Taxonomic and molecular identification of Verpa bohemica: A newly explored fungi from Rajouri (J&K), India

A species of mushroom, Verpa bohemica was collected from lower Shivalik range of moist temperate Conifer forest of Rajouri and identified on the basis of morphological and molecular characterization. Universal fungus primers (ITS1 and ITS4) were used in amplification process of target region of rDNA (ITS1 5.8S I). Bioinformatics approach was followed for its molecular identification. Its rDNA sequence, when aligned in GenBank by performing BLAST, matches 100% with Verpa bohemica. The rDNA sequence of this species forms a distinct clade from the rest of species of the same genus. This species is being reported and explored first time from Rajouri Dist. of Jammu & Kashmir, India.&nbsp

Influence of cooling rate on the magnetic properties of Hf-Co-Fe-B melt-spun alloy

In the present work, Hf2Co9.5Fe1.5B melt-spun (MS) alloy is synthesized by employing melt spinning at different wheel speeds viz. 16, 20, 24 and 28 m/s to study the effect of quenching on the thermal, structural, microstructural and magnetic properties. The phase purity and the magnetic behaviour of the MS ribbons are highly dependent on the cooling rate that is controlled by altering the tangential wheel speed during melt spinning. Cooling rates are found to increase with increase in wheel speed with a concurrent decrease in the ribbon thickness owing to the increase in the heat transfer coefficient at the thermal contact. The best phase purity and the magnetic properties are found for the ribbons melt-spun at 28 m/s. This could be attributed to the high cooling rate 2.3 x 10(7) K/s causing crystallization of hard magnetic Hf2Co11B phase leading to refined grain size. A maximum coercivity (H-C) similar to 2.18 kOe, remanence ratio (M-r/M-s) similar to 0.61, an appreciable magnetic energy product (BH)(max) similar to 3 MGOe observed in the MS ribbons at 28 m/s illustrates the critical role of wheel speed in the enhancement of permanent magnetic properties in a single-step without annealing. XRD patterns reveal that the alloy was found to crystallize in orthorhombic Hf2Co11B in addition to cubic Co and Hf6Co23 phases. FE-SEM analysis is carried out to realize the grain morphology and phase identification. The current work exhibits the efficacy of rapid quenching by melt spinning as an effective technique in the development of high-performance Hf2Co9.5Fe1.5B rare-earth-free permanent magnet alloy for future energy applications in the high-temperature regime

Influence of wheel speed and ageing on nanostructure and magnetic properties of Cr-doped MnBi magnetic material

In the present work, Mn47Bi50Cr3 ribbons were synthesised employing melt spinning at different wheel speeds ranging from 16 to 28 m/s, to study the effect of quenching rate on the microstructure, morphology and magnetic properties of rapidly solidified alloy. X-ray diffraction studies indicate that the FWHM of diffraction peaks for MnBi increases with the increase in wheel speed, leading to a concurrent decrease in the mean grain size. This could be attributed to the high cooling rate causing homogeneous nucleation leading to refined grain size. The maximum value of coercivity of 11.9 kOe and saturation magnetisation of 54.2 emu/g was obtained for the alloy melt spun at 20 m/s, indicating dependence of coercivity on the grain size, and its orientation, which is largely controlled by the wheel speed. Also, XRD pattern confirms that the MnBi phase fraction is found to be maximum at this wheel speed. Therefore, high-performance nanocrystalline Mn47Bi50Cr3 magnetic material has been synthesised by adjusting the wheel speed and thereby tuning the quench rate. In addition, the phase transformation and variation with respect to temperature and time were studied using thermal analysis technique. Stability of magnetic properties of the alloy with respect to time was also studied after the ageing process

Swyer syndrome presenting as primary infertility

Swyer syndrome was first described by Jim Swyer in 1955. It is a form of “Pure gonadal dysgenesis”. The affected female has 46, XY karyotype. A 21 year old married female came with complaints of primary infertility. On examination she has normal built with normal secondary sexual characteristics. She had normal vaginal opening with small uterus. Serum FSH was 71.54 mIU/ml. Thyroid and Prolactin was in normal range. Karyotype showed genotype of 46, XY. Diagnostic laparoscopy showed streak gonads, small uterus, and normal patent fallopian tubes. Diagnosis of Swyer syndrome was made

