Seasonal variation of Aeromycoflora of Chhattisgarh Institute of Medical Sciences (CIMS), Bilaspur, C.G. (India)

This study was carried out to study the diversity of fungi in hospital environment. Gravity settle plate method was used for the isolation of aeromycoflora. The investigation period for this study was from July 2011 to June 2012. During this investigation total 62 fungal species (571 fungal colonies) belonging to 33 fungal genera were isolated. Various environmental factors: wind, moisture, temperature and air pollution affect and alter the density and frequency of a fungal species in any medium. Furthermore, temperature, water potential, humidity and pH have a critical influence on the growth and survival of fungi. It was observed the concentration of the spores in the air varies from season to season probably due to variation in meteorological parameters

A Review of Second Generation of Terrestrial Digital Video Broadcasting System

Digital broadcast systems have increasingly been deployed for various services such as Digital Video Broadcasting (i.e. DVB-S, DVB-T, etc.) and Digital Audio Broadcasting (DAB). Classical digital broadcast systems were designed with fixed modulation guarantee reliable communication even with very hostile channel environment.  DVB-T2 terrestrial television standard is becoming increasingly important.  The emergence of it is motivated by the higher spectral efficiency and adopting  transition from analogue TV to DVB-T2, or transition from DVB-T to DVB-T2. It can reduce the transmission cost per program and deliver HD services economically viable. It introduces a new technique to improve performance in channels with frequency selective fading. If in addition improved source coding (MPEG-4) is employed, the gain in broadcast transmission is remarkable.   Keywords: DVB-T, DVB-T2, Digital video broadcastin

Segmentation Techniques through Machine Based Learning for Latent Fingerprint Indexing and Identification

201-208Latent fingerprints have become most important evidence in law enforcement department and forensic agencies worldwide. It is also very important evidence in forensic applications to identify criminals as it is mostly encountered in crime scenes. Segmentation is one of the solutions to extract quality features. Fingerprint indexing reduces the search space without compromising accuracy. In this paper, minutiae based rotational and translational features and a global matching approach in combination with local matching is used in order to boost the indexing efficiency. Also, a machine learning (ML) based segmentation model is designed as a binary classification model to classify local blocks into foreground and background. Average indexed time as well as accuracy for full as well as partial fingerprints is tabulated by varying the template sminutiae

STUDY OF VEGETATION IN PT. RAVISHANKAR SHUKLA UNIVERSITY CAMPUS, RAIPUR CHHATTISGARH WITH SPECIAL REFERENCE TO STATISTICS DEPARTMENT

ABSTRACT Plants used for treatment of various diseases are of significant value throughout the world. Among the plant diversity some of them have great potential to treat many diseases which are referred as medicinal plants. The main aim of the present study is to focus on the diversity of plants for further utility and conservation. Current research is a useful account on medicininal plant in statistics department Pt. Ravishankar Shukla University, campus Raipur (Chhattisgarh). A survey on plant diversity was made during 01 June 2013 to 16 June 2013 . After field survey, observed medicinal plants were listed: by botanical name, family, habit, uses and propagation with the help of available literature. Total of 56 medicinal plants species belonging to 26 families were recorded, which indicate the heterogenous floristic composition in the University campus. Maximum species diversity was recorded under the family Fabaceae. Over the recorded medicinal plants 78% plants were propagated by their seeds. Herbaceous medicinal plants showed their maximum presence in the study area

Segmentation Techniques through Machine Based Learning for Latent Fingerprint Indexing and Identification

Latent fingerprints have become most important evidence in law enforcement department and forensic agencies worldwide. It is also very important evidence in forensic applications to identify criminals as it is mostly encountered in crime scenes. Segmentation is one of the solutions to extract quality features. Fingerprint indexing reduces the search space without compromising accuracy. In this paper, minutiae based rotational and translational features and a global matching approach in combination with local matching is used in order to boost the indexing efficiency. Also, a machine learning (ML) based segmentation model is designed as a binary classification model to classify local blocks into foreground and background. Average indexed time as well as accuracy for full as well as partial fingerprints is tabulated by varying the template sminutiae

Latent Fingerprint Indexing for Faster Retrieval from Dataset with Image Enhancement Technique

730-753Since decades fingerprints have been the prime source in identification of suspects latent fingerprints are compared and examined with rolled and plain fingerprints which are stored in the dataset. The common challenges which are faced while examining latent fingerprints are background noise, nonlinear distortions, poor ridge clarity and partial impression of the finger. As conventional methods of Segmentation doesn’t perform well on latent fingerprints. The current advancement in machine learning based segmentation approach has been showing good results in terms of segmentation accuracy but lacks to provide accurate result in terms of matching accuracy. As one of the problem faced in matching latent fingerprint is low clarity of ridge-valley pattern which results in detection of false minutiae and poor matching accuracy. A multilayer processing of artificial neural network based segmentation is proposed to minimize the detection of false minutiae and increase the matching accuracy. This approach is designed on binary classification model where the simulation will be carried out on IIIT-D latent fingerprint dataset. Segmentation will be divided into full and partial impression fingerprints which are then compared with minutiae with the database using local and global matching algorithm. An improvised result is received which is more accurate as compared to the previous algorithms

