bluuebunny/crossref_metadata_2025_split huggingface dataset:
Doing gender and sexuality while doing group
Authors: Klevermann Nils 
Published: June 2024

ZusammenfassungIm DFG-geförderten Projekt „Doing gender and sexuality while doing group. Zur Verschränkung von kollektiver und personaler (Selbst‑)Bildung in der queeren Jugendarbeit“ wird erforscht, wie Jugendliche und junge Erwachsene in der queeren Gruppenarbeit zu lesbischen, schwulen, bisexuellen, trans*, inter*, asexuellen/aromantischen und queeren Jugendlichen (LSBT*IAQ) gebildet werden und sich selbst bilden. Das ethnographische Forschungsprojekt analysiert, wie durch diese Bildungsangebote eine Sozialität geschaffen wird, in der Jugendliche eine Identität ausbilden und einüben (können). Im Zentrum des Erkenntnisinteresses steht die Verschränkung von individuellen und kollektiven Bildungsprozessen.
Microbiome origin and stress-related changes in bacterial abundance of the photosymbiotic sea slug Berghia stephanieae (Á. Valdés, 2005)
Authors: Sickinger Corinna , Brackwehr Sofie Marie , Melo Clavijo Jenny , Gasperoni Gilles , Tierling Sascha , Preisfeld Angelika , Christa Gregor 
Published: June 2024

AbstractThe precise mechanisms that allow animals and phototrophic organisms to form a stable photosymbiotic relationship are still unknown. While previous studies focused on genomic adaptations of the animal host, more recent research looked into the role of bacteria in photosymbiosis. Here, we analyzed the core microbiome of the sea slug Berghia stephanieae and its food source Exaiptasia diaphana to understand if the microbiome and the linked bacterial metabolic pathways differ between unstable and stable photosymbiosis. This sea slug feeds solely on the model cnidarian E. diaphana and steals their photobionts which the slug can only maintain for a week. We additionally examined the influence of light and starvation stress on the slug’s bacterial composition, which are common experimental set-ups to elucidate the photosymbiotic relationship in the slugs. Our results show that the core microbiome of B. stephanieae and E. diaphana differed significantly suggesting that the slug’s microbiome is not obtained from its food source or the water column and indicates a vertical transmission. Further, differences in metabolic pathways imply that the microbiome of B. stephanieae does not support a stable photosymbiosis due to an insufficient nitrogen cycle on part of the photobiont. Starving the slugs induced a shift towards an increased abundance of potential pathogens and led to a downregulation in the sulphur cycle. Yet, starvation in darkness resulted in the depletion of most bacteria and induced a metabolic switch toward bacterial nitrogen fixation. This emphasizes that different holobiont members contribute to essential nutrient cycles, and it is important to look beyond the photobiont to understand the sea slug holobiont.
Identification and expression analysis of nuclear factor Y transcription factor genes under drought, cold and Eldana infestation in sugarcane (Saccharum spp. hybrid)
Authors: le Roux Jancke , Jacob Robyn , Fischer Riëtte , van der Vyver Christell 
Published: June 2024

Abstract
Background
The Nuclear Factor Y (NF-Y) transcription factor (TF) gene family plays a crucial role in plant development and response to stress. Limited information is available on this gene family in sugarcane.

Objectives
To identify sugarcane NF-Y genes through bioinformatic analysis and phylogenetic association and investigate the expression of these genes in response to abiotic and biotic stress.

Methods
Sugarcane NF-Y genes were identified using comparative genomics from functionally annotated Poaceae and Arabidopsis species. Quantitative PCR and transcriptome analysis assigned preliminary functional roles to these genes in response to water deficit, cold and African sugarcane borer (Eldana saccharina) infestation.

Results
We identify 21 NF-Y genes in sugarcane. Phylogenetic analysis revealed three main branches representing the subunits with potential discrepancies present in the assignment of numerical names of some NF-Y putative orthologs across the different species. Gene expression analysis indicated that three genes, ShNF-YA1, A3 and B3 were upregulated and two genes, NF-YA4 and A7 were downregulated, while three genes were upregulated, ShNF-YB2, B3 and C4, in the plants exposed to water deficit and cold stress, respectively. Functional involvement of NF-Y genes in the biotic stress response were also detected where three genes, ShNF-YA6, A3 and A7 were downregulated in the early resistant (cv. N33) response to Eldana infestation whilst only ShNF-YA6 was downregulated in the susceptible (cv. N11) early response.

