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  1. Article ; Online: Protective effects of myricetin and morin on neurological damage in Aβ

    Guo, Linli / Zhao, Yanan / Kong, Zhengqiao / Liu, Ruihua / Liu, Ping

    Journal of chemical neuroanatomy

    2024  Volume 137, Page(s) 102404

    Abstract: Alzheimer's disease (AD) is a degenerative neurological disorder with unclear pathogenesis. Single-target drugs have very limited efficacy in treating AD, but synthetic multi-target drugs have poor efficacy and safety. Therefore, finding suitable natural ...

    Abstract Alzheimer's disease (AD) is a degenerative neurological disorder with unclear pathogenesis. Single-target drugs have very limited efficacy in treating AD, but synthetic multi-target drugs have poor efficacy and safety. Therefore, finding suitable natural multi-target drugs against AD is of great interest for research studies. We chose two flavonols, myricetin and morin, for the relevant study. In this study, we used microinjection of Aβ
    Language English
    Publishing date 2024-02-27
    Publishing country Netherlands
    Document type Journal Article
    ZDB-ID 639443-7
    ISSN 1873-6300 ; 0891-0618
    ISSN (online) 1873-6300
    ISSN 0891-0618
    DOI 10.1016/j.jchemneu.2024.102404
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 2363: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (2.OG)
    ab Jg. 2022: Lesesaal (EG)

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  2. Article ; Online: Designing functional Li

    Zeng, Zihao / Lei, Hai / Li, Jiexiang / Yuan, Zhengqiao / Wang, Bing / Zhao, Wenqing / Yang, Yue / Ge, Peng

    Chemical communications (Cambridge, England)

    2024  Volume 60, Issue 22, Page(s) 3059–3062

    Abstract: A chemical-physical investigation proved that the loss of active Li represents the main mechanism of capacity-fading in spent ... ...

    Abstract A chemical-physical investigation proved that the loss of active Li represents the main mechanism of capacity-fading in spent LiFePO
    Language English
    Publishing date 2024-03-12
    Publishing country England
    Document type Journal Article
    ZDB-ID 1472881-3
    ISSN 1364-548X ; 1359-7345 ; 0009-241X
    ISSN (online) 1364-548X
    ISSN 1359-7345 ; 0009-241X
    DOI 10.1039/d4cc00227j
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: Interpretable and Predictive Deep Neural Network Modeling of the SARS-CoV-2 Spike Protein Sequence to Predict COVID-19 Disease Severity

    Bahrad A. Sokhansanj / Zhengqiao Zhao / Gail L. Rosen

    Biology, Vol 11, Iss 1786, p

    2022  Volume 1786

    Abstract: Through the COVID-19 pandemic, SARS-CoV-2 has gained and lost multiple mutations in novel or unexpected combinations. Predicting how complex mutations affect COVID-19 disease severity is critical in planning public health responses as the virus continues ...

    Abstract Through the COVID-19 pandemic, SARS-CoV-2 has gained and lost multiple mutations in novel or unexpected combinations. Predicting how complex mutations affect COVID-19 disease severity is critical in planning public health responses as the virus continues to evolve. This paper presents a novel computational framework to complement conventional lineage classification and applies it to predict the severe disease potential of viral genetic variation. The transformer-based neural network model architecture has additional layers that provide sample embeddings and sequence-wide attention for interpretation and visualization. First, training a model to predict SARS-CoV-2 taxonomy validates the architecture’s interpretability. Second, an interpretable predictive model of disease severity is trained on spike protein sequence and patient metadata from GISAID. Confounding effects of changing patient demographics, increasing vaccination rates, and improving treatment over time are addressed by including demographics and case date as independent input to the neural network model. The resulting model can be interpreted to identify potentially significant virus mutations and proves to be a robust predctive tool. Although trained on sequence data obtained entirely before the availability of empirical data for Omicron, the model can predict the Omicron’s reduced risk of severe disease, in accord with epidemiological and experimental data.
    Keywords COVID-19 ; SARS-CoV-2 ; coronavirus ; deep learning ; neural networks ; machine learning ; Biology (General) ; QH301-705.5
    Subject code 006
    Language English
    Publishing date 2022-12-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  4. Article: Interpretable and Predictive Deep Neural Network Modeling of the SARS-CoV-2 Spike Protein Sequence to Predict COVID-19 Disease Severity.

    Sokhansanj, Bahrad A / Zhao, Zhengqiao / Rosen, Gail L

    Biology

    2022  Volume 11, Issue 12

    Abstract: Through the COVID-19 pandemic, SARS-CoV-2 has gained and lost multiple mutations in novel or unexpected combinations. Predicting how complex mutations affect COVID-19 disease severity is critical in planning public health responses as the virus continues ...

