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  1. Article: The effect of neutral electrolyzed water as a disinfectant of eggshells artificially contaminated with Listeria monocytogenes

    Cano, Jose

    Food Science & Nutrition, 7(7):2252-2260

    2019  

    Abstract: Neutral electrolyzed water (NEW) was tested as a disinfectant against Listeria monocytogenes on the surface of table eggs. Eggs were collected from a single Bovans White flock and were exposed to L. monocytogenes. Artificially contaminated eggs were ... ...

    Abstract Neutral electrolyzed water (NEW) was tested as a disinfectant against Listeria monocytogenes on the surface of table eggs. Eggs were collected from a single Bovans White flock and were exposed to L. monocytogenes. Artificially contaminated eggs were divided into three different treatment groups: NEW, 2% citric acid solution (CAS), and saline solution (SS). To evaluate the bactericidal effect, the Mexican norm for antimicrobial activity determination protocol was performed. The observed bactericidal effect was compared against those obtained from CAS and SS. Bacterial cells present on the eggshells were quantified. NEW exhibited a significantly higher bactericidal effect than CAS when evaluated on the surfaces of chicken eggshells (6.11 log10CFU/ml reduction in vitro and a 2.18 log10 CFU/egg reduction on eggs vs. 1.06 log10CFU/ml in vitro reduction and 1.74 log10CFU/egg). Additionally, CAS was found to react with the carbonate egg shield, resulting in a loss of cuticle integrity. Mineral content of NEW‐treated eggshells was similar to SS‐treated eggshells; however, CAS‐treated eggshells showed a significant decrease in phosphorous concentration compared to NEW treatment. In this study, we demonstrated the effect of NEW and CAS on the integrity of the L. monocytogenes wall using transmission electron microscopy. To the best of our knowledge, this is the first report of the effect of NEW against L. monocytogenes on eggshells. Our results show that NEW is a viable alternative solution for the disinfection of table eggs that does not affect the cuticle or shell.
    Keywords Listeria monocytogenes ; egg disinfection ; electrolyzed water
    Language English
    Document type Article
    Database Repository for Life Sciences

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  2. Book ; Conference proceedings: Cereal science and technology for feeding ten billion people

    Molina Cano, José Luis

    genomics era and beyond ; proceedings for the Meeting of Eucarpia Cereal Section "Cereal Science and Technology for Feeding Ten Billion People: Genomics Era and Beyond", Lleida (Spain), 13 - 17 November 2006

    (Options méditerranéennes : Serie A, Séminaires Méditerranéens ; 81)

    2008  

    Institution International Centre for Advanced Mediterranean Agronomic Studies
    Event/congress Meeting of the Eucarpia Cereal Section Cereal Science and Technology for Feeding Ten Billion People: Genomics Era and Beyond (2006, Lleida)
    Author's details CIHEAM ... Scientific ed.: J. L. Molina Cano
    Series title Options méditerranéennes : Serie A, Séminaires Méditerranéens ; 81
    Options méditerranéennes
    Options méditerranéennes ; Serie A, Séminaires Méditerranéens
    Collection Options méditerranéennes
    Options méditerranéennes ; Serie A, Séminaires Méditerranéens
    Language English
    Size 481 S. : Ill., graph. Darst.
    Publisher Zaragoza
    Publishing place INO Reproducciones
    Publishing country Spain
    Document type Book ; Conference proceedings
    HBZ-ID HT016130807
    ISBN 2-85352-404-3 ; 978-2-85352-404-9
    Database Catalogue ZB MED Nutrition, Environment, Agriculture

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    In stock of ZB MED Bonn / Germany

    Shelf markLoan statusLocation
    094/289 available for loan (reading room) Freihandmagazin (U1)

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  3. Book ; Thesis: Somatische Komorbidität bei stationären Patienten mit Depressionen oder Schizophrenie

    Cano, José

    2007  

    Author's details vorgelegt von José Cano
    Language German
    Size 88 Bl. : graph. Darst.
    Publishing country Germany
    Document type Book ; Thesis
    Thesis / German Habilitation thesis Bochum, Univ., Diss., 2007
    HBZ-ID HT015454004
    Database Catalogue ZB MED Medicine, Health

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    In stock of ZB MED Cologne/Königswinter

    Shelf markLoan statusLocation
    2008 E 1490 order for loan Magazin

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  4. Article ; Online: Deciphering the role of brainstem glycinergic neurons during startle and prepulse inhibition.

    Huang, Wanyun / Cano, Jose C / Fénelon, Karine

    Brain research

    2024  Volume 1836, Page(s) 148938

    Abstract: Prepulse inhibition (PPI) of the auditory startle response, a key measure of sensorimotor gating, diminishes with age and is impaired in various neurological conditions. While PPI deficits are often associated with cognitive impairments, their reversal ... ...

