bluuebunny/crossref_metadata_2025_split huggingface dataset:
Transcription-coupled DNA–protein crosslink repair by CSB and CRL4CSA-mediated degradation
Authors: van Sluis Marjolein , Yu Qing , van der Woude Melanie , Gonzalo-Hansen Camila , Dealy Shannon C. , Janssens Roel C. , Somsen Hedda B. , Ramadhin Anisha R. , Dekkers Dick H. W. , Wienecke Hannah Lena , Demmers Joris J. P. G. , Raams Anja , Davó-Martínez Carlota , Llerena Schiffmacher Diana A. , van Toorn Marvin , Häckes David , Thijssen Karen L. , Zhou Di , Lammers Judith G. , Pines Alex , Vermeulen Wim , Pothof Joris , Demmers Jeroen A. A. , van den Berg Debbie L. C. , Lans Hannes , Marteijn Jurgen A. 
Published: April 2024

AbstractDNA–protein crosslinks (DPCs) arise from enzymatic intermediates, metabolism or chemicals like chemotherapeutics. DPCs are highly cytotoxic as they impede DNA-based processes such as replication, which is counteracted through proteolysis-mediated DPC removal by spartan (SPRTN) or the proteasome. However, whether DPCs affect transcription and how transcription-blocking DPCs are repaired remains largely unknown. Here we show that DPCs severely impede RNA polymerase II-mediated transcription and are preferentially repaired in active genes by transcription-coupled DPC (TC-DPC) repair. TC-DPC repair is initiated by recruiting the transcription-coupled nucleotide excision repair (TC-NER) factors CSB and CSA to DPC-stalled RNA polymerase II. CSA and CSB are indispensable for TC-DPC repair; however, the downstream TC-NER factors UVSSA and XPA are not, a result indicative of a non-canonical TC-NER mechanism. TC-DPC repair functions independently of SPRTN but is mediated by the ubiquitin ligase CRL4CSA and the proteasome. Thus, DPCs in genes are preferentially repaired in a transcription-coupled manner to facilitate unperturbed transcription.
Endogenous aldehyde-induced DNA–protein crosslinks are resolved by transcription-coupled repair
Authors: Oka Yasuyoshi , Nakazawa Yuka , Shimada Mayuko , Ogi Tomoo 
Published: April 2024

AbstractDNA–protein crosslinks (DPCs) induced by aldehydes interfere with replication and transcription. Hereditary deficiencies in DPC repair and aldehyde clearance processes cause progeria, including Ruijs–Aalfs syndrome (RJALS) and AMeD syndrome (AMeDS) in humans. Although the elimination of DPC during replication has been well established, how cells overcome DPC lesions in transcription remains elusive. Here we show that endogenous aldehyde-induced DPC roadblocks are efficiently resolved by transcription-coupled repair (TCR). We develop a high-throughput sequencing technique to measure the genome-wide distribution of DPCs (DPC-seq). Using proteomics and DPC-seq, we demonstrate that the conventional TCR complex as well as VCP/p97 and the proteasome are required for the removal of formaldehyde-induced DPCs. TFIIS-dependent cleavage of RNAPII transcripts protects against transcription obstacles. Finally, a mouse model lacking both aldehyde clearance and TCR confirms endogenous DPC accumulation in actively transcribed regions. Collectively, our data provide evidence that transcription-coupled DPC repair (TC-DPCR) as well as aldehyde clearance are crucial for protecting against metabolic genotoxin, thus explaining the molecular pathogenesis of AMeDS and other disorders associated with defects in TCR, such as Cockayne syndrome.
Emergence of fractal geometries in the evolution of a metabolic enzyme
Authors: Sendker Franziska L. , Lo Yat Kei , Heimerl Thomas , Bohn Stefan , Persson Louise J. , Mais Christopher-Nils , Sadowska Wiktoria , Paczia Nicole , Nußbaum Eva , del Carmen Sánchez Olmos María , Forchhammer Karl , Schindler Daniel , Erb Tobias J. , Benesch Justin L. P. , Marklund Erik G. , Bange Gert , Schuller Jan M. , Hochberg Georg K. A. 
Published: April 2024

