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26 results on '"Mandal, Swati"'

3. eIF5A

Author

Park, Myung Hee, Mandal, Swati, Mandal, Ajeet, Wolff, Edith C, and Parsyan, Armen, editor

Published
2014
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8. Genome-scale RNA interference screen identifies antizyme 1(OAZ1) as a target for improvement of recombinant protein production in mammalian cells†

Author

Xiao, Su, Chen, Yu Chi, Buehler, Eugene, Mandal, Swati, Mandal, Ajeet, Betenbaugh, Michael, HeePark, Myung, Martin, Scott, and Shiloach, Joseph

Subjects
Genetic Enhancement, HEK293 Cells, Cell Survival, Gene Targeting, Chromosome Mapping, High-Throughput Nucleotide Sequencing, Humans, Proteins, RNA Interference, Protein Engineering, Article, Recombinant Proteins
Abstract

For the purpose of improving recombinant protein production from mammalian cells, an unbiased, high-throughput whole-genome RNA interference screen was conducted using human embryonic kidney 293 (HEK 293) cells expressing firefly luciferase. A 21,585 human genes were individually silenced with three different siRNAs for each gene. The screen identified 56 genes that led to the greatest improvement in luciferase expression. These genes were found to be included in several pathways involved in spliceosome formation and mRNA processing, transcription, metabolic processes, transport, and protein folding. The 10 genes that most enhanced protein expression when downregulated, were further confirmed by measuring the effect of their silencing on the expression of three additional recombinant proteins. Among the confirmed genes, OAZ1-the gene encoding the ornithine decarboxylase antizyme1-was selected for detailed investigation, since its silencing improved the reporter protein production without affecting cell viability. Silencing OAZ1 caused an increase of the ornithine decarboxylase enzyme and the cellular levels of putrescine and spermidine; an indication that increased cellular polyamines enhances luciferase expression without affecting its transcription. The study shows that OAZ1 is a novel target for improving expression of recombinant proteins. The genome-scale screening performed in this work can establish the foundation for targeted design of an efficient mammalian cell platform for various biotechnological applications. Biotechnol. Bioeng. 2016;113: 2403-2415. © 2016 Wiley Periodicals, Inc.

Published
2016

20. Novel Arylimidamides for Treatment of Visceral Leishmaniasis

Author

Wang, Michael Zhuo, primary, Zhu, Xiaohua, additional, Srivastava, Anuradha, additional, Liu, Qiang, additional, Sweat, J. Mark, additional, Pandharkar, Trupti, additional, Stephens, Chad E., additional, Riccio, Ed, additional, Parman, Toufan, additional, Munde, Manoj, additional, Mandal, Swati, additional, Madhubala, Rentala, additional, Tidwell, Richard R., additional, Wilson, W. David, additional, Boykin, David W., additional, Hall, James Edwin, additional, Kyle, Dennis E., additional, and Werbovetz, Karl A., additional

Published
2010
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22. Hand Gesture Recognition Using Fusion of Features

Author

Mandal, Swati and Mandal, Swati

Abstract

As the world proceeds with an increase in digitization, Human-computer interface has a wide variety of scope in this digital world. Hand gesture recognition is one such domain which has a number of applications in Human-computer interface(HCI). Hand gestures recognition can be used in application such as automobile interface, medicine industries, gaming zones, public services etc. Among several gestures based recognition system, Hand Gesture is one of the easiest and convenient ways to communicate with the computer. Hand gestures are basically used by deaf and dumb people, so various sign languages are used. In this project, American Sign Language is used. This work is carried out in three steps: i) preprocessing, ii) Feature Extraction, iii) Classification. In the preprocessing step the input image which is exposed to illumination, rotation, and scaling is dealt with. The extraction of hand region out of the entire image is a challenging task, in this work YCbCr color space based skin color detection is done for hand gesture image for both uniform background and complex background. Various Morphological operations are also performed on the binary image for removing the unwanted redundancies from the image. For feature extraction, Edge Orientation Histogram (EOH), Block Based features (BBF), and Scale Invariant Feature Transform (SIFT)is used. The former is a contour based feature extraction approach in which only hand gesture boundary is considered for extraction of features. The second one is the block based feature extraction algorithm; in this, the shape of the gesture image is taken for generating the feature vector. The later one is Scale-invariant feature transform, this algorithm is mainly used in object and scene recognition due to its flexibility towards images which are prone to scaling and rotation variance. Fusion of edge orientation histogram and block based features are also done to make the system more efficient. The third step in hand gesture recogn

23. Hand Gesture Recognition Using Fusion of Features

Author

Mandal, Swati and Mandal, Swati

Abstract

As the world proceeds with an increase in digitization, Human-computer interface has a wide variety of scope in this digital world. Hand gesture recognition is one such domain which has a number of applications in Human-computer interface(HCI). Hand gestures recognition can be used in application such as automobile interface, medicine industries, gaming zones, public services etc. Among several gestures based recognition system, Hand Gesture is one of the easiest and convenient ways to communicate with the computer. Hand gestures are basically used by deaf and dumb people, so various sign languages are used. In this project, American Sign Language is used. This work is carried out in three steps: i) preprocessing, ii) Feature Extraction, iii) Classification. In the preprocessing step the input image which is exposed to illumination, rotation, and scaling is dealt with. The extraction of hand region out of the entire image is a challenging task, in this work YCbCr color space based skin color detection is done for hand gesture image for both uniform background and complex background. Various Morphological operations are also performed on the binary image for removing the unwanted redundancies from the image. For feature extraction, Edge Orientation Histogram (EOH), Block Based features (BBF), and Scale Invariant Feature Transform (SIFT)is used. The former is a contour based feature extraction approach in which only hand gesture boundary is considered for extraction of features. The second one is the block based feature extraction algorithm; in this, the shape of the gesture image is taken for generating the feature vector. The later one is Scale-invariant feature transform, this algorithm is mainly used in object and scene recognition due to its flexibility towards images which are prone to scaling and rotation variance. Fusion of edge orientation histogram and block based features are also done to make the system more efficient. The third step in hand gesture recogn

