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  1. Sze-Yin, S., Lai-Hoong, C.
    MyJurnal
    The objective of this work was to study the effects of trehalose and maltodextrin on Chinese
    steamed bread (CSB) prepared from frozen dough. Trehalose (0.1 and 0.2% w/w) and
    maltodextrin (1 and 2% w/w) were added and CSB prepared from the fresh dough and the
    frozen dough was characterized in terms of spread ratio, specific volume, staling index and
    stress relaxation properties. Upon frozen storage, spread ratio and specific volume of CSB,
    and elasticity of the bread crumb were reduced. The extend of deterioration was significantly
    reduced with the addition of 0.1% trehalose and 2% maltodextrin. Excessive addition of
    trehalose and maltodextrin was found to cause detrimental effects to CSB quality.
  2. Hermawan A, Amrillah T, Riapanitra A, Ong WJ, Yin S
    Adv Healthc Mater, 2021 10;10(20):e2100970.
    PMID: 34318999 DOI: 10.1002/adhm.202100970
    A fully integrated, flexible, and functional sensing device for exhaled breath analysis drastically transforms conventional medical diagnosis to non-invasive, low-cost, real-time, and personalized health care. 2D materials based on MXenes offer multiple advantages for accurately detecting various breath biomarkers compared to conventional semiconducting oxides. High surface sensitivity, large surface-to-weight ratio, room temperature detection, and easy-to-assemble structures are vital parameters for such sensing devices in which MXenes have demonstrated all these properties both experimentally and theoretically. So far, MXenes-based flexible sensor is successfully fabricated at a lab-scale and is predicted to be translated into clinical practice within the next few years. This review presents a potential application of MXenes as emerging materials for flexible and wearable sensor devices. The biomarkers from exhaled breath are described first, with emphasis on metabolic processes and diseases indicated by abnormal biomarkers. Then, biomarkers sensing performances provided by MXenes families and the enhancement strategies are discussed. The method of fabrications toward MXenes integration into various flexible substrates is summarized. Finally, the fundamental challenges and prospects, including portable integration with Internet-of-Thing (IoT) and Artificial Intelligence (AI), are addressed to realize marketization.
  3. Wen D, Cheng Z, Li J, Zheng X, Yao W, Dong X, et al.
    J Neurosci Methods, 2021 Nov 01;363:109353.
    PMID: 34492241 DOI: 10.1016/j.jneumeth.2021.109353
    BACKGROUND: The application of deep learning models to electroencephalogram (EEG) signal classification has recently become a popular research topic. Several deep learning models have been proposed to classify EEG signals in patients with various neurological diseases. However, no effective deep learning model for event-related potential (ERP) signal classification is yet available for amnestic mild cognitive impairment (aMCI) with type 2 diabetes mellitus (T2DM).

    METHOD: This study proposed a single-scale multi-input convolutional neural network (SSMICNN) method to classify ERP signals between aMCI patients with T2DM and the control group. Firstly, the 18-electrode ERP signal on alpha, beta, and theta frequency bands was extracted by using the fast Fourier transform, and then the mean, sum of squares, and absolute value feature of each frequency band were calculated. Finally, these three features are converted into multispectral images respectively and used as the input of the SSMICNN network to realize the classification task.

    RESULTS: The results show that the SSMICNN can fuse MSI formed by different features, SSMICNN enriches the feature quantity of the neural network input layer and has excellent robustness, and the errors of SSMICNN can be simultaneously transmitted to the three convolution channels in the back-propagation phase. Comparison with Existing Method(s): SSMICNN could more effectively identify ERP signals from aMCI with T2DM from the control group compared to existing classification methods, including convolution neural network, support vector machine, and logistic regression.

    CONCLUSIONS: The combination of SSMICNN and MSI can be used as an effective biological marker to distinguish aMCI patients with T2DM from the control group.

  4. Yi X, Yin S, Huang L, Li H, Wang Y, Wang Q, et al.
    Sci Total Environ, 2021 Jun 01;771:144644.
    PMID: 33736175 DOI: 10.1016/j.scitotenv.2020.144644
    Chlorine radical plays an important role in the formation of ozone and secondary aerosols in the troposphere. It is hence important to develop comprehensive emissions inventory of chlorine precursors in order to enhance our understanding of the role of chlorine chemistry in ozone and secondary pollution issues. Based on a bottom-up methodology, this study presents a comprehensive emission inventory for major atomic chlorine precursors in the Yangtze River Delta (YRD) region of China for the year 2017. Four primary chlorine precursors are considered in this study: hydrogen chloride (HCl), fine particulate chloride (Cl-) (Cl- in PM2.5), chlorine gas (Cl2), and hypochlorous acid (HClO) with emissions estimated for twelve source categories. The total emissions of these four species in the YRD region are estimated to be 20,424 t, 15,719 t, 1556 and 9331 t