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Myelin-associated glycoprotein (MAG) binds to the nerve cell surface and inhibits nerve regeneration. The nerve cell surface ligand(s) for MAG are not established, although sialic acid-bearing glycans have been implicated. We identify the nerve cell surface gangliosides GD1a and GT1b as specific functional ligands for MAG-mediated inhibition of neurite outgrowth from primary rat cerebellar granule neurons. MAG-mediated neurite outgrowth inhibition is attenuated by (i) neuraminidase treatment of the neurons; (ii) blocking neuronal ganglioside biosynthesis; (ii) genetically modifying the terminal structures of nerve cell surface gangliosides; and (iv) adding highly specific IgG-class antiganglioside mAbs. Furthermore, neurite outgrowth inhibition is mimicked by highly multivalent clustering of GD1a or GT1b by using pre-complexed antiganglioside Abs. These data implicate the nerve cell surface gangliosides GD1a and GT1b as functional MAG ligands and suggest that the first step in MAG inhibition is multivalent ganglioside clustering.

Advancements in the Geospatial Technology has brought about benefits to various fields in science and technology. The education and the capacity building of geospatial technology plays a very important role within these fields. The current practice of the education is basically dominated by the teacher, huge syllabus, non-relevant knowledge and having very little opportunity for the discussions between the students and teachers. Instead of the unidirectional, monolithic, rigid and traditional teaching in practice, it requires a change to dynamic, evolving, in-process and gradual system of learning to shape the knowledge society. This generates creative, innovative human beings to train them to perform based on the scientific reasoning and empirical evidence in their respective fields. In order to develop, there are three important components: content, practice and cross-cutting to be established as a strategy in the data savvy environment. The ‘content’ may shift to more emphasis on higher order skills of constructing explanations, the ‘practice’ would enhance critical thinking as well as synthesis and ‘cross-cutting’ would synergize the performance expectations. Hence, the modified education and capacity building programmes advocate to move to a competency based model and the imperatives motivate the better use of the technology. This paper explains multiple levels that exists across academic, research and practitioner community that have potential to benefit from geospatial technology and it determines appropriate curriculum, pedagogy and evaluation strategies. It also maps an appropriate framework and approaches for multi-level education and capacity building considering the recent developments in geospatial technology.

The present study addresses the potential of RADARSAT-2 data for Land Use Land Cover (LULC) Classification in parts of Ahmedabad, Gujarat, India. Texture measures of the original SAR data were obtained by the Gray Level Co-occurrence Matrix (GLCM). Results suggested False Colour Composite (FCC) of Mean, Homogeneity and Entropy showed a good discrimination of different land cover classes. Further, Principal Component Analysis (PCA) was also applied to the eight texture measures and FCC of Principal components is generated. Unsupervised classification is carried out for the above generated FCCs and accuracy assessment is carried out. The result of classification shows that the PCA generated from GLCM texture measures could obtain higher accuracy than using only the classification carried out by texture measures. Overall results of the study suggested possible use of single polarization and single date Radarsat-2 data for LULC classification with better accuracy using PCA generated image.

The Udaipur urban agglomeration was selected to analyse the urban heat island (UHI) effect in the area from 2005 to 2017. Landsat 7 ETM+ sensor data was used to derive land surface temperatures (LST) and map landscape characteristics. The agglomeration was classified into seven land use land cover classes including agriculture cropped, agriculture fallow, barren, built up, scrub, vegetation and water using unsupervised classification method. The LST results were obtained using NDVI method. Urban heat island intensities were mapped using z-score method. Regions with UHI values of above 2 standard deviations were considered to reflect UHI effect. Results show an overall decrease of 10.2 percent in agricultural cropped and fallow lands. The scrub class also shows a moderate decline of 2.3 percent. Increase in area were observed for built up, barren and vegetation classes by 8.8, 2.1 and 1.9 percent respectively. The dominant LULC change was the transformation of agricultural lands to built-up class. The class-wise mean LST observations increase in order of water, vegetation, cropped, fallow, built up, scrub and barren land. The mean LST records an increase of 1.7° C from 30.1° C to 31.8° C over the study period. Results suggest that UHI effects are more prominent in urban fringe area corresponding with the built up and barren land cover classes contrary to its core area. Built up areas surrounded by barren lands left vacant for city’s sprawl in the future exhibit highest UHI intensities.

Dungarpur, one of the most backward districts of India, is a predominantly tribal region of Rajasthan state. Ground water is the major source of drinking water in the region. High concentrations of Fluoride (F) and Iron (Fe) have been reported in the region which poses high risk of rampant water-borne diseases. This study evaluates the ground water quality for drinking purpose in terms of 10 hydro-geochemical and two biological parameters in context of causative factors like land use and geology of the region. The occurrence of water borne diseases in resident population has been examined in association with quality of drinking water and priority areas for policy intervention have been identified. Water samples have been collected from 173 drinking water sources and 346 households consuming water from the selected sources have been surveyed for prevalence of water borne diseases. Water quality surfaces have been mapped in terms of F, Total Dissolved Solids (TDS), Hardness, Fe, alkalinity, Faecal Coliform (FC), E. Coli, and Water Quality Index (WQI) computed on the basis of 10 geochemical parameters, using Inverse Distance Weighted (IDW) method. Results reveal that the north-eastern part comprising Aspur, Sabla and part of Sagwara tehsils predominantly has ‘very poor’ to ‘unsuitable’ water quality. The western part has excessive faecal contamination. Entire district has high levels of TDS and hardness, while excessive Fe and F occur in specific regions. Disease incidence closely corresponds to the geochemical and microbial composition of water. Higher values of WQI correspond with occurrence of Phyllites and Mica Schists.

