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  1. Article: Editorial: Interpretable predictive analytics for precision cardio-oncology preventive care.

    Zhou, Jiandong / Liu, Tong / Roever, Leonardo / Zhang, Qingpeng

    Frontiers in cardiovascular medicine

    2024  Volume 11, Page(s) 1377749

    Language English
    Publishing date 2024-03-06
    Publishing country Switzerland
    Document type Editorial
    ZDB-ID 2781496-8
    ISSN 2297-055X
    ISSN 2297-055X
    DOI 10.3389/fcvm.2024.1377749
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Numbers Needed to Treat for Preventing Adverse Cardiovascular Outcomes for Sodium-Glucose Cotransporter 2 Inhibitors vs. Dipeptidyl Peptidase 4 Inhibitors: The Hong Kong Diabetes Study.

    Tse, Gary / Lee, Sharen / Liu, Tong / Zhou, Jiandong

    Cardiovascular drugs and therapy

    2024  Volume 38, Issue 2, Page(s) 391–392

    MeSH term(s) Humans ; Dipeptidyl-Peptidase IV Inhibitors/adverse effects ; Hong Kong ; Numbers Needed To Treat ; Hypoglycemic Agents/adverse effects ; Diabetes Mellitus, Type 2/diagnosis ; Diabetes Mellitus, Type 2/drug therapy ; Glucose ; Sodium ; Retrospective Studies
    Chemical Substances Dipeptidyl-Peptidase IV Inhibitors ; Hypoglycemic Agents ; Glucose (IY9XDZ35W2) ; Sodium (9NEZ333N27)
    Language English
    Publishing date 2024-01-20
    Publishing country United States
    Document type Letter
    ZDB-ID 639068-7
    ISSN 1573-7241 ; 0920-3206
    ISSN (online) 1573-7241
    ISSN 0920-3206
    DOI 10.1007/s10557-024-07550-8
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.B 3057: Show issues Location:
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  3. Article ; Online: Publisher Correction: Direct probing of single-molecule chemiluminescent reaction dynamics under catalytic conditions in solution.

    Zhang, Ziqing / Dong, Jinrun / Yang, Yibo / Zhou, Yuan / Chen, Yuang / Xu, Yang / Feng, Jiandong

    Nature communications

    2024  Volume 15, Issue 1, Page(s) 1052

    Language English
    Publishing date 2024-02-05
    Publishing country England
    Document type Published Erratum
    ZDB-ID 2553671-0
    ISSN 2041-1723 ; 2041-1723
    ISSN (online) 2041-1723
    ISSN 2041-1723
    DOI 10.1038/s41467-024-45496-5
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article: Microstructure and Mechanical Properties of Ni-20Cr-Eu

    Zhou, Yihong / Yue, Huifang / Ma, Zhaohui / Guo, Zhancheng / Zhang, Jiandong / Wang, Lijun / Yan, Guoqing

    Materials (Basel, Switzerland)

    2023  Volume 16, Issue 4

    Abstract: ... Ni-20Cr- ... ...

    Abstract Ni-20Cr-Eu
    Language English
    Publishing date 2023-02-09
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2487261-1
    ISSN 1996-1944
    ISSN 1996-1944
    DOI 10.3390/ma16041473
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Article ; Online: A Novel MBAS-RF Approach to Predict Mechanical Properties of Geopolymer-Based Compositions

    Shuzhao Chen / Mengmeng Zhou / Xuyang Shi / Jiandong Huang

    Gels, Vol 9, Iss 434, p

    2023  Volume 434

    Abstract: Using gels to replace a certain amount of cement in concrete is conducive to the green concrete industry, while testing the compressive strength (CS) of geopolymer concrete requires a substantial amount of substantial effort and expense. To solve the ... ...

    Abstract Using gels to replace a certain amount of cement in concrete is conducive to the green concrete industry, while testing the compressive strength (CS) of geopolymer concrete requires a substantial amount of substantial effort and expense. To solve the above issue, a hybrid machine learning model of a modified beetle antennae search (MBAS) algorithm and random forest (RF) algorithm was developed in this study to model the CS of geopolymer concrete, in which MBAS was employed to adjust the hyperparameters of the RF model. The performance of the MBAS was verified by the relationship between 10-fold cross-validation (10-fold CV) and root mean square error (RMSE) value, and the prediction performance of the MBAS and RF hybrid machine learning model was verified by evaluating the correlation coefficient (R) and RMSE values and comparing with other models. The results show that the MBAS can effectively tune the performance of the RF model; the hybrid machine learning model had high R values (training set R = 0.9162 and test set R = 0.9071) and low RMSE values (training set RMSE = 7.111 and test set RMSE = 7.4345) at the same time, which indicated that the prediction accuracy was high; NaOH molarity was confirmed as the most important parameter regarding the CS of geopolymer concrete, with the importance score of 3.7848, and grade 4/10 mm was confirmed as the least important parameter, with the importance score of 0.5667.
    Keywords gels ; concrete ; hybrid machine learning model ; compressive strength ; Science ; Q ; Chemistry ; QD1-999 ; Inorganic chemistry ; QD146-197 ; General. Including alchemy ; QD1-65
    Subject code 690
    Language English
    Publishing date 2023-05-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  6. Article ; Online: Improvement of Computational Efficiency and Accuracy by Firefly Algorithm and Random Forest for Compressive Strength Modeling of Recycled Concrete

    Yong Liu / Yang Wang / Mengmeng Zhou / Jiandong Huang

    Sustainability, Vol 15, Iss 9170, p

    2023  Volume 9170

    Abstract: It is an important direction for the sustainable development of pavement to mix the discarded concrete blocks with gradation according to a certain proportion after crushing, cleaning and other technological processes, partially or completely replace ... ...

    Abstract It is an important direction for the sustainable development of pavement to mix the discarded concrete blocks with gradation according to a certain proportion after crushing, cleaning and other technological processes, partially or completely replace aggregate, and then add cement, water, and so on to make recycled concrete for pavement paving, but the traditional evaluation model for the compressive strength (CS) of recycled concrete cannot meet the requirements of efficient calculation. To address such issues, the present research proposed to apply the firefly algorithm (FA) to optimize the random forest (RF) model. The results were demonstrated by comparing the consistency of predicted and actual values, and also by analyzing the correlation coefficient (R) and root-mean-square error (RMSE). Higher R values (0.9756 and 0.9328) and lower RMSE values (3.0752 and 6.4369) for the training and test sets present the reliability of the FA and RF hybrid machine learning model. To understand the influence law of input indexes on the output index, the importance and sensitivity of variables are further analyzed. The results displayed that effective water-cement ratio (WC) and nominal maximum recycled concrete aggregate size (NMR) have the greatest impact on the output variable, with importance scores of 2.5947 and 2.4315, respectively, while the change in the recycled concrete aggregate replacement rate (RCA) has a weak influence, with an importance score of 0.4695. Introducing FA to RF for the compressive strength modeling of recycled concrete can significantly improve the computational efficiency and accuracy.
    Keywords recycled concrete ; machine learning ; firefly algorithm ; random forest ; compressive strength ; Environmental effects of industries and plants ; TD194-195 ; Renewable energy sources ; TJ807-830 ; Environmental sciences ; GE1-350
    Subject code 690
    Language English
    Publishing date 2023-06-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

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    Inter-library loan at ZB MED

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  7. Article ; Online: A Novel MBAS-RF Approach to Predict Mechanical Properties of Geopolymer-Based Compositions.

    Chen, Shuzhao / Zhou, Mengmeng / Shi, Xuyang / Huang, Jiandong

    Gels (Basel, Switzerland)

    2023  Volume 9, Issue 6

    Abstract: Using gels to replace a certain amount of cement in concrete is conducive to the green concrete industry, while testing the compressive strength (CS) of geopolymer concrete requires a substantial amount of substantial effort and expense. To solve the ... ...