Conclusions
Our research findings establish a foundation for investigating the function of ShNF-Ys and offer candidate genes for stress-resistant breeding and improvement in sugarcane.
Data-Driven Earthquake Multi-impact Modeling: A Comparison of Models
Authors: Patten Hamish , Anderson Loake Max , Steinsaltz David 
Published: June 2024

AbstractIn this study, a broad range of supervised machine learning and parametric statistical, geospatial, and non-geospatial models were applied to model both aggregated observed impact estimate data and satellite image-derived geolocated building damage data for earthquakes, via regression- and classification-based models, respectively. For the aggregated observational data, models were ranked via predictive performance of mortality, population displacement, building damage, and building destruction for 375 observations across 161 earthquakes in 61 countries. For the satellite image-derived data, models were ranked via classification performance (damaged/unaffected) of 369,813 geolocated buildings for 26 earthquakes in 15 countries. Grouped k-fold, 3-repeat cross validation was used to ensure out-of-sample predictive performance. Feature importance of several variables used as proxies for vulnerability to disasters indicates covariate utility. The 2023 Türkiye–Syria earthquake event was used to explore model limitations for extreme events. However, applying the AdaBoost model on the 27,032 held-out buildings of the 2023 Türkiye–Syria earthquake event, predictions had an AUC of 0.93. Therefore, without any geospatial, building-specific, or direct satellite image information, this model accurately classified building damage, with significantly improved performance over satellite image trained models found in the literature.
A New Method to Identify the Maximum Time Interval between Individual Events in Compound Rainstorm and Heatwave Events
Authors: Zhang Junlin , Xu Wei , Qiao Yu , Liao Xinli , Meng Chenna , Han Qinmei 
Published: June 2024

AbstractGrowing evidence indicates that extreme heat and rain may occur in succession within short time periods and cause greater impacts than individual events separated in time and space. Therefore, many studies have examined the impacts of compound hazard events on the social-ecological system at various scales. The definition of compound events is fundamental for such research. However, there are no existing studies that support the determination of time interval between individual events of a compound rainstorm and heatwave (CRH) event, which consists of two or more potentially qualifying component heatwave and rainstorm events. To address the deficiency in defining what individual events can constitute a CRH event, this study proposed a novel method to determine the maximum time interval for CRH events through the change in CRH event frequency with increasing time interval between individual events, using southern China as a case study. The results show that the threshold identified by the proposed method is reasonable. For more than 90% of the meteorological stations, the frequency of CRH events has reached a maximum when the time interval is less than or equal to the threshold. This study can aid in time interval selection, which is an important step for subsequent study of CRH events.
Perioperative Management of Hemophilia Patients
Authors: Lowell Aliena E. , Calgi Michael P. , Caruso Joseph J. , Man Louise M. , McNeil John S. 
Published: June 2024

Abstract
Purpose of Review
Hemophilia is a rare, typically inherited, condition where a specific clotting factor is reduced or even near absent. Patients with hemophilia who present for an invasive procedure, whether elective or urgent, are at increased risk of bleeding intraoperatively and postoperatively.

Recent Findings
Ten years ago, most patients with hemophilia with moderate or severe disease were treated with standard half-life factor replacement therapy, either prophylactic or on-demand. Now, patients may present on extended half-life factor therapy, or on a non-factor hemostatic therapy, or as a recipient of gene therapy. Further complicating the challenge of caring for these patients is that most of these new modalities will typically require supplementation with traditional factor therapy when surgery is required.