Diagnostic algorithms and practice guidelines that adjust or “correct” their outputs on the basis of a patient’s race or ethnicity guide decisions in ways that may direct more attention or resources to white patients than to members of racial and ethnic minorities.

As a result of increasing use of wind energy as a sustainable source of electricity, large Wind Parks with numerous Wind Turbines have been constructed. Wind turbines are extremely tall objects consisting of stationary and moving parts. The presence of wind turbines in the vicinity of weather radar systems can significantly impact their performance, leading to false alarms and errors in radar measurements. Accurate weather forecasting is challenging in this circumstance. Large Radar Cross Section (RCS) of wind turbines results in interference, also known asWind Turbine Clutter (WTC) orWind Turbine Interference (WTI), within and beyond the radar main beam, Multipath Interference (MPI), and phenomena referred to as ”shadowing effects” behind the wind turbines. These effects vary significantly in both time and space as a result of various wind turbine operations and meteorological conditions. It can often be difficult to distinguish wind turbine returns from weather-like signals. For the assessment of WTC or WTI, it is essential to understand the scattering properties of these wind turbines. In this paper, the bistatic scattering characteristics of a wind park turbine using a Unmanned Aerial Vehicle (UAV)-mounted receiver recording C-band weather radar signals were investigated by determining the average received power (PRxAvg (θs)) and RCS of wind turbine as a function of the scattering angle. For this purpose, the measurements and data provided by the German Meteorological Service (DWD, DeutscherWetterdienst) were utilised. The average received power as a function of scattering angle (θs) was calculated by using I-Q (In-phase and Quadrature) signals. Forward, back and side scattering of the calculated average received power were analysed separately. Moreover, Front-to-Back ratio, Front-to-Right side ratio and Front-to-Left side ratio were calculated and compared using forward, back and side scatter values. RCS values were also calculated depending on the scattering angle (θs) of the wind turbine.

Land cover mapping using remote-sensing imagery has attracted significant attention in recent years. Classification of land use and land cover is an advantage of remote sensing technology which provides all information about land surface. Numerous studies have investigated land cover classification using different broad array of sensors, resolution, feature selection, classifiers, Classification Techniques and other features of interest from over the past decade. One, Pixel based image classification technique is widely used in the world which works on their per pixel spectral reflectance. Classification algorithms such as parallelepiped, minimum distance, maximum likelihood, Mahalanobis distance are some of the classification algorithms used in this technique. Other, Object based image classification is one of the most adapted land cover classification technique in recent time which also considers other parameters such as shape, colour, smoothness, compactness etc. apart from the spectral reflectance of single pixel.At present, there is a possibility of getting the more accurate information about the land cover classification by using latest technology, recent and relevant algorithms according to our study. In this study a combination of pixel-by-pixel image classification and object based image classification is done using different platforms like ArcGIS and e-cognition, respectively. The aim of the study is to analyze LULC pattern using satellite imagery and GIS for the Ahmedabad district in the state of Gujarat, India using a LISS-IV imagery acquired from January to April, 2017. The over-all accuracy of the classified map is 84.48% with Producer’s and User’s accuracy as 89.26% and 84.47% respectively. Kappa statistics for the classified map are calculated as 0.84. This classified map at 1:10,000 scale generated using recent available high resolution space borne data is a valuable input for various research studies over the study area and also provide useful information to town planners and civic authorities. The developed technique can be replicated for generating such LULC maps for other study areas as well.

Approximately by the year 2030, 40% of population of a country will be urbanised. This indicates a tremendous opportunity in sector of constructing units for fulfilling Residential as well as commercial requirements. Construction activity takes place on land, it must be noted that land solely is not responsible. There are various regulations which affect the extent to which construction/land utilization can takes place. The two most significant factors which affects utilization of land are Zoning and Development Control regulation. Zoning will broadly determine land use while DCR varies depending on size of plot, height achieved by a construction activity and purpose for which it is being used. Development plan determines zone in which a land will be lying (i.e., from Macroscopic point of view), it determines the activity permitted and largely the FSI allotted for each zone. While General Development Control Regulation (GDCR) gives detailed structure regarding permitted activities for the land as well as minimum area of construction depending on its typology. In addition to its height as well as margin depends on factors like Road length and surrounding structure. Using the buildable area of a plot, the total built-up area in a city can be calculated based on the FSI provided in various zones that helps in providing sufficient infrastructure for the future It also gives an estimate on how much land needs to be opened up in future to accommodate the future population Study focuses on developing a Geospatial solution which can incorporate all these factors when a particular construction activity needs to be conducted. By obtaining buildable are one can forecast various infrastructural elements which needs to be implant along with various emergency provision of Fire Safety and Identify the Roof tops to fixed up the Solar penal, develop a public utility, other such.