Machine learning-driven optimization of plasma-catalytic dry reforming of methane

This study investigates the dry reformation of methane(DRM)over Ni/Al_(2)O_(3)catalysts in a dielectric barrier discharge(DBD)non-thermal plasma reactor.A novel hybrid machine learning(ML)model is developed to optimize the plasma-catalytic DRM reaction with limited experimental data.To address the non-linear and complex nature of the plasma-catalytic DRM process,the hybrid ML model integrates three well-established algorithms:regression trees,support vector regression,and artificial neural networks.A genetic algorithm(GA)is then used to optimize the hyperparameters of each algorithm within the hybrid ML model.The ML model achieved excellent agreement with the experimental data,demonstrating its efficacy in accurately predicting and optimizing the DRM process.The model was subsequently used to investigate the impact of various operating parameters on the plasma-catalytic DRM performance.We found that the optimal discharge power(20 W),CO_(2)/CH_(4)molar ratio(1.5),and Ni loading(7.8 wt%)resulted in the maximum energy yield at a total flow rate of∼51 mL/min.Furthermore,we investigated the relative significance of each operating parameter on the performance of the plasma-catalytic DRM process.The results show that the total flow rate had the greatest influence on the conversion,with a significance exceeding 35%for each output,while the Ni loading had the least impact on the overall reaction performance.This hybrid model demonstrates a remarkable ability to extract valuable insights from limited datasets,enabling the development and optimization of more efficient and selective plasma-catalytic chemical processes.

Imatinib blocks tyrosine phosphorylation of Smad4 and restores TGF-β growth-suppressive signaling in BCR-ABL1-positive leukemia

Loss of TGF-β-mediated growth suppression is a major contributor to the development of cancers,best exemplified by loss-offunction mutations in genes encoding components of the TGF-βsignaling pathway in colorectal and pancreatic cancers.Alternatively,gain-of-function oncogene mutations can also disrupt antiproliferative TGF-βsignaling.However,the molecular mechanisms underlying oncogene-induced modulation of TGF-βsignaling have not been extensively investigated.Here,we show that the oncogenic BCR-ABL1 of chronic myelogenous leukemia(CML)and the cellular ABL1 tyrosine kinases phosphorylate and inactivate Smad4 to block antiproliferative TGF-βsignaling.Mechanistically,phosphorylation of Smad4 at Tyr195,Tyr301,and Tyr322 in the linker region interferes with its binding to the transcription co-activator p300/CBP,thereby blocking the ability of Smad4 to activate the expression of cyclin-dependent kinase(CDK)inhibitors and induce cell cycle arrest.In contrast,the inhibition of BCR-ABL1 kinase with Imatinib prevented Smad4 tyrosine phosphorylation and re-sensitized CML cells to TGF-β-induced antiproliferative and pro-apoptotic responses.Furthermore,expression of phosphorylation-site-mutated Y195F/Y301F/Y322F mutant of Smad4 in Smad4-null CML cells enhanced antiproliferative responses to TGF-β,whereas the phosphorylation-mimicking Y195E/Y301E/Y322E mutant interfered with TGF-βsignaling and enhanced the in vivo growth of CML cells.These findings demonstrate the direct role of BCR-ABL1 tyrosine kinase in suppressing TGF-βsignaling in CML and explain how Imatinib-targeted therapy restored beneficial TGF-βanti-growth responses.

化工技术在生物冶金过程强化中的研究进展

随着高品位矿物的消耗枯竭,生产成本高、环境污染严重的传统火法冶炼技术已不能适应我国经济社会可持续发展的需要。以生物堆浸和生物槽浸为代表的生物冶金技术具有操作简单、成本低廉、环境友好等特点,可用于低品位、复杂矿物的处理,目前已广泛应用于铜、金等有价金属的回收利用。但是,生物冶金过程中相对较慢的反应速度是限制其应用范围进一步扩大的主要原因。生物冶金技术的基本过程与化工过程紧密相关,化工技术的发展在历史上曾极大地促进生物冶金技术的推广应用,通过化工技术强化生物冶金过程仍是未来生物冶金研究的主要方向之一。因此,本工作综述了近年来与生物冶金有关的化工过程研究进展,主要从工业应用角度分析了生物堆浸和生物槽浸过程的影响因素,以期帮助研究者更好地了解相关领域的发展现状,正确把握研究方向。