AbstractFractals are patterns that are self-similar across multiple length-scales1. Macroscopic fractals are common in nature2–4; however, so far, molecular assembly into fractals is restricted to synthetic systems5–12. Here we report the discovery of a natural protein, citrate synthase from the cyanobacterium Synechococcus elongatus, which self-assembles into Sierpiński triangles. Using cryo-electron microscopy, we reveal how the fractal assembles from a hexameric building block. Although different stimuli modulate the formation of fractal complexes and these complexes can regulate the enzymatic activity of citrate synthase in vitro, the fractal may not serve a physiological function in vivo. We use ancestral sequence reconstruction to retrace how the citrate synthase fractal evolved from non-fractal precursors, and the results suggest it may have emerged as a harmless evolutionary accident. Our findings expand the space of possible protein complexes and demonstrate that intricate and regulatable assemblies can evolve in a single substitution.
Generative models improve fairness of medical classifiers under distribution shifts
Authors: Ktena Ira , Wiles Olivia , Albuquerque Isabela , Rebuffi Sylvestre-Alvise , Tanno Ryutaro , Roy Abhijit Guha , Azizi Shekoofeh , Belgrave Danielle , Kohli Pushmeet , Cemgil Taylan , Karthikesalingam Alan , Gowal Sven 
Published: April 2024

AbstractDomain generalization is a ubiquitous challenge for machine learning in healthcare. Model performance in real-world conditions might be lower than expected because of discrepancies between the data encountered during deployment and development. Underrepresentation of some groups or conditions during model development is a common cause of this phenomenon. This challenge is often not readily addressed by targeted data acquisition and ‘labeling’ by expert clinicians, which can be prohibitively expensive or practically impossible because of the rarity of conditions or the available clinical expertise. We hypothesize that advances in generative artificial intelligence can help mitigate this unmet need in a steerable fashion, enriching our training dataset with synthetic examples that address shortfalls of underrepresented conditions or subgroups. We show that diffusion models can automatically learn realistic augmentations from data in a label-efficient manner. We demonstrate that learned augmentations make models more robust and statistically fair in-distribution and out of distribution. To evaluate the generality of our approach, we studied three distinct medical imaging contexts of varying difficulty: (1) histopathology, (2) chest X-ray and (3) dermatology images. Complementing real samples with synthetic ones improved the robustness of models in all three medical tasks and increased fairness by improving the accuracy of clinical diagnosis within underrepresented groups, especially out of distribution.
High resolution optical and acoustic remote sensing datasets of the Puck Lagoon
Authors: Janowski Łukasz , Skarlatos Dimitrios , Agrafiotis Panagiotis , Tysiąc Paweł , Pydyn Andrzej , Popek Mateusz , Kotarba-Morley Anna M. , Mandlburger Gottfried , Gajewski Łukasz , Kołakowski Mateusz , Papadaki Alexandra , Gajewski Juliusz 
Published: April 2024

AbstractThe very shallow marine basin of Puck Lagoon in the southern Baltic Sea, on the Northern coast of Poland, hosts valuable benthic habitats and cultural heritage sites. These include, among others, protected Zostera marina meadows, one of the Baltic’s major medieval harbours, a ship graveyard, and likely other submerged features that are yet to be discovered. Prior to this project, no comprehensive high-resolution remote sensing data were available for this area. This article describes the first Digital Elevation Models (DEMs) derived from a combination of airborne bathymetric LiDAR, multibeam echosounder, airborne photogrammetry and satellite imagery. These datasets also include multibeam echosounder backscatter and LiDAR intensity, allowing determination of the character and properties of the seafloor. Combined, these datasets are a vital resource for assessing and understanding seafloor morphology, benthic habitats, cultural heritage, and submerged landscapes. Given the significance of Puck Lagoon’s hydrographical, ecological, geological, and archaeological environs, the high-resolution bathymetry, acquired by our project, can provide the foundation for sustainable management and informed decision-making for this area of interest.
Fabrication of glass/madar fibers reinforced hybrid epoxy composite: a comprehensive study on the material stability
Authors: Raja Thandavamoorthy , Yuvarajan D. , Ali Saheb , Dhanraj G. , Kaliappan Nandagopal 
Published: April 2024

