Search

Coniferyl aldehyde dehydrogenase (CALDH) catalyzes the oxidation of coniferyl aldehyde to ferulic acid. Because ferulic acid has a relevant role in the structure and recalcitrance of the cell wall, inhibition of CALDH can reduce its levels and increase the digestibility of lignocellulosic biomass. We prospected in silico a selective inhibitor of CALDH of Zea mays. The ZmaysCALDH gene was identified by homology with the corresponding gene of Arabidopsis thaliana. The sequence was translated and analyzed, and the quaternary structure was modeled. A set of 20 putative inhibitors were screened from a virtual library and docked in the active site of ZmaysCALDH, and daidzin (DZN) was selected as an enzyme inhibitor. The stability of the ZmaysCALDH–DZN complex was evaluated by molecular dynamics simulations of the monomeric and tetrameric forms. For evaluation of kinetic analysis, ZmaysCALDH activity was determined in vitro by high-performance liquid chromatography. In comparison to the DZN-free control, the data obtained indicated constant Vmax and enhanced Km. Altogether, in silico and in vitro findings indicated that DZN inhibited ZmaysCALDH competitively. The DZN-induced inhibition of ZmaysCALDH could be a valuable and promising approach to studies on ferulic acid biosynthesis and saccharification of lignocellulosic biomass.

The diffuse attenuation coefficient of downwelling irradiance (Kd) is an essential parameter for inland waters research by remotely sensing the water transparency. Lately, Kd semi-analytical algorithms substituted the empirical algorithms widely employed. The purpose of this research was to reparametrize a semi-analytical algorithm to estimate Kd and then apply it to a Sentinel-2 MSI time-series (2017&ndash, 2019) for the Tr&ecirc, s Marias reservoir, Brazil. The results for the Kd semi-analytical reparametrization achieved good accuracies, reaching mean absolute percentage errors (MAPE) for bands B2, B3 and B4 (492, 560 and 665 nm), lower than 21% when derived from in-situ remote sensing reflectance (Rrs), while for MSI Data, a derived MAPE of 12% and 38% for B2 and B3, respectively. After the application of the algorithm to Sentinel-2 images time-series, seasonal patterns were observed in the results, showing high Kd values at 492 nm during the rainy periods, mainly in the tributary mouths, possibly due to an increase in the surface runoff and inflows and outflow rates in the reservoir watershed.

Made available in DSpace on 2020-12-10T20:04:51Z (GMT). No. of bitstreams: 0 Previous issue date: 2020-07-09 Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) Monitoramento Ambiental por Satelites no Bioma Amazonia-Banco Nacional de Desenvolvimento Economico e Social (MSA-BNDES) Optical water types (OWTs) can represent diverse ranges of Chlorophyll-a (Chl-a), total suspended matter (TSM), and colored dissolved organic matter (CDOM) concentrations, which make them extremely useful for monitoring water quality, for example, detecting eutrophic conditions or tuning remote sensing algorithms. In this study, the objective is to assess OWTs found in Brazilian waters using in situ remote sensing reflectance (R-rs), acquired for water bodies encompassing a wide range of optical characteristics. Eight OWTs are obtained based on R-rs spectral shape and magnitude, which represent different limnological characteristics of Brazilian waters. The OWT 1 is clear waters with low TSM, Chl-a, and CDOM (median ((x) over tilde): TSM of 2.64 g m(-3), Chl-a of 6.04 mg m(-3), and CDOM of 0.6 m(-1)); OWT 2 represents moderate turbid waters (TSM (x) over tilde: 5.14); OWTs 3, 4, and 5 are characterized by waters with high Chl-a concentration ((x) over tilde: 33.1, 39.6, and 180.4 mg m(-3), respectively); OWT 6 is characterized with the highest CDOM concentration ((x) over tilde: 4.07 m(-1)); OWTs 7 and 8 consist of waters with the highest TSM concentrations from terrestrial input ((x) over tilde: 19.55 and 93.25, respectively). Hence, those OWTs could support satellite monitoring by helping to tune algorithms and also providing wide spatial-temporal monitoring. Natl Inst Space Res, Remote Sensing Div, Sao Jose Dos Campos, Brazil Univ Fed Pelotas, CDTec, Pelotas, RS, Brazil Natl Inst Space Res, Image Proc Div, Sao Jose Dos Campos, Brazil Univ Fed Parana, Ctr Marine Studies, Pontal Do Parana, Brazil Sao Paulo State Univ, Dept Cartog, Presidente Prudente, Brazil Sao Paulo State Univ, Dept Cartog, Presidente Prudente, Brazil CAPES: 001 FAPESP: 2014/23903-9 FAPESP: 2013/09045-7 FAPESP: 2012/19821-1 FAPESP: 2008/56252-0 Monitoramento Ambiental por Satelites no Bioma Amazonia-Banco Nacional de Desenvolvimento Economico e Social (MSA-BNDES): 1022114003005

