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2. SAR and Optical data fusion based on Anisotropic Diffusion with PCA and Classification using Patch-based with LBP

Author

Shakya, Achala, Biswas, Mantosh, and Pal, Mahesh

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

SAR (VV and VH polarization) and optical data are widely used in image fusion to use the complimentary information of each other and to obtain the better-quality image (in terms of spatial and spectral features) for the improved classification results. This paper uses anisotropic diffusion with PCA for the fusion of SAR and optical data and patch-based SVM Classification with LBP (LBP-PSVM). Fusion results with VV polarization performed better than VH polarization using considered fusion method. For classification, the performance of LBP-PSVM using S1 (VV) with S2, S1 (VH) with S2 is compared with SVM classifier (without patch) and PSVM classifier (with patch), respectively. Classification results suggests that the LBP-PSVM classifier is more effective in comparison to SVM and PSVM classifiers for considered data.

Published
2021

3. IRX-1D: A Simple Deep Learning Architecture for Remote Sensing Classifications

Author

Pal, Mahesh, Akshay, and Teja, B. Charan

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

We proposes a simple deep learning architecture combining elements of Inception, ResNet and Xception networks. Four new datasets were used for classification with both small and large training samples. Results in terms of classification accuracy suggests improved performance by proposed architecture in comparison to Bayesian optimised 2D-CNN with small training samples. Comparison of results using small training sample with Indiana Pines hyperspectral dataset suggests comparable or better performance by proposed architecture than nine reported works using different deep learning architectures. In spite of achieving high classification accuracy with limited training samples, comparison of classified image suggests different land cover classes are assigned to same area when compared with the classified image provided by the model trained using large training samples with all datasets., Comment: Want to improve this manuscript as it is not accepted by journal in present form

Published
2020

4. Patch Based Classification of Remote Sensing Data: A Comparison of 2D-CNN, SVM and NN Classifiers

Author

Pal, Mahesh, Akshay, Rohilla, Himanshu, and Teja, B. Charan

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Pixel based algorithms including back propagation neural networks (NN) and support vector machines (SVM) have been widely used for remotely sensed image classifications. Within last few years, deep learning based image classifier like convolution neural networks (2D-CNN) are becoming popular alternatives to these classifiers. In this paper, we compare performance of patch based SVM and NN with that of a deep learning algorithms comprising of 2D-CNN and fully connected layers. Similar to CNN which utilise image patches to derive features for further classification, we propose to use patches as an input in place of individual pixel with both SVM and NN classifiers. Two datasets, one multispectral and other hyperspectral data was used to compare the performance of different classifiers. Results with both datasets suggest the effectiveness of patch based SVM and NN classifiers in comparison to state of art 2D-CNN classifier., Comment: 8 pages, 2 Figures

Published
2020

5. Deep neural network for pier scour prediction

Author

Pal, Mahesh

Subjects
Computer Science - Neural and Evolutionary Computing, Computer Science - Machine Learning
Abstract

With the advancement in computing power over last decades, deep neural networks (DNN), consisting of two or more hidden layers with large number of nodes, are being suggested as an alternate to commonly used single-hidden-layer neural networks (ANN). DNN are found to be flexible models with a very large number of parameters, thus making them capable of modelling very complex and highly nonlinear relationships existing between inputs and outputs. This paper investigates the potential of a DNN consisting of 3 hidden layers (100, 80 and 50 nodes) to predict the local scour around bridge piers using field data. To update the weights and bias of DNN, an adaptive learning rate optimization algorithm was used. The dataset consists of 232 pier scour measurements, out of which a total of 154 data were used to train whereas remaining 78 data to test the created model. A correlation coefficient value of 0.957 (root mean square error = 0.306m) was achieved by DNN in comparison to 0.938 (0.388m) by ANN, indicating an improved performance by DNN for scour depth perdition. Encouraging performance on the used dataset in the work suggests the need of more studies on the use of DNN for various civil engineering applications., Comment: 7 pages, 2 figure, conference paper

