Response of Bio-priming in okra for vegetable production

The field experiment was conducted at District Seed Farm, Bidhan Chandra Krishi Viswavidyalaya, Kalyani, Nadia, West Bengal in summer season of 2011 and 2012. Pre-sowing bio-priming was done with Trichoderma viride and Pseudomonas fluorescens with an un-primed control to assess the trend of okra varieties viz., Lalu, Arka Anamika, Ramya, Satsira, Lady Luck,Debpusa Jhar,Japani Jhar and Barsha Laxmi  due to bio-priming of seeds towards vegetable production Significant variation among the varieties was noted for all the characters studied. Okra variety Lalu gave highest vegetable yield per plant in both years and it was statistically at par with Arka Anamika. Vegetable yield per plant was increased by 4.33 to 20.08% in first year and 3.68 to 19.60% in second year with T. viride as compared to P. fluorescens and un-primed control. Individual varieties indicated that vegetable yield per plant was maximum with Lalu when priming was made with both the bio-inoculants followed by Arka Anamika during both years. Hence, Lalu and Arka Anamika may be recommended for experimental region for higher yield and pre-sowing seed bio-priming may be recommended with both T.viride and  P. fluorescens for enhanced vegetable yield of okra. &nbsp

Evaluation of Economic Losses due to Coccidiosis in Poultry Industry in India

Coccidiosis is an old parasitic disease, prevalent all over the country and has a significant impact on poultry production. In this paper, economic loss to poultry industry has been estimated considering the major economic parameters. The estimation has revealed that commercial broiler industry is a major sufferer due to coccidiosis wherein 95.61 per cent of the total economic loss occurs due to the disease. The commercial layer industry shares 3.53 per cent economic loss, mainly due to cost of chemoprophylaxis and reduced egg production. A comparison across economic traits has revealed that loss is maximum due to reduced body weight gain, followed by increased FCR (23.74%) and chemoprophylaxis (2.83%) in the total loss due to coccidiosis in broiler industry of India. The overall comparison of economic traits for all the types of poultry sector it has shown that reduced body wt gain and increased FCR are the major parameters from which 68.08 per cent and 22.70 per cent annual loss has occurred in the total loss from coccidiosis in India during the year 2003-04. The total loss due to coccidiosis has been found to be of Rs 1.14 billion (approx) for the year 2003-04. The study has observed that generation of this data across different geographical regions will be helpful to conclude about the global economic loss due to coccidiosis in the poultry industry.Agricultural and Food Policy,

Predictive Data Mining: Promising Future and Applications

Predictive analytics is the branch of data mining concerned with the prediction of future probabilities and trends. The central element of predictive analytics is the predictor, a variable that can be measured for an individual or other entity to predict future behavior. For example, an insurance company is likely to take into account potential driving safety predictors such as age, gender, and driving record when issuing car insurance policies. Multiple predictors are combined into a predictive model, which, when subjected to analysis, can be used to forecast future probabilities with an acceptable level of reliability. In predictive modeling, data is collected, a statistical model is formulated, predictions are made and the model is validated (or revised) as additional data becomes available. Predictive analytics are applied to many research areas, including meteorology, security, genetics, economics, and marketing. In this paper, we have done an extensive study on various predictive techniques with all its future directions and applications in various areas are being explaine

A PROSPECTIVE STUDY ON EVALUATION OF DIAGNOSTIC PERFORMANCE OF GENEXPERT MTB IN CSF FOR EARLY DIAGNOSIS OF TUBERCULAR MENINGITIS IN A TERTIARY CARE HOSPITAL ODISHA.

Aim The study aims to establish the diagnostic accuracy of GeneXpert MTB/RIF in CSF for early diagnosis of tubercular meningitis and to compare the efficacy of CSF GeneXpert MTB/RIF with CSF culture for mycobacterium. Methodology This was a prospective, cross-sectional study conducted in the Department of Medicine, Neurology, S.C.B Medical College, Cuttack. All patients of age >18 with clinical features suggestive of tubercular meningitis were included in the study. All routine blood tests were performed along with malaria, leptospira, chest radiograph, CT scan or MRI (selected patients) and CSF study to exclude other causes of meningitis. CSF sample subjected to biochemistry, cytology, ZN stain, MGIT culture, and Xpert MTB/RIF. The positive results for each test (ZN stain, MGIT culture, and Xpert MTB/RIF) were compared using Pearson’s chi-squared test. All statistical analyses were done using the SPSS 21.0 version. Results Out of 100 suspected TBM patients 40 were finally classified as definite TBM, 6 were probable TBM, 2 were possible TBM, and 52 were not having TBM. Tubercular meningitis occurred more commonly in the population 21-40 years and in males. The overall sensitivity of CSF GeneXpert MTB/RIF, Zn stain, and MGIT culture was 62.5%, 29.16%, and 66.5% respectively, and specificity of 100% for each in diagnosing TBM. Rifampicin resistance was detected only in two cases. Conclusion GeneXpert MTB/RIF test can rapidly confirm a diagnosis of TBM with 62.5% sensitivity and 100% specificity, along with rifampicin resistance. It can be a useful diagnostic method in patients of suspected TBM either AFB smear-negative or positive due to its rapidity and simultaneous detection of rifampicin resistance. Recommendation Positive GeneXpert results are to be read cautiously and should be well correlated with the clinical and treatment history of the patient

