bluuebunny/crossref_metadata_2025_split huggingface dataset:
The key role of sufficiency for low demand-based carbon neutrality and energy security across Europe
Authors: Wiese Frauke , Taillard Nicolas , Balembois Emile , Best Benjamin , Bourgeois Stephane , Campos José , Cordroch Luisa , Djelali Mathilde , Gabert Alexandre , Jacob Adrien , Johnson Elliott , Meyer Sébastien , Munkácsy Béla , Pagliano Lorenzo , Quoilin Sylvain , Roscetti Andrea , Thema Johannes , Thiran Paolo , Toledano Adrien , Vogel Bendix , Zell-Ziegler Carina , Marignac Yves 
Published: October 2024

AbstractA detailed assessment of a low energy demand, 1.5 ∘C compatible pathway is provided for Europe from a bottom-up, country scale modelling perspective. The level of detail enables a clear representation of the potential of sufficiency measures. Results show that by 2050, 50% final energy demand reduction compared to 2019 is possible in Europe, with at least 40% of it attributable to various sufficiency measures across all sectors. This reduction enables a 77% renewable energy share in 2040 and 100% in 2050, with very limited need for imports from outside of Europe and no carbon sequestration technologies. Sufficiency enables increased fairness between countries through the convergence towards a more equitable share of energy service levels. Here we show, that without sufficiency measures, Europe misses the opportunity to transform energy demand leaving considerable pressure on supply side changes combined with unproven carbon removal technologies.
Serum metabolite and metal ions profiles for breast cancer screening
Authors: Wojtowicz Wojciech , Tarkowski R. , Olczak A. , Szymczycha-Madeja A. , Pohl P. , Maciejczyk A. , Trembecki Ł. , Matkowski R. , Młynarz Piotr 
Published: October 2024

AbstractEnhancing early-stage breast cancer detection requires integrating additional screening methods with current diagnostic imaging. Omics screening, using easily collectible serum samples, could serve as an initial step. Alongside biomarker identification capabilities, omics analysis allows for a comprehensive analysis of prevalent histological types—DCIS and IDC. Employing metabolomics, metallomics, and machine learning, could yield accurate screening models with valuable insights into organism responses. Serum samples of confirmed breast cancer patients were utilized to analyze metabolite and metal ion profiles, using two distinct analysis methods, proton NMR for metabolomics and ICP-OES for metallomics. The resulting responses were then subjected to discriminant analysis, progression biomarker exploration, examination of correlations between patients’ metabolites and metal ions, and the impact of age and menopause status. Measured NMR spectra and metabolite relative integrals were used to achieve statistically significant discrimination through MVA between breast cancer and control groups. The analysis identified 24 metabolites and 4 metal ions crucial for discrimination. Furthermore, four metabolites were associated with disease progression. Additionally, there were important correlations and relationships between metabolite relative integrals, metal ion concentrations, and age/menopausal status subgroups. Quantified relative integrals allowed for discrimination between studied subgroups, validated with a holdout set. Feature importance and statistical analysis for metabolomics and metallomics extracted a set of common entities which in combination provides valuable insights into ongoing molecular disturbances and disease progression.
eBryoSoil: a citizen science application to monitor changes in soil ecosystems
Authors: Mira André F. , Hortal Joaquín , Portela Ana Paula , Albertos Belén , Estébanez Belén , Branquinho Cristina , Vieira Cristiana , Hespanhol Helena , Draper Isabel , Marques Joana , Monteiro Juliana , Leo María , Hurtado Pilar , Ochoa-Hueso Raúl , Varela Zulema , Medina Nagore G. 
Published: October 2024

