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  1. Book ; Online: Nonlinear Analysis and Optimization with Applications

    Du, Wei-Shih / Chu, Liang-Ju / He, Fei / Precup, Radu

    2022  

    Keywords Research & information: general ; Mathematics & science ; best proximity point ; fixed point ; monotone mappings ; relatively cyclic nonexpansive mappings ; partially ordered Banach spaces ; modified BBM equations ; white noise ; Brownian motion ; travelling wave solutions ; wick-type stochastic ; admissible spaces ; hybrid contraction ; interpolative contraction ; b-metric spaces ; simulation function ; m-metric space ; proximal αp-admissible ; αp-admissible weak (F,φ)-proximal contraction ; G-proximal graphic contraction ; φ-best proximity point ; Fourier data ; reconstruction ; multivariate approximation ; piecewise smooth ; projection methods ; strong convergence ; extragradient method ; monotone mapping ; variational inequalities ; critical index ; relaxation time ; time-translation invariance breaking and restoration ; market crash ; COVID-19 ; Gompertz approximants ; split common null point ; resolvent ; metric resolvent ; split minimization problem ; split equilibrium problem ; Banach space ; multiple-sets split feasibility problem ; strictly pseudocontractive mappings ; nonexpansive mappings ; viscossity iterative scheme ; fixed point problem ; n-Banach space ; cubic mappings ; quartic mappings ; the generalized Hyers-Ulam stability ; maximal element ; sizing-up function ; μ-bounded quasi-ordered set ; critical point ; fuzzy mapping ; Ekeland's variational principle ; Caristi's fixed point theorem ; Takahashi's nonconvex minimization theorem ; essential distance
    Size 1 electronic resource (208 pages)
    Publisher MDPI - Multidisciplinary Digital Publishing Institute
    Publishing place Basel
    Document type Book ; Online
    Note English ; Open Access
    HBZ-ID HT021290611
    ISBN 9783036520452 ; 3036520457
    Database ZB MED Catalogue: Medicine, Health, Nutrition, Environment, Agriculture

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  2. Article ; Online: Efficacy and Safety of Corticosteroid Therapy in Patients With Cardiac Arrest.

    He, Fei

    Critical care medicine

    2022  Volume 50, Issue 2, Page(s) e217–e218

    MeSH term(s) Adrenal Cortex Hormones/adverse effects ; Cardiopulmonary Resuscitation ; Heart Arrest/chemically induced ; Heart Arrest/drug therapy ; Humans
    Chemical Substances Adrenal Cortex Hormones
    Language English
    Publishing date 2022-01-19
    Publishing country United States
    Document type Letter ; Comment
    ZDB-ID 197890-1
    ISSN 1530-0293 ; 0090-3493
    ISSN (online) 1530-0293
    ISSN 0090-3493
    DOI 10.1097/CCM.0000000000005326
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 1261: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
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    Jg. 1995 - 2021: Lesesall (1.OG)
    ab Jg. 2022: Lesesaal (EG)

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  3. Article: Response of Root-Associated Bacterial Communities to Different Degrees of Soft Rot Damage in

    He, Fei

    Frontiers in microbiology

    2021  Volume 12, Page(s) 652758

    Abstract: Bacterial soft rot is a destructive disease that restricts the development of the konjac ( ...

    Abstract Bacterial soft rot is a destructive disease that restricts the development of the konjac (
    Language English
    Publishing date 2021-07-08
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2587354-4
    ISSN 1664-302X
    ISSN 1664-302X
    DOI 10.3389/fmicb.2021.652758
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article ; Online: Graph Neural Network-Based EEG Classification: A Survey.

    Klepl, Dominik / Wu, Min / He, Fei

    IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society

    2024  Volume 32, Page(s) 493–503

    Abstract: Graph neural networks (GNN) are increasingly used to classify EEG for tasks such as emotion recognition, motor imagery and neurological diseases and disorders. A wide range of methods have been proposed to design GNN-based classifiers. Therefore, there ... ...