    Abstract Through the COVID-19 pandemic, SARS-CoV-2 has gained and lost multiple mutations in novel or unexpected combinations. Predicting how complex mutations affect COVID-19 disease severity is critical in planning public health responses as the virus continues to evolve. This paper presents a novel computational framework to complement conventional lineage classification and applies it to predict the severe disease potential of viral genetic variation. The transformer-based neural network model architecture has additional layers that provide sample embeddings and sequence-wide attention for interpretation and visualization. First, training a model to predict SARS-CoV-2 taxonomy validates the architecture's interpretability. Second, an interpretable predictive model of disease severity is trained on spike protein sequence and patient metadata from GISAID. Confounding effects of changing patient demographics, increasing vaccination rates, and improving treatment over time are addressed by including demographics and case date as independent input to the neural network model. The resulting model can be interpreted to identify potentially significant virus mutations and proves to be a robust predctive tool. Although trained on sequence data obtained entirely before the availability of empirical data for Omicron, the model can predict the Omicron's reduced risk of severe disease, in accord with epidemiological and experimental data.
    Language English
    Publishing date 2022-12-08
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2661517-4
    ISSN 2079-7737
    ISSN 2079-7737
    DOI 10.3390/biology11121786
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Article ; Online: Designing Sphere-like FeSe

    Zeng, Zihao / Liu, Junchang / Yuan, Zhengqiao / Dong, Yu / Zhao, Wenqing / Yuan, Shaohui / Xie, Siyan / Jing, Mingjun / Wu, Tianjing / Ge, Peng

    Journal of colloid and interface science

    2023  Volume 648, Page(s) 149–160

    Abstract: Due to their low cost and high stability, sodium-ion batteries have been increasingly studied. However, their further development is limited by the relative energy density, resulting in the search for high-capacity anodes. ... ...

    Abstract Due to their low cost and high stability, sodium-ion batteries have been increasingly studied. However, their further development is limited by the relative energy density, resulting in the search for high-capacity anodes. FeSe
    Language English
    Publishing date 2023-06-03
    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.2023.06.005
    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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  6. Article ; Online: Interpretable and Predictive Deep Neural Network Modeling of the SARS-CoV-2 Spike Protein Sequence to Predict COVID-19 Disease Severity

    Sokhansanj, Bahrad A. / Zhao, Zhengqiao / Rosen, Gail L.

    Biology (Basel). 2022 Dec. 08, v. 11, no. 12

    2022  

    Abstract: Through the COVID-19 pandemic, SARS-CoV-2 has gained and lost multiple mutations in novel or unexpected combinations. Predicting how complex mutations affect COVID-19 disease severity is critical in planning public health responses as the virus continues ...

    Abstract Through the COVID-19 pandemic, SARS-CoV-2 has gained and lost multiple mutations in novel or unexpected combinations. Predicting how complex mutations affect COVID-19 disease severity is critical in planning public health responses as the virus continues to evolve. This paper presents a novel computational framework to complement conventional lineage classification and applies it to predict the severe disease potential of viral genetic variation. The transformer-based neural network model architecture has additional layers that provide sample embeddings and sequence-wide attention for interpretation and visualization. First, training a model to predict SARS-CoV-2 taxonomy validates the architecture’s interpretability. Second, an interpretable predictive model of disease severity is trained on spike protein sequence and patient metadata from GISAID. Confounding effects of changing patient demographics, increasing vaccination rates, and improving treatment over time are addressed by including demographics and case date as independent input to the neural network model. The resulting model can be interpreted to identify potentially significant virus mutations and proves to be a robust predctive tool. Although trained on sequence data obtained entirely before the availability of empirical data for Omicron, the model can predict the Omicron’s reduced risk of severe disease, in accord with epidemiological and experimental data.
    Keywords COVID-19 infection ; Severe acute respiratory syndrome coronavirus 2 ; amino acid sequences ; demographic statistics ; disease severity ; genetic variation ; metadata ; neural networks ; patients ; public health ; risk reduction ; taxonomy ; vaccination ; viruses
    Language English
    Dates of publication 2022-1208
    Publishing place Multidisciplinary Digital Publishing Institute
    Document type Article ; Online
    ZDB-ID 2661517-4
    ISSN 2079-7737
    ISSN 2079-7737
    DOI 10.3390/biology11121786
    Database NAL-Catalogue (AGRICOLA)

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  7. Article ; Online: A Well-Designed Implement for Promoting Population Health and Property via Insurance

    Zhengqiao Liu / Li Zhao / Yang-Che Wu / Ming-Che Chuang / Mei-Chih Wang

    Frontiers in Public Health, Vol

    2022  Volume 9

    Abstract: The frequency and intensity of catastrophes (including natural disasters and pandemics) rise and damage the population's health, life and property more seriously. In order to protect population health and wealth via full insurance indemnity, many ... ...

