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  1. Article ; Online: Emerging opportunities for gene editing therapies in India.

    Ghosh, Arkasubhra / Maiti, Souvik / Chakraborty, Debojyoti

    Nature medicine

    2024  Volume 30, Issue 2, Page(s) 324–325

    MeSH term(s) Gene Editing ; Genetic Therapy ; CRISPR-Cas Systems/genetics ; India
    Language English
    Publishing date 2024-01-19
    Publishing country United States
    Document type Letter
    ZDB-ID 1220066-9
    ISSN 1546-170X ; 1078-8956
    ISSN (online) 1546-170X
    ISSN 1078-8956
    DOI 10.1038/s41591-023-02752-3
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    Zs.A 4212: Show issues Location:
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  2. Article ; Online: Erythema ab igne: a cutaneous marker of prolonged thermal exposure.

    Chandra, Atanu / Sil, Abheek / Das, Souvik / Chakraborty, Uddalak

    BMJ case reports

    2023  Volume 16, Issue 8

    MeSH term(s) Humans ; Erythema Ab Igne
    Language English
    Publishing date 2023-08-17
    Publishing country England
    Document type Case Reports ; Journal Article
    ISSN 1757-790X
    ISSN (online) 1757-790X
    DOI 10.1136/bcr-2023-256612
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  3. Article ; Online: Enigma of Red, Still Eye: Orbital Inflammatory Syndrome.

    Sen, Subhro Sankar / Das, Debarup / Chakraborty, Uddalak / Dubey, Souvik / Ray, Biman Kanti / Pandit, Alak

    Neurology India

    2024  Volume 72, Issue 1, Page(s) 186–188

    Language English
    Publishing date 2024-02-29
    Publishing country India
    Document type Journal Article
    ZDB-ID 415522-1
    ISSN 1998-4022 ; 0028-3886
    ISSN (online) 1998-4022
    ISSN 0028-3886
    DOI 10.4103/neurol-india.Neurol-India-D-24-00058
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    Zs.A 698: Show issues Location:
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  4. Article ; Online: Rare genetic disorders in India: Current status, challenges, and CRISPR-based therapy.

    Bhattacharyya, Pallabi / Mehndiratta, Kanikah / Maiti, Souvik / Chakraborty, Debojyoti

    Journal of biosciences

    2024  Volume 49

    Abstract: Rare genetic diseases are a group of life-threatening disorders affecting significant populations worldwide and posing substantial challenges to healthcare systems globally. India, with its vast population, is also no exception. The country harbors ... ...

    Abstract Rare genetic diseases are a group of life-threatening disorders affecting significant populations worldwide and posing substantial challenges to healthcare systems globally. India, with its vast population, is also no exception. The country harbors millions of individuals affected by these fatal disorders, which often result from mutations in a single gene. The emergence of CRISPR-Cas9 technology, however, has ushered in a new era of hope in genetic therapies. CRISPR-based treatments hold the potential to precisely edit and correct diseasecausing mutations, offering tailored solutions for rare genetic diseases in India. This review explores the landscape of rare genetic diseases in India along with national policies and major challenges, and examines the implications of CRISPR-based therapies for potential cure. It delves into the potential of this technology in providing personalized and effective treatments. However, alongside these promising prospects, some ethical considerations, regulatory challenges, and concerns about the accessibility of CRISPR therapies are also discussed since addressing these issues is crucial for harnessing the full power of CRISPR in tackling rare genetic diseases in India. By taking a multidisciplinary approach that combines scientific advancements, ethical principles, and regulatory frameworks, these complexities can be reconciled, paving the way for innovative and impactful healthcare solutions for rare diseases in India.
    MeSH term(s) Humans ; Gene Editing ; CRISPR-Cas Systems/genetics ; Rare Diseases/epidemiology ; Rare Diseases/genetics ; Rare Diseases/therapy ; Genetic Therapy ; India
    Language English
    Publishing date 2024-02-21
    Publishing country India
    Document type Review ; Journal Article
    ZDB-ID 756157-x
    ISSN 0973-7138 ; 0250-5991
    ISSN (online) 0973-7138
    ISSN 0250-5991
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    Zs.A 1907: Show issues Location:
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  5. Book ; Online: DPA-WNO

    Tushar / Chakraborty, Souvik

    A gray box model for a class of stochastic mechanics problem

    2023  

    Abstract: The well-known governing physics in science and engineering is often based on certain assumptions and approximations. Therefore, analyses and designs carried out based on these equations are also approximate. The emergence of data-driven models has, to a ...

