Involvement of endorphins in emotional hyperthermia of rats

When rats were confronted with the novel experience of a new environment and stressful handling procedure their body temperature increased within minutes. At the same time β-endorphin-like immunoreactivity in the plasma increased dramatically. The stress-induced hyperthermia could be antagonized or reversed by the active, not however, by the inactive enantiomer of naloxone. The data provide evidence for a physiological role of endorphins.     

The germacradienolide isabelin from Zexmenia valerii: stereochemistry and conformation of scandenolide and deoxymikanolide

The isolation of the germacradienolide isabelin from Zexmenia valerii is reported. In contrast to isabelin, the closely related lactones deoxymikanolide and scandenolide exist in solution in one conformation only, which is similar to that of dihvdromikanolide in the solid state. The C-3 stereochemistry of scandenolide is revised.     

Trehalose-6-phosphate-mediated phenotypic change in Acinetobacter baumannii

The stress protectant trehalose is synthesized in Acinetobacter baumannii from UPD-glucose and glucose-6-phosphase via the OtsA/OtsB pathway. Previous studies proved that deletion of otsB led to a decreased virulence, the inability to grow at 45°C and a slight reduction of growth at high salinities indicating that trehalose is the cause of these phenotypes. We have questioned this conclusion by producing ∆otsA and ∆otsBA mutants and studying their phenotypes. Only deletion of otsB, but not deletion of otsA or otsBA, led to growth impairments at high salt and high temperature. The intracellular concentrations of trehalose and trehalose-6-phosphate were measured by NMR or enzymatic assay. Interestingly, none of the mutants accumulated trehalose any more but the ∆otsB mutant with its defect in trehalose-6-phosphate phosphatase activity accumulated trehalose-6-phosphate. Moreover, expression of otsA in a ∆otsB background under conditions where trehalose synthesis is not induced led to growth inhibition and the accumulation of trehalose-6-phosphate. Our results demonstrate that trehalose-6-phosphate affects multiple physiological activities in A. baumannii ATCC 19606.     

Molecular docking analysis of beta-catenin with compounds derived from Lycopersicon esculentum

Beta-catenin is linked with colorectal cancer (CRC). Therefore, it is of interest to design and develop novel compounds to combat CRC. Hence, we document compounds (chlorogenic acid, gallic acid, protocatechuic acid, quercetin and vanillic acid) from Lycopersicon esculentum with optimal binding features for further consideration.     

Using Manifold Learning for Atlas Selection in Multi-Atlas Segmentation

Multi-atlas segmentation has been widely used to segment various anatomical structures. The success of this technique partly relies on the selection of atlases that are best mapped to a new target image after registration. Recently, manifold learning has been proposed as a method for atlas selection. Each manifold learning technique seeks to optimize a unique objective function. Therefore, different techniques produce different embeddings even when applied to the same data set. Previous studies used a single technique in their method and gave no reason for the choice of the manifold learning technique employed nor the theoretical grounds for the choice of the manifold parameters. In this study, we compare side-by-side the results given by 3 manifold learning techniques (Isomap, Laplacian Eigenmaps and Locally Linear Embedding) on the same data set. We assess the ability of those 3 different techniques to select the best atlases to combine in the framework of multi-atlas segmentation. First, a leave-one-out experiment is used to optimize our method on a set of 110 manually segmented atlases of hippocampi and find the manifold learning technique and associated manifold parameters that give the best segmentation accuracy. Then, the optimal parameters are used to automatically segment 30 subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). For our dataset, the selection of atlases with Locally Linear Embedding gives the best results. Our findings show that selection of atlases with manifold learning leads to segmentation accuracy close to or significantly higher than the state-of-the-art method and that accuracy can be increased by fine tuning the manifold learning process.