A study on light trap catches of some rice pests in relation to meteorological factors

The effect of meteorological factors on the population build up of green leafhopper Nephotettix virescens Dist (Cicadellidae, Hemiptera), plant hoppers Cofana spectra Dist (Delphacidae, Hemiptera) and C. yasumatsui Young (Kolla mimica, Hemiptera) and rice gundhi bug Leptocoriza acuta Thunberg (Alydidae, Hemiptera) in rice growing season (July to November) was studied through light trap collection during ten years (1988–1997). Maximum populations of Nephotettix virescens Dist (Cicadellidae, Hemiptera) and C. yasumatsui Young (Kolla mimica, Hemiptera) were recorded in the third week of October during all the years. Cofana spectra Dist had maintained peak activity in respect of population in the last week of September and third week of October. Leptocoriza acuta Thunberg (Alydidae, Hemiptera) had maximum population in second and third weeks of October during the aforesaid period. No meteorological factors have significant effect on the population build up of Nephotettix virescens Dist, Cofana spectra Dist and C. yasumatsui Young in the month of October. In the case of Leptocoriza acuta Thunberg, no other factor but rainfall had positive correlation of order 0.857 with population build up in the fourth week of September.Key words/phrases: Meteorological factors, path analysis, regression analysis, rice insect pests SINET: Ethiopian Journal of Science: Vol. 27 (2) 2004: 165–17

Assessment of genetic and biochemical diversity of ecologically variant ectomycorrhizal Russula sp. from India

The aim of this study was to develop the phylogenetic relationship amongst the ecologically variant Russula species by using polymerase chain reaction (PCR) based technique, random amplified polymorphic DNA (RAPD) and isozyme analyses. Two groups could be characterized amongst the total isolates by cluster analyses. Protease, cellulase, glutamate dehydrogenase,  pectinase and acid phosphatase designated band P220.16, C472.18, GLD130.21, Pe569.12 and AP472.12, respectively, were common in all the isolates and four monomorphic RAPD bands viz; 818, 512, 298 and 201 bp were also diversified in the isolates. This common band reveals that diversity of these alleles or loci in all ecologically variant isolates. Thus, the present studies discuss the genetic diversity of ecologically variant Russula species on the basis of RAPD and isozyme analysis

Groundnut Entered Post-genome Sequencing Era: Opportunities and Challenges in Translating Genomic Information from Genome to Field

Cultivated groundnut or peanut (Arachis hypogaea) is an allopolyploid crop with a large complex genome and genetic barrier for exchanging genetic diversity from its wild relatives due to ploidy differences. Optimum genetic and genomic resources are key for accelerating the process for trait mapping and gene discovery and deploying diagnostic markers in genomics-assisted breeding. The better utilization of different aspects of peanut biology such as genetics, genomics, transcriptomics, proteomics, epigenomics, metabolomics, and interactomics can be of great help to groundnut genetic improvement program across the globe. The availability of high-quality reference genome is core to all the “omics” approaches, and hence optimum genomic resources are a must for fully exploiting the potential of modern science into conventional breeding. In this context, groundnut is passing through a very critical and transformational phase by making available the required genetic and genomic resources such as reference genomes of progenitors, resequencing of diverse lines, transcriptome resources, germplasm diversity panel, and multi-parent genetic populations for conducting high-resolution trait mapping, identification of associated markers, and development of diagnostic markers for selected traits. Lastly, the available resources have been deployed in translating genomic information from genome to field by developing improved groundnut lines with enhanced resistance to root-knot nematode, rust, and late leaf spot and high oleic acid. In addition, the International Peanut Genome Initiative (IPGI) have made available the high-quality reference genome for cultivated tetraploid groundnut which will facilitate better utilization of genetic resources in groundnut improvement. In parallel, the development of high-density genotyping platforms, such as Axiom_Arachis array with 58 K SNPs, and constitution of training population will initiate the deployment of the modern breeding approach, genomic selection, for achieving higher genetic gains in less time with more precision

Heterogeneous expression and functional evaluation of in silico characterized recombinant OmpC of Salmonella Typhimurium as a functional poultry vaccine to eradicate zoonotic transmission

