Subtle changes in the dynamics between NO and superoxide can modulate the character of oxidative and nitrosative reaction pathways (32). Control experiments were performed to test whether MβCD-cholesterol treatment was influencing DAF-triazole yield by altering cellular superoxide formation. Inclusion of either the enzyme superoxide dismutase (1 milliunit/ml) or porphyrin mimetic Mn(III)TMPyP (5 μm) did not significantly alter the enhanced nitrosation profiles for DAF in the buffer solutions in the presence of MβCD-cholesterol-treated cells relative to their untreated counterparts. Collectively, these data support the hypothesis that increased plasma membrane cholesterol levels correspondingly decrease NO entry into the cell, favoring a greater level of NO autooxidation and nitrosation of DAF present in the extracellular medium.
Gas Dynamics By Radhakrishnan Pdf Free 37
Abstract:The physiological properties of biological soft matter are the product of collective interactions, which span many time and length scales. Recent computational modeling efforts have helped illuminate experiments that characterize the ways in which proteins modulate membrane physics. Linking these models across time and length scales in a multiscale model explains how atomistic information propagates to larger scales. This paper reviews continuum modeling and coarse-grained molecular dynamics methods, which connect atomistic simulations and single-molecule experiments with the observed microscopic or mesoscale properties of soft-matter systems essential to our understanding of cells, particularly those involved in sculpting and remodeling cell membranes.Keywords: molecular dynamics; coarse-grained model; membrane proteins
In fluid dynamics, stagnation pressure is the static pressure at a stagnation point in a fluid flow.[1] At a stagnation point the fluid velocity is zero. In an incompressible flow, stagnation pressure is equal to the sum of the free-stream static pressure and the free-stream dynamic pressure.[2]
The two points of interest are 1) in the freestream flow at relative speed v \displaystyle v where the pressure is called the "static" pressure, (for example well away from an airplane moving at speed v \displaystyle v ); and 2) at a "stagnation" point where the fluid is at rest with respect to the measuring apparatus (for example at the end of a pitot tube in an airplane).
Farming systems of industrial agriculture are based on strongly simplified crop sequences, standardised crop management and systematic use of chemical inputs: Haber-Bosch-based nitrogen and pesticides. They also rely on potassium and phosphorus fertilisers and, in irrigated areas, on water withdrawals (Box 1). To avoid the risk of limited or reduced yields, farmers often apply more fertilisers and pesticides than needed due to their relatively low prices (Caron et al. 2014; Cordell et al. 2011; Struik et al. 2014). To address current economic constraints and environmental regulations, these chemical input-based farming systems currently seek to optimise inputs according to spatiotemporal plant/animal requirements and to limit pollution (Fig. 2). In other words, to deal with sustainability issues and regulations, farmers managing chemical input-based farming systems follow an efficiency-based modernisation pathway (Hill 1998; Introduction). Most often, it corresponds to incremental adaptations of farming systems (Park et al. 2012). One challenge is to accurately assess the levels of input ecosystem services in time and space to optimise the amounts of additional external inputs required to reach desired production levels. Precision-agriculture technologies based on sensors in the soil or on the crop, machinery, drones, planes and satellites allow monitoring of the dynamics of multiple variables and optimisation of required inputs. They are well developed to deal with nutrient cycling (especially nitrogen) and weeds (e.g. weeding robots, targeted pesticide applications). In addition, farmers use cultivars and animal breeds which are less sensitive to limiting or reducing factors while exhibiting yields which are as high or higher (defining factors). These technologies may allow farming systems to increase input-use efficiency, reduce environmental impacts and, depending on the technology costs, economic performance. Amortising these technologies may lead farmers to continue to increase the size of their farm to reach suitable economies of scale. Environmental regulations can lead farmers to introduce more substantial changes, such as cover crops in nitrogen-sensitive areas or landscape features that minimise diffusion of pollutants in aquatic ecosystems. In this case, sowing and destruction dates of cover crops are determined to comply with environmental regulations.
