Updated land use and land cover information improves biomass burning emission estimates

Biomass burning (BB) emissions negatively impact the biosphere and human lives. Orbital remote sensing and modelling are used to estimate BB emissions on regional to global scales, but these estimates are subject to errors related to the parameters, data, and methods available. For example, emission factors (mass emitted by species during BB per mass of dry matter burned) are based on land use and land cover (LULC) classifications that vary considerably across products. In this work, we evaluate how BB emissions vary in the PREP-CHEM-SRC emission estimator tool (version 1.8.3) when it is run with original LULC data from MDC12Q1 (collection 5.1) and newer LULC data from MapBiomas (collection 6.0). We compare the results using both datasets in the Brazilian Amazon and Cerrado biomes during the 2002–2020 time series. A major reallocation of emissions occurs within Brazil when using the MapBiomas product, with emissions decreasing by 788 Gg (−1.91% year−1) in the Amazon and emissions increasing by 371 Gg (2.44% year−1) in the Cerrado. The differences identified are mostly associated with the better capture of the deforestation process in the Amazon and forest formations in Northern Cerrado with the MapBiomas product, as emissions in forest-related LULCs decreased by 5260 Gg in the Amazon biome and increased by 1676 Gg in the Cerrado biome. This is an important improvement to PREP-CHEM-SRC, which could be considered the tool to build South America’s official BB emission inventory and to provide a basis for setting emission reduction targets and assessing the effectiveness of mitigation strategies

Structure of complement C3(H2O) revealed by quantitative cross-linking/mass spectrometry and modelling:QCLMS and modelling reveals structure of C3(H2O)

The slow but spontaneous and ubiquitous formation of C3(H(2)O), the hydrolytic and conformationally rearranged product of C3, initiates antibody-independent activation of the complement system that is a key first line of antimicrobial defense. The structure of C3(H(2)O) has not been determined. Here we subjected C3(H(2)O) to quantitative cross-linking/mass spectrometry (QCLMS). This revealed details of the structural differences and similarities between C3(H(2)O) and C3, as well as between C3(H(2)O) and its pivotal proteolytic cleavage product, C3b, which shares functionally similarity with C3(H(2)O). Considered in combination with the crystal structures of C3 and C3b, the QCMLS data suggest that C3(H(2)O) generation is accompanied by the migration of the thioester-containing domain of C3 from one end of the molecule to the other. This creates a stable C3b-like platform able to bind the zymogen, factor B, or the regulator, factor H. Integration of available crystallographic and QCLMS data allowed the determination of a 3D model of the C3(H(2)O) domain architecture. The unique arrangement of domains thus observed in C3(H(2)O), which retains the anaphylatoxin domain (that is excised when C3 is enzymatically activated to C3b), can be used to rationalize observed differences between C3(H(2)O) and C3b in terms of complement activation and regulation

Bacterial Toxins and the Nervous System: Neurotoxins and Multipotential Toxins Interacting with Neuronal Cells

Toxins are potent molecules used by various bacteria to interact with a host organism. Some of them specifically act on neuronal cells (clostridial neurotoxins) leading to characteristics neurological affections. But many other toxins are multifunctional and recognize a wider range of cell types including neuronal cells. Various enterotoxins interact with the enteric nervous system, for example by stimulating afferent neurons or inducing neurotransmitter release from enterochromaffin cells which result either in vomiting, in amplification of the diarrhea, or in intestinal inflammation process. Other toxins can pass the blood brain barrier and directly act on specific neurons

Reply to Comment on “Comparison of Cloud Cover Detection Algorithms on Sentinel–2 Images of the Amazon Tropical Forest”

In their comments about our paper, the authors remark on two issues regarding our results relating to the MACCS-ATCOR Joint Algorithm (MAJA). The first relates to the sub-optimal performance of this algorithm under the conditions of our tests, while the second corresponds to an error in our interpretation of MAJA’s bit mask. To answer the first issue, we acknowledge MAJA’s capacity to improve its performance as the number of images increases with time. However, in our paper, we used the images we had available at the time we wrote our paper. Regarding the second issue, we misread the MAJA’s bit mask and mistakenly labelled shadows as clouds. We regret our error and here we present the updated tables and images. We corrected our estimation and, consequently, there is an increment in MAJA’s accuracy in the detection of clouds and cloud shadows. However, these increments are not enough to change the conclusion of our original paper

Spatio-temporal change detection from multidimensional arrays : Detecting deforestation from MODIS time series

Growing availability of long-term satellite imagery enables change modeling with advanced spatio-temporal statistical methods. Multidimensional arrays naturally match the structure of spatio-temporal satellite data and can provide a clean modeling process for complex spatio-temporal analysis over large datasets. Our study case illustrates the detection of breakpoints in MODIS imagery time series for land cover change in the Brazilian Amazon using the BFAST (Breaks For Additive Season and Trend) change detection framework. BFAST includes an Empirical Fluctuation Process (EFP) to alarm the change and a change point time locating process. We extend the EFP to account for the spatial autocorrelation between spatial neighbors and assess the effects of spatial correlation when applying BFAST on satellite image time series. In addition, we evaluate how sensitive EFP is to the assumption that its time series residuals are temporally uncorrelated, by modeling it as an autoregressive process. We use arrays as a unified data structure for the modeling process, R to execute the analysis, and an array database management system to scale computation. Our results point to BFAST as a robust approach against mild temporal and spatial correlation, to the use of arrays to ease the modeling process of spatio-temporal change, and towards communicable and scalable analysis