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Abstract: We investigated the assumption, necessary for comparison between bottom-up scaling of plot level fluxes to top-down scaling of regional models, that small plots of sap flux are representative of the transpiration component of regional land-surface atmosphere water vapor interactions. We conducted sap flux measurements in three stands dominated by sugar maple (Acer saccharum Marsh.) representing old-growth (Sylvania, SV), second-growth (Willow Creek, WC), and thinned second-growth stands (Hay Creek, HC). Using the sap flux-scaled estimates of transpiration we tested whether (1) differences in A. saccharum transpiration between the three stands could be explained by leaf area index and/or stand inventory measures, and (2) the Terrestrial Regional Ecosystem Exchange Simulator (TREES) model could capture differences in the response of transpiration to environmental variables. We found large differences between the three stands over two growing seasons in A. saccharum canopy transpiration per unit ground area (1.61, 3.66, and 0.85mmday−1 for SV, WC, and HC respectively) and canopy transpiration per unit leaf area (0.31, 1.00, and 0.21mmday−1 for SV, WC, and HC respectively). While none of these differences could be explained with stand or environmental variables, the TREES model was able to effectively capture the half-hourly temporal variability in the sap flux data. TREES incorporates an adaptive parameterization scheme which improves upon traditional sensitivity tests of models. By incorporating both simple plant hydraulic theory and adaptive parameterization we were able to minimize the necessary parameters to those that are sensible and easily applied regionally. Our results indicate that while the assumption of uniform water loss from A. saccharum forests regionally is incorrect, models that incorporate simple plant hydraulics effectively capture the dynamics of transpiration across disparate stands. [Copyright &y& Elsevier]

We examined physiological parameter tradeoffs in modeling stomatal control of transpiration from a number of forest species. Measurements of sapflux, micrometeorology, and leaf area index were made in stands representing 85% of the forest ecosystems around the WLEF eddy flux tower in northern Wisconsin. A Jarvis-based canopy conductance model was used to simulate canopy transpiration (EC) for five tree species from these stands. They consisted of conifers and deciduous species in both upland and wetland locations. Parameter estimation was used to assess the tradeoffs between physiological parameters used in the calculation of stomatal conductance. These tradeoffs were then evaluated against current theory on stomatal regulation of leaf water potential. The results show that the best simulations of EC were obtained with values of maximum stomatal conductance (gSmax) and stomatal sensitivity to vapor pressure deficit (δ) that closely followed this hydraulic theory. The model predictions reveal a large variation in the strategies used to regulate water potential among species. Aspen showed the greatest tendency towards efficiency, indicating that it has high EC under low vapor pressure deficit (D) conditions, but is susceptible to rapid EC decline at moderate to high D. Other species showed more conservative water use. The results indicate that inter-specific differences in dynamic response to D can produce large spatial variation in EC under typical environmental conditions. [Copyright &y& Elsevier]

All land surface process models require parameters that are proxies for spatial processes that are impractical or impossible to measure. Recent developments in model parameter estimation theory suggest that information obtained from calibrating such models is inherently uncertain in nature. As a consequence, identification of optimum parameter values is often highly non–specific. A calibration framework using fuzzy logic is presented to deal with such uncertain information. An application of this technique to calibrate the sub–canopy controls on transpiration in a land surface process model demonstrates that objective estimates of parameter values and expected ranges of predictions can be obtained with suitable choices for objective functions. An iterative refinement in parameter estimates was possible with conditional sampling techniques. The automated approach was able to correctly identify parameter tradeoffs such that two strongly different sets of parameters could. [ABSTRACT FROM AUTHOR]

Abstract: We studied the potential of systemically administered aminoglycosides as a therapy for retinal degeneration resulting from premature termination codon (PTC) mutations. Aminoglycosides were systemically delivered to two rodent models of retinal degeneration: a transgenic rat model of dominant disease caused by a PTC in rhodopsin (S334ter); and a mouse model of recessive disease (rd12) caused by a PTC in the retinoid isomerase Rpe65. Initial luciferase reporter assays were undertaken to measure the efficiency of gentamicin-induced read-through in vitro. These experiments indicated that gentamicin treatment induced on average a 5.3% extra read-through of the S334ter PTC in vitro, but did not affect the rd12 PTC. Beginning at postnatal day 5, animals received daily subcutaneous injections of gentamicin or geneticin at a range of doses. The effect of the treatment on retinal degeneration was examined by histopathology and electroretinography (ERG). Systemic treatment with aminoglycoside significantly increased the number of surviving photoreceptors in the S334ter rat model over several weeks of treatment, but was not effective in slowing the retinal degeneration in the rd12 mouse model. Similarly, ERG recordings indicated better preservation of retinal function in the treated S334ter rats, but no difference was observed in the rd12 mice. Daily subcutaneous injection of 12.5μg/g gentamicin was the only regimen that inhibited retinal degeneration without apparent adverse systemic side effects. Reduced effectiveness beyond postnatal day 50 correlated with reduced ocular penetration of drug as seen in gentamicin-Texas red (GTTR) conjugation experiments. We conclude that, in the rat model, an ∼5% reduction of abnormal truncated protein is sufficient to enhance photoreceptor survival. Such a change in truncated protein is consistent with beneficial effects seen when aminoglycosides has been used in other, non-ocular animal models. In the rd12 mouse, lack of efficacy was seen despite this particular PTC being theoretically more sensitive to aminoglycoside modification. We conclude that aminoglycoside read-through of PTCs in vitro and in vivo cannot be predicted just from genomic context. Because there is considerable genetic heterogeneity amongst retinal degenerations, pharmacologic therapies that are not gene-specific have significant appeal. Our findings suggest that if adverse issues such as systemic toxicity and limited ocular penetration can be overcome, small molecule therapeutics, such as aminoglycosides, which target classes of mutation could hold considerable potential as therapies for retinal disease. [Copyright &y& Elsevier]

• We associate changes in biophysical traits to expression frequency of gene modules. • Bayesian networks can be used to provide probabilities, linking expression levels to traits. • Implemented process models with transcriptomic information capture genotypic and environmental variation. Process-based models of plant productivity provide a means of yield prediction that can inform best practices in agriculture and land management. However, current biophysical models fail in capturing both genotypic and phenotypic variation under changing environmental conditions. Physiological traits affecting final yield and ecosystem productivity are the result of gene expression, protein translation, and metabolite formation which are controlled by specific alleles acting alone and in response to time and environmental cues. Biophysical interactions take place simultaneously across several scales of time and space, giving rise to high levels of complexity and limiting the predictive capacity of existing analytical approaches. While statistical methods can partially quantify genotype-by-environment connections, biophysical process models miss exploring genotypic diversity and they cannot currently be implemented with -omics data, i.e. the entire collection of entities such as gene transcripts, metabolites, and proteins. Here we present a novel framework that utilizes Bayesian networks to provide probabilities that link gene expression levels to trait occurrence. We propose the use of these gained probabilities to inform parameters of existing biophysical models. Merging transcriptomic information into process-based models allows for greater integration of empirical data and processes across different scales in complex systems, while increasing the likelihood of growth predictions under unknown environmental conditions. Image, graphical abstract [ABSTRACT FROM AUTHOR]