PHYSICAL QUALITY OF A YELLOW LATOSSOL UNDER INTEGRATED CROP-LIVESTOCK SYSTEM

Soil physical quality is essential to global sustainability of agroecosystems, once it is related to processes that are essential to agricultural crop development. This study aimed to evaluate physical attributes of a Yellow Latossol under different management systems in the savanna area in the state of Piaui. This study was developed in Urucui southwest of the state of Piaui. Three systems of soil management were studied: an area under conventional tillage (CT) with disk plowi and heavy harrow and soybean crop; an area under no-tillage with soybean-maize rotation and millet as cover crop (NT + M); two areas under Integrated Crop. Livestock System, with five-month pasture grazing and soybean cultivation and then continuous pasture grazing (ICL + S and ICL + P, respectively). Also, an area under Native Forest (NF) was studied. The soil depths studied were 0.00-0.05, 0.05-0.10 and 0.10-0.20 m. Soil bulk density, as well as porosity and stability of soil aggregates were analyzed as physical attributes. Anthropic action has changed the soil physical attributes, in depth, in most systems studied, in comparison to NF. In the 0.00 to 0.05 m depth, ICL + P showed higher soil bulk density value. As to macroporosity, there was no difference between the management systems studied and NF. The management systems studied changed the soil structure, having, as a result, a small proportion of soil in great aggregate classes (MWD). Converting native forest into agricultural production systems changes the soil physical quality. The Integrated Crop-Livestock System did not promote the improvement in soil physical quality.34371772

Toward High Performance Computing Education

High Performance Computing (HPC) is the ability to process data and perform complex calculations at extremely high speeds. Current HPC platforms can achieve calculations on the order of quadrillions of calculations per second with quintillions on the horizon. The past three decades witnessed a vast increase in the use of HPC across different scientific, engineering and business communities, for example, sequencing the genome, predicting climate changes, designing modern aerodynamics, or establishing customer preferences. Although HPC has been well incorporated into science curricula such as bioinformatics, the same cannot be said for most computing programs. This working group will explore how HPC can make inroads into computer science education, from the undergraduate to postgraduate levels. The group will address research questions designed to investigate topics such as identifying and handling barriers that inhibit the adoption of HPC in educational environments, how to incorporate HPC into various curricula, and how HPC can be leveraged to enhance applied critical thinking and problem solving skills. Four deliverables include: (1) a catalog of core HPC educational concepts, (2) HPC curricula for contemporary computing needs, such as in artificial intelligence, cyberanalytics, data science and engineering, or internet of things, (3) possible infrastructures for implementing HPC coursework, and (4) HPC-related feedback to the CC2020 project

Diversity of lactic acid bacteria of the bioethanol process

<p>Abstract</p> <p>Background</p> <p>Bacteria may compete with yeast for nutrients during bioethanol production process, potentially causing economic losses. This is the first study aiming at the quantification and identification of Lactic Acid Bacteria (LAB) present in the bioethanol industrial processes in different distilleries of Brazil.</p> <p>Results</p> <p>A total of 489 LAB isolates were obtained from four distilleries in 2007 and 2008. The abundance of LAB in the fermentation tanks varied between 6.0 × 10⁵and 8.9 × 10⁸CFUs/mL. Crude sugar cane juice contained 7.4 × 10⁷to 6.0 × 10⁸LAB CFUs. Most of the LAB isolates belonged to the genus <it>Lactobacillus </it>according to rRNA operon enzyme restriction profiles. A variety of <it>Lactobacillus </it>species occurred throughout the bioethanol process, but the most frequently found species towards the end of the harvest season were <it>L. fermentum </it>and <it>L. vini</it>. The different rep-PCR patterns indicate the co-occurrence of distinct populations of the species <it>L. fermentum </it>and <it>L. vini</it>, suggesting a great intraspecific diversity. Representative isolates of both species had the ability to grow in medium containing up to 10% ethanol, suggesting selection of ethanol tolerant bacteria throughout the process.</p> <p>Conclusions</p> <p>This study served as a first survey of the LAB diversity in the bioethanol process in Brazil. The abundance and diversity of LAB suggest that they have a significant impact in the bioethanol process.</p

TRY plant trait database - enhanced coverage and open access

Plant traits-the morphological, anatomical, physiological, biochemical and phenological characteristics of plants-determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait-based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits-almost complete coverage for 'plant growth form'. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait-environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives