bluuebunny/crossref_metadata_2025_split huggingface dataset:
Winter tick sharing between ungulates in the Greater Yellowstone Ecosystem and implications for apparent competition
Authors: Koser Troy , Martin Alynn , Courtemanch Alyson , Thompson Laura , Wise Benjamin , Fralick Gary , Dewey Sarah , Girard Amy , Scurlock Brandon , Rogerson Jared , Oyen Kennan , Cross Paul 
Published: January 2025

AbstractHost species heterogeneity can drive parasite dynamics through variation in host competency as well as host abundance. We explored how elk (Cervus canadensis) with apparent subclinical infestations of winter tick (Dermacentor albipictus) may be a cryptic reservoir and drive winter tick dynamics, impacting moose (Alces alces) populations. We found that winter tick infestation loads did not vary remarkably between both host species and winter ticks sourced from elk and moose produced similar numbers of larvae which activated within 5 days of each other. We also found similar larval densities in habitats predominately used by elk, moose, and both host species. Our analysis of 2793 informative single‐nucleotide polymorphisms showed genetic differentiation among tick populations that were only ~75 km apart, but fewer differences among ticks from elk or moose in the same locality, suggesting sharing of winter ticks across host species. Despite the clinical signs of high winter tick infestations being most apparent on moose, elk may be critical drivers of winter tick population dynamics and indirectly compete with moose in areas where they outnumber moose populations, a common characteristic of ungulate communities in western North America. Management interventions aimed at addressing winter tick issues on moose may wish to consider the movement patterns and abundance of cryptic reservoirs like elk.
Recommendations for developing, documenting, and distributing data products derived from NEON data
Authors: Atkins Jeff W. , Aho Kelly S. , Chen Xuan , Elmore Andrew J. , Fiorella Rich , Luo Wenqi , Lombardozzi Danica , Lunch Claire , Manak Leah , de Pablo Luis X. , Myers‐Pigg Allison N. , Record Sydne , Qiu Tong , Reed Samuel , Ruddell Benjamin , Strange Brandon , Torrens Christa L. , Yule Kelsey , Richardson Andrew D. 
Published: January 2025

AbstractThe National Ecological Observatory Network (NEON) provides over 180 distinct data products from 81 sites (47 terrestrial and 34 freshwater aquatic sites) within the United States and Puerto Rico. These data products include both field and remote sensing data collected using standardized protocols and sampling schema, with centralized quality assurance and quality control (QA/QC) provided by NEON staff. Such breadth of data creates opportunities for the research community to extend basic and applied research while also extending the impact and reach of NEON data through the creation of derived data products—higher level data products derived by the user community from NEON data. Derived data products are curated, documented, reproducibly‐generated datasets created by applying various processing steps to one or more lower level data products—including interpolation, extrapolation, integration, statistical analysis, modeling, or transformations. Derived data products directly benefit the research community and increase the impact of NEON data by broadening the size and diversity of the user base, decreasing the time and effort needed for working with NEON data, providing primary research foci through the development via the derivation process, and helping users address multidisciplinary questions. Creating derived data products also promotes personal career advancement to those involved through publications, citations, and future grant proposals. However, the creation of derived data products is a nontrivial task. Here we provide an overview of the process of creating derived data products while outlining the advantages, challenges, and major considerations.
Costs and benefits of maternal nest choice: Trade‐offs between brood survival and thermal stress in bees
Authors: deHaan Jessie L. , Maretzki Jesse , Skandalis Adonis , Tattersall Glenn J. , Richards Miriam H. 
Published: January 2025