    Abstract The well-known governing physics in science and engineering is often based on certain assumptions and approximations. Therefore, analyses and designs carried out based on these equations are also approximate. The emergence of data-driven models has, to a certain degree, addressed this challenge; however, the purely data-driven models often (a) lack interpretability, (b) are data-hungry, and (c) do not generalize beyond the training window. Operator learning has recently been proposed as a potential alternative to address the aforementioned challenges; however, the challenges are still persistent. We here argue that one of the possible solutions resides in data-physics fusion, where the data-driven model is used to correct/identify the missing physics. To that end, we propose a novel Differentiable Physics Augmented Wavelet Neural Operator (DPA-WNO). The proposed DPA-WNO blends a differentiable physics solver with the Wavelet Neural Operator (WNO), where the role of WNO is to model the missing physics. This empowers the proposed framework to exploit the capability of WNO to learn from data while retaining the interpretability and generalizability associated with physics-based solvers. We illustrate the applicability of the proposed approach in solving time-dependent uncertainty quantification problems due to randomness in the initial condition. Four benchmark uncertainty quantification and reliability analysis examples from various fields of science and engineering are solved using the proposed approach. The results presented illustrate interesting features of the proposed approach.
    Keywords Computer Science - Machine Learning
    Subject code 006
    Publishing date 2023-09-24
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  6. Article ; Online: Genome sequence of

    Chakraborty, Souvik / Benoit, Joshua B / Rowe, Annette R / Sackett, Joshua D

    Microbiology resource announcements

    2023  Volume 12, Issue 11, Page(s) e0050923

    Abstract: Understanding microbe-host interactions is key to combating disease transmission by mosquitoes. Here, we report the genome sequence ... ...

    Abstract Understanding microbe-host interactions is key to combating disease transmission by mosquitoes. Here, we report the genome sequence of
    Language English
    Publishing date 2023-10-16
    Publishing country United States
    Document type Journal Article
    ISSN 2576-098X
    ISSN (online) 2576-098X
    DOI 10.1128/MRA.00509-23
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  7. Book ; Online: Randomized prior wavelet neural operator for uncertainty quantification

    Garg, Shailesh / Chakraborty, Souvik

    2023  

    Abstract: In this paper, we propose a novel data-driven operator learning framework referred to as the \textit{Randomized Prior Wavelet Neural Operator} (RP-WNO). The proposed RP-WNO is an extension of the recently proposed wavelet neural operator, which boasts ... ...

    Abstract In this paper, we propose a novel data-driven operator learning framework referred to as the \textit{Randomized Prior Wavelet Neural Operator} (RP-WNO). The proposed RP-WNO is an extension of the recently proposed wavelet neural operator, which boasts excellent generalizing capabilities but cannot estimate the uncertainty associated with its predictions. RP-WNO, unlike the vanilla WNO, comes with inherent uncertainty quantification module and hence, is expected to be extremely useful for scientists and engineers alike. RP-WNO utilizes randomized prior networks, which can account for prior information and is easier to implement for large, complex deep-learning architectures than its Bayesian counterpart. Four examples have been solved to test the proposed framework, and the results produced advocate favorably for the efficacy of the proposed framework.
    Keywords Statistics - Machine Learning ; Computer Science - Machine Learning
    Subject code 006
    Publishing date 2023-02-02
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  8. Book ; Online: Transfer learning based multi-fidelity physics informed deep neural network

    Chakraborty, Souvik

    2020  

    Abstract: For many systems in science and engineering, the governing differential equation is either not known or known in an approximate sense. Analyses and design of such systems are governed by data collected from the field and/or laboratory experiments. This ... ...

