bluuebunny/crossref_metadata_2025_split huggingface dataset:
Benthic marine invertebrate herbivores diversify their algal diets in winter
Authors: Yoshioka R. M. , Galloway A. W. E. , Schram J. B. , Bell L. E. , Kroeker K. J. 
Published: January 2025

AbstractChanges in the seasonal environment substantially influence organisms. Understanding how species respond to such changes, such as in their feeding interactions and physiology, is key to predicting their resilience to both seasonal and longer term climate changes. The Sitka Sound in Southeast Alaska, USA, is an attractive natural laboratory for studying change, as the marine environment experiences substantial seasonal fluctuations in parameters such as temperature, pH, and productivity between summer and winter. By sampling a suite of dominant macrophyte algae and their benthic herbivores in winter (January) and summer (July) 2019, we investigated how producers and their primary consumers may respond to seasonal change inferred through fatty acid trophic markers. We used a fatty acid logratio selection and analysis approach to ask the following: (1) Do fatty acid biomarkers for algae differ between seasons? (2) Do the same fatty acid biomarkers differ between seasons when applied to herbivores, with herbivore fatty acids tracking the presumed trophic resources? (3) Do fatty acid biomarkers for herbivores differ between seasons, when considered independently from algae? Comparing logratio sets selected each for the algae and the herbivore fatty acids, we found that algae fatty acids were different between our sampling seasons, but the algae‐selected fatty acid logratios did not clearly separate most herbivores by season. In contrast, the herbivore‐selected logratios strongly distinguished herbivore species between the January and June samplings. Further, dispersions of fatty acid logratios were greater within herbivore species in the winter than in the summer, potentially due to the utilization of more diverse resources when preferred algae are less abundant. In total, results suggest that herbivores are not simply tracking the seasonal fatty acid changes in the dominant algae as trophic resources; instead, herbivore fatty acid seasonal changes may occur largely through some other nonexclusive mechanisms, such as omnivory, reductions or shifts in dietary composition, and/or endogenous physiological responses. Thus, the benthic herbivores in our study appear not to be locked into their diets when faced with seasonal change and may use a range of strategies, including diet diversification, to cope with environmental variation.
Resource selection and survival of plains sharp‐tailed grouse at a wind energy facility
Authors: Kelly Carly S. , LeBeau Chad W. , Beck Jeffrey L. , Solem Alex , Morey Hilary , Smith Kurt T. 
Published: January 2025

AbstractAs the demand for wind energy development increases across much of the Great Plains region, there is a need to understand how this type of energy generation may impact wildlife. Due to their extensive range across areas with high wind resources, plains sharp‐tailed grouse (Tympanuchus phasianellus jamesiTympanuchus phasianellus jamesi) represent a valuable species to evaluate how selection and survival are associated with existing wind energy infrastructure. We used spatial and demographic data collected from radio‐marked female sharp‐tailed grouse to evaluate resource selection (nest, brood‐rearing, and breeding season) and survival (nest and female) near existing wind energy infrastructure during the April to August breeding season over a 3‐year period from 2020 to 2022 in northeastern South Dakota, USA. We monitored 119 GPS‐marked females captured at eight leks over the study period. We did not find evidence that females selected nest sites in relation to wind energy infrastructure but found that females with broods and females during the breeding season (April–August) avoided areas near high densities of wind turbines within 1.0 and 5.0 km of their home range, respectively. We found consistent selection for lower lengths of transmission lines across all life stages at the home range scale. We did not detect an effect of wind energy infrastructure on nest or female survival. Based on the results of our study, limiting the siting (the process of selecting the optimal location for a project and the associated features) of wind turbines within 5.0 km of sharp‐tailed grouse breeding habitat may represent an important siting tool to minimize avoidance of otherwise suitable habitats.
Deep Learning Method for Airfoil Flow Field Simulation Based on Unet++
Authors: Ruiling Xie , Jie Xu , Jianping Chen , Peizhi Tan 
Published: January 2025

