Detection of oil pollution impacts on vegetation using multifrequency SAR, multispectral images with fuzzy forest and random forest methods.

Oil pollution harms terrestrial ecosystems. There is an urgent requirement to improve on existing methods for detecting, mapping and establishing the precise extent of oil-impacted and oil-free vegetation. This is needed to quantify existing spill extents, formulate effective remediation strategies and to enable effective pipeline monitoring strategies to identify leakages at an early stage. An effective oil spill detection algorithm based on optical image spectral responses can benefit immensely from the inclusion of multi-frequency Synthetic Aperture Radar (SAR) data, especially when the effect of multi-collinearity is sufficiently reduced. This study compared the Fuzzy Forest (FF) and Random Forest (RF) methods in detecting and mapping oil-impacted vegetation from a post spill multispectral optical sentinel 2 image and multifrequency C and X Band Sentinel - 1, COSMO Skymed and TanDEM-X SAR images. FF and RF classifiers were employed to discriminate oil-spill impacted and oil-free vegetation in a study area in Nigeria. Fuzzy Forest uses specific functions for the selection and use of uncorrelated variables in the classification process to yield an improved result. This method proved an efficient variable selection technique addressing the effects of high dimensionality and multi-collinearity, as the optimization and use of different SAR and optical image variables generated more accurate results than the RF algorithm in densely vegetated areas. An Overall Accuracy (OA) of 75% was obtained for the dense (Tree Cover Area) vegetation, while cropland and grassland areas had 59.4% and 65% OA respectively. However, RF performed better in Cropland areas with OA = 75% when SAR-optical image variables were used for classification, while both methods performed equally well in Grassland areas with OA = 65%. Similarly, significant backscatter differences (P < 0.005) were observed in the C-Band backscatter sample mean of polluted and oil-free TCA, while strong linear associations existed between LAI and backscatter in grassland and TCA. This study demonstrates that SAR based monitoring of petroleum hydrocarbon impacts on vegetation is feasible and has high potential for establishing oil-impacted areas and oil pipeline monitoring.

Mapping terrestrial oil spill impact using machine learning random forest and Landsat 8 OLI imagery: a case site within the Niger Delta region of Nigeria.

Terrestrial oil pollution is one of the major causes of ecological damage within the Niger Delta region of Nigeria and has caused a considerable loss of mangroves and arable croplands since the discovery of crude oil in 1956. The exact extent of landcover loss due to oil pollution remains uncertain due to the variability in factors such as volume and size of the oil spills, the age of oil, and its effects on the different vegetation types. Here, the feasibility of identifying oil-impacted land in the Niger Delta region of Nigeria with a machine learning random forest classifier using Landsat 8 (OLI spectral bands) and Vegetation Health Indices is explored. Oil spill incident data for the years 2015 and 2016 were obtained from published records of the National Oil Spill Detection and Response Agency and Shell Petroleum Development Corporation. Various health indices and spectral wavelengths from visible, near-infrared, and shortwave infrared bands were fused and classified using the machine learning random forest classifier to distinguish between oil-free and oil spill-impacted landcover. This provided the basis for the identification of the best variables for discriminating oil polluted from unpolluted land. Results showed that better results for discriminating oil-free and oil polluted landcovers were obtained when individual landcover types were classified separately as opposed to when the full study area image including all landcover types was classified at once. Similarly, the results also showed that biomass density plays a significant role in the characterization and classification of oil contaminated and oil-free pixels as tree cover areas showed higher classification accuracy compared to cropland and grassland.

Formal [4 + 2] cycloaddition of imines with alkoxyisocoumarins.

A new preparation of δ-lactams is reported. In the presence of a Lewis acid promoter, alkoxyisocoumarins engage a range of N-aryl and N-alkyl imines to form δ-lactams with a pendent carboalkoxy substituent. A sulfonamide-thiourea catalyst enables the synthesis of these products in moderate to good enantioselectivities.

Catalytic Enantioselective Synthesis of Lactams through Formal [4+2] Cycloaddition of Imines with Homophthalic Anhydride.

An amide-thiourea compound, operating through a novel ion pairing mechanism, is an efficient organocatalyst for the asymmetric reaction of homophthalic anhydride with imines. N-aryl and N-alkyl imines readily undergo formal [4+2] cycloaddition to provide lactams with high levels of enantio- and diastereoselectivity. The nature of the key chiral ion pair intermediate was elucidated by DFT calculations.

The Effect of Remote Ischemic Conditioning and Glyceryl Trinitrate on Perioperative Myocardial Injury in Cardiac Bypass Surgery Patients: Rationale and Design of the ERIC-GTN Study.

Remote ischemic conditioning (RIC) using transient limb ischemia/reperfusion has been reported to reduce perioperative myocardial injury in patients undergoing coronary artery bypass grafting and/or valve surgery. The role of intravenous glyceryl trinitrate (GTN) therapy administered during cardiac surgery as a cardioprotective agent and whether it interferes with RIC cardioprotection is not clear and is investigated in the ERIC-GTN trial ( http://www.clinicaltrials.gov: NCT01864252). The ERIC-GTN trial is a single-site, double-blind, randomized, placebo-controlled study. Consenting adult patients (age > 18 years) undergoing elective coronary artery bypass grafting ± valve surgery with blood cardioplegia will be eligible for inclusion. Two hundred sixty patients will be randomized to 1 of 4 treatment groups following anesthetic induction: (1) RIC alone, a RIC protocol comprising three 5-minute cycles of simultaneous upper-arm and thigh cuff inflation/deflation followed by an intravenous (IV) placebo infusion; (2) GTN alone, a simulated sham RIC protocol followed by an IV GTN infusion; (3) RIC + GTN, a RIC protocol followed by an IV GTN infusion; and (4) neither RIC nor GTN, a sham RIC protocol followed by IV placebo infusion. The primary endpoint will be perioperative myocardial injury as quantified by the 72-hour area-under-the-curve serum high-sensitivity troponin T. The ERIC-GTN trial will determine whether intraoperative GTN therapy is cardioprotective during cardiac surgery and whether it affects RIC cardioprotection.

Redox-neutral α-sulfenylation of secondary amines: ring-fused N,S-acetals.

Secondary amines react with thiosalicylaldehydes in the presence of catalytic amounts of acetic acid to generate ring-fused N,S-acetals in redox-neutral fashion. A broad range of amines undergo α-sulfenylation, including challenging substrates such morpholine, thiomorpholine, and piperazines. Computational studies employing density functional theory indicate that acetic acid reduces the energy barriers of two separate steps, both of which involve proton transfer.