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  1. Article ; Online: A Generative Model for Generic Light Field Reconstruction.

    Chandramouli, Paramanand / Gandikota, Kanchana Vaishnavi / Goerlitz, Andreas / Kolb, Andreas / Moeller, Michael

    IEEE transactions on pattern analysis and machine intelligence

    2022  Volume 44, Issue 4, Page(s) 1712–1724

    Abstract: Recently deep generative models have achieved impressive progress in modeling the distribution of training data. In this work, we present for the first time a generative model for 4D light field patches using variational autoencoders to capture the data ... ...

    Abstract Recently deep generative models have achieved impressive progress in modeling the distribution of training data. In this work, we present for the first time a generative model for 4D light field patches using variational autoencoders to capture the data distribution of light field patches. We develop a generative model conditioned on the central view of the light field and incorporate this as a prior in an energy minimization framework to address diverse light field reconstruction tasks. While pure learning-based approaches do achieve excellent results on each instance of such a problem, their applicability is limited to the specific observation model they have been trained on. On the contrary, our trained light field generative model can be incorporated as a prior into any model-based optimization approach and therefore extend to diverse reconstruction tasks including light field view synthesis, spatial-angular super resolution and reconstruction from coded projections. Our proposed method demonstrates good reconstruction, with performance approaching end-to-end trained networks, while outperforming traditional model-based approaches on both synthetic and real scenes. Furthermore, we show that our approach enables reliable light field recovery despite distortions in the input.
    Language English
    Publishing date 2022-03-04
    Publishing country United States
    Document type Journal Article
    ISSN 1939-3539
    ISSN (online) 1939-3539
    DOI 10.1109/TPAMI.2020.3039841
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article: Susceptibility of SARS Coronavirus-2 infection in domestic and wild animals: a systematic review.

    Rao, Sudhanarayani S / Parthasarathy, Krupakar / Sounderrajan, Vignesh / Neelagandan, K / Anbazhagan, Pradeep / Chandramouli, Vaishnavi

    3 Biotech

    2022  Volume 13, Issue 1, Page(s) 5

    Abstract: Animals and viruses have constantly been co-evolving under natural circumstances and pandemic like situations. They harbour harmful viruses which can spread easily. In the recent times we have seen pandemic like situations being created as a result of ... ...

    Abstract Animals and viruses have constantly been co-evolving under natural circumstances and pandemic like situations. They harbour harmful viruses which can spread easily. In the recent times we have seen pandemic like situations being created as a result of the spread of deadly and fatal viruses. Coronaviruses (CoVs) are one of the wellrecognized groups of viruses. There are four known genera of Coronavirus family namely, alpha (α), beta (β), gamma (γ), and delta (δ). Animals have been infected with CoVs belonging to all four genera. In the last few decades the world has witnessed an emergence of severe acute respiratory syndromes which had created a pandemic like situation such as SARS CoV, MERS-CoV. We are currently in another pandemic like situation created due to the uncontrolled spread of a similar coronavirus namely SARSCoV-2. These findings are based on a small number of animals and do not indicate whether animals can transmit disease to humans. Several mammals, including cats, dogs, bank voles, ferrets, fruit bats, hamsters, mink, pigs, rabbits, racoon dogs, and white-tailed deer, have been found to be infected naturally by the virus. Certain laboratory discoveries revealed that animals such as cats, ferrets, fruit bats, hamsters, racoon dogs, and white-tailed deer can spread the illness to other animals of the same species. This review article gives insights on the current knowledge about SARS-CoV-2 infection and development in animals on the farm and in domestic community and their impact on society.
    Language English
    Publishing date 2022-12-11
    Publishing country Germany
    Document type Journal Article ; Review
    ZDB-ID 2600522-0
    ISSN 2190-5738 ; 2190-572X
    ISSN (online) 2190-5738
    ISSN 2190-572X
    DOI 10.1007/s13205-022-03416-8
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: Kalium channelrhodopsins effectively inhibit neurons.

