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  1. Article: The Underrated Gut Microbiota Helminths, Bacteriophages, Fungi, and Archaea.

    Garcia-Bonete, Maria Jose / Rajan, Anandi / Suriano, Francesco / Layunta, Elena

    Life (Basel, Switzerland)

    2023  Volume 13, Issue 8

    Abstract: The microbiota inhabits the gastrointestinal tract, providing essential capacities to the host. The microbiota is a crucial factor in intestinal health and regulates intestinal physiology. However, microbiota disturbances, named dysbiosis, can disrupt ... ...

    Abstract The microbiota inhabits the gastrointestinal tract, providing essential capacities to the host. The microbiota is a crucial factor in intestinal health and regulates intestinal physiology. However, microbiota disturbances, named dysbiosis, can disrupt intestinal homeostasis, leading to the development of diseases. Classically, the microbiota has been referred to as bacteria, though other organisms form this complex group, including viruses, archaea, and eukaryotes such as fungi and protozoa. This review aims to clarify the role of helminths, bacteriophages, fungi, and archaea in intestinal homeostasis and diseases, their interaction with bacteria, and their use as therapeutic targets in intestinal maladies.
    Language English
    Publishing date 2023-08-18
    Publishing country Switzerland
    Document type Journal Article ; Review
    ZDB-ID 2662250-6
    ISSN 2075-1729
    ISSN 2075-1729
    DOI 10.3390/life13081765
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Systematic study of Co-free LiNi

    Seenivasan, Manojkumar / Yang, Chun-Chen / Wu, She-Huang / Chang, Jeng-Kuei / Jose, Rajan

    Journal of colloid and interface science

    2024  Volume 661, Page(s) 1070–1081

    Abstract: The growing use of EVs and society's energy needs require safe, affordable, durable, and eco-friendly high-energy lithium-ion batteries (LIBs). To this end, we synthesized and investigated the removal of Co from Al-doped Ni-rich cathode materials, ... ...

    Abstract The growing use of EVs and society's energy needs require safe, affordable, durable, and eco-friendly high-energy lithium-ion batteries (LIBs). To this end, we synthesized and investigated the removal of Co from Al-doped Ni-rich cathode materials, specifically LiNi
    Language English
    Publishing date 2024-02-08
    Publishing country United States
    Document type Journal Article
    ZDB-ID 241597-5
    ISSN 1095-7103 ; 0021-9797
    ISSN (online) 1095-7103
    ISSN 0021-9797
    DOI 10.1016/j.jcis.2024.02.040
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Bonn / Germany

    Z 71/707: Show issues

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  3. Article ; Online: Preface.

    Emdad, Luni / Atfi, Azeddine / Gogna, Rajan / Trevino, Jose G / Fisher, Paul B

    Advances in cancer research

    2023  Volume 159, Page(s) xiii–xviii

    Language English
    Publishing date 2023-11-30
    Publishing country United States
    Document type Editorial
    ZDB-ID 127-2
    ISSN 2162-5557 ; 0065-230X
    ISSN (online) 2162-5557
    ISSN 0065-230X
    DOI 10.1016/S0065-230X(23)00049-0
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    In stock of ZB MED Cologne/Königswinter

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  4. Article ; Online: Three new species of Promicrogaster Brues & Richardson (Hymenoptera: Braconidae) from India.

    Ranjith, A P / Fernandez-Triana, Jose / Sushama, V / Rajmohana, K / Priyadarsanan, Dharma Rajan

    Zootaxa

    2024  Volume 5403, Issue 3, Page(s) 357–368

    Abstract: The Indian fauna of the microgastrine genus Promicrogaster is reviewed and three new species are described: P. constricta, P. flava, and P. incompleta, all authored by Ranjith & Fernandez-Triana. Until now only one species had been reported from India. ... ...