Published
2019

6. Comparative Analysis of Different Factors Affecting Groundwater Potential Using Geospatial Techniques.

Author

Nagal, Balbir, Prabhakar, A. K., and Pal, Mahesh

Subjects
ANALYTIC hierarchy process, GEOGRAPHIC information systems, THEMATIC maps, GROUNDWATER management, SOIL classification
Abstract

Groundwater is an essential natural resource for supporting biological existence on Earth. It is also helpful in promoting socioeconomic development. The management of this resource is of utmost importance, particularly when there is over depletion for fulfilling the demand. In this study, an investigation was conducted to evaluate the role of the factors responsible for groundwater potential (GWP) in the Ratmau Rao basin (Uttarakhand). GWP is controlled by various parameters, such as geological formations, slopes, soil types, drainage networks, and surface runoff, for which a thorough study is needed. Geographic information system (GIS) and remote sensing technologies are suitable platforms for spatiotemporal monitoring of groundwater storage (GWS). This further helps in the sustainable use and effective management of groundwater. The analytical hierarchy process (AHP) was implemented in this study within a GIS environment to assess the elements that affect the GWP. The quality and accuracy of hydrogeological inputs can be improved by satellite imagery. Geospatial technologies offer valuable insights into complex hydrogeological systems, which are extremely important for places in Himalayan foothills that are confronted with the issue of groundwater depletion. The study covers an area of 323 km2, utilizing remote sensing technology with field surveys to provide maps related to hydrogeomorphology (HG), drainage density (DD), soil, land use land cover (LULC), lineament density (LD), and slope. Different parameters were analysed using multicriteria decision analysis (MCDA) to evaluate the effects of each criterion on the GWP using the AHP. In this research, among all six factors affecting the GWP, HG affects the GWP the most, with an influencing weight of 0.425, followed by DD (0.227). Furthermore, soil type and LULC had impacts, with weights of 0.127 and 0.108, respectively. LD and slope have the least impact on GWP, as their influence weights are only 0.074 and 0.038, respectively. Overall, HG has the greatest influence on the GWP, followed by DD, soil type, LULC, LD, and slope. [ABSTRACT FROM AUTHOR]

Published
2024
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8. Support vector machines/relevance vector machine for remote sensing classification: A review

Author

Pal, Mahesh

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Learning
Abstract

Kernel-based machine learning algorithms are based on mapping data from the original input feature space to a kernel feature space of higher dimensionality to solve a linear problem in that space. Over the last decade, kernel based classification and regression approaches such as support vector machines have widely been used in remote sensing as well as in various civil engineering applications. In spite of their better performance with different datasets, support vector machines still suffer from shortcomings such as visualization/interpretation of model, choice of kernel and kernel specific parameter as well as the regularization parameter. Relevance vector machines are another kernel based approach being explored for classification and regression with in last few years. The advantages of the relevance vector machines over the support vector machines is the availability of probabilistic predictions, using arbitrary kernel functions and not requiring setting of the regularization parameter. This paper presents a state-of-the-art review of SVM and RVM in remote sensing and provides some details of their use in other civil engineering application also., Comment: 19 pages

Published
2011

10. Geographic Information Systems in Evaluation and Visualization of Construction Schedule

Author

Bansal, V. K. and Pal, Mahesh

Subjects
Computer Science - Other Computer Science
Abstract

Commercially available scheduling tools such as Primavera and Microsoft Project fail to provide information pertaining to the spatial aspects of construction project. A methodology using geographical information systems (GIS) is developed to represent spatial aspects of the construction progress graphically by synchronizing it with construction schedule. The spatial aspects are depicted by 3D model developed in AutoCAD and construction schedule is generated using Microsoft Excel. Spatial and scheduling information are linked together into the GIS environment (ArcGIS). The GIS-based system developed in this study may help in better understanding the schedule along with its spatial aspects., Comment: Presented in Second ESRI Asia-Pacific User Conference New Delhi, 2007

Published
2008

11. Multiclass Approaches for Support Vector Machine Based Land Cover Classification

Author

Pal, Mahesh

Subjects
Computer Science - Neural and Evolutionary Computing, Computer Science - Computer Vision and Pattern Recognition
Abstract

SVMs were initially developed to perform binary classification; though, applications of binary classification are very limited. Most of the practical applications involve multiclass classification, especially in remote sensing land cover classification. A number of methods have been proposed to implement SVMs to produce multiclass classification. A number of methods to generate multiclass SVMs from binary SVMs have been proposed by researchers and is still a continuing research topic. This paper compares the performance of six multi-class approaches to solve classification problem with remote sensing data in term of classification accuracy and computational cost. One vs. one, one vs. rest, Directed Acyclic Graph (DAG), and Error Corrected Output Coding (ECOC) based multiclass approaches creates many binary classifiers and combines their results to determine the class label of a test pixel. Another catogery of multi class approach modify the binary class objective function and allows simultaneous computation of multiclass classification by solving a single optimisation problem. Results from this study conclude the usefulness of One vs. One multi class approach in term of accuracy and computational cost over other multi class approaches., Comment: 16 pages, MapIndia 2005 conference

Published
2008

12. Support Vector classifiers for Land Cover Classification

Author

Pal, Mahesh and Mather, Paul M.

Subjects
Computer Science - Neural and Evolutionary Computing, Computer Science - Computer Vision and Pattern Recognition
Abstract

Support vector machines represent a promising development in machine learning research that is not widely used within the remote sensing community. This paper reports the results of Multispectral(Landsat-7 ETM+) and Hyperspectral DAIS)data in which multi-class SVMs are compared with maximum likelihood and artificial neural network methods in terms of classification accuracy. Our results show that the SVM achieves a higher level of classification accuracy than either the maximum likelihood or the neural classifier, and that the support vector machine can be used with small training datasets and high-dimensional data., Comment: 11 pages, 1 figure, Published in MapIndia Conference 2003

Published
2008
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13. Extreme Learning Machine for land cover classification

Author

Pal, Mahesh

Subjects
Computer Science - Neural and Evolutionary Computing, Computer Science - Computer Vision and Pattern Recognition
Abstract

This paper explores the potential of extreme learning machine based supervised classification algorithm for land cover classification. In comparison to a backpropagation neural network, which requires setting of several user-defined parameters and may produce local minima, extreme learning machine require setting of one parameter and produce a unique solution. ETM+ multispectral data set (England) was used to judge the suitability of extreme learning machine for remote sensing classifications. A back propagation neural network was used to compare its performance in term of classification accuracy and computational cost. Results suggest that the extreme learning machine perform equally well to back propagation neural network in term of classification accuracy with this data set. The computational cost using extreme learning machine is very small in comparison to back propagation neural network., Comment: 6 pages, mapindia 2008 conference

Published
2008
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18. Factors influencing the accuracy of remote sensing classifications : a comparative study

Author

Pal, Mahesh

Subjects
526, G Geography (General)
Abstract

Within last 20 years, a number of methods have been employed for classifying remote sensing data, including parametric methods (e.g. the maximum likelihood classifier) and non-parametric classifiers (such as neural network classifiers).Each of these classification algorithms has some specific problems which limits its use. This research studies some alternative classification methods for land cover classification and compares their performance with the well established classification methods. The areas selected for this study are located near Littleport (Ely), in East Anglia, UK and in La Mancha region of Spain. Images in the optical bands of the Landsat ETM+ for year 2000 and InSAR data from May to September of 1996 for UK area, DAIS hyperspectral data and Landsat ETM+ for year 2000 for Spain area are used for this study. In addition, field data for the year 1996 were collected from farmers and for year 2000 were collected by field visits to both areas in the UK and Spain to generate the ground reference data set. The research was carried out in three main stages.The overall aim of this study is to assess the relative performance of four approaches to classification in remote sensing - the maximum likelihood, artificial neural net, decision tree and support vector machine methods and to examine factors which affect their performance in term of overall classification accuracy. Firstly, this research studies the behaviour of decision tree and support vector machine classifiers for land cover classification using ETM+ (UK) data. This stage discusses some factors affecting classification accuracy of a decision tree classifier, and also compares the performance of the decision tree with that of the maximum likelihood and neural network classifiers. The use of SVM requires the user to set the values of some parameters, such as type of kernel, kernel parameters, and multi-class methods as these parameters can significantly affect the accuracy of the resulting classification. This stage involves studying the effects of varying the various user defined parameters and noting their effect on classification accuracy. It is concluded that SVM perform far better than decision tree, maximum likelihood and neural network classifiers for this type of study. The second stage involves applying the decision tree, maximum likelihood and neural network classifiers to InSAR coherence and intensity data and evaluating the utility of this type of data for land cover classification studies. Finally, the last stage involves studying the response of SVMs, decision trees, maximum likelihood and neural classifier to different training data sizes, number of features, sampling plan, and the scale of the data used. The conclusion from the experiments presented in this stage is that the SVMs are unaffected by the Hughes phenomenon, and perform far better than the other classifiers in all cases. The performance of decision tree classifier based feature selection is found to be quite good in comparison with MNF transform. This study indicates that good classification performance depends on various parameters such as data type, scale of data, training sample size and type of classification method employed.

Published
2002

20. A transformer-based cloud detection approach using Sentinel 2 imageries

Author

Singh, Rohit, Biswas, Mantosh, and Pal, Mahesh

Abstract

Presence of clouds blocks the view of Earth’s surface objects in optical imageries, thus compromising their application and usability. Identifying and removing the clouds become a crucial task during image preprocessing. Recently deep learning (DL)-based cloud detection methods have shown improved performance, but capturing global semantic features and long-range dependencies necessitates a careful selection of DL classifiers to further enhance their effectiveness. Keeping this in view, the present study proposes a novel spatial-spectral attention transformer for cloud detection (SSATR-CD) with a spatial-spectral attention module that generates an enhanced feature map to replace convolution by using the image patches directly. To implement the proposed approach, a new Sentinel-2 data set with various types of cloud covers over India (IndiaS2) was created and tested with the proposed method. Alongside this, an additional benchmarked data set (WHUS2-CD) was also considered to check the ability of the proposed model to different regions of the world by applying model-based transfer learning. The result highlights the effectiveness and efficiency of the SSATR-CD approach in both cases.

Published
2023
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27. Modeling pile capacity using support vector machines and generalized regression neural network

Author

Pal, Mahesh and Deswal, Surinder

Subjects
Neural networks -- Evaluation, Piling (Civil engineering) -- Design and construction, Piling (Civil engineering) -- Mechanical properties, Regression analysis -- Methods, Strains and stresses -- Measurement, Stress relaxation (Materials) -- Measurement, Stress relieving (Materials) -- Measurement, Neural network, Earth sciences, Engineering and manufacturing industries, Science and technology
Abstract

This note investigates the potential of support vector machines based regression approach to model the static pile capacity from dynamic stress-wave data. A data set of 105 prestressed precast high strength concrete spun pipe piles is used. Radial basis function and polynomial kernel based support vector machines were used to model the total pile capacity and results were compared with a generalized regression neural network approach. A total of 81 data set were used to train, whereas the remaining 24 data sets were used to test the created model. A correlation coefficient value of 0.977 was achieved by generalized regression neural network in comparison to values of 0.967 and 0.964 achieved by radial basis function and polynomial kernel based support vector machines, respectively. Results suggest an improved performance by generalized regression neural network based approach in comparison to support vector machines but polynomial kernel based support vector machines provide a linear relationship to predict total pile capacity using stress-wave data. CE Database subject headings: Piles; Neural networks; Vector analysis; Stress wave.

Published
2008

28. Generating, evaluating, and visualizing construction schedule with geographic information systems

Author

Bansal, V.K. and Pal, Mahesh

Subjects
Geographic information systems -- Usage, Industrial project management -- Methods, Project management -- Methods, Scheduling (Management) -- Methods, Geology -- Statistical methods, Geology -- Research, Geographic information system, Computers, Engineering and manufacturing industries, Science and technology
Abstract

Linking of the activities in a critical path method schedule with the corresponding elements of a three-dimensional (3D) model makes the project sequence easier to understand. Although some commercial tools allow the planner to build a 4D model and create graphical simulation of the construction process, it still lacks features like generation and manipulation of a 4D model within a single environment. This paper presents a geographic information systems (GIS) based methodology for scheduling as an alternative to the existing 4D computer aided design tools. A methodology to build a 3D model and link it with the construction schedule using several in-house scripts written in GIS environment is discussed. It allows the planner to understand the construction schedule quickly by linking the activities of the schedule with the corresponding 3D components as well as to visualize a buildable schedule on a computer screen. The proposed methodology utilizes the dynamic linkage between the activities in the schedule and corresponding 3D components, thus, making it possible to detect the incompleteness and logical errors in the schedule sequence. The ability of GIS to maintain spatial data (i.e., 3D components corresponding to each activity) in separate themes, which can be superimposed spatially, is utilized. The related nonspatial information (like schedule, material type and quantity, safety and quality control recommendations, etc.) are stored in the attribute tables of corresponding components, which can be extracted from the database maintained within the GIS itself. CE Database subject headings: Geographic information systems; Scheduling; Construction management; Project management; Information systems.

Published
2008

39. The sensitivity of mapping methods to reference data quality:training supervised image classifications with imperfect reference data

Author

Foody, Giles M., Pal, Mahesh, Rocchini, Duccio, Garzon-Lopez, Carol X., and Bastin, Lucy

Abstract

The accuracy of a map is dependent on the reference dataset used in its construction. Classification analyses used in thematic mapping can, for example, be sensitive to a range of sampling and data quality concerns. With particular focus on the latter, the effects of reference data quality on land cover classifications from airborne thematic mapper data are explored. Variations in sampling intensity and effort are highlighted in a dataset that is widely used in mapping and modelling studies; these may need accounting for in analyses. The quality of the labelling in the reference dataset was also a key variable influencing mapping accuracy. Accuracy varied with the amount and nature of mislabelled training cases with the nature of the effects varying between classifiers. The largest impacts on accuracy occurred when mislabelling involved confusion between similar classes. Accuracy was also typically negatively related to the magnitude of mislabelled cases and the support vector machine (SVM), which has been claimed to be relatively insensitive to training data error, was the most sensitive of the set of classifiers investigated, with overall classification accuracy declining by 8% (significant at 95% level of confidence) with the use of a training set containing 20% mislabelled cases.

Published
2016

40. Major bioactive phenolics in Bergenia species from the Indian Himalayan region: Method development, validation and quantitative estimation using UHPLC-QqQLIT-MS/MS

Author

Pandey, Renu, primary, Kumar, Brijesh, additional, Meena, Baleshwar, additional, Srivastava, Mukesh, additional, Mishra, Tripti, additional, Tiwari, Vandana, additional, Pal, Mahesh, additional, Nair, Narayanan K., additional, Upreti, Dalip K., additional, and Rana, Tikam Singh, additional

Published
2017
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42. An evaluation of the efficacy of ethanolic extract of Nigella sativa L. (Kalonji) on the clinical parameters of moderate-to-severe gingivitis: A split-mouth clinical study.

Author

Singh, Vibha, Gupta, Abhaya, Verma, Umesh, Mishra, Tripti, and Pal, Mahesh

Subjects
BLACK cumin, GINGIVITIS, NEEM, GINGER, PERIODONTAL disease, TOOTH mobility
Abstract