AbstractBiological soil covers (BSCs) play a pivotal role in ecosystem functioning by enhancing soil stability, mediating nutrient cycling, and influencing soil hydrology. Recognized as ecosystem engineers, they can physically modify, maintain, or create habitats, facilitating plant community development. Through these intricate interactions, BSCs contribute significantly to ecological processes, highlighting their importance in the overall health and functionality of the ecosystems of the Iberian Peninsula. Here we present the results obtained from the contributions of the citizen scientists uploaded from November 2019 to January 2021 with eBryoSoil, an app that allows citizens to participate in mapping the BSC communities across the Iberian Peninsula. Here, we emphasize the importance of habitats and consequently, their interaction with climatic variables for the persistence of BSCs (lichens and bryophytes). Conservation efforts targeted at preserving diverse habitats are essential to ensure the continued presence of lichen and bryophyte communities. Despite challenges posed by the SARS-CoV-2 outbreak, this citizen science project demonstrated success in utilizing a specifically tailored app to gather valuable information on BSC communities, providing insights into their vulnerability to climate change. This program serves as an illustrative example of how citizen science can effectively identify and study vulnerable habitats, offering a blueprint for future studies focused on understudied organisms.
Sensory cues, behavior and fur-based drying in the rat wetness response
Authors: Attah Augustine Triumph , Negrón-Moreno Paola N. , Amigo-Duran Macarena , Zhang Linghua , Kenngott Max , Brecht Michael , Clemens Ann M. 
Published: October 2024

AbstractIt never rains in standard lab-confinements; thus we have limited understanding of animal reactions to water and wetness. To address this issue, we sprayed water on different body parts of rats and measured drying and fur temperature by thermal imaging while manipulating behavior, sensory cues and fur. Spraying water on rats resulted in fur changes (hair clumping, apex formation), grooming, shaking, and scratching. Anesthesia abolished behavioral responses, interfered with fur changes, and slowed drying. Spraying water on different body parts resulted in differential behavioral drying responses. Spraying the head resulted in grooming and shaking responses; water evaporated from the head twice as fast as water sprayed on the animal’s back or belly. We observed no effect of whisker removal on post-water-spraying behavior. In contrast, local anesthesia of dorsal facial skin reduced post-water-spraying behavioral responses. Shaving of head fur drastically enhanced post-water-spraying behaviors, but reduced water loss during drying; indicating that fur promotes evaporation, acting in tandem with behavior to mediate drying. Excised wet fur patches dried and cooled faster than shaved excised wet skin. Water was sucked into distal hair tips, where it evaporated. We propose the wet-fur-heat-pump-hypothesis; fur might extract heat required for drying by cooling ambient air.
Spatial-temporal graph neural networks for groundwater data
Authors: Taccari Maria Luisa , Wang He , Nuttall Jonathan , Chen Xiaohui , Jimack Peter K. 
Published: October 2024

AbstractThis paper introduces a novel application of spatial-temporal graph neural networks (ST-GNNs) to predict groundwater levels. Groundwater level prediction is inherently complex, influenced by various hydrological, meteorological, and anthropogenic factors. Traditional prediction models often struggle with the nonlinearity and non-stationary characteristics of groundwater data. Our study leverages the capabilities of ST-GNNs to address these challenges in the Overbetuwe area, Netherlands. We utilize a comprehensive dataset encompassing 395 groundwater level time series and auxiliary data such as precipitation, evaporation, river stages, and pumping well data. The graph-based framework of our ST-GNN model facilitates the integration of spatial interconnectivity and temporal dynamics, capturing the complex interactions within the groundwater system. Our modified Multivariate Time Graph Neural Network model shows significant improvements over traditional methods, particularly in handling missing data and forecasting future groundwater levels with minimal bias. The model’s performance is rigorously evaluated when trained and applied with both synthetic and measured data, demonstrating superior accuracy and robustness in comparison to traditional numerical models in long-term forecasting. The study’s findings highlight the potential of ST-GNNs in environmental modeling, offering a significant step forward in predictive modeling of groundwater levels.
Nationwide study on development and validation of a risk prediction model for CIN3+ and cervical cancer in Estonia
Authors: Tisler Anna , Võrk Andres , Tammemägi Martin , Ojavee Sven Erik , Raag Mait , Šavrova Aleksandra , Nygård Mari , Nygård Jan F. , Stankunas Mindaugas , Kivite-Urtane Anda , Uusküla Anneli 
Published: October 2024