    Abstract For many systems in science and engineering, the governing differential equation is either not known or known in an approximate sense. Analyses and design of such systems are governed by data collected from the field and/or laboratory experiments. This challenging scenario is further worsened when data-collection is expensive and time-consuming. To address this issue, this paper presents a novel multi-fidelity physics informed deep neural network (MF-PIDNN). The framework proposed is particularly suitable when the physics of the problem is known in an approximate sense (low-fidelity physics) and only a few high-fidelity data are available. MF-PIDNN blends physics informed and data-driven deep learning techniques by using the concept of transfer learning. The approximate governing equation is first used to train a low-fidelity physics informed deep neural network. This is followed by transfer learning where the low-fidelity model is updated by using the available high-fidelity data. MF-PIDNN is able to encode useful information on the physics of the problem from the {\it approximate} governing differential equation and hence, provides accurate prediction even in zones with no data. Additionally, no low-fidelity data is required for training this model. Applicability and utility of MF-PIDNN are illustrated in solving four benchmark reliability analysis problems. Case studies to illustrate interesting features of the proposed approach are also presented.
    Keywords Computer Science - Machine Learning ; Physics - Computational Physics ; Statistics - Machine Learning
    Subject code 006
    Publishing date 2020-05-19
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  9. Book ; Online: Discovering interpretable Lagrangian of dynamical systems from data

    Tripura, Tapas / Chakraborty, Souvik

    2023  

    Abstract: A complete understanding of physical systems requires models that are accurate and obeys natural conservation laws. Recent trends in representation learning involve learning Lagrangian from data rather than the direct discovery of governing equations of ... ...

    Abstract A complete understanding of physical systems requires models that are accurate and obeys natural conservation laws. Recent trends in representation learning involve learning Lagrangian from data rather than the direct discovery of governing equations of motion. The generalization of equation discovery techniques has huge potential; however, existing Lagrangian discovery frameworks are black-box in nature. This raises a concern about the reusability of the discovered Lagrangian. In this article, we propose a novel data-driven machine-learning algorithm to automate the discovery of interpretable Lagrangian from data. The Lagrangian are derived in interpretable forms, which also allows the automated discovery of conservation laws and governing equations of motion. The architecture of the proposed framework is designed in such a way that it allows learning the Lagrangian from a subset of the underlying domain and then generalizing for an infinite-dimensional system. The fidelity of the proposed framework is exemplified using examples described by systems of ordinary differential equations and partial differential equations where the Lagrangian and conserved quantities are known.
    Keywords Statistics - Machine Learning ; Condensed Matter - Disordered Systems and Neural Networks ; Computer Science - Machine Learning
    Subject code 531
    Publishing date 2023-02-08
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  10. Book ; Online: Neuroscience inspired scientific machine learning (Part-1)

    Garg, Shailesh / Chakraborty, Souvik

    Variable spiking neuron for regression

    2023  

    Abstract: Redundant information transfer in a neural network can increase the complexity of the deep learning model, thus increasing its power consumption. We introduce in this paper a novel spiking neuron, termed Variable Spiking Neuron (VSN), which can reduce ... ...

    Abstract Redundant information transfer in a neural network can increase the complexity of the deep learning model, thus increasing its power consumption. We introduce in this paper a novel spiking neuron, termed Variable Spiking Neuron (VSN), which can reduce the redundant firing using lessons from biological neuron inspired Leaky Integrate and Fire Spiking Neurons (LIF-SN). The proposed VSN blends LIF-SN and artificial neurons. It garners the advantage of intermittent firing from the LIF-SN and utilizes the advantage of continuous activation from the artificial neuron. This property of the proposed VSN makes it suitable for regression tasks, which is a weak point for the vanilla spiking neurons, all while keeping the energy budget low. The proposed VSN is tested against both classification and regression tasks. The results produced advocate favorably towards the efficacy of the proposed spiking neuron, particularly for regression tasks.
    Keywords Computer Science - Neural and Evolutionary Computing ; Computer Science - Machine Learning
    Subject code 006
    Publishing date 2023-11-15
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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