ABSTRACTThis paper investigates the accuracy of U‐Net++ networks in predicting Reynolds‐Averaged Navier‐Stokes (RANS) solutions. The study employs the symbolic distance function (SDF) to represent geometry and flow conditions, utilizing parameterized airfoil data from the UIUC (University of Illinois at Urbana‐Champaign) airfoil datasets. The research assesses the performance of multiple trained neural networks in predicting pressure and velocity distributions. Specifically, the study examines the influence of varying network weights on solution accuracy. Through the optimization of the model, the research demonstrates that the mean relative error is below 1.72% for a range of previously unseen wing shapes, with a computational speedup factor of up to 1,000× in certain scenarios. The accuracy achieved by this model underscores the significant potential of deep learning‐based approaches as reliable tools for aerodynamic design and optimization.
The Transcriptomic and Gene Fusion Landscape of Pleomorphic Salivary Gland Adenomas
Authors: Afshari Maryam Kakay , Tejera Nevado Paloma , Fehr André , Huang Junchi , Jäwert Fredrik , Nilsson Jonas A. , Stenman Göran , Andersson Mattias K. 
Published: January 2025

ABSTRACTPleomorphic adenoma (PA) is the most common salivary gland tumor. PAs are characterized by chromosomal rearrangements of 8q12 and 12q14‐15, leading to gene fusions involving the PLAG1 and HMGA2 oncogenes. Here, we performed the first comprehensive study of the transcriptomic and gene fusion landscape of 38 cytogenetically characterized PAs. RNA‐seq identified PLAG1 or HMGA2 fusions in 33/38 cases (87%), of which 15 were novel fusions. Fusions were found also in tumors with normal karyotype, demonstrating that they are generated by cryptic rearrangements. PLAG1 was mainly activated by promoter swapping and HMGA2 by truncation of its 3′‐part. RNA‐seq revealed upregulation of genes involved in extracellular matrix production, WNT‐signaling, and epithelial‐mesenchymal transition in PA compared to normal salivary tissue. Principal component analysis identified two PA subclusters characterized by PLAG1‐ and HMGA2‐activation, respectively, that differed in expression of genes involved in the immune system, cell adhesion, and microenvironment remodeling. Moreover, comparative analyses of PA and salivary carcinomas revealed that PA resembles myoepithelial carcinoma. Our study reveals new oncogenic gene fusions and expands our knowledge about the molecular underpinnings of PA.
General Kernel Machine Methods for Multi‐Omics Integration and Genome‐Wide Association Testing With Related Individuals
Authors: Little Amarise , Zhao Ni , Mikhaylova Anna , Zhang Angela , Ling Wodan , Thibord Florian , Johnson Andrew D. , Raffield Laura M. , Curran Joanne E. , Blangero John , O'Connell Jeffrey R. , Xu Huichun , Rotter Jerome I. , Rich Stephen S. , Rice Kenneth M. , Chen Ming‐Huei , Reiner Alexander , Kooperberg Charles , Vu Thao , Hou Lifang , Fornage Myriam , Loos Ruth J.F. , Kenny Eimear , Mathias Rasika , Becker Lewis , Smith Albert V. , Boerwinkle Eric , Yu Bing , Thornton Timothy , Wu Michael C. 
Published: January 2025

ABSTRACTIntegrating multi‐omics data may help researchers understand the genetic underpinnings of complex traits and diseases. However, the best ways to integrate multi‐omics data and use them to address pressing scientific questions remain a challenge. One important and topical problem is how to assess the aggregate effect of multiple genomic data types (e.g. genotypes and gene expression levels) on a phenotype, particularly while accommodating routine issues, such as having related subjects' data in analyses. In this paper, we extend an existing composite kernel machine regression model to integrate two multi‐omics data types, while accommodating for general correlation structures amongst outcomes. Due to the kernel machine regression framework, our methods allow for the integration of high‐dimensional omics data with small, nonlinear, and interactive effects, and accommodation of general study designs. Here, we focus on scientific questions that aim to assess the association between a functional grouping (such as a gene or a pathway) and a quantitative trait of interest. We use a kernel machine regression to integrate the two multi‐omics data types, as they may relate to the trait, and perform a global test of association. We demonstrate the advantage of this approach over single data type association tests via simulation. Finally, we apply this method to a large, multi‐ethnic data set to investigate how predicted gene expression and rare genetic variation may be related to two platelet traits.
Climate Risk and Foreign Direct Investment Entry Mode
Authors: Li Chengchun , Luo Yun , De Vita Glauco 
Published: January 2025