    Ott, Stanislav / Xu, Sangyu / Lee, Nicole / Hong, Ivan / Anns, Jonathan / Suresh, Danesha Devini / Zhang, Zhiyi / Zhang, Xianyuan / Harion, Raihanah / Ye, Weiying / Chandramouli, Vaishnavi / Jesuthasan, Suresh / Saheki, Yasunori / Claridge-Chang, Adam

    Nature communications

    2024  Volume 15, Issue 1, Page(s) 3480

    Abstract: The analysis of neural circuits has been revolutionized by optogenetic methods. Light-gated chloride-conducting anion channelrhodopsins (ACRs)-recently emerged as powerful neuron inhibitors. For cells or sub-neuronal compartments with high intracellular ... ...

    Abstract The analysis of neural circuits has been revolutionized by optogenetic methods. Light-gated chloride-conducting anion channelrhodopsins (ACRs)-recently emerged as powerful neuron inhibitors. For cells or sub-neuronal compartments with high intracellular chloride concentrations, however, a chloride conductance can have instead an activating effect. The recently discovered light-gated, potassium-conducting, kalium channelrhodopsins (KCRs) might serve as an alternative in these situations, with potentially broad application. As yet, KCRs have not been shown to confer potent inhibitory effects in small genetically tractable animals. Here, we evaluated the utility of KCRs to suppress behavior and inhibit neural activity in Drosophila, Caenorhabditis elegans, and zebrafish. In direct comparisons with ACR1, a KCR1 variant with enhanced plasma-membrane trafficking displayed comparable potency, but with improved properties that include reduced toxicity and superior efficacy in putative high-chloride cells. This comparative analysis of behavioral inhibition between chloride- and potassium-selective silencing tools establishes KCRs as next-generation optogenetic inhibitors for in vivo circuit analysis in behaving animals.
    MeSH term(s) Animals ; Caenorhabditis elegans/genetics ; Neurons/metabolism ; Neurons/physiology ; Optogenetics/methods ; Zebrafish ; Channelrhodopsins/metabolism ; Channelrhodopsins/genetics ; Humans ; Drosophila ; Potassium Channels/metabolism ; Potassium Channels/genetics ; Chlorides/metabolism ; Animals, Genetically Modified ; Behavior, Animal ; HEK293 Cells ; Drosophila melanogaster
    Chemical Substances Channelrhodopsins ; Potassium Channels ; Chlorides
    Language English
    Publishing date 2024-04-24
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, N.I.H., Extramural
    ZDB-ID 2553671-0
    ISSN 2041-1723 ; 2041-1723
    ISSN (online) 2041-1723
    ISSN 2041-1723
    DOI 10.1038/s41467-024-47203-w
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article ; Online: Phytomolecules Repurposed as Covid-19 Inhibitors: Opportunity and Challenges.

    Chandramouli, Vaishnavi / Niraj, Shekhar Kumar / Nair, Krishna G / Joseph, Jerrine / Aruni, Wilson

    Current microbiology

    2021  Volume 78, Issue 10, Page(s) 3620–3633

    Abstract: The SARS-CoV-2 virus has spread worldwide to cause a full blown pandemic since 2020. To date, several promising synthetic therapeutics are repurposed and vaccines through different stages of clinical trials were approved and being administered, but still ...

    Abstract The SARS-CoV-2 virus has spread worldwide to cause a full blown pandemic since 2020. To date, several promising synthetic therapeutics are repurposed and vaccines through different stages of clinical trials were approved and being administered, but still the efficacy of the drugs and vaccines are yet to be decoded. This article highlights the importance of traditional medicinal plants and the phytomolecules derived from them, which possess in vitro antiviral and anti-CoV properties and further explores their potential as inhibitors to molecular targets of SARS-CoV-2 that were evaluated by in silico approaches. Botanicals in traditional medicinal systems have been investigated for anti-SARS-CoV-2 activity through in silico and in vitro studies. However, information linking structure of phytomolecules to their antiviral activity is limited. Most phytomolecules with anti-CoV activity were studied for inhibition of the human ACE2 receptor through which the virus enters host cells, and non-structural proteins 3CL
    MeSH term(s) Antiviral Agents/pharmacology ; COVID-19 ; Humans ; Pandemics ; SARS-CoV-2
    Chemical Substances Antiviral Agents
    Language English
    Publishing date 2021-08-26
    Publishing country United States
    Document type Journal Article ; Review
    ZDB-ID 134238-1
    ISSN 1432-0991 ; 0343-8651
    ISSN (online) 1432-0991
    ISSN 0343-8651
    DOI 10.1007/s00284-021-02639-x
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Article: Phytomolecules Repurposed as Covid-19 Inhibitors: Opportunity and Challenges

    Chandramouli, Vaishnavi / Niraj, Shekhar Kumar / Nair, Krishna G. / Joseph, Jerrine / Aruni, Wilson

    Current microbiology. 2021 Oct., v. 78, no. 10

    2021  

    Abstract: The SARS-CoV-2 virus has spread worldwide to cause a full blown pandemic since 2020. To date, several promising synthetic therapeutics are repurposed and vaccines through different stages of clinical trials were approved and being administered, but still ...

