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A Fluorescent Alternative to the Synthetic Strigolactone GR24

Molecular Plant 2013年第1期6卷 100-112页

作者： Amanda Rasmussen Thomas Heugebaert Cedrick Matthys Rik Van Deun Francois-Didier Boyer1 Sofie Goormachtig christian stevens Danny Geelen Plant Production Faculty of Bioscience Engineering Ghent University Coupure links 653 Ghent 9000 Belgium Research Group SynBioC Department of Sustainable Organic Chemistry and Technology Faculty of Bioscience Engineering Ghent University Coupurelinks 653 B-9000 Gent Belgium Department of Plant Systems Biology VIB Ghent 9052 Belgium Department of Plant Biotechnologyand Bioinformatics Ghent University Ghent 9052 Belgium f-element Coordination Chemistry Lab Inorganic and Physical Chemistry Department Ghent University Krijgslaan 281--$3 9000 Gent Belgium Centre de Recherche de Gif Institut de Chimie des Substances Naturelles UPR2301 CNRS INRA 1 avenue de la Terrasse F-91198 Gif-sur-Yvette Cedex France

Strigolactones have recently been implicated in both above- and below-ground developmental pathways in higher plants. To facilitate the molecular and chemical properties of strigolactones in vitro and in vivo, we have devel- oped a fluorescent strigolactone molecule, CISA-1, synthesized via a novel method which was robust, high-yielding, and used simple starting materials. We demonstrate that CISA-1 has a broad range of known strigolactone activities and further report on an adventitious rooting assay in Arabidopsis which is a highly sensitive and rapid method for testing biological activity of strigolactone analogs. In this rooting assay and the widely used Orobanche germination assay, CISA-1 showed stronger biological activity than the commonly tested GR24. CISA-1 and GR24 were equally effective at inhibiting branching in Arabidopsis inflorescence stems. In both the branching and adventitious rooting assay, we also demonstrated that CISA-1 activity is dependent on the max strigolactone signaling pathway. In water methanol solu- tions, CISA-1 was about threefold more stable than GR24, which may contribute to the increased activity observed in the various biological tests.

关键词： strigolactones adventitious rooting branching fluorescent markers parasitic weed seed germination.

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Machine Learning in Chemical Engineering:Strengths,Weaknesses,Opportunities,and Threats

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Engineering 2021年第9期7卷 1201-1211页

作者： Maarten R.Dobbelaere Pieter P.Plehiers Ruben Van de Vijver christian V.stevens Kevin M.Van Geem Laboratory for Chemical Technology Department of MaterialsTextiles and Chemical EngineeringGhent UniversityGhent 9052Belgium SynBioC Research Group Department of Green Chemistry and TechnologyFaculty of Bioscience EngineeringGhent UniversityGhent 9000Belgium

Chemical engineers rely on models for design,research,and daily decision-making,often with potentially large financial and safety *** efforts a few decades ago to combine artificial intelligence and chemical engineering for modeling were unable to fulfill the *** the last five years,the increasing availability of data and computational resources has led to a resurgence in machine learning-based *** recent efforts have facilitated the roll-out of machine learning techniques in the research field by developing large databases,benchmarks,and representations for chemical applications and new machine learning *** learning has significant advantages over traditional modeling techniques,including flexibility,accuracy,and execution *** strengths also come with weaknesses,such as the lack of interpretability of these black-box *** greatest opportunities involve using machine learning in time-limited applications such as real-time optimization and planning that require high accuracy and that can build on models with a self-learning ability to recognize patterns,learn from data,and become more intelligent over *** greatest threat in artificial intelligence research today is inappropriate use because most chemical engineers have had limited training in computer science and data ***,machine learning will definitely become a trustworthy element in the modeling toolbox of chemical engineers.

