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Anwesha Banerjee

Bio: Anwesha Banerjee is an academic researcher from Jadavpur University. The author has contributed to research in topics: Hjorth parameters & Eye movement. The author has an hindex of 9, co-authored 35 publications receiving 227 citations. Previous affiliations of Anwesha Banerjee include Indian Council of Medical Research & National Brain Research Centre.

Papers
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Journal ArticleDOI
TL;DR: In this paper, a comprehensive report to investigate diagnostic biomarkers for AD may be identified by from magnetoencephalography (MEG) data, which can also be utilized for the same pursuit in combination with other imaging modalities.
Abstract: Neural oscillations were established with their association with neurophysiological activities and the altered rhythmic patterns are believed to be linked directly to the progression of cognitive decline. Magnetoencephalography (MEG) is a non-invasive technique to record such neuronal activity due to excellent temporal and fair amount of spatial resolution. Single channel, connectivity as well as brain network analysis using MEG data in resting state and task-based experiments were analyzed from existing literature. Single channel analysis studies reported a less complex, more regular and predictable oscillations in Alzheimer's disease (AD) primarily in the left parietal, temporal and occipital regions. Investigations on both functional connectivity (FC) and effective (EC) connectivity analysis demonstrated a loss of connectivity in AD compared to healthy control (HC) subjects found in higher frequency bands. It has been reported from multiplex network of MEG study in AD in the affected regions of hippocampus, posterior default mode network (DMN) and occipital areas, however, conclusions cannot be drawn due to limited availability of clinical literature. Potential utilization of high spatial resolution in MEG likely to provide information related to in-depth brain functioning and underlying factors responsible for changes in neuronal waves in AD. This review is a comprehensive report to investigate diagnostic biomarkers for AD may be identified by from MEG data. It is also important to note that MEG data can also be utilized for the same pursuit in combination with other imaging modalities.

65 citations

Journal ArticleDOI
TL;DR: A comparative study of different methods for Electrooculogram classification to utilize it to control rehabilitation aids is presented.

38 citations

Journal ArticleDOI
TL;DR: This review gives a detailed account of the significance of compartmentalization during HSV pathogenesis and highlights the undiscovered areas in the HSV cell biology research which demand attention for devising improved therapeutics against the infection.
Abstract: Alpha (α)-herpesviruses (HSV-1 and HSV-2), like other viruses, are obligate intracellular parasites. They hijack the cellular machinery to survive and replicate through evading the defensive responses by the host. The viral genome of herpes simplex viruses (HSVs) contains viral genes, the products of which are destined to exploit the host apparatus for their own existence. Cellular modulations begin from the entry point itself. The two main gateways that the virus has to penetrate are the cell membrane and the nuclear membrane. Changes in the cell membrane are triggered when the glycoproteins of HSV interact with the surface receptors of the host cell, and from here, the components of the cytoskeleton take over. The rearrangement in the cytoskeleton components help the virus to enter as well as transport to the nucleus and back to the cell membrane to spread out to the other cells. The entire carriage process is also mediated by the motor proteins of the kinesin and dynein superfamily and is directed by the viral tegument proteins. Also, the virus captures the cell's most efficient cargo carrying system, the endoplasmic reticulum (ER)-Golgi vesicular transport machinery for egress to the cell membrane. For these reasons, the host cell has its own checkpoints where the normal functions are halted once a danger is sensed. However, a cell may be prepared for the adversities from an invading virus, and it is simply commendable that the virus has the antidote to these cellular strategies as well. The HSV viral proteins are capable of limiting the use of the transcriptional and translational tools for the cell itself, so that its own transcription and translation pathways remain unhindered. HSV prefers to constrain any self-destruction process of the cell-be it autophagy in the lysosome or apoptosis by the mitochondria, so that it can continue to parasitize the cell for its own survival. This review gives a detailed account of the significance of compartmentalization during HSV pathogenesis. It also highlights the undiscovered areas in the HSV cell biology research which demand attention for devising improved therapeutics against the infection.

31 citations

Journal ArticleDOI
30 Sep 2021-Viruses
TL;DR: In this paper, different stages of the Dengue virus infection cycle inside mammalian host cells and how host proteins are exploited by the virus in the course of infection as well as how the host counteracts the virus by eliciting different antiviral responses.
Abstract: Dengue is a mosquito-borne viral disease (arboviral) caused by the Dengue virus. It is one of the prominent public health problems in tropical and subtropical regions with no effective vaccines. Every year around 400 million people get infected by the Dengue virus, with a mortality rate of about 20% among the patients with severe dengue. The Dengue virus belongs to the Flaviviridae family, and it is an enveloped virus with positive-sense single-stranded RNA as the genetic material. Studies of the infection cycle of this virus revealed potential host targets important for the virus replication cycle. Here in this review article, we will be discussing different stages of the Dengue virus infection cycle inside mammalian host cells and how host proteins are exploited by the virus in the course of infection as well as how the host counteracts the virus by eliciting different antiviral responses.

