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Förster Resonance Energy Transfer (FRET) has become an immensely powerful tool to profile intra- and inter-molecular interactions. Through fusion of genetically encoded fluorescent proteins (FPs) researchers have been able to detect protein oligomerization, receptor activation, and protein translocation among other biophysical phenomena. Recently, two bright monomeric red fluorescent proteins, mRuby3 and mScarlet-I, have been developed. These proteins offer much improved physical properties compared to previous generations of monomeric red FPs that should help facilitate more general adoption of Green/Red FRET. Here we assess the ability of these two proteins, along with mCherry, to act as a FRET acceptor for the bright, monomeric, green-yellow FP mNeonGreen using intensiometric FRET and 2-photon Fluorescent Lifetime Imaging Microscopy (FLIM) FRET techniques. We first determined that mNeonGreen was a stable donor for 2-photon FLIM experiments under a variety of imaging conditions. We then tested the red FP's ability to act as FRET acceptors using mNeonGreen-Red FP tandem construct. With these constructs we found that mScarlet-I and mCherry are able to efficiently FRET with mNeonGreen in spectroscopic and FLIM FRET. In contrast, mNeonGreen and mRuby3 FRET with a much lower efficiency than predicted in these same assays. We explore possible explanations for this poor performance and determine mRuby3's protein maturation properties are a major contributor. Overall, we find that mNeonGreen is an excellent FRET donor, and both mCherry and mScarlet-I, but not mRuby3, act as practical FRET acceptors, with the brighter mScarlet-I out performing mCherry in intensiometric studies, but mCherry out performing mScarlet-I in instances where consistent efficiency in a population is critical. [ABSTRACT FROM AUTHOR]

Skeletal muscle (SM) health and integrity is dependent on the dynamic balance between protein synthesis and degradation, and central to this process is the availability of amino acids (AA) in the amino pool. While Doxorubicin (DOX) remains one of the most widely used chemotherapeutic agents for the treatment of solid and hematological malignancies, little is known of the effect of the drug on SM, particularly its effect on the availability of amino acids in the tissue. The purpose of this study was to examine the effect of DOX administration on vascular, interstitial and intracellular concentrations of AA in SM of the rat up to 8 days after the administration of a 1.5 or 4.5 mg/kg i.p. dose of DOX. In the plasma, total amino acids (TAA) were significantly increased compared to control where greater (P<0.05) concentrations were observed following the 1.5 mg/kg dose compared to the 4.5 mg/kg dose. Compared to control, the 1.5 mg/kg dose resulted in an increase (P<0.05) in interstitial TAA whereas the 4.5 mg/kg resulted in a sustained decrease (P<0.05). Intracellular TAA, essential amino acids (EAA) and branched-chain amino acids (BCAA) where significantly increased in each muscle group analyzed, following the 1.5 and 4.5 mg/kg doses compared to control. This study provides important insight into the amino acid response following DOX chemotherapy and presents a substantial foundation for future studies focused on reducing SM damage and recovery by targeting amino acid metabolism. [ABSTRACT FROM AUTHOR]

Doxorubicin remains one of the most widely used chemotherapeutic agents however its effect on healthy tissue, such as skeletal muscle, remains poorly understood. The purpose of the current study was to examine the accumulation of doxorubicin (DOX) and its metabolite doxorubicinol (DOXol) in skeletal muscle of the rat up to 8 days after the administration of a 1.5 or 4.5 mg kg-1 i.p. dose. Subsequent to either dose, DOX and DOXol were observed in skeletal muscle throughout the length of the experiment. Interestingly an efflux of DOX was examined after 96 hours, followed by an apparent re-uptake of the drug which coincided with a spike and rapid decrease of plasma DOX concentrations. The interstitial space within the muscle did not appear to play a significant rate limiting compartment for the uptake or release of DOX or DOXol from the tissue to the circulation. Furthermore, there was no evidence that DOX preferentially accumulated in a specific muscle group with either dose. It appears that the sequestering of drug in skeletal muscle plays an acute and important role in the systemic availability and metabolism of DOX which may have a greater impact on the clinical outcome than previously considered. [ABSTRACT FROM AUTHOR]

Global climate is changing due to increasing anthropogenic emissions of greenhouse gases. Forest managers need growth and yield models that can be used to predict future forest dynamics during the transition period of present-day forests under a changing climatic regime. In this study, we developed a forest growth and yield model that can be used to predict individual-tree growth under current and projected future climatic conditions. The model was constructed by integrating historical tree growth records with predictions from an ecological process-based model using neural networks. The new model predicts basal area (BA) and volume growth for individual trees in pure or mixed species forests. For model development, tree-growth data under current climatic conditions were obtained using over 3000 permanent sample plots from the Province of Nova Scotia, Canada. Data to reflect tree growth under a changing climatic regime were projected with JABOWA-3 (an ecological process-based model). Model validation with designated data produced model efficiencies of 0.82 and 0.89 in predicting individual-tree BA and volume growth. Model efficiency is a relative index of model performance, where 1 indicates an ideal fit, while values lower than zero means the predictions are no better than the average of the observations. Overall mean prediction error (BIAS) of basal area and volume growth predictions was nominal (i.e., for BA: -0.0177 cm2 5-year-1 and volume: 0.0008 m3 5-year-1). Model variability described by root mean squared error (RMSE) in basal area prediction was 40.53 cm2 5-year-1 and 0.0393 m3 5-year-1 in volume prediction. The new modelling approach has potential to reduce uncertainties in growth and yield predictions under different climate change scenarios. This novel approach provides an avenue for forest managers to generate required information for the management of forests in transitional periods of climate change. Artificial intelligence technology has substantial potential in forest modelling. [ABSTRACT FROM AUTHOR]