AbstractTransitioning to an individualized risk-based approach can significantly enhance cervical cancer screening programs. We aimed to derive and internally validate a prediction model for assessing the risk of cervical intraepithelial neoplasia grade 3 or higher (CIN3+) and cancer in women eligible for screening. This retrospective study utilized data from the Estonian electronic health records, including 517,884 women from the health insurance database and linked health registries. We employed Cox proportional hazard regression, incorporating reproductive and medical history variables (14 covariates), and utilized the least absolute shrinkage and selection operator (LASSO) for variable selection. A 10-fold cross-validation for internal validation of the model was used. The main outcomes were the performance of discrimination and calibration. Over the 8-year follow-up, we identified 1326 women with cervical cancer and 5929 with CIN3+, with absolute risks of 0.3% and 1.1%, respectively. The prediction model for CIN3 + and cervical cancer had good discriminative power and was well calibrated Harrell’s C of 0.74 (0.73–0.74) (calibration slope 1.00 (0.97–1.02) and 0.67 (0.66–0.69) (calibration slope 0.92 (0.84–1.00) respectively. A developed model based on nationwide electronic health data showed potential utility for risk stratification to supplement screening efforts. This work was supported through grants number PRG2218 from the Estonian Research Council, and EMP416 from the EEA (European Economic Area) and Norway Grants.
Insect responses to seasonal time constraints under global change are facilitated by warming and counteracted by invasive alien predators
Authors: Sniegula Szymon , Stoks Robby , Golab Maria J. 
Published: October 2024

AbstractIn seasonal environments, organisms with complex life cycles not only contend with seasonal time constraints (TC) but also increasingly face global change stressors that may interfere with responses to TC. Here, we tested how warming and predator stress imposed during the egg and larval stages shaped life history and behavioural responses to TC in the temperate damselflyIschnura elegans. Eggs from early and late clutches in the season were subjected to ambient and 4 °C warming temperature and the presence or absence of predator cues from perch and signal crayfish. After hatching, larvae were retained at the same thermal regime, and the predator treatment was continued or not up to emergence. The late eggs decreased their development time, especially under warming and when not exposed to predator cues. However, the late eggs increased their development time when exposed to predator cues, especially to crayfish cues. The TC decreased survival of late larvae that were as eggs exposed to crayfish cues, indicating a carry-over effect. The TC and warming additively reduced late larvae development time to emergence. Independent of the TC, predator cue effects on development time were stronger during the egg than during the larval stage. The late individuals expressed lower mass at emergence, which mirrored the size difference between field-collected mothers. Warming caused a higher mass at emergence. The late individuals increased their boldness and showed a higher number of moves, whereas warming caused a decreased boldness. There was no predator cue effect on larval behaviour. The results indicate that late individuals compensate for late season egg laying, which is facilitated under warming but counteracted under predation risk, especially when imposed by the crayfish.
Novel paediatric case of a spinal high-grade astrocytoma with piloid features in a patient with Noonan Syndrome
Authors: Staunton Jordan , Ajuyah Pamela , Harris Angela , Mayoh Chelsea , Wong Marie , Rumford Megan , Sullivan Patricia J. , Ekert Paul G. , Fuentes-Bolanos Noemi , Cowley Mark J. , Lau Loretta M. S. , Ziegler David S. , Barahona Paulette , Manoharan Neevika 
Published: October 2024

AbstractNoonan Syndrome (NS) is associated with an increased risk of low-grade central nervous system tumours in children but only very rarely associated with high-grade gliomas. Here we describe the first reported case of a spinal high-grade astrocytoma with piloid features (HGAP) in a child with NS. This case was a diagnostic and treatment dilemma, prior to whole-genome germline and tumour sequencing, tumour transcriptome sequencing and DNA methylation analysis. The methylation profile matched strongly with HGAP and sequencing identified somatic FGFR1 and NF1 variants and a PTPN11 germline pathogenic variant. Therapeutic targets were identified but also alterations novel to HGAP such as differential expression of VEGFA and PD-L1. The germline PTPN11 finding has not been previously described in individuals with HGAP. This case underscores the power of precision medicine from a diagnostic, therapeutic and clinical management perspective, and describes an association between HGAP and NS which has not previously been reported.
Discovery of Trypanosoma brucei inhibitors enabled by a unified synthesis of diverse sulfonyl fluorides
Authors: Mantilla Brian S. , White Jack S. , Mosedale William R. T. , Gomm Andrew , Nelson Adam , Smith Terry K. , Wright Megan H. 
Published: October 2024

AbstractSets of electrophilic probes are generally prepared using a narrow toolkit of robust reactions, which tends to limit both their structural and functional diversity. A unified synthesis of skeletally-diverse sulfonyl fluorides was developed that relied upon photoredox-catalysed dehydrogenative couplings between hetaryl sulfonyl fluorides and hydrogen donor building blocks. A set of 32 diverse probes was prepared, and then screened against Trypanosoma brucei. Four of the probes were found to have sub-micromolar anti-trypanosomal activity. A chemical proteomic approach, harnessing an alkynylated analogue and broad-spectrum fluorophosphonate tools, provided insights into the observed anti-trypanosomal activity, which likely stems from covalent modification of multiple protein targets. It is envisaged that the unified diversity-oriented approach may enable the discovery of electrophilic probes that have value in the elucidation of biological and biomedical mechanisms.
Catalytic hydrodeoxygenation and C–C coupling of lignin and its derivatives into renewable jet-fuel-range cycloalkanes
Authors: Diao Xinyong , Xiong Ying , Shi Yawen , Ma Longlong , Dong Chenglong , Zhang Shengbo , Ji Na 
Published: October 2024

This review provides an in-depth understanding of the synthesis pathways and corresponding catalytic systems for the production of jet-fuel-range cycloalkanes from lignin and its derivatives via catalytic hydrodeoxygenation and C–C coupling.
Massive MIMO uplink and downlink joint representation based on couple dictionary learning
Authors: Guan Qing Yang 
Published: October 2024

AbstractThe challenge of jointly representing both the uplink (UL) and downlink (DL) in massive multiple input multiple output (MIMO) systems have been tackled. Considering the angular reciprocity, a couple dictionary learning (CDL) support to enhance performance and address high complexity has been introduced. This approach minimizes the number of pilots and improves accuracy. Currently, accuracy analysis of UL/DL representation primarily relies on simulation. To bridge this gap, a proportion factor (PF) operator is proposed for CDL to assess accuracy. Specifically, a qualitative analysis formula is provided for accuracy and an optimal upper bound is established. Through theoretical proof, it is demonstrated that the accuracy of CDL for representation is mainly influenced by the cross‐correlation between the pilot matrix and the dictionary matrix. Inspired by PF operator, an optimal couple dictionary learning (OCDL) algorithm using singular value decomposition (SVD) is introduced to obtain dictionary updating, aiming at high‐precision representation. By establishing normalized mean squared error (NMSE), successful representation ratio, bit error rate (BER), and constellation performance, an OCDL algorithm that outperforms existing methods is showcased and channel representation accuracy is enhanced significantly.
An enhanced sensitivity‐based combined control method of battery energy storage systems for voltage regulation in PV‐rich residential distribution networks
Authors: Rezaei Farzaneh , Esmaeili Saeid 
Published: October 2024

AbstractCommercial off‐the‐shelf (OTS) photovoltaic systems coupled with battery energy storage units (PV‐BES) are typically designed to increase household self‐consumption, neglecting their potential for voltage regulation in low voltage distribution networks (LVDNs). This work proposes an enhanced sensitivity‐based combined (ESC) control method for voltage regulation, using BES control as level 1 and reactive power compensation as level 2. A centralized controller manages charging/discharging intervals, while local inverters handle real‐time power rates and reactive power, ensuring effective LVDN voltage regulation. The BES set points are obtained concerning the measured local bus voltage and according to enhanced sensitivity coefficients. The enhancement algorithm ensures that the full capacity of BES is utilized and that there is adequate capacity during charging and discharging time intervals. The proposed method, tested on 8‐bus and 116‐bus LV test feeders, outperforms OTS and an adaptive decentralized (AD) control method by completely preventing overvoltage issues, minimizing various changes in the direction of BES power, and reducing voltage deviation without significantly affecting consumers' grid dependency.
An online frequency allocation strategy for multi‐carrier radar against spot jammer
Authors: Shan Zhao , Wang Lei , Zhang Zhe , Liu Yimin 
Published: October 2024