Introduction: Gingivitis is a relatively innocuous and reversible inflammation of gingiva. If left untreated, it might progress involving the deeper supporting periodontal tissues of the tooth with consequent mobility and tooth loss. Compelling literature has suggested the role of local antibacterial and anti-inflammatory agents as an adjunct to scaling and root planing (gold standard) for treating periodontal diseases. Various herbs such as Nimba (Azadirachta indica A. Juss), Babbula (Vachellia nilotica (L.) P.J.H. Hurter & Mabb.) and turmeric (Curcuma long a L.) have been used for gingivitis since ancient times. Nigella sativa L. (Kalonji) is one such herb known for its remarkable anti-inflammatory, antioxidant and antimicrobial properties and thus has been utilized in the present study. Aim: The aim of the study was to explore the clinical efficacy of different ethanolic solutions of N. sativa in moderate-to-severe gingivitis patients. Materials and Methods: It is a split-mouth clinical study with 24 patients of moderate-to-severe gingivitis from the age group of 25–45 years. Recruited individuals were divided in to group I1, group II1and group III1 (scaling and root planning i.e., control) and group I2, group II2and group III2 (experimental). Three doses of solution 1 (1:3), solution 2 (1:1) and solution 3 (3:1) were administered to the experimental groups for 3 consecutive days. The clinical parameters, i.e., gingival index (GI) and plaque index (PI) were recorded at baseline, 14 days and 28 days in all the individuals. ANOVA test was used in the study for statistical analysis. Results: Intergroup comparison in terms of GI showed statistically significant difference at 14th and 28th day from baseline between I1 &I2,at only 28th day between II1 & II2and insignificant difference between III1 & III2 at all time intervals from baseline. On intragroup comparison, statistically significant reduction in GI in all groups from baseline till 28 days was found, but among experimental groups best result was seen in group III2(P < 0.001; F value 153.75). As far as PI is concerned, intergroup comparison between different groups displayed statistically significant difference from baseline to 14th and 28th day between all groups i.e I1& I2, II1& II2and III1& III2. On intragroup comparison, statistically significant reduction in PI in all control groups i.e I1, II1 & III1was found, but among experimental groups only group III2 provided statistically significant reduction (P < 0.001, F value 30.40). Conclusion: The results of this study indicate that, the ethanolic extract of N. sativa is effective in the treatment of moderate to severe gingivitis. [ABSTRACT FROM AUTHOR]

Published
2019
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43. Fatty acid composition and antibacterial potential of Cassia tora(leaves and stem) collected from different geographic areas of India

Author

Shukla, Shipra, Hegde, Satisha, Kumar, Anil, Chaudhary, Gaurav, Tewari, Shri Krishna, Upreti, Dalip Kumar, and Pal, Mahesh

Abstract

The comparative analysis of the fatty acid composition of Cassia tora(leaves and stem) was determined using gas chromatography–mass spectrometry. Twenty-seven fatty acids were identified in C. tora(leaves and stem) which was collected from three different geographical areas of India: Lucknow (Uttar Pradesh), Nainital (Uttarakhand), and Bhavnagar (Gujarat), coded as CT-1, CT-2, and CT-3, respectively. The gas chromatography–mass spectrometry analysis showed the presence of various saturated and unsaturated fatty acids. The major fatty acids found were palmitic acid, linoleic acid, linolenic acid, margaric acid, melissic acid, and behenic acid. The highest amounts of saturated fatty acids were found in leaves of C. toracollected from Bhavnagar (Gujarat) (60.7% ± 0.5%). Thus, the study reveals that C. torahas a major amount of nutritionally important fatty acids, along with significant antimicrobial potential. Fatty acids play a significant role in the development of fat products with enhanced nutritional value and clinical application. Remarkable differences were found in the present study between fatty acid profiles of C. toracollected from different locations in India. To the best of our knowledge there is no previously reported comparative study of the fatty acids of C. tora.

Published
2024
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48. Anti-termite activity of essential oil and its components from Myristica fragrans against Microcerotermes beesoni

Author

Pal, Mahesh, Kumar, Rajesh, and Tewari, Sri Krishan

Abstract

The essential oil obtained by hydrodistillation of the fruits of Myristica fragrans was analyzed by GC and GC-MS. Twenty eight compounds were identified representing 95.9% of the oil. The major constituents of the oil were α-pinene (6.4%), Sabinene (37.7%), β-pinene (7.3%), myrcene (3.5%),Limonene (4.7%),Terpine-4-ol (5.8%), safrole (3.4%) and myristicin (6.8%).The essential oil and its major constituents were evaluated at different dilution against Microcerotermes beesoni, test termite. The LC50 value of fruit essential oil is 28.6 mg/g. Furthermore, exposure to myristicin caused 100% mortality at a dosage of 5 mg/g after 14d. @JASEMJ. Appl. Sci. Environ. Manage. Sept, 2011, Vol. 15 (3) 559 - 561

Published
2013

49. Pharmacognostic and pharmacological evaluation of Eulaliopsis binata plant extracts by measuring in vitro/in vivo safety profile and anti-microbial potential.

