Agrobacterium-Mediated Genetic Transformation of Withania coagulans (Dunal) with rol A Genes and Its Antioxidant Potential

In ancient times, Withania coagulans Dunal was used as a therapeutic plant for the treatment of several diseases. This report aims to examine the effect of Agrobacterium tumefactions-mediated transformation of W. coagulans with the rolA gene to enhance secondary metabolite production, antioxidant activity, and anticancer activity of transformed tissues. Before transgenic plant production, the authors designed an efficient methodology for in vitro transformation. In this study, leaf explants were cultured on Murashage and Skoog (MS) media containing different amounts of naphthalene acetic acid (NAA) and benzyl adenine (BA). The best performance for inducing embryogenic callus was in MS medium containing 4 μM NAA and 6.0 μM BA, while the best results for shooting (100%) were obtained at 8 μM benzyl adenine. On the other hand, direct shooting was attained by subculturing leaves on MS medium supplemented with 8 μM benzyl adenine. Prolonged shoots showed excellent in vitro rooting results (80%) with 12 μM indole-3-butyric acid (IBA). The samples were precultivated for 3 days and were followed by 48 h infection with A. tumefaciens strain GV3101 having pCV002. Then, a vector expressed the rol A gene of strain Agrobacterium rhizogenes. Furthermore, three independent transgenic shoot lines and one callus line (T2) were produced and exhibited stable integration of transgene rol A genes, as revealed by PCR analysis. Transgenic strains showed a significant increase in antioxidant potential as compared to untransformed plants. Additionally, LC-MS analysis showed that the transformed strains have a higher withanolide content as compared to untransformed ones. Moreover, the reduced proliferation of prostate cancer cells was observed after treatment with extracts of transgenic plants. Furthermore, these transformed plants exhibited superior antioxidant capability and higher withanolide content than untransformed ones. In conclusion, the reported data can be used to select withanolide-rich germplasm from transformed cell cultures.

Assessment of hybrid machine learning algorithms using TRMM rainfall data for daily inflow forecasting in Três Marias Reservoir, eastern Brazil

This study investigates the application of the Gaussian Radial Basis Function Neural Network (GRNN), Gaussian Process Regression (GPR), and Multilayer Perceptron Optimized by Particle Swarm Optimization (MLP-PSO) models in analyzing the relationship between rainfall and runoff and in predicting runoff discharge. These models utilize autoregressive input vectors based on daily-observed TRMM rainfall and TMR inflow data. The performance evaluation of each model is conducted using statistical measures to compare their effectiveness in capturing the complex relationships between input and output variables. The results consistently demonstrate that the MLP-PSO model outperforms the GRNN and GPR models, achieving the lowest root mean square error (RMSE) across multiple input combinations. Furthermore, the study explores the application of the Empirical Mode Decomposition-Hilbert-Huang Transform (EMD-HHT) in conjunction with the GPR and MLP-PSO models. This combination yields promising results in streamflow prediction, with the MLP-PSO-EMD model exhibiting superior accuracy compared to the GPR-EMD model. The incorporation of different components into the MLP-PSO-EMD model significantly improves its accuracy. Among the presented scenarios, Model M4, which incorporates the simplest components, emerges as the most favorable choice due to its lowest RMSE values. Comparisons with other models reported in the literature further underscore the effectiveness of the MLP-PSO-EMD model in streamflow prediction. This study offers valuable insights into the selection and performance of different models for rainfall-runoff analysis and prediction.

Predicting Thermal Conductivities of Formations From Other Known Properties

Abstract

Measuring the thermal properties of rocks and rock-fluid systems is difficult and time consuming and the results from such measurements are of limited value unless complete descriptions of the rock and fluids are given. A need exists for a method of predicting thermal behavior from other more easily measurable properties. Presented here are correlations developed for predicting the thermal conductivity of consolidated sandstones from a knowledge of density, porosity, permeability, and formation resistivity factor. Values for all these properties are available from well logs or core properties are available from well logs or core analysis data. Also obtained were correlations for estimating the thermal conductivity of liquid-saturated sandstones from a knowledge of the conductivities of dry sandstone. The thermal conductivity of most rocks decreases with increasing temperature and a method of estimating this effect is presented. The effect of pressure on conductivities is generally small, but may be predicted from a knowledge of the bulk predicted from a knowledge of the bulk compressibility of the rock.

Introduction

Although thermal recovery processes have been applied in the petroleum industry for many years, there is still a lack of basic thermal data with which predict the performance of these processes. Much of the thermal conductivity work reported in the literature lacks a complete description of the physical properties of the rocks used, and in physical properties of the rocks used, and in addition, most of the thermal conductivity measurements have been made at room temperature and at atmospheric pressure. The work reported in this paper deals with the thermal conductivity of typical porous rocks at simulated subsurface conditions of temperature, pressure, and saturation. Because thermal conductivity is difficult to measure, emphasis has been placed here on methods of predicting thermal conductivity from other more easily measured properties as well as on methods of predicting the effects of temperature, pressure, and liquid saturation on thermal properties. pressure, and liquid saturation on thermal properties. RELATIONSHIP OF THERMAL CONDUCTIVITY TO OTHER PHYSICAL PROPERTIES

The thermal conductivities of dry rocks have been shown to be functions of density, porosity, grain size and shape, cementation, and mineral composition. The first two properties are easy to measure and precise values may, be assigned for correlation purposes. Grain size and shape and cementation are difficult to quantify. There are, however, other related properties that can be used to characterize these properties for use in correlations. Permeability and formation resistivity factors are probably most closely related to these properties and are readily measurable as unique properties and are readily measurable as unique values. Precise mineral composition values are generally not available, and even A they were, it would be difficult to introduce them into correlations. The high thermal conductivity of quartz seems to have a predominating influence, and thus for most sandstones containing quartz in moderate amounts, the effects of other minerals can be ignored. Many efforts have been made to relate thermal conductivity to the physical properties of porous rocks. These efforts have been reviewed in rather complete detail by Scorer and Anand. Unfortunately, most of the correlations developed require a knowledge of the thermal conductivity of the rock matrix or the dry rock at some known porosity. Although some simple correlations have porosity. Although some simple correlations have been obtained, these are for specific systems and are not applicable generally. Probably the most useful work in this area is that reported by Zierfuss and Van der Vliet. Basing their analysis on 36 sandstones having a wide range in measured properties, they obtained a correlation between properties, they obtained a correlation between effective porosity and the product of thermal conductivity and formation resistivity factor. A fourth-order polynomial fit of thermal conductivity and fractional porosity was obtained by regression analysis. Their data also seemed to indicate that thermal conducting increases with permeability, this being attributed to conduction with permeability.

SPEJ

P. 267