    Abstract Using gels to replace a certain amount of cement in concrete is conducive to the green concrete industry, while testing the compressive strength (CS) of geopolymer concrete requires a substantial amount of substantial effort and expense. To solve the above issue, a hybrid machine learning model of a modified beetle antennae search (MBAS) algorithm and random forest (RF) algorithm was developed in this study to model the CS of geopolymer concrete, in which MBAS was employed to adjust the hyperparameters of the RF model. The performance of the MBAS was verified by the relationship between 10-fold cross-validation (10-fold CV) and root mean square error (RMSE) value, and the prediction performance of the MBAS and RF hybrid machine learning model was verified by evaluating the correlation coefficient (R) and RMSE values and comparing with other models. The results show that the MBAS can effectively tune the performance of the RF model; the hybrid machine learning model had high R values (training set R = 0.9162 and test set R = 0.9071) and low RMSE values (training set RMSE = 7.111 and test set RMSE = 7.4345) at the same time, which indicated that the prediction accuracy was high; NaOH molarity was confirmed as the most important parameter regarding the CS of geopolymer concrete, with the importance score of 3.7848, and grade 4/10 mm was confirmed as the least important parameter, with the importance score of 0.5667.
    Language English
    Publishing date 2023-05-24
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2813982-3
    ISSN 2310-2861 ; 2310-2861
    ISSN (online) 2310-2861
    ISSN 2310-2861
    DOI 10.3390/gels9060434
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  8. Article ; Online: What can we learn from the AV crashes? - An association rule analysis for identifying the contributing risky factors.

    Liu, Pei / Guo, Yanyong / Liu, Pan / Ding, Hongliang / Cao, Jiandong / Zhou, Jibiao / Feng, Zhongxiang

    Accident; analysis and prevention

    2024  Volume 199, Page(s) 107492

    Abstract: The objective of this study is to explore the contributing risky factors to Autonomous Vehicle (AV) crashes and their interdependencies. AV crash data between 2015 and 2023 were collected from the autonomous vehicle collision report published by ... ...

    Abstract The objective of this study is to explore the contributing risky factors to Autonomous Vehicle (AV) crashes and their interdependencies. AV crash data between 2015 and 2023 were collected from the autonomous vehicle collision report published by California Department of Motor Vehicles (DMV). AV crashes were categorized into four types based on vehicle damage. AV crashes features including crash location and time, driving mode, vehicle movements, crash type and vehicle damage, traffic conditions, and among others were used as potential risk factors. Association Rule Mining methods (ARM) were utilized to identify sets of contributing risky factors that often occur together in AV crashes. Several association rules suggest that AV crashes result from complex interactions between road factors, vehicle factors, and environmental conditions. No damage and minor crashes are more likely affected by the road features and traffic conditions. In contrast, the movements of vehicles are more sensitive to severe AV crashes. Improper vehicle operations could increase the probability of severe AV crashes. In addition, results suggest that adverse weather conditions could increase the damage of AV crashes. AV interactions with roadside infrastructure or vulnerable road users on wet road surfaces during the night could potentially lead to significant loss of life and property. Furthermore, the safety effects of vehicle mode on the different AV crash damage are revealed. In some contexts, the autonomous driving mode can mitigate the risk of crash damages compared with conventional driving mode. The findings of this study should be indicative of policy measures and engineering countermeasures that improve the safety and efficiency of AV on the road, ultimately improving road transportation's overall safety and reliability.
    MeSH term(s) Humans ; Accidents, Traffic/prevention & control ; Reproducibility of Results ; Autonomous Vehicles ; Engineering ; Risk Factors
    Language English
    Publishing date 2024-02-29
    Publishing country England
    Document type Journal Article
    ZDB-ID 210223-7
    ISSN 1879-2057 ; 0001-4575
    ISSN (online) 1879-2057
    ISSN 0001-4575
    DOI 10.1016/j.aap.2024.107492
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 2461: 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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  9. Article: Phosphorus addition increases stability and complexity of co-occurrence network of soil microbes in an artificial

