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  1. Article ; Online: Dyeing and UV Protective Properties of Chitosan-Modified Cotton Fabric Treated with Black Rice Extract

    Ke Li / Xiaowen Li / Yawei Li / Chang Wu

    Journal of Natural Fibers, Vol 20, Iss

    2023  Volume 1

    Abstract: Black rice is known as a health-promoting food for its abundant content of anthocyanins. The main objective of this paper is to get functional and eco-friendly materials dyed with black rice extract. In this research work, chitosan-modified cotton fabric ...

    Abstract Black rice is known as a health-promoting food for its abundant content of anthocyanins. The main objective of this paper is to get functional and eco-friendly materials dyed with black rice extract. In this research work, chitosan-modified cotton fabric was dyed with the black rice extract, and the fabric’s CIELab color characteristic values (L*, a*, b*, C*), color strength (K/S) value, and UPF value were investigated closely. The K/S value and UPF value of dyed samples depend on temperature, time, and pH. It is worth noting that the acid medium favored the dyeing to obtain a purple-red color and achieve a larger K/S value and UPF value. The results showed that chitosan-modified cotton fabric dyed with black rice extract had good UV resistance and color fastness.
    Keywords cotton fabrics ; chitosan ; black rice extract ; dyeing ; color strength ; anti-ultraviolet property ; Science ; Q ; Textile bleaching ; printing ; etc ; TP890-933
    Subject code 660
    Language English
    Publishing date 2023-04-01T00:00:00Z
    Publisher Taylor & Francis Group
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Article ; Online: Optimizing the evaluation of gene-targeted panels for tumor mutational burden estimation

    Yawei Li / Yuan Luo

    Scientific Reports, Vol 11, Iss 1, Pp 1-

    2021  Volume 11

    Abstract: Abstract Though whole exome sequencing (WES) is the gold-standard for measuring tumor mutational burden (TMB), the development of gene-targeted panels enables cost-effective TMB estimation. With the growing number of panels in clinical trials, developing ...

    Abstract Abstract Though whole exome sequencing (WES) is the gold-standard for measuring tumor mutational burden (TMB), the development of gene-targeted panels enables cost-effective TMB estimation. With the growing number of panels in clinical trials, developing a statistical method to effectively evaluate and compare the performance of different panels is necessary. The mainstream method uses R-squared value to measure the correlation between the panel-based TMB and WES-based TMB. However, the performance of a panel is usually overestimated via R-squared value based on the long-tailed TMB distribution of the dataset. Herein, we propose angular distance, a measurement used to compute the extent of the estimated bias. Our extensive in silico analysis indicates that the R-squared value reaches a plateau after the panel size reaches 0.5 Mb, which does not adequately characterize the performance of the panels. In contrast, the angular distance is still sensitive to the changes in panel sizes when the panel size reaches 6 Mb. In particular, R-squared values between the hypermutation-included dataset and the non-hypermutation dataset differ widely across many cancer types, whereas the angular distances are highly consistent. Therefore, the angular distance is more objective and logical than R-squared value for evaluating the accuracy of TMB estimation for gene-targeted panels.
    Keywords Medicine ; R ; Science ; Q
    Subject code 310
    Language English
    Publishing date 2021-10-01T00:00:00Z
    Publisher Nature Portfolio
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  3. Article ; Online: Effect of the Cross-Section of a Porous Burner on the Combustion Stability Limit of Premixed Oxy-Methane Flames

    Mingjian Liao / Zhu He / Xiong Liang / Yawei Li / Xuecheng Xu

    ACS Omega, Vol 8, Iss 50, Pp 48258-

    2023  Volume 48268

    Keywords Chemistry ; QD1-999
    Language English
    Publishing date 2023-12-01T00:00:00Z
    Publisher American Chemical Society
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  4. Article ; Online: High-Performance of Electrocatalytic CO 2 Reduction on Defective Graphene-Supported Cu 4 S 2 Cluster

    Qiyan Zhang / Yawei Li / Haiyan Zhu / Bingbing Suo

    Catalysts, Vol 12, Iss 454, p

    2022  Volume 454

    Abstract: Electrochemical CO 2 reduction reaction (CO 2 RR) to high-value chemicals is one of the most splendid approaches to mitigating environmental threats and energy shortage. In this study, the catalytic performance of CO 2 RR on defective graphene-supported ... ...