AbstractThe present study aims to examine the characteristics of a composite material composed of glass/madar fibers and porcelain particles, which are reinforced with epoxy. A compression molding technique achieves the fabrication of this composite. A comprehensive characterization was conducted by employing a mixture of analytical techniques, including X-ray Diffraction (XRD), mechanical testing, Scanning Electron Microscopy (SEM), Dynamic Mechanical Analysis (DMA), and Thermogravimetric Analysis (TGA). The composition of the composite was determined using X-ray diffraction (XRD) analysis, which demonstrated the successful integration of porcelain fillers. The material exhibited notable mechanical properties, rendering it appropriate for utilization in structural applications. The utilization of SEM facilitated the examination of the microstructure of the composite material, thereby providing a deeper understanding of the interactions between the fibers and the matrix. DMA results revealed the glass/madar composite contained 4.2% higher viscoelastic properties when the addition of porcelain filler, thermal stability was improved up to the maximum temperature of 357 °C. This study provided significant insights into the properties of a hybrid epoxy composite consisting of glass/madar fibers reinforced porcelain particles.
RETRACTED ARTICLE: Break oily water emulsion during petroleum enhancing production processes using green approach for the synthesis of SnCuO@FeO nanocomposite from microorganisms
Authors: Hosny M. , Mubarak Mahmoud F. , El-Sheshtawy H. S. , Hosny R. 
Published: April 2024

AbstractThe aim of this work was to synthesize a green nanoparticle SnCuO@FeO nanocomposite core–shell to break oily water emulsions during petroleum-enhancing production processes as an alternative to chemical and physical processes. In this study, eight bacterial isolates (MHB1–MHB8) have been isolated from tree leaves, giant reeds, and soil samples. The investigation involved testing bacterial isolates for their ability to make FeO nanoparticles and choosing the best producers. The selected isolate (MHB5) was identified by amplification and sequencing of the 16S rRNA gene as Bacillus paramycoides strain OQ878685. MHB5 produced the FeO nanoparticles with the smallest particle size (78.7 nm) using DLS. XRD, FTIR, and TEM were used to characterize the biosynthesized nanoparticles. The jar experiment used SnCuO@FeO with different ratios of Sn to CuO (1:1, 2:1, and 3:1) to study the effect of oil concentration, retention time, and temperature. The most effective performance was observed with a 1:1 ratio of Sn to CuO, achieving an 85% separation efficiency at a concentration of 5 mg/L, for a duration of 5 min, and at a temperature of 373 K. Analysis using kinetic models indicates that the adsorption process can be accurately described by both the pseudo-first-order and pseudo-second-order models. This suggests that the adsorption mechanism likely involves a combination of film diffusion and intraparticle diffusion. Regarding the adsorption isotherm, the Langmuir model provides a strong fit for the data, while the D-R model indicates that physical interactions primarily govern the adsorption mechanism. Thermodynamic analysis reveals a ∆H value of 18.62 kJ/mol, indicating an exothermic adsorption process. This suggests that the adsorption is a favorable process, as energy is released during the process. Finally, the synthesized green SnCuO@FeO nanocomposite has potential for use in advanced applications in the oil and gas industry to help the industry meet regulatory compliance, lower operation costs, reduce environmental impact, and enhance production efficiency.
Cervicocephalic kinaesthesia reveals novel subgroups of motor control impairments in patients with neck pain
Authors: Majcen Rosker Ziva , Rosker Jernej 
Published: April 2024

AbstractCervical-spine sensorimotor control is associated with chronicity and recurrence of neck pain (NP). Tests used to measure sensorimotor impairments lack consistency in studied parameters. Interpretation is often based on either a handful or numerous parameters, without considering their possible interrelation. Different aspects of motor-control could be studied with different parameters, but this has not yet been addressed. The aim of this study was to determine if different parameters of cervical position (JPE) and movement (Butterfly) sense tests represent distinct components of motor-control strategies in patients with chronic NP. Principal component analysis performed on 135 patients revealed three direction-specific (repositioning from flexion, extension or rotations) and one parameter-specific (variability of repositioning) component for JPE, two difficulty-specific (easy or medium and difficult trajectory) and one movement-specific (undershooting a target) component for Butterfly test. Here we report that these components could be related to central (neck repositioning and control of cervical movement) and peripheral sensorimotor adaptations (variability of repositioning) present in NP. New technologies allow extraction of greater number of parameters of which hand-picking could lead to information loss. This study adds towards better identification of diverse groups of parameters offering potentially clinically relevant information and improved functional diagnostics for patients with NP.
Numerical study of transient absorption saturation in single-layer graphene for optical nanoscopy applications
Authors: Kariman Behjat S. , Diaspro Alberto , Bianchini Paolo 
Published: April 2024