Securing Cloud from Tampering and Duplication

Cloud computing is the most emerging technology today which is used by most of the social media sites to store the data. The data stored on the cloud is private data of the user so it must not be tampered by other entities. The previous system has worked on reducing the storage space by copying and archiving data but on the cost of reduced performance rate. We propose a system to enhance the storage space by performing deduplication on data and shuffling the data,between the number of directories within cloud after particular interval of time to avoid the tracking of data to enhance the security. The backup of the data will be taken timely into the back up directory. The proposed system will provide ease to use the cloud

Synthesis and biological evaluation of thiazolidinedione derivatives of chalcones and flavones as antihyperglycemic and antidyslipidemic agents

A series of chalcone and flavone derivatives (6a-d, 9a-f) based on 2,4-thiazolidinedione have been synthesized and evaluated for in vivo antihyperglycemic activity in sucrose loaded (SLM) and streptozotocin (STZ) induced diabetic animal models and also for antidyslipidemic activity in the triton model. Compounds 9d, 9e, and 9f exhibited potent blood glucose-lowering activity in both SLM and STZ models. Compounds 6c, 6d, and 9c, 9e, and 9f showed moderate lipid-lowering activity. The selected most potent compounds 6d and 9e were also studied in db/db mice for both antihyperglycemic and antidyslipidemic activity

Synthesis and biological evaluation of thiazolidinedione derivatives of chalcones and flavones as antihyperglycemic and antidyslipidemic agents

579-588A series of chalcone and flavone derivatives (6a-d, 9a-f) based on 2,4-thiazolidinedione have been synthesized and evaluated for in vivo antihyperglycemic activity in sucrose loaded (SLM) and streptozotocin (STZ) induced diabetic animal models and also for antidyslipidemic activity in the triton model. Compounds 9d, 9e, and 9f exhibited potent blood glucose-lowering activity in both SLM and STZ models. Compounds 6c, 6d, and 9c, 9e, and 9f showed moderate lipid-lowering activity. The selected most potent compounds 6d and 9e were also studied in db/db mice for both antihyperglycemic and antidyslipidemic activity

Shifting the limits in wheat research and breeding using a fully annotated reference genome

Introduction: Wheat (Triticum aestivum L.) is the most widely cultivated crop on Earth, contributing about a fifth of the total calories consumed by humans. Consequently, wheat yields and production affect the global economy, and failed harvests can lead to social unrest. Breeders continuously strive to develop improved varieties by fine-tuning genetically complex yield and end-use quality parameters while maintaining stable yields and adapting the crop to regionally specific biotic and abiotic stresses. Rationale: Breeding efforts are limited by insufficient knowledge and understanding of wheat biology and the molecular basis of central agronomic traits. To meet the demands of human population growth, there is an urgent need for wheat research and breeding to accelerate genetic gain as well as to increase and protect wheat yield and quality traits. In other plant and animal species, access to a fully annotated and ordered genome sequence, including regulatory sequences and genome-diversity information, has promoted the development of systematic and more time-efficient approaches for the selection and understanding of important traits. Wheat has lagged behind, primarily owing to the challenges of assembling a genome that is more than five times as large as the human genome, polyploid, and complex, containing more than 85% repetitive DNA. To provide a foundation for improvement through molecular breeding, in 2005, the International Wheat Genome Sequencing Consortium set out to deliver a high-quality annotated reference genome sequence of bread wheat. Results: An annotated reference sequence representing the hexaploid bread wheat genome in the form of 21 chromosome-like sequence assemblies has now been delivered, giving access to 107,891 high-confidence genes, including their genomic context of regulatory sequences. This assembly enabled the discovery of tissue- and developmental stage–related gene coexpression networks using a transcriptome atlas representing all stages of wheat development. The dynamics of change in complex gene families involved in environmental adaptation and end-use quality were revealed at subgenome resolution and contextualized to known agronomic single-gene or quantitative trait loci. Aspects of the future value of the annotated assembly for molecular breeding and research were exemplarily illustrated by resolving the genetic basis of a quantitative trait locus conferring resistance to abiotic stress and insect damage as well as by serving as the basis for genome editing of the flowering-time trait. Conclusion: This annotated reference sequence of wheat is a resource that can now drive disruptive innovation in wheat improvement, as this community resource establishes the foundation for accelerating wheat research and application through improved understanding of wheat biology and genomics-assisted breeding. Importantly, the bioinformatics capacity developed for model-organism genomes will facilitate a better understanding of the wheat genome as a result of the high-quality chromosome-based genome assembly. By necessity, breeders work with the genome at the whole chromosome level, as each new cross involves the modification of genome-wide gene networks that control the expression of complex traits such as yield. With the annotated and ordered reference genome sequence in place, researchers and breeders can now easily access sequence-level information to precisely define the necessary changes in the genomes for breeding programs. This will be realized through the implementation of new DNA marker platforms and targeted breeding technologies, including genome editing