    Abstract The frequency and intensity of catastrophes (including natural disasters and pandemics) rise and damage the population's health, life and property more seriously. In order to protect population health and wealth via full insurance indemnity, many countries set up a public catastrophe insurance scheme (PCIS) to maintain the function of catastrophe insurance markets. Little literature discusses the smart payment way of contributions charged by PCIS. This article design a model to describe the upward trend and cyclic frequency and intensity of catastrophic events. Such characteristics also promote the business cycle of the insurance industry. We analyze the changes in catastrophic insurer's capital structures under three cases of that the volume-based charges to the PCIS may come from equity holders or policyholders or both. PCIS may entail a shift of equity capital toward minimum solvency requirements, and then adverse incentives regarding insurer's security level arise. Various numerical experiments illustrate the changes in equity position, default probabilities, or expected policyholder deficits. The results show that the payment way of contributions should be designed carefully, not only with regard to PCIS's finance balance but also the resultant incentives and effects.
    Keywords catastrophe insurance ; public catastrophe insurance scheme ; population health and property ; minimum solvency requirement ; default risk ; Public aspects of medicine ; RA1-1270
    Subject code 336
    Language English
    Publishing date 2022-01-01T00:00:00Z
    Publisher Frontiers Media S.A.
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  8. Article ; Online: Keeping up with the genomes

    Zhengqiao Zhao / Alexandru Cristian / Gail Rosen

    BMC Bioinformatics, Vol 21, Iss 1, Pp 1-

    efficient learning of our increasing knowledge of the tree of life

    2020  Volume 23

    Abstract: Abstract Background It is a computational challenge for current metagenomic classifiers to keep up with the pace of training data generated from genome sequencing projects, such as the exponentially-growing NCBI RefSeq bacterial genome database. When new ...

    Abstract Abstract Background It is a computational challenge for current metagenomic classifiers to keep up with the pace of training data generated from genome sequencing projects, such as the exponentially-growing NCBI RefSeq bacterial genome database. When new reference sequences are added to training data, statically trained classifiers must be rerun on all data, resulting in a highly inefficient process. The rich literature of “incremental learning” addresses the need to update an existing classifier to accommodate new data without sacrificing much accuracy compared to retraining the classifier with all data. Results We demonstrate how classification improves over time by incrementally training a classifier on progressive RefSeq snapshots and testing it on: (a) all known current genomes (as a ground truth set) and (b) a real experimental metagenomic gut sample. We demonstrate that as a classifier model’s knowledge of genomes grows, classification accuracy increases. The proof-of-concept naïve Bayes implementation, when updated yearly, now runs in 1/4 t h of the non-incremental time with no accuracy loss. Conclusions It is evident that classification improves by having the most current knowledge at its disposal. Therefore, it is of utmost importance to make classifiers computationally tractable to keep up with the data deluge. The incremental learning classifier can be efficiently updated without the cost of reprocessing nor the access to the existing database and therefore save storage as well as computation resources.
    Keywords Incremental learning ; Naïve Bayes taxanomic classifier ; RefSeq ; Metagenomics ; Computer applications to medicine. Medical informatics ; R858-859.7 ; Biology (General) ; QH301-705.5
    Subject code 006
    Language English
    Publishing date 2020-09-01T00:00:00Z
    Publisher BMC
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  9. Article: Amino Acid

    ValizadehAslani, Taha / Zhao, Zhengqiao / Sokhansanj, Bahrad A / Rosen, Gail L

    Biology

    2020  Volume 9, Issue 11

    Abstract: Machine learning algorithms can learn mechanisms of antimicrobial resistance from the data of DNA sequence without any a priori information. Interpreting a trained machine learning algorithm can be exploited for validating the model and obtaining new ... ...

    Abstract Machine learning algorithms can learn mechanisms of antimicrobial resistance from the data of DNA sequence without any a priori information. Interpreting a trained machine learning algorithm can be exploited for validating the model and obtaining new information about resistance mechanisms. Different feature extraction methods, such as SNP calling and counting nucleotide
    Language English
    Publishing date 2020-10-28
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2661517-4
    ISSN 2079-7737
    ISSN 2079-7737
    DOI 10.3390/biology9110365
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  10. Article ; Online: Controlling of Ni-Based Composites in Salt Melt Synthesis with High Sodium-Ion Storage Performance.

    Zeng, Zihao / Yuan, Shaohui / Yi, Chenxing / Zhao, Wenqing / Yuan, Zhengqiao / Dong, Yu / Zhu, Jinliang / Yang, Yue / Ge, Peng

    ACS applied materials & interfaces

    2022  Volume 14, Issue 46, Page(s) 52067–52078

    Abstract: Owing to its fascinating properties (such as high theoretical specific capacity and considerable conductivity), nickel sulfide (NiS) was investigated comprehensively as an anode material in sodium-ion batteries. However, they still suffered from volume ... ...

    Abstract Owing to its fascinating properties (such as high theoretical specific capacity and considerable conductivity), nickel sulfide (NiS) was investigated comprehensively as an anode material in sodium-ion batteries. However, they still suffered from volume expansion and sluggish kinetics, resulting in serious cycle capabilities. Herein, through controlling the kind of molten salts (Na
    Language English
    Publishing date 2022-11-08
    Publishing country United States
    Document type Journal Article
    ISSN 1944-8252
    ISSN (online) 1944-8252
    DOI 10.1021/acsami.2c17568
    Database MEDical Literature Analysis and Retrieval System OnLINE

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