bluuebunny/crossref_metadata_2025_split huggingface dataset:
Characterization of clinical data for patient stratification in moderate osteoarthritis with support vector machines, regulatory network models, and verification against osteoarthritis Initiative data
Authors: Segarra-Queralt Maria , Galofré Mar , Tio Laura , Monfort Jordi , Monllau Joan Carlos , Piella Gemma , Noailly Jérôme 
Published: May 2024

AbstractKnee osteoarthritis (OA) diagnosis is based on symptoms, assessed through questionnaires such as the WOMAC. However, the inconsistency of pain recording and the discrepancy between joint phenotype and symptoms highlight the need for objective biomarkers in knee OA diagnosis. To this end, we study relationships among clinical and molecular data in a cohort of women (n = 51) with Kellgren–Lawrence grade 2–3 knee OA through a Support Vector Machine (SVM) and a regulation network model. Clinical descriptors (i.e., pain catastrophism, depression, functionality, joint pain, rigidity, sensitization and synovitis) are used to classify patients. A Youden’s test is performed for each classifier to determine optimal binarization thresholds for the descriptors. Thresholds are tested against patient stratification according to baseline WOMAC data from the Osteoarthritis Initiative, and the mean accuracy is 0.97. For our cohort, the data used as SVM inputs are knee OA descriptors, synovial fluid proteomic measurements (n = 25), and transcription factor activation obtained from regulatory network model stimulated with the synovial fluid measurements. The relative weights after classification reflect input importance. The performance of each classifier is evaluated through ROC-AUC analysis. The best classifier with clinical data is pain catastrophism (AUC = 0.9), highly influenced by funcionality and pain sensetization, suggesting that kinesophobia is involved in pain perception. With synovial fluid proteins used as input, leptin strongly influences every classifier, suggesting the importance of low-grade inflammation. When transcription factors are used, the mean AUC is limited to 0.608, which can be related to the pleomorphic behaviour of osteoarthritic chondrocytes. Nevertheless, funcionality has an AUC of 0.7 with a decisive importance of FOXO downregulation. Though larger and longitudinal cohorts are needed, this unique combination of SVM and regulatory network model shall help to stratify knee OA patients more objectively.
PAU Smart Seeder: a novel way forward for rice residue management in North-west India
Authors: Manpreet-Singh  , Chaleka A. T. , Goyal Rajesh , Gupta Naveen , Singh Arshdeep , Singh Manpreet , Sharma Sandeep , Dixit Anoop Kumar , Malik Anurag , Al-Ansari Nadhir , Mattar Mohamed A. 
Published: May 2024

AbstractIn winter, the paddy residues become wet during morning and late evening due to dew, which restricts the operation of sowing machines (Happy Seeder and Super Seeder) into paddy residues, as wet residues do not slide on furrow openers/tines. A PAU Smart Seeder (PSS) was developed and evaluated for a four-wheel tractor that can sow wheat with optimum crop establishment in combined harvested rice fields. The PSS were evaluated for its performance under varying straw load, forward speed, and rotor speed in terms of fuel consumption, field capacity, seed emergence, and grain yield. The crop establishment and wheat yield of PSS was also compared with the existing straw management machines Happy Seeder (HS) and Super Seeder (SS) under heavy paddy residue conditions. The effect of the straw load was more pronounced on dependent variables than the effect of the speed index. PSS performance was best at a forward speed of 2.6 km h−1, rotor speed of 127.5 rpm, and a straw load of 6 t ha−1. Average fuel consumption using PSS was lower than SS but higher than HS. Wheat emergence was higher by 15.6 and 25.7% on the PSS plots compared to HS and SS, respectively. Average wheat grain yield in PSS plots was significantly higher by 12.7 and 18.9% than SS and HS, respectively in one experiment, while the grain yield was similar for both PSS and HS in other experiments. PSS has a novel mechanism to manage paddy straw and simultaneously sow wheat into a heavy straw load (> 8 t ha−1) mixture of anchored and loose straw. In conclusion, PSS showed promise for in-situ management of rice straw as it eliminates most of the operational problems encountered by the existing seeders (HS and SS).
Assessment of image quality and impact of deep learning-based software in non-contrast head CT scans
Authors: Bos Denise , Demircioğlu Aydin , Neuhoff Julia , Haubold Johannes , Zensen Sebastian , Opitz Marcel K. , Drews Marcel A. , Li Yan , Styczen Hanna , Forsting Michael , Nassenstein Kai 
Published: May 2024