    Abstract The Indian fauna of the microgastrine genus Promicrogaster is reviewed and three new species are described: P. constricta, P. flava, and P. incompleta, all authored by Ranjith & Fernandez-Triana. Until now only one species had been reported from India. An illustrated key for the Oriental region, including all previously described species, is also provided.
    MeSH term(s) Animals ; Hymenoptera ; India
    Language English
    Publishing date 2024-01-22
    Publishing country New Zealand
    Document type Journal Article
    ISSN 1175-5334
    ISSN (online) 1175-5334
    DOI 10.11646/zootaxa.5403.3.5
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Book ; Online: DeltaNN

    Louloudakis, Nikolaos / Gibson, Perry / Cano, José / Rajan, Ajitha

    Assessing the Impact of Computational Environment Parameters on the Performance of Image Recognition Models

    2023  

    Abstract: Image recognition tasks typically use deep learning and require enormous processing power, thus relying on hardware accelerators like GPUs and TPUs for fast, timely processing. Failure in real-time image recognition tasks can occur due to sub-optimal ... ...

    Abstract Image recognition tasks typically use deep learning and require enormous processing power, thus relying on hardware accelerators like GPUs and TPUs for fast, timely processing. Failure in real-time image recognition tasks can occur due to sub-optimal mapping on hardware accelerators during model deployment, which may lead to timing uncertainty and erroneous behavior. Mapping on hardware accelerators is done using multiple software components like deep learning frameworks, compilers, and device libraries, that we refer to as the computational environment. Owing to the increased use of image recognition tasks in safety-critical applications like autonomous driving and medical imaging, it is imperative to assess their robustness to changes in the computational environment, as the impact of parameters like deep learning frameworks, compiler optimizations, and hardware devices on model performance and correctness is not yet well understood. In this paper we present a differential testing framework, DeltaNN, that allows us to assess the impact of different computational environment parameters on the performance of image recognition models during deployment, post training. DeltaNN generates different implementations of a given image recognition model for variations in environment parameters, namely, deep learning frameworks, compiler optimizations and hardware devices and analyzes differences in model performance as a result. Using DeltaNN, we conduct an empirical study of robustness analysis of three popular image recognition models using the ImageNet dataset. We report the impact in terms of misclassifications and inference time differences across different settings. In total, we observed up to 72% output label differences across deep learning frameworks, and up to 81% unexpected performance degradation in terms of inference time, when applying compiler optimizations.

    Comment: 11 pages, 10 figures, 2 tables
    Keywords Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Machine Learning ; Computer Science - Software Engineering ; Electrical Engineering and Systems Science - Systems and Control
    Subject code 006
    Publishing date 2023-06-05
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  6. Book ; Online: Fault Localization for Buggy Deep Learning Framework Conversions in Image Recognition

    Louloudakis, Nikolaos / Gibson, Perry / Cano, José / Rajan, Ajitha

    2023  

    Abstract: When deploying Deep Neural Networks (DNNs), developers often convert models from one deep learning framework to another (e.g., TensorFlow to PyTorch). However, this process is error-prone and can impact target model accuracy. To identify the extent of ... ...

    Abstract When deploying Deep Neural Networks (DNNs), developers often convert models from one deep learning framework to another (e.g., TensorFlow to PyTorch). However, this process is error-prone and can impact target model accuracy. To identify the extent of such impact, we perform and briefly present a differential analysis against three DNNs widely used for image recognition (MobileNetV2, ResNet101, and InceptionV3) converted across four well-known deep learning frameworks (PyTorch, Keras, TensorFlow (TF), and TFLite), which revealed numerous model crashes and output label discrepancies of up to 72%. To mitigate such errors, we present a novel approach towards fault localization and repair of buggy deep learning framework conversions, focusing on pre-trained image recognition models. Our technique consists of four stages of analysis: 1) conversion tools, 2) model parameters, 3) model hyperparameters, and 4) graph representation. In addition, we propose various strategies towards fault repair of the faults detected. We implement our technique on top of the Apache TVM deep learning compiler, and we test it by conducting a preliminary fault localization analysis for the conversion of InceptionV3 from TF to TFLite. Our approach detected a fault in a common DNN converter tool, which introduced precision errors in weights, reducing model accuracy. After our fault localization, we repaired the issue, reducing our conversion error to zero.

