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Cloning Successive Generations of Industrial Hemp (Cannabis sativa) to Assess Cannabinoid Profiles

Agricultural Sciences 2022年第12期13卷 1291-1308页

作者： Cassandra Perrone paul kline John DuBois Department of Biology Middle Tennessee State University Murfreesboro USA Department of Chemistry Middle Tennessee State University Murfreesboro USA Tennessee Center for Botanical Medicine Research Middle Tennessee State University Murfreesboro USA

Industrial hemp (Cannabis sativa) has made a remarkable impact worldwide due to the plant’s beneficial properties and versatile use. Vegetative cuttings (clones) of C. sativa are the preferred propagation technique to be the most effective in retaining the same genetic information and reducing hybridization and mutations. The objective of this project was to assess cannabinoid profile concentrations of successively cloned generations of 5 varieties: Cherry, Cherry Blossom, Cherry × Workhorse, Sour Space Candy, and The Wife. This research project focused on the idea that every cloned plant contains the exact same genetic information and, therefore, should have the same metabolic profile of cannabinoids through all the successive generations grown, which is shown to be true. Plants were cloned for multiple generations using stem cuttings and a commercial cloner. As plants matured, they were set in environmental conditions to stimulate flowering and buds were harvested and analyzed for cannabinoid contents using HPLC. Several generations of each variety were successfully cloned. As many as 17 different cannabinoids were analyzed and the results of this study show that there is not a significant difference in cannabinoids over successive generations, showing no major trends.

关键词： Industrial Hemp Cannabis Cloning Cannabinoids

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众脑之力:通过开放神经影像数据和大规模协作催化神经精神疾病的创新发现

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Science Bulletin 2024年第10期69卷 1536-1555页

作者：鲁彬陈骁 Francisco Xavier Castellanos paul M.Thompson 左西年臧玉峰严超赣 CAS Key Laboratory of Behavioral Science Institute of PsychologyBeijing 100101China Department of Psychology University of Chinese Academy of SciencesBeijing 100101China Department of Child and Adolescent Psychiatry NYU Grossman School of MedicineNew York 10016USA d Nathan kline Institute for Psychiatric Research Orangeburg 10962USA Imaging Genetics Center Mark&Mary Stevens Institute for Neuroimaging&InformaticsKeck School of MedicineUniversity of Southern CaliforniaLos Angeles 90033USA Developmental Population Neuroscience Research Center IDG/McGovern Institute for Brain ResearchBeijing Normal UniversityBeijing 100875China National Basic Science Data Center Beijing 100190China Centre for Cognition and Brain Disorders The Affiliated Hospital of Hangzhou Normal UniversityHangzhou 310004China Institute of Psychological Science Hangzhou Normal UniversityHangzhou 310030China Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairment Hangzhou 311121China International Big-Data Center for Depression Research Institute of PsychologyChinese Academy of SciencesBeijing 100101China Magnetic Resonance Imaging Research Center Institute of PsychologyChinese Academy of SciencesBeijing 100101China

Recent advances in open neuroimaging data are enhancing our comprehension of neuropsychiatric *** pooling images from various cohorts,statistical power has increased,enabling the detection of subtle abnormalities and robust associations,and fostering new research *** collaborations in imaging have furthered our knowledge of the neurobiological foundations of brain disorders and aided in imaging-based prediction for more targeted ***-scale magnetic resonance imaging initiatives are driving innovation in analytics and supporting generalizable psychiatric *** also emphasize the significant role of big data in understanding neural mechanisms and in the early identification and precise treatment of neuropsychiatric ***,challenges such as data harmonization across different sites,privacy protection,and effective data sharing must be *** proper governance and open science practices,we conclude with a projection of how large-scale imaging resources and collaborations could revolutionize diagnosis,treatment selection,and outcome prediction,contributing to optimal brain health.

关键词： Magnetic resonance imaging Neuropsychiatric disorders Big data Open science Artificial intelligence

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心理学大曝光

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1992年

作者：保罗·凯林(paul kline)

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