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  1. Article ; Online: Analysis of Characteristics of Endometrial Carcinoma in Peri- and Postmenopausal Women with Abnormal Uterine Bleeding.

    Yanli, Ye / Mei, Wang Tian / Cong, Li

    BioMed research international

    2024  Volume 2024, Page(s) 6509171

    Abstract: Objective: To analyze the menstrual characteristics of endometrial carcinoma and investigate whether abnormal uterine bleeding in the perimenopausal period differs from postmenopausal bleeding.: Methods: We conducted a retrospective analysis of 928 ... ...

    Abstract Objective: To analyze the menstrual characteristics of endometrial carcinoma and investigate whether abnormal uterine bleeding in the perimenopausal period differs from postmenopausal bleeding.
    Methods: We conducted a retrospective analysis of 928 cases of endometrial carcinoma in patients admitted from January 2016 to December 2022. We gathered fundamental clinical data and analyzed distinct clinical risk factors between the perimenopausal and postmenopausal groups. Furthermore, we computed the statistical variances in menarche, regular menstrual cycles, and the duration of abnormal uterine bleeding.
    Results: Perimenopausal patients with endometrial carcinoma exhibit similar factors to postmenopausal patients, especially if they have a history of menstrual cycles lasting more than 30 years, hypertension, abnormal uterine bleeding for over 1 year, and a high risk of endometrial carcinoma. Early intervention for abnormal uterine bleeding during the perimenopausal stage can prevent up to 80% of women from developing endometrial carcinoma.
    Conclusion: Perimenopause women experiencing abnormal uterine bleeding should be mindful of the risk of endometrial carcinoma, as this awareness can substantially decrease the occurrence of the disease.
    MeSH term(s) Humans ; Female ; Postmenopause ; Retrospective Studies ; Endometrial Neoplasms/complications ; Endometrial Neoplasms/epidemiology ; Uterine Hemorrhage ; Early Intervention, Educational
    Language English
    Publishing date 2024-02-24
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2698540-8
    ISSN 2314-6141 ; 2314-6133
    ISSN (online) 2314-6141
    ISSN 2314-6133
    DOI 10.1155/2024/6509171
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  2. Article ; Online: Sources of COVID-19 Information Seeking and their Associations with Self-Perceived Mental Health among Canadians

    Yanli Li

    The International Journal of Information, Diversity, & Inclusion, Vol 5, Iss

    2021  Volume 3

    Abstract: Using two datasets from the Canadian Perspectives Survey Series (CPSS), this study provides a longitudinal analysis of information sources Canadians consulted regarding COVID-19, and their associations with poor self-perceived mental health (SPMH) during ...

    Abstract Using two datasets from the Canadian Perspectives Survey Series (CPSS), this study provides a longitudinal analysis of information sources Canadians consulted regarding COVID-19, and their associations with poor self-perceived mental health (SPMH) during March and July 2020. Nearly 20% of Canadians reported poor SPMH. The logistic regression results revealed that at Time 2 (July 2020), after controlling for demographic, socio-economic, and psycho-behavioural factors, using social media was significantly associated with higher odds of poor SPMH than using six other information sources including news outlets, federal health agencies, provincial health agencies, provincial daily announcements, places of employment, and other sources (for example, schools, colleges, universities). Checking the accuracy of online information more frequently was also associated with lower odds of poor SPMH.
    Keywords information sources ; COVID-19 ; self-perceived mental health ; social media ; Bibliography. Library science. Information resources ; Z ; Communities. Classes. Races ; HT51-1595
    Subject code 302
    Language English
    Publishing date 2021-09-01T00:00:00Z
    Publisher University of Hawai'i Library & Information Science Program
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  3. Article: Self supervised learning based emotion recognition using physiological signals.

    Zhang, Min / Cui, YanLi

    Frontiers in human neuroscience

    2024  Volume 18, Page(s) 1334721

    Abstract: Introduction: The significant role of emotional recognition in the field of human-machine interaction has garnered the attention of many researchers. Emotion recognition based on physiological signals can objectively reflect the most authentic emotional ...

