Static scheduling of multiple workflows with soft deadlines in non-dedicated heterogeneous environments

2016 ◽  
Vol 55 ◽  
pp. 51-61 ◽  
Author(s):  
Klavdiya Bochenina ◽  
Nikolay Butakov ◽  
Alexander Boukhanovsky
Keyword(s):  
Heterogeneous Environments ◽  
Static Scheduling

Development of Strategies for Glycopeptide Synthesis: An Overview on the Glycosidic Linkage

2020 ◽  
Vol 24 (21) ◽  
pp. 2475-2497
Author(s):  
Andrea Verónica Rodríguez-Mayor ◽  
German Jesid Peralta-Camacho ◽  
Karen Johanna Cárdenas-Martínez ◽  
Javier Eduardo García-Castañeda
Keyword(s):  
Chemical Synthesis ◽  
Solid Phase ◽  
Building Blocks ◽  
Heterogeneous Environments ◽  
Phase Synthesis ◽  
Low Concentrations ◽  
Biological Sources ◽  
Synthetic Routes ◽  
The Impact ◽  
Potential Use

Glycoproteins and glycopeptides are an interesting focus of research, because of their potential use as therapeutic agents, since they are related to carbohydrate-carbohydrate, carbohydrate-protein, and carbohydrate-lipid interactions, which are commonly involved in biological processes. It has been established that natural glycoconjugates could be an important source of templates for the design and development of molecules with therapeutic applications. However, isolating large quantities of glycoconjugates from biological sources with the required purity is extremely complex, because these molecules are found in heterogeneous environments and in very low concentrations. As an alternative to solving this problem, the chemical synthesis of glycoconjugates has been developed. In this context, several methods for the synthesis of glycopeptides in solution and/or solid-phase have been reported. In most of these methods, glycosylated amino acid derivatives are used as building blocks for both solution and solid-phase synthesis. The synthetic viability of glycoconjugates is a critical parameter for allowing their use as drugs to mitigate the impact of microbial resistance and/or cancer. However, the chemical synthesis of glycoconjugates is a challenge, because these molecules possess multiple reaction sites and have a very specific stereochemistry. Therefore, it is necessary to design and implement synthetic routes, which may involve various protection schemes but can be stereoselective, environmentally friendly, and high-yielding. This review focuses on glycopeptide synthesis by recapitulating the progress made over the last 15 years.

scholarly journals Evaluation of water loss in transit and surface runoff in a Brazilian semi-arid basin

2020 ◽  
Vol 15 (4) ◽  
pp. 1
Author(s):  
Cristian Epifanio Toledo ◽  
João Carlos Mohn Nogueira ◽  
Alexandre De Amorim Camargo
Keyword(s):  
Water Loss ◽  
Surface Runoff ◽  
High Efficiency ◽  
Heterogeneous Environments ◽  
Runoff Coefficient ◽  
Water Losses ◽  
Proposed Model ◽  
Reservoir Basin ◽  
In Transit ◽  
Semi Arid

The objective of this work was to propose and evaluate a model to estimate transit water losses and surface runoff in a Brazilian semi-arid basin, fundamental components in the hydrological studies of the region, such as in the verification of hydrological connectivity. The study area was the Orós Reservoir Basin, located in the state of Ceará. The modeling of transit water loss and surface runoff were developed based on the work of Araújo and Ribeiro (1996) and Peter et al. (2014). In the proposed model, the parameter of loss in transit (k) was estimated at 0.027 km-1 for a section of the river basin, and when simulated for other stretches it provided good flow results at the end of the stretch, obtaining an NSE of 82%. The value of the runoff coefficient was estimated at 3% and when evaluating a spatial variation of this coefficient in the basin, the values varied from 2% to 12%, and the use of specialized runoff coefficient (RC) values promoted a higher NSE in the discharge simulation in the basin. It is concluded that the proposed model to estimate transit water losses and surface runoff demonstrated a high efficiency in the simulation of hydrological processes. The basin of Orós reservoir presented a high variability of the coefficient of surface runoff, justifying the need for a greater spatiality of this coefficient in heterogeneous environments.

scholarly journals Mapping the adaptive landscape of a major agricultural pathogen reveals evolutionary constraints across heterogeneous environments

2021 ◽  
Author(s):  
Anik Dutta ◽  
Fanny E. Hartmann ◽  
Carolina Sardinha Francisco ◽  
Bruce A. McDonald ◽  
Daniel Croll
Keyword(s):  
Life History ◽  
Large Scale ◽  
Life History Traits ◽  
Genetic Correlations ◽  
Stress Factors ◽  
Life History Trait ◽  
Adaptive Landscape ◽  
Heterogeneous Environments ◽  
Growth Responses ◽  
Trade Offs

AbstractThe adaptive potential of pathogens in novel or heterogeneous environments underpins the risk of disease epidemics. Antagonistic pleiotropy or differential resource allocation among life-history traits can constrain pathogen adaptation. However, we lack understanding of how the genetic architecture of individual traits can generate trade-offs. Here, we report a large-scale study based on 145 global strains of the fungal wheat pathogen Zymoseptoria tritici from four continents. We measured 50 life-history traits, including virulence and reproduction on 12 different wheat hosts and growth responses to several abiotic stressors. To elucidate the genetic basis of adaptation, we used genome-wide association mapping coupled with genetic correlation analyses. We show that most traits are governed by polygenic architectures and are highly heritable suggesting that adaptation proceeds mainly through allele frequency shifts at many loci. We identified negative genetic correlations among traits related to host colonization and survival in stressful environments. Such genetic constraints indicate that pleiotropic effects could limit the pathogen’s ability to cause host damage. In contrast, adaptation to abiotic stress factors was likely facilitated by synergistic pleiotropy. Our study illustrates how comprehensive mapping of life-history trait architectures across diverse environments allows to predict evolutionary trajectories of pathogens confronted with environmental perturbations.

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