients; and clinician will disregard AI recommendations if deemed harmful or suboptimal and seek alternatives.
Quantifying the shift of public export finance from fossil fuels to renewable energy
Authors: Censkowsky Philipp , Waidelich Paul , Shishlov Igor , Steffen Bjarne 
Published: January 2025

Abstract
By providing guarantees and direct lending, public export credit agencies (ECAs) de-risk and thus enable energy projects worldwide. Despite their importance for global greenhouse gas emission pathways, a systematic assessment of ECAs’ role and financing patterns in the low-carbon energy transition is still needed. Using commercial transaction data, here we analyze 921 energy deals backed by ECAs from 31 OECD and non-OECD countries (excluding Canada) between 2013 and 2023. We find that while the share of renewables in global ECA energy commitments rose substantially between 2013 and 2023, ECAs remain heavily involved in the fossil fuel sector, with support varying substantially across technologies, value chain stages, and countries. Portfolio ‘greening’ is primarily driven by members of the E3F climate club, impacting deal financing structures and shifting finance flows towards high-income countries. Our results call for reconsidering ECA mandates and strengthening international climate-related cooperation in export finance.
Maize monoculture supported pre-Columbian urbanism in southwestern Amazonia
Authors: Lombardo Umberto , Hilbert Lautaro , Bentley McKenzie , Bronk Ramsey Christopher , Dudgeon Kate , Gaitan-Roca Albert , Iriarte José , Mejía Ramón Andrés G. , Quezada Sergio , Raczka Marco , Watling Jennifer G. , Neves Eduardo , Mayle Francis 
Published: January 2025

Abstract
The Casarabe culture (500–1400 ce), spreading over roughly 4,500 km2 of the monumental mounds region of the Llanos de Moxos, Bolivia, is one of the clearest examples of urbanism in pre-Columbian (pre-1492 ce) Amazonia. It exhibits a four-tier hierarchical settlement pattern, with hundreds of monumental mounds interconnected by canals and causeways1,2. Despite archaeological evidence indicating that maize was cultivated by this society3, it is unknown whether it was the staple crop and which type of agricultural farming system was used to support this urban-scale society. Here, we address this issue by integration of remote sensing, field survey and microbotanical analyses, which shows that the Casarabe culture invested heavily in landscape engineering, constructing a complex system of drainage canals (to drain excess water during the rainy season) and newly documented savannah farm ponds (to retain water in the dry season). Phytolith analyses of 178 samples from 18 soil profiles in drained fields, farm ponds and forested settings record the singular and ubiquitous presence of maize (Zea mays) in pre-Columbian fields and farm ponds, and an absence of evidence for agricultural practices in the forest. Collectively, our findings show how the Casarabe culture managed the savannah landscape for intensive year-round maize monoculture that probably sustained its relatively large population. Our results have implications for how we conceive agricultural systems in Amazonia, and show an example of a Neolithic-like, grain-based agrarian economy in the Amazon.
C-terminal amides mark proteins for degradation via SCF–FBXO31
Authors: Muhar Matthias F. , Farnung Jakob , Cernakova Martina , Hofmann Raphael , Henneberg Lukas T. , Pfleiderer Moritz M. , Denoth-Lippuner Annina , Kalčic Filip , Nievergelt Ajse S. , Peters Al-Bayati Marwa , Sidiropoulos Nikolaos D. , Beier Viola , Mann Matthias , Jessberger Sebastian , Jinek Martin , Schulman Brenda A. , Bode Jeffrey W. , Corn Jacob E. 
Published: January 2025

Abstract
During normal cellular homeostasis, unfolded and mislocalized proteins are recognized and removed, preventing the build-up of toxic byproducts1. When protein homeostasis is perturbed during ageing, neurodegeneration or cellular stress, proteins can accumulate several forms of chemical damage through reactive metabolites2,3. Such modifications have been proposed to trigger the selective removal of chemically marked proteins3–6; however, identifying modifications that are sufficient to induce protein degradation has remained challenging. Here, using a semi-synthetic chemical biology approach coupled to cellular assays, we found that C-terminal amide-bearing proteins (CTAPs) are rapidly cleared from human cells. A CRISPR screen identified FBXO31 as a reader of C-terminal amides. FBXO31 is a substrate receptor for the SKP1–CUL1–F-box protein (SCF) ubiquitin ligase SCF–FBXO31, which ubiquitylates CTAPs for subsequent proteasomal degradation. A conserved binding pocket enables FBXO31 to bind to almost any C-terminal peptide bearing an amide while retaining exquisite selectivity over non-modified clients. This mechanism facilitates binding and turnover of endogenous CTAPs that are formed after oxidative stress. A dominant human mutation found in neurodevelopmental disorders reverses CTAP recognition, such that non-amidated neosubstrates are now degraded and FBXO31 becomes markedly toxic. We propose that CTAPs may represent the vanguard of a largely unexplored class of modified amino acid degrons that could provide a general strategy for selective yet broad surveillance of chemically damaged proteins.
A deep learning analysis for dual healthcare system users and risk of opioid use disorder
Authors: Yin Ying , Workman Elizabeth , Ma Phillip , Cheng Yan , Shao Yijun , Goulet Joseph L. , Sandbrink Friedhelm , Brandt Cynthia , Spevak Christopher , Kean Jacob T. , Becker William , Libin Alexander , Shara Nawar , Sheriff Helen M. , Butler Jorie , Agrawal Rajeev M. , Kupersmith Joel , Zeng-Trietler Qing 
Published: January 2025

