Oleocanthal Exerts Antitumor Effects on Human Liver and Colon Cancer Cells Through ROS Generation

The beneficial health properties of the Mediterranean diet are well recognized. The principle source of fat in Mediterranean diet is extra-virgin olive oil (EVOO). Oleocanthal (OC) is a naturally occurring minor phenolic compound isolated from EVOO, which has shown a potent anti-inflammatory activity, by means of its ability to inhibit the cyclooxygenase (COX) enzymes COX-1 and COX-2. A large body of evidence indicates that phenols exhibit anticancer activities. The aim of the present study was to evaluate the potential anticancer effects of OC in hepatocellular carcinoma (HCC) and colorectal carcinoma (CRC) models. A panel of human HCC (HepG2, Huh7, Hep3B and PLC/PRF/5) and CRC (HT29, SW480) cell lines was used. Cells were treated with OC, and cell viability and apoptosis were evaluated. Compared with classical commercially available COX inhibitors (ibuprofen, indomethacin, nimesulide), OC was more effective in inducing cell growth inhibition in HCC and CRC cells. Moreover, OC inhibited colony for mation and i nduced ap optosis, as confirmed by PARP cleavage, activation of caspases 3/7 and chromatin condensation. OC treatment in a dose dependent-manner induced expression of \uce\ub3H2AX, a marker of DNA damage, increased intracellular ROS production and caused mitochondrial depolarization. Moreover, the effects of OC were suppressed by the ROS scavenger N-acetyl-L-cysteine. Finally, OC was not toxic in primary normal human hepatocytes. In conclusion, OC treatment was found to exert a potent anticancer activity against HCC and CRC cells. Taken together, our findings provide preclinical support of the chemotherapeutic potential of EVOO against cancer

A blended TROPOMI+GOSAT satellite data product for atmospheric methane using machine learning to correct retrieval biases

Satellite observations of dry-column methane mixing ratios (XCH4) from shortwave infrared (SWIR) solar backscatter radiation provide a powerful resource to quantify methane emissions in service of climate action. The TROPOspheric Monitoring Instrument (TROPOMI), launched in October 2017, provides global daily coverage at a 5.5 × 7 km2 (nadir) pixel resolution, but its methane retrievals can suffer from biases associated with SWIR surface albedo, scattering from aerosols and cirrus clouds, and across-track variability (striping). The Greenhouse gases Observing SATellite (GOSAT) instrument, launched in 2009, has better spectral characteristics and its methane retrieval is much less subject to biases, but its data density is 250 times sparser than TROPOMI. Here, we present a blended TROPOMI+GOSAT methane product obtained by training a machine learning (ML) model to predict the difference between TROPOMI and GOSAT co-located measurements, using only predictor variables included in the TROPOMI retrieval, and then applying the correction to the complete TROPOMI record from April 2018 to present. We find that the largest corrections are associated with coarse aerosol particles, high SWIR surface albedo, and across-track pixel index. Our blended product corrects a systematic difference between TROPOMI and GOSAT over water, and it features corrections exceeding 10 ppb over arid land, persistently cloudy regions, and high northern latitudes. It reduces the TROPOMI spatially variable bias over land (referenced to GOSAT data) from 14.3 to 10.4 ppb at a 0.25∘ × 0.3125∘ resolution. Validation with Total Carbon Column Observing Network (TCCON) ground-based column measurements shows reductions in variable bias compared with the original TROPOMI data from 4.7 to 4.4 ppb and in single-retrieval precision from 14.5 to 11.9 ppb. TCCON data are all in locations with a SWIR surface albedo below 0.4 (where TROPOMI biases tend to be relatively low), but they confirm the dependence of TROPOMI biases on SWIR surface albedo and coarse aerosol particles, as well as the reduction of these biases in the blended product. Fine-scale inspection of the Arabian Peninsula shows that a number of hotspots in the original TROPOMI data are removed as artifacts in the blended product. The blended product also corrects striping and aerosol/cloud biases in single-orbit TROPOMI data, enabling better detection and quantification of ultra-emitters. Residual coastal biases can be removed by applying additional filters. The ML method presented here can be applied more generally to validate and correct data from any new satellite instrument by reference to a more established instrument.</p

A bias-corrected GEMS geostationary satellite product for nitrogen dioxide using machine learning to enforce consistency with the TROPOMI satellite instrument