    Abstract Graph neural networks (GNN) are increasingly used to classify EEG for tasks such as emotion recognition, motor imagery and neurological diseases and disorders. A wide range of methods have been proposed to design GNN-based classifiers. Therefore, there is a need for a systematic review and categorisation of these approaches. We exhaustively search the published literature on this topic and derive several categories for comparison. These categories highlight the similarities and differences among the methods. The results suggest a prevalence of spectral graph convolutional layers over spatial. Additionally, we identify standard forms of node features, with the most popular being the raw EEG signal and differential entropy. Our results summarise the emerging trends in GNN-based approaches for EEG classification. Finally, we discuss several promising research directions, such as exploring the potential of transfer learning methods and appropriate modelling of cross-frequency interactions.
    MeSH term(s) Humans ; Emotions ; Entropy ; Learning ; Neural Networks, Computer ; Electroencephalography
    Language English
    Publishing date 2024-01-25
    Publishing country United States
    Document type Journal Article
    ZDB-ID 1166307-8
    ISSN 1558-0210 ; 1063-6528 ; 1534-4320
    ISSN (online) 1558-0210
    ISSN 1063-6528 ; 1534-4320
    DOI 10.1109/TNSRE.2024.3355750
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 4726: Show issues Location:
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    Jg. 1995 - 2021: Lesesall (2.OG)
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  5. Article ; Online: Information Geometry Theoretic Measures for Characterizing Neural Information Processing from Simulated EEG Signals.

    Hua, Jia-Chen / Kim, Eun-Jin / He, Fei

    Entropy (Basel, Switzerland)

    2024  Volume 26, Issue 3

    Abstract: In this work, we explore information geometry theoretic measures for characterizing neural information processing from EEG signals simulated by stochastic nonlinear coupled oscillator models for both healthy subjects and Alzheimer's disease (AD) patients ...

    Abstract In this work, we explore information geometry theoretic measures for characterizing neural information processing from EEG signals simulated by stochastic nonlinear coupled oscillator models for both healthy subjects and Alzheimer's disease (AD) patients with both eyes-closed and eyes-open conditions. In particular, we employ information rates to quantify the time evolution of probability density functions of simulated EEG signals, and employ causal information rates to quantify one signal's instantaneous influence on another signal's information rate. These two measures help us find significant and interesting distinctions between healthy subjects and AD patients when they open or close their eyes. These distinctions may be further related to differences in neural information processing activities of the corresponding brain regions, and to differences in connectivities among these brain regions. Our results show that information rate and causal information rate are superior to their more traditional or established information-theoretic counterparts, i.e., differential entropy and transfer entropy, respectively. Since these novel, information geometry theoretic measures can be applied to experimental EEG signals in a model-free manner, and they are capable of quantifying non-stationary time-varying effects, nonlinearity, and non-Gaussian stochasticity presented in real-world EEG signals, we believe that they can form an important and powerful tool-set for both understanding neural information processing in the brain and the diagnosis of neurological disorders, such as Alzheimer's disease as presented in this work.
    Language English
    Publishing date 2024-02-28
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2014734-X
    ISSN 1099-4300 ; 1099-4300
    ISSN (online) 1099-4300
    ISSN 1099-4300
    DOI 10.3390/e26030213
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  6. Article: Integrating Protein Structure Prediction and Bayesian Optimization for Peptide Design.

    Manshour, Negin / He, Fei / Wang, Duolin / Xu, Dong

    Research square

    2024  

    Abstract: Peptide design, with the goal of identifying peptides possessing unique biological properties, stands as a crucial challenge in peptide-based drug discovery. While traditional and computational methods have made significant strides, they often encounter ... ...

