Developing adaptive capacity in times of climate change in central rural Vietnam: exploring smallholders’ learning and governance

Climate change already affects Vietnam in virtually all sectors. Agriculture in small communities is particularly vulnerable to current and projected climate change impacts. Many of the smallholder farmers in Vietnam have limited adaptive capacity to deal with these impacts. Increasingly social learning is proposed as an important mechanism to build the adaptive capacity of local farming communities. However, little is known about the interplay between social learning and adaptive capacity and how adaptive capacity could be increased in a complex hierarchical governance setting that is typical in a country like Vietnam. The dissertation therefore aims to elicit and explore the ways through which social learning can increase the adaptive capacity of smallholder farmers in central Vietnam to respond to climate change impacts. Four research questions are addressed: (i) what insights does the existing body of climate change adaptation literature provide into the interplay between social learning and adaptive capacity?; (ii) what do smallholder farmers in Vietnam perceive as their current adaptive capacity and what enables or constrains them in increasing it?; (iii) how can social learning configurations strengthen the adaptive capacity of farming communities?; and (iv) how do different levels of government enable and constrain the process of building adaptive capacity and social learning of smallholder farmers to respond to impacts of climate change in Vietnam? Overall, the dissertation shows that social learning offers many possibilities to help farmers adapt to climate change, but that climate change adaptation in developing countries creates specific contextual conditions that require an adaptive capacity-focused perspective. An adequate learning configuration that can successfully help farmers build their adaptive capacity, considers responsive design, facilitation, monitoring, and evaluation steps. Furthermore, efforts of increasing adaptive capacity should not only focus on technical, social and human dimensions, but also on market conditions. The critical importance in creating an environment that enables social learning is the role of government across different levels. In order for the Vietnamese government to be more actively involved in building adaptive capacity through social learning, investments in transparent legal institutions, efficient use of limited available resources, and enhancing capacity of local policy actors will be critical in helping smallholder farmers learn how to adapt to climate change impacts.</p

Crystallographic characterization of two novel crystal forms of human insulin induced by chaotropic agents and a shift in pH-4

<p><b>Copyright information:</b></p><p>Taken from "Crystallographic characterization of two novel crystal forms of human insulin induced by chaotropic agents and a shift in pH"</p><p>http://www.biomedcentral.com/1472-6807/7/83</p><p>BMC Structural Biology 2007;7():83-83.</p><p>Published online 19 Dec 2007</p><p>PMCID:PMC2241603.</p><p></p>shown to bind in the pocket created by the two flanking tyrosine residues. The side chain of the tyrosine to the right in is missing in the pdb file. In (b) the same structures are superposed with the C222urea structure (orange). The side chain of the left tyrosine is flipped to accommodate the hexamer-hexamer interaction shown in (c), where a neighboring hexamer from the asymmetric unit is included (grey). The tyrosine side chain of the second hexamer occupies the same position as the phenolic compounds

Crystallographic characterization of two novel crystal forms of human insulin induced by chaotropic agents and a shift in pH-1

<p><b>Copyright information:</b></p><p>Taken from "Crystallographic characterization of two novel crystal forms of human insulin induced by chaotropic agents and a shift in pH"</p><p>http://www.biomedcentral.com/1472-6807/7/83</p><p>BMC Structural Biology 2007;7():83-83.</p><p>Published online 19 Dec 2007</p><p>PMCID:PMC2241603.</p><p></p>B chains in the C222structures have the PheB1 residue in an extended conformation (the top most population). Labels indicate chain names used in the final PDB files. For illustrative purpose, the side chain of the C-terminal LysB29 is included in the figures to illustrate the flexibility. This side-chain was subsequently omitted from several chains in the final PDB files due to disordered electron density

Crystallographic characterization of two novel crystal forms of human insulin induced by chaotropic agents and a shift in pH-5

<p><b>Copyright information:</b></p><p>Taken from "Crystallographic characterization of two novel crystal forms of human insulin induced by chaotropic agents and a shift in pH"</p><p>http://www.biomedcentral.com/1472-6807/7/83</p><p>BMC Structural Biology 2007;7():83-83.</p><p>Published online 19 Dec 2007</p><p>PMCID:PMC2241603.</p><p></p>rily directed towards the carbonyl oxygen GlnA5 but surrounding carbonyl oxygens from SerA9 and IleA10 are within reasonable distances. Marked distances are given in Ångström (Å)

Crystallographic characterization of two novel crystal forms of human insulin induced by chaotropic agents and a shift in pH-0

