Results 161 to 170 of about 908,634 (266)
Dual‐Array Nano Configuration for High‐Performance Metastable β Titanium Alloys
Advanced Science, EarlyView.This work resolves a fundamental paradox in metastable alloys by establishing a novel dislocation‐phase coupling mechanism, enabling dynamic microstructure control for simultaneous thermal stability and deformability. Geometrically ordered α‐nanograins achieve unprecedented 500°C strength‐ductility synergy (863 MPa UTS, 78.3% elongation).Tianle Li, Renhao Wu, Jiabao Liu, Sang‐Ho Oh, Xiang Wu, Hyojin Park, Byeong‐Joo Lee, Hidemi Kato, Hyoung Seop Kim, Xiaochun Liu, Xifeng Li +10 morewiley +1 more sourceTendon Organoids Enable Functional Tendon Rejuvenation Through ALKBH5‐Dependent RNA Demethylation
Advanced Science, EarlyView.FT organoids reverse the aged phenotype of tendon cells, reinstating a fetal‐like state. This breakthrough establishes a potent cell source for tendon tissue engineering, effectively advancing regenerative medicine. ABSTRACT
Adult tendon injuries pose a major clinical challenge due to limited self‐repair capacity, resulting in suboptimal regeneration ...Tian Qin, Aini Pan, Zhuoning Miao, Yanyan Zhao, Xianan Mo, Heng Sun, Chunmei Fan, Junyu Guo, Bingbing Wu, Weiliang Shen, Qiangqiang Zheng, Jun Lu, Xi Jiang, Zi Yin, Xiao Chen +14 morewiley +1 more sourceObservations of the Cabibbo-Suppressed decays , and the Cabibbo-Favored decay ${\Lambda_{\boldsymbol c}^{\bf +}\to n\pi^{\bf +}\pi^{\bf 0}} $, ${n\pi^{+}\pi^{\bf -}\pi^{\bf +} }$ and the Cabibbo-Favored decay ${\Lambda_{c}^{\bf +}\to nK^{\bf -}\pi^{\bf +}\pi^{\bf +} }$
, 2023 Ablikim, M., Achasov, M. N., Baldini Ferroli, R., Gong, W. X., Gradl, W., Greco, M., Gu, L. M., Gu, M. H., Gu, Y. T., Y Guan, C., Guo, A. Q., Guo, L. B., Guo, R. P., Balossino, I., Guo, Y. P., Guskov, A., Han, W. Y., Hao, X. Q., Harris, F. A., He, K. K., He, K. L., Heinsius, F. H., Heinz, C. H., Heng, Y. K., Ban, Y., Herold, C., Hou, G. Y., Hou, Y. R., Hou, Z. L., Hu, H. M., Hu, J. F., Hu, T., Hu, Y., Huang, G. S., Huang, K. X., Batozskaya, V., Huang, L. Q., Huang, X. T., Huang, Y. P., Huang, Z., Hussain, T., Hüsken, N., Imoehl, W., Irshad, M., Jackson, J., Jaeger, S., Becker, D., Janchiv, S., Jang, E., Jeong, J. H., Ji, Q., Ji, Q. P., Ji, X. B., Ji, X. L., Ji, Y. Y., Jia, Z. K., Jiang, S. S., Begzsuren, K., Jiang, X. S., Jiang, Y., Jiao, J. B., Jiao, Z., Jin, S., Jin, Y., Jing, M. Q., Johansson, T., Kalantar-Nayestanaki, N., Kang, X. S., Berger, N., Kappert, R., Kavatsyuk, M., Ke, B. C., Keshk, I. K., Khoukaz, A., Kiuchi, R., Kliemt, R., Koch, L., Kolcu, O. B., Kopf, B., Bertani, M., Kuemmel, M., Kuessner, M., Kupsc, A., Kühn, W., Lane, J. J., Lange, J. S., Larin, P., Lavania, A., Lavezzi, L., Lei, Z. H., Bettoni, D., Leithoff, H., Lellmann, M., Lenz, T., Li, C., Li, C., Li, C. H., Li, Cheng, Li, D. M., Li, F., Li, G., Bianchi, F., Li, H., Li, H., Li, H. B., Li, H. J., Li, H. N., Li, J. Q., Li, J. S., Li, J. W., Li, Ke, J