Summary
An in-depth understanding of all the potential treatment options for hemophilia is essential when anesthesiologists care for a patient with hemophilia. And there are several perioperative arenas (neuraxial procedures, point-of-care coagulation tests such as ROTEM or TEG, and cardiopulmonary bypass) where hemophilia creates unique considerations.
Spatial-temporal memory enhanced multi-level attention network for origin-destination demand prediction
Authors: Lu Jiawei , Pan Lin , Ren Qianqian 
Published: June 2024

AbstractOrigin-destination demand prediction is a critical task in the field of intelligent transportation systems. However, accurately modeling the complex spatial-temporal dependencies presents significant challenges, which arises from various factors, including spatial, temporal, and external influences such as geographical features, weather conditions, and traffic incidents. Moreover, capturing multi-scale dependencies of local and global spatial dependencies, as well as short and long-term temporal dependencies, further complicates the task. To address these challenges, a novel framework called the Spatial-Temporal Memory Enhanced Multi-Level Attention Network (ST-MEN) is proposed. The framework consists of several key components. Firstly, an external attention mechanism is incorporated to efficiently process external factors into the prediction process. Secondly, a dynamic spatial feature extraction module is designed that effectively captures the spatial dependencies among nodes. By incorporating two skip-connections, this module preserves the original node information while aggregating information from other nodes. Finally, a temporal feature extraction module is proposed that captures both continuous and discrete temporal dependencies using a hierarchical memory network. In addition, multi-scale features cascade fusion is incorporated to enhance the performance of the proposed model. To evaluate the effectiveness of the proposed model, extensively experiments are conducted on two real-world datasets. The experimental results demonstrate that the ST-MEN model achieves excellent prediction accuracy, where the maximum improvement can reach to 19.1%.
Conformalized prescriptive machine learning for uncertainty-aware automated decision making: the case of goodwill requests
Authors: Haas Stefan , Hüllermeier Eyke 
Published: June 2024

AbstractDue to the inherent presence of uncertainty in machine learning (ML) systems, the usage of ML is until now out of scope for many critical (financial) business processes. One such process is goodwill assessment at car manufacturers, where a large part of goodwill cases is still assessed manually by human experts. To increase the degree of automation while still providing an overall reliable assessment service, we propose a selective uncertainty-aware automated decision making approach based on uncertainty quantification through conformal prediction. In our approach, goodwill requests are still shifted to human experts in case the risk of a wrong assessment is too high. Nevertheless, ML can be introduced into the process with reduced and controllable risk. We hereby determine the risk of wrong ML assessments through two hierarchical conformal predictors that make use of the prediction set and interval size as the main criteria for quantifying uncertainty. We also utilize conformal prediction’s property to output empty prediction sets if no prediction is significant enough and abstain from an automatic decision in that case. Instead of providing mathematical guarantees for limited risk, we focus on the risk vs. degree of automation trade-off and how a business decision maker can select in an a posteriori fashion a trade-off that best suits the business problem at hand from a set of pareto optimal solutions. We also show empirically on a goodwill data set of a BMW National Sales Company that by only selecting certain requests for automated decision making we can significantly increase the accuracy of automatically processed requests. For instance, from 92 to 98% for labor and from 90 to 98% for parts contributions respectively, while still maintaining a degree of automation of approximately 70%.
Bayesian hierarchical network autocorrelation models for estimating direct and indirect effects of peer hospitals on outcomes of hospitalized patients
Authors: Chen Guanqing , O’Malley A. James 
Published: June 2024

Abstract
When an hypothesized peer effect (also termed social influence or contagion) is believed to act between units (e.g., hospitals) above the level at which data is observed (e.g., patients), a network autocorrelation model may be embedded within a hierarchical data structure thereby formulating the peer effect as a dependency between latent variables. In such a situation, a patient’s own hospital can be thought of as a mediator between the effects of peer hospitals and their outcome. However, as in mediation analyses, there may be interest in allowing the effects of peer units to directly impact patients of other units. To accommodate these possibilities, we develop two hierarchical network autocorrelation models that allow for direct and indirect peer effects between hospitals when modeling individual outcomes of the patients cared for at the hospitals. A Bayesian approach is used for model estimation while a simulation study assesses the performance of the models and sensitivity of results to different prior distributions. We construct a United States New England region patient-sharing hospital network and apply newly developed Bayesian hierarchical models to study the diffusion of robotic surgery and hospital peer effects in patient outcomes using a cohort of United States Medicare beneficiaries in 2016 and 2017. The comparative fit of models to the data is assessed using Deviance information criteria tailored to hierarchical models that include peer effects as latent variables.
Monitoring and tracking changes in sensitivity to zoxamide fungicide in Plasmopara viticola in Italy
Authors: Nanni Irene M. , Oggiano Ivano , Laricchia Maria , Cempini Martina , Collina Marina 
Published: June 2024