关键词： Artificial intelligence Machine learning Reaction engineering Process engineering

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Artificial Intelligence in Steam Cracking Modeling: A Deep Learning Algorithm for Detailed Effluent Prediction

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Engineering 2019年第6期5卷 1027-1040页

作者： Pieter PPlehiers Steffen HSymoens Ismaël Amghizar Guy B.Marin christian V.stevens Kevin M.Van Geem Laboratory for Chemical Technology Department of MaterialsTextiles and Chemical EngineeringGhent UniversityGent 9052Belgium SynBioC Research Group Department of Green Chemistry and TechnologyFaculty of Bioscience EngineeringGhent UniversityGent 9000Belgium

Chemical processes can bene t tremendously from fast and accurate ef uent composition prediction for plant design, control, and optimization. The Industry 4.0 revolution claims that by introducing machine learning into these elds, substantial economic and environmental gains can be achieved. The bottleneck for high-frequency optimization and process control is often the time necessary to perform the required detailed analyses of, for example, feed and product. To resolve these issues, a framework of four deep learning arti cial neural networks (DL ANNs) has been developed for the largest chemicals production process steam cracking. The proposed methodology allows both a detailed characterization of a naphtha feedstock and a detailed composition of the steam cracker ef uent to be determined, based on a limited number of commercial naphtha indices and rapidly accessible process characteristics. The detailed char- acterization of a naphtha is predicted from three points on the boiling curve and paraf ns, iso-paraf ns, ole ns, naphthenes, and aronatics (PIONA) characterization. If unavailable, the boiling points are also estimated. Even with estimated boiling points, the developed DL ANN outperforms several established methods such as maximization of Shannon entropy and traditional ANNs. For feedstock reconstruction, a mean absolute error (MAE) of 0.3 wt% is achieved on the test set, while the MAE of the ef uent predic- tion is 0.1 wt%. When combining all networks using the output of the previous as input to the next the ef uent MAE increases to 0.19 wt%. In addition to the high accuracy of the networks, a major bene t is the negligible computational cost required to obtain the predictions. On a standard Intel i7 processor, predictions are made in the order of milliseconds. Commercial software such as COILSIM1D performs slightly better in terms of accuracy, but the required central processing unit time per reaction is in the order of seconds. This tremendous speed-up an

关键词： Artificial intelligence Deep learning Steam cracking Artificial neural networks

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New Strigolactone Analogs as Plant Hormones with Low Activities in the Rhizosphere

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Molecular Plant 2014年第4期7卷 675-690页

作者： Francois-Didier Boyer Alexandre de Saint Germain Jean-Bernard Pouvreau Guillaume Clave Jean-Paul Pillot Amelie Roux Amanda Rasmussen Stephen Depuydt Dominique Lauresserguesh Nicolas Frei dit Frey Thomas S.A. Heugebaert christian V. stevens Danny Geelen Sofie Goormachtig Catherine Rameau Institut Jean-Pierre Bourgin UMR1318 Institut National de la Recherche Agronomique (INRA)-AgroParisTech Route de Saint-Cyr (RD 10) F-78026 Versailles Cedex France Centre de Recherche de Gif Institut de Chimie des Substances Naturelles UPR2301 Centre National de la Recherche Scientifique (CNRS) 1 avenue de la Terrasse F-91198 Gif-sur-Yvette Cedex France Laboratoire de Biologie et de Pathologie Vegetales EA 1157 SFR 4207 QUASAV Universite de Nantes 44322 Nantes France Plant Production Faculty of Bioscience Engineering Ghent University Coupure links 653 Ghent 9000 Belgium Present address: School of Biosciences Division of Plant and Crop Production The University of Nottingham Sutton Bonington Leicestershire LE12 5RD UK Department of Plant Systems Biology VIB Ghent 9052 Belgium Department of Plant Biotechnology and Bioinformatics Ghent University Ghent 9052 Belgium Laboratoire de Recherche en Sciences Vegetales UMR 5546 Universite de Toulouse- Centre National de la Recherche Scientifique (CNRS) BP 42617 Auzeville F-31326 Castanet-Tolosan France Research Group SynBioC Department of Sustainable Organic Chemistry and Technology Faculty of Bioscience Engineering Ghent University Coupure links 653 B-9000 Gent Belgium