    Zhou, Xiaoguo / Hu, Yutong / Li, Huijun / Sheng, Jiandong / Cheng, Junhui / Zhao, Tingting / Zhang, Yuanmei

    Frontiers in microbiology

    2024  Volume 15, Page(s) 1289022

    Abstract: Introduction: Understanding the response of cross-domain co-occurrence networks of soil microorganisms to phosphorus stability and the resulting impacts is critical in ecosystems, but the underlying mechanism is unclear in artificial grassland ... ...

    Abstract Introduction: Understanding the response of cross-domain co-occurrence networks of soil microorganisms to phosphorus stability and the resulting impacts is critical in ecosystems, but the underlying mechanism is unclear in artificial grassland ecosystems.
    Methods: In this study, the effects of four phosphorus concentrations, P0 (0 kg P ha
    Results and discussion: The results of the present study showed that phosphorus addition significantly altered the stem number, biomass and plant height of the Leymus chinensis but had no significant effect on the soil bacterial or fungal alpha (ACE) diversity or beta diversity. The phosphorus treatments all increased the cross-domain co-occurrence network edge, node, proportion of positively correlated edges, edge density, average degree, proximity to centrality, and robustness and increased the complexity and stability of the bacterial-fungal cross-domain co-occurrence network after 3 years of continuous phosphorus addition. Among them, fungi (Ascomycota, Basidiomycota, Mortierellomycota and Glomeromycota) play important roles as keystone species in the co-occurrence network, and they are significantly associated with soil AN, AK and EC. Finally, the growth of Leymus chinensis was mainly due to the influence of the soil phosphorus content and AN. This study revealed the factors affecting the growth of Leymus chinense in artificial grasslands in arid areas and provided a theoretical basis for the construction of artificial grasslands.
    Language English
    Publishing date 2024-03-27
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2587354-4
    ISSN 1664-302X
    ISSN 1664-302X
    DOI 10.3389/fmicb.2024.1289022
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  10. Article ; Online: Quantitative Single-Molecule Electrochemiluminescence Bioassay.

    Zhu, Wenxin / Dong, Jinrun / Ruan, Guoxiang / Zhou, Yuan / Feng, Jiandong

    Angewandte Chemie (International ed. in English)

    2023  Volume 62, Issue 7, Page(s) e202214419

    Abstract: A single-molecule electrochemiluminescence bioassay is developed here which allows imaging and direct quantification of single biomolecules. Imaging single biomolecules is realized by localizing the electrochemiluminescence events of the labeled ... ...

    Abstract A single-molecule electrochemiluminescence bioassay is developed here which allows imaging and direct quantification of single biomolecules. Imaging single biomolecules is realized by localizing the electrochemiluminescence events of the labeled molecules. Such an imaging system allows mapping the spatial distribution of biomolecules with electrochemiluminescence and contains quantitative single-molecule insights. We further quantify biomolecules by spatiotemporally merging the repeated reactions at one molecule site and then counting the clustered molecules. The proposed single-molecule electrochemiluminescence bioassay is used to detect carcinoembryonic antigen, showing a limit of detection of 67 attomole concentration which is 10 000 times better than conventional electrochemiluminescence bioassays. This spatial resolution and sensitivity enable single-molecule electrochemiluminescence bioassay a new toolbox for both specific bioimaging and ultrasensitive quantitative analysis.
    MeSH term(s) Nanotechnology ; Diagnostic Imaging ; Biological Assay
    Language English
    Publishing date 2023-01-12
    Publishing country Germany
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2011836-3
    ISSN 1521-3773 ; 1433-7851
    ISSN (online) 1521-3773
    ISSN 1433-7851
    DOI 10.1002/anie.202214419
    Database MEDical Literature Analysis and Retrieval System OnLINE

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