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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Talia JOfCloudComp2019 8.png|240px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Mandrioli Molecules2019 24-11.png|240px]]</div>
'''"[[Journal:A view of programming scalable data analysis: From clouds to exascale|A view of programming scalable data analysis: From clouds to exascale]]"'''
'''"[[Journal:Fast detection of 10 cannabinoids by RP-HPLC-UV method in Cannabis sativa L.|Fast detection of 10 cannabinoids by RP-HPLC-UV method in Cannabis sativa L.]]"'''


Scalability is a key feature for big data analysis and machine learning frameworks and for applications that need to analyze very large and real-time data available from data repositories, social media, sensor networks, smartphones, and the internet. Scalable big data analysis today can be achieved by parallel implementations that are able to exploit the computing and storage facilities of high-performance computing (HPC) systems and [[cloud computing]] systems, whereas in the near future exascale systems will be used to implement extreme-scale [[data analysis]]. Here is discussed how cloud computing currently supports the development of scalable data mining solutions and what the main challenges to be addressed and solved for implementing innovative data analysis applications on exascale systems currently are. ('''[[Journal:A view of programming scalable data analysis: From clouds to exascale|Full article...]]''')<br />
[[wikipedia:Cannabis|Cannabis]] has regained much attention as a result of updated legislation authorizing many different uses, and it can be classified on the basis of the content of [[wikipedia:Tetrahydrocannabinol|Δ9-tetrahydrocannabinol]] (Δ9-THC), a psychotropic substance for which there are legal limitations in many countries. For this purpose, accurate qualitative and quantitative determination is essential. The relationship between THC and [[wikipedia:Cannabidiol|cannabidiol]] (CBD) is also significant, as the latter substance is endowed with many specific and non-psychoactive proprieties. For these reasons, it becomes increasingly important and urgent to utilize fast, easy, validated, and harmonized procedures for determination of [[wikipedia:Cannabinoid|cannabinoids]]. The procedure described herein allows rapid determination of 10 cannabinoids from the [[wikipedia:Inflorescence|inflorescences]] of ''Cannabis sativa'' L. by extraction with organic solvents. Separation and subsequent detection are by [[wikipedia:Reversed-phase chromatography|reversed-phase]] [[high-performance liquid chromatography]] with ultraviolet detector (RP-HPLC-UV). ('''[[Journal:Fast detection of 10 cannabinoids by RP-HPLC-UV method in Cannabis sativa L.|Full article...]]''')<br />
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Revision as of 16:43, 20 January 2020

Fig1 Mandrioli Molecules2019 24-11.png

"Fast detection of 10 cannabinoids by RP-HPLC-UV method in Cannabis sativa L."

Cannabis has regained much attention as a result of updated legislation authorizing many different uses, and it can be classified on the basis of the content of Δ9-tetrahydrocannabinol (Δ9-THC), a psychotropic substance for which there are legal limitations in many countries. For this purpose, accurate qualitative and quantitative determination is essential. The relationship between THC and cannabidiol (CBD) is also significant, as the latter substance is endowed with many specific and non-psychoactive proprieties. For these reasons, it becomes increasingly important and urgent to utilize fast, easy, validated, and harmonized procedures for determination of cannabinoids. The procedure described herein allows rapid determination of 10 cannabinoids from the inflorescences of Cannabis sativa L. by extraction with organic solvents. Separation and subsequent detection are by reversed-phase high-performance liquid chromatography with ultraviolet detector (RP-HPLC-UV). (Full article...)

Recently featured:

What is this sensor and does this app need access to it?
AI meets exascale computing: Advancing cancer research with large-scale high-performance computing
Building infrastructure for African human genomic data management