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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig2 Palmieri Molecules2019 24-19.png|240px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig2 Berezin PLoSCompBio23 19-12.png|240px]]</div>
'''"[[Journal:Identification of Cannabis sativa L. (hemp) retailers by means of multivariate analysis of cannabinoids|Identification of Cannabis sativa L. (hemp) retailers by means of multivariate analysis of cannabinoids]]"'''
'''"[[Journal:Ten simple rules for managing laboratory information|Ten simple rules for managing laboratory information]]"'''
 
[[Information]] is the cornerstone of [[research]], from experimental data/[[metadata]] and computational processes to complex inventories of reagents and equipment. These 10 simple rules discuss best practices for leveraging [[laboratory information management system]]s (LIMS) to transform this large information load into useful scientific findings. The development of [[mathematical model]]s that can predict the properties of biological systems is the holy grail of [[computational biology]]. Such models can be used to test biological hypotheses, guide the development of biomanufactured products, engineer new systems meeting user-defined specifications, and much more ... ('''[[Journal:Ten simple rules for managing laboratory information|Full article...]]''')<br />


In this work, the concentration of nine [[wikipedia:Cannabinoid|cannabinoid]]s—six neutral cannabinoids (THC, CBD, CBC, CBG, CBN, and CBDV) and three acidic cannabinoids (THCA, CBGA, and CBDA)—was used to identify the Italian retailers of ''[[wikipedia:Cannabis sativa|Cannabis sativa]]'' L. ([[wikipedia:Hemp|hemp]]), reinforcing the idea that the practice of categorizing hemp samples only using THC and CBD is inadequate. A [[high-performance liquid chromatography]]–[[tandem mass spectrometry]] (HPLC-MS/MS) method was developed for screening and simultaneously analyzing the nine cannabinoids in 161 hemp samples sold by four retailers located in different Italian cities. The hemp samples dataset was analyzed by [[wikipedia:Univariate analysis|univariate]] and [[wikipedia:Multivariate analysis|multivariate analysis]], with the aim to identify the associated hemp retailers without using any other [[information]] on the hemp samples such as [[wikipedia:Cannabis strains|''Cannabis'' strains]], seeds, soil and cultivation characteristics, geographical origin, product storage, etc. The univariate analysis highlighted that the hemp samples could not be differentiated by using any of the nine cannabinoids analyzed. ('''[[Journal:Identification of Cannabis sativa L. (hemp) retailers by means of multivariate analysis of cannabinoids|Full article...]]''')<br />
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Latest revision as of 18:03, 10 June 2024

Fig2 Berezin PLoSCompBio23 19-12.png

"Ten simple rules for managing laboratory information"

Information is the cornerstone of research, from experimental data/metadata and computational processes to complex inventories of reagents and equipment. These 10 simple rules discuss best practices for leveraging laboratory information management systems (LIMS) to transform this large information load into useful scientific findings. The development of mathematical models that can predict the properties of biological systems is the holy grail of computational biology. Such models can be used to test biological hypotheses, guide the development of biomanufactured products, engineer new systems meeting user-defined specifications, and much more ... (Full article...)

Recently featured: