miR-146a deficiency in hematopoietic cells is not involved in the development of atherosclerosis. by Alberto Del Monte, Ana B Arroyo, María J Andrés-Manzano, Nuria García-Barberá, María S Caleprico, Vicente Vicente, Vanessa Roldán, Rocío González-Conejero, Constantino Martínez, Vicente Andrés

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The azimuthal correlation between the leading jet and the scattered lepton in deep inelastic scattering at HERA by ZEUS Collaboration, I. Abt, R. Aggarwal, V. Aushev, O. Behnke, A. Bertolin, I. Bloch, I. Brock, N. H. Brook, R. Brugnera, A. Bruni, P. J. Bussey, A. Caldwell, C. D. Catterall, J. Chwastowski, J. Ciborowski, R. Ciesielski, A. M. Cooper-Sarkar, M. Corradi, R. K. Dementiev, S. Dusini, J. Ferrando, B. Foster, E. Gallo, D. Gangadharan, A. Garfagnini, A. Geiser, G. Grzelak, C. Gwenlan, D. Hochman, N. Z. Jomhari, I. Kadenko, U. Karshon, P. Kaur, R. Klanner, U. Klein, I. A. Korzhavina, N. Kovalchuk, M. Kuze, B. B. Levchenko, A. Levy, B. Löhr, E. Lohrmann, A. Longhin, F. Lorkowski, E. Lunghi, I. Makarenko, J. Malka, S. Masciocchi, K. Nagano, J. D. Nam, Yu. Onishchuk, E. Paul, I. Pidhurskyi, A. Polini, M. Przybycień, A. Quintero, M. Ruspa, U. Schneekloth, T. Schörner-Sadenius, I. Selyuzhenkov, M. Shchedrolosiev, L. M. Shcheglova, N. Sherrill, I. O. Skillicorn, W. Słomiński, A. Solano, L. Stanco, N. Stefaniuk, B. Surrow, K. Tokushuku, O. Turkot, T. Tymieniecka, A. Verbytskyi, W. A. T. Wan Abdullah, K. Wichmann, M. Wing, S. Yamada, Y. Yamazaki, A. F. Żarnecki, O. Zenaiev

“…The data are equally well described by Monte Carlo predictions that supplement leading-order matrix elements with parton showering.…”
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Groundwater–CO₂ emissions relationship in Dutch peatlands derived by machine learning using airborne and ground-based eddy covariance data by L. M. van der Poel, L. V. Bataille, B. Kruijt, W. Franssen, W. Jans, J. Biermann, A. Rietman, A. J. V. Buzacott, Y. van der Velde, R. Boelens, R. W. A. Hutjes

“…Using spatiotemporal data, we train and optimize a boosted regression tree (BRT) machine learning algorithm to predict immediate CO<span class="inline-formula">₂</span> fluxes and use Shapley values and various simulations to interpret the model's outputs. …”
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Diffuse attenuation coefficient and bathymetry retrieval in shallow water environments by integrating satellite laser altimetry with optical remote sensing by Changda Liu, Huan Xie, Qi Xu, Jie Li, Yuan Sun, Min Ji, Xiaohua Tong

“…Finally, the neural network model accurately predicted the bathymetry in the two regions. The accuracy of the bathymetric maps improved significantly with seafloor classification, as indicated by reductions in root mean square error (RMSE) of 0.12 m and 0.15 m, and in mean absolute percentage error (MAPE) by 2.24 % and 5.87 %, respectively. …”
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Spatial and Temporal Variability of Rainfall Erosivity in the Niyang River Basin by Qingqin Bai, Lei Wang, Yangzong Cidan

“…This study enhances the understanding of rainfall erosive forces in the plateau region and provides a scientific basis for predicting soil loss, developing effective erosion control measures, and ensuring sustainable land use.…”
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Clinical and Epidemiological Manifestations of Ixodic Tick-Borne Borreliosis Foci in the Tomsk region by O. V. Voronkova, E. N. Ilyinskikh, A. A. Rudikov, T. N. Poltoratskaya, I. E. Esimova, L. V. Lukashova, M. R. Karpova

“…The study of the genotypic diversity of pathogenic borrelias in relation to the species diversity of vectors, the analysis of the clinical manifestations of different etiological variants of tick-borne borreliosis (mono- and mixed infections), as well as the development of an algorithm for differential diagnostic search and a model for predicting the outcomes of the infectious process in tick-borne borreliosis and mixed infections are priority directions of problem-oriented scientific research in Tomsk region.…”
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Crosstalk between the serine/threonine kinase StkP and the response regulator ComE controls the stress response and intracellular survival of Streptococcus pneumoniae. by Germán E Piñas, Nicolás M Reinoso-Vizcaino, Nubia Y Yandar Barahona, Paulo R Cortes, Rosario Duran, Chandan Badapanda, Ankita Rathore, Dario R Bichara, Melina B Cian, Nadia B Olivero, Daniel R Perez, José Echenique

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Research on the Movement Patterns of Bed-load Streaky Structure Transport and the Mechanism of Fluid-solid Coupling by YAO Jiateng, XIE Xiaoxi, WANG Hao, ZHAO Lin, XIE Haonan, ZHEN Rongtian

“…Based on the located centroids and vector velocities of bedload particles, the Kalman filter algorithm was employed to calculate both measured and predicted values under the given conditions. …”
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Global Feature Focusing and Information Enhancement Network for Occluded Pedestrian Detection by ZHENG Kaikui, JI Kangyou, LI Jun, LI Qiming

“…This mechanism first uses the bilinear interpolation algorithm to adjust the spatial resolution of different scale feature maps to be consistent. …”
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Peculiarities of Clinical Indices, Duration and Complication of Acute Pancreatitis in Patients With Comorbid Obesity by Kebkalo A., Tkachuk O., Reiti A.

