Tata Consultancy Services Limited

Council of Scientific and Industrial Research

Indian Association for the Cultivation of Science

Ayan Mukherjee

Arpan Pal

Soma Bandyopadhyay

Arijit Ukil

Chetanya Puri

Rituraj Singh

Arindam Talukdar

Biswajit Kundu

Rohan Banerjee

Dipyaman Ganguly

Barnali Paul

Swarnali Roy

Oindrila Rahaman

Tanushyam Chattopadhyay

Anirban Dutta Choudhury

Snehasis Banerjee

Shreyasi Datta

Sundeep Khandelwal

Murali Poduval

Ishan Sahu

Varsha Sharma

Debayan Mukherjee

Sourav Pal

Benu Brata Das

Subhajit Roy

Raychaudhuri Deblina

Debomita Bhattacharya

Chowdhuri Srijita Paul

Subhendu K Das

Dipayan Sarkar

Roopkatha Bhattacharya

Amrit Raj Ghosh

Shounak Roy

The invention described herein relates to the compounds of Formula I for treating diseases and disorders for which inhibition or modulation of the topoisomerase I enzyme produces a physiologically beneficial response, in particular for the treatment of breast cancer. Also provided is the process of preparing compounds of Formula I.

Bicycle topoisomerase i inhibiting compounds, process for preparation and use thereof

A method and system for a feature subset-classifier pair for a classification task. The classification task corresponds to automatically classifying data associated with a subject(s) or object(s) of interest into an appropriate class based on a feature subset selected among a plurality of features extracted from the data and a classifier selected from a set of classifier types. The method proposed includes simultaneously determining the feature subset-classifier pair based on a relax-greedy {feature subset, classifier} approach utilizing sub-greedy search process based on a patience function, wherein the feature subset-classifier pair provides an optimal combination for more accurate classification. The automatic joint selection is time efficient solution, effectively speeding up the classification task.

Method and system for joint selection of a feature subset-classifier pair for a classification task

The present invention provides novel purine based compounds of formula 1, method of preparation of purine based compounds and its composition useful for inhibiting signalling through Toll-like receptors. These compounds are useful in inhibiting immune stimulation involving toll-like receptor 9 (TLR9). These can be used in treatment of autoimmune disease and inflammation where aberrant activation of TLR9 plays role. 

Purine based compounds as toll-like receptor 9 antagonist

Current technologies analyze electrocardiogram (ECG) signals for a long duration, which is not always a practical scenario. Moreover the current scenarios perform a binary classification between normal and Atrial Fibrillation (AF) only, whereas there are many abnormal rhythms apart from AF. Conventional systems/methods have their own limitations and may tend to misclassify ECG signals, thereby resulting in an unbalanced multi-label classification problem. Embodiments of the present disclosure provide systems and methods that are robust and more efficient for classifying rhythms for example, normal, AF, other abnormal rhythms and noisy ECG recordings by implementing a spectrogram based noise removal that obtains clean ECG signal from an acquired single-lead ECG signal, an optimum feature selection at each layer of classification that selects optimum features from a pool of extracted features, and a multi-layer cascaded binary classifier that identifies rhythms in the clean ECG signal at each layer of the classifier.

Cascaded binary classifier for identifying rhythms in a single-lead electrocardiogram (ECG) signal

The present invention relates to small molecule 4-(piperazin-1-yl)quinazolin-2-amino compounds with formula (I) useful for inhibiting signalling by certain toll-like receptors (TLRs), especially TLR9. Toll-like receptors (TLRs) are members of the larger family of evolutionarily conserved pattern recognition receptors which are critical first line of defense for self-nonself discrimination by the host immune response. Aberrant TLR9 activation is implicated in autoreactive inflammation in different autoimmune diseases. The invention depicts compounds with formula (I), composition and methods can be used in a number of clinical applications, including as pharmaceutical agents and methods for treating conditions involving unwanted immune activity due to TLR9 activation. 

Blocking toll-like receptor 9 signaling with small molecule antagonist

Development of sensor data based descriptive and prescriptive system involves machine learning tasks like classification and regression. Any such system development requires the involvement of different stake-holders for obtaining features. Such features typically obtained are not interpretable for 1-D sensor signals. Embodiments of the present disclosure provide systems and methods that perform signal analysis for features extraction and interpretation thereof wherein input is raw signal data where origin of a feature is traced to signal data, and mapped to domain/application knowledge. Feature(s) are extracted using deep learning network(s) and machine learning (ML) model(s) are implemented for sensor data analysis to perform causality analysis for prognostics. Layer(s) (say last layer) of Deep Network(s) contains the automatically derived features that can be used for ML tasks. Parameter(s) tuning is performed based on the set of features that were recommended by the system to determined performance of systems (or applications) under consideration.

Signal analysis systems and methods for features extraction and interpretation thereof

Traditionally known classification methods of non-stationary physiological audio signals as noisy and clean involve human intervention, may involve dependency on particular type of classifier and further analyses is carried out on classified clean signals. However, in non-stationary audio signals a major portion may end up being classified as noisy and hence may get rejected which may cause missing of intelligence which could have been derived from lightly noisy audio signals that may be critical. The present disclosure enables automation of classification based on auto-thresholding and statistical isolation wherein noisy signals are further classified as highly noisy and lightly noisy through continuous dynamic learning.

Noisy signal identification from non-stationary audio signals

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Kolkata, India

Ayan Mukherjee