Abstract
The opioid crisis has disproportionately affected U.S. veterans, leading the Veterans Health Administration to implement opioid prescribing guidelines. Veterans who receive care from both VA and non-VA providers—known as dual-system users—have an increased risk of Opioid Use Disorder (OUD). The interaction between dual-system use and demographic and clinical factors, however, has not been previously explored. We conducted a retrospective study of 856,299 patient instances from the Washington DC and Baltimore VA Medical Centers (2012–2019), using a deep neural network (DNN) and explainable Artificial Intelligence to examine the impact of dual-system use on OUD and how demographic and clinical factors interact with it. Of the cohort, 146,688(17%) had OUD, determined through Natural Language Processing of clinical notes and ICD-9/10 diagnoses. The DNN model, with a 78% area under the curve, confirmed that dual-system use is a risk factor for OUD, along with prior opioid use or other substance use. Interestingly, a history of other drug use interacted negatively with dual-system use regarding OUD risk. In contrast, older age was associated with a lower risk of OUD but interacted positively with dual-system use. These findings suggest that within the dual-system users, patients with certain risk profiles warrant special attention.
Artificial intelligence for image recognition in diagnosing oral and oropharyngeal cancer and leukoplakia
Authors: Schmidl Benedikt , Hütten Tobias , Pigorsch Steffi , Stögbauer Fabian , Hoch Cosima C. , Hussain Timon , Wollenberg Barbara , Wirth Markus 
Published: January 2025

Abstract
Visual diagnosis is one of the key features of squamous cell carcinoma of the oral cavity (OSCC) and oropharynx (OPSCC), both subsets of head and neck squamous cell carcinoma (HNSCC) with a heterogeneous clinical appearance. Advancements in artificial intelligence led to Image recognition being introduced recently into large language models (LLMs) such as ChatGPT 4.0. This exploratory study, for the first time, evaluated the application of image recognition by ChatGPT to diagnose squamous cell carcinoma and leukoplakia based on clinical images, with images without any lesion as a control group. A total of 45 clinical images were analyzed, comprising 15 cases each of SCC, leukoplakia, and non-lesion images. ChatGPT 4.0 was tasked with providing the most likely diagnosis based on these images in scenario one. In scenario two the image and the clinical history were provided, whereas in scenario three only the clinical history was given. The results and the accuracy of the LLM were rated by two independent reviewers and the overall performance was evaluated using the modified Artificial Intelligence Performance Index (AIPI. In this study, ChatGPT 4.0 demonstrated the ability to correctly identify leukoplakia cases using image recognition alone, while the ability to diagnose SCC was insufficient, but improved by including the clinical history in the prompt. Providing only the clinical history resulted in a misclassification of most leukoplakia and some SCC cases. Oral cavity lesions were more likely to be diagnosed correctly. In this exploratory study of 45 images of oral lesions, ChatGPT 4.0 demonstrated a convincing performance for detecting SCC only when the clinical history was added, whereas Leukoplakia was detected solely by image recognition. ChatGPT is therefore currently insufficient for reliable OPSCC and OSCC diagnosis, but further technological advancements may pave the way for the use in the clinical setting.
Spatial accuracy of dose delivery significantly impacts the planning target volume margin in linear accelerator-based intracranial stereotactic radiosurgery
Authors: Takahashi Yuta , Oshika Riki , Tachibana Rie , Shirai Katsuyuki , Asakura Hiroshi , Miyazaki Masayoshi , Sagawa Tomohiro , Takahashi Shinichi , Kuwae Tsunekazu , Kojima Hironori , Nishiyama Shiro , Nemoto Hikaru , Ishihara Yoshitomo , Umeda Mariko , Kijima Kotaro , Kobayashi Daisuke , Suzuki Keiji , Nozawa Yuki , Hoshida Kento , Kitagawa Tomoki , Endo Hiromitsu , Matsunaga Yuki , Itagaki Hiroya , Ishida Mayumi , Kanahara Shigeru , Horita Ryo , Hori Daisuke , Tachibana Hidenobu 
Published: January 2025

Abstract
The impact of three-dimensional (3D) dose delivery accuracy of C-arm linacs on the planning target volume (PTV) margin was evaluated for non-coplanar intracranial stereotactic radiosurgery (SRS). A multi-institutional 3D starshot test using beams from seven directions was conducted at 22 clinics using Varian and Elekta linacs with X-ray CT-based polymer gel dosimeters. Variability in dose delivery accuracy was observed, with the distance between the imaging isocenter and each beam exceeding 1 mm at one institution for Varian and nine institutions for Elekta. The calculated PTV margins for Varian and Elekta linacs that could cover the gross tumor volume with 95% probability at 95% of the institutions were 2.3 and 3.5 mm, respectively, in the superior–inferior direction. However, with multifactorial system management (i.e., high-accuracy 3D dose delivery with rigorous linac quality assurance, strict patient immobilization, and high intra-fractional positioning accuracy), these margins could be reduced to 1.0 mm and 1.5 mm, respectively. The findings indicate significant millimeter-level variability in 3D dose delivery accuracy among linacs installed in clinical settings. Thus, maximizing a linac’s 3D dose delivery accuracy is essential to achieve the required PTV margin in intracranial SRS.