Results 201 to 210 of about 2,651,920 (362)

Technological-Knowledge Dynamics in Lab-Equipment Models of Quality Ladders [PDF]

The Perpetual Inventory Model (PIM) assumes that, in each period, an arbitrary constant fraction of technological-knowledge stock is lost. By connecting the aggregate resource constraint with firms’ market value, we give a theoretical background to the ...
Pedro Mazeda Gil, Óscar Afonso
core  

Highly multiplexed digital PCR assay for simultaneous quantification of variant allele frequencies and copy number alterations of KRAS and GNAS in pancreatic cancer precursors

Molecular Oncology, EarlyView.
Combining melting curve analysis enhances the multiplexing capability of digital PCR. Here, we developed a 14‐plex assay to simultaneously measure single nucleotide mutations and amplifications of KRAS and GNAS, which are common driver genes in pancreatic cancer precursors. This assay accurately quantified variant allele frequencies in clinical samples
Junko Tanaka, Tatsuo Nakagawa, Yusuke Ono, Yoshio Kamura, Takeshi Ishida, Hidemasa Kawabata, Kenji Takahashi, Hiroki Sato, Andrew S. Liss, Yusuke Mizukami, Takahide Yokoi   +10 more
wiley   +1 more source

Analysis of Technological Portfolios for CO2 stabilizations and Effects of Technological Changes [PDF]

In this study, cost-effective technological options to stabilize CO2 concentrations at 550, 500, and 450 ppmv are evaluated using a world energy systems model of linear programming with a high regional resolution.
Fuminori Sano, Keigo Akimoto, Takashi Homma, Toshimasa Tomoda   +3 more
core  

Exploration of heterogeneity and recurrence signatures in hepatocellular carcinoma

Molecular Oncology, EarlyView.
This study leveraged public datasets and integrative bioinformatic analysis to dissect malignant cell heterogeneity between relapsed and primary HCC, focusing on intercellular communication, differentiation status, metabolic activity, and transcriptomic profiles.
Wen‐Jing Wu, Jianchao Wang, Fuqing Chen, Xuefeng Wang, Bin Lan, Ruyi Fu, Hong Wen, Fangfang Chen, Wei Hong, Tian‐Yu Tang, Ying He, Gang Chen, Jianyin Zhou, Hai‐Long Piao, Di Chen, Shu‐Yong Lin   +15 more
wiley   +1 more source

SIMULATION OF VIRTUAL TIME NORMS FOR TECHNOLOGICAL PROCESSES

, 2003
Romualdas Tamošaitis, Leonarda Gargasaite   +1 more
openalex   +2 more sources

Electrical performances of AlInN/GaN HEMTs. A comparison with AlGaN/GaN HEMTs with similar technological process [PDF]

, 2011
Olivier Jardel, Guillaume Callet, Jérémy Dufraisse, Michele Piazza, Nicolas Sarazin, E. Chartier, M. Oualli, Raphaël Aubry, Tibault Reveyrand, Jean-Claude Jacquet, Marie‐Antoinette di Forte Poisson, E. Morvan, S. Piotrowicz, S.L. Delage   +13 more
openalex   +1 more source

Comparing self‐reported race and genetic ancestry for identifying potential differentially methylated sites in endometrial cancer: insights from African ancestry proportions using machine learning models

Molecular Oncology, EarlyView.
Integrating ancestry, differential methylation analysis, and machine learning, we identified robust epigenetic signature genes (ESGs) and Core‐ESGs in Black and White women with endometrial cancer. Core‐ESGs (namely APOBEC1 and PLEKHG5) methylation levels were significantly associated with survival, with tumors from high African ancestry (THA) showing ...
Huma Asif, J. Julie Kim
wiley   +1 more source

Finite Element Modelling of Mechanical Phenomena Connected to the Technological Process of Continuous Casting of Steel

, 2004
J. Heger
openalex   +2 more sources

Democratization is the determinant of technological change [PDF]

The purpose of this paper is to analyze the relationship between democracy and technological innovation. The primary findings are that most free countries, measured with liberal, participatory, and constitutional democracy index, have higher ...
Mario Coccia
core  

Escape from TGF‐β‐induced senescence promotes aggressive hallmarks in epithelial hepatocellular carcinoma cells

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, Dilara Demirci, Sude Eris, Bengisu Dayanc, Ece Cakiroglu, Merve Basol, Merve Uysal, Gulcin Cakan‐Akdogan, Fang Liu, Mehmet Ozturk, Gökhan Karakülah, Serif Senturk   +11 more
wiley   +1 more source