    Abstract The SARS-CoV-2 virus has spread worldwide to cause a full blown pandemic since 2020. To date, several promising synthetic therapeutics are repurposed and vaccines through different stages of clinical trials were approved and being administered, but still the efficacy of the drugs and vaccines are yet to be decoded. This article highlights the importance of traditional medicinal plants and the phytomolecules derived from them, which possess in vitro antiviral and anti-CoV properties and further explores their potential as inhibitors to molecular targets of SARS-CoV-2 that were evaluated by in silico approaches. Botanicals in traditional medicinal systems have been investigated for anti-SARS-CoV-2 activity through in silico and in vitro studies. However, information linking structure of phytomolecules to their antiviral activity is limited. Most phytomolecules with anti-CoV activity were studied for inhibition of the human ACE2 receptor through which the virus enters host cells, and non-structural proteins 3CLᵖʳᵒ and PLᵖʳᵒ. Although the proteases are ideal anti-CoV targets, information on plant-based inhibitors for the CoV structural proteins, e.g., spike, envelope, membrane, nucleocapsid required further investigations. In absence of scientific evaluations through in vitro and biocompatibility studies, plant-based antivirals fall short as treatment options. Plant-based anti-SARS-CoV-2 therapeutics can be promising alternatives to their synthetic counterparts as they are economical and bear fewer chances of toxicity, side effects, and viral resistance. Our review could provide a systematic overview of the potential phytomolecules which can be repurposed and subjected to further modes of experimental evaluation to qualify for use in treatment and prophylaxis of SARS-CoV-2 infections.
    Keywords COVID-19 infection ; Severe acute respiratory syndrome coronavirus 2 ; antiviral agents ; antiviral properties ; biocompatibility ; computer simulation ; disease prevention ; humans ; nucleocapsid ; pandemic ; proteinases ; therapeutics ; toxicity ; viruses
    Language English
    Dates of publication 2021-10
    Size p. 3620-3633.
    Publishing place Springer US
    Document type Article
    Note Review
    ZDB-ID 134238-1
    ISSN 1432-0991 ; 0343-8651
    ISSN (online) 1432-0991
    ISSN 0343-8651
    DOI 10.1007/s00284-021-02639-x
    Database NAL-Catalogue (AGRICOLA)

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  6. Book ; Online: A Generative Model for Generic Light Field Reconstruction

    Chandramouli, Paramanand / Gandikota, Kanchana Vaishnavi / Goerlitz, Andreas / Kolb, Andreas / Moeller, Michael

    2020  

    Abstract: Recently deep generative models have achieved impressive progress in modeling the distribution of training data. In this work, we present for the first time a generative model for 4D light field patches using variational autoencoders to capture the data ... ...

    Abstract Recently deep generative models have achieved impressive progress in modeling the distribution of training data. In this work, we present for the first time a generative model for 4D light field patches using variational autoencoders to capture the data distribution of light field patches. We develop a generative model conditioned on the central view of the light field and incorporate this as a prior in an energy minimization framework to address diverse light field reconstruction tasks. While pure learning-based approaches do achieve excellent results on each instance of such a problem, their applicability is limited to the specific observation model they have been trained on. On the contrary, our trained light field generative model can be incorporated as a prior into any model-based optimization approach and therefore extend to diverse reconstruction tasks including light field view synthesis, spatial-angular super resolution and reconstruction from coded projections. Our proposed method demonstrates good reconstruction, with performance approaching end-to-end trained networks, while outperforming traditional model-based approaches on both synthetic and real scenes. Furthermore, we show that our approach enables reliable light field recovery despite distortions in the input.
    Keywords Electrical Engineering and Systems Science - Image and Video Processing ; Computer Science - Computer Vision and Pattern Recognition
    Subject code 004
    Publishing date 2020-05-13
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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