    Abstract Introduction: The significant role of emotional recognition in the field of human-machine interaction has garnered the attention of many researchers. Emotion recognition based on physiological signals can objectively reflect the most authentic emotional states of humans. However, existing labeled Electroencephalogram (EEG) datasets are often of small scale.
    Methods: In practical scenarios, a large number of unlabeled EEG signals are easier to obtain. Therefore, this paper adopts self-supervised learning methods to study emotion recognition based on EEG. Specifically, experiments employ three pre-defined tasks to define pseudo-labels and extract features from the inherent structure of the data.
    Results and discussion: Experimental results indicate that self-supervised learning methods have the capability to learn effective feature representations for downstream tasks without any manual labels.
    Language English
    Publishing date 2024-04-09
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2425477-0
    ISSN 1662-5161
    ISSN 1662-5161
    DOI 10.3389/fnhum.2024.1334721
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  4. Article ; Online: Direct

    Wan, Yanli / Zhao, Yixing / Li, Yaling / Zhang, Zhenwei / Li, Sen / Tian, Tingfang / Wang, Li

    Nanoscale

    2024  Volume 16, Issue 5, Page(s) 2504–2512

    Abstract: ... ...

    Abstract CsPbX
    Language English
    Publishing date 2024-02-01
    Publishing country England
    Document type Journal Article
    ZDB-ID 2515664-0
    ISSN 2040-3372 ; 2040-3364
    ISSN (online) 2040-3372
    ISSN 2040-3364
    DOI 10.1039/d3nr04876d
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  5. Article ; Online: A novel epileptic seizure prediction method based on synchroextracting transform and 1-dimensional convolutional neural network.

    Ra, Jee Sook / Li, Tianning / YanLi

    Computer methods and programs in biomedicine

    2023  Volume 240, Page(s) 107678

    Abstract: Background and objective: Epilepsy is a serious brain disorder affecting more than 50 million people worldwide. If epileptic seizures can be predicted in advance, patients can take measures to avoid unfortunate consequences. Important approaches for ... ...

    Abstract Background and objective: Epilepsy is a serious brain disorder affecting more than 50 million people worldwide. If epileptic seizures can be predicted in advance, patients can take measures to avoid unfortunate consequences. Important approaches for epileptic seizure predictions are often signal transformation and classification using electroencephalography (EEG) signals. A time-frequency (TF) transformation, such as the short-term Fourier transform (STFT), has been widely used over many years but curtailed by the Heisenberg uncertainty principle. This research focuses on decomposing epileptic EEG signals with a higher resolution so that an epileptic seizure can be predicted accurately before its episodes.
    Methods: This study applies a synchroextracting transformation (SET) and singular value decomposition (SET-SVD) to improve the time-frequency resolution. The SET is a more energy-concentrated TF representation than classical TF analysis methods.
    Results: The pre-seizure classification method employing a 1-dimensional convolutional neural network (1D-CNN) reached an accuracy of 99.71% (the CHB-MIT database) and 100% (the Bonn University database). The experiments on the CHB-MIT show that the accuracy, sensitivity and specificity from the SET-SVD method, compared with the results of the STFT, are increased by 8.12%, 6.24% and 13.91%, respectively. In addition, a multi-layer perceptron (MLP) was also used as a classifier. Its experimental results also show that the SET-SVD generates a higher accuracy, sensitivity and specificity by 5.0%, 2.41% and 11.42% than the STFT, respectively.
    Conclusions: The results of two classification methods (the MLP and 1D-CNN) show that the SET-SVD has the capacity to extract more accurate information than the STFT. The 1D-CNN model is suitable for a fast and accurate patient-specific EEG classification.
    MeSH term(s) Humans ; Epilepsy/diagnosis ; Seizures/diagnosis ; Neural Networks, Computer ; Sensitivity and Specificity ; Electroencephalography/methods ; Signal Processing, Computer-Assisted ; Algorithms
    Language English
    Publishing date 2023-06-18
    Publishing country Ireland
    Document type Journal Article
    ZDB-ID 632564-6
    ISSN 1872-7565 ; 0169-2607
    ISSN (online) 1872-7565
    ISSN 0169-2607
    DOI 10.1016/j.cmpb.2023.107678
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    In stock of ZB MED Cologne/Königswinter

    Zs.B 521: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 2021: Bestellungen von Artikeln über das Online-Bestellformular
    ab Jg. 2022: Lesesaal (EG)

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  6. Article: 2-Hydroxy-4-methoxybenzaldehyde, a more effective antifungal aroma than vanillin and its derivatives against

    Li, Qian / Wang, Chong / Xiao, Hongying / Zhang, Yiming / Xie, Yanli

    Frontiers in microbiology

    2024  Volume 15, Page(s) 1359947

    Abstract: ... Fusarium ... ...