AbstractOptimal nest site selection is crucial in animals whose offspring are completely dependent on the shelter of a nest. Parental decisions influencing nest thermal conditions are particularly important because temperature strongly influences juvenile activity, metabolism, growth, developmental rate, survival, and adult body size. In small ectotherms such as bees, maternal decisions to nest in sun‐exposed or shady sites can lead to marked differences in thermal microenvironments inside nests. Small carpenter bees (Ceratina calcarata) strongly prefer to nest in sun but also prefer nesting substrates more frequently found in shade, suggesting that nest site selection is based on a trade‐off between costs and benefits of warmer versus cooler nest sites. We investigated the consequences of sun and shade nesting for mothers and their offspring using a field experiment in which mothers and newly founded nests were placed in sunny or shady habitats. Maternal costs and benefits in each treatment were quantified by comparing maternal foraging effort, nest size, number of brood provisioned, and number and size of live offspring. These demographic measures allowed us to estimate fitness for mothers nesting in sun versus shade. For juvenile bees from sun and shade nests, we quantified two thermal traits, high‐temperature tolerance (CTmax) and metabolic rate. Mothers in sun nests had significantly higher nesting success, with 59% of all nests producing brood, while mothers in shade nests experienced only 32% success. Successful sun nests actually contained fewer live brood (5.2 ± 3.0, mean ± SD) than shade nests (6.9 ± 3.3), but their higher success rates meant that maternal fitness was higher in sun than in shade. However, sun nesting entailed clear costs to brood, which were significantly smaller, less likely to survive to adulthood, and had significantly elevated CTmax, suggesting that thermal stress during development necessitated them to shunt resources from growth to thermoprotection. The maternal preferences for sun nesting optimize maternal fitness despite the evident costs to juveniles developing in sun‐exposed nests.
Synthesis and Characterisation of {PdNO}10 Pincer Complexes
Authors: Sinclair Matthew J. G. , Roig Nil , Tsoureas Nikolaos , Alonso Mercedes , Chaplin Adrian B. 
Published: January 2025

AbstractThe synthesis and characterisation of two square planar {PdNO}10 pincer complexes of the form [Pd(pincer)(NO)]+ (pincer = 2,6‐(tBu2PCH2)2C5H3N, 1; 2,6‐(tBu2PO)2C5H3N, 2) are reported. These complexes are readily isolated by phosphine substitution of T‐shaped [Pd(PtBu3)2(NO)]+ 3 in THF and the bent nitrosyl coordination mode observed in 3 is retained, as evidenced by X‐ray diffraction (∠PdNO ∼ 120°), IR spectroscopy and analysis of isotopically enriched samples by 15N NMR spectroscopy. Effective oxidation states of Pd0/NO+ are calculated for 1–3 and nitrosyl coordination is principally attributed to metal‐centred σ‐bonding, with supplementary π‐backbonding. Computational analysis, however, indicates that the Pd−NO bonds in 1 and 2 have greater PdI/NO• character and σ‐bonding is more prominent than in 3. These differences in bonding are manifested experimentally in more red‐shifted nitrosyl stretching frequencies and the propensity of 1 and 2 to react with dichloromethane to afford palladium(II) chloride derivatives.
Synthesis of Sulfone Containing‐Spirocycles, ‐Pentafulvene Dyes and Their Photophysical Properties
Authors: Mishra Gitanjali , Indu Potnuru , Sasmal Mukesh , Thirupathi Barla 
Published: January 2025

AbstractA highly regioselective synthesis of sulfone‐containing spiro[4.4]nonanes has been achieved by the reaction of 2‐phenacyl‐1,3‐indandiones with 3‐(phenylsulfonyl)propiolates at ambient temperature. Furthermore, the synthesized compounds were transformed into pentafulvene dyes via unprecedented C−C bond rearrangement and their photophysical properties are also outlined in this article.
Cardo Bisphenol Fluorene Fused with Dibenzo[g,p]chrysene for a High Refractive Index Monomer
Authors: Yura Toko , Noda Rion , Okada Ikuma , Guo Zhenfeng , Nakanishi Takashi , Yamaoka Yousuke , Iwasawa Tetsuo 
Published: January 2025

AbstractSynthesis and properties of a new cardo‐typed bis‐phenol are described. A dibenzo[g,p]chrysene (DBC) core that has one ester group at the bay was prepared, and then intramolecular Friedel‐Crafts acylation gave a key cyclopentanone intermediate. Finally, two‐fold arylation successfully yielded a DBC‐linked bis‐phenol. This new DBC‐linked bis‐phenol was compared with the prototype bis‐phenol fluorene (BPF). The structure‐activity relationship between these two bis‐phenols reveals that the expansion of the π‐conjugated system using a skeletal DBC influences the quality of its attributes with a refractive index of 1.83. This comparative study provides us a basis for future cardo structure material chemistry.
Does Social Cohesion Predict Justification of Extremist Violence? Evidence From the Sahel Region in Burkina Faso
Authors: Varela Micaela , Bulska Dominika , Bilali Rezarta , Xu Ruoxi 
Published: January 2025

ABSTRACTMany governments have invested in interventions that foster social cohesion to promote peace and prevent the rise of violent extremism. Yet, we know little about the relationship between social cohesion and support for violent extremism. This research (N = 5016) examined cross‐sectional relationships between different dimensions of social cohesion and violent extremism in 132 villages in the Sahel region of Burkina Faso (Study 1). In addition, using longitudinal data from 66 villages (Study 2: N = 2508), we assessed whether social cohesion predicts violence justification six months later. At the individual level, sense of community, outgroup trust and institutional trust were associated with lower justification of violence. Community participation was associated with higher justification of violence in villages with high levels of community participation. Between‐village effects revealed that justification of violence was lower in villages with higher aggregate levels of ingroup and outgroup trust but lower levels of community participation.
Integrated SegFlow, µSIA, and UPLC for Online Sialic Acid Quantitation of Glycoproteins Directly from Bioreactors
Authors: Chemmalil Letha , Kulkarni Tanmay , Raman Mathura , Singh Priya , Qian Yueming , Chumsae Chris , McHugh Kyle , Huang Zhuangrong , Hodgman Eric , Borys Michael C. , Ding Julia , Li Gloria , Leone Anthony 
Published: January 2025

ABSTRACTThis study emphasizes the critical importance of closely monitoring and controlling the sialic acid content in therapeutic glycoproteins, including EPO, interferon‐γ, Orencia, Enbrel, and others, as the level of sialylation directly impacts their pharmacokinetics (PK), immunogenicity, potency, and overall clinical performance due to its influence on protein clearance via hepatic asialoglycoprotein receptors (ASGPR). The ASGPR recognizes and binds to glycoproteins exposed to terminal galactose or N‐acetylgalactosamine residues, leading to receptor‐mediated endocytosis. Recent studies have demonstrated that sialylation of O‐linked glycan plays a role in protecting against macrophage galactose lectin (MGL)‐mediated clearance. In addition to the impact on serum half‐life, sialylation can influence other clinical outcomes, including immunogenicity, potency, and cytotoxicity. Therefore, the level of sialic acid is a critical quality attribute (CQA), and monitoring and regulating sialylation has become a regulatory requirement to ensure desired clinical performance. To achieve consistent levels of sialic acid‐to‐protein ratio, the time of upstream harvest and conductivity of downstream wash buffers must be tightly regulated based on the sialic acid content. Therefore, the utilization of process analytical technology (PAT) tools for generating real‐time or near‐real‐time sialic acid content is a business‐critical requirement. This work demonstrates the utility of an integrated PAT system for near real‐time online sialic acid measurements. The system consists of a micro‐sequential injection analyzer (µSIA) interfaced with SegFlow and an ultra performance liquid chromatography (UPLC). The fully automated architecture exemplifies the execution of online sampling, automatic sample preparation, and subsequent online UPLC analysis. This carefully orchestrated PAT framework effectively supports the requirements of QbD‐driven continuous bioprocessing.
Stakeholder Risk Perception About Heat: An Interview‐Based Study Among Outdoor Workers in South India
Authors: Ragupathy Sneha , Annadata Shanmukh Pranavi , Latha P. K. , Garg Sarada Satyamoorthy , Venugopal Vidhya 
Published: January 2025

