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

DNA fingerprint variability within and among the silkworm Bombyx mori varieties and estimation of their genetic relatedness using Bkm-derived probe

Genetic diversity within and among 13 silkworm varieties (6 diapausing and 7 nondiapausing) that differ in various quantitative and qualitative characters of economic importance was determined by DNA fingerprinting using Bkm-derived 2(8) probe. A high degree of genetic similarity was observed within each variety studied. Based on fingerprints of pooled DNA, the genetic similarity among various varieties was calculated. The dendrogram constructed using UPGMA resulted in the 13 varieties resolving into two major clusters. These two clusters were comprised of five nondiapausing as one group and five diapausing varieties as the other. The genetic similarity estimated within and among silkworms is consistent with the pedigrees and geographical distribution of the varieties. Our study has demonstrated that the variability of DNA fingerprints within and among silkworm can provide an essential basis on which breeders may plan crossbreeding strategies to produce potentially heterotic hybrids

Towards operational detection of forest ecosystem changes in protected areas

This paper discusses the application of the Cross-Correlation Analysis (CCA) technique to multi-spatial resolution Earth Observation (EO) data for detecting and quantifying changes in forest ecosystems in two different protected areas, located in Southern Italy and Southern India. The input data for CCA investigation were elaborated from the forest layer extracted from an existing Land Cover/Land Use (LC/LU) map (time T1) and a more recent (T2, with T2 > T1) single date image. The latter consist of a High Resolution (HR) Landsat 8 OLI image and a Very High Resolution (VHR) Worldview-2 image, which were analysed separately. For the Italian site, the forest layer (1:5000) was first compared to the HR Landsat 8 OLI image and then to the VHR Worldview-2 image. For the Indian site, the forest layer (1:50,000) was compared to the Landsat 8 OLI image then the changes were interpreted using Worldview-2. The changes detected through CCA, at HR only, were compared against those detected by applying a traditional NDVI image differencing technique of two Landsat scenes at T1 and T2. The accuracy assessment, concerning the change maps of the multi-spatial resolution outputs, was based on stratified random sampling. The CCA technique allowed an increase in the value of the overall accuracy: from 52% to 68% for the Italian site and from 63% to 82% for the Indian site. In addition, a significant reduction of the error affecting the stratified changed area estimation for both sites was obtained. For the Italian site, the error reduction became significant at VHR (?2 ha) in respect to HR (?32 ha) even though both techniques had comparable overall accuracy (82%) and stratified changed area estimation. The findings obtained support the conclusions that CCA technique can be a useful tool to detect and quantify changes in forest areas due to both legal and illegal interventions, including relatively inaccessible sites (e.g., tropical forest) with costs remaining rather low. The data obtained through CCA intervention could not only support the commitments undertaken by the European Habitats Directive (92/43/EEC) and the Convention of Biological Diversity (CBD) but also satisfy UN Sustainable Development Goals (SDG)

Report on Ecological Niche Modeling (ENM). BIO SOS Biodiversity Multisource Monitoring System: from Space TO Species (BIO SOS) Deliverable D6.7, pp54 http://www.biosos.wur.nl/UK/Deliverables/

The Deliverable analyses four case studies and builds different approaches of Ecological Niche Models. In all the cases habitat typology is the main variable that emerges as most significant in explaining the model of ecological niche. The results have both theoretical importance (for instance, the use of GHCs improves the prediction of the model for the distribution of some species in Alta Murgia Parco Nazionale) and practical relevance for stakeholders (for instance, the models enable generation of a risk map predicting areas of potential vulnerability to damage by wild boars in Alta Murgia). Presence-absence species distribution data on Alta Murgia were provided by the management Authority of the National Park (i.e., Ente Parco). BIO_SOS project would like to thank Ente Parco Nazionale dell’ Alta Murgi