The Geostationary Environment Monitoring Spectrometer (GEMS) launched in February 2020 is now providing continuous daytime hourly observations of nitrogen dioxide (NO2) columns over eastern Asia (5° S–45° N, 75–145° E) with 3.5 × 7.7 km2 pixel resolution. These data provide unique information to improve understanding of the sources, chemistry, and transport of nitrogen oxides (NOx) with implications for atmospheric chemistry and air quality, but opportunities for direct validation are very limited. Here we correct the operational level-2 (L2) NO2 vertical column densities (VCDs) from GEMS with a machine learning (ML) model to match the much sparser but more mature observations from the low Earth orbit TROPOspheric Monitoring Instrument (TROPOMI), preserving the data density of GEMS but making them consistent with TROPOMI. We first reprocess the GEMS and TROPOMI operational L2 products to use common prior vertical NO2 profiles (shape factors) from the GEOS-Chem chemical transport model. This removes a major inconsistency between the two satellite products and greatly improves their agreement with ground-based Pandora NO2 VCD data in source regions. We then apply the ML model to correct the remaining differences, Δ(GEMS–TROPOMI), using the GEMS NO2 VCDs and retrieval parameters as predictor variables. We train the ML model with colocated GEMS and TROPOMI NO2 VCDs, taking advantage of TROPOMI off-track viewing to cover the wide range of effective zenith angles (EZAs) observed by GEMS. The two most important predictor variables for Δ(GEMS–TROPOMI) are GEMS NO2 VCD and EZA. The corrected GEMS product is unbiased relative to TROPOMI and shows a diurnal variation over source regions more consistent with Pandora than the operational product.</p

Satellite quantification of methane emissions from South American countries: a high-resolution inversion of TROPOMI and GOSAT observations

We use 2021 TROPOMI and GOSAT satellite observations of atmospheric methane in an analytical inversion to quantify national methane emissions from South America at up to 25 km × 25 km resolution. From the inversion, we derive optimal posterior estimates of methane emissions, adjusting a combination of national anthropogenic emission inventories reported by individual countries to the United Nations Framework Convention on Climate Change (UNFCCC), the UNFCCC-based Global Fuel Exploitation Inventory (GFEIv2), and the Emissions Database for Global Atmospheric Research (EDGARv7) as prior estimates. We also evaluate two alternative wetland emission inventories (WetCHARTs and LPJ-wsl) as prior estimates. Our best posterior estimates for wetland emissions are consistent with previous inventories for the Amazon but lower for the Pantanal and higher for the Paraná. Our best posterior estimate of South American anthropogenic emissions is 48 (41–56) Tg a−1, where numbers in parentheses are the range from our inversion ensemble. This is 55 % higher than our prior estimate and is dominated by livestock (65 % of anthropogenic total). We find that TROPOMI and GOSAT observations can effectively optimize and separate national emissions by sector for 10 of the 13 countries and territories in the region, 7 of which account for 93 % of continental anthropogenic emissions: Brazil (19 (16–23) Tg a−1), Argentina (9.2 (7.9–11) Tg a−1), Venezuela (7.0 (5.5–9.9) Tg a−1), Colombia (5.0 (4.4–6.7) Tg a−1), Peru (2.4 (1.6–3.9) Tg a−1), Bolivia (0.96 (0.66–1.2) Tg a−1), and Paraguay (0.93 (0.88–1.0) Tg a−1). Our estimates align with the prior estimates for Brazil, Bolivia, and Paraguay but are significantly higher for other countries. Emissions in all countries are dominated by livestock (mainly enteric fermentation) except for oil–gas in Venezuela and landfills in Peru. Methane intensities from the oil–gas industry are high in Venezuela (33 %), Colombia (6.5 %), and Argentina (5.9 %). The livestock sector shows the largest difference between our top-down estimate and the UNFCCC prior estimates, and even countries using complex bottom-up methods report UNFCCC emissions significantly lower than our posterior estimate. These discrepancies could stem from underestimations in IPCC-recommended bottom-up calculations or uncertainties in the inversion from aggregation error and the prior spatial distribution of emissions.</p

Using spectral sensors to determine photosynthetic photon flux density in daylight – A theoretical approach

Precise determination of the photosynthetic photon flux density (PPFD) is important for illumination in modern horticultural systems. This paper presents two methods to calculate the PPFD of daylight spectra using low cost optic sensors. The first method uses the spectral sensitivity functions of spectral sensors to recreate the quantum sensitivity curve of quantum sensors. Two sets of spectral sensitivity functions are compared. The second method calculates the PPFD based on the calculated correlated colour temperature and a spectral reconstruction using the CIE daylight model. It is demonstrated that all methods offer a useful estimation of the PPFD with correspondingly similar daylight spectra, but the supposedly simpler method, which is based on weighting of the individual channels, is more stable against deviations from the CIE daylight model. </jats:p

Genetic association of interleukin-6 polymorphism (-174 G/C) with chronic liver diseases and hepatocellular carcinoma.