ABSTRACTHeat waves in Southeast Asia are expected to intensify in the upcoming decades thereby raising the vulnerability of at‐risk workers to heat‐related illnesses (HRIs). Identifying and strengthening workers' self‐protection knowledge is crucial to effective heat adaptation and management. The study aimed to investigate outdoor workers' perceptions of heat‐related risks, changes in protective behavior during hotter seasons, knowledge and awareness of regional heat action plans (HAPs), and protection measures. It aimed to find effective means of communication and strategies for improving heat protection among these workers. We used a validated and structured mixed‐method survey questionnaire and one‐on‐one interviews to assess 140 outdoor workers' heat‐related risk perceptions, protective behavior changes during hot seasons, and knowledge of regional HAPs protective measures in April–July 2022. The estimated worker's seasonal average WBGT exposure using meteorological data was 34.4°C ± 0.02°C, which exceeded acceptable limits. Heat was a big concern for outdoor workers, the study revealed. Workers believed that knowing about heat hazards could help reduce individual risks. Many workers were aware of the heat's health risks but felt the nature of their jobs prevented them from taking precautions. Some workers assumed that business owners' concerns about productivity and cooling costs would prevent government measures to safeguard at‐risk employees from succeeding. Workers responded more positively to business owners' HAP communications. Workers are more likely to use practical, simple sector‐based warning HAPs. We urgently need management policy reforms to protect millions of workers in low‐ and middle‐income countries (LMICs), and the implementation of cost‐effective, practical, and sustainable heat protection infrastructure and behavioral change solutions through trusted channels is crucial.
A Next Available Appointment (NAA) Tool to Better Manage Patient Delay Risk and Patient Scheduling Expectations in Specialist Clinics
Authors: Riahi Vahid , Rolls David , Diouf Ibrahima , Khanna Sankalp , O'Sullivan Kim , Jayasena Rajiv 
Published: January 2025

ABSTRACTEvery year there are approximately 3 million new outpatient specialist clinic appointments at local hospital networks in Victoria, Australia. Growing daily demand for these services leads to high‐volume waiting lists and therefore long appointment delays for patients. This phenomenon emphasises the importance of providing analytics and tools to assist with waiting list management in outpatient specialist clinics. In this paper, we developed a novel Next Available Appointment (NAA) tool, to assist clinicians to manage delayed‐appointment risk and improve the patient experience by aligning the expected and actual day of the appointment. The NAA uses simulation to determine the earliest available week for a patient appointment on or after the timeframe requested by the clinician, considering the current waiting list and future planned clinician availability. It was validated using 3 years of historical waiting list information across several scenarios chosen to capture operational diversity. As a practical example, a scenario chosen for implementation within the clinic's operational setting achieved a simulated reduction in overdue appointments from 41% to 25% (i.e., a reduction of 47,000 overdue appointments over 3 years). We also provided early details on the implementation of the tool currently underway.
Data Mining of Infertility and Factors Influencing Its Development: A Finding From a Prospective Cohort Study of RaNCD in Iran
Authors: Heydarian Hosna , Abbasi Masoumeh , Najafi Farid , Darbandi Mitra 
Published: January 2025

ABSTRACTBackground and AimsInfertility, as defined by the World Health Organization, is the inability to conceive after 12 months of regular, unprotected intercourse. This study aimed to identify factors influencing infertility by applying data mining techniques, specifically rule‐mining methods, to analyze diverse patient data and uncover relevant insights. This approach involves a thorough analysis of patients' clinical characteristics, dietary habits, and overall conditions to identify complex patterns and relationships that may contribute to infertility.MethodsIn this study, we examined the impact of lifestyle factors on infertility using machine learning and data mining techniques, specifically Association Rules. The study included a total of 4437 women who participated in the Ravansar Non‐Communicable Disease Cohort study. Among the remaining participants, 434 were infertile. We utilized 38 variables to generate the relevant association rules.ResultsAs a result, the analysis reveals that 97% of infertile women are likely to cook for more than 2 h and engage in standing activities. Additionally, 94% of infertile women are likely to have central obesity. Infertile women also have a 73% chance of reusing cooking oil and a 74% chance of consuming fried food at least once a week. The likelihood of infertility increases to 98% among women who use more than 24 eggs per month and to 97% among those who consume moldy jam or syrup. The evaluation of these associations was further supported by measures of support, confidence, and lift.ConclusionThis study showed that key lifestyle factors linked to infertility, underscoring the role of lifestyle in reproductive health. These findings suggest that targeted interventions and lifestyle changes may help reduce infertility rates. Further research is needed to confirm these associations and investigate the underlying mechanisms.
Identification and Functional Analysis of PANoptosis‐Associated Genes in the Progression From Sepsis to ARDS
Authors: Lu Zhong‐Hua , Tang Yan , Chen Hu , Liu Feng , Liu Mei , Fu Lu , Wang Xian‐Kai , Li Ming‐Juan , Yu Wei‐Li , Sun Yun 
Published: January 2025