    Abstract Prepulse inhibition (PPI) of the auditory startle response, a key measure of sensorimotor gating, diminishes with age and is impaired in various neurological conditions. While PPI deficits are often associated with cognitive impairments, their reversal is routinely used in experimental systems for antipsychotic drug screening. Yet, the cellular and circuit-level mechanisms of PPI remain unclear, even under non-pathological conditions. We recently showed that brainstem neurons located in the caudal pontine reticular nucleus (PnC) expressing the glycine transporter type 2 (GlyT2
    Language English
    Publishing date 2024-04-13
    Publishing country Netherlands
    Document type Journal Article
    ZDB-ID 1200-2
    ISSN 1872-6240 ; 0006-8993
    ISSN (online) 1872-6240
    ISSN 0006-8993
    DOI 10.1016/j.brainres.2024.148938
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 161: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (1.OG)
    ab Jg. 2022: Lesesaal (EG)

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  5. Article ; Online: Transitivity and intransitivity in soil bacterial networks.

    Verdú, Miguel / Alcántara, Julio M / Navarro-Cano, Jose A / Goberna, Marta

    The ISME journal

    2023  Volume 17, Issue 12, Page(s) 2135–2139

    Abstract: Competition can lead to the exclusion of bacterial taxa when there is a transitive relationship among competitors with a hierarchy of competitive success. However, competition may not prevent bacterial coexistence if competitors form intransitive loops, ... ...

    Abstract Competition can lead to the exclusion of bacterial taxa when there is a transitive relationship among competitors with a hierarchy of competitive success. However, competition may not prevent bacterial coexistence if competitors form intransitive loops, in which none is able to outcompete all the rest. Both transitive and intransitive competition have been demonstrated in bacterial model systems. However, in natural soil microbial assemblages competition is typically understood as a dominance relationship leading to the exclusion of weak competitors. Here, we argue that transitive and intransitive interactions concurrently determine the structure of soil microbial communities. We explain why pairwise interactions cannot depict competition correctly in complex communities, and propose an alternative through the detection of strongly connected components (SCCs) in microbial networks. We finally analyse the existence of SCCs in soil bacterial communities in two Mediterranean ecosystems, for illustrative purposes only (rather than with the aim of providing a methodological tool) due to current limitations, and discuss future avenues to experimentally test the existence of SCCs in nature.
    MeSH term(s) Ecosystem ; Soil ; Models, Biological ; Bacteria/genetics
    Chemical Substances Soil
    Language English
    Publishing date 2023-10-19
    Publishing country England
    Document type Journal Article ; Review
    ZDB-ID 2406536-5
    ISSN 1751-7370 ; 1751-7362
    ISSN (online) 1751-7370
    ISSN 1751-7362
    DOI 10.1038/s41396-023-01540-8
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Bonn / Germany

    Z 7773: Show issues

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  6. Book ; Online: Productive Reproducible Workflows for DNNs

    Gibson, Perry / Cano, José

    A Case Study for Industrial Defect Detection

    2022  

    Abstract: As Deep Neural Networks (DNNs) have become an increasingly ubiquitous workload, the range of libraries and tooling available to aid in their development and deployment has grown significantly. Scalable, production quality tools are freely available under ...

    Abstract As Deep Neural Networks (DNNs) have become an increasingly ubiquitous workload, the range of libraries and tooling available to aid in their development and deployment has grown significantly. Scalable, production quality tools are freely available under permissive licenses, and are accessible enough to enable even small teams to be very productive. However within the research community, awareness and usage of said tools is not necessarily widespread, and researchers may be missing out on potential productivity gains from exploiting the latest tools and workflows. This paper presents a case study where we discuss our recent experience producing an end-to-end artificial intelligence application for industrial defect detection. We detail the high level deep learning libraries, containerized workflows, continuous integration/deployment pipelines, and open source code templates we leveraged to produce a competitive result, matching the performance of other ranked solutions to our three target datasets. We highlight the value that exploiting such systems can bring, even for research, and detail our solution and present our best results in terms of accuracy and inference time on a server class GPU, as well as inference times on a server class CPU, and a Raspberry Pi 4.

    Comment: 7 pages, 5 figures, AccML 2022
    Keywords Computer Science - Machine Learning ; Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Performance ; Computer Science - Software Engineering
    Subject code 006
    Publishing date 2022-06-19
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  7. 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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  8. 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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  9. 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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  10. Book ; Conference proceedings: New trends in barley quality for malting and feeding

    Molina Cano, José Luis

    proceedings of the Barcelon, Spain seminar, 15 - 16 November 1990

    (Options méditerranéennes : Série A, Séminaires Méditerranéens ; 20)

    1991  

    Institution International Centre for Advanced Mediterranean Agronomic Studies
    Author's details organized by C.I.H.E.A.M. ... Ed. by: J. L. Molina-Cano
    Series title Options méditerranéennes : Série A, Séminaires Méditerranéens ; 20
    Options méditerranéennes
    Options méditerranéennes ; Série A, Séminaires Méditerranéens
    Collection Options méditerranéennes
    Options méditerranéennes ; Série A, Séminaires Méditerranéens
    Language English
    Size 93 S. : Ill., graph. Darst.
    Publisher Lavoisiers diffusion éd
    Publishing place Cachan
    Publishing country France
    Document type Book ; Conference proceedings
    Note Zsfassung in engl. und franz. Sprache
    HBZ-ID HT017061693
    Database Catalogue ZB MED Nutrition, Environment, Agriculture

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    In stock of ZB MED Bonn / Germany

    Shelf markLoan statusLocation
    114/777 available for loan (reading room) Freihandmagazin (U1)

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