Li, L., Adlarson, P., Bianco, E., Li, L. K., Li, Lei, Li, M. H., Li, P. R., Li, S. X., Li, S. Y., Li, T., Li, W. D., Li, W. G., Li, X. H., Bloms, J., Li, X. L., Li, Xiaoyu, Li, Y. G., Li, Z. X., Li, Z. Y., Liang, C., Liang, H., Liang, H., Liang, H., Liang, Y. F., Bortone, A., Liang, Y. T., Liao, G. R., Liao, L. Z., Libby, J., Limphirat, A., Lin, C. X., Lin, D. X., Lin, T., Liu, B. J., Liu, C., Boyko, I., Liu, C. X., Liu, D., Liu, F. H., Liu, Fang, Liu, Feng, Liu, G. M., Liu, H., Liu, H. B., Liu, H. M., Liu, Huanhuan, Briere, R. A., Liu, Huihui, Liu, J. B., Liu, J. L., Liu, J. Y., Liu, K., Liu, K. Y., Liu, Ke, Liu, L., Liu, Lu, Liu, M. H., Brueggemann, A., Liu, P. L., Liu, Q., Liu, S. B., Liu, T., Liu, W. K., Liu, W. M., Liu, X., Liu, Y., Liu, Y. B., Liu, Z. A., Cai, H., Liu, Z. Q., Lou, X. C., Lu, F. X., Lu, H. J., Lu, J. G., Lu, X. L., Lu, Y., Lu, Y. P., Lu, Z. H., Luo, C. L., Cai, X., Luo, M. X., Luo, T., Luo, X. L., Lyu, X. R., Lyu, Y. F., Ma, F. C., Ma, H. L., Ma, L. L., Ma, M. M. +199 moreopenaire +1 more sourceCellPolaris: Transfer Learning for Gene Regulatory Network Construction to Guide Cell State Transitions
Advanced Science, EarlyView.CellPolaris decodes how transcription factors guide cell fate by building gene regulatory networks from transcriptomic data using transfer learning. It generates tissue‐ and cell‐type‐specific networks, identifies master regulators in cell state transitions, and simulates TF perturbations in developmental processes.Guihai Feng, Xin Qin, Jiahao Zhang, Wuliang Huang, Yiyang Zhang, Wentao Cui, Yao Chen, Shirui Li, Wenhao Liu, Yao Tian, Yana Liu, Jingxi Dong, Ping Xu, Zhenpeng Man, Guole Liu, Zhongming Liang, Xinlong Jiang, Xiaodong Yang, Pengfei Wang, Ge Yang, Hongmei Wang, Xuezhi Wang, Ming‐Han Tong, Yuanchun Zhou, Shihua Zhang, Yiqiang Chen, Yong Wang, Xin Li +27 morewiley +1 more sourceA Patient‐Derived Organ‐on‐Chip Platform for Modeling the Tumor Microenvironment and Drug Responses in Pancreatic Cancer
Advanced Science, EarlyView.Researchers have developed a patient‐derived organ‐on‐a‐chip model for pancreatic cancer by integrating cancer cells with supportive stromal and immune cells inside a microfluidic device. This system mimics the tumor microenvironment, enabling personalized testing of chemotherapy and immunotherapy, and offering new insights into how targeting ...Darbaz Adnan, Natan Roberto de Barros, Luca S. Santovito, Xuhong Cheng, Kristi M. Lawrence, Mariah K. Barnett, Martine D. Boetto, Neal Mehta, Ajaypal Singh, Lin Cheng, Xiangsheng Huang, Faraz Bishehsari +11 morewiley +1 more source
