Scots pine transfer effect models for growth and survival in Sweden and Finland

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Genotype-by-environment interactions and the dynamic relationship between tree vitality and height in northern Pinus sylvestris

Tree health and growth rate must both be considered in Scots pine breeding for harsh areas such as northern Sweden. Univariate (UV) and multivariate (MV) multi-environment trial (MET) analyses of tree vitality (a measure of tree health) and height (a measure of growth rate) were conducted for four series of open-pollinated Scots pine progeny trials (20 trials total), to evaluate age trends, patterns, and drivers of genotype-by-environment interaction (G × E). The lowest standard errors were obtained for the MV MET analyses, indicating that MVanalyses are preferable to UVanalyses. By incorporating factor-analytic structures, the most complex data sets could be handled, suggesting that factor-analytic analyses are preferred for evaluation of forest progeny trials. We detected strong patterns of G × E for both tree vitality and height, and the driver of G × E was found mainly to be differences in degree day temperature sum, such that G × E was higher between trials with more contrasting temperature sums. The genetic correlations, between vitality and height within sites, were generally positive and were driven by the harshness of the trial; mild trials had lower genetic correlations than did harsh trials. The sign of the across-site genetic correlations between vitality and height changed from positive to negative in some cases, as the differences between the temperature sum of the trials increased. These findings support the hypothesis that tree height assessed in harsh environments with low survival is likely to reflect health and survival ability to a greater extent than growth capacity

Reliable Strategy for Analysis of Complex Biosensor Data

When using biosensors, analyte biomolecules of several different concentrations are percolated over a chip with immobilized ligand molecules that form complexes with analytes. However, in many cases of biological interest, e.g., in antibody interactions, complex formation steady-state is not reached. The data measured are so-called sensorgram, one for each analyte concentration, with total complex concentration vs time. Here we present a new four-step strategy for more reliable processing of this complex kinetic binding data and compare it with the standard global fitting procedure. In our strategy, we first calculate a dissociation graph to reveal if there are any heterogeneous interactions. Thereafter, a new numerical algorithm, AIDA, is used to get the number of different complex formation reactions for each analyte concentration level. This information is then used to estimate the corresponding complex formation rate constants by fitting to the measured sensorgram one by one. Finally, all estimated rate constants are plotted and clustered, where each cluster represents a complex formation. Synthetic and experimental data obtained from three different QCM biosensor experimental systems having fast (close to steady-state), moderate, and slow kinetics (far from steady-state) were evaluated using the four-step strategy and standard global fitting. The new strategy allowed us to more reliably estimate the number of different complex formations, especially for cases of complex and slow dissociation kinetics. Moreover, the new strategy proved to be more robust as it enables one to handle system drift, i.e., data from biosensor chips that deteriorate over time.Peer reviewe

Thermodynamic and kinetic approaches for evaluation of monoclonal antibody - Lipoprotein interactions

Two complementary instrumental techniques were used, and the data generated was processed with advanced numerical tools to investigate the interactions between anti-human apoB-100 monoclonal antibody (anti-apoB-100 Mab) and apoB-100 containing lipoproteins. Partial Filling Affinity Capillary Electrophoresis (PF-ACE) combined with Adsorption Energy Distribution (AED) calculations provided information on the heterogeneity of the interactions without any a priori model assumptions. The AED calculations evidenced a homogenous binding site distribution for the interactions. Quartz Crystal Microbalance (QCM) studies were used to evaluate thermodynamics and kinetics of the Low-Density Lipoprotein (LDL) and anti-apoB-100 Mab interactions. High affinity and selectivity were observed, and the emerging data sets were analysed with so called Interaction Maps. In thermodynamic studies, the interaction between LDL and anti-apoB-100 Mab was found to be predominantly enthalpy driven. Both techniques were also used to study antibody interactions with Intermediate-Density (IDL) and Very Low Density (VLDL) Lipoproteins. By screening affinity constants for IDL-VLDL sample in a single injection we were able to distinguish affinity constants for both subpopulations using the numerical Interaction Map tool. (C) 2016 Elsevier Inc. All rights reserved.Peer reviewe