Diversity of fungi as human pathogen

Worldwide human pathogenic fungi cause I nvasive diseases, pose a serious and growing health problem and are a major cause of death. Superficial mycosis is more prevalent in tropical and subtropical countries including India, where heat and moisture play an important role in promoting of Anthropophilic dermatophytes and tends to get worse during summer, with symptoms alleviating during the winter. Such fungi are known as Dermatophytes and usually colonize the outer layer of the skin, occasionally invade subcutaneous tissues, resulting in kerion development of ringworm symptoms. These symptoms develop by a number of different fungal species e.g. Trichophyton , Microsporum  and Epidermatophyton are proved most common causative agents. Such fungi attack various parts of the body and lead to Dermatophytosis as Tinea pedis (athlete's foot) affects  on the feet; Tinea unguium on the fingernails and toenails; Tinea corporis on the arms, legs and trunk, Tinea cruris (jock itch) groin area ; Tinea manuum  hands and palm area ,Tinea capitis on the scalp, Tinea barbae affects facial hair; Tinea faciei on the face etc..  The other superficial mycoses (not classic ringworm or dermatophytes) are Tinea versicolor caused by Malassezia furfur and Tinea nigra caused by Hortaea werneckii

Antimicrobial effectiveness of Nano Silver Fluoride Varnish in reducing Streptococcus mutans in saliva and plaque biofilm when compared with Chlorhexidine and Sodium Fluoride Varnishes

This in vivo study was done to investigate the antimicrobial effectiveness of Nano Silver fluoride, Sodium fluoride and Chlorhexidine when used as a varnish on Streptococcus mutans (S.mutans) in saliva and plaque biofilm. 120 caries free subjects, aged

Design and Characterization of Dual Drug Loaded Microspheres for Colon Drug Targeting

The present investigation was focus to prepared and characterized eudragit coated pectin microspheres for the delivery of mesalamine and prednisolone to the colon. The pectin microspheres were prepared using emulsion dehydration technique. A 33 full factorial design (three variables in three levels) was employed to evaluate the combined effect of the selected independent variables: drugs to polymer ratio, emulsifier concentrations and stirring speed on dependent variables such as particle size and size distribution, percentage yield, % drug entrapment and swelling ratio. Optimized formulation i.e. F18, F24, and F27 were coated with eudragit S100 by the solvent evaporation technique to prevent drug release in the stomach. Eudragit S100 coated pectin microspheres were further characterized for coating thickness and in-vitro release kinetics. The cumulative percent drug release of mesalamine and prednisolone from formulation in pH 7.4 phosphate buffer was found to be 97.01 + 1.35% and 96.89 + 0.67% for mesalamine and prednisolone, respectively. Optimized formulation (F24) was characterized for in-vivo studies. The eudragit-coated pectin microspheres may improve therapeutic efficacy by local action and reduce the side effects by minimizing the systemic absorption of mesalamine and prednisolone. Amalgamation of mesalamine and prednisolone in therapeutic regimen will show synergism action for treatment of UC.     &nbsp

Detecting Crop Health using Machine Learning Techniques in Smart Agriculture System

699-706The crop diseases can’t detected accurately by only analysing separate disease basis. Only with the help of making comprehensive analysis framework, users can get the predictions of most expected diseases. In this research, IOT and machine learning based technique capable of processing acquisition, analysis and detection of crop health information in the same platform is introduced. The proposed system supports distinguished services by monitoring crop and also managed its data, devices and models. This system also supports data sharing and communication with the help of IOT using unmanned aerial vehicle (UAV) and maintains high communication standards even in bad communication environment. Therefore, IOT and machine learning ensures the high accuracy of disease prediction in crop. The proposed integrated system is capable of detecting health of crop through analysis of multi-spectral images captured through the IOT associated UAV. The various machine learning is also applied to test the performance of our system and compared with the existing disease detection methods

An Optimized Approach for Feature Extraction in Multi-Relational Statistical Learning

537-542Various features come from relational data often used to enhance the prediction of statistical models. The features increases as the feature space increases. We proposed a framework, which generates the features for feature selection using support vector machine with (1) augmentation of relational concepts using classification-type approach (2) various strategy to generate features. Classification are used to increase the productivity of feature space by adding new techniques used to create new features and lead to enhance the accuracy of the model. The feature generation in run-time lead to the building of models with higher accuracy despite generating features in advance. Our results in different applications of data mining in different relations are far better from existing results

Detecting Crop Health using Machine Learning Techniques in Smart Agriculture System

The crop diseases can’t detected accurately by only analysing separate disease basis. Only with the help of making comprehensive analysis framework, users can get the predictions of most expected diseases. In this research, IOT and machine learning based technique capable of processing acquisition, analysis and detection of crop health information in the same platform is introduced. The proposed system supports distinguished services by monitoring crop and also managed its data, devices and models. This system also supports data sharing and communication with the help of IOT using unmanned aerial vehicle (UAV) and maintains high communication standards even in bad communication environment. Therefore, IOT and machine learning ensures the high accuracy of disease prediction in crop. The proposed integrated system is capable of detecting health of crop through analysis of multi-spectral images captured through the IOT associated UAV. The various machine learning is also applied to test the performance of our system and compared with the existing disease detection methods