The highly rearranged mitochondrial genomes of the crabs Maja crispata and Maja squinado (Majidae) and gene order evolution in Brachyura

Abstract We sequenced the mitochondrial genomes of the spider crabs Maja crispata and Maja squinado (Majidae, Brachyura). Both genomes contain the whole set of 37 genes characteristic of Bilaterian genomes, encoded on both \u3b1- and \u3b2-strands. Both species exhibit the same gene order, which is unique among known animal genomes. In particular, all the genes located on the \u3b2-strand form a single block. This gene order was analysed together with the other nine gene orders known for the Brachyura. Our study confirms that the most widespread gene order (BraGO) represents the plesiomorphic condition for Brachyura and was established at the onset of this clade. All other gene orders are the result of transformational pathways originating from BraGO. The different gene orders exhibit variable levels of genes rearrangements, which involve only tRNAs or all types of genes. Local homoplastic arrangements were identified, while complete gene orders remain unique and represent signatures that can have a diagnostic value. Brachyura appear to be a hot-spot of gene order diversity within the phylum Arthropoda. Our analysis, allowed to track, for the first time, the fully evolutionary pathways producing the Brachyuran gene orders. This goal was achieved by coupling sophisticated bioinformatic tools with phylogenetic analysis

Can preferential credit programs speed up the adoption of low-carbon agricultural systems in Mato Grosso, Brazil? Results from bioeconomic microsimulation

The need to balance agricultural production and environmental protection shifted the focus of Brazilian land-use policy toward sustainable agriculture. In 2010, Brazil established preferential credit lines to finance investments into low-carbon integrated agricultural systems of crop, livestock and forestry. This article presents a simulation-based empirical assessment of integrated system adoption in the state of Mato Grosso, where highly mechanized soybean–cotton and soybean–maize doublecrop systems currently prevail. We employ bioeconomic modeling to explicitly capture the heterogeneity of farm level costs and benefits of adoption. By parameterizing and validating our simulations with both empirical and experimental data, we evaluate the effectiveness of the ABC Integration credit through indicators such as land-use change, adoption rates and budgetary costs of credit provision. Alternative scenarios reveal that specific credit conditions might speed up the diffusion of low-carbon agricultural systems in Mato Grosso

Comparisons of regression and machine learning methods for estimating mangrove above-ground biomass using multiple remote sensing data in the red River Estuaries of Vietnam

Currently, remote sensing platforms provide state-of-the-art data for multiple purposes including applications related to coastal wetlands. Mangrove above-ground biomass (MAGB) together with its extent is considered well correlated with the habitats’ environmental and economic values. Above-ground biomass can be estimated by models that integrate remote sensing, field data and statistical information. However, it remains difficult to decide which model and which data offer the best performance for any one study location. Hence, this study aims to assess the spatial change in MAGB over a 45-year period and investigate different approaches to quantify this change through linear and multi linear regression models. Specifically, we test a non-linear model (Multivariate Adaptive Regression Splines; MARS), and non-parametric machine learning models, to predict MAGB using vegetation indices and biophysical variables derived from optical remote sensing data from Sentinel-2, Landsat-8, SPOT-7 and synthetic aperture radar remote sensing data from ALOS-2. The multi linear regression (MLR) and the MARS models were trained by field measured MAGB data to a good level of accuracy (R2 = 0.80 and RMSE = 5.56 Mg ha−1 for MLR and R2 = 0.89, RMSE = 5.42 Mg ha−1 for MARS). These models were subsequently applied to Landsat 2, 5 and 8 time-series images to assess changes in MAGB values and mangrove forest extent over the period 1975 to 2020. To ensure accurate training data for the models, we conducted field work to measure MAGB in 24 plots measured in May 2019. Findings showed that the MARS model generated MAGB values with higher accuracy than linear regression and multi linear regression models. Uses of vegetation indices (Normalized Differenced Vegetation Index, Soil-adjusted Vegetation Index, Green-Normalized Differenced Vegetation Index, Simple Ratio, and Red-edge Simple Ratio) generated MAGB values with accuracy slightly higher than using biophysical variables (Leaf area index, Fraction of Absorbed Radiation, Fractional vegetation cover, and Leaf chlorophyll content). Sentinel-2 and Landsat 8 were effective data sources for MAGB estimates, while SPOT-7 and ALOS-2 produced acceptable MAGB accuracy. Modelling the Landsat time series found an increase in both MAGB values and forest extent over the 1975–2020 period. The MARS model, Sentinel-2, Landsat 8 and vegetation indices are the recommended models and data to use to measure MAGB and could be used to understand changes in MAGB and forest extent at national and regional scales

Corn cob silica as an antibacterial support for silver nanoparticles: efficacy on Escherichia coli and Listeria monocytogenes

There is great potential to combine bioresource and recycled materials with nanotechnology for industrial and environmental applications. In a novel approach, silver (Ag) nanoparticles (Ag NPs) were imbedded on amine-functionalized silica obtained from corn cob (ACCS) to produce a composite material that can be used to inactivate bacteria. Transmission electron microscope (TEM) images show near-uniform ACCS particles (34.7 ± 8.6 nm diameter), with Ag NPs (5–10 nm diameter) homogenously dispersed on the surfaces. The potential of ACCS-Ag NPs to rapidly inactivate gram-negative Escherichia coli ATCC 8739 and gram-positive Listeria monocytogenes was investigated. A four-log (> 99.99%) inactivation of the E. coli was achieved within 30 min with 4 mg of ACCS-Ag NPs in a 40-mL PBS suspension (1 × 105 CFU/mL). Extended exposure of ACCS-Ag NP may be required to inactivate L. monocytogenes, suggesting the ACCS-Ag NP composite will be less practical for gram-positive bacteria due to thick cell wall and alternative formulations may need to be developed. Result shows that the potential of corn cob silica as an alternative, eco-friendly support matrix for applications such as bacterial inactivation. The Ag-imbedded, amine-functionalized corn cob silica demonstrates how bio-waste can be combined with nanotechnology to produce useful materials.by Jaehong Shim Payal Mazumder and Manish Kuma