Simple, quick and green isolation of cannabinoids from complex natural product extracts using sustainable mesoporous materials (Starbon®)

The current process to purify CBD from C. sativa extract is long and intensive, requiring several steps such as winterification for 48 hours at -45 °C and high-temperature, high vacuum distillation. These processes are capital intensive and generate large amounts of toxic solvent waste. In contrast, the solid phase extraction (SPE) methodology proposed herein will change the way CBD is obtained, doing so in a single step that is fast and reusable. Furthermore, the new process is simple and easily implemented and does not require any intensive operator training. Starbon® A300 was successfully employed as the stationary phase in SPE taking Cannabis sativa extract in hexane to selectively physisorb the cannabinoids onto the surface, followed by ethanol to bring about desorption at up to 93% (by GC-FID). A similar one pot system was also proven, using Fedora hemp stem dust as feedstock, with extraction and adsorption in supercritical CO2 followed by desorption in ethanol

A shared role for RBF1 and dCAP-D3 in the regulation of transcription with consequences for innate immunity

Previously, we discovered a conserved interaction between RB proteins and the Condensin II protein CAP-D3 that is important for ensuring uniform chromatin condensation during mitotic prophase. The Drosophila melanogaster homologs RBF1 and dCAP-D3 co-localize on non-dividing polytene chromatin, suggesting the existence of a shared, non-mitotic role for these two proteins. Here, we show that the absence of RBF1 and dCAP-D3 alters the expression of many of the same genes in larvae and adult flies. Strikingly, most of the genes affected by the loss of RBF1 and dCAP-D3 are not classic cell cycle genes but are developmentally regulated genes with tissue-specific functions and these genes tend to be located in gene clusters. Our data reveal that RBF1 and dCAP-D3 are needed in fat body cells to activate transcription of clusters of antimicrobial peptide (AMP) genes. AMPs are important for innate immunity, and loss of either dCAP-D3 or RBF1 regulation results in a decrease in the ability to clear bacteria. Interestingly, in the adult fat body, RBF1 and dCAP-D3 bind to regions flanking an AMP gene cluster both prior to and following bacterial infection. These results describe a novel, non-mitotic role for the RBF1 and dCAP-D3 proteins in activation of the Drosophila immune system and suggest dCAP-D3 has an important role at specific subsets of RBF1-dependent genes

Innate Immune Responses of Drosophila melanogaster Are Altered by Spaceflight

Alterations and impairment of immune responses in humans present a health risk for space exploration missions. The molecular mechanisms underpinning innate immune defense can be confounded by the complexity of the acquired immune system of humans. Drosophila (fruit fly) innate immunity is simpler, and shares many similarities with human innate immunity at the level of molecular and genetic pathways. The goals of this study were to elucidate fundamental immune processes in Drosophila affected by spaceflight and to measure host-pathogen responses post-flight. Five containers, each containing ten female and five male fruit flies, were housed and bred on the space shuttle (average orbit altitude of 330.35 km) for 12 days and 18.5 hours. A new generation of flies was reared in microgravity. In larvae, the immune system was examined by analyzing plasmatocyte number and activity in culture. In adults, the induced immune responses were analyzed by bacterial clearance and quantitative real-time polymerase chain reaction (qPCR) of selected genes following infection with E. coli. The RNA levels of relevant immune pathway genes were determined in both larvae and adults by microarray analysis. The ability of larval plasmatocytes to phagocytose E. coli in culture was attenuated following spaceflight, and in parallel, the expression of genes involved in cell maturation was downregulated. In addition, the level of constitutive expression of pattern recognition receptors and opsonins that specifically recognize bacteria, and of lysozymes, antimicrobial peptide (AMP) pathway and immune stress genes, hallmarks of humoral immunity, were also reduced in larvae. In adults, the efficiency of bacterial clearance measured in vivo following a systemic infection with E. coli post-flight, remained robust. We show that spaceflight altered both cellular and humoral immune responses in Drosophila and that the disruption occurs at multiple interacting pathways