AbstractSpot jamming poses a significant threat to radar detection due to its ability to rapidly intercept radar signals and emit high‐power interference. A novel Cognitive Multi‐Carrier Radar (CMCR) is proposed, which utilises a detection waveform comprising multiple narrow bands to reduce the disruption from the spot jammer. Additionally, it can analyse the target’s Radar Cross‐Section (RCS) characteristics and proactively select frequencies with high RCS for detection, thereby enhancing target detection capability. The online frequency allocation problem of CMCR is a Multi‐Armed Bandit (MAB) problem with a combinatorial action space. To tackle the challenge from the huge action space, an efficient aDaptive Accept Reject (DART) method is employed, which significantly reduces the regret of CMCR in the detection task. Moreover, a slide‐window change point indicator is proposed to monitor the change of target’s RCS, which enables CMCR to be applicable in scenarios where target’s RCS varies. Finally, the effectiveness and robustness of CMCR are through extensive numerical experiments.
Enhancing underwater target detection: Fusion of spatio‐temporal incompletely‐aligned AIS and sonar information via DTW and multi‐head attention mechanism
Authors: Zhao Wenbo , Cheng Xinghua , Wang Dezhi , Xiong Xiaodan , Zhang Xiaoshuang 
Published: October 2024

AbstractIn the field of underwater target detection, the passive sonar is an important means of long‐distance target detection. The sonar detection information typically includes both surface and underwater targets, whereas it is a great challenge on effectively distinguishing between surface and underwater targets solely based on sonar information. Effective fusion of sonar and AIS (Automatic Identification System) data can leverage their complementary nature to compensate for the limitation of sonar information. However, the sonar information and AIS information are acquired based on different detection principles and systems, which are essentially multi‐source heterogeneous information with obvious spatio‐temporal misalignment in nature. Existing fusion methods normally struggle to effectively align sonar and AIS data in both time and space subject to the complexity of the problem. In this study, the Dynamic Time Warping (DTW) algorithm is applied to align sonar and AIS data in the time domain. In addition, a deep learning algorithm with multi‐head attention mechanism is proposed to achieve the spatial alignment of sonar and AIS data, where the matching between the surface targets in AIS data and the same surface targets in sonar data can also be successfully achieved. It provides a priori knowledge to enhance the underwater target detection of the passive sonar by eliminating the interference of the surface targets. Based on the attention mechanism, the abstract features extracted from the intermediate‐layer of the neural networks are found to be effective to represent the typical features of the target motion trajectories, which also demonstrates the effectiveness of the attention mechanism. The experiment results show that the proposed method can successfully achieve a MatchingSucccessRate of over 95% between the AIS targets and sonar detection targets.
Intraoperative bleeding model for swine gastric endoscopic submucosal dissection via heparinization
Authors: Masunaga Teppei , Sasaki Motoki , Sato Moe , Minezaki Daisuke , Morioka Kohei , Tojo Anna , Sakurai Hinako , Iwata Kentaro , Miyazaki Kurato , Kubosawa Yoko , Mizutani Mari , Akimoto Teppei , Takatori Yusaku , Kawasaki Shintaro , Matsuura Noriko , Nakayama Atsushi , Sujino Tomohisa , Takabayashi Kaoru , Yahagi Naohisa , Nakajima Kiyokazu , Kato Motohiko 
Published: October 2024

Abstract
Background and study aims: Live swine have a high degree of coagulation and aggregation and using them for training about how to manage intraoperative bleeding during endoscopic submucosal dissection (ESD) is unsatisfactory. This study aimed to identify the appropriate heparin dose in an intraoperative bleeding model and validate its applicability.
Methods: First, we explored the dose of heparin required for a swine bleeding model in which the activated clotting time reached and maintained the upper limit of measurement (1500 s) after 10 minutes. Second, we compared intraoperative bleeding and hematoma frequency during ESD for 2-cm lesions between the heparinized bleeding model and control groups. Intraoperative bleeding was classified according to the Forrest classification.
Results: The combination of a bolus (300 U/kg), continuous infusion (300 U/kg/h), and a bolus dose (150 U/kg) of heparin 10 minutes after the first infusion was identified as the dose for the bleeding model. Five ESDs were performed in each heparinized bleeding model and the control group. The median number of intraoperative bleeds was significantly higher in the heparinized model than in the control group (5 interquartile range [IQR] 4–7 vs. 3 [IQR 0–4, P = 0.028). All of the intraoperative bleeding events oozing (Forrest Ib) rather than spurting (Forrest Ia). The median number of hematomas was significantly higher in the heparinized model group than in the control group (3 [IQR 1–4] vs. 0 [IQR 0–1], P = 0.023).
Conclusions: High doses of heparin significantly increased intraoperative bleeding and hematoma during swine ESD.