ABSTRACTThe paper examines how climate risk impacts the strategic entry mode choices—between greenfield investments and cross‐border mergers and acquisitions (M&As)—of multinational enterprises (MNEs) into foreign markets. Our study adds to existing literature through a cross‐countries empirical analysis and uses a newly developed dataset, the Multinational Revenue, Employment, and Investment Database (MREID), that accounts for how both physical and transition risks of climate change affect MNEs' entry mode choice. Physical risk refers to the tangible impacts of climate change such as extreme weather events, while transition risk involves regulatory and policy changes associated with moving towards a low‐carbon economy. Using data for 139 source countries and 134 destination countries over the period from 2010 to 2021, we find that an increase in the physical risk of climate change leads to MNEs choosing greenfield investment when entering a new market while a higher level of transition risk discourages greenfield investment. Physical risk has a negative and significant influence on MNEs' entry choice of using cross‐border M&A. There is a positive and significant correlation between transition risk and cross‐border M&A though such a relationship is not robust. Industrial‐level evidence shows a similar pattern in the majority of the industries. Our findings provide policymakers with guidelines helping to mitigate the negative impact of climate change on business decisions at the global level.
Local Inflammation Precedes Diaphragm Wasting and Fibrotic Remodelling in a Mouse Model of Pancreatic Cancer
Authors: Neyroud Daria , D'Lugos Andrew C. , Trevino Enrique J. , Callaway Chandler S. , Lamm Jacqueline , Laitano Orlando , Poole Brittney , Deyhle Michael R. , Brantley Justina , Le Lam , Judge Andrew R. , Judge Sarah M. 
Published: January 2025

ABSTRACTBackgroundCancer cachexia represents a debilitating muscle wasting condition that is highly prevalent in gastrointestinal cancers, including pancreatic ductal adenocarcinoma (PDAC). Cachexia is estimated to contribute to ~30% of cancer‐related deaths, with deterioration of respiratory muscles suspected to be a key contributor to cachexia‐associated morbidity and mortality. In recent studies, we identified fibrotic remodelling of respiratory accessory muscles as a key feature of human PDAC cachexia.MethodsTo gain insight into mechanisms driving respiratory muscle wasting and fibrotic remodelling in response to PDAC, we conducted temporal histological and transcriptomic analyses on diaphragm muscles harvested from mice‐bearing orthotopic murine pancreatic (KPC) tumours at time points reflective of precachexia (D8 and D10), mild–moderate cachexia (D12 and D14) and advanced cachexia (endpoint).ResultsDuring the precachexia phase, diaphragms showed significant leukocyte infiltration (+3‐fold to +13‐fold; D8—endpoint vs. Sham, p < 0.05) and transcriptomic enrichment of inflammatory processes associated with tissue injury that remained increased through endpoint. Diaphragm inflammation was followed by increases in PDGFR‐ɑ+ fibroadipogenic progenitors (+2.5 to +3.8‐fold; D10—endpoint vs. Sham, p < 0.05), fibre atrophy (−16% to −24%, D12 to endpoint vs. Sham, p < 0.05), ECM expansion (+1.5 to +1.8‐fold; D14—endpoint vs. Sham, p < 0.05), collagen accumulation (+3.8‐fold; endpoint vs. Sham, p = 0.0013) and reductions in breathing frequency (−55%, p = 0.0074) and diaphragm excursion (−43%, p = 0.0006). These biological processes were supported by changes in the diaphragm transcriptome. Ingenuity pathway analysis predicted factors involved in inflammatory responses to tissue injury, including TGF‐β1, angiotensin and PDGF BB, as top upstream regulators activated in diaphragms prior to and throughout cachexia progression, while PGC‐1α and the insulin receptor were among the top upstream regulators predicted to be suppressed. The transcriptomic dataset further revealed progressive disturbances to networks involved in lipid, glucose and oxidative metabolism, activation of the unfolded protein response and neuromuscular junction remodelling associated with denervation.ConclusionsIn summary, our data support leukocyte infiltration and expansion of PDGFRα mesenchymal progenitors as early events that precede wasting and fibrotic remodelling of the diaphragm in response to PDAC that may also underlie metabolic disturbances, weakness and respiratory complications.
Past hydro‐pedological events and trend reflected in the Mid–Late Holocene alluvial record of the Middle Rhône Valley and tributaries: A new reference for South East France
Authors: Berger Jean‐Francois , Brochier Jacques Leopold 
Published: January 2025

