This paper presents distributions of topological observables in inclusive three- and four-jet events produced in pp collisions at a centre-of-mass energy of 7[Formula: see text] with a data sample collected by the CMS experiment corresponding to a luminosity of 5.1[Formula: see text]. The distributions are corrected for detector effects, and compared with several event generators based on two- and multi-parton matrix elements at leading order. Among the considered calculations, MadGraph interfaced with pythia6 displays the overall best agreement with data.
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http://dx.doi.org/10.1140/epjc/s10052-015-3491-9 | DOI Listing |
Oncoimmunology
December 2025
Molecular and Translational Oncology Division, Biomedical Innovation Unit, CIEMAT, Madrid, Spain.
High-risk non-muscle-invasive bladder cancer (NMIBC) presents high recurrence and progression rates. Despite the use of Bacillus Calmette-Guérin gold-standard immunotherapy and the recent irruption of anti-PD-1/PD-L1 drugs, we are missing a comprehensive understanding of the tumor microenvironment (TME) that may help us find biomarkers associated to treatment outcome. Here, we prospectively analyzed TME composition and PD-L1 expression of tumor and non-tumoral tissue biopsies from 73 NMIBC patients and used scRNA-seq, transcriptomic cohorts and tissue micro-array to validate the prognostic value of cell types of interest.
View Article and Find Full Text PDFBrain Inform
December 2024
Brain Cognition and Intelligent Computing Lab, Department of Artificial Intelligence, School of Informatics, Xiamen University, Xiamen, Fujian, China.
EEG-based emotion recognition uses high-level information from neural activities to predict emotional responses in subjects. However, this information is sparsely distributed in frequency, time, and spatial domains and varied across subjects. To address these challenges in emotion recognition, we propose a novel neural network model named Temporal-Spectral Graph Convolutional Network (TSGCN).
View Article and Find Full Text PDFInt J Mol Sci
November 2024
Department of Biological Sciences, Faculty of Science, National University of Singapore, 10 Kent Ridge Crescent, Singapore 119260, Singapore.
Water exists in the beginning and hydrates all matter. Life emerged in water, requiring three essential components in compartmentalized spaces: (1) universal energy sources driving biochemical reactions and processes, (2) molecules that store, encode, and transmit information, and (3) functional players carrying out biological activities and structural organization. Phosphorus has been selected to create adenosine triphosphate (ATP) as the universal energy currency, nucleic acids for genetic information storage and transmission, and phospholipids for cellular compartmentalization.
View Article and Find Full Text PDFJ Chem Inf Model
December 2024
Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058 Zhejiang, China.
Three-dimensional (3D) molecular generation models employ deep neural networks to simultaneously generate both topological representation and molecular conformations. Due to their advantages in utilizing the structural and interaction information on targets, as well as their reduced reliance on existing bioactivity data, these models have attracted widespread attention. However, limited training and testing data sets and the unexpected biases inherent in single evaluation metrics pose a significant challenge in comparing these models in practical settings.
View Article and Find Full Text PDFFront Neurosci
November 2024
Global R&D Center, China FAW Corporation Limited, Changchun, China.
Brain-computer interfaces (BCIs) establish a direct communication pathway between the brain and external devices and have been widely applied in upper limb rehabilitation for hemiplegic patients. However, significant individual variability in motor imagery electroencephalogram (MI-EEG) signals leads to poor generalization performance of MI-based BCI decoding methods to new patients. This paper proposes a Multi-scale Frequency domain Feature-based Dynamic graph Attention Network (MFF-DANet) for upper limb MI decoding in hemiplegic patients.
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