ABSTRACTBackgroundSepsis and acute respiratory distress syndrome (ARDS) are common inflammatory conditions in intensive care, with ARDS significantly increasing mortality in septic patients. PANoptosis, a newly discovered form of programmed cell death involving multiple cell death pathways, plays a critical role in inflammatory diseases. This study aims to elucidate the PANoptosis‐related genes (PRGs) and their involvement in the progression of sepsis to ARDS.MethodsThis study analyzed differentially expressed genes (DEGs) associated with PRGs to explore their role in the progression of immune disorders from sepsis to septic ARDS. A diagnostic prediction model was constructed based on key PRGs identified through bioinformatics analysis. Functional enrichment analyses were conducted to determine pathway involvement, and correlations with immune cells were assessed. Mendelian randomization analysis was applied to investigate potential causal links between specific PRGs and ARDS. Immunohistochemical analysis was used to evaluate PRG expression in lung tissue.ResultsThe prediction model effectively distinguished septic ARDS patients from those with sepsis. NDRG1 expression was elevated in ARDS, while DDX3X, PTPRC, and TNFSF8 were downregulated. NDRG1 showed a positive correlation with activated dendritic cells, whereas DDX3X, PTPRC, and TNFSF8 were positively associated with neutrophils and negatively correlated with CD56bright NK cells. Functional enrichment analysis highlighted spliceosome function, MAPK signaling, endocytosis, and antigen processing pathways as significantly associated with these PRGs. Mendelian randomization suggested a causal link between NDRG1 and ARDS, and immunohistochemical analysis revealed its predominant expression near vascular walls. In a mouse model of sepsis, suppression of NDRG1 alleviated lung injury.ConclusionPANoptosis may contribute to immune dysregulation in sepsis‐associated ARDS. NDRG1 is identified as a potential therapeutic target, offering new avenues for mitigating ARDS progression and improving patient outcomes.
Potential Biomarkers for Predicting the Risk of Developing Into Long COVID After COVID‐19 Infection
Authors: Hou Zhiyong , Ming Yu , Liu Jun , Wang Zhong 
Published: January 2025

ABSTRACTBackgroundLong COVID, a heterogeneous condition characterized by a range of physical and neuropsychiatric presentations, can be presented with a proportion of COVID‐19‐infected individuals.MethodsTranscriptomic data sets of those within gene expression profiles of COVID‐19, long COVID, and healthy controls were retrieved from the GEO database. Differentially expressed genes (DEGs) falling under COVID‐19 and long COVID were identified with R packages, and contemporaneously conducted module detection was performed with the Modular Pharmacology Platform (http://112.86.129.72:48081/). The integration of both DEGs and differentially expressed module‐genes (DEMGs) regarding long COVID and COVID‐19 was intersected by following Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA).ResultsThere were 11 and 62 differentially expressed modules, 1837 and 179 DEGs, as well as 103 and 508 DEMGs acquiring identified for both COVID‐19 and long COVID, notably enriched in the immune‐correlated signaling pathways. The immune infiltrating cells of long COVID and COVID‐19 were comparatively and respectively assessed via CIBERSORT, ssGSEA, and xCell algorithms. Subsequently, the screening of hub genes involved employing the SVM‐RFE, RF, XGBoost algorithms, and logistic regression analysis. Among the 67 candidate genes were processed with machine learning algorithms and logistic regression, a subgroup consisting of CEP55, CDCA2, MELK, and DEPDC1B, was at last identified as potential biomarkers for predicting the risk of the progression into long COVID after COVID‐19 infections. The predicting performance of the potential biomarkers was quantified with a ROC value of 0.8762542, which proved the combination of potential biomarkers provided the highest performance.ConclusionsIn summary, we identified a subgroup of potential biomarkers for predicting the risk of the progression into long COVID after COVID‐19 infection, which could be partly elucidation of the associated molecular mechanisms for long COVID.