AbstractTransient absorption, or pump–probe microscopy is an absorption-based technique that can explore samples ultrafast dynamic properties and provide fluorescence-free contrast mechanisms. When applied to graphene and its derivatives, this technique exploits the graphene transient response caused by the ultrafast interband transition as the imaging contrast mechanism. The saturation of this transition is fundamental to allow for super-resolution optical far-field imaging, following the reversible saturable optical fluorescence transitions (RESOLFT) concept, although not involving fluorescence. With this aim, we propose a model to numerically compute the temporal evolution under saturation conditions of the single-layer graphene molecular states, which are involved in the transient absorption. Exploiting an algorithm based on the fourth order Runge–Kutta (RK4) method, and the density matrix approach, we numerically demonstrate that the transient absorption signal of single-layer graphene varies linearly as a function of excitation intensity until it reaches saturation. We experimentally verify this model using a custom pump–probe super-resolution microscope. The results define the intensities necessary to achieve super-resolution in a pump–probe nanoscope while studying graphene-based materials and open the possibility of predicting such a saturation process in other light-matter interactions that undergo the same transition.
Genomic wide association study and selective sweep analysis identify genes associated with improved yield under drought in Turkish winter wheat germplasm
Authors: Sehgal Deepmala , Rathan Nagenahalli Dharmegowda , Özdemir Fatih , Keser Mesut , Akin Beyhan , Dababat Abdelfattah A. , Koc Emrah , Dreisigacker Susanne , Morgounov Alexey 
Published: April 2024

AbstractA panel comprising of 84 Turkish winter wheat landraces (LR) and 73 modern varieties (MV) was analyzed with genome wide association study (GWAS) to identify genes/genomic regions associated with increased yield under favorable and drought conditions. In addition, selective sweep analysis was conducted to detect signatures of selection in the winter wheat genome driving the differentiation between LR and MV, to gather an understanding of genomic regions linked to adaptation and yield improvement. The panel was genotyped with 25 K wheat SNP array and phenotyped for agronomic traits for two growing seasons (2018 and 2019) in Konya, Turkey. Year 2018 was treated as drought environment due to very low precipitation prior to heading whereas year 2019 was considered as a favorable season. GWAS conducted with SNPs and haplotype blocks using mixed linear model identified 18 genomic regions in the vicinities of known genes i.e., TaERF3-3A, TaERF3-3B, DEP1-5A, FRIZZY PANICLE-2D, TaSnRK23-1A, TaAGL6-A, TaARF12-2A, TaARF12-2B, WAPO1, TaSPL16-7D, TaTGW6-A1, KAT-2B, TaOGT1, TaSPL21-6B, TaSBEIb, trs1/WFZP-A, TaCwi-A1-2A and TaPIN1-7A associated with grain yield (GY) and yield related traits. Haplotype-based GWAS identified five haplotype blocks (H1A-42, H2A-71, H4A-48, H7B-123 and H7B-124), with the favorable haplotypes showing a yield increase of > 700 kg/ha in the drought season. SNP-based GWAS, detected only one larger effect genomic region on chromosome 7B, in common with haplotype-based GWAS. On an average, the percentage variation (PV) explained by haplotypes was 8.0% higher than PV explained by SNPs for all the investigated traits. Selective sweep analysis detected 39 signatures of selection between LR and MV of which 15 were within proximity of known functional genes controlling flowering (PRR-A1, PPR-D1, TaHd1-6B), GY and GY components (TaSus2-2B, TaGS2-B1, AG1-1A/WAG1-1A, DUO-A1, DUO-B1, AG2-3A/WAG2-3A, TaLAX1, TaSnRK210-4A, FBP, TaLAX1, TaPIL1 and AP3-1-7A/WPA3-7A) and 10 regions underlying various transcription factors and regulatory genes. The study outcomes contribute to utilization of LR in breeding winter wheat.
Forecasting the strength of preplaced aggregate concrete using interpretable machine learning approaches
Authors: Javed Muhammad Faisal , Fawad Muhammad , Lodhi Rida , Najeh Taoufik , Gamil Yaser 
Published: April 2024