27 citations

Proceedings ArticleDOI
01 Dec 2012
TL;DR: New approach to control the motorized human computer interface using electrooculogram (EOG) is proposed, which is the potential generated in due the movement of the eyeballs and can be acquired from the surrounding region of eye socket.
Abstract: Human computer interfacing (HCI) technology has emerged as a new pathway towards the improvement of different rehabilitative aids. In this paper, new approach to control the motorized human computer interface using electrooculogram (EOG) is proposed. A mobility interface controlled by eye movements has been developed to help the disabled individuals with motor impairment who cannot even speak. Electrooculogram(EOG) is the potential generated in due the movement of the eyeballs and can be acquired from the surrounding region of eye socket. The signal is easy to acquire noninvasively and has a simple pattern. A low cost data acquisition system for EOG is designed. Horizontal electrooculographic signal is recorded by placing electrodes at the outer region of the orbit of eyes, and a reference electrode at neck. Using different combinations of eye movements in right and left direction a simple control strategy has been developed to drive motors. Control signals have been first generated using 8051 microcontroller. To meet the problems occurred while using 8051, ATMEGA microcontroller has been adapted. Directional movements of a small prototype of mobility aid (a toy car) with DC motors in right, left and forward is controlled and start and stop of movement is also implemented with ATMEGA. These control signals can be further used to command rehabilitative assistive device with eye movement sequences.

21 citations


Cited by
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Journal ArticleDOI
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

13,246 citations

Journal ArticleDOI
21 Jan 2020-Sensors
TL;DR: This paper covers a few classes of sensors, using contactless methods as well as contact and skin-penetrating electrodes for human emotion detection and the measurement of their intensity and proposes their classification.
Abstract: Automated emotion recognition (AEE) is an important issue in various fields of activities which use human emotional reactions as a signal for marketing, technical equipment, or human–robot interaction. This paper analyzes scientific research and technical papers for sensor use analysis, among various methods implemented or researched. This paper covers a few classes of sensors, using contactless methods as well as contact and skin-penetrating electrodes for human emotion detection and the measurement of their intensity. The results of the analysis performed in this paper present applicable methods for each type of emotion and their intensity and propose their classification. The classification of emotion sensors is presented to reveal area of application and expected outcomes from each method, as well as their limitations. This paper should be relevant for researchers using human emotion evaluation and analysis, when there is a need to choose a proper method for their purposes or to find alternative decisions. Based on the analyzed human emotion recognition sensors and methods, we developed some practical applications for humanizing the Internet of Things (IoT) and affective computing systems.

227 citations

01 Jan 2009
TL;DR: The Naive Bayesian Classifier is defined as “nothing but uncertainty”.
Abstract: 将Naive Bayesian Classifier(简单贝叶斯网络分类器)用于遥感影像的分类,并对其主要问题如特征选择和后验概率推理等展开研究。使用K2结构学习算法选出具有类别可分性的波段,进一步利用互信息测试对遥感波段之间的相关性做分析,去除冗余信息。特征(波段)的条件独立性假设简化了联合概率的计算,以较小的计算代价获得后验概率。在此基础上,将Naive Bayesian Classifier用于多光谱和高光谱影像的分类,获得很好的性能和相当高的稳健性。

164 citations

Journal Article
Sarah C Smith1
TL;DR: Most studies indicate that computer operators who view their video display terminals (VDT) report more eye related problems than non VDT office workers.
Abstract: Since computer use is such a visually demanding task, vision problems and symptoms have become very common in today’s work place . Most studies indicate that computer operators who view their video display terminals (VDT) report more eye related problems than non VDT office workers. NIOSH Survey (National Institute of Occupational Safety and Health) has reported that visual symptoms occur in 75-90 % of VDT workers as opposed to 22 % musculoskeletal disorders (carpel tunnel syndrome) in computer users .

144 citations

Proceedings ArticleDOI
15 Sep 2015
TL;DR: An architecture based on head-and eye-tracking data is introduced in this study and several features are analyzed, showing promising results towards in-vehicle driver-activity recognition.
Abstract: This paper presents a novel approach to automated recognition of the driver's activity, which is a crucial factor for determining the take-over readiness in conditionally autonomous driving scenarios. Therefore, an architecture based on head-and eye-tracking data is introduced in this study and several features are analyzed. The proposed approach is evaluated on data recorded during a driving simulator study with 73 subjects performing different secondary tasks while driving in an autonomous setting. The proposed architecture shows promising results towards in-vehicle driver-activity recognition. Furthermore, a significant improvement in the classification performance is demonstrated due to the consideration of novel features derived especially for the autonomous driving context.

103 citations