ABSTRACTThis paper proposes a new approach to quantitative geomorphology and paleopedology in the mid‐Rhône valley (MRV) based on a compilation of geomorphological metadata from 25 tributaries of the Rhône river and subordinate floodbasins, including 51 geomorphological and paleopedological sites. This study compares two types of hydrogeomorphological data formalization: a temporal similarities analysis between paleosols and fluvial aggradation of sub‐basins, and a quantitative analysis by summed probability distributions comparing floodplain stability and activity phases in cumulative density functions of categorized 14C and chronocultural dates. Alluvial chronologies at centennial to millennial time scales, coinciding with a modification in sedimentation rate, or river style, demonstrate temporal and spatial patterning of MRV alluviation, pedogenesis and erosion across the western Prealpine region, with detection of 24 main Holocene alternative periods (activity versus stability). To explore possible large‐scale hydroclimatic teleconnections, the Holocene MRV results are aligned, and correlations are made, with those of a range of paleohydrological proxy studies from all Rhône drainage basins, Western Europe and the Mediterranean. The MRV rivers respond to the majority of Holocene rapid climatic changes and from 4000/3000 cal a
bp to an accelerated sediment delivery and torrentiality from slopes and headwaters, which results in the development of sedimentary cascades from upstream to downstream in the Rhône valley to the delta (the main drivers are discussed in detail in in a companion paper to follow).
Thermal Desorption Hyphenated to Comprehensive Two‐Dimensional Gas Chromatography‐Time‐of‐Flight Mass Spectrometry for Trace Analysis in Raw Renewable Gases–Application to Hydrothermal Gasification
Authors: Legendre Agathe , Bsaibes Sandy , Paijens Claudia , Jores Clément De Saint , Senné Célia , Dugay José , Cuccia Lorena , Castro Dairo Ballestas , Thiébaut Didier , Vial Jérôme 
Published: January 2025

ABSTRACTIn the context of the energy transition, European countries pursue the common goal of increasing the share of renewable gases (from anaerobic digestion, pyrogasification, and hydrothermal gasification for instance) in the gas mix. Although produced gases are mainly composed of methane after upgrading, impurities of various natures and quantities may also be present in the produced raw gases and still after upgrading, including volatile organic compounds (VOCs) at trace levels that may have an impact on different stages of the gas chain even at low concentrations. These new renewable and/or low‐carbon gases imply the need to develop new analytical tools to deeply characterize them, and thus fully manage their integration into the gas value chain. In this study focused on VOC analysis, a sampling approach using pre‐concentration on an adsorbent phase was developed for gas sampling to ensure rapid collection, requiring only a small volume (≈ 100 mL). Traces were then analyzed by thermal desorption hyphenated to a comprehensive two‐dimensional gas chromatography time‐of‐flight mass spectrometer. After identifying the compounds, a semi‐quantification method was developed to provide an estimation of the concentration of VOCs for each chemical family. The method was first developed on a biomethane sample before being successfully applied to a raw gas produced by hydrothermal gasification, in which around 250 compounds were detected at a trace level (total concentration equivalent at 200 µg/L).
A Predictive Model for Secondary Posttonsillectomy Hemorrhage in Pediatric Patients: An 8‐Year Retrospective Study
Authors: Ge Yuting , Chang Wenchuan , Xie Lixiao , Gao Yan , Xu Yue , Zhu Huie 
Published: January 2025