AbstractIn this retrospective study, we aimed to assess the objective and subjective image quality of different reconstruction techniques and a deep learning-based software on non-contrast head computed tomography (CT) images. In total, 152 adult head CT scans (77 female, 75 male; mean age 69.4 ± 18.3 years) obtained from three different CT scanners using different protocols between March and April 2021 were included. CT images were reconstructed using filtered-back projection (FBP), iterative reconstruction (IR), and post-processed using a deep learning-based algorithm (PS). Post-processing significantly reduced noise in FBP-reconstructed images (up to 15.4% reduction) depending on the protocol, leading to improvements in signal-to-noise ratio of up to 19.7%. However, when deep learning-based post-processing was applied to FBP images compared to IR alone, the differences were inconsistent and partly non-significant, which appeared to be protocol or site specific. Subjective assessments showed no significant overall improvement in image quality for all reconstructions and post-processing. Inter-rater reliability was low and preferences varied. Deep learning-based denoising software improved objective image quality compared to FBP in routine head CT. A significant difference compared to IR was observed for only one protocol. Subjective assessments did not indicate a significant clinical impact in terms of improved subjective image quality, likely due to the low noise levels in full-dose images.
Development of an alginate–chitosan biopolymer composite with dECM bioink additive for organ-on-a-chip articular cartilage
Authors: Upadhyay Upasna , Kolla Saketh , Maredupaka Siddhartha , Priya Swapna , Srinivasulu Kamma , Chelluri Lakshmi Kiran 
Published: May 2024

AbstractIn vitro use of articular cartilage on an organ-on-a-chip (OOAC) via microfluidics is challenging owing to the dense extracellular matrix (ECM) composed of numerous protein moieties and few chondrocytes, which has limited proliferation potential and microscale translation. Hence, this study proposes a novel approach for using a combination of biopolymers and decellularised ECM (dECM) as a bioink additive in the development of scalable OOAC using a microfluidic platform. The bioink was tested with native chondrocytes and mesenchymal stem cell-induced chondrocytes using biopolymers of alginate and chitosan composite hydrogels. Two-dimensional (2D) and three-dimensional (3D) biomimetic tissue construction approaches have been used to characterise the morphology and cellular marker expression (by histology and confocal laser scanning microscopy), viability (cell viability dye using flow cytometry), and genotypic expression of ECM-specific markers (by quantitative PCR). The results demonstrated that the bioink had a significant impact on the increase in phenotypic and genotypic expression, with a statistical significance level of p < 0.05 according to Student’s t-test. The use of a cell-laden biopolymer as a bioink optimised the niche conditions for obtaining hyaline-type cartilage under culture conditions, paving the way for testing mechano-responsive properties and translating these findings to a cartilage-on-a-chip microfluidics system.
FAT1 inhibits the proliferation of DLBCL cells via increasing the m6A modification of YAP1 mRNA
Authors: Wang Tian-long , Miao Xiao-juan , Shuai Yan-rong , Sun Hao-ping , Wang Xiao , Yang Min , Zhang Nan 
Published: May 2024

AbstractEmerging evidence shows that FAT atypical cadherin 1 (FAT1) mutations occur in lymphoma and are associated with poorer overall survival. Considering that diffuse large B cell lymphoma (DLBCL) is the category of lymphoma with the highest incidence rate, this study aims to explore the role of FAT1 in DLBCL. The findings demonstrate that FAT1 inhibits the proliferation of DLBCL cell lines by downregulating the expression of YAP1 rather than by altering its cellular localization. Mechanistic analysis via meRIP-qPCR/luciferase reporter assays showed that FAT1 increases the m6A modification of YAP1 mRNA 3′UTR and the subsequent binding of heterogeneous nuclear ribonucleoprotein D (HNRNPD) to the m6A modified YAP1 mRNA, thus decreasing the stability of YAP1 mRNA. Furthermore, FAT1 increases YAP1 mRNA 3′UTR m6A modification by decreasing the activity of the TGFβ-Smad2/3 pathway and the subsequent expression of ALKBH5, which is regulated at the transcriptional level by Smad2/3. Collectively, these results reveal that FAT1 inhibits the proliferation of DLBCL cells by increasing the m6A modification of the YAP1 mRNA 3’UTR via the TGFβ-Smad2/3-ALKBH5 pathway. The findings of this study therefore indicate that FAT1 exerts anti-tumor effects in DLBCL and may represent a novel target in the treatment of this form of lymphoma.
Climate summits and protests have a strong impact on climate change media coverage in Germany
Authors: Lochner Jakob H. , Stechemesser Annika , Wenz Leonie 
Published: May 2024

AbstractMedia inform the public, thereby influencing societal debates and political decisions. Despite climate change’s importance, drivers of media attention to climate change remain differently understood. Here we assess how different sociopolitical and extreme weather events affect climate change media coverage, both immediately and in the weeks following the event. To this end, we construct a data set of over 90,000 climate change articles published in nine major German newspapers over the past three decades and apply fixed effects panel regressions to control for confounders. We find that United Nations Climate Change Conferences affect coverage most strongly and most persistently. Climate protests incite climate coverage that extends well beyond the reporting on the event itself, whereas many articles on weather extremes do not mention climate change. The influence of all events has risen over time, increasing the media prominence of climate change.
naRNA-LL37 composite DAMPs define sterile NETs as self-propagating drivers of inflammation
Authors: Bork Francesca , Greve Carsten L , Youn Christine , Chen Sirui , N C Leal Vinicius , Wang Yu , Fischer Berenice , Nasri Masoud , Focken Jule , Scheurer Jasmin , Engels Pujan , Dubbelaar Marissa , Hipp Katharina , Zalat Baher , Szolek Andras , Wu Meng-Jen , Schittek Birgit , Bugl Stefanie , Kufer Thomas A , Löffler Markus W , Chamaillard Mathias , Skokowa Julia , Kramer Daniela , Archer Nathan K , Weber Alexander N R 
Published: May 2024

AbstractNeutrophil extracellular traps (NETs) are a key antimicrobial feature of cellular innate immunity mediated by polymorphonuclear neutrophils (PMNs). NETs counteract microbes but are also linked to inflammation in atherosclerosis, arthritis, or psoriasis by unknown mechanisms. Here, we report that NET-associated RNA (naRNA) stimulates further NET formation in naive PMNs via a unique TLR8-NLRP3 inflammasome-dependent pathway. Keratinocytes respond to naRNA with expression of psoriasis-related genes (e.g., IL17, IL36) via atypical NOD2-RIPK signaling. In vivo, naRNA drives temporary skin inflammation, which is drastically ameliorated by genetic ablation of RNA sensing. Unexpectedly, the naRNA-LL37 ‘composite damage-associated molecular pattern (DAMP)’ is pre-stored in resting neutrophil granules, defining sterile NETs as inflammatory webs that amplify neutrophil activation. However, the activity of the naRNA-LL37 DAMP is transient and hence supposedly self-limiting under physiological conditions. Collectively, upon dysregulated NET release like in psoriasis, naRNA sensing may represent both a potential cause of disease and a new intervention target.
Design of cobalt phosphate/nickel phosphate films with improved electrochromic performance
Authors: Chi Xiaotong , Chen Dairong , Wang Ting , Jiao Xiuling 
Published: May 2024

The composite metal phosphate film (CoHPO4/NiHPO4 film) shows excellent electrochromic properties including good optical modulation, fast switching time, high charge capacity, high coloration efficiency and outstanding cycling stability.
Study on the corrosion behaviour of carbon nanotubes/graphene/WPU coated steel
Authors: Shi Yiqing , Peng Xinyuan , Ye Zhiguo , Ding Yi , Liu Sen 
Published: May 2024

AbstractThe bond strength, resistivity and corrosion resistance of Q235 steel coated with composites containing various concentrations of carbon nanotubes, graphene and waterborne polyurethane (CNTs/Gr/WPU) were evaluated in a 5% NaCl solution. Additionally, the galvanic corrosion between the coated steel and galvanised steel was investigated through galvanic potential, galvanic current, corrosion morphology and corrosion rate. The results show that the CNTs/Gr/WPU‐coated steel exhibits excellent adhesion and conductivity. Moreover, the incorporation of CNTs and Gr can effectively enhance the corrosion resistance of the CNTs/Gr/WPU‐coated steels in the 5% NaCl solution. The trends of the galvanic potentials and galvanic current densities of CNTs/Gr/WPU‐coated steel with different mass fractions coupled with galvanised steel are similar. The CNTs/Gr/WPU‐coated steels exhibit severe galvanic corrosion when coupled with galvanised steel.
ADF‐Net: Attention‐guided deep feature decomposition network for infrared and visible image fusion
Authors: Shen Sen , Zhang Taotao , Dong Haidi , Yuan ShengZhi , Li Min , Xiao RenKai , Zhang Xiaohui 
Published: May 2024

