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Author

Neha Jain

Other affiliations: Indian Institutes of Technology
Bio: Neha Jain is an academic researcher from Indian Institute of Technology Delhi. The author has contributed to research in topic(s): Protein folding & Equilibrium unfolding. The author has an hindex of 3, co-authored 6 publication(s) receiving 82 citation(s). Previous affiliations of Neha Jain include Indian Institutes of Technology.

Papers
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Proceedings ArticleDOI
01 Sep 2013
TL;DR: This paper proposes a novel temporal representation of Gait using Pal and Pal Entropy (GPPE) for each cycle of the silhouettes using Principal component analysis to create a feature matrix.
Abstract: Human Gait recognition is one of the most promising research areas at the moment. Gait is the style or manner of walking on foot. Gait recognition aims to identify individuals by the manner in which they walk. Existing Gait representations which capture both motion and appearance information are sensitive to changes in various covariate conditions such as carrying and clothing. In this paper, we propose a novel temporal representation of Gait using Pal and Pal Entropy (GPPE) for each cycle of the silhouettes. The Principal component analysis is applied to each of the features extracted to create a feature matrix. Support Vector Machine (SVM) is used for training and testing of individuals by the proposed method. Extensive experiments on the Treadmill dataset and the CASIA datasets A, B, C have been carried out to demonstrate the effectiveness of the proposed representation of Gait.

64 citations

Journal ArticleDOI
TL;DR: A comparative study of various species of DHFR shows that zDHFR has comparable thermodynamic stability with human counterpart and thus proved to be a good in vitro model system for structure- function relationship studies.
Abstract: The folding and unfolding mechanisms of a small monomeric protein, Dihydrofolate reductase (1.5.1.3.) from a new variant, Zebrafish (zDHFR) has been studied through GdnHCl denaturation, followed by its refolding through dilution of the denaturant. Intrinsic and extrinsic fluorescence, far-UV CD and enzyme activity were employed to monitor structural and functional changes due to chemical denaturation. The unfolding transitions monitored by intrinsic fluorescence showed that GdnHCl based denaturation of zDHFR is reversible. At low concentration of the denaturant, zDHFR forms intermediate species as reflected by increased fluorescence intensity compared to the native and fully unfolded form. Equilibrium unfolding transition study of zDHFR induced by GdnHCl exhibited three- state process. The non- coincidence of fluorescence and far-UVCD based transitions curves support the establishment of three state model of zDHFR protein which involves native, intermediate and unfolded forms. Analysis of the equilibrium unfolding transition suggests the presence of non- native intermediate species. A comparative study of various species of DHFR shows that zDHFR has comparable thermodynamic stability with human counterpart and thus proved to be a good in vitro model system for structure- function relationship studies. Understanding various conformational states during the folding unfolding process of the zDHFR protein may provide important clues towards designing inhibitors against this important protein involved in cell cycle regulation.

9 citations

Journal ArticleDOI
TL;DR: Minichaperone can act as a very good in vitro as well as in vivo chaperone model for monitoring assisted protein folding phenomenon and indicates that it enhances the thermal stability of the enzyme.
Abstract: The maintenance of thermal stability is a major issue in protein engineering as many proteins tend to form inactive aggregates at higher temperatures. Zebrafish DHFR, an essential protein for the survival of cells, shows irreversible thermal unfolding transition. The protein exhibits complete unfolding and loss of activity at 50 °C as monitored by UV-Visible, fluorescence and far UV-CD spectroscopy. The heat induced inactivation of zDHFR follows first–order kinetics and Arrhenius law. The variation in the value of inactivation rate constant, k with increasing temperatures depicts faster inactivation at elevated temperatures. We have attempted to study the chaperoning ability of a shorter variant of GroEL (minichaperone) and compared it with that of conventional GroEL-GroES chaperone system. Both the chaperone system prevented the aggregation and assisted in refolding of zDHFR. The rate of thermal inactivation was significantly retarded in the presence of chaperones which indicate that it enhances the thermal stability of the enzyme. As minichaperone is less complex, and does not require high energy co-factors like ATP, for its function as compared to conventional GroEL-GroES system, it can act as a very good in vitro as well as in vivo chaperone model for monitoring assisted protein folding phenomenon.