AbstractPlasmopara viticola is controlled by fungicides with different modes of action, which include zoxamide. Zoxamide was developed in 1998 by Dow AgroSciences LIC (Indianapolis, IN) and has been commercialized since 2001 by Rohm and Haas Company. This fungicide is highly effective against oomycetes, used for foliar application in vine crops, potato, and other vegetables to control oomycete-induced diseases. Zoxamide acts by causing mitotic arrest by binding to β-tubulin, inhibiting tubulin polymerization and cell division of the pathogen. In the past three decades, the management of fungicide resistance has emerged as a significant concern for both growers and regulatory authorities; this is primarily due to the substantial increase in resistance, in terms of occurrence and spread, towards most fungicide groups. The monitoring of the effectiveness of fungicides is the only way to accurately identify as soon as possible the onset of resistance. In this study, we were interested in tracking the changes in the sensitivity of zoxamide to P. viticola populations collected during a 6-year monitoring (2017–2022) in two Italian locations: the autonomous province of Trento and the autonomous region of Friuli-Venezia Giulia. Bioassays on leaf discs were carried out, and EC50 and MIC values were elaborated. From our results, zoxamide showed for a long period of time high sensitivity, but over the last years we observed a change, as in 2022 most of the samples tested in these two regions showed MIC > 100 mg L−1.
The Combination Between Super Absorbent Polymers (SAPs) and Biofertilizers Could be an Ecofriendly Approach for Soil Chemical Properties Improving and Sustainable Wheat (Triticum Sativum) Production in Sandy Loam Soil
Authors: Negim Osama I. A. , Moharam Moustafa H. A. , Elsayed Elsayed F. , Thabit Fatma N. 
Published: June 2024

AbstractSustainable agriculture aims to provide food needs while improving soil health and protecting it from degradation and contamination from excessive chemical fertilizer use. Sandy-textured soils have low fertility and water-holding capacity. This study assessed the integrated impact of super absorbent polymers (SAPs) and biofertilizer application on the soil chemical characteristics and wheat growth parameters in sandy loam soil. Two super absorbent polymers (SAPs) included Barbary plant G3 (P1) and Aqua Gool polymer (P2), and four microbial inoculations (Trichoderma harzianum (T), Actinomycetes (Streptomyces rochei and Streptomyces atrovirens) (AC1 and AC2), and Bacillus subtilis (B)) as biofertilizers were used in our pot experiment. The SAPs were applied to soil at a level of 0.2% (w/w), while biofertilizers were applied in the form of microbial cell suspensions (50 ml per pot) in addition to treating wheat seed with these suspensions during cultivation. Wheat plants were irrigated every 8 days to field capacity level. Amending soil with super absorbent polymers and microbes either individually or in combination significantly reduced pH and EC, increased organic matter level, and the availability of macro- and micronutrients in soil. Wheat growth metrics, including shoot length, tiller number, biomass accumulation, leaf area, and grain yield, exhibited considerable enhancements relative to the plants of the control treatment. The interaction between P1 polymer and Streptomyces atrovirens (AC2) showed the highest performance in improving the almost studied parameters. The application of SAPs with microbial biofertilizers offers a promising eco-friendly method for enhancing soil health and wheat yield.
Do NGOs recognise the climate-biodiversity nexus? Insights from 107 climate NGOs in Germany
Authors: Oelschläger Lucie Maj , Kim Rakhyun E. 
Published: June 2024

