Results 71 to 80 of about 706,179 (292)

Molecular and phylogenetic analyses of the complete MADS-box transcription factor family in Arabidopsis: New openings to the MADS World [PDF]

, 2003
MADS-box transcription factors are key regulators of several plant development processes. Analysis of the complete Arabidopsis genome sequence revealed 107 genes encoding MADS-box proteins, of which 84re of unknown function.
H. Cook, Angenent, G.C., J. Busscher, Jacqueline Busscher, Stefan de Folter, Colombo, L., Richard M. Ingram, B. Davies, Kieffer, M., Kater, M.M., M. Kater, Lucia Colombo, S. de Folder, M. Kieffer, R. Ingram, L. Colombo, D. Horner, C. Favalli, Favalli, C., Busscher, J., Lucie Par̆enicová, Brendan Davies, Ingram, R.M., Martin Kieffer, G. Angenent, de Folter, S., Cristina Favalli, Cook, H.E., Holly E. Cook, Davies, B., Parenicova, L., Folter, S., de, Horner, D.S., L. Perenicova, David S. Horner, Martin M. Kater, Gerco C. Angenent   +36 more
core   +1 more source

Targeting Lactate‐Driven Stromal Autophagy via MCT1 Disrupts the Immunosuppressive Niche and Sensitizes Pancreatic Cancer to PD‐1 Blockade

Advanced Science, EarlyView.
Tumor‐derived lactate activates PSCs through MCT1‐mediated Vps34 lactylation and autophagy. These activated PSCs secrete CXCL9/10, upregulating PD‐1 on CD8+ T cells via the CXCR3/STAT3 axis to foster immunosuppression. Disrupting this metabolic crosstalk by targeting MCT1 effectively sensitizes pancreatic cancer to PD‐1 blockade, presenting a promising
Wenfeng Zhuo, Rong Hu, Yuhang Hu, Ping Hu, Shengbo Han, Guozheng Lv, Zhu Zeng, Yong Zhao, Yang Li, Yan Huang, Yingsong Zhao, Hongda Wang, Guangyu Zhao, Eryang Zhao, Gang Zhao   +14 more
wiley   +1 more source

K-Domain Technology: Constitutive Expression of a Blueberry Keratin-Like Domain Mimics Expression of Multiple MADS-Box Genes in Enhancing Maize Grain Yield

Frontiers in Plant Science, 2021
MADS-box genes are considered as the foundation of all agronomic traits because they play essential roles in almost every aspect of plant reproductive development.
Guo-qing Song, Xue Han
doaj   +1 more source

Machine Learning Interatomic Potentials for Energy Materials: Architectures, Training Strategies, and Applications

Advanced Energy Materials, EarlyView.
Machine learning interatomic potentials bridge quantum accuracy and computational efficiency for materials discovery. Architectures from Gaussian process regression to equivariant graph neural networks, training strategies including active learning and foundation models, and applications in solid‐state electrolytes, batteries, electrocatalysts ...
In Kee Park, Saeed Pourasad, Jinhong Mun, Arunkumar Chitteth Rajan, Tae Hyeon Park, Anand Rohit, Mohammad Zafari, Tan‐Lien Pham, Eunjae Jung, Bonseung Koo, Miran Ha, Gi Beom Sim, Hyun Gyu Park, Won Woong Choi, Kwanghyeon Jo, Soohaeng Yoo Willow, David ChangMo Yang, Chang Woo Myung, Geunsik Lee, Kwang S. Kim   +19 more
wiley   +1 more source

Magnetic field‐assisted concentration swing adsorption for CO2 capture over Fe3O4/fibrous nanosilica‐PEI adsorbent

AIChE Journal, EarlyView.
Abstract Magnetic field‐assisted concentration swing adsorption (CSA) provides an electrification‐compatible alternative to conventional temperature swing adsorption (TSA) by enabling rapid heating/cooling and energy‐efficient regeneration under near‐isothermal conditions, thereby eliminating the need for sensible heat input.
Xiaohao Jia, Fateme Rezaei
wiley   +1 more source

Solid–liquid equilibria in the LiOH–ethanol–water system: Solubility measurements and thermodynamic modeling

AIChE Journal, EarlyView.
Abstract The demand for LiOH is driven by the growth of the electric vehicle industry. Evaporative crystallization of LiOH·H2O is energy intensive, whereas ethanol‐based antisolvent crystallization has emerged as a more sustainable alternative. From a process design perspective, the crystallization yield depends on the ethanol dosage, and thermodynamic
Xiaoqi Xu, Chentao He, Janet A. W. Elliott, Liuyin Xia   +3 more
wiley   +1 more source

Genome-wide identification of a MADS-box transcription factor family and their expression during floral development in Coptis teeta wall

BMC Plant Biology
Background MADS-box transcription factors have been shown to be involved in multiple developmental processes, including the regulation of floral organ formation and pollen maturation. However, the role of the MADS-box gene family in floral development of
Shao-Feng Duan, Ji-Chen Yu, Timothy Charles Baldwin, Yuan Yuan, Gui-Sheng Xiang, Rui Cui, Yan Zhao, Xin-Chun Mo, Ying-Chun Lu, Yan-Li Liang   +9 more
doaj   +1 more source

Characterization of a Tobacco MADS-Box Gene Homologous to AGL2

Molecules and Cells, 1998
Flower development is regulated by a group of regulatory factors containing the MADS box domain. Recently, we isolated and characterized a tobacco MADS-box gene, NtMADS1, which is homologous to AGL2 in Arabidopsis. Sequence analysis revealed that NtMADS1 encodes a putative MADS-box protein homologous to the AGL2 with 68.4% identity and 82% similarity ...
Chung, YY, Lee, KJ, An, G
openaire   +2 more sources

Macrophage Phenotype Detection Methodology on Textured Surfaces via Nuclear Morphology Using Machine Learning

Advanced Intelligent Discovery, EarlyView.
A novel machine learning approach classifies macrophage phenotypes with up to 98% accuracy using only nuclear morphology from DAPI‐stained images. Bypassing traditional surface markers, the method proves robust even on complex textured biomaterial surfaces. It offers a simpler, faster alternative for studying macrophage behavior in various experimental
Oleh Mezhenskyi, Kateryna Andriienkova, Tetyana Goldanova, Ezgi Antmen, Thibaut Soubrie, Nihal Engin Vrana   +5 more
wiley   +1 more source

MADS-box gene family in rice: genome-wide identification, organization and expression profiling during reproductive development and stress-6

, 2011
Copyright information:Taken from "MADS-box gene family in rice: genome-wide identification, organization and expression profiling during reproductive development and stress"http://www.biomedcentral.com/1471-2164/8/242BMC Genomics 2007;8():242-242 ...
Rita Arora (43314), Vijay Pal Singh (43318), Akhilesh K Tyagi (14881), Sanjay Kapoor (43319), Pinky Agarwal (43315), Ashok Kumar Singh (43317), Swatismita Ray (43316)   +6 more
core   +1 more source