    Comment: 5 pages, 3 figures, 1 table
    Keywords Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Machine Learning ; Computer Science - Software Engineering ; Electrical Engineering and Systems Science - Systems and Control
    Subject code 006
    Publishing date 2023-06-10
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  7. Book ; Online: MutateNN

    Louloudakis, Nikolaos / Gibson, Perry / Cano, José / Rajan, Ajitha

    Mutation Testing of Image Recognition Models Deployed on Hardware Accelerators

    2023  

    Abstract: The increased utilization of Artificial Intelligence (AI) solutions brings with it inherent risks, such as misclassification and sub-optimal execution time performance, due to errors introduced in their deployment infrastructure because of problematic ... ...

    Abstract The increased utilization of Artificial Intelligence (AI) solutions brings with it inherent risks, such as misclassification and sub-optimal execution time performance, due to errors introduced in their deployment infrastructure because of problematic configuration and software faults. On top of that, AI methods such as Deep Neural Networks (DNNs) are utilized to perform demanding, resource-intensive and even safety-critical tasks, and in order to effectively increase the performance of the DNN models deployed, a variety of Machine Learning (ML) compilers have been developed, allowing compatibility of DNNs with a variety of hardware acceleration devices, such as GPUs and TPUs. Furthermore the correctness of the compilation process should be verified. In order to allow developers and researchers to explore the robustness of DNN models deployed on different hardware accelerators via ML compilers, in this paper we propose MutateNN, a tool that provides mutation testing and model analysis features in the context of deployment on different hardware accelerators. To demonstrate the capabilities of MutateNN, we focus on the image recognition domain by applying mutation testing to 7 well-established models utilized for image classification. We instruct 21 mutations of 6 different categories, and deploy our mutants on 4 different hardware acceleration devices of varying capabilities. Our results indicate that models are proven robust to changes related to layer modifications and arithmetic operators, while presenting discrepancies of up to 90.3% in mutants related to conditional operators. We also observed unexpectedly severe performance degradation on mutations related to arithmetic types of variables, leading the mutants to produce the same classifications for all dataset inputs.

    Comment: 7 pages, 7 figures
    Keywords Computer Science - Machine Learning ; Computer Science - Software Engineering ; Electrical Engineering and Systems Science - Systems and Control
    Subject code 006
    Publishing date 2023-06-02
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  8. Article ; Online: CYLD in health and disease

    José L. Marín-Rubio / Ishier Raote / Joseph Inns / Carol Dobson-Stone / Neil Rajan

    Disease Models & Mechanisms, Vol 16, Iss

    2023  Volume 6

    Keywords cyld ; cylindroma ; frontotemporal dementia ; genetics ; skin tumours ; ubiquitination ; Medicine ; R ; Pathology ; RB1-214
    Language English
    Publishing date 2023-06-01T00:00:00Z
    Publisher The Company of Biologists
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  9. Article ; Online: CYLD in health and disease.

    Marín-Rubio, José L / Raote, Ishier / Inns, Joseph / Dobson-Stone, Carol / Rajan, Neil

    Disease models & mechanisms

    2023  Volume 16, Issue 6

    Abstract: CYLD lysine 63 deubiquitinase (CYLD) is a ubiquitin hydrolase with important roles in immunity and cancer. Complete CYLD ablation, truncation and expression of alternate isoforms, including short CYLD, drive distinct phenotypes and offer insights into ... ...

