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We establish sufficient conditions for the oscillation of all solutions to the second-order impulsive differential equation $$displaylines{ ig(r(t)x'(t)ig)'+p(t)x'(t)+q(t)x(t)+sum_{i=1}^n q_i(t)|x(t)|^{alpha_i}hbox{sgn} x(t)=e(t),quad teq au_k,cr x({au_k}^+)=a_k x({au_k}),quad x'({au_k}^+)=b_k x'({au_k}). }$$ The results obtained in this paper extend some of the existing results and are illustrated by examples.

Microsatellite loci were isolated from the genomic DNA of the Asian rice gall midge, Orseolia oryzae (Wood-Mason) using a hybridization capture approach. A total of 90 non-redundant primer pairs, representing unique loci, were designed. These simple sequence repeat (SSR) markers represented di (72%), tri (15.3%), and complex repeats (12.7%). Three biotypes of gall midge (20 individuals for each biotype) were screened using these SSRs. The results revealed that 15 loci were hyper variable and showed polymorphism among different biotypes of this pest. The number of alleles ranged from two to 11 and expected heterozygosity was above 0.5. Inheritance studies with three markers (observed to be polymorphic between sexes) revealed sex linked inheritance of two SSRs (Oosat55 and Oosat59) and autosomal inheritance of one marker (Oosat43). These markers will prove to be a useful tool to devise strategies for integrated pest management and in the study of biotype evolution in this important rice pest.

The Embedded Zero-tree Wavelet (EZW) is a lossy compression method that allows for progressive transmission of a compressed image. By exploiting the natural zero-trees found in a wavelet decomposed image, the EZW algorithm is able to encode large portions of insignificant regions of an still image with a minimal number of bits. The upshot of this encoding is an algorithm that is able to achieve relatively high peak signal to noise ratios (PSNR) for high compression levels. The EZW algorithm is to encode large portions of insignificant regions of an image with a minimal number of bits. Vector Quantization (VQ) method can be performed as a post processing step to reduce the coded file size. Vector Quantization (VQ) method can be reduces redundancy of the image data in order to be able to store or transmit data in an efficient form. It is demonstrated by experimental results that the proposed method outperforms several well-known lossless image compression techniques for still images that contain 256 colors or less.

Biosorption equilibrium and kinetics of Pb(II) and Hg(II) on coconut shell carbon (CSC) were investigated by batch equilibration method. The effects of pH, adsorbent dosage, contact time, temperature and initial concentration of Pb(II) and Hg(II) on the activated carbon of coconut shell wastes were studied. Maximum adsorption of Pb(II) occurred at pH 4.5 and Hg(II) at pH 6. The sorptive mechanism followed the pseudo second order kinetics. The equilibrium data were analysed by Langmuir, Freundlich and Dubinin-Radushkevich isotherm models. The equilibration data fitted well with both Langmuir and Freundlich isotherm model. The Langmuir adsorption capacity for Pb(II) was greater than Hg(II). The mean free energy of adsorption calculated from Dubinin-Radushkevich (D-R) isotherm model indicated that the adsorption of metal ions was found to be by chemical ion exchange. Thermodynamic parameter showed that the sorption process of Pb(II) onto SDC was feasible, spontaneous and endothermic under studied conditions. A comparison was evaluated for the two metals.

At present, health prediction in contemporary life set off very much indispensable. Big Data exploration plays a major contribution to predict subsequent status of health and offers outstanding health consequence to people. A lot of research is persisting on predictive analytics utilizing optimized machine learning techniques to disclose healthier decision making. Big Data analytics strengthens exceptional opening to predict future health condition from health criterions and bestow finest outcomes. We used Big Data Predictive Analytic model for heart disease prediction using Z-score Normalized Iterative African Buffalo Optimization (ZN-IABO). It fills the missing values in the database based on Z-score Normalized Data Pre-processing that standardize the scores on same scale by dividing score deviation by standard deviation. Z-score normalization model is suitable for huge data sets especially for big data. Next, Iterative African Buffalo Optimization based Feature Selection process is applied to the pre-processed data that addresses pre-mature convergence. In the simulation, the proposed ZN-IABO method is also compared with numerous well-known algorithms ZN-IABO (without optimization), imperialist competitive algorithm, and FODW. The result illustrates that this proposed algorithm is very competitive compared with the cardiovascular disease dataset for addressing the theoretical issue and superior to solving the real-world issue. The proposed ZN-IABO method (with optimization), achieves enhancement in the heart disease prediction accuracy by 17%, minimization of heart disease prediction time, error rate, and space complexity by 22%, 39%, and 37% as compared to the ZN-IABO (without optimization), imperialist competitive algorithm, FODW, MLBO respectively. [ABSTRACT FROM AUTHOR]

Most of the research on personal networking and deep learning have been conducted separately. Crossovers between the two fields have just emerged. This article provides a quick introduction to the fundamentals of deep learning, as well as the most recent advancements in the field. Other methodologies and platforms for deploying deep learning on personal network systems are also discussed and compared. Self-Sustained Personal Network (SSPN) is utilized for a wide range of networking functions, including computation, text analytics, and many more. Using deep learning approaches, this study demonstrates how to individualize personal networking activities in order to get the greatest performance in complicated settings with more efficient ones. Other features of deep learning modeling, such as supervised or unsupervised task learning skills with Generative Adversarial Networks (GAN), Convolutional Neural Network (CNN) and deep reinforcement learning, which aims to explain the capabilities of deep learning approaches to operate in an automated and intelligent way are described. Moreover, this research aims to provide the conceptual roadmap between network researchers to real-time networking practitioners who implement deep learning approaches in personal networks. As a result of the implementing deep learning algorithms into real time network installation, new and powerful tools have emerged. This research study develops an efficient learning protocol by predicting future traffic in the personal network using a time series forecast of past traffic. Furthermore, while the algorithm is run, the future personal network numbers are generated randomly. A deep learning method is used in the different algorithms to reduce the normalized mean square error. The correlation function and error histograms are shown in the results and discussion section. By examining the overlap between these modern trend algorithms for self- sustained personal networks, this research study tries to bridge the gap between the deep learning and computer networks. [ABSTRACT FROM AUTHOR]

Introduction: Polycystic ovarian syndrome (PCOS) is the main cause of anovulatory infertility. Various combination of drugs have been tried to induce ovulation in PCOS patients with varied result. So, this study was planned to compare the effect of bromocriptine combined with Clomiphene Citrate and Clomiphene Citrate alone, in patients of polycystic ovarian syndrome with normal prolactin level. Materials & Methods: On the basis of inclusion and exclusion criteria, seventy four PCOS patients with normal prolactin level (< 20 ng/ml) and BMI between 20-30 were randomly assigned into two groups. One group (n=38) received 50 mg clomiphene citrate (CC) from day3 to day7. The other group (CC+Bcrt) was given 50 mg of clomiphene citrate from day3 to day7 along with 0.8mg of bromocriptine daily for full cycle (n=36). Both the groups were treated for 3 cycles. The outcomes were measured by the hormonal status, follicular size, ovulation rate and pregnancy outcomes. Results: The serum prolactin level was normal in both the groups before treatment. After 3 cycles the prolactin level decreased in (CC+Bcrt) group (p< 0.01). Follicular development (size >15mm) was observed in 30 patients (78.9%) in CC group and 28 patients (82.3%) in CC+Bcrt group. There was no significant change in hormonal status (LH, FSH and Estradiol) of both the groups. The rate of ovulation was 69.4% in CC group and 75.8% in CC+Bcrt group. During the treatment period, nine patients in CC group and seven patients in CC+Bcrt group became pregnant. Conclusion: There is no added benefit of bromocriptine with clomiphene citrate as compared to clomiphene alone in ovulation induction as well as pregnancy outcomes in PCOS patients with normal prolactin.

In the present study freshwater commercially cultivable Indian major carp Labeo rohita fingerlings was used to evaluate the synergistic effect of chlorpyrifos, an organophosphate insecticide and cypermethrin , a synthetic pyrethroid insecticide widely used in agricultural fields. The fishes were maintained at sublethal concentration for a period of 14 and 21 days. Histological examination was carried out in gills, liver and muscle at the end of exposure period to study the extent of damage at safe concentration.

Genomic aberrations are common in cancers and the long arm of chromosome 1 is known for its frequent amplifications in breast cancer. However, the key candidate genes of 1q, and their contribution in breast cancer pathogenesis remain unexplored. We have analyzed the gene expression profiles of 1635 breast tumor samples using meta-analysis based approach and identified clinically significant candidates from chromosome 1q. Seven candidate genes including exonuclease 1 (EXO1) are consistently over expressed in breast tumors, specifically in high grade and aggressive breast tumors with poor clinical outcome. We derived a EXO1 co-expression module from the mRNA profiles of breast tumors which comprises 1q candidate genes and their co-expressed genes. By integrative functional genomics investigation, we identified the involvement of EGFR, RAS, PI3K / AKT, MYC, E2F signaling in the regulation of these selected 1q genes in breast tumors and breast cancer cell lines. Expression of EXO1 module was found as indicative of elevated cell proliferation, genomic instability, activated RAS/AKT/MYC/E2F1 signaling pathways and loss of p53 activity in breast tumors. mRNA-drug connectivity analysis indicates inhibition of RAS/PI3K as a possible targeted therapeutic approach for the patients with activated EXO1 module in breast tumors. Thus, we identified seven 1q candidate genes strongly associated with the poor survival of breast cancer patients and identified the possibility of targeting them with EGFR/RAS/PI3K inhibitors.

• Anatomic myocardial features significantly capture reduced ejection fraction. • Segment CMR effectively captures myocardial boundary in short and long-axis views. • BOA aids the tuning of ELM hyper-parameters and choice of relevant feature set. • Bio-inspired ELM framework effectively detects cardiovascular disorder progression. This study focuses on integration of anatomical left ventricle myocardium features and optimized extreme learning machine (ELM) for discrimination of subjects with normal, mild, moderate and severe abnormal ejection fraction (EF). The physiological alterations in myocardium have diagnostic relevance to the etiology of cardiovascular diseases (CVD) with reduced EF. This assessment is carried out on cardiovascular magnetic resonance (CMR) images of 104 subjects available in Kaggle Second Annual Data Science Bowl. The Segment CMR framework is used to segment myocardium from cardiac MR images, and it is subdivided into 16 sectors. 86 clinically significant anatomical features are extracted and subjected to ELM framework. Regularization coefficient and hidden neurons influence the prediction accuracy of ELM. The optimal value for these parameters is achieved with the butterfly optimizer (BO). A comparative study of BOELM framework with different activation functions and feature set has been conducted. Among the individual feature set, myocardial volume at ED gives a better classification accuracy of 83.3% compared to others. Further, the given BOELM framework is able to provide higher multi-class accuracy of 95.2% with the entire feature set than ELM. Better discrimination of healthy and moderate abnormal subjects is achieved than other sub groups. The combined anatomical sector wise myocardial features assisted BOELM is able to predict the severity levels of CVDs. Thus, this study supports the radiologists in the mass diagnosis of cardiac disorder. [ABSTRACT FROM AUTHOR]

Big data comprises the structured, semi-structured and unstructured data collected by organization mined for the predictive analytics. Heart disease is the common disease that caused the peoples worldwide. Early heart disease prediction is an essential process for diverse healthcare providers to save their lives. Heart disease prediction is carried out with signs, symptoms and physical examination of patient. Data pre-processing, feature selection and classification process are performed for efficient heart disease prediction. Data pre-processing is carried out to refill the missing values in the input database. The feature selection process is performed to choose the relevant features from pre-processed data. The classification process is performed to classify the input data as normal or abnormal data for performing heart disease prediction. Many researchers carried out their research on the heart disease prediction. But, the accuracy level was not increased and time consumption was not minimized during the heart disease prediction. In order to address these problems, existing heart disease prediction method was reviewed. [ABSTRACT FROM AUTHOR]

The present study aimed to evaluate the influence of bioregulators on physiological attributes and yield components of TNAU Papaya CO. 8 variety during 2017 - 2018. The study involved two factors viz., growth retardants as factor I and stress tolerance promoting bioregulators as factor II. The treatments involving growth retardants (Factor I) were water spray (G1) as control, 250 ppm of pro hexadione calcium (G2), 500 ppm of mepiquat chloride (G3) and 500 ppm of chlormequat chloride (G4). Similarly, the stress tolerance promoting bioregulators (Factor II) comprised of water spray as control (S1), 100 ppm of methyl jasmonate (S2), 100 ppm of salicylic acid (S3) and 0.6 g/ L of strobilurins (S4). The bioregulators were applied as foliar spray at the fourth and eighth months after transplanting. Initially, growth retardants were sprayed and one week after that, the stress tolerance promoting bioregulators were sprayed. Observations were recorded on number of leaves, leaf area index, leaf gas exchange parameters, papaya ring spot virusdisease incidence and yield components. Among the treatments, higher number of leaves (52.03) were observed in the combination involving 250 ppm of prohexadione calcium and 100 ppm of salicylic acid (G2S3) and leaf area index (2.73) was highly influenced by 500 ppm of chlormequat chloride and 100 ppm of salicylic acid (G4S3). Leaf gas exchange parameters were found to be superior with 250 ppm of prohexadione calcium and 100 ppm of salicylic acid treatment (net photosynthetic rate - 16.46 µmol cm-2 s-1, stomatal conductance - 0.51 mol m-2s-1, transpiration rate - 7.58 mmol m-2s-1) as compared to control. Significant differences were also observed among the treatments for yield and yield components. The fruit yield was significantly improved by the treatment involving 500 ppm of mepiquat chloride and 100 ppm of salicylic acid (G3S3 - 61.76 kg plant-1), indicating the possibility for enhancing papaya yield under field conditions where PRSV is prevalent. [ABSTRACT FROM AUTHOR]

Bio efficacy of virulent isolate of entomopathogenic fungi, Beauveria bassiana (MK918495) was evaluated against two spotted spider mite, Tetranychus urticae on tuberose under pot culture condition at Insectary, Department of Agricultural Entomology, TNAU, Coimbatore. For effective working of any insect pathogen, it is mandatory to develop an effective formulation. Keeping this point in view, in the present study, compatibility of the fungal isolate with two different oils, viz., ground nut and sunflower were tested at different concentrations, viz., 1, 3, 5 and 10%. The results revealed that the groundnut oil was compatible with B. bassiana at all concentrations with respect to colony growth and spore yield compared to sunflower oil which was compatible only at one percent. Based on the results, oil in water and oil based formulations were prepared. At higher concentration of 10%, groundnut oil recorded the radial colony growth of 36.90 mm diameter and spore yield of 1.2 x 108 spores mL-1. Among the seven formulations (A-G) tested, formulation D which contained surfactant mixture having HLB value of 10.00 (53.33% Tween 80 and 46.67% Span 80) formed a better suspension with less flocculation layer and dispersion of conidia in spray fluid and was found to be better as it formed a good emulsion with water by mere shaking. Results of pot culture studies revealed that groundnut oil based fungal formulation was effective with a cumulative mean mite population reduction of 64.98% followed by crude fungal spore suspension (62.16%) and both were statistically on par with each other. Standard chemical check, Fenazaquin 10% EC @ 1.5 mL L-1 recorded significantly highest population reduction of mites (77.20%). [ABSTRACT FROM AUTHOR]