bluuebunny/crossref_metadata_2025_split huggingface dataset:
Cancer cells impair monocyte-mediated T cell stimulation to evade immunity
Authors: Elewaut Anais , Estivill Guillem , Bayerl Felix , Castillon Leticia , Novatchkova Maria , Pottendorfer Elisabeth , Hoffmann-Haas Lisa , Schönlein Martin , Nguyen Trung Viet , Lauss Martin , Andreatta Francesco , Vulin Milica , Krecioch Izabela , Bayerl Jonas , Pedde Anna-Marie , Fabre Naomi , Holstein Felix , Cronin Shona M. , Rieser Sarah , Laniti Denarda Dangaj , Barras David , Coukos George , Quek Camelia , Bai Xinyu , Muñoz i Ordoño Miquel , Wiesner Thomas , Zuber Johannes , Jönsson Göran , Böttcher Jan P. , Vanharanta Sakari , Obenauf Anna C. 
Published: November 2024

Abstract
The tumour microenvironment is programmed by cancer cells and substantially influences anti-tumour immune responses1,2. Within the tumour microenvironment, CD8+ T cells undergo full effector differentiation and acquire cytotoxic anti-tumour functions in specialized niches3–7. Although interactions with type 1 conventional dendritic cells have been implicated in this process3–5,8–10, the underlying cellular players and molecular mechanisms remain incompletely understood. Here we show that inflammatory monocytes can adopt a pivotal role in intratumoral T cell stimulation. These cells express Cxcl9, Cxcl10 and Il15, but in contrast to type 1 conventional dendritic cells, which cross-present antigens, inflammatory monocytes obtain and present peptide–major histocompatibility complex class I complexes from tumour cells through ‘cross-dressing’. Hyperactivation of MAPK signalling in cancer cells hampers this process by coordinately blunting the production of type I interferon (IFN-I) cytokines and inducing the secretion of prostaglandin E2 (PGE2), which impairs the inflammatory monocyte state and intratumoral T cell stimulation. Enhancing IFN-I cytokine production and blocking PGE2 secretion restores this process and re-sensitizes tumours to T cell-mediated immunity. Together, our work uncovers a central role of inflammatory monocytes in intratumoral T cell stimulation, elucidates how oncogenic signalling disrupts T cell responses through counter-regulation of PGE2 and IFN-I, and proposes rational combination therapies to enhance immunotherapies.
MCM double hexamer loading visualized with human proteins
Authors: Weissmann Florian , Greiwe Julia F. , Pühringer Thomas , Eastwood Evelyn L. , Couves Emma C. , Miller Thomas C. R. , Diffley John F. X. , Costa Alessandro 
Published: November 2024

AbstractEukaryotic DNA replication begins with the loading of the MCM replicative DNA helicase as a head-to-head double hexamer at origins of DNA replication1–3. Our current understanding of how the double hexamer is assembled by the origin recognition complex (ORC), CDC6 and CDT1 comes mostly from budding yeast. Here we characterize human double hexamer (hDH) loading using biochemical reconstitution and cryo-electron microscopy with purified proteins. We show that the human double hexamer engages DNA differently from the yeast double hexamer (yDH), and generates approximately five base pairs of underwound DNA at the interface between hexamers, as seen in hDH isolated from cells4. We identify several differences from the yeast double hexamer in the order of factor recruitment and dependencies during hDH assembly. Unlike in yeast5–8, the ORC6 subunit of the ORC is not essential for initial MCM recruitment or hDH loading, but contributes to an alternative hDH assembly pathway that requires an intrinsically disordered region in ORC1, which may work through a MCM–ORC intermediate. Our work presents a detailed view of how double hexamers are assembled in an organism that uses sequence-independent replication origins, provides further evidence for diversity in eukaryotic double hexamer assembly mechanisms9, and represents a first step towards reconstitution of DNA replication initiation with purified human proteins.
BFR2: a curated ribosomal reference dataset for benthic foraminifera
Authors: Holzmann Maria , Nguyen Ngoc-Loi , Angeles Inès Barrenechea , Pawlowski Jan 
Published: November 2024

