Results 141 to 150 of about 1,478,008 (318)
A VLIW processor with reconfigurable instruction set for embedded applications [PDF]
F. Campi +5 more
openalex +1 more source
A‐to‐I editing of miRNAs, particularly miR‐200b‐3p, contributes to HGSOC progression by enhancing cancer cell proliferation, migration and 3D growth. The edited form is linked to poorer patient survival and the identification of novel molecular targets.
Magdalena Niemira +14 more
wiley +1 more source
A retargetable, ultra-fast instruction set simulator [PDF]
Jianwen Zhu, D.D. Gajski
openalex +1 more source
Experience as an “Instructional Set” in Negotiation [PDF]
Frederick R. Kling, Albert E. Myers
openaire +4 more sources
YAP1::TFE3 mediates endothelial‐to‐mesenchymal plasticity in epithelioid hemangioendothelioma
The YAP1::TFE3 fusion protein drives endothelial‐to‐mesenchymal transition (EndMT) plasticity, resulting in the loss of endothelial characteristics and gain of mesenchymal‐like properties, including resistance to anoikis, increased migratory capacity, and loss of contact growth inhibition in endothelial cells.
Ant Murphy +9 more
wiley +1 more source
Scalable custom instructions identification for instruction-set extensible processors [PDF]
Pan Yu, Tulika Mitra
openalex +1 more source
Emerging role of ARHGAP29 in melanoma cell phenotype switching
This study gives first insights into the role of ARHGAP29 in malignant melanoma. ARHGAP29 was revealed to be connected to tumor cell plasticity, promoting a mesenchymal‐like, invasive phenotype and driving tumor progression. Further, it modulates cell spreading by influencing RhoA/ROCK signaling and affects SMAD2 activity. Rho GTPase‐activating protein
Beatrice Charlotte Tröster +3 more
wiley +1 more source
Characterizing embedded applications for instruction-set extensible processors [PDF]
Pan Yu, Tulika Mitra
openalex +1 more source
We evaluated circulating tumor DNA (ctDNA) detection in advanced pancreatic cancer using DNA methylation, cell‐free DNA fragment lengths, and 5′ end motifs. Machine learning models were trained to estimate ctDNA levels from each feature and their combination.
Morten Lapin +10 more
wiley +1 more source
Improving Instruction Set Architecture learning results [PDF]
José M. Claver +2 more
openalex +1 more source