<p><b>Copyright information:</b></p><p>Taken from "Crystallographic characterization of two novel crystal forms of human insulin induced by chaotropic agents and a shift in pH"</p><p>http://www.biomedcentral.com/1472-6807/7/83</p><p>BMC Structural Biology 2007;7():83-83.</p><p>Published online 19 Dec 2007</p><p>PMCID:PMC2241603.</p><p></p>The flanking hexamers are located around the central hexamer at an angle of ~110°. The local non-crystallographic three-fold axis of the two outer hexamers is almost orthogonal to the central non-crystallographic three-fold axis. The zinc atoms are illustrated as large spheres to mark the position of the three-fold axes. The hexamers are numbered from I to III. (b) The crystal packing in the C222space group drawn with main chain trace with the asymmetric unit in magenta. (c) The crystal packing of the human insulin in space group C2. The asymmetric unit molecule is colored magenta. The inserts in (b) and (c) show crystals of the C222and C2 forms, respectively

Ligand-Controlled Assembly of Hexamers, Dihexamers, and Linear Multihexamer Structures by the Engineered Acylated Insulin Degludec

Insulin degludec, an engineered acylated insulin, was recently reported to form a soluble depot after subcutaneous injection with a subsequent slow release of insulin and an ultralong glucose-lowering effect in excess of 40 h in humans. We describe the structure, ligand binding properties, and self-assemblies of insulin degludec using orthogonal structural methods. The protein fold adopted by insulin degludec is very similar to that of human insulin. Hexamers in the R₆ state similar to those of human insulin are observed for insulin degludec in the presence of zinc and resorcinol. However, under conditions comparable to the pharmaceutical formulation comprising zinc and phenol, insulin degludec forms finite dihexamers that are composed of hexamers in the T₃R₃ state that interact to form an R₃T₃–T₃R₃ structure. When the phenolic ligand is depleted and the solvent condition thereby mimics that of the injection site, the quaternary structure changes from dihexamers to a supramolecular structure composed of linear arrays of hundreds of hexamers in the T₆ state and an average molar mass, <i>M</i>₀, of 59.7 × 10³ kg/mol. This novel concept of self-assemblies of insulin controlled by zinc and phenol provides the basis for the slow action profile of insulin degludec. To the best of our knowledge, this report for the first time describes a tight linkage between quaternary insulin structures of hexamers, dihexamers, and multihexamers and their allosteric state and its origin in the inherent propensity of the insulin hexamer for allosteric half-site reactivity

A Scalable High-performance Topographic Flow Direction Algorithm for Hydrological Information Analysis

Hydrological information analyses based on Digital Elevation Models (DEM) provide hydrological properties derived from high-resolution topographic data represented as an elevation grid. Flow direction is one of the most computationally intensive functions in the current implementation of TauDEM, a broadly used high-performance hydrological analysis software in hydrology community. Hydrologic flow direction defines a flow field on the DEM that directs flow from each grid cell to one or more of its neighbors. This is a local computation for the majority of grid cells, but becomes a global calculation for the geomorphologically motivated procedure in TauDEM to route flow across flat regions. As the resolution of DEM becomes higher, the computational bottleneck of this function hinders the use of these DEM data in large-scale studies. This paper presents an efficient parallel flow direction algorithm that identifies spatial features (e.g., flats) and reduces the number of sequential and parallel iterations needed to compute their geomorphologically motivated flow direction. Numerical experiments show that our algorithm outperformed the existing parallel D8 algorithm in TauDEM by two orders of magnitude. The new parallel algorithm exhibited desirable scalability on Stampede and ROGER supercomputers

Cartoon representation of the crystal structure of the B25C-dimer.

<p><b>A:</b> The A chain is coloured in green and the B chain is shown in blue. The additional disulphide bond is shown by stick representation (yellow). An omit map was calculated by omitting the Sulphur atom of B25C. The resulting difference electron density Fo-Fc map is coloured in orange at σ-level = 3.0. It is clear from the structure that the two monomers are linked by a disulfide bond between the two adjoining B25C. <b>B:</b> Comparison of the B25C structure (blue) with that of the porcine in-sulin (PDB code 1B2E) (grey). The Cα trace shows that the two structures have a high resemblance with minor deviations in Cα positions at residue B21E and B29K.</p

Data collection and refinement statistics.

a<p> <i>R_merge = Σ|I_i−I|/ΣI where I_i is an individual intensity measurement and I is the mean intensity for this reflection.</i></p>b<p> <i>R value = crystallographic R-factor = Σ|F_obs|−|F_calc|/Σ|F_obs|, where Fobs and Fcalc are the observed and calculated structure factors respectively. R_free value is the same as R value but calculated on 5% of the data not included in the refinement.</i></p>c<p> <i>Root-mean-square deviations of the parameters from their ideal values.</i></p

The experimental parameters determined from the fit in Figure 2 and results previously determined for hexameric insulin of human and porcine origin.

*<p> <i>Measured value.,</i></p>**<p> <i>Probably affected by non-ideality because of high concentration.</i></p