Author

Kumar, Vinod, Sharma, Arun K., Rajput, Satyendra K., Pal, Mahesh, and Dhiman, Neerupma

Subjects
PLANT extracts, DISC diffusion tests (Microbiology), FOLIAR diagnosis, FLAVONOIDS, POWDERS
Abstract

The present investigation emphasizes the pharmacognostic and phytochemical screening of Eulaliopsis binata and further evaluates the extracts of this plant for toxicological profile and anti-bacterial potential based on in vivo/in vitro assays. Microscopy, powder characteristics of the leaf material, and physicochemical and phytochemical screening were assessed for pharmacognostic evaluation. Dry leaves of Eulaliopsis binata were extracted using different solvents (methanol, ethyl acetate, and hexane), and the extracts obtained were further investigated for in vitro/in vivo toxicological study. Moreover, acute toxicity was assessed by evaluating the anti-oxidant defense system and anatomical damage in vital organs. In addition, anti-bacterial activity of all the extracts was assessed by the Kirby-Bauer method. Physicochemical and microscopic observations showed the unique identification mark for leaf powder and leaf transverse section. Phytochemical investigation evidenced the presence of flavonoids and phenolic contents in the methanolic extract. All extracts were found to be hemocompatible and exhibited no induction of behavioral alteration and no alteration in the anti-oxidant potential and anatomical structure of the vital organs. On the other hand, the methanolic extract showed a significant upsurge in the reduced glutathione level, whereas all extracts showed significant anti-bacterial potential in a dose-dependent manner. Eulaliopsis binata has inimitable pharmacognostical characteristics, good safety profile and significant anti-oxidant and anti-bacterial potential that show immense possibility for its further investigation for pharmacological use. [ABSTRACT FROM AUTHOR]

Published
2018
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50. Chemical composition and cytotoxic activity of methanol extract and its fractions of streblus asper leaves on human cancer cell lines.

Author

Rawat, Preeti, Kumar, Anil, Singh, Tryambak, and Pal, Mahesh

Subjects
MORACEAE, ANTINEOPLASTIC agents, CELL-mediated cytotoxicity, METHANOL, GAS chromatography/Mass spectrometry (GC-MS)
Abstract

Background: Streblus asper, family Moraceae is well-known important medicinal plant used in the Indian system of medicine. In Ayurveda, stem bark of S. asper is recommended against elephantiasis for which there is still no any other effective medicine in the modern system of medicine. Objectives: In the present work, methanol extract (SAM) and its fractions of S. asper leave tested for in vitro anticancer activity against cancer cell lines (MCF-7, A-549, Hep-G2, and K-562) which claims its folklore importance in cancer and gas chromatography-mass spectrometry identification of extracts was also performed. Materials and Methods: Shade dried plant material was extracted with methanol and fractionated sequentially with hexane, chloroform, and butanol. Results: All tested extracts found highly effective against human lung cancer cell line (A-549) with IC50 <10 μg/mL. On Hep-G2 cancer cell line, only chloroform fraction are highly active with IC50 <10 μg/mL. Methanol and hexane fraction showed potent anticancer activity on K-562 cancer cell line with IC50 <10 μg/mL. Conclusion: Qualitative phytochemical analysis confirmed the presence of fatty acids, phytosterol, triterpenoids, polyol, sugar acid, aldehyde, diterpene, terpene, carboxylic compounds, acid and sugar in S. asper leaves extract. Topmost abundant compounds in SAM are α-D-glucopyranoside (10.60%), glycerol (7.96%), myo-inositol (4.90%), and butanedioic acid (3.30%). Hexane consists of the higher amount of hexadecanoic acid (18.07%), octadecanoic acid (7.39%), β-sitosterol (4.50%), and α-D-glucopyranoside (4.03%). Higher component in chloroform extract is lupenyl acetate (11.25%). Abbreviations used: SRB: Sulforhodamine B assay; SAM: Methanol extract; SAH: Hexane extract; SAC: Chloroform extract. [ABSTRACT FROM AUTHOR]

Published
2018
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