International audience; The modeling of underwater light field is essential for the understanding of biogeochemical processes, such as photosynthesis, carbon fluxes, and sediment transports in inland waters. Water-column light attenuation can be quantified by the diffuse attenuation coefficient of the downwelling irradiance (Kd) using semi-analytical algorithms (SAA). However, the accuracy of these algorithms is currently limited in highly turbid environments, such as Amazon Floodplains, due to the SAA parametrization steps. In this study, we assessed an SAA approach for Kd retrieval using a sizeable (n = 239) and diverse dataset (e.g., Kd (490) ranging from almost 0 to up to 30 m-1 with mean values of 5.75 ± 3.94 m-1) in Amazon freshwater ecosystem. The main framework of this study consists of i) re-parametrization of a quasi-analytical algorithm using regional in-situ inherent optical properties (IOPs) and ii) application and validation of SAA for Kd retrieval using in-situ and Sentinel-2/MSI (n = 49) derived from Remote Sensing Reflectance (Rrs). Overall, the performance of the calibrated SAA was satisfactory for both in-situ and satellite Rrs. The validation results with in-situ data achieved a Mean Absolute Percentage Error (MAPE) lower than 22%, Correlation Coefficient (R) > 0.80, Root Mean Square Error (RMSE) lower than 1.7 m-1, and bias between 0.73 and 1.34 for simulated visible bands of Sentinel-2/MSI (490, 560 and 660 nm) (VIS). The results using MSI imagery were similar to those of in-situ, with R > 0.9, MAPE < 20%, RMSE < 1.25 m-1, and bias between 0.98 and 1.10 for VIS bands, which illustrate the viability of this methodology for Kd mapping in Amazon Floodplain Lakes. Therefore, this study demonstrates a successful application of satellite remote sensing data for the spatialization of the Kd in the optically complex waters of Amazon Basin, which is essential for the ecological management of the Amazon Floodplain Lakes.

Remote sensing imagery are fundamental to increasing the knowledge about sediment dynamics in the middle-lower Amazon floodplains. Moreover, they can help to understand both how climate change and how land use and land cover changes impact the sediment exchange between the Amazon River and floodplain lakes in this important and complex ecosystem. This study investigates the suitability of Landsat-8 and Sentinel-2 spectral characteristics in retrieving total (TSS) and inorganic (TSI) suspended sediments on a set of Amazon floodplain lakes in the middle-lower Amazon basin using in situ Remote Sensing Reflectance (Rrs) measurements to simulate Landsat 8/OLI (Operational Land Imager) and Sentinel 2/MSI (Multispectral Instrument) bands and to calibrate/validate several TSS and TSI empirical algorithms. The calibration was based on the Monte Carlo Simulation carried out for the following datasets: (1) All-Dataset, consisting of all the data acquired during four field campaigns at five lakes spread over the lower Amazon floodplain (n = 94); (2) Campaign-Dataset including samples acquired in a specific hydrograph phase (season) in all lakes. As sample size varied from one season to the other, n varied from 18 to 31; (3) Lake-Dataset including samples acquired in all seasons at a given lake with n also varying from 17 to 67 for each lake. The calibrated models were, then, applied to OLI and MSI scenes acquired in August 2017. The performance of three atmospheric correction algorithms was also assessed for both OLI (6S, ACOLITE, and L8SR) and MSI (6S, ACOLITE, and Sen2Cor) images. The impact of glint correction on atmosphere-corrected image performance was assessed against in situ glint-corrected Rrs measurements. After glint correction, the L8SR and 6S atmospheric correction performed better with the OLI and MSI sensors, respectively (Mean Absolute Percentage Error (MAPE) = 16.68% and 14.38%) considering the entire set of bands. However, for a given single band, different methods have different performances. The validated TSI and TSS satellite estimates showed that both in situ TSI and TSS algorithms provided reliable estimates, having the best results for the green OLI band (561 nm) and MSI red-edge band (705 nm) (MAPE < 21%). Moreover, the findings indicate that the OLI and MSI models provided similar errors, which support the use of both sensors as a virtual constellation for the TSS and TSI estimate over an Amazon floodplain. These results demonstrate the applicability of the calibration/validation techniques developed for the empirical modeling of suspended sediments in lower Amazon floodplain lakes using medium-resolution sensors.

Apesar da extensa cobertura florestal nativa, usos como pastagem extensiva e cultivo florestal também estão presentes na Área de Proteção Ambiental da Serra da Mantiqueira (APA-SM). Além disto, há também ausência de um plano de manejo, com pouco estudo ambiental sobre sua dinâmica de uso e ocupação. Este trabalho buscou avaliar a variação temporal da cobertura florestal para a APA-SM entre os anos de 1999 e 2017 utilizando interpretação de imagens orbitais dos sensores Landsat-5/TM e Landsat-8/OLI. A imagem de 1999 foi classificada com posterior atualização manual até o ano de 2017. Em seguida, buscando avaliar o nível de fragmentação das florestas nativas, foram adotadas métricas de paisagem nas imagens classificadas. Como resultado, foi diagnosticado um aumento da cobertura florestal (floresta nativa com floresta plantada) pela expansão e surgimento de novos fragmentos, enquanto que a conversão de florestas para outros usos foi baixa. Apesar disso, as florestas nativas apresentaram uma redução de seu percentual de cobertura em relação à cobertura florestal total e as métricas de paisagem evidenciaram fragmentação da floresta nativa. O significativo número de novos fragmentos de pequena extensão pode indicar, em determinados casos, prognóstico positivo para a classe de cobertura florestal nativa em crescimento, apesar da fragmentação do habitat.

Using remote sensing for monitoring trophic states of inland waters relies on the calibration of chlorophyll-a (chl-a) bio-optical algorithms. One of the main limiting factors of calibrating those algorithms is that they cannot accurately cope with the wide chl-a concentration ranges in optically complex waters subject to different trophic states. Thus, this study proposes an optical hybrid chl-a algorithm (OHA), which is a combined framework of algorithms for specific chl-a concentration ranges. The study area is Ibitinga Reservoir characterized by high spatiotemporal variability of chl-a concentrations (3–1000 mg/m3). We took the following steps to address this issue: (1) we defined optical classes of specific chl-a concentration ranges using Spectral Angle Mapper (SAM); (2) we calibrated/validated chl-a bio-optical algorithms for each trophic class using simulated Sentinel-2 MSI (Multispectral Instrument) bands; (3) and we applied a decision tree classifier in MSI/Sentinel-2 image to detect the optical classes and to switch to the suitable algorithm for the given class. The results showed that three optical classes represent different ranges of chl-a concentration: class 1 varies 2.89–22.83 mg/m3, class 2 varies 19.51–87.63 mg/m3, and class 3 varies 75.89–938.97 mg/m3. The best algorithms for trophic classes 1, 2, and 3 are the 3-band (R2 = 0.78; MAPE - Mean Absolute Percentage Error = 34.36%), slope (R2 = 0.93; MAPE = 23.35%), and 2-band (R2 = 0.98; MAPE = 20.12%), respectively. The decision tree classifier showed an accuracy of 95% for detecting SAM’s optical trophic classes. The overall performance of OHA was satisfactory (R2 = 0.98; MAPE = 26.33%) using in situ data but reduced in the Sentinel-2 image (R2 = 0.42; MAPE = 28.32%) due to the temporal gap between matchups and the variability in reservoir hydrodynamics. In summary, OHA proved to be a viable method for estimating chl-a concentration in Ibitinga Reservoir and the extension of this framework allowed a more precise chl-a estimate in eutrophic inland waters.