Strigolactones （SLs） are known not only as plant hormones, but also as rhizosphere signals for establishing symbiotic and parasitic interactions. The design of new specific SL analogs is a challenging goal in understanding the basic plant biology and is also useful to control plant architectures without favoring the development of parasitic plants. Two different molecules （23 （3＇-methyI-GR24）, 31 （thia-3＇-methyl-debranone-like molecule）） already described, and a new one （AR36）, for which the synthesis is presented, are biologically compared with the well-known GR24 and the recently identified CISA-1. These different structures emphasize the wide range of parts attached to the D-ring for the bioactivity as a plant hormone. These new compounds possess a common dimethylbutenolide motif but their structure varies in the ABC part of the molecules： 23 has the same ABC part as GR24, while 31 and AR36 carry, respectively, an aromatic ring and an acyclic carbon chain. Detailed information is given for the bioactivity of such derivatives in strigolactone synthesis or in perception mutant plants （pea rmsl and rms4, Arabidopsis max2 and, max4） for different hormonal functions along with their action in the rhizosphere on arbuscular mycorrhizal hyphal growth and parasitic weed germination.

关键词： strigolactones synthetic analogs Pisum sativum Arabidopsis root parasitic plants Gigaspora rosea plant growth regulator.

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Fast indirect measurement of PCDD/F TEQ emission from municipal solid waste incineration:a review

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Waste Disposal and Sustainable Energy 2019年第1期1卷 39-51页

作者： Xuan Cao Shengyong Lu William R.stevens Huadong Zhong Kaicheng Wu Xiaodong Li Jianhua Yan State Key Laboratory of Clean Energy Utilization Zhejiang University38 Zheda RoadHangzhou 310027China Zhejiang Fuchunjiang Environmental Technology Research Co. Ltd.Hangzhou 311401China College of Health Sciences Kentucky Christian UniversityGraysonKY 41143USA

Landfilling,a traditional and major method for municipal solid waste management,is gradually being taken place by ***,the contamination problem caused by polychlorinated dibenzo-p-dioxin and dibenzofuran(PCDD/F)emissions from incineration has also brought much *** offline method based on high-resolution gas chromatography coupled with high-resolution mass spectrometry for the determination of PCDD/F emissions is expensive,infrequent and *** of PCDD/F indicators to achieve fast indirect measurements for PCDD/Fs is a much more promising *** categories of potential PCDD/F indicators are discussed in this paper:CO,total hydrocarbons,specific PCDD/F congeners,polycyclic aromatic hydrocarbons,chlorophenols and *** performance as PCDD/F indicators are verified in the following criteria:(1)structural similarities;(2)known mechanistic arguments;(3)known cor-relation values from the literature and(4)*** considering the four factors,chlorobenzenes may be the best suitable PCDD/F *** analytical method based on Photon Ionization coupled with Time-of-Flight Mass Spectrometry has been proved effective for the online/atline measurements for PCDD/F indicators for the prediction of TEQ in incineration ***,thermal desorption and gas chromatography can be applied to provide extra separation and concentration for very complex matrices with extremely low concentrations.

关键词： PCDD/Fs MSW incineration Indirect measurement Correlation Indicators

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冰上舞蹈搭档

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疯狂英语（新策略） 2019年第11期 18-20页

作者： Kate stevens 张尽染(译) 不详

语篇导读本文是一篇新闻报道,主要报道的是一对美国冰上舞蹈搭档的励志故事。他们苦练花样滑冰,立志进军北京冬奥会。

关键词：新闻报道冰上舞蹈花样滑冰搭档北京冬奥会励志立志

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