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  1. Article ; Online: Deep learning hybrid model for analyzing and predicting the impact of imported malaria cases from Africa on the rise of Plasmodium falciparum in China before and during the COVID-19 pandemic

    Eric Kamana / Jijun Zhao

    PLoS ONE, Vol 18, Iss

    2023  Volume 12

    Keywords Medicine ; R ; Science ; Q
    Language English
    Publishing date 2023-01-01T00:00:00Z
    Publisher Public Library of Science (PLoS)
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Article ; Online: Deep learning hybrid model for analyzing and predicting the impact of imported malaria cases from Africa on the rise of Plasmodium falciparum in China before and during the COVID-19 pandemic.

    Eric Kamana / Jijun Zhao

    PLoS ONE, Vol 18, Iss 12, p e

    2023  Volume 0287702

    Abstract: Background Plasmodium falciparum cases are rising in China due to the imported malaria cases from African countries. The main goal of this study is to examine the impact of imported malaria cases in African countries on the rise of P. falciparum cases in ...

    Abstract Background Plasmodium falciparum cases are rising in China due to the imported malaria cases from African countries. The main goal of this study is to examine the impact of imported malaria cases in African countries on the rise of P. falciparum cases in China before and during the COVID-19 pandemic. Methods A generalized regression model was used to investigate the association of time trends between imported malaria cases from 45 African countries and P. falciparum cases in 31 provinces of China from 2012 to 2018 before the COVID-19 pandemic and during the COVID-19 pandemic from October 2020 to May 2021. Based on the analysis, we proposed a statistical and deep learning hybrid approach to model the resurgence of malaria in China using monthly data of P. falciparum from 2004 to 2016. This study builds a hybrid model known as the ARIMA-GRU approach for modeling the P. falciparum cases in all provinces of China and the number of malaria deaths in China before and during the COVID-19 pandemic. Results The analysis showed an emerging link between the rise of imported malaria cases from Africa and P. falciparum cases in many provinces of China. Many imported malaria cases from Africa were P. falciparum cases. The proposed deep learning model achieved a high prediction accuracy score on the testing dataset of 96%. Conclusion The study provided an analysis of the reduction of P. falciparum cases and deaths caused by imported P. falciparum cases during the COVID-19 pandemic due to the control measures regarding the limitation of international travel in China. The Chinese government has to prepare the imported malaria control measures after the normalization of international travel, to prevent the resurgence of malaria disease in China.
    Keywords Medicine ; R ; Science ; Q
    Subject code 950
    Language English
    Publishing date 2023-01-01T00:00:00Z
    Publisher Public Library of Science (PLoS)
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  3. Article ; Online: Mechanisms in the Catalytic Reduction of N 2 O by CO over the M 13 @Cu 42 Clusters of Aromatic-like Inorganic and Metal Compounds

    Ziyang Liu / Haifeng Wang / Yan Gao / Jijun Zhao

    Molecules, Vol 28, Iss 4485, p

    2023  Volume 4485

    Abstract: Metal aromatic substances play a unique and important role in both experimental and theoretical aspects, and they have made tremendous progress in the past few decades. The new aromaticity system has posed a significant challenge and expansion to the ... ...

    Abstract Metal aromatic substances play a unique and important role in both experimental and theoretical aspects, and they have made tremendous progress in the past few decades. The new aromaticity system has posed a significant challenge and expansion to the concept of aromaticity. From this perspective, based on spin-polarized density functional theory (DFT) calculations, we systematically investigated the doping effects on the reduction reactions of N 2 O catalyzed by CO for M 13 @Cu 42 (M = Cu, Co, Ni, Zn, Ru, Rh, Pd, Pt) core–shell clusters from aromatic-like inorganic and metal compounds. It was found that compared with the pure Cu 55 cluster, the strong M–Cu bonds provide more structural stability for M 13 @Cu 42 clusters. Electrons that transferred from the M 13 @Cu 42 to N 2 O promoted the activation and dissociation of the N–O bond. Two possible reaction modes of co-adsorption (L-H) and stepwise adsorption (E-R) mechanisms over M 13 @Cu 42 clusters were thoroughly discovered. The results showed that the exothermic phenomenon was accompanied with the decomposition process of N 2 O via L-H mechanisms for all of the considered M 13 @Cu 42 clusters and via E-R mechanisms for most of the M 13 @Cu 42 clusters. Furthermore, the rate-limiting step of the whole reactions for the M 13 @Cu 42 clusters were examined as the CO oxidation process. Our numerical calculations suggested that the Ni 13 @Cu 42 cluster and Co 13 @Cu 42 clusters exhibited superior potential in the reduction reactions of N 2 O by CO; especially, Ni 13 @Cu 42 clusters are highly active, with very low free energy barriers of 9.68 kcal/mol under the L-H mechanism. This work demonstrates that the transition metal core encapsulated M 13 @Cu 42 clusters can present superior catalytic activities towards N 2 O reduction by CO.
    Keywords DFT ; Cu 55 cluster ; aromatic-like inorganic and metal compounds ; Organic chemistry ; QD241-441
    Subject code 290
    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)