4 citations

Journal ArticleDOI
TL;DR: It was observed that 100 mM proline does not show any significant stabilization to either DHFRs and the human protein is relatively less stable than the E. coli counterpart.
Abstract: A protein, differing in origin, may exhibit variable physicochemical behaviour, difference in sequence homology, fold and function. Thus studying structure-function relationship of proteins from altered sources is meaningful in the sense that it may give rise to comparative aspects of their sequence-structure-function relationship. Dihydrofolate reductase is an enzyme involved in cell cycle regulation. It is a significant enzyme as.a target for developing anticancer drugs. Hence, detailed understanding of structure-function relationships of wide variants of the enzyme dihydrofolate reductase would be important for developing an inhibitor or an antagonist against the enzyme involved in the cellular developmental processes. In this communication, we have reported the comparative structure-function relationship between E. coli and human dihydrofolate reductase. The differences in the unfolding behaviour of these two proteins have been investigated to understand various properties of these two proteins like relative' stability differences and variation in conformational changes under identical denaturing conditions. The equilibrium unfolding mechanism of dihydrofolate reductase proteins using guanidine hydrochloride as a denaturant in the presence of various types of osmolytes has been monitored using loss in enzymatic activity, intrinsic tryptophan fluorescence and an extrinsic fluorophore 8-anilino-1-naphthalene-sulfonic acid as probes. It has been observed that osmolytes, such as 1M sucrose, and 30% glycerol, provided enhanced stability to both variants of dihydrofolate reductase. Their level of stabilisation has been observed to be dependent on intrinsic protein stability. It was observed that 100 mM proline does not show any 'significant stabilisation to either of dihydrofolate reductases. In the present study, it has been observed that the human protein is relatively less stable than the E.coli counterpart.

3 citations

Journal ArticleDOI
TL;DR: Observations suggest that the minichaperone works by carrying out repeated cycles of binding aggregation-prone protein MalZ in a relatively compact conformation and in a partially folded but active state, and releasing them to attempt to fold in solution.
Abstract: The isolated apical domain of GroEL consisting of residues 191–345 (known as “minichaperone”) binds and assists the folding of a wide variety of client proteins without GroES and ATP, but the mechanism of its action is still unknown. In order to probe into the matter, we have examined minichaperone-mediated folding of a large aggregation prone protein Maltodextrin-glucosidase (MalZ). The key objective was to identify whether MalZ exists free in solution, or remains bound to, or cycling on and off the minichaperone during the refolding process. When GroES was introduced during refolding process, production of the native MalZ was inhibited. We also observed the same findings with a trap mutant of GroEL, which stably captures a predominantly non-native MalZ released from minichaperone during refolding process, but does not release it. Tryptophan and ANS fluorescence measurements indicated that refolded MalZ has the same structure as the native MalZ, but that its structure when bound to minichaperone is different. Surface plasmon resonance measurements provide an estimate for the equilibrium dissociation constant KD for the MalZ-minichaperone complex of 0.21 ± 0.04 μM, which are significantly higher than for most GroEL clients. This showed that minichaperone interacts loosely with MalZ to allow the protein to change its conformation and fold while bound during the refolding process. These observations suggest that the minichaperone works by carrying out repeated cycles of binding aggregation-prone protein MalZ in a relatively compact conformation and in a partially folded but active state, and releasing them to attempt to fold in solution.

2 citations


Cited by
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Journal ArticleDOI
TL;DR: A specialized deep convolutional neural network architecture for gait recognition that is less sensitive to several cases of the common variations and occlusions that affect and degrade gait Recognition performance.
Abstract: Gait recognition is a biometric technique used in determining the identity of humans based on the style and the manner of their walk. Its performance is often degraded by covariate factors such as carrying condition changes, clothing condition changes, and viewing angle variations. Recently, machine learning based techniques have produced promising results for challenging classification problems. Since, a deep convolutional neural network (CNN) is one of the most advanced machine learning techniques with the ability to approximate complex non-linear functions, we develop a specialized deep CNN architecture for Gait Recognition. The proposed architecture is less sensitive to several cases of the common variations and occlusions that affect and degrade gait recognition performance. It can also handle relatively small data sets without using any augmentation or fine-tuning techniques. The majority of previous approaches to gait recognition have used subspace learning methods which have several shortcomings that we avoid. Our specialized deep CNN model can obtain competitive performance when tested on the CASIA-B large gait dataset.

