Induction of resistance in tomato against Helicoverpa armigera (Hubner) using biofertilizers

Based on preliminary and confirmatory field screening of 321 tomato accessions for resistance against fruit borer, Helicoverpa armigera (Hubner), a promising accession Varushanadu Local was selected for studying induction of resistance using biofertilizers viz., Azospirillum, Phosphobacteria, Pseudomonas and K-solubilizer. In comparison, a susceptible check, I979 was also evaluated. The feeding preference of H. armigera larva was the least towards Varushanadu Local than I979 irrespective of the biofertilizer. Among the biofertilizers K-solubilizer treated plants were the least preferred than others. A trend was observed in both the free choice and confinement tests. ÂÂ

Modeling Volume Loss of Heat Treated Al 6061 Composites Using an Artificial Neural Network

AbstractIn the present study, artificial neural network (ANN) approach was used to predict the volume loss of heat treated Al 6061 metal matrix composites reinforced with 10% SiC particles and 2% graphite particles. Composite was produced using stir casting process. Volume loss of composite was measured during wear testing in a pin on disc apparatus. Microstructure examination at wear surface was investigated by Scanning Electron Microscope (SEM). In Artificial Neural Network (ANN), Multi Layer Perceptron (MLP) architecture with back-propagation neural network that uses gradient descent learning algorithm is utilized. The results clearly revealed that the developed ANN model is reliable and accurate

A novel medium for the enhanced cell growth and production of prodigiosin from Serratia marcescens isolated from soil

BACKGROUND: Prodigiosin produced by Serratia marcescens is a promising drug owing to its reported characteristics of having antifungal, immunosuppressive and antiproliferative activity. From an industrial point of view the necessity to obtain a suitable medium to simultaneously enhance the growth of Serratia marcescens and the pigment production was the aim of this work. The usage of individual fatty acid as substrate in industries would be cost-effective in the long run and this paved the way for us to try the effect of different fatty acid-containing seeds and oils of peanut, sesame and coconut as source of substrate. RESULTS: The addition of sugars only showed slight enhancement of prodigiosin production in nutrient broth but not in fatty acid containing seed medium. The powdered peanut broth had supported better growth of Serratia marcescens and higher yield of prodigiosin when compared with the existing nutrient broth and peptone glycerol broth. A block in prodigiosin production was seen above 30°C in nutrient broth, but the fatty acid seed medium used by us supported prodigiosin production upto 42°C though the yields were lower than what was obtained at 28°C. From the results, the fatty acid form of carbon source has a role to play in enhanced cell growth and prodigiosin production. CONCLUSION: We conclude by reporting that the powdered and sieved peanut seed of different quality grades were consistent in yielding a fourty fold increase in prodigiosin production over the existing media. A literature survey on the composition of the different media components in nutrient broth, peptone glycerol broth and the fatty acid containing seeds and oils enabled us to propose that the saturated form of fatty acid has a role to play in enhanced cell growth and prodigiosin production. This work has also enabled us to report that the temperature related block of prodigiosin biosynthesis varies with different media and the powdered peanut broth supports prodigiosin production at higher temperatures. The medium suggested in this work is best suitable from an industrial point of view in being economically feasible, in terms of the higher prodigiosin yield and the extraction of prodigiosin described in this paper is simple with minimal wastage

An Efficient Ensemble Method Using K-Fold Cross Validation for the Early Detection of Benign and Malignant Breast Cancer

In comparison to all other malignancies, breast cancer is the most common form of cancer, among women. Breast cancer prediction has been studied by several researchers and is considered a serious threat to women. Clinicians are finding it difficult to create a treatment approach that will help patients live longer, due to the lack of solid predictive models. Rates of this malignancy have been observed to rise, more with industrialization and urbanization, as well as with early detection facilities. It is still considerably more prevalent in very developed countries, but it is rapidly spreading to developing countries as well. The purpose of this work is to offer a report on the disease of breast cancer in which we used available technical breakthroughs to construct breast cancer survivability prediction models. The Machine Learning (ML) techniques, namely Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Decision Tree (DT) Classifier, Random Forests (RF), and Logistic Regression (LR) is used as base Learners and their performance has been compared with the ensemble method, eXtreme Gradient Boosting (XGBoost).  For performance comparison, we employed the k-fold cross-validation method to measure the unbiased estimate of these prediction models. The results indicated that XGBoost outperformed with an accuracy of 97.81% compared to other ML algorithms

An Efficient Ensemble Method Using K-Fold Cross Validation for the Early Detection of Benign and Malignant Breast Cancer

In comparison to all other malignancies, breast cancer is the most common form of cancer, among women. Breast cancer prediction has been studied by several researchers and is considered a serious threat to women. Clinicians are finding it difficult to create a treatment approach that will help patients live longer, due to the lack of solid predictive models. Rates of this malignancy have been observed to rise, more with industrialization and urbanization, as well as with early detection facilities. It is still considerably more prevalent in very developed countries, but it is rapidly spreading to developing countries as well. The purpose of this work is to offer a report on the disease of breast cancer in which we used available technical breakthroughs to construct breast cancer survivability prediction models. The Machine Learning (ML) techniques, namely Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Decision Tree (DT) Classifier, Random Forests (RF), and Logistic Regression (LR) is used as base Learners and their performance has been compared with the ensemble method, eXtreme Gradient Boosting (XGBoost).  For performance comparison, we employed the k-fold cross-validation method to measure the unbiased estimate of these prediction models. The results indicated that XGBoost outperformed with an accuracy of 97.81% compared to other ML algorithms

Tailoring the Effects of Titanium Dioxide (TiO2) and Polyvinyl Alcohol (PVA) in the Separation and Antifouling Performance of Thin-Film Composite Polyvinylidene Fluoride (PVDF) Membrane

In this study, thin-film composite (TFC) polyvinylidene fluoride (PVDF) membranes were synthesized by coating with titanium dioxide (TiO2)/polyvinyl alcohol (PVA) solution by a dip coating method and cross-linked with glutaraldehyde. Glutaraldehyde (GA) acted as a cross-linking agent to improve the thermal and chemical stability of the thin film coating. The incorporation of TiO2 in the film enhanced the hydrophilicity of the membrane and the rejection of dyes during filtration. The layer of TiO2 nanoparticles on the PVDF membranes have mitigated the fouling effects compared to the plain PVDF membrane. The photocatalytic performance was studied at different TiO2 loading for the photodegradation of dyes (reactive blue (RB) and methyl orange (MO)). The results indicated that the thin film coating of TiO2/PVA enhanced photocatalytic performance and showed good reusability under UV irradiation. This study showed that nearly 78% MO and 47% RB were removed using the TFC membrane. This work provides a new vision in the fabrication of TFC polymeric membranes as an efficient wastewater treatment tool

Comparison of fouling mechanisms in low-pressure membrane (MF/UF) filtration of secondary effluent

Membrane filtration in municipal wastewater treatment is being increasingly used to improve the quality of water and increase the productivity of existing plants. However, membrane fouling encountered in reclamation of municipal wastewater represents serious design and operational concern. There are several fouling models which are being developed and used as a powerful tool to increase the understanding of the fouling mechanisms and its key characteristics that influence the design of optimal process and operating conditions. This study investigates and compares the fouling mechanisms of three different types of polymeric and ceramic ultrafiltration (UF) and microfiltration (MF) membranes in the recovery of water from secondary effluent. The result demonstrated that ceramic UF membrane produced very high quality of water compared to polymeric UF and ceramic MF membranes. Out of four fouling models used to fit the experimental flux data, cake filtration and pore narrowing and complete pore blocking models predicted the initial fluxes of polymeric UF membrane more accurately. On the other hand, the cake filtration and pore narrowing models predicted the performance of ceramic UF membrane. Whereas, pore narrowing model predicted the performance of ceramic MF membrane more precisely compared to other three models. Further, the application of unified membrane fouling index (UMFI) was used to assess the fouling potential of the membranes. Good agreement between UMFI and other models was found. &copy; 2013 Copyright Balaban Desalination Publications.<br /

A preliminary study on the volume reduction of pre-treatment sludge in seawater desalination by forward osmosis

Forward Osmosis (FO) can be applied to recover water from the pre-treatment sludge of seawater reverse osmosis process. This study investigated the effect of the concentration of two draw solutions (MgCl2 and NaCl) in the reduction of Fe(OH)3 sludge volume and the effect of cross flow velocity on flux through FO membrane. Higher the concentration of NaCl and MgCl2 higher the water flux observed. However, the percentage increase was not significant due to the occurrence of internal concentration polarisation. MgCl2 draws marginally increased water flux than NaCl, when the conditions of feed and draw solutions were similar. Increase in cross flow velocity (from 0.25 to 1.0&thinsp;m/s) marginally changed the flux with both draw solutions as higher cross flow velocities were unproductive to beat the external CP effect along the membrane surface. However, at 1&thinsp;m/s, highest fluxes were obtained for both draw solutions.<br /

Extraction process optimization of flavonoid and in vitro amylase inhibitory effect of purified quercetin derivative from Amorphophallus paeoniifolius tubers

544-556Amorphophallus paeoniifolius (Elephant foot yam) is a prominent tuberous plant utilized across several parts of India to treat various ailments such as a tumour, haemorrhage, microbial infections, cough, bronchitis, diabetes, anaemia, and hepato-gastro and cardiovascular diseases. In this context, the present study aims to optimize the extraction process of the flavonoid and to study the in vitro amylase inhibitory effect of purified flavonoid moiety. The Shake flask method with different extraction solvents was adopted to quantify the flavonoid content. Central composite design (CCD) based response surface methodology (RSM) was formulated to optimize the extraction process. Three-dimensional preparative chromatography (3D PTLC) was executed to purify the flavonoid content and high-resolution liquid chromatography-mass spectrometry (HRLC-MS) was adopted to predict the structure. 3,5-dinitrosalicylic acid (DNS) based spectrophotometry method was used to determine the amylase inhibitory property. All the analyses were subjected to standard statistical tests. The developed model for the extraction optimization process was found to be near significant (P = 0.242) with temperature as a significant variable (P = 0.029), and a 107-fold increase (71.11±0.5 mg/g tissue) of flavonoid content was recorded. A strong yellow colour spot (flavonoid fraction) was eluted using 3D PTLC technique and the molecule was identified as quercetin derivative (m/z 447) by the direct MS method. Significant amylase inhibition (36.1±2.1%) recorded by purified quercetin derivative has documented the utilization of A. paeoniifolius tubers as classical traditional medicine

Determination of the optimal pre-processing technique for spectral data of oil palm leaves with respect to nutrient

Precision agriculture with regard to crop science was introduced to apply only the required and optimal amount of fertiliser, which inspired the present study of nutrient prediction for oil palm using spectroradiometer with wavelengths ranging from 350 to 2500 nm. Partial least square (PLS) method was used to develop a statistical model to interpret spectral data for nutrient deficiency of nitrogen (N), phosphorus (P), potassium (K), magnesium (Mg), calcium (Ca) and boron (B) of oil palm. Prior to the development of the PLS model, pre-processing was conducted to ensure only the smooth and best signals were studied, which includes the multiplicative scatter correction (MSC), first and second derivatives and standard normal variate (SNV), Gaussian filter and Savitzky-Golay smoothing. The MSC technique was the optimal overall pre-treatment method for nutrients in this study, with highest prediction R2 of 0.91 for N and lowest RMSEP value of 0.00 for P