Interferon Alpha Characterization and Its Comparative Expression in PBM Cells of Capra hircus and Antelope cervicapra Cultured in the Presence of TLR9 Agonist

TLR9 plays pivotal role in innate immune responses through upregulation of costimulatory molecules and induction of proinflammatory cytokines like type I interferons including interferon alpha (IFNA). The present study characterized IFNA cDNA and predicted protein sequences in goat and black buck. Response of the PBM cells to TLR9 agonist CpG ODN C and Phorbol Myristate Acetate (PMA) was evaluated by realtime PCR. IFNA coding sequences were amplified from leukocyte cDNA and cloned in pGEMT-easy vector for nucleotide sequencing. Sequence analysis revealed 570 bp, IFNA ORF encoding 189 amino acids in goat and black buck. Black buck and goat IFNA has 92.1% to 94.7% and 93% to 95.6% similarity at nucleotide level, 86.3% to 89.5% and 70.9% to 91.6% identity at amino acid level with other ruminants, respectively. Nonsynonymous substitutions exceeding synonymous substitutions indicated IFNA evolved through positive selection among ruminants. In spite of lower total leukocyte count, the innate immune cells like monocytes and neutrophils were more in black buck compared to goat. In addition, CpG ODN C-stimulated PBM cells revealed raised IFNA transcript in black buck than goat. These findings indicate sturdy genetically governed immune system in wild antelope black buck compared to domestic ruminant goat

Prophesying the Short-Term Dynamics of the Crude Oil Future Price by Adopting the Survival of the Fittest Principle of Improved Grey Optimization and Extreme Learning Machine

Crude oil market analysis has become one of the emerging financial markets and the volatility effect of the market is paramount and has been considered as an issue of utmost importance. This study examines the dynamics of this volatile market of crude oil by employing a hybrid approach based on an extreme learning machine (ELM) as a regressor and the improved grey wolf optimizer (IGWO) for prophesying the crude oil rate for West Texas Intermediate (WTI) and Brent crude oil datasets. The datasets are augmented using technical indicators (TIs) and statistical measures (SMs) to obtain better insight into the forecasting ability of this proposed model. The differential evolution (DE) strategy has been used for evolution and the survival of the fittest (SOF) principle has been used for elimination while implementing the GWO to achieve better convergence rate and accuracy. Whereas, the algorithmic simplicity, use of less parameters, and easy implementation of DE efficiently decide the evolutionary patterns of wolves in GWO and the SOF principle updates the wolf pack based on the fitness value of each wolf, thereby ensuring the algorithm does not fall into local optimum. Furthermore, the comparison and analysis of the proposed model with other models, such as ELM–DE, ELM–Particle Swarm Optimization (ELM–PSO), and ELM–GWO shows that the predictability evidence obtained substantially achieves better performance for ELM–IGWO with respect to faster error convergence rate and mean square error (MSE) during training and testing phases. The sensitivity study of the proposed ELM–IGWO provides better results in terms of the performance measures, such as Theil’s U, mean absolute error (MAE), average relative variance (ARV), mean average percentage error (MAPE), and minimal computational time

A framework for crime data analysis using relationship among named entities

Many crime reports are available online in various blogs and Newswire. Though manual annotation of these massive reports is quite tedious for crime data analysis, it gives an overall crime scenario of all over the world. This motivates us to propose a framework for crime data analysis based on the online reports. Initially, the method extracts the crime reports and identifies named entities. The intermediate sequence of context words between every consecutive pair of named entities is termed as a crime vector that provides relationships between the entities. The feature vectors for each entity pair are generated from these crime vectors using the Word2Vec model. The paper considers three different types of named entity pairs to facilitate the major crime data analysis task, and for each type, similarity between every pair of entities is measured using respective feature vectors. For each type of named entity pair, a separate weighted graph is generated with entity pairs as vertices and similarity score between them as the weight of the corresponding edge. Then, Infomap, a graph-based clustering algorithm, is applied to obtain optimal set of clusters of entity pairs and a representative entity pair of each cluster. Each cluster is labelled by the relationship, represented by the crime vector, of its representative entity pair. In reality, all the entity pairs in a cluster may not reflect contextual similarity with their representative entity pair. So the clusters are further partitioned into subclusters based on WordNet-based path similarity measure which makes the entity pairs in each subcluster more contextually similar compared to their original cluster. These subclusters provide us various statistical crime information over the time period. The method is experimented only using the crime reports related to crime against women in India. The experimental results demonstrate the effectiveness and superiority of the method compared to others for analysing the crime data