Role of an Agro Technique for the Development of Medicinal Plant: Shalparni

Vrikshayurveda a classical science of botanical field, this science deals with growth and development of plants. This science mainly suggests uses of Kunapa jala and Pancha gavya for the high productivity of crops. This concept works around organic farming for effective plant growth. Vrikshayurvedha mentioned Kunapajala as organic liquid manure which is a fermentation product and acts as plant nutrients. There are two types of Kunapajala mainly in practices viz; herbal and non-herbal which is prepared according to the procedures described in Vrikshayurvedha. Considering importance of this we planned a study to investigate role of Vrikshayurvedha concept for the development of medicinal plant; Shalparni. Present study investigated role of Kunapajala in the growth and % yield of plant Shalparni. This study observed that Kunapajala treatment offered best response with respect to root yield and soil physico-chemical parameters. Kunapajala increased plant yield quantitatively as well as qualitatively

A human somatostatin receptor (SSTR3), located on chromosome 22, displays preferential affinity for somatostatin-14 like peptides

AbstractWe report here on the cloning of a human intronless gene encoding a member of the G-protein linked somatostatin (SST) receptor subfamily, termed SSTR3. Based on the deduced amino acid sequence, this gene encodes a 418 amino acid protein displaying sequence similarity, particularly within putative transmembrane domains, with the recently cloned human SSTR1 (62%), SSTR2 (64%) and SSTR4 (58%) receptors. Membranes prepared from COS-7 cells transiently expressing the human SSTR3 gene bound [125I]Leu8,d-Trp22,-Tyr22 SST-28 in a saturable manner with high affinity (~200 pM) and with rank order of potency (d-Trp8 SST-14 > SST-14 > SMS-201-995 > SST-28) indicative of a somatostatin-14 selective receptor. The pharmacological profile of the expressed human SSTR3 receptor is similar but not identical to that reported for the rat homolog [(1992) J. Biol. Chem. 267,20422] where the peptide selectivity is SST-28 ≧ SST-14 XXX SMS-201-995. Northern blot analysis reveals the presence of an SSTR3 mRNA species of ~5 kb in various regions of the monkey brain, including the frontal cortex, cerebellum, medulla, amygdala, with little or no SSTR3 mRNA detectable in brain regions such as the striatum, hippocampus, and olfactory tubercle. The SSTR3 receptor gene maps to human chromosome 22. The existence of at least four distinct human genes encoding somatostatin-14 selective receptors with diverse pharmacological specificities may help to account for some of the multiple biological actions of somatostatin under normal and pathological conditions

CAS-MINE: Providing personalized services in context-aware applications by means of generalized rules

Context-aware systems acquire and exploit information on the user context to tailor services to a particular user, place, time, and/or event. Hence, they allowservice providers to adapt their services to actual user needs, by offering personalized services depending on the current user context. Service providers are usually interested in profiling users both to increase client satisfaction and to broaden the set of offered services. Novel and efficient techniques are needed to tailor service supply to the user (or the user category) and to the situation inwhich he/she is involved. This paper presents the CAS-Mine framework to efficiently discover relevant relationships between user context data and currently asked services for both user and service profiling. CAS-Mine efficiently extracts generalized association rules, which provide a high-level abstraction of both user habits and service characteristics depending on the context. A lazy (analyst-provided) taxonomy evaluation performed on different attributes (e.g., a geographic hierarchy on spatial coordinates, a classification of provided services) drives the rule generalization process. Extracted rules are classified into groups according to their semantic meaning and ranked by means of quality indices, thus allowing a domain expert to focus on the most relevant patterns. Experiments performed on three context-aware datasets, obtained by logging user requests and context information for three real applications, show the effectiveness and the efficiency of the CAS-Mine framework in mining different valuable types of correlations between user habits, context information, and provided services

Evaluation and identification of promising pearl millet germplasm for grain and fodder traits