    Abstract Electrochemical CO 2 reduction reaction (CO 2 RR) to high-value chemicals is one of the most splendid approaches to mitigating environmental threats and energy shortage. In this study, the catalytic performance of CO 2 RR on defective graphene-supported Cu 4 S 2 clusters as well as isolated Cu 4 X n (X = O, S, Se; n = 2, 4) was systematically investigated based on density functional theory (DFT) computations. Calculation results revealed that the most thermodynamically feasible product is CH 3 OH among the C1 products on Cu 4 X 2 clusters, in which the Cu 4 S 2 cluster has the best activity concerning CH 3 OH synthesis with a limiting potential of −0.48 V. When the Cu 4 S 2 cluster was further supported on defective graphene, the strong interaction between cluster and substrate could greatly improve the performance via tuning the electronic structure and improving the stability of the Cu 4 S 2 cluster. The calculated free energy diagram indicated that it is also more energetically preferable for CH 3 OH production with a low limiting potential of −0.35 V. Besides, the defective graphene support has a significant ability to suppress the competing reactions, such as the hydrogen evolution reaction (HER) and CO and HCOOH production. Geometric structures, limiting potentials, and reduction pathways were also discussed to gain insight into the reaction mechanism and to find the minimum-energy pathway for C1 products. We hope this work will provide theoretical reference for designing and developing advanced supported Cu-based electrocatalysts for CO 2 reduction.
    Keywords electrochemical CO 2 reduction reaction ; DFT calculation ; Cu 4 X 2 nanocluster ; defective graphene-supported ; Chemical technology ; TP1-1185 ; Chemistry ; QD1-999
    Subject code 290
    Language English
    Publishing date 2022-04-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  5. Article ; Online: Statistical and machine learning methods for spatially resolved transcriptomics data analysis

    Zexian Zeng / Yawei Li / Yiming Li / Yuan Luo

    Genome Biology, Vol 23, Iss 1, Pp 1-

    2022  Volume 23

    Abstract: Abstract The recent advancement in spatial transcriptomics technology has enabled multiplexed profiling of cellular transcriptomes and spatial locations. As the capacity and efficiency of the experimental technologies continue to improve, there is an ... ...

    Abstract Abstract The recent advancement in spatial transcriptomics technology has enabled multiplexed profiling of cellular transcriptomes and spatial locations. As the capacity and efficiency of the experimental technologies continue to improve, there is an emerging need for the development of analytical approaches. Furthermore, with the continuous evolution of sequencing protocols, the underlying assumptions of current analytical methods need to be re-evaluated and adjusted to harness the increasing data complexity. To motivate and aid future model development, we herein review the recent development of statistical and machine learning methods in spatial transcriptomics, summarize useful resources, and highlight the challenges and opportunities ahead.
    Keywords Biology (General) ; QH301-705.5 ; Genetics ; QH426-470
    Language English
    Publishing date 2022-03-01T00:00:00Z
    Publisher BMC
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  6. Article ; Online: Incorporating Transformers and Attention Networks for Stock Movement Prediction

    Yawei Li / Shuqi Lv / Xinghua Liu / Qiuyue Zhang

    Complexity, Vol

    2022  Volume 2022

    Abstract: Predicting stock movements is a valuable research field that can help investors earn more profits. As with time-series data, the stock market is time-dependent and the value of historical information may decrease over time. Accurate prediction can be ... ...