    Abstract CYLD lysine 63 deubiquitinase (CYLD) is a ubiquitin hydrolase with important roles in immunity and cancer. Complete CYLD ablation, truncation and expression of alternate isoforms, including short CYLD, drive distinct phenotypes and offer insights into CYLD function in inflammation, cell death, cell cycle progression and cell transformation. Research in diverse model systems has shown that these are mediated via CYLD regulation of cellular pathways including the NF-κB, Wnt and TGF-β pathways. Recent biochemical advances and models have offered new insights into the regulation and function of CYLD. In addition, recent discoveries of gain-of-function germline pathogenic CYLD variants in patients with a neurodegenerative phenotype contrast with the more widely known loss-of-function mutations seen in patients with CYLD cutaneous syndrome and with sporadic cancers. Here, we provide a current review of mechanistic insights into CYLD function gained from CYLD animal models, as well as an update on the role of CYLD in human disease.
    MeSH term(s) Animals ; Humans ; Cell Death ; Cell Division ; Inflammation ; Models, Animal ; Models, Biological ; Deubiquitinating Enzyme CYLD/genetics
    Chemical Substances CYLD protein, human (EC 3.4.19.12) ; Deubiquitinating Enzyme CYLD (EC 3.4.19.12)
    Language English
    Publishing date 2023-06-30
    Publishing country England
    Document type Review ; Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2451104-3
    ISSN 1754-8411 ; 1754-8403
    ISSN (online) 1754-8411
    ISSN 1754-8403
    DOI 10.1242/dmm.050093
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  10. Article: Dengue and scrub typhus co-infection in children: Experience of a teaching hospital in an endemic area.

    Jose, Priya / Rajan, Nishanth / Kommu, Peter Prasanth Kumar / Krishnan, Lalitha

    Indian journal of public health

    2022  Volume 66, Issue 3, Page(s) 292–294

    Abstract: Background: Dengue fever and scrub typhus are considered an endemic disease in the Indian subcontinent. The epidemiology and clinical presentations are complex and vary each year.: Objective: The objective of this study was to estimate the prevalence ...

    Abstract Background: Dengue fever and scrub typhus are considered an endemic disease in the Indian subcontinent. The epidemiology and clinical presentations are complex and vary each year.
    Objective: The objective of this study was to estimate the prevalence of coinfection with scrub typhus in children diagnosed with dengue fever.
    Methods: A retrospective hospital-based, cross-sectional study was done in the Department of Pediatrics of a teaching hospital in Puducherry. All children (0-14 years) who had enzyme-linked immunosorbent assay (ELISA) reported scrub typhus among those diagnosed with dengue fever (NS1Ag or immunoglobulin M ELISA positivity) during 2012-2016. Medical records with incomplete data were excluded from the study. Odds ratio was calculated to find out the association of coinfections. An independent t-test was used to find out the statistical significance. P < 0.05 was considered statistically significant.
    Results: Atypical features of dengue were present in 250/318 (78.6%) children. Coinfections were seen in 62/318 (19.4%) children. Scrub typhus was the most common (n = 51/62, 82.2%). The chance of scrub typhus in a dengue serology-positive child is significant when the symptoms are atypical or protracted (OR- 2.6, P = 0.033).
    Conclusion: High index of suspicion should be present in endemic dengue and scrub typhus coinfection.
    MeSH term(s) Child ; Coinfection/complications ; Coinfection/epidemiology ; Cross-Sectional Studies ; Dengue/epidemiology ; Hospitals, Teaching ; Humans ; Immunoglobulin M ; India/epidemiology ; Orientia tsutsugamushi ; Retrospective Studies ; Scrub Typhus/complications
    Chemical Substances Immunoglobulin M
    Language English
    Publishing date 2022-09-16
    Publishing country India
    Document type Journal Article
    ZDB-ID 800737-8
    ISSN 2229-7693 ; 0019-557X
    ISSN (online) 2229-7693
    ISSN 0019-557X
    DOI 10.4103/ijph.ijph_2052_21
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.B 760: Show issues Location:
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