Whole-genome sequence of Corynebacterium pseudotuberculosis 262 biovar equi isolated from cow milk

We report the complete genome sequence of Corynebacterium pseudotuberculosis 262, isolated from a bovine host. C. pseudotuberculosis is an etiological agent of diseases with medical and veterinary relevance. The genome contains 2,325,749 bp, 52.8% G C content, 2,022 coding sequences (CDS), 50 pseudogenes, 48 tRNAs, and 12 rRNAs

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

The Chemical Percolation Devolatilization Model Applied to the Devolatilization of Coal in High Intensity Acoustic Fields

The chemical percolation devolatilization model (CPD) was extended for the prediction of drying and devolatilization of coal particles in high intensity acoustic fields found in Rijke tube reactors. The acoustic oscillations enhance the heat and mass transfer processes in the fuel bed as well as in the freeboard, above the grate. The results from simulations in a Rijke tube combustor have shown an increase in the rate of water evaporation and thermal degradation of the particles. The devolatilization model, based on chemical percolation, applied in pulsating regime allowed the dynamic prediction on the yields of CO, CO2, CH4, H2O, other light gases as well as tar which are important on ignition and stabilization of flames. The model predicted the quantity and form of nitrogen containing species generated during devolatilization, for which knowledge is strategically indispensable for reducing pollutant emissions (NOx) in flames under acoustic excitation

A Brazilian Space Launch System for the Small Satellite Market

At present, most small satellites are delivered as hosted payloads on large launch vehicles. Considering the current technological development, constellations of small satellites can provide a broad range of services operating at designated orbits. To achieve that, small satellite customers are seeking cost-effective launch services for dedicated missions. This paper deals with performance and cost assessments of a set of launch vehicle concepts based on a solid propellant rocket engine (S-50) under development by the Institute of Aeronautics and Space (Brazil) with support from the Brazilian Space Agency. Cost estimation analysis, based on the TRANSCOST model, was carried out taking into account the costs of launch system development, vehicle fabrication, direct and indirect operation cost. A cost-competitive expendable launch system was identified by using three S-50 solid rocket motors for the first stage, one S-50 engine for the second stage and a flight-proven cluster of pressure-fed liquid engines for the third stage. This launch system, operating from the Alcantara Launch Center, located at 2 ∘ 20’ S, would deliver satellites from the 500 kg class in typical polar missions with a specific transportation cost of about US$39,000 per kilogram of payload at a rate of 12 launches per year, in dedicated missions. At a low inclined orbit, vehicle payload capacity increased, decreasing the specific transportation cost to about 32,000 US$/kg. Cost analysis also showed that vehicle development effort would claim 781 work year, or less than 80 million dollars. Vehicle fabrication accounted for 174 work year representing less than 23 million dollars per unit. The launch system based on the best concept would, therefore, deploy small satellite constellations in cost-effective dedicated launches, 224 work year per flight, from the Alcantara Launch Center in Brazil