Carneywaller8207

The restoration and reforestation of 12 million hectares of forests by 2030 are amongst the leading mitigation strategies for reducing carbon emissions within the Brazilian Nationally Determined Contribution targets assumed under the Paris Agreement. Understanding the dynamics of forest cover, which steeply decreased between 1985 and 2018 throughout Brazil, is essential for estimating the global carbon balance and quantifying the provision of ecosystem services. To know the long-term increment, extent, and age of secondary forests is crucial; however, these variables are yet poorly quantified. Here we developed a 30-m spatial resolution dataset of the annual increment, extent, and age of secondary forests for Brazil over the 1986-2018 period. Land-use and land-cover maps from MapBiomas Project (Collection 4.1) were used as input data for our algorithm, implemented in the Google Earth Engine platform. This dataset provides critical spatially explicit information for supporting carbon emissions reduction, biodiversity, and restoration policies, enabling environmental science applications, territorial planning, and subsidizing environmental law enforcement.The scientific application of 3D imaging has evolved significantly over recent years. These techniques make it possible to study internal features by non-destructive analysis. Despite its potential, the development of 3D imaging in the Geosciences is behind other fields due to the high cost of commercial software and the scarce free alternatives. Most free software was designed for the Health Sciences, and the pre-settled workflows are not suited to geoscientific materials. Thus, an outstanding challenge in the Geosciences is to define workflows using free alternatives for Computed Tomography (CT) data processing, promoting data sharing, reproducibility, and the development of specific extensions. We present CroSSED, a processing sequence for 3D reconstructions of CT data, using 3DSlicer, a popular application in medical imaging. Its usefulness is exemplified in the study of burrows that have low-density contrast with respect to the host sediment. For geoscientists who have access to CT data and wish to reconstruct 3D structures, this method offers a wide range of possibilities and contributes to open-science and applied CT studies.Biodegradable materials, including the widely used poly (lactic-co-glycolic acid) (PLGA) nanoparticles contained in slow-release drug formulations, scaffolds and implants, are ubiquitous in modern biomedicine and are considered inert or capable of being metabolized through intermediates such as lactate. selleck inhibitor However, in the presence of metabolic stress, such as in obesity, the resulting degradation products may play a detrimental role, which is still not well understood. We evaluated the effect of intravenously-administered PLGA nanoparticles on the gut-liver axis under conditions of caloric excess in C57BL/6 mice. Our results show that PLGA nanoparticles accumulate and cause gut acidification in the cecum, accompanied by significant changes in the microbiome, with a marked decrease of Firmicutes and Bacteroidetes. This was associated with transcriptomic reprogramming in the liver, with a downregulation of mitochondrial function, and an increase in key enzymatic, inflammation and cell activation pathways. No changes were observed in systemic inflammation. Metagenome analysis coupled with publicly available microarray data suggested a mechanism of impaired PLGA degradation and intestinal acidification confirming an important enterohepatic axis of metabolite-microbiome interaction resulting in maintenance of metabolic homeostasis. link2 Thus, our results have important implications for the investigation of PLGA use in metabolically-compromised clinical and experimental settings.Self-sorting double network hydrogels comprising orthogonal supramolecular nanofibers have attracted attention as artificially-regulated multi-component systems. Regulation of network patterns of self-sorted nanofibers is considered as a key for potential applications such as optoelectronics, but still challenging owing to a lack of useful methods to prepare and analyze the network patterns. Herein, we describe the selective construction of two distinct self-sorting network patterns, interpenetrated and parallel, by controlling the kinetics of seed formation with dynamic covalent oxime chemistry. Confocal imaging reveals the interpenetrated self-sorting network was formed upon addition of O-benzylhydroxylamine to a benzaldehyde-tethered peptide-type hydrogelator in the presence of lipid-type nanofibers. We also succeed in construction of a parallel self-sorting network through deceleration of seed formation using a slow oxime exchange reaction. Through careful observation, the formation of peptide-type seeds and nanofibers is shown to predominantly occur on the surface of the lipid-type nanofibers via highly dynamic and thermally-fluctuated processes.Most invertebrates in the ocean begin their lives with planktonic larval phases that are critical for dispersal and distribution of these species. Larvae are particularly vulnerable to environmental change, so understanding interactive effects of environmental stressors on larval life is essential in predicting population persistence and vulnerability of species. Here, we use a novel experimental approach to rear larvae under interacting gradients of temperature, salinity, and ocean acidification, then model growth rate and duration of Olympia oyster larvae and predict the suitability of habitats for larval survival. We find that temperature and salinity are closely linked to larval growth and larval habitat suitability, but larvae are tolerant to acidification at this scale. We discover that present conditions in the Salish Sea are actually suboptimal for Olympia oyster larvae from populations in the region, and that larvae from these populations might actually benefit from some degree of global ocean change. Our models predict a vast decrease in mean pelagic larval duration by the year 2095, which has the potential to alter population dynamics for this species in future oceans. Additionally, we find that larval tolerance can explain large-scale biogeographic patterns for this species across its range.Inspired by the structures of virus capsids, chemists have long pursued the synthesis of their artificial molecular counterparts through self-assembly. Building nanoscale hierarchical structures to simulate double-shell virus capsids is believed to be a daunting challenge in supramolecular chemistry. Here, we report a double-shell cage wherein two independent metal-organic polyhedra featuring Platonic and Archimedean solids are nested together. The inner (3.2 nm) and outer (3.3 nm) shells do not follow the traditional "small vs. large" pattern, but are basically of the same size. Furthermore, the assembly of the inner and outer shells is based on supramolecular recognition, a behavior analogous to the assembly principle found in double-shell viruses. These two unique nested characteristics provide a new model for Matryoshka-type assemblies. The inner cage can be isolated individually and proves to be a potential molecular receptor to selectively trap guest molecules.The transport properties of iron under Earth's inner core conditions are essential input for the geophysical modelling but are poorly constrained experimentally. Here we show that the thermal and electrical conductivity of iron at those conditions remains high even if the electron-electron-scattering (EES) is properly taken into account. This result is obtained by ab initio simulations taking into account consistently both thermal disorder and electronic correlations. Thermal disorder suppresses the non-Fermi-liquid behavior of the body-centered cubic iron phase, hence, reducing the EES; the total calculated thermal conductivity of this phase is 220 Wm-1 K-1 with the EES reduction not exceeding 20%. The EES and electron-lattice scattering are intertwined resulting in breaking of the Matthiessen's rule with increasing EES. In the hexagonal close-packed iron the EES is also not increased by thermal disorder and remains weak. Our main finding thus holds for the both likely iron phases in the inner core.Group 3 innate lymphoid cells (ILC3) are an important regulator for immunity, inflammation and tissue homeostasis in the intestine, but how ILC3 activation is regulated remains elusive. Here we identify a new circular RNA (circRNA) circKcnt2 that is induced in ILC3s during intestinal inflammation. Deletion of circKcnt2 causes gut ILC3 activation and severe colitis in mice. Mechanistically, circKcnt2, as a nuclear circRNA, recruits the nucleosome remodeling deacetylase (NuRD) complex onto Batf promoter to inhibit Batf expression; this in turn suppresses Il17 expression and thereby ILC3 inactivation to promote innate colitis resolution. Furthermore, Mbd3-/-Rag1-/- and circKcnt2-/-Rag1-/- mice develop severe innate colitis following dextran sodium sulfate (DSS) treatments, while simultaneous deletion of Batf promotes colitis resolution. In summary, our data support a function of the circRNA circKcnt2 in regulating ILC3 inactivation and resolution of innate colitis.Most marine fish species express life-history changes across temperature gradients, such as faster growth, earlier maturation, and higher mortality at higher temperature. However, such climate-driven effects on life histories and population dynamics remain unassessed for most fishes. For 332 Indo-Pacific fishes, we show positive effects of temperature on body growth (but with decreasing asymptotic length), reproductive rates (including earlier age-at-maturation), and natural mortality for all species, with the effect strength varying among habitat-related species groups. Reef and demersal fishes are more sensitive to temperature changes than pelagic and bathydemersal fishes. Using a life table, we show that the combined changes of life histories upon increasing temperature tend to facilitate population growth for slow life-history populations, but reduce it for fast life-history ones. Within our data, lower proportions (25-30%) of slow life-history fishes but greater proportions of fast life-history fishes (42-60%) show declined population growth rates under 1 °C warming. Together, these findings suggest prioritizing sustainable management for fast life-history species.Chest CT is emerging as a valuable diagnostic tool for clinical management of COVID-19 associated lung disease. Artificial intelligence (AI) has the potential to aid in rapid evaluation of CT scans for differentiation of COVID-19 findings from other clinical entities. Here we show that a series of deep learning algorithms, trained in a diverse multinational cohort of 1280 patients to localize parietal pleura/lung parenchyma followed by classification of COVID-19 pneumonia, can achieve up to 90.8% accuracy, with 84% sensitivity and 93% specificity, as evaluated in an independent test set (not included in training and validation) of 1337 patients. link3 Normal controls included chest CTs from oncology, emergency, and pneumonia-related indications. The false positive rate in 140 patients with laboratory confirmed other (non COVID-19) pneumonias was 10%. AI-based algorithms can readily identify CT scans with COVID-19 associated pneumonia, as well as distinguish non-COVID related pneumonias with high specificity in diverse patient populations.