Effect of the G375C and G346E Achondroplasia Mutations on FGFR3 Activation

Two mutations in FGFR3, G380R and G375C are known to cause achondroplasia, the most common form of human dwarfism. The G380R mutation accounts for 98% of the achondroplasia cases, and thus has been studied extensively. Here we study the effect of the G375C mutation on the phosphorylation and the cross-linking propensity of full-length FGFR3 in HEK 293 cells, and we compare the results to previously published results for the G380R mutant. We observe identical behavior of the two achondroplasia mutants in these experiments, a finding which supports a direct link between the severity of dwarfism phenotypes and the level and mechanism of FGFR3 over-activation. The mutations do not increase the cross-linking propensity of FGFR3, contrary to previous expectations that the achondroplasia mutations stabilize the FGFR3 dimers. Instead, the phosphorylation efficiency within un-liganded FGFR3 dimers is increased, and this increase is likely the underlying cause for pathogenesis in achondroplasia. We further investigate the G346E mutation, which has been reported to cause achondroplasia in one case. We find that this mutation does not increase FGFR3 phosphorylation and decreases FGFR3 cross-linking propensity, a finding which raises questions whether this mutation is indeed a genetic cause for human dwarfism

Biological and Non-Biological Methods for Lignocellulosic Biomass Deconstruction

Owing to their abundance and cost-effectiveness, lignocellulosic materials have attracted increasing attention in clean energy technologies over the last decade. However, the complex polymer structure in these residues makes it difficult to extract the fermentable sugars. Therefore, various pretreatment regimes have been used resulting in the breaking of lignocelluloses’ physical and chemical structures, thereby enhancing the availability of the polysaccharides which are subsequently hydrolysed into different biocommodities. This chapter provides an evaluation of some of the latest exploited methodologies that are used in the pretreatment of lignocellulosic materials. Moreover, the chapter discusses the advantages and disadvantages of each method

Changes in tropical forest: assessing different detection techniques

INTRODUCTION: The monitoring of forest ecosystem state involves the detection of changes which may have occurred in the specific area. The operational definition of ecosystem mapping and monitoring proposed by Maes et al.(2014) suggests that ecosystem changes can be quantified through Land Cover/Land Use (LC/LU) class changes. The detection of LC/LU class changes implies not only the identification of when and where they may have occurred, but also the definition of both the type and magnitude of target (e.g., forest) class transitions from time T1 to time T2, with T1<T2, along with the quantification of class modifications. The changes thus detected can then be used to identify anthropic and other pressures acting on the area (Nagendra et al., 2014; Sorrano et al., 2014). The present study compares the data obtained through the Cross-Correlation Analysis (CCA) technique, developed by the American company Earthsat, Inc., with those resulting from a traditional unsupervised technique in the detection of changes in tropical forest ecosystem. The CCA technique has already been used by Koeln and Bissonnette, (2000) and Civco et al. (2002) to analyse High Resolution (HR) (e.g., Landsat TM) and Medium Resolution (MR) imagery (e.g., MERIS). More recently, Tarantino et al. (2016) have applied the CCA technique to Very High Resolution (VHR) data (e.g., WorldView-2) to detect grassland ecosystems changes. Focusing on a protected area in Southern India, the present study investigates the advantages in terms of costs and Overall Accuracy (OA) of the CCA technique. A brief description of materials and methods used will be followed by indications of the study area and input data. Thereafter, the accuracy of the results obtained and their discussion will provide support to the operational implementation of the CCA technique and its application to tropical forest monitoring

Effects of landscape context on the invasive species lantana camara in Biligiri Rangaswamy temple tiger reserve, India

Non-native invasive species establish in favourable habitats in alien regions. Such favourable habitats are largely determined by local climatic, soil and biogeographic factors. Modelling these factors can help managers to identify areas of possible risk of invasion. This paper uses logistic regression modelling to identify variables conducive to high invasion in a tropical mixed forest in a biodiversity hotspot region in India. Using presence-absence data of an invasive species Lantana camara and local habitat variables from increasing buffer distances around sampling locations along with broad scale climatic parameters, we identify the variables that support invasion and spread. Results indicated that the percentage of moist deciduous forest at a distance of 50 m around the plot was significantly related to the invasion of L. camara. The study demonstrates the facilitation by moist deciduous forests to the growth and spread of L. camara in this region, and highlights the importance of using data at multiple scales for modelling invasion