Salmonellosis is one of the major global health concerns leading to millions of deaths annually. The present vaccines not being up to the mark necessitate the need for the development of new generation vaccines. Outer membrane proteins (Omps) of several Gram negative bacteria have been investigated and found to be immunogenic and protective. The present study explores the potential of a major porin protein (OmpC) of Salmonella Typhimurium, as a vaccine candidate. The OmpC 3D structure and its potential to bind effectively with antibodies and generate humoral response was investigated using in silico docking, and expressed in a heterogeneous Escherichia coli M15 host strain. The rOmpC was purified and its immunopotential was evaluated in vitro by western blotting and in vivo in three weeks old chicks. The recombinant OmpC produced a significant humoral response and in vaccinated birds 100% survival rate was observed along with delay in the shedding of organism in droppings. These findings indicate that the rOmpC vaccination prevents mortality in chicken and lowers fecal shedding in droppings.Key words: Outer membrane protein (Omp), Salmonellosis, Salmonella Typhimurium, subunit vaccines, porins,rigid-body docking

Analysis of Computer Network Security Storage System Based on Cloud Computing Environment

A fundamental component of cloud computers from a business perspective is that users are allowed to use any desire and pay with a product that desire. Its cloud services were accessible anytime and anywhere consumers needed them. As a result, consumers are free to purchase whatever IT services they want, and they don't have to worry about how easy things can be managed. The remote server is used in a new information storage computing architecture that is considered an Internet generation. Ensuring security, material at resource providers' sites is a challenge that must be addressed in cloud technology. Thus, rather than reliance on a single provider for knowledge storing, this research implies developing construction for protection of knowledge stockpiling with a variation of operations, in which knowledge is scrambled and divided into numerous cipher frames and distributed across a large number of provider places. This support was applied to provide greater security, scalability, or reliability that was suggested according to the new structure. This paper, presented an encoded model for the cloud environment to improve security. The proposed model comprises the parity metadata for the database management provision to the provider. In the developed encoder chunks parity is not stored within the single resources with the provision of the available information chunks. The constructed security architecture in the RAID layer increases the dependability of the data with the deployment of the RAID 10 deployment. The developed RAID-based encoder chunks exhibit improved efficiency for the higher uptime at a minimal cost

Recommendation Model-Based 5G Network and Cognitive System of Cloud Data with AI Technique in IOMT Applications

Recommender system provides the significant suggestion towards the effective service offers for the vast range of big data. The Internet of Things (IoT) environment exhibits the value added application services to the customer with the provision of the effective collection and processing of information. In the extension of the IoT, Internet of Medical Things (IoMT) is evolved for the patient healthcare monitoring and processing. The data collected from the IoMT are stored and processed with the cognitive system for the data transmission between the users. However, in the conventional system subjected to challenges of processing big data while transmission with the cognitive radio network. In this paper, developed a effective cognitive 5G communication model with the recommender model for the IoMT big data processing. The proposed model is termed as Ranking Strategy Internet of Medical Things (RSIoMT). The proposed RSIoMT model uses the distance vector estimation between the feature variables with the ranking. The proposed RSIoMT model perform the recommender model with the ranking those are matches with the communication devices for improved wireless communication quality. The proposed system recommender model uses the estimation of direct communication link between the IoMT variables in the cognitive radio system. The proposed RSIoMT model evaluates the collected IoMT model data with the consideration of the four different healthcare datasets for the data transmission through cognitive radio network. Through the developed model the performance of the system is evaluated based on the deep learning model with the consideration of the collaborative features. The simulation analysis is comparatively examined based on the consideration of the wireless performance. Simulation analysis expressed that the proposed RSIoMT model exhibits the superior performance than the conventional classifier. The comparative analysis expressed that the proposed mode exhibits ~3 – 4% performance improvement over the conventional classifiers. The accuracy of the  developed model achieves 99% which is ~3 – 9% higher than the conventional classifier. In terms of the channel performance, the proposed RSIoMT model exhibits the reduced recommender relay selection count of 1 while the other technique achieves the relay value of 13 which implies that proposed model performance is ~4-6% higher than the other techniques

Management of a Fungal Perinephric Abscess (PNA): Dilemma Revisited

ABSTRACT Candida species can cause a wide variety of clinical syndromes, especially in immunocompromised and diabetic patients. Primary perirenal candidial abscess has been a rare reported entity. Here we report a rare case of primary PNA due to Candida species in an immunocompetent patient with diabetes mellitus and its minimal invasive management. Retrospective and prospective analysis of clinical, laboratory and radiological records along with continued follow up of patient was done. This patient was 48-year-old man, admitted with burning in micturition, right flank discomfort and low grade fever since 1 year. Abdominal ultrasound and computerized tomography were suggestive of a PNA of the right kidney. Candida species was isolated from sample obtained by C.T. guided needle aspiration. Culture of aspirate showed sensitivity to azoles. Systemic antifungal therapy based on culture report was started in form of oral drug. The patient responded well leading to resolution of lump and the fever. Appropriate timely treatment appears to be having a promising role in definitive therapy for renal and PNA due to Candida even in immunocompetent host with predisposing factors such as diabetes mellitus. This case highlights the fact that fungal infections should be included in the differential diagnosis of PNA in such patients