    Abstract Peptide design, with the goal of identifying peptides possessing unique biological properties, stands as a crucial challenge in peptide-based drug discovery. While traditional and computational methods have made significant strides, they often encounter hurdles due to the complexities and costs of laboratory experiments. Recent advancements in deep learning and Bayesian Optimization have paved the way for innovative research in this domain. In this context, our study presents a novel approach that effectively combines protein structure prediction with Bayesian Optimization for peptide design. By applying carefully designed objective functions, we guide and enhance the optimization trajectory for new peptide sequences. Benchmarked against multiple native structures, our methodology is tailored to generate new peptides to their optimal potential biological properties.
    Language English
    Publishing date 2024-03-11
    Publishing country United States
    Document type Preprint
    DOI 10.21203/rs.3.rs-4045284/v1
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  7. Article ; Online: Molecular level toxicity effects of As(V) on Folsomia candida: Integrated transcriptomics and metabolomics analyses.

    Lin, Xianglong / Wang, Weiran / He, Fei / Hou, Hong / Guo, Fei

    The Science of the total environment

    2024  Volume 922, Page(s) 171409

    Abstract: Arsenic (As) is a widespread metalloid with well-known toxicity. To date, numerous studies have focused on individual level toxicity (e.g., growth and reproduction) of As to typical invertebrate springtails in soils, however, the molecular level toxicity ...

    Abstract Arsenic (As) is a widespread metalloid with well-known toxicity. To date, numerous studies have focused on individual level toxicity (e.g., growth and reproduction) of As to typical invertebrate springtails in soils, however, the molecular level toxicity and mechanism was poorly understood. Here, an integrated transcriptomics and metabolomics approach was used to reveal responses of Folsomia candida exposed to As(V) of 10 and 60 mg kg
    MeSH term(s) Animals ; Arsenic/metabolism ; Soil Pollutants/metabolism ; Arthropods ; Gene Expression Profiling ; Metabolomics ; Chromatin/metabolism ; Peptide Hydrolases/metabolism ; Chitin/metabolism ; Soil/chemistry
    Chemical Substances Arsenic (N712M78A8G) ; Soil Pollutants ; Chromatin ; Peptide Hydrolases (EC 3.4.-) ; Chitin (1398-61-4) ; Soil
    Language English
    Publishing date 2024-03-01
    Publishing country Netherlands
    Document type Journal Article
    ZDB-ID 121506-1
    ISSN 1879-1026 ; 0048-9697
    ISSN (online) 1879-1026
    ISSN 0048-9697
    DOI 10.1016/j.scitotenv.2024.171409
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 949: 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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  8. Article: Application of standardized management and effect evaluation of chronic obstructive pulmonary disease patients using the big data center of the Internet of Things.

    Chen, Xiaoping / He, Fei / Jiang, Yan / Chen, Xuezhen / Yan, Yubing

    Digital health

    2024  Volume 10, Page(s) 20552076241237706

    Abstract: Objective: Early detection, diagnosis, treatment and management of chronic obstructive pulmonary disease can lower morbidity and perhaps mortality. This study aimed to evaluate the effect of the application of standardized management against the ... ...

    Abstract Objective: Early detection, diagnosis, treatment and management of chronic obstructive pulmonary disease can lower morbidity and perhaps mortality. This study aimed to evaluate the effect of the application of standardized management against the background of the rapid development of the big data center of modern internet of things technology.
    Methods: Participants ≥40 years of age with chronic obstructive pulmonary disease presenting at Xiamen Medical College Affiliated Haicang Hospital from October 2019 to October 2020 were selected as the observation patients based on the Internet of Things big data center for chronic obstructive pulmonary disease standardized management, and control patients from the community were selected for without down to the chronic obstructive pulmonary disease standardized management. Follow-up after 2 years of patient health records and acute episodes using the World Health Organization Quality of Life Questionnaire-Brief version to evaluate the quality of life of the two groups revealed differences.
    Results: The results of comparative analysis of the number of acute attacks before and after follow-up in the observation and control groups after propensity score matching showed that the decrease in acute episodes before and after in the observation group was significant compared with that in the control group (
    Conclusion: In this study, we analyzed the application of modern internet of things technology in the management of chronic obstructive pulmonary disease patients, discussed the effect of standardized management, and promoted the self-management of chronic obstructive pulmonary disease patients. The effectiveness and continuity of the standardized management model for chronic obstructive pulmonary disease implemented in Xiamen city based on the internet of things big data center were considered true and effective.
    Language English
    Publishing date 2024-03-15
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2819396-9
    ISSN 2055-2076
    ISSN 2055-2076
    DOI 10.1177/20552076241237706
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  9. Article ; Online: Exploring Bitter and Sweet: The Application of Large Language Models in Molecular Taste Prediction.