Results are presented of experiments aiming to evaluate and characterize available germplasm lines, landraces and core collection lines of pearl millet and to exploit genetic variability so as to identify promising germplasm lines for metric traits for utilization in different breeding programmes and develop early and high-yielding potential hybrids/open-pollinated cultivars for grain and fodder in drier areas of India. The materials consisted of 2375 germplasm accessions, 180 landraces and 504 core collection lines. The germplasm lines and landraces were evaluated during the 2006 rainy season in Mandor, Jodhpur, Rajasthan, India. The accessions were sown in 22 blocks. Each block consisted of 120 accessions and 4 controls (J 2405, J 2340, NMP 24 and PP 29). The pearl millet core collection was also evaluated during the 2006 rainy season at 3 locations, i.e. AICPMIP, Mandor, Jodhpur; Agricultural Research Station, Rajasthan Agricultural University, Durgapura, Jaipur, Rajasthan; and Millet Research Station, Junagadh Agricultural University, Jamnagar, Gujarat. The core collection accessions were accommodated in 19 blocks. Each block consisted of 27 test accessions and 3 controls, namely IP 17862 (ICTP 8203), IP 22281 (Raj 171) (grain type) and IP 3616 (fodder type). After every 9 test accessions, one of the 3 controls was sown. Observations were recorded on morphological/metric traits, i.e. vigour, days to 50% flowering, days to maturity, plant height, productive tillers per plant, total tillers per plant, nodes per plant, sheath length, blade length and width, panicle exsertion, length, thickness and density, bristle length, 1000-grain weight, grain yield, grain yield potential, green fodder yield, green fodder yield potential, and overall plant aspect

Seedless Pattern Growth of Quasi-Aligned ZnO Nanorod Arrays on Cover Glass Substrates in Solution

A hybrid technique for the selective growth of ZnO nanorod arrays on wanted areas of thin cover glass substrates was developed without the use of seed layer of ZnO. This method utilizes electron-beam lithography for pattern transfer on seedless substrate, followed by solution method for the bottom-up growth of ZnO nanorod arrays on the patterned substrates. The arrays of highly crystalline ZnO nanorods having diameter of 60 ± 10 nm and length of 750 ± 50 nm were selectively grown on different shape patterns and exhibited a remarkable uniformity in terms of diameter, length, and density. The room temperature cathodluminescence measurements showed a strong ultraviolet emission at 381 nm and broad visible emission at 585–610 nm were observed in the spectrum

HI Fluctuations at Large Redshifts: III - Simulating the Signal Expected at GMRT

We simulate the distribution of neutral hydrogen (HI) at the redshifts z=1.3 and 3.4 using a cosmological N-body simulation along with a prescription for assigning HI masses to the particles. The HI is distributed in clouds whose properties are consistent with those of the damped Lyman-\alpha absorption systems (DLAs) seen in quasar spectra. The clustering properties of these clouds are identical to those of the dark matter. We use this to simulate the redshifted HI emission expected at 610 MHz and 325 MHz, two of the observing bands a the GMRT. These are used to predict the correlations expected between the complex visibilities measured at different baselines and frequencies in radio-interferometric observations with the GMRT. The visibility correlations directly probe the power spectrum of HI fluctuations at the epoch when the HI emission originated, and this holds the possibility of using HI observations to study large-scale structures at high z.Comment: Submitted to JApA, 12 Latex pages including 6 figure

Data Sciences Technology for Homeland Security Information Management and Knowledge Discovery

The Department of Homeland Security (DHS) has vast amounts of data available, but its ultimate value cannot be realized without powerful technologies for knowledge discovery to enable better decision making by analysts. Past evidence has shown that terrorist activities leave detectable footprints, but these footprints generally have not been discovered until the opportunity for maximum benefit has passed. The challenge faced by the DHS is to discover the money transfers, border crossings, and other activities in advance of an attack and use that information to identify potential threats and vulnerabilities. The data to be analyzed by DHS comes from many sources ranging from news feeds, to raw sensors, to intelligence reports, and more. The amount of data is staggering; some estimates place the number of entities to be processed at 1015. The uses for the data are varied as well, including entity tracking over space and time, identifying complex and evolving relationships between entities, and identifying organization structure, to name a few. Because they are ideal for representing relationship and linkage information, semantic graphs have emerged as a key technology for fusing and organizing DHS data. A semantic graph organizes relational data by using nodes to represent entities and edges to connect related entities. Hidden relationships in the data are then uncovered by examining the structure and properties of the semantic graph

Ontology-guided data preparation for discovering genotype-phenotype relationships

International audienceComplexity of post-genomic data and multiplicity of mining strategies are two limits to Knowledge Discovery in Databases (KDD) in life sciences. Because they provide a semantic frame to data and because they benefit from the progress of semantic web technologies, bio-ontologies should be considered for playing a key role in the KDD process. In the frame of a case study relative to the search of genotype-phenotype relationships, we demonstrate the capability of bio-ontologies to guide data selection during the preparation step of the KDD process. We propose three scenarios to illustrate how domain knowledge can be taken into account in order to select or aggregate data to mine, and consequently how it can facilitate result interpretation at the end of the process