AbstractTo effectively enhance the ability to acquire information by making full use of the complementary features of infrared and visible images, the widely used image fusion algorithm is faced with challenges such as information loss and image blurring. In response to this issue, the authors propose a dual‐branch deep hierarchical fusion network (ADF‐Net) guided by an attention mechanism. Initially, the attention convolution module extracts the shallow features of the image. Subsequently, a dual‐branch deep decomposition feature extractor is introduced, where in the transformer encoder block (TEB) employs remote attention to process low‐frequency global features, while the CNN encoder block (CEB) extracts high‐frequency local information. Ultimately, the global fusion layer based on TEB and the local fusion layer based on CEB produce the fused image through the encoder. Multiple experiments demonstrate that ADF‐Net excels in various aspects by utilizing two‐stage training and an appropriate loss function for training and testing.
Influence of power quality and stack temperature on the specific energy consumption of alkaline electrolyzer
Authors: Dong Yannan , Chen Zhe , Li Xiangjun , Wang Haixin , Han Zijiao , Ma Shaohua , Yao Xin 
Published: May 2024

AbstractThe efficiency of the power‐to‐hydrogen (P2H) alkaline electrolyzer (AE) is the primary factor constraint on its large‐scale application. Decreasing the specific energy consumption (SEC) of AE is positive to enhancing the efficiency of P2H system. However, the factors influencing the SEC of AE are yet to be fully revealed. In this paper, the influence of power quality and stack temperature on the SEC of AE is explored. Firstly, the models of double star rectifier and PWM current‐type rectifier are established. And the mathematical model on the SEC of rectifiers is proposed. The influence of power quality on the SEC is analyzed by comparing the current and voltage waveforms under four types of rectifiers. Secondly, the model of AE that considers the stack temperature is proposed. Unlike the existing simplified models, the proposed model can assess the SEC considering changes in stack temperature. Finally, the results show that the SEC with the 6‐pulse rectifier is 10.99% and 11.98% higher than that with the double‐star rectifier and PWM current‐type rectifier. The stack temperature is inversely related to the SEC of AE. This study provides a reference for reducing the SEC and enhancing the efficiency of the P2H system.
Identifying type II quasars at intermediate redshift with few-shot learning photometric classification
Authors: Cunha P. A. C. , Humphrey A. , Brinchmann J. , Morais S. G. , Carvajal R. , Gomes J. M. , Matute I. , Paulino-Afonso A. 
Published: May 2024

Context. A sub-population of AGNs where the central engine is obscured are known as type II quasars (QSO2s). These luminous AGNs have a thick and dusty torus that obscures the accretion disc from our line of sight. Thus, their special orientation allows for detailed studies of the AGN-host co-evolution. Increasing the sample size of QSO2 sources in critical redshift ranges is crucial for understanding the interplay of AGN feedback, the AGN-host relationship, and the evolution of active galaxies.Aims. We aim to identify QSO2 candidates in the ‘redshift desert’ using optical and infrared photometry. At this intermediate redshift range (i.e. 1 ≤ ɀ ≤ 2), most of the prominent optical emission lines in QSO2 sources (e.g. CIVλl549; [OIII]λλ4959, 5008) fall either outside the wavelength range of the SDSS optical spectra or in particularly noisy wavelength ranges, making QSO2 identification challenging. Therefore, we adopted a semi-supervised machine learning approach to select candidates in the SDSS galaxy sample.Methods. Recent applications of machine learning in astronomy focus on problems involving large data sets, with small data sets often being overlooked. We developed a ‘few-shot’ learning approach for the identification and classification of rare-object classes using limited training data (200 sources). The new AMELIA pipeline uses a transfer-learning based approach with decision trees, distance-based, and deep learning methods to build a classifier capable of identifying rare objects on the basis of an observational training data set.Results. We validated the performance ofAMELIAby addressing the problem of identifying QSO2s at 1 ≤ ɀ ≤ 2 using SDSS and WISE photometry, obtaining an F1-score above 0.8 in a supervised approach. We then usedAMELIAto select new QSO2 candidates in the ‘redshift desert’ and examined the nature of the candidates using SDSS spectra, when available. In particular, we identified a sub-population of [NeV]λ3426 emitters at ɀ ~ 1.1, which are highly likely to contain obscured AGNs. We used X-ray and radio crossmatching to validate our classification and investigated the performance of photometric criteria from the literature showing that our candidates have an inherent dusty nature. Finally, we derived physical properties for our QSO2 sample using photoionisation models and verified the AGN classification using an SED fitting.Conclusions. Our results demonstrate the potential of few-shot learning applied to small data sets of rare objects, in particular QSO2s, and confirms that optical-IR information can be further explored to search for obscured AGNs. We present a new sample of candidates to be further studied and validated using multi-wavelength observations.
Thermal and magnetic field structure of near-equatorial coronal holes
Authors: Hegde M. , Hiremath K. M. 
Published: May 2024