The Quality of the Estimators of the ETI

Measuring the elasticity of taxable income (ETI) is central for tax policy design. Yet, there are few arguments which support or infirm that current methods yield measurements of the ETI that can be trusted. Our first purpose is to use simulation methods to assess the bias and precision of the prevalent methods used in the literature (IV estimation and bunching methods). Thereby, we aim at (i) explaining the huge differences in empirical results, and (ii) providing arguments in favor of or against using these methods. Our second purpose is to suggest indirect inference estimation to improve the quality of the measurement. We find that the IV regression estimators may suffer from considerable bias and be quite imprecise, whereas the bunching estimators perform better in our controlled environment. We also show that using more of the information available in the data, estimators based on indirect inference principles produce more precise estimates of the ETI than any of the most commonly used methods

A maximum likelihood bunching estimator of the elasticity of taxable income

This paper develops a maximum likelihood (ML) bunching estimator of the elasticity of taxable income (ETI). Our structural approach provides a natural framework to simultaneously account for unobserved preference heterogeneity and optimization errors and for measuring their relative importance. We characterize the conditions under which the parameters of the model are identified and show that the ML estimator performs well in terms of bias and precision. The paper also contains an empirical application using Swedish data, showing that both the ETI and the standard deviation of the optimization friction are precisely estimated, albeit relatively small

Alternative parametric bunching estimators of the ETI

We propose a maximum likelihood (ML) based method to improve the bunching approach of measuring the elasticity of taxable income (ETI), and derive the estimator for several model settings that are prevalent in the literature, such as perfect bunching, bunching with optimization frictions, notches, and heterogeneity in the ETI. We show that the ML estimator is more precise and likely less biased than ad-hoc bunching estimators that are typically used in the literature. In the case of optimization frictions in the form of random shocks to earnings, the ML estimation requires a prior of the average size of such shocks. The results obtained in the presence of a notch can differ substantially from those obtained using ad-hoc approaches. If there is heterogeneity in the ETI, the elasticity of the individuals who bunch exceeds the average elasticity in the population

The quality of the estimators of the ETI

The elasticity of taxable income (ETI) is a central statistic for tax policy design. One purpose of the present paper is to use Monte Carlo simulation techniques to assess the bias and precision of the prevalent estimators in the literature, the IV-regression estimator and the bunching estimator. Thereby, we aim to provide arguments in favor of, or against, using these methods. Another is to suggest indirect inference estimation to improve the quality of the measurement of the ETI. While IV-regression estimators perform well in terms of bias under certain conditions, they are more variable than bunching estimators. We also find that bunching estimators can be biased downward. The estimators based on indirect inference principles are practically unbiased and more precise than the other estimators

Alternative parametric bunching estimators of the ETI

We propose a maximum likelihood (ML) based method to improve the bunching approach of measuring the elasticity of taxable income (ETI), and derive the estimator for several model settings that are prevalent in the literature, such as perfect bunching, bunching with optimization frictions, notches, and heterogeneity in the ETI. We show that the ML estimator is more precise and likely less biased than ad-hoc bunching estimators that are typically used in the literature. In the case of optimization frictions in the form of random shocks to earnings, the ML estimation requires a prior of the average size of such shocks. The results obtained in the presence of a notch can differ substantially from those obtained using ad-hoc approaches. If there is heterogeneity in the ETI, the elasticity of the individuals who bunch exceeds the average elasticity in the population

Maximum Likelihood Bunching Estimators of the ETI

We propose a maximum likelihood method to improve the bunching approach of estimating the elasticity of taxable income (ETI), and derive estimators for several model settings such as bunching with optimization frictions, notches, and heterogeneity in the ETI. Modelling optimization frictions explicitly, our estimators fit the data of several published studies very well. In the presence of a notch, the results can differ substantially from those obtained using the polynomial approach. If there is heterogeneity in the ETI, the elasticity among those who bunch exceeds the average elasticity in the population

Alternative parametric bunching estimators of the ETI

We propose a maximum likelihood (ML) based method to improve the bunching approach of measuring the elasticity of taxable income (ETI), and derive the estimator for several model settings that are prevalent in the literature, such as perfect bunching, bunching with optimization frictions, notches, and heterogeneity in the ETI. We show that the ML estimator is more precise and likely less biased than ad-hoc bunching estimators that are typically used in the literature. In the case of optimization frictions in the form of random shocks to earnings, the ML estimation requires a prior of the average size of such shocks. The results obtained in the presence of a notch can differ substantially from those obtained using ad-hoc approaches. If there is heterogeneity in the ETI, the elasticity of the individuals who bunch exceeds the average elasticity in the population