80 citations

Journal ArticleDOI
TL;DR: A method to select the most discriminative human body part based on group Lasso of motion to reduce the intra-class variation so as to improve the recognition performance is proposed.
Abstract: Gait recognition is an emerging biometric technology that identifies people through the analysis of the way they walk. The challenge of model-free based gait recognition is to cope with various intra-class variations such as clothing variations, carrying conditions and angle variations that adversely affect the recognition performance. This paper proposes a method to select the most discriminative human body part based on group Lasso of motion to reduce the intra-class variation so as to improve the recognition performance. The proposed method is evaluated using CASIA Gait Dataset B. Experimental results demonstrate that the proposed technique gives promising results.

80 citations

Journal ArticleDOI
TL;DR: The proposed gait feature extraction process is performed in the spatio-temporal domain and the performance of the proposed method is promising for the case of normal walking, and is outstanding for the cases of partial occlusion caused by walking with carrying a bag and walking with varying a cloth type.
Abstract: Gait has been known as an effective biometric feature to identify a person at a distance, e.g., in video surveillance applications. Many methods have been proposed for gait recognitions from various different perspectives. It is found that these methods rely on appearance (e.g., shape contour, silhouette)-based analyses, which require preprocessing of foreground–background segmentation (FG/BG). This process not only causes additional time complexity, but also adversely influences performances of gait analyses due to imperfections of existing FG/BG methods. Besides, appearance-based gait recognitions are sensitive to several variations and partial occlusions, e.g., caused by carrying a bag and varying a cloth type. To avoid these limitations, this paper proposes a new framework to construct a new gait feature directly from a raw video. The proposed gait feature extraction process is performed in the spatio-temporal domain. The space-time interest points (STIPs) are detected by considering large variations along both spatial and temporal directions in local spatio-temporal volumes of a raw gait video sequence. Thus, STIPs are allocated, where there are significant movements of human body in both space and time. A histogram of oriented gradients and a histogram of optical flow are computed on a 3D video patch in a neighborhood of each detected STIP, as a STIP descriptor. Then, the bag-of-words model is applied on each set of STIP descriptors to construct a gait feature for representing and recognizing an individual gait. When compared with other existing methods in the literature, it has been shown that the performance of the proposed method is promising for the case of normal walking, and is outstanding for the case of partial occlusion caused by walking with carrying a bag and walking with varying a cloth type.

68 citations

Book ChapterDOI
28 Oct 2017
TL;DR: This work proposes a novel pose-based gait recognition approach that is more robust to the clothing and carrying variations, and a pose- based temporal-spatial network (PTSN) is proposed to extract the temporal- Spatial features, which effectively improve the performance of gait Recognition.
Abstract: One of the most attractive biometric techniques is gait recognition, since its potential for human identification at a distance. But gait recognition is still challenging in real applications due to the effect of many variations on the appearance and shape. Usually, appearance-based methods need to compute gait energy image (GEI) which is extracted from the human silhouettes. GEI is an image that is obtained by averaging the silhouettes and as result the temporal information is removed. The body joints are invariant to changing clothing and carrying conditions. We propose a novel pose-based gait recognition approach that is more robust to the clothing and carrying variations. At the same time, a pose-based temporal-spatial network (PTSN) is proposed to extract the temporal-spatial features, which effectively improve the performance of gait recognition. Experiments evaluated on the challenging CASIA B dataset, show that our method achieves state-of-the-art performance in both carrying and clothing conditions.

63 citations

Journal ArticleDOI
TL;DR: A comprehensive overview of existing robust gait recognition methods is provided to provide researchers with state of the art approaches in order to help advance the research topic through an understanding of basic taxonomies, comparisons, and summaries of the state-of-the-art performances on several widely used gait Recognition datasets.
Abstract: Gait recognition has emerged as an attractive biometric technology for the identification of people by analysing the way they walk. However, one of the main challenges of the technology is to address the effects of inherent various intra-class variations caused by covariate factors such as clothing, carrying conditions, and view angle that adversely affect the recognition performance. The main aim of this survey is to provide a comprehensive overview of existing robust gait recognition methods. This is intended to provide researchers with state of the art approaches in order to help advance the research topic through an understanding of basic taxonomies, comparisons, and summaries of the state-of-the-art performances on several widely used gait recognition datasets.

50 citations