“…The score ​​equal to or greater than 8 predicted a more severe course of the disease. The CT severity index was also calculated on the basis of computed tomography of the abdominal cavity with intravenous contrast after 7 days of hospitalization. …”
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YOLOv8-Based Estimation of Estrus in Sows Through Reproductive Organ Swelling Analysis Using a Single Camera by Iyad Almadani, Mohammed Abuhussein, Aaron L. Robinson

“…In the first stage, we calculated the Mean Squared Error (MSE) between the ground truth keypoints of the labia distance and the distance between the predicted keypoints, and we performed the same calculation for the distance between the clitoris and perineum. …”
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Joint analysis of single-cell RNA sequencing and bulk transcriptome reveals the heterogeneity of the urea cycle of astrocytes in glioblastoma by Minfeng Tong, Qi Tu, Lude Wang, Huahui Chen, Xing Wan, Zhijian Xu

“…Conclusion: We set up a novel prognostic model for predicting the survival of GB patients by integrating bulk RNA-seq and scRNA-seq data. …”
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Network and systems biology approaches help investigate gene regulatory interactions between Salmonella disease and host in chickens: Model‐based in silico evidence combined with g... by Reza Tohidi, Hoda Javaheri Bargourooshi, Arash Javanmard

“…To start, we used free computational tools and online bioinformatics resources, including predicting gene function using a multiple association network integration algorithm (geneMania), the Kyoto Encyclopedia of Genes and Genomes, the Annotation, Visualization and Integrated Discovery (DAVID) database and the stimulator of interferon genes. …”
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Chinese Massage (Tuina) Attenuates Knee Osteoarthritis by Modulating Autophagy-Related Cytokines: A Multidisciplinary Methodological Investigation by Wang Z, Zhao C, Li M, Zhang L, Diao J, Wu Y, Yang T, Shi M, Lei Y, Wang Y, Li M, Bian Y, Zhou Y, Xu H

“…A Support Vector Machine (SVM) algorithm was used to conduct a secondary analysis of the experimental dataset to predict comprehensive therapeutic effects and synergistic correlations among the indicators.Results: MR analysis revealed causal relationships between eight cytokines and KOA at genetically determined levels, with seven exhibiting autophagy-related regulatory properties. …”
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Medicare part D prescribing for direct oral anticoagulants in the United States: Cost, use and the "rubber effect". by Panayiotis D Ziakas, Irene S Kourbeti, Loukia S Poulou, Georgios S Vlachogeorgos, Eleftherios Mylonakis

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Evaluating the Pattern of Forest Cover Changes Using Fuzzy Object-Oriented Techniques (Case Study: Kaleybar County) by Majid Pishnamaz Ahmadi, keyvan Mohammadzadeh, seyed Asadolah Hejazi

“…The results were then calculated and finalized in ArcGIS software after accurate evaluation. 3-Results and Discussion In this research, images in 200 scales sorted consecutively from 1 to 200 were segmented using low to high multi-functional hierarchical segmentation approach with shape coefficient of 0.4 and compression coefficient of 0.5 in order to construct LV graphs and the appropriate scales for image segmentation were determined using the plotted graphs. By predicting the appropriate scale for creating image units using the algorithm (ESP), the scale of 15 with coefficients of shape and compression 0.3 and 0.5 respectively was scaled as the appropriate scale for extraction of Landsat 5 and 7 satellite images, and the scale of 130 with shape coefficient of 0.4 and compression coefficient of 0.5 was chosen as the appropriate scale for Landsat 8 satellite OLI images. …”
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Numerical Simulation of Dynamic Response of Slab-Column Structures under Fire-Explosion Coupled Effects by LIU Jingbo, YANG Qiong, WU Lei, WANG Chengquan, YU Xiongfei, FANG Mingyu, DONG Tengfang

“…The damage, deformation, and stiffness loss in the coupled scenario are considerably greater than what would be predicted by a simple superposition of the individual hazards. …”
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FEATURES OF LOCAL IMMUNITY IN PATIENTS WITH OROPHARYNGEAL CANCER by O. I. Keith, M. N. Tilliashaihov, P. V. Svetitsky, E. Yu. Zlatnik, G. I. Zakora, A. A. Ganiev, G. P. Nistratov

“…Modern laboratory techniques allow predicting patient’s condition before and during the course of treatment; they were implemented into clinical practice for successful treatment correction. …”
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Pulmonary Hemodynamic Correlates and Prognostic Value of the Cardiopulmonary Exercise Score in Patients With Left Heart Failure by Qianqian Chen, Wande Yu, Peipei Cheng, Mengyu Zhang, Wei Li, Dandan Wei, Hang Zhang, Hanjia Gao, Xiaoxia Fu

“…The objective of this study is to develop an algorithm by using data from cardiopulmonary exercise testing (CPET) to assess the severity of pulmonary hemodynamics and predict clinical worsening and mortality in patients with heart failure. …”
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Research on Model Reconstruction Methods for Partial Point Cloud Information Loss by Zhengping CHANG, Guoshuai YUAN, Jiuhua LI, Yang ZHAO, Zhongqi WANG

“…At the present stage, assembly performance analysis requires model refactoring to predict assembly errors. The refactoring of a model of point cloud inverse reconstruction can reflect real physical information, which solves the deviation between simulation and actual results. …”
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