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  4. Article ; Online: Bidirectional matching and aggregation network for few-shot relation extraction

    Zhongcheng Wei / Wenjie Guo / Yunping Zhang / Jieying Zhang / Jijun Zhao

    PeerJ Computer Science, Vol 9, p e

    2023  Volume 1272

    Abstract: Few-shot relation extraction is used to solve the problem of long tail distribution of data by matching between query instances and support instances. Existing methods focus only on the single direction process of matching, ignoring the symmetry of the ... ...

    Abstract Few-shot relation extraction is used to solve the problem of long tail distribution of data by matching between query instances and support instances. Existing methods focus only on the single direction process of matching, ignoring the symmetry of the data in the process. To address this issue, we propose the bidirectional matching and aggregation network (BMAN), which is particularly powerful when the training data is symmetrical. This model not only tries to extract relations for query instances, but also seeks relational prototypes about the query instances to validate the feature representation of the support set. Moreover, to avoid overfitting in bidirectional matching, the data enhancement method was designed to scale up the number of instances while maintaining the scope of the instance relation class. Extensive experiments on FewRel and FewRel2.0 public datasets are conducted and evaluate the effectiveness of BMAN.
    Keywords Relation extraction ; Few-shot learning ; Prototypical network ; Knowledge graph ; Long-tail distribution ; Electronic computers. Computer science ; QA75.5-76.95
    Subject code 006
    Language English
    Publishing date 2023-03-01T00:00:00Z
    Publisher PeerJ Inc.
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  5. Article ; Online: Strain softened bending modulus of graphene oxide

    Songding Yu / Lei Jiao / Jijun Zhao / Lizhao Liu

    Carbon Trends, Vol 7, Iss , Pp 100167- (2022)

    2022  

    Abstract: Graphene oxide is attractive in flexible materials due to its biocompatibility. Herein, flexibility of graphene oxide under uniaxial tensile strain has been studied in term of the bending modulus. It is demonstrated that tensile strain plays an effective ...

    Abstract Graphene oxide is attractive in flexible materials due to its biocompatibility. Herein, flexibility of graphene oxide under uniaxial tensile strain has been studied in term of the bending modulus. It is demonstrated that tensile strain plays an effective role in softening the bending modulus of graphene oxide. The strained graphene oxide can be even more flexible than graphene. The mechanism of strain softened bending modulus is discussed where weakened atomic bonding accounts directly for the enhanced flexibility. Particularly, an important factor for softened bending modulus is proposed, i.e. alignment of epoxide groups. The degradation of bending modulus is more prominent when the epoxides are aligned along the bending direction. This work not only proposes an effective method to enhance the flexibility of graphene oxide, but also unveils the mechanism of softened bending modulus, which could be useful in design of highly flexible materials.
    Keywords Graphene oxide ; Tensile strain ; Flexibility ; Bending modulus ; Chemistry ; QD1-999
    Language English
    Publishing date 2022-04-01T00:00:00Z
    Publisher Elsevier
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  6. Article ; Online: Predicting the impact of climate change on the re-emergence of malaria cases in China using LSTMSeq2Seq deep learning model

    Eric Kamana / Jijun Zhao / Di Bai

    BMJ Open, Vol 12, Iss

    a modelling and prediction analysis study

    2022  Volume 3

    Keywords Medicine ; R
    Language English
    Publishing date 2022-03-01T00:00:00Z
    Publisher BMJ Publishing Group
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  7. Article ; Online: Modulated Autophagy by MicroRNAs in Osteoarthritis Chondrocytes

    Yinghao Yu / Jijun Zhao

    BioMed Research International, Vol

    2019  Volume 2019

    Abstract: Osteoarthritis (OA) is a chronic joint disease characterized by articular cartilage regression. The etiology of OA is diverse, the exact pathogenesis of which remains unclear. Autophagy is a conserved maintenance mechanism in eukaryotic cells. ... ...