    Abstract Predicting stock movements is a valuable research field that can help investors earn more profits. As with time-series data, the stock market is time-dependent and the value of historical information may decrease over time. Accurate prediction can be achieved by mining valuable information with words on social platforms and further integrating it with actual stock market conditions. However, many methods still cannot effectively dig deep into hidden information, integrate text and stock prices, and ignore the temporal dependence. Therefore, to solve the above problems, we propose a transformer-based attention network framework that uses historical text and stock prices to capture the temporal dependence of financial data. Among them, the transformer model and attention mechanism are used for feature extraction of financial data, which has fewer applications in the financial field, and effective analysis of key information to achieve an accurate prediction. A large number of experiments have proved the effectiveness of our proposed method. The actual simulation experiment verifies that our model has practical application value.
    Keywords Electronic computers. Computer science ; QA75.5-76.95
    Subject code 332
    Language English
    Publishing date 2022-01-01T00:00:00Z
    Publisher Hindawi-Wiley
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  7. Article ; Online: Integrated Optimization of Stop Planning and Timetabling for Demand-Responsive Transport in High-Speed Railways

    Yawei Li / Baoming Han / Ruixia Yang / Peng Zhao

    Applied Sciences, Vol 13, Iss 1, p

    2022  Volume 551

    Abstract: The high-speed railways have made rapid developments in recent years. Fulfilling passenger demand and providing precise train services are the core problems to be solved in railway operation. This paper proposes an optimization strategy for demand- ... ...

    Abstract The high-speed railways have made rapid developments in recent years. Fulfilling passenger demand and providing precise train services are the core problems to be solved in railway operation. This paper proposes an optimization strategy for demand-responsive transport to integrate train-stop planning and timetabling in high-speed railways. Passenger travel information, including their origins, destinations and expected departure times is taken as input. A mixed integer linear programming model is established to obtain an effective service plan, which consists of train stop pattern, passenger ride plan and train arrival/departure times at all stations. The optimization objective is to minimize the remaining passenger demand and train travel time. Finally, the proposed method is applied to a real-world case, and a series of several experiments are conducted to prove the efficiency and validity of the proposed model. The results suggest that the proposed approach could generate efficient service plans which are responsive to passenger demand.
    Keywords high-speed railway ; demand-responsive ; stop planning ; timetabling ; integrated optimization ; Technology ; T ; Engineering (General). Civil engineering (General) ; TA1-2040 ; Biology (General) ; QH301-705.5 ; Physics ; QC1-999 ; Chemistry ; QD1-999
    Subject code 380
    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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  8. Article ; Online: The effects of sport expertise and shot results on basketball players' action anticipation.

    Yawei Li / Tian Feng

    PLoS ONE, Vol 15, Iss 1, p e

    2020  Volume 0227521

    Abstract: The purpose of the present cross-sectional study was to clarify the effects of sport expertise and shot results on the action anticipation of basketball players. Eighty-eight male subjects participated in this study, namely, 30 collegiate basketball ... ...

    Abstract The purpose of the present cross-sectional study was to clarify the effects of sport expertise and shot results on the action anticipation of basketball players. Eighty-eight male subjects participated in this study, namely, 30 collegiate basketball players, 28 recreational basketball players and 30 non-athletes. Each participant performed a shot anticipation task in which he watched the shooting phase, rising phase, high point and falling phase of a free throw and predicted the fate of the ball. The results showed that the collegiate players and recreational players demonstrated higher accuracy than the non-athletes for the falling phase but not for the other temporal conditions. Analysis of the shot results demonstrated that for made shots, the collegiate players and recreational players provided more accurate predictions than the non-athletes. These results suggested that the experienced players required a sufficient amount of information to be able to make accurate judgements and demonstrated that the experts' judgement bias for made shots was independent of the temporal condition.
    Keywords Medicine ; R ; Science ; Q
    Subject code 796
    Language English
    Publishing date 2020-01-01T00:00:00Z
    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: Integrated Optimization of Rolling Stock Scheduling and Flexible Train Formation Based on Passenger Demand for an Intercity High-Speed Railway

    Peng Zhao / Yawei Li / Baoming Han / Ruixia Yang / Zhiping Liu

    Sustainability, Vol 14, Iss 5650, p

    2022  Volume 5650

    Abstract: The key features of an intercity high-speed railway (IHSR) include its high frequency, the short intervals, and the short distances covered. The mode of rolling stock scheduling generally uses fixed segments. In view of the fact that intercity passenger ... ...