AbstractBiodiversity loss and climate change are deeply intertwined and constitute a complex and interdependent crisis. These interlinked sustainability issues need to be addressed through an integrated approach. However, governments have been slow to integrate the need to protect both climate and biodiversity into environmental governance. NGOs, as non-profit organisations promoting public good, are seen as having the potential to recognise and advocate for this much-needed holistic protection of climate and biodiversity. But to what extent and in what ways do NGOs recognise this climate-biodiversity nexus? This article addresses these questions through an analysis of 107 German climate NGOs, examining their 190 annual reports from two time periods, 2016/2017 and 2021/2022. We found that, while German climate NGOs have made significant progress in recognising the climate-biodiversity nexus, they lack recognition of the role of biodiversity in stabilising the climate and the unintended negative consequences of climate policies and measures for biodiversity, highlighting that NGOs are still far from being holistic environmental stewards. Climate NGOs need to integrate the interplay between climate and biodiversity objectives into their organisational activities and align their mandates to promote synergies and minimise trade-offs between climate and biodiversity actions. We suggest that one way forward is to increase collaboration between NGOs. NGOs could then complement each other’s expertise and be better placed to jointly contribute to finding integrated solutions for climate and biodiversity protection and mainstreaming biodiversity into climate policy.
Application of the Bagged Trees Technique on Retrieving the Nighttime Ionospheric Peak Density From OI‐135.6 nm Airglow
Authors: Lin Chi‐Yen , Liu Jann‐Yenq , Chien‐Hung Lin Charles , Rajesh P. K. , Duann Yi , Wen Yun‐Cheng 
Published: June 2024

AbstractThe NASA global‐scale observations of the limb and disk (GOLD) mission is a measurement opportunity to scan the far ultraviolet airglow at ∼134–162 nm over the American Hemisphere since October 2018. The FORMOSAT‐7/COSMIC‐2 (F7/C2) satellite mission has provided thousands of daily radio occultation soundings in the low‐ and mid‐latitude regions since July 2019. The nighttime OI–135.6 nm emission is mainly through radiative recombination, and the radiance is used to derive the peak electron density. Comparison with corresponding F7/C2 observations demonstrates good correlation in low‐latitudes, while is overestimated near mid‐latitudes in winter, induced by the photoelectrons emanating from magnetically conjugate Hemisphere. The machine learning technique Bagged Trees is implemented to develop an intensity to peak density model training from GOLD and F7/C2 observations. The validation demonstrates that Bagged Trees peak‐density has less influence from conjugate photoelectrons and indicates the power of machine learning techniques for geophysics data processing.
Sources of Southern Hemisphere Marine Aerosols: Insights From Carbonaceous Fraction Concentration and Stable Carbon Isotope Analysis
Authors: Gu Weihua , Xie Zhouqing , Jiang Bei , Yue Fange , Yu Xiawei , Chen Afeng 
Published: June 2024

AbstractMarine carbonaceous aerosols, originating from marine and continental sources, are significant global aerosol components. The understanding of marine carbonaceous aerosols is currently limited, especially in the Southern Hemisphere. Furthermore, there is an ongoing debate regarding the contributions of marine fresh and ancient carbon to marine aerosols. To address these gaps, we conducted an extensive investigation utilizing a long‐term data set of aerosol samples collected during six Antarctic cruises (28°N–78°S) from 2013 to 2020. Our analysis revealed an average organic carbon (OC) concentration of 1.29 ± 1.15 μg/m3 and an element carbon (EC) concentration of 0.13 ± 0.18 μg/m3 in the samples. These concentrations varied within a range spanning from background marine samples to those impacted by substantial continental transport. Fossil fuel combustion remained the primary source of continental influence in the marine environment, as evidenced by the OC/EC ratio. The δ13CTC value for all samples range from −22.3‰ to −28.4‰, with a mean value of −26.3 ‰. Using a three‐endmember isotopic source model, we find that continental carbonaceous aerosols make substantial contributions in the Eastern Indian Ocean (81 ± 4%), while their prevalence is lower in the Southern Ocean (SO) (44 ± 20%). In contrast to mid‐latitudes, primary marine aerosol of the SO exhibits a significantly higher contribution from the fresh carbon pool (52 ± 19%). Furthermore, our study suggests that SO sea ice may play a potential role in driving emissions from the fresh carbon pool. These findings contribute to a comprehensive understanding of the effects of carbonaceous aerosols on climate change and the ocean‐atmosphere carbon cycle.