    Song, Renxiu / Liu, Kaifeng / He, Qizheng / He, Fei / Han, Weiwei

    Journal of chemical information and modeling

    2024  

    Abstract: The perception of bitter and sweet tastes is a crucial aspect of human sensory experience. Concerns over the long-term use of aspartame, a widely used sweetener suspected of carcinogenic risks, highlight the importance of developing new taste modifiers. ... ...

    Abstract The perception of bitter and sweet tastes is a crucial aspect of human sensory experience. Concerns over the long-term use of aspartame, a widely used sweetener suspected of carcinogenic risks, highlight the importance of developing new taste modifiers. This study utilizes Large Language Models (LLMs) such as GPT-3.5 and GPT-4 for predicting molecular taste characteristics, with a focus on the bitter-sweet dichotomy. Employing random and scaffold data splitting strategies, GPT-4 demonstrated superior performance, achieving an impressive 86% accuracy under scaffold partitioning. Additionally, ChatGPT was employed to extract specific molecular features associated with bitter and sweet tastes. Utilizing these insights, novel molecular compounds with distinct taste profiles were successfully generated. These compounds were validated for their bitter and sweet properties through molecular docking and molecular dynamics simulation, and their practicality was further confirmed by ADMET toxicity testing and DeepSA synthesis feasibility. This research highlights the potential of LLMs in predicting molecular properties and their implications in health and chemical science.
    Language English
    Publishing date 2024-05-07
    Publishing country United States
    Document type Journal Article
    ZDB-ID 190019-5
    ISSN 1549-960X ; 0095-2338
    ISSN (online) 1549-960X
    ISSN 0095-2338
    DOI 10.1021/acs.jcim.4c00681
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    Zs.A 1230: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
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  10. Article ; Online: The neutrophil-to-lymphocyte ratio is associated with the frequency of delayed neurologic sequelae in patients with carbon monoxide poisoning.

    Xu, Dawei / Mei, Tianshu / He, Fei

    Scientific reports

    2023  Volume 13, Issue 1, Page(s) 19706

    Abstract: Delayed neurologic sequelae (DNS) is a common complication in patients with carbon monoxide poisoning (COP). We aimed to investigate the association of the neutrophil-to-lymphocyte ratio (NLR) with the frequency of DNS in COP patients. A total of 371 COP ...

    Abstract Delayed neurologic sequelae (DNS) is a common complication in patients with carbon monoxide poisoning (COP). We aimed to investigate the association of the neutrophil-to-lymphocyte ratio (NLR) with the frequency of DNS in COP patients. A total of 371 COP patients were investigated in retrospective and prospective studies. A receiver operator curve (ROC) test was performed to evaluate the ability of the NLR to predict DNS in COP patients. The retrospective study included 288 COP patients, of whom 84 (29.2%) were confirmed to have DNS, and 1 (0.3%) died within 28 days. The NLR in the DNS group was significantly higher than that in the non-DNS group (6.84 [4.22-12.43] vs. 3.23 [1.91-5.60] × 10
    MeSH term(s) Humans ; Carbon Monoxide Poisoning/complications ; Retrospective Studies ; Prospective Studies ; Neutrophils ; Lymphocytes ; ROC Curve
    Language English
    Publishing date 2023-11-11
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2615211-3
    ISSN 2045-2322 ; 2045-2322
    ISSN (online) 2045-2322
    ISSN 2045-2322
    DOI 10.1038/s41598-023-47214-5
    Database MEDical Literature Analysis and Retrieval System OnLINE

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