Interleukin-6 (IL-6) is a pleiotropic cytokine which is expressed in many inflammatory cells in response to different types of stimuli, regulating a number of biological processes. The IL-6 gene is polymorphic in both the 5' and 3' flanking regions and more than 150 single nucleotide polymorphisms have been identified so far. Genetic polymorphisms of IL-6 may affect the outcomes of several diseases, where the presence of high levels of circulating IL-6 have been correlated to the stage and/or the progression of the disease itself. The -174 G/C polymorphism is a frequent polymorphism, that is located in the upstream regulatory region of the IL-6 gene and affects IL-6 production. However, the data in the literature on the genetic association between the -174 G/C polymorphism and some specific liver diseases characterized by different etiologies are still controversial. In particular, most of the studies are quite unanimous in describing a correlation between the presence of the high-producer genotype and a worse evolution of the chronic liver disease. This is valid for patients with hepatitis C virus (HCV)-related chronic hepatitis and liver cirrhosis and hepatocellular carcinoma (HCC) whatever the etiology. Studies in hepatitis B virus-related chronic liver diseases are not conclusive, while specific populations like non alcoholic fatty liver disease/non-alcoholic steatohepatitis, autoimmune and human immunodeficiency virus/HCV co-infected patients show a higher prevalence of the low-producer genotype, probably due to the complexity of these clinical pictures. In this direction, a systematic revision of these data should shed more light on the role of this polymorphism in chronic liver diseases and HCC

Using spectral sensors to determine photosynthetic photon flux density in daylight - A theoretical approach

Precise determination of the photosynthetic photon flux density (PPFD) is important for illumination in modern horticultural systems. This paper presents two methods to calculate the PPFD of daylight spectra using low cost optic sensors. The first method uses the spectral sensitivity functions of spectral sensors to recreate the quantum sensitivity curve of quantum sensors. Two sets of spectral sensitivity functions are compared. The second method calculates the PPFD based on the calculated correlated colour temperature and a spectral reconstruction using the CIE daylight model. It is demonstrated that all methods offer a useful estimation of the PPFD with correspondingly similar daylight spectra, but the supposedly simpler method, which is based on weighting of the individual channels, is more stable against deviations from the CIE daylight model

Rottebehaelter zur mechanischen und biologischen Vorreinigung von haeuslichem Abwasser - Optimierung eines Rottebehaelters Schlussbericht

SIGLEAvailable from TIB Hannover: F03B1239 / FIZ - Fachinformationszzentrum Karlsruhe / TIB - Technische InformationsbibliothekBundesministerium fuer Bildung und Forschung, Berlin (Germany); Arbeitsgemeinschaft Industrieller Forschungsvereinigungen e.V., Koeln (Germany)DEGerman

Environmental effects over the first 2½ rotation periods of a fertilised poplar short rotation coppice

A short rotation coppice (SRC) with poplar was established in a randomised fertilisation experiment on sandy loam soil in Potsdam (Northeast Germany). The main objective of this study was to assess if negative environmental effects as nitrogen leaching and greenhouse gas emissions are enhanced by mineral nitrogen (N) fertiliser applied to poplar at rates of 0, 50 and 75 kg N ha−1 year−1 and how these effects are influenced by tree age with increasing number of rotation periods and cycles of organic matter decomposition and tree growth after each harvesting event. Between 2008 and 2012, the leaching of nitrate (NO3 −) was monitored with self-integrating accumulators over 6-month periods and the emissions of the greenhouse gases (GHG) nitrous oxide (N2O) and carbon dioxide (CO2) were determined in closed gas chambers. During the first 4 years of the poplar SRC, most nitrogen was lost through NO3 − leaching from the main root zone; however, there was no significant relationship to the rate of N fertilisation. On average, 5.8 kg N ha−1 year−1 (13.0 kg CO2equ) was leached from the root zone. Nitrogen leaching rates decreased in the course of the 4-year study parallel to an increase of the fine root biomass and the degree of mycorrhization. In contrast to N leaching, the loss of nitrogen by N2O emissions from the soil was very low with an average of 0.61 kg N ha−1 year−1 (182 kg CO2equ) and were also not affected by N fertilisation over the whole study period. Real CO2 emissions from the poplar soil were two orders of magnitude higher ranging between 15,122 and 19,091 kg CO2 ha−1 year−1 and followed the rotation period with enhanced emission rates in the years of harvest. As key-factors for NO3 − leaching and N2O emissions, the time after planting and after harvest and the rotation period have been identified by a mixed effects mode