AbstractPreplaced aggregate concrete (PAC) also known as two-stage concrete (TSC) is widely used in construction engineering for various applications. To produce PAC, a mixture of Portland cement, sand, and admixtures is injected into a mold subsequent to the deposition of coarse aggregate. This process complicates the prediction of compressive strength (CS), demanding thorough investigation. Consequently, the emphasis of this study is on enhancing the comprehension of PAC compressive strength using machine learning models. Thirteen models are evaluated with 261 data points and eleven input variables. The result depicts that xgboost demonstrates exceptional accuracy with a correlation coefficient of 0.9791 and a normalized coefficient of determination (R2) of 0.9583. Moreover, Gradient boosting (GB) and Cat boost (CB) also perform well due to its robust performance. In addition, Adaboost, Voting regressor, and Random forest yield precise predictions with low mean absolute error (MAE) and root mean square error (RMSE) values. The sensitivity analysis (SA) reveals the significant impact of key input parameters on overall model sensitivity. Notably, gravel takes the lead with a substantial 44.7% contribution, followed by sand at 19.5%, cement at 15.6%, and Fly ash and GGBS at 5.9% and 5.1%, respectively. The best fit model i.e., XG-Boost model, was employed for SHAP analysis to assess the relative importance of contributing attributes and optimize input variables. The SHAP analysis unveiled the water-to-binder (W/B) ratio, superplasticizer, and gravel as the most significant factors influencing the CS of PAC. Furthermore, graphical user interface (GUI) have been developed for practical applications in predicting concrete strength. This simplifies the process and offers a valuable tool for leveraging the model's potential in the field of civil engineering. This comprehensive evaluation provides valuable insights to researchers and practitioners, empowering them to make informed choices in predicting PAC compressive strength in construction projects. By enhancing the reliability and applicability of predictive models, this study contributes to the field of preplaced aggregate concrete strength prediction.
HARNet in deep learning approach—a systematic survey
Authors: Kumar Neelam Sanjeev , Deepika G. , Goutham V. , Buvaneswari B. , Reddy R. Vijaya Kumar , Angadi Sanjeevkumar , Dhanamjayulu C. , Chinthaginjala Ravikumar , Mohammad Faruq , Khan Baseem 
Published: April 2024

AbstractA comprehensive examination of human action recognition (HAR) methodologies situated at the convergence of deep learning and computer vision is the subject of this article. We examine the progression from handcrafted feature-based approaches to end-to-end learning, with a particular focus on the significance of large-scale datasets. By classifying research paradigms, such as temporal modelling and spatial features, our proposed taxonomy illuminates the merits and drawbacks of each. We specifically present HARNet, an architecture for Multi-Model Deep Learning that integrates recurrent and convolutional neural networks while utilizing attention mechanisms to improve accuracy and robustness. The VideoMAE v2 method (https://github.com/OpenGVLab/VideoMAEv2) has been utilized as a case study to illustrate practical implementations and obstacles. For researchers and practitioners interested in gaining a comprehensive understanding of the most recent advancements in HAR as they relate to computer vision and deep learning, this survey is an invaluable resource.
Novel T cell exhaustion gene signature to predict prognosis and immunotherapy response in thyroid carcinoma from integrated RNA-sequencing analysis
Authors: Li Yang , Wang Zhen , Lu Fangting , Miao Yahu , Feng Qing , Zhu Weixi , Kang Qingqing , Chen Yijing , Zhang Qiu 
Published: April 2024

AbstractExhausted CD8+ T lymphocytes and tumor-associated macrophages play critical roles in determining cancer prognosis and the efficacy of immunotherapy. Our study revealed a negative correlation between exhausted CD8+ T lymphocytes and prognosis in thyroid carcinoma (THCA). Consensus clustering divided patients into two subgroups of exhaustion with different prognoses, as defined by marker genes of exhausted CD8+ T cells. Subsequently, we constructed an eight-gene prognostic signature, and developed a risk score named the exhaustion-related gene score (ERGS) to forecast both prognosis and immunotherapy response in THCA. Bulk RNA sequencing analysis revealed a higher prevalence of M2 macrophages, indicative of an immunosuppressive tumor microenvironment (TME), in the high-ERGS group. Single-cell RNA sequencing showed that SPP1+ macrophages and CD14+ monocytes infiltrations were positively associated with higher ERGS. Functionally, it was determined that SPP1+ macrophages exert an immunosuppressive role, while CD14+ monocytes were implicated in promoting tumor progression and angiogenesis. Analysis of cell–cell interactions between SPP1+ macrophages and T cells highlighted the activation of the SPP1-CD44 and MIF-CD74 axes, both of which could foster an immunosuppressive TME. Therapeutic strategies that target SPP1+ macrophages, CD14+ monocytes, and the SPP1-CD44 and MIF-CD74 axes may potentially improve the prognosis and amplify the immunotherapy response in THCA patients.