    Abstract Osteoarthritis (OA) is a chronic joint disease characterized by articular cartilage regression. The etiology of OA is diverse, the exact pathogenesis of which remains unclear. Autophagy is a conserved maintenance mechanism in eukaryotic cells. Dysfunction of chondrocyte autophagy is regarded as a crucial pathogenesis of cartilage degradation in OA. MircoRNAs (miRNAs) are a category of small noncoding RNAs, acting as posttranscriptional modulators that regulate biological processes and cell signaling pathways via target genes. A series of miRNAs are involved in the progression of chondrocyte autophagy and are connected with numerous factors and pathways. This article focuses on the mechanisms of chondrocyte autophagy in OA and reviews the role of miRNA in their modulation. Potentially relevant miRNAs are also discussed in order to provide new directions for future research and improve our understanding of the autophagic network of miRNAs.
    Keywords Medicine ; R
    Language English
    Publishing date 2019-01-01T00:00:00Z
    Publisher Hindawi Limited
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  8. Article ; Online: Analysis and prediction of hand, foot and mouth disease incidence in China using Random Forest and XGBoost.

    Delin Meng / Jun Xu / Jijun Zhao

    PLoS ONE, Vol 16, Iss 12, p e

    2021  Volume 0261629

    Abstract: Hand, foot and mouth disease (HFMD) is an increasingly serious public health problem, and it has caused an outbreak in China every year since 2008. Predicting the incidence of HFMD and analyzing its influential factors are of great significance to its ... ...

    Abstract Hand, foot and mouth disease (HFMD) is an increasingly serious public health problem, and it has caused an outbreak in China every year since 2008. Predicting the incidence of HFMD and analyzing its influential factors are of great significance to its prevention. Now, machine learning has shown advantages in infectious disease models, but there are few studies on HFMD incidence based on machine learning that cover all the provinces in mainland China. In this study, we proposed two different machine learning algorithms, Random Forest and eXtreme Gradient Boosting (XGBoost), to perform our analysis and prediction. We first used Random Forest to examine the association between HFMD incidence and potential influential factors for 31 provinces in mainland China. Next, we established Random Forest and XGBoost prediction models using meteorological and social factors as the predictors. Finally, we applied our prediction models in four different regions of mainland China and evaluated the performance of them. Our results show that: 1) Meteorological factors and social factors jointly affect the incidence of HFMD in mainland China. Average temperature and population density are the two most significant influential factors; 2) Population flux has different delayed effect in affecting HFMD incidence in different regions. From a national perspective, the model using population flux data delayed for one month has better prediction performance; 3) The prediction capability of XGBoost model was better than that of Random Forest model from the overall perspective. XGBoost model is more suitable for predicting the incidence of HFMD in mainland China.
    Keywords Medicine ; R ; Science ; Q
    Subject code 006
    Language English
    Publishing date 2021-01-01T00:00:00Z
    Publisher Public Library of Science (PLoS)
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  9. Article ; Online: Atomic Wires of Transition Metal Chalcogenides

    Chanjuan Shang / Li Fu / Si Zhou / Jijun Zhao

    JACS Au, Vol 1, Iss 2, Pp 147-

    A Family of 1D Materials for Flexible Electronics and Spintronics

    2020  Volume 155

    Keywords Chemistry ; QD1-999
    Language English
    Publishing date 2020-12-01T00:00:00Z
    Publisher American Chemical Society
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  10. Article ; Online: Selective C−C Coupling by Spatially Confined Dimeric Metal Centers

    Yanyan Zhao / Si Zhou / Jijun Zhao

    iScience, Vol 23, Iss 5, Pp - (2020)

    2020  

    Abstract: Summary: Direct conversion of carbon dioxide (CO2) to high-energy fuels and high-value chemicals is a fascinating sustainable strategy. For most of the current electrocatalysts for CO2 reduction, however, multi-carbon products are inhibited by large ... ...

    Abstract Summary: Direct conversion of carbon dioxide (CO2) to high-energy fuels and high-value chemicals is a fascinating sustainable strategy. For most of the current electrocatalysts for CO2 reduction, however, multi-carbon products are inhibited by large overpotentials and low selectivity. Herein, we exploit dispersed 3d transition metal dimers as spatially confined dual reaction centers for selective reduction of CO2 to liquid fuels. Various nitrogenated holey carbon monolayers are shown to be promising templates to stabilize these metal dimers and dictate their electronic structures, allowing precise control of the catalytic activity and product selectivity. By comprehensive first-principles calculations, we screen the suitable transition metal dimers that universally have high activity for ethanol (C2H5OH). Furthermore, remarkable selectivity for C2H5OH against other C1 and C2 products is found for Fe2 dimer anchored on C2N monolayer. The role of electronic coupling between the metal dimer and the carbon substrates is thoroughly elucidated.
    Keywords Catalysis ; Atomic Electronic Structure ; Energy Sustainability ; Numerical Method in Materials Science ; Science ; Q
    Subject code 540
    Language English
    Publishing date 2020-05-01T00:00:00Z
    Publisher Elsevier
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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