Genome-based trait prediction in multi- environment breeding trials in groundnut

Genomic selection (GS) can be an efficient and cost-effective breeding approach which captures both small- and large-effect genetic factors and therefore promises to achieve higher genetic gains for complex traits such as yield and oil content in groundnut. A training population was constituted with 340 elite lines followed by genotyping with 58 K ‘Axiom_Arachis’ SNP array and phenotyping for key agronomic traits at three locations in India. Four GS models were tested using three different random cross-validation schemes (CV0, CV1 and CV2). These models are: (1) model 1 (M1 = E + L) which includes the main effects of environment (E) and line (L); (2) model 2 (M2 = E + L + G) which includes the main effects of markers (G) in addition to E and L; (3) model 3 (M3 = E + L + G + GE), a naïve interaction model; and (4) model 4 (E + L + G + LE + GE), a naïve and informed interaction model. Prediction accuracy estimated for four models indicated clear advantage of the inclusion of marker information which was reflected in better prediction accuracy achieved with models M2, M3 and M4 as compared to M1 model. High prediction accuracies (> 0.600) were observed for days to 50% flowering, days to maturity, hundred seed weight, oleic acid, rust@90 days, rust@105 days and late leaf spot@90 days, while medium prediction accuracies (0.400–0.600) were obtained for pods/plant, shelling %, and total yield/plant. Assessment of comparative prediction accuracy for different GS models to perform selection for untested genotypes, and unobserved and unevaluated environments provided greater insights on potential application of GS breeding in groundnut

Estimation of greenhouse gas emissions from spontaneous combustion/fire of coal in opencast mines – Indian context

There are a significant number of uncontrolled coal mine fires (primarily due to spontaneous combustion of coal), which are currently burning all over the world. These spontaneous combustion sources emit greenhouse gases (GHGs). A critical review reveals that there are no standard measurement methods to estimate GHG emissions from mine fire/spontaneous combustion areas. The objective of this research paper was to estimate GHGs emissions from spontaneous combustion of coals in the Indian context. A sampling chamber (SC) method was successfully used to assess emissions at two locations of the Enna Opencast Project (OCP), Jharia Coalfield (JCF), for 3 months. The study reveals that measured cumulative average emission rate for CO2 varies from 75.02 to 286.03 gs−1m−1 and CH4 varies from 41.49 to 40.34 gs−1m−1 for low- and medium-temperature zones. The total GHG emissions predicted from this single fire affecting mines of JCF vary from 16.86 to 20.19 Mtyr−

Bianchi Type-II String Cosmological Models in Normal Gauge for Lyra's Manifold with Constant Deceleration Parameter

The present study deals with a spatially homogeneous and anisotropic Bianchi-II cosmological models representing massive strings in normal gauge for Lyra's manifold by applying the variation law for generalized Hubble's parameter that yields a constant value of deceleration parameter. The variation law for Hubble's parameter generates two types of solutions for the average scale factor, one is of power-law type and other is of the exponential form. Using these two forms, Einstein's modified field equations are solved separately that correspond to expanding singular and non-singular models of the universe respectively. The energy-momentum tensor for such string as formulated by Letelier (1983) is used to construct massive string cosmological models for which we assume that the expansion ($\theta$) in the model is proportional to the component $\sigma^{1}_{~1}$ of the shear tensor $\sigma^{j}_{i}$. This condition leads to $A = (BC)^{m}$, where A, B and C are the metric coefficients and m is proportionality constant. Our models are in accelerating phase which is consistent to the recent observations. It has been found that the displacement vector $\beta$ behaves like cosmological term $\Lambda$ in the normal gauge treatment and the solutions are consistent with recent observations of SNe Ia. It has been found that massive strings dominate in the decelerating universe whereas strings dominate in the accelerating universe. Some physical and geometric behaviour of these models are also discussed.Comment: 24 pages, 10 figure