24. Hand Gesture Recognition Using Fusion of Features

Author

Mandal, Swati and Mandal, Swati

Abstract

As the world proceeds with an increase in digitization, Human-computer interface has a wide variety of scope in this digital world. Hand gesture recognition is one such domain which has a number of applications in Human-computer interface(HCI). Hand gestures recognition can be used in application such as automobile interface, medicine industries, gaming zones, public services etc. Among several gestures based recognition system, Hand Gesture is one of the easiest and convenient ways to communicate with the computer. Hand gestures are basically used by deaf and dumb people, so various sign languages are used. In this project, American Sign Language is used. This work is carried out in three steps: i) preprocessing, ii) Feature Extraction, iii) Classification. In the preprocessing step the input image which is exposed to illumination, rotation, and scaling is dealt with. The extraction of hand region out of the entire image is a challenging task, in this work YCbCr color space based skin color detection is done for hand gesture image for both uniform background and complex background. Various Morphological operations are also performed on the binary image for removing the unwanted redundancies from the image. For feature extraction, Edge Orientation Histogram (EOH), Block Based features (BBF), and Scale Invariant Feature Transform (SIFT)is used. The former is a contour based feature extraction approach in which only hand gesture boundary is considered for extraction of features. The second one is the block based feature extraction algorithm; in this, the shape of the gesture image is taken for generating the feature vector. The later one is Scale-invariant feature transform, this algorithm is mainly used in object and scene recognition due to its flexibility towards images which are prone to scaling and rotation variance. Fusion of edge orientation histogram and block based features are also done to make the system more efficient. The third step in hand gesture recogn

25. Hand Gesture Recognition Using Fusion of Features

Author

Mandal, Swati and Mandal, Swati

Abstract

As the world proceeds with an increase in digitization, Human-computer interface has a wide variety of scope in this digital world. Hand gesture recognition is one such domain which has a number of applications in Human-computer interface(HCI). Hand gestures recognition can be used in application such as automobile interface, medicine industries, gaming zones, public services etc. Among several gestures based recognition system, Hand Gesture is one of the easiest and convenient ways to communicate with the computer. Hand gestures are basically used by deaf and dumb people, so various sign languages are used. In this project, American Sign Language is used. This work is carried out in three steps: i) preprocessing, ii) Feature Extraction, iii) Classification. In the preprocessing step the input image which is exposed to illumination, rotation, and scaling is dealt with. The extraction of hand region out of the entire image is a challenging task, in this work YCbCr color space based skin color detection is done for hand gesture image for both uniform background and complex background. Various Morphological operations are also performed on the binary image for removing the unwanted redundancies from the image. For feature extraction, Edge Orientation Histogram (EOH), Block Based features (BBF), and Scale Invariant Feature Transform (SIFT)is used. The former is a contour based feature extraction approach in which only hand gesture boundary is considered for extraction of features. The second one is the block based feature extraction algorithm; in this, the shape of the gesture image is taken for generating the feature vector. The later one is Scale-invariant feature transform, this algorithm is mainly used in object and scene recognition due to its flexibility towards images which are prone to scaling and rotation variance. Fusion of edge orientation histogram and block based features are also done to make the system more efficient. The third step in hand gesture recogn

26. High-throughput screening of amastigotes of Leishmania donovani clinical isolates against drugs using a colorimetric beta-lactamase assay.

Author

Mandal S, Maharjan M, Ganguly S, Chatterjee M, Singh S, Buckner FS, and Madhubala R

Subjects
Animals, Antiprotozoal Agents pharmacology, Antiprotozoal Agents therapeutic use, Genes, Reporter, Humans, Leishmania donovani cytology, Leishmania donovani drug effects, Leishmaniasis, Cutaneous drug therapy, Leishmaniasis, Visceral drug therapy, beta-Lactamases genetics, Biological Assay methods, Leishmania donovani metabolism, beta-Lactamases metabolism
Abstract

A simple colorimetric beta-lactamase assay for quantifying Leishmania amastigotes in macrophages grown in microtiter plates has been reported. The beta-lactamase gene was integrated into the rRNA region of the genome, thereby allowing for high-level stable expression of the enzyme. Both visceral leishmaniasis (VL) and post-kala azar dermal leishmaniasis (PKDL) isolates were transfected with beta-Lactamase gene. These beta-lactamase-expressing promastigotes were used for infecting intracellular J774A.1 macrophages in vitro. Quantification was done by a colorimetric readout with CENTA beta-lactamase as substrate and with an optical density plate reader. The assay was carried out in 96-well plates. Results obtained demonstrate that this methodology could be a valuable high-throughput screening assay for checking efficacy of anti-leishmanial drugs in the clinical isolates.

Published
2009

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