ABSTRACTObjectivesPosttonsillectomy hemorrhage (PTH) is a common and potentially life‐threatening complication in pediatric tonsillectomy. Early identification and prediction of PTH are of great significance. Currently, there are very few tools available for clinicians to accurately assess the risk of PTH. This study aimed to develop and validate a predictive model for secondary PTH.MethodsA retrospective analysis was conducted on 492 individuals who underwent tonsillectomy or tonsillotomy in Children's Hospital of Soochow University from July 1st, 2015 to December 31th, 2023. The study population was randomly divided into the training set and the validation set at a ratio of 7:3. Univariate logistic regression analysis was used to screen features. Multivariate logistic regression and seven machine learning algorithms were used to construct predictive models. Discrimination, calibration, and clinical utility were used to compare the predictive performance. The SHapley Additive exPlanation (SHAP) method was used to interpret the results of the best‐performing model.ResultsOne multivariate logistic regression model and seven machine learning models were constructed. The XGBoost model yielded the best performance in the validation set. The SHAP method ranked the features of the XGBoost model based on their importance and provided both global and local explanations of the model.ConclusionThis study established a machine learning‐based predictive model for secondary PTH, which may enable clinicians to accurately assess the risk of secondary PTH in children.Level of Evidence4
Public–private partnership in pipelining science of acute care ecosystem: Insights from Taiwan's Presidential Hackathon
Authors: Chen Chao‐Wen , Yeh Yung‐Sung , Chan Ta‐Chien , Wu Yi‐Syuan 
Published: January 2025

AbstractIntroductionThe acute care system faced significant challenges in managing healthcare emergencies due to a lack of coordination between emergency services and logistical support. This disorganization undermined collaboration and response efficiency.MethodsTaiwan's Presidential Hackathon introduced an innovative approach to improving the trauma system by integrating digital pipeline science through public–private partnerships (PPPs). This initiative specifically addressed inefficiencies and complexities in the acute care ecosystem, brought to light by the catastrophic 2014 gas explosion in Kaohsiung City.ResultsThe hackathon led to the development of a unified digital platform for emergency data management. This platform significantly enhanced communication, data sharing, and coordination across healthcare sectors, culminating in the implementation of a digital pre‐hospital emergency care system across multiple administrative regions.ConclusionOur experience demonstrated the effectiveness of leveraging digital technologies, PPPs, and the hackathon model to revolutionize emergency healthcare management and response systems through cross‐sector collaboration.
Bridging research and practice in a learning health system: Developing and refining an embedded scholars program through insights from scholars and clinical mentors
Authors: Sherrill Windsor Westbrook , Hall Luke , Fredendall Lawrence , Evatt Janet Hoffman 
Published: January 2025

AbstractIntroductionA learning health system (LHS) necessitates collaboration to produce translational health research. This experience report examines the integration of Clemson University scholars into clinical departments of Prisma Health–Upstate in South Carolina, highlighting their experiences working alongside clinician mentors to inform and facilitate research translation. Particularly, this study aims to explore the interpersonal and structural factors influencing the success of an embedded scholar program, focusing on enablers and barriers to collaboration, knowledge integration, and mentorship within the LHS.MethodsNine embedded scholar and 12 mentor semi‐structured interviews were conducted. This qualitative study initially used an inductive technique to analyze responses thematically. After thematic saturation was achieved, deductive analysis was utilized to further organize enablers and barriers across the following five categories: (1) Scholar Integration, (2) Scholar Autonomy, (3) Mentor Support, (4) Programmatic Outcomes, and (5) Institutional Dynamics.ResultsWe found 10 major program‐related enablers and barriers to successfully embedding scholars. These were clinical environment adaptation, mentor interaction, research management, balance of independence, role clarity, resource provision, research application and quality, scholar development, organizational support, and policy and procedure alignment. Findings reveal that effective mentorship, organizational alignment, and resource availability are critical enablers of program success, while misaligned expectations, limited institutional support, and insufficient scholar integration into clinical environments are barriers.ConclusionEvaluating specific components of embedded scholar programs can uncover best practices and innovation opportunities in the LHS. These provide a great opportunity to enhance the mentorship mechanisms between clinical mentors and embedded researchers. As research on embedded scholars in a LHS progresses, fostering structured mentoring relationships may serve as an impetus to bridge the gap between research and clinical practice. Further study is needed to operationalize these relationships effectively.