    Abstract Fusarium graminearum
    Language English
    Publishing date 2024-02-26
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2587354-4
    ISSN 1664-302X
    ISSN 1664-302X
    DOI 10.3389/fmicb.2024.1359947
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  7. Article ; Online: Characterization of the complete chloroplast genome sequence of

    Yi, Lianghui / Wang, Yanli / Li, Yunze / Zhang, Dandan / Tong, Wei

    Mitochondrial DNA. Part B, Resources

    2024  Volume 9, Issue 4, Page(s) 461–464

    Abstract: ... Camellia ... ...

    Abstract Camellia tetracocca
    Language English
    Publishing date 2024-04-04
    Publishing country England
    Document type Journal Article
    ISSN 2380-2359
    ISSN (online) 2380-2359
    DOI 10.1080/23802359.2024.2316067
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  8. Article ; Online: Cationic Hypervalent Chalcogen Bond Catalysis on the Povarov Reaction: Reactivity and Stereoselectivity.

    Zhao, Chang / Li, Ying / Wang, Yanjiang / Zeng, Yanli

    Chemistry (Weinheim an der Bergstrasse, Germany)

    2024  Volume 30, Issue 24, Page(s) e202400555

    Abstract: Chalcogen bond catalysis, particularly cationic hypervalent chalcogen bond catalysis, is considered to be an effective strategy for organocatalysis. In this work, the cationic hypervalent chalcogen bond catalysis for the Povarov reaction between N- ... ...

    Abstract Chalcogen bond catalysis, particularly cationic hypervalent chalcogen bond catalysis, is considered to be an effective strategy for organocatalysis. In this work, the cationic hypervalent chalcogen bond catalysis for the Povarov reaction between N-benzylideneaniline and ethyl vinyl ether was investigated by density functional theory (DFT). The catalytic reaction involves the cycloaddition process and the proton transfer process, and the rate-determining step is the cycloaddition process. Cationic hypervalent tellurium derivatives bearing CF
    Language English
    Publishing date 2024-03-07
    Publishing country Germany
    Document type Journal Article
    ZDB-ID 1478547-X
    ISSN 1521-3765 ; 0947-6539
    ISSN (online) 1521-3765
    ISSN 0947-6539
    DOI 10.1002/chem.202400555
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  9. Article ; Online: Sol-Gel Derived Tungsten Doped VO

    Ding, Xiaoming / Li, Yanli / Zhang, Yubo

    Molecules (Basel, Switzerland)

    2023  Volume 28, Issue 9

    Abstract: Vanadium dioxide ( ... ...

    Abstract Vanadium dioxide (VO
    Language English
    Publishing date 2023-04-27
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 1413402-0
    ISSN 1420-3049 ; 1431-5165 ; 1420-3049
    ISSN (online) 1420-3049
    ISSN 1431-5165 ; 1420-3049
    DOI 10.3390/molecules28093778
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    In stock of ZB MED Cologne/Königswinter

    Zs.MO 81: Show issues

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  10. Article ; Online: Halogen Bond Catalysis: A Physical Chemistry Perspective.

    Li, Ying / Zhao, Chang / Wang, Zhuo / Zeng, Yanli

    The journal of physical chemistry. A

    2024  Volume 128, Issue 3, Page(s) 507–527

    Abstract: As important noncovalent interactions, halogen bonds have been widely used in material science, supramolecular chemistry, medicinal chemistry, organocatalysis, and other fields. In the past 15 years, halogen bond catalysis has become a developed field in ...

    Abstract As important noncovalent interactions, halogen bonds have been widely used in material science, supramolecular chemistry, medicinal chemistry, organocatalysis, and other fields. In the past 15 years, halogen bond catalysis has become a developed field in organocatalysis for the catalysts' advantages of being environmentally friendly, inexpensive, and recyclable. Halogen bonds can induce various organic reactions, and halogen bond catalysis has become a powerful alternative to the fully explored hydrogen bond catalysis. From a physical chemistry view, this perspective provides an overview of the latest progress and key examples of halogen bond catalysis via activation of the lone pair systems of organic functional group, π systems, and metal complexes. The research progresses in halogen bond catalysis by our group were also introduced.
    Language English
    Publishing date 2024-01-12
    Publishing country United States
    Document type Journal Article ; Review
    ISSN 1520-5215
    ISSN (online) 1520-5215
    DOI 10.1021/acs.jpca.3c06363
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