Results 81 to 90 of about 1,157,336 (324)
Influence of Stirrup-Tie Shape on Inelastic Cyclic Response of Flanged Reinforced Concrete Flexural Members [PDF]
, 1981 Three reinforced concrete flanged sections (T-sections) having different shapes of shear reinforcement were subjected to cyclic inelastic flexure representative of what framed structure members might-be forced to endure during a severe earthquake. The
Scribner, C.F., Wilhelm, D.R.
core
Molecular Oncology, EarlyView.TGF‐β has a complex role in cancer, exhibiting both tumor‐suppressive and tumor‐promoting properties. Using a series of differentiated tumoroids, derived from different stages and mutational background of colorectal cancer patients, we replicate this duality of TGF‐β in vitro. Notably, the atypical but highly aggressive KRASQ22K mutation rendered early‐
Theresia Mair +17 more
wiley +1 more source
Transverse Reinforcement in Reinforced Concrete Columns
sspjce, 2013 Abstract In the article we are dealing with the influence of transverse reinforcement to the resistance of a cross-section of the reinforced concrete columns and also with the effective detailing of the column reinforcement. We are verifying the correctness of design guides for detailing of transverse reinforcement.
Peter Veróny, Štefan Gramblička
openaire +3 more sources
Molecular Oncology, EarlyView.Chronic TGF‐β exposure drives epithelial HCC cells from a senescent state to a TGF‐β resistant mesenchymal phenotype. This transition is characterized by the loss of Smad3‐mediated signaling, escape from senescence, enhanced invasiveness and metastatic potential, and upregulation of key resistance modulators such as MARK1 and GRM8, ultimately promoting
Minenur Kalyoncu +11 more
wiley +1 more source
Reactive Reinforcement Learning in Asynchronous Environments
Frontiers in Robotics and AI, 2018 The relationship between a reinforcement learning (RL) agent and an asynchronous environment is often ignored. Frequently used models of the interaction between an agent and its environment, such as Markov Decision Processes (MDP) or Semi-Markov Decision
Jaden B. Travnik +6 more
doaj +1 more source
Coupling, Reinforcement, and Speciation [PDF]
The American Naturalist, 2018 During the process of speciation, populations may diverge for traits and at their underlying loci that contribute barriers to gene flow. These barrier traits and barrier loci underlie individual barrier effects, by which we mean the contribution that a barrier locus or trait-or some combination of barrier loci or traits-makes to overall isolation.
Butlin, Roger, Smadja, Carole
openaire +6 more sources
Molecular Oncology, EarlyView.This article advocates integrating temporal dynamics into cancer research. Rather than relying on static snapshots, researchers should increasingly consider adopting dynamic methods—such as live imaging, temporal omics, and liquid biopsies—to track how tumors evolve over time.
Gautier Follain +3 more
wiley +1 more source
Molecular Oncology, EarlyView.B‐cell chronic lymphocytic leukemia (B‐CLL) and monoclonal B‐cell lymphocytosis (MBL) show altered proteomes and phosphoproteomes, analyzed using mass spectrometry, protein microarrays, and western blotting. Identifying 2970 proteins and 316 phosphoproteins, including 55 novel phosphopeptides, we reveal BCR and NF‐kβ/STAT3 signaling in disease ...
Paula Díez +17 more
wiley +1 more source
Genomics‐led approach to drug testing in models of undifferentiated pleomorphic sarcoma
Molecular Oncology, EarlyView.GA text Genomic data from undifferentiated pleomorphic sarcoma patients and preclinical models were used to inform a targeted drug screen. Selected compounds were tested in 2D and 3D cultures of UPS cell lines. A combination of trametinib and infigratinib was synergistic in the majority of UPS cell lines tested, which was further confirmed in an ex ...
Piotr J. Manasterski +19 more
wiley +1 more source
Optimizing thermodynamic trajectories using evolutionary and gradient-based reinforcement learning [PDF]
, 2019 Using a model heat engine, we show that neural network-based reinforcement learning can identify thermodynamic trajectories of maximal efficiency. We consider both gradient and gradient-free reinforcement learning.
Beeler, Chris +5 more
core