AbstractBenthic foraminifera are one of the major groups of marine protists that also occur in freshwater and terrestrial habitats. They are widely used to monitor current and past environmental conditions. Over the last three decades, thousands of DNA sequences have been obtained from benthic foraminiferal isolates. The results of this long-term effort are compiled here in the form of the first curated benthic foraminiferal ribosomal reference dataset (BFR2). The present dataset contains over 5000 sequences of a fragment of the 18S rDNA gene, which is recognized as the DNA barcode of foraminifera. The sequences represent 279 species and 204 genera belonging to 91 families. Thirteen percent of these sequences have not been assigned to any morphologically described group and may represent species new to science. Furthermore, forty-five percent of the sequences have not been previously published. The BFR2 dataset aims to collect all DNA barcodes of benthic foraminifera and to provide a much-needed reference dataset for the rapidly developing field of molecular foraminiferal studies.
Comparison of MRI head motion indicators in 40,969 subjects informs neuroimaging study design
Authors: Teo Thomas Wei Jun , Saffari Seyed Ehsan , Chan Ling Ling , Welton Thomas 
Published: November 2024

AbstractHead motion during MRI compromises image quality for clinical assessments and research. Active motion reduction strategies are effective but rarely applied due to uncertainty in their value for a given study. The ability to anticipate motion based on group characteristics would aid effective neuroimaging study design. This study compared putative motion indicators for their association to fMRI head motion in a large UK Biobank cohort (n = 40,969, aged 54.9 ± 7.5 years, 53% male). Body Mass Index (BMI; βadj = .050, p < .001) and ethnicity (βadj = 0.068, p < 0.001) were the strongest indicators of head motion. A ten-point increase in BMI, which is the difference between “healthy” and “obese”, corresponded to a 51% increase in motion. Findings were similar in a subgroup with no lifetime diagnoses (n = 6858). Motion was not significantly increased in individuals with psychiatric disorders, musculoskeletal disorders, or diabetes. The hypertension subgroup exhibited significantly increased motion (p = 0.048). Cognitive task performance (t = 110.83, p < 0.001) and prior scan experience (t = 7.16, p < 0.001) were associated with increased head motion. Our results inform decision making for implementation of motion reduction strategies in MRI. BMI outweighs other motion indicators, while blood pressure, age, smoking and caffeine consumption are relatively less influential. Disease diagnosis alone is not a good indicator of MRI head motion.
Association between the CYP2B6 polymorphisms and nonnucleoside reverse transcriptase inhibitors drug-induced liver injury: a systematic review and meta-analysis
Authors: Chanhom Noppadol , Sonjan Janjira , Inchai Jarupat , Udomsinprasert Wanvisa , Chaikledkaew Usa , Suvichapanich Supharat , Mahasirimongkol Surakameth , Jittikoon Jiraphun 
Published: November 2024

AbstractNevirapine (NVP) and Efavirenz (EFV) can cause antiretroviral drug-induced liver injury (ARVDILI). The objectives of this study were to summarize and analyze existing data on pharmacogenomics associated with nonnucleoside reverse transcriptase inhibitors drug-induced liver injury using systematic review and meta-analysis. This study systematically searched the relevant studies regarding pharmacogenes related to ARVDILI from online databases. Genes-encoding proteins were further analyzed using the STRING program to determine the protein-protein interactions (PPI). CYP2B6 polymorphisms were further meta-analyzed. Seventeen genes have been shown to be significantly associated with ARVDILI. Illustration from STRING analysis, CYP2B6, CYP1A1, and CYP2D6 enzymes have been recognized as central proteins linked to all other analyzed proteins. Meta-analysis illustrated that CYP2B6 *1/*6 (OR = 1.83; 95% CI: 1.15–2.90; P = 0.01), *6/*6 (OR = 2.48; 95% CI: 1.28–4.79; P = 0.007), and *1/*6 plus *6/*6 (OR = 1.94; 95% CI: 1.24–3.01; P = 0.003) were associated with risks of EFV-induced liver injury. Moreover, CYP2B6 *1/*6 (OR = 0.44; 95% CI: 0.22–0.91; P = 0.03) and a group combining individuals with either *1/*6 or *6/*6 (OR = 0.42; 95% CI: 0.21–0.84; P = 0.01) were associated with reduced risks of NVP-induced liver injury. This meta-analysis revealed an association between CYP2B6 genetic polymorphism and susceptibility to ARVDILI.
Enhanced warming and bacterial biomass production as key factors for coastal hypoxia in the southwestern Baltic Sea
Authors: Hepach Helmke , Piontek Judith , Bange Hermann W. , Barthelmeß Theresa , von Jackowski Anabel , Engel Anja 
Published: November 2024

AbstractCoastal ecosystems are affected by a multitude of anthropogenic stressors. As the Baltic Sea ecosystems rank among the most altered marine ecosystems worldwide, they represent ideal model regions to study ecosystem responses to anthropogenic pressures. Our statistical analysis of data including dissolved organic carbon and nitrogen, as well as bacterial abundance and -biomass production from the time-series station Boknis Eck in the southwestern Baltic Sea reveals that bacterial biomass production intensifies towards summer following the phytoplankton spring bloom. Moreover, warming, especially very high temperatures in summer, enhances stratification and bacterial biomass production despite long-term reduction in nutrient input. A strong decrease in oxygen in the bottom layer is possibly linked to this. We detect an increasing trend in bacterial biomass production along with intensifying warming and stratification, and more frequently occurring hypoxia despite ongoing restoration efforts. If this trend continues, the coastal Baltic Sea ecosystem is likely to be altered even further. Coastal ecosystems play pivotal roles in mitigating impacts of climate change but if destroyed, they may amplify climate change further calling for stronger ecosystem management strategies.
Perspectives on innovative non-fertilizer applications of sewage sludge for mitigating environmental and health hazards
Authors: Fini Elham H. , Kazemi Mohammadjavad , Poulikakos Lily , Lazorenko Georgy , Akbarzade Vajiheh , Lamanna Anthony , Lammers Peter 
Published: November 2024

AbstractAs waste production increases and resources become limited, sewage sludge presents a valuable resource with potential beyond traditional land use and incineration. This review emphasizes exploring innovative non-fertilizer applications of sewage sludges and advocates for viewing wastewater treatment plants as sources of valuable feedstock and carbon sequestration. Innovative uses include integrating sewage sludge into construction materials such as asphalt pavements, geopolymer, cementitious composites, and masonry blocks. These methods not only immobilize heavy metals and mitigate environmental hazards but also support carbon sequestration, contrasting with incineration and land application methods that release carbon into the atmosphere. The review also addresses emerging technologies like bio-adhesives, bio-binders for asphalt, hydrogels, bioplastics, and corrosion inhibitors. It highlights the recovery of valuable materials from sewage sludge, including phosphorus, oils, metals, cellulose, and polyhydroxyalkanoates as well as enzyme production. By focusing on these non-fertilizer applications, this review presents a compelling case for re-envisioning wastewater treatment plants as sources of valuable feedstock and carbon sequestration, supporting global efforts to manage waste effectively and enhance sustainability.
Feature pyramid attention network for audio‐visual scene classification
Authors: Zhou Liguang , Zhou Yuhongze , Qi Xiaonan , Hu Junjie , Lam Tin Lun , Xu Yangsheng 
Published: November 2024

AbstractAudio‐visual scene classification (AVSC) poses a formidable challenge owing to the intricate spatial‐temporal relationships exhibited by audio‐visual signals, coupled with the complex spatial patterns of objects and textures found in visual images. The focus of recent studies has predominantly revolved around extracting features from diverse neural network structures, inadvertently neglecting the acquisition of semantically meaningful regions and crucial components within audio‐visual data. The authors present a feature pyramid attention network (FPANet) for audio‐visual scene understanding, which extracts semantically significant characteristics from audio‐visual data. The authors’ approach builds multi‐scale hierarchical features of sound spectrograms and visual images using a feature pyramid representation and localises the semantically relevant regions with a feature pyramid attention module (FPAM). A dimension alignment (DA) strategy is employed to align feature maps from multiple layers, a pyramid spatial attention (PSA) to spatially locate essential regions, and a pyramid channel attention (PCA) to pinpoint significant temporal frames. Experiments on visual scene classification (VSC), audio scene classification (ASC), and AVSC tasks demonstrate that FPANet achieves performance on par with state‐of‐the‐art (SOTA) approaches, with a 95.9 F1‐score on the ADVANCE dataset and a relative improvement of 28.8%. Visualisation results show that FPANet can prioritise semantically meaningful areas in audio‐visual signals.
Obtaining diverse solutions for reliable multi‐microgrid network design
Authors: Zhang Tao , Yao Xingyi , Li Wenhua , Wang Rui 
Published: November 2024

AbstractThe multi‐microgrid network structure optimisation problem (MNSDOP) aims to minimise the circuit lengths while maximising power transmission stability, a crucial aspect for system robustness enhancement. While existing literature primarily focuses on achieving Pareto optimal solutions there is an overlooked emphasis on solution diversity. In situations like MNSDOPs, several optimal solutions might possess identical or comparable objective values, classifying them under multimodal multi‐objective optimisation roblems (MMOPs). Offering an array of optimal solutions equips decision‐makers with a holistic understanding of the problem, aiding in the identification of preferred solutions. Motivated by this gap, we introduce a multimodal multi‐objective evolutionary algorithm tailored for MNSDOPs, termed MMO‐BM. We also propose a diversity evaluation metric specifically for binary optimisation problems, substantially amplifying the exploration capabilities of evolutionary algorithms. In addition, a novel matrix‐oriented DE operator is proposed to accerate the convergence process. Experimental evaluations attest to our method's prowess in yielding diverse and high‐quality solutions.
Typical characteristic mining and load evaluation for the difficulties of power supply–demand balance regulation
Authors: Li Junwei , Yu Yang , Mi Zengqiang , Wu Jian 
Published: November 2024

AbstractPower systems face significant challenges in maintaining power balance because of the stochasticity of sources and loads. This unpredictability makes it difficult to characterize the typical demand for grid power balance regulation, which results in a lack of clear objectives for evaluating the contributions of demand‐side users to power balance. To bridge these gaps, in this paper, a new approach is proposed on the basis of the inherent difficulties in grid power balance regulation. First, a method for portraying the change in power balance regulation demand is presented, which emphasizes the trend characteristics of time‐varying regulation demand through the weighting of trend segments. Second, demand periods and curves are calculated on the basis of a regulation capacity of 5% of the maximum load, and their distribution during the monthly cycle is analysed. Finally, to elucidate the contribution of random samples to typical demand features, a sample weighting method utilizing clustering categories is proposed and a distance‐minimum optimization model is constructed to estimate typical features by leveraging the “group effect” concept. Actual power data from a region in China are selected for verification, confirming that the proposed typical value calculation method is more representative of random sampling situations. Moreover, considering the power grid's time‐varying nature, the evaluation of the contribution of demand‐side users to the power grid is improved.
Geo‐spatial traffic behaviour analysis and anomaly detection for ITS applications
Authors: Akdag Erkut , D'Amicantonio Giacomo , Vijverberg Julien , Stajan David , Beers Bart , De With Peter H. N. , Bondarev Egor 
Published: November 2024

AbstractUnderstanding the behaviour of traffic participants within the geo‐spatial context of road/intersection topology is a vital prerequisite for any smart ITS application. This article presents a video‐based traffic analysis and anomaly detection system covering the complete data processing pipeline, including sensor data acquisition, analysis, and digital twin reconstruction. The system solves the challenge of geo‐spatial mapping of captured visual data onto the road/intersection topology by semantic analysis of aerial data. Additionally, the automated camera calibration component enables instant camera pose estimation to map traffic agents onto the road/intersection surface accurately. A novel aspect is approaching the anomaly detection problem by AI analysis of both the spatio‐temporal visual clues and the geo‐spatial trajectories for all type of traffic participants, such as pedestrians, bicyclists, and vehicles. This enables recognition of anomalies related to either traffic‐rule violations, for example, jaywalking, improper turns, zig‐zag driving, unlawful stops, or behavioural anomalies: littering, accidents, falling, vandalism, violence, infrastructure collapse etc. The method achieves leading anomaly detection results on benchmark datasets World Cup 2014, UCF‐Crime, XD‐Violence, and ShanghaiTech. All the obtained results are streamed and rendered in real‐time by the developed TGX digital twin visualizer. The complete system has been deployed and validated on the roads of Helmond town in The Netherlands.
Machine learning‐driven design of dual‐band antennas using PGGAN and enhanced feature mapping
Authors: Tuen Lung‐Fai , Li Ching‐Lieh , Chi Yu‐Jen , Chiu Chien‐Ching , Chen Po Hsiang 
Published: November 2024

AbstractThis paper presents a systematic antenna design methodology that integrates machine learning, leveraging the progressive growth technique of Progressive Growing of GANs (PGGAN) to generate images of various dual‐band PIFA‐like antenna structures. The process involves using data augmentation methods to generate 4180 antenna samples. In the latent space, the authors employ Latin Hypercube Sampling to maintain diversity and combine it with the Hough Transform to enhance the edge features of the antennas while providing labelling functionality. This labelling method strengthens the relationship between antenna frequency and wavelength characteristics. The paper clearly outlines the design process, starting from the simulation of two types of single‐frequency PIFA‐like antennas (2.45 and 5.2 GHz, respectively) to the completion of PGGAN's generation task, resulting in a novel dual‐band Wi‐Fi PIFA‐like antenna structure. The measurement results of the dual‐band antennas exhibit excellent consistency with the simulation results.
A state evaluation and fault diagnosis strategy for substation relay protection system integrating multiple intelligent algorithms
Authors: Wang Jiajun , Jing Shiyi , Yao Yu , Wang Kunlun , Li Bo 
Published: November 2024

AbstractEnsuring the operational reliability of substation relay protection systems through rapid defect diagnosis and state assessment is crucial for maintaining power system stability. This study introduces a new diagnostic framework that combines improved particle swarm optimization, K‐means clustering algorithms, support vector machine (SVM), and learning vector quantization neural networks to provide a comprehensive fault diagnosis and prediction model for relay protection systems. The model commences by identifying critical metrics for system state evaluation, employing an improved analytic hierarchy process to allocate weights to these indicators, and introducing variable weights theory to improve dependability of outcomes. The model enhances SVM with learning vector quantization for precise state prediction by utilizing operational data from substation relay protection systems. Improved particle swarm optimization optimizes key SVM parameters to improve accuracy. In order to effectively classify defect categories, the K‐means clustering algorithm is implemented. The model's efficacy, stability, and comprehensive applicational potential have been confirmed through experimental trials, which represent substantial progress in the field of substation fault management.
Investigating explosive events in a 3D quiet-Sun model: Transition region and coronal response
Authors: Chen Yajie , Peter Hardi , Przybylski Damien 
Published: November 2024

Context. Transition region explosive events are characterized by the non-Gaussian profiles of the emission lines that form at transition region temperatures, and they are believed to be manifestations of small-scale reconnection events in the transition region.
Aims. Traditionally, the enhanced emission at the line wings is interpreted as bi-directional outflows generated by the reconnection of oppositely directed magnetic fields. We investigate whether the 2D picture also holds in a more realistic setup of a 3D radiation magnetohydrodynamic (MHD) quiet-Sun model. We also compare the thermal responses in the transition region and corona of different events.
Methods. We took a 3D self-consistent quiet-Sun model extending from the upper convection zone to the lower corona calculated using the MURaM code. We first synthesized the Si IV line profiles from the model and then located the profiles which show signatures of bi-directional flows. These tend to appear along network lanes, and most do not reach coronal temperatures. We isolated two hot events (around 1 MK) and one cool event (order of 0.1 MK) and examined the magnetic field evolution in and around these selected events. Furthermore, we investigated why some explosive events reach coronal temperatures, while most remain cool. We also examined the emission of these events as seen in the 174 Å passband of the Extreme Ultraviolet Imager (EUI) on board Solar Orbiter and all coronal passbands of the Atmospheric Imaging Assembly (AIA) on board the Solar Dynamics Observatory (SDO).
Results. The field lines around two events reconnect at small angles (i.e., they undergo component reconnection). The third case is associated with the relaxation of a highly twisted flux rope. All three events reveal signatures in the synthesized EUI 174 Å images. The intensity variations in two events are dominated by variations of the coronal emissions, while the cool component seen in the respective channel contributes significantly to the intensity variation in one case. In comparison, one hot event is embedded in regions with higher magnetic field strength and heating rates while the densities are comparable, and the other hot event is heated to coronal temperatures mainly because of the low density.
Conclusions. Small-scale heating events seen in the extreme-ultraviolet (EUV) channels of AIA or EUI might be hot or cool. Our results imply that the major difference between the events in which coronal counterparts dominate or not is the amount of converted magnetic energy and/or density in and around the reconnection region.