Context.Coronal holes are low-density and unipolar magnetic field structures in the solar corona that trigger geomagnetic disturbances on the Earth. Hence, it is important to understand the genesis and evolutionary behavior of these coronal activity features during their passage across the solar disk.Aims.We study the day-to-day latitudinal variations of thermal and magnetic field structures of near-equatorial coronal holes. For this purpose, eight years of full-disk SOHO/EIT 195 Å calibrated images were used.Methods.Using the response curves of the SOHO/EIT channels and assuming thermodynamic equilibrium, we estimated the temperature structure of coronal holes. From the latitudinal variation in the magnetic pressure, we inferred the magnitude of the magnetic field structure of coronal holes.Results.Except for the temperatureT, we find that the variations in the average photon fluxF, in the radiative energyE, in the areaA, and in the magnitude of the magnetic field structure |B| of coronal holes depend on latitude. The typical average values of the estimated physical parameters areA ∼ 3.8(±0.5)×1020cm2,F ∼ 2.3(±0.2)×1013photons cm−2s−1,E ∼ 2.32(±0.5)×103ergs cm−2s−1,T ∼ 0.94(±0.1)×106K and |B|∼0.01(±0.001) G.Conclusions.When coronal holes are anchored in the convection zone, these activity features would be expected to rotate differentially. The thermal wind balance and isorotation of coronal holes with the solar plasma therefore implies a measurable temperature difference between the equator and the two poles. Contrary to this fact, the variation in the thermal structure of near-equatorial coronal holes is independent of latitude, which leads to the conclusion that coronal holes must rotate rigidly and are likely to be initially anchored below the tachocline. This confirms our previous study.
Blind and robust estimation of adaptive optics point spread function and diffuse halo with sharp-edged objects
Authors: Berdeu Anthony 
Published: May 2024

Context. Initially designed to detect and characterise exoplanets, extreme adaptive optics (AO) systems open a new window onto the Solar System by resolving its small bodies. Nonetheless, their study remains limited by the accuracy of the knowledge of the AO-corrected point spread function (AO-PSF) that degrades their image and produces a bright halo, potentially hiding faint moons in their close vicinity.Aims. To overcome the random nature of AO-PSFs, I aim to develop a method that blindly recovers the PSF and its faint structured extensions directly into the data of interest, without any prior on the instrument or the object’s shape. The objectives are both to deconvolve the object and to properly estimate and remove its surrounding halo to highlight potential faint companions.Methods. My method first estimated the PSF core via a parametric model fit, under the assumption of a sharp-edged flat object. Then, the resolved object and the PSF extensions were alternatively deconvolved with a robust method, insensitive to model outliers, such as cosmic rays or unresolved moons. Finally, the complex halo produced by the AO system was modelled and removed from the data.Results. The method is validated on realistic simulations with an on-sky AO-PSF from the SPHERE/ZIMPOL instrument. On real data, the proposed blind deconvolution algorithm strongly improves the image sharpness and retrieves details on the surface of asteroids. In addition, their moons are visible in all tested epochs despite important variability in turbulence conditions.Conclusions. My method shows the feasibility of retrieving the complex features of AO-PSFs directly from the data of interest. It paves the way towards more precise studies of asteroid surfaces and the discovery and characterisation of Solar System moons in archival data or with future instruments on extremely large telescopes with ever more complex AO-PSFs.