    Abstract The key features of an intercity high-speed railway (IHSR) include its high frequency, the short intervals, and the short distances covered. The mode of rolling stock scheduling generally uses fixed segments. In view of the fact that intercity passenger demand has the characteristics of large fluctuations in terms of time and direction, the use of the traditional rolling stock scheduling plan with a fixed train formation will result in a mismatch between the train formation and passenger demand. In order to improve the matching of train formation and passenger demand and increase the utilization rate of rolling stocks, this paper puts forward the concept of flexible train formation by time period and constructs an integrated optimization model of the rolling stock scheduling and flexible train formation based on passenger demand. The model aims at minimizing the number of rolling stocks, the amount of coupling/decoupling necessary, and the deadhead time. The model takes into account constraints such as the connection method used, the source and destination of the rolling stock, the total amount of rolling stock, and the use of a flexible train formation. In addition, the Gurobi solver is used to accurately solve the problem through the linearization of the model. This paper also provides an example of the Beijing–Tianjin IHSR as a verification of the feasibility and effectiveness of the proposed model. The example compares the indicators in the fixed and flexible train formation modes. The results of the research show that, on the premise of meeting passenger demand, the flexible train formation mode can reduce the cost of rolling stock; increase the efficiency of rolling stock; improve the balance of rolling stock scheduling; and, consequently, provide a reference for the optimization of rolling stock scheduling plan with the background of “cost reduction and efficiency increase” in the railway industry.
    Keywords flexible train formation by time period ; passenger demand ; rolling stock scheduling plan ; fixed train formation ; flexible train formation ; turnaround and connection mode ; Environmental effects of industries and plants ; TD194-195 ; Renewable energy sources ; TJ807-830 ; Environmental sciences ; GE1-350
    Subject code 380
    Language English
    Publishing date 2022-05-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  10. Article ; Online: Machine Learning for Lung Cancer Diagnosis, Treatment, and Prognosis

    Yawei Li / Xin Wu / Ping Yang / Guoqian Jiang / Yuan Luo

    Genomics, Proteomics & Bioinformatics, Vol 20, Iss 5, Pp 850-

    2022  Volume 866

    Abstract: The recent development of imaging and sequencing technologies enables systematic advances in the clinical study of lung cancer. Meanwhile, the human mind is limited in effectively handling and fully utilizing the accumulation of such enormous amounts of ... ...

    Abstract The recent development of imaging and sequencing technologies enables systematic advances in the clinical study of lung cancer. Meanwhile, the human mind is limited in effectively handling and fully utilizing the accumulation of such enormous amounts of data. Machine learning-based approaches play a critical role in integrating and analyzing these large and complex datasets, which have extensively characterized lung cancer through the use of different perspectives from these accrued data. In this review, we provide an overview of machine learning-based approaches that strengthen the varying aspects of lung cancer diagnosis and therapy, including early detection, auxiliary diagnosis, prognosis prediction, and immunotherapy practice. Moreover, we highlight the challenges and opportunities for future applications of machine learning in lung cancer.
    Keywords Omics dataset ; Imaging dataset ; Feature extraction ; Prediction ; Immunotherapy ; Biology (General) ; QH301-705.5
    Subject code 006 ; 610
    Language English
    Publishing date 2022-10-01T00:00:00Z
    Publisher Elsevier
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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