The first agriculture model identified corresponds to chemical input-based farming systems (specialised cash-crop and livestock farms) embedded in globalised commodity-based food systems (lower left quadrant of Fig. 4). This is the dominant agriculture model in Western Europe and USA (Levidow et al. 2014; Lyson and Guptill 2004). Economic resilience of these farming systems to price variability and biophysical hazards can be supported respectively by contracts and insurance schemes, both provided by globalised commodity-based food supply chains. These insurance instruments may lead farmers to plant riskier cash crops more often, resulting in relatively more monocultures (Müller and Kreuer 2016). In this agriculture model, large companies and retailers often retain most of the added value (Sect. 3.1). Because they are integrated in dynamics of large-scale commodity-based food systems, these farming systems are often poorly connected to local natural resource management issues and strategies, leading to conflicts over issues such as water shortages due to irrigation, water quality due to pollution and erosion due to bare soils. A typical example of this decoupling is the world soya bean market, which grew strongly during the 1990s and led to high environmental impacts in regions where soya bean is grown (e.g. pesticide pollution, deforestation) as well in those where it is used as feed for specialised and concentrated livestock enterprises (i.e. nitrogen emissions) (Billen et al. 2014a, b, Sect. 3.1).
Biological input-based farming systems usually are also embedded in and mainly interact with globalised commodity-based food supply chains for the supply of biological inputs (e.g. biostimulants, biopesticides, external organisms) and trading of their products. However, they may evolve due to additional opportunities provided by circular economies to replace chemical inputs with biological inputs and for diversification (e.g. biomass production for bioenergy). These two degrees of integration in globalised commodity-based food systems helped us to distinguish two agriculture models (lower left and right quadrants in Fig. 4). When biological input-based farming systems are involved in circular economies, they are more embedded in territorial socio-economic dynamics (Sect. 3.2).
Research on dynamics of nutrient availability is also needed to manage organic fertilisation effectively, especially in the context of an increasing diversity of organic resources and development of minimum tillage. Furthermore, management of soil organic matter must be designed not only to consider organic matter as a source of plant nutrients but also to foster carbon sequestration or prevent soil erosion. Consequently, interactions between several ecosystem services have to be analysed (Kirkby et al. 2014; Noellemeyer and Six 2015). Here also, user-friendly decision-support systems have to be developed.
In addition to generic knowledge about the functioning of agricultural ecosystems and landscapes, research should also develop participatory procedures and operational tools to design and assess diversified farming systems and landscapes, possibly based on modelling, and operational methods to monitor their dynamics (Duru et al. 2015a, b; Freeman et al. 2015; Mastrangelo et al. 2014; Reed et al. 2016; Voinov et al. 2016). For example, research could develop tools to design the spatial distribution and management of non-crop habitats at farm and landscape levels (e.g. Tzilivakis et al. 2016). More generally, how new information and communication technologies (ICT) can be used to render scientific knowledge accessible and operational or to collect experience feedback from farmers have to be explored (Dowd et al. 2014; Duru et al. 2015a).
Our analytical framework led us to identify agronomic knowledge gaps of each agriculture model and transversal ones, such as conditions for their co-existence from farm, local, regional and global levels. For agriculture models involving chemical input- and biological input-based farming systems, development of operational knowledge about the best management practices to follow to apply the Right Product, Right Rate, Right Time, and Right Place remains a scientific challenge. When these farming systems are involved in circular economies, agro-industrial ecology is required to provide decision-support systems to analyse, design and assess circular economy options. For the three agriculture models involving biodiversity-based farming systems, the main agronomic research issues involve development of (i) breeding procedures for cultivars adapted to provide targeted ecosystem services in different production situations; (ii) actionable agro-ecological knowledge in specific production situations about relationships between management practices, associated biodiversity and ecosystem services; and (iii) participatory procedures and operational tools to design and assess diversified farming systems and landscapes and to monitor their dynamics. Biodiversity-based farming systems involved in circular economies and alternative food systems require operational knowledge to develop integrated food-energy systems. When involved in integrated landscape approaches, knowledge is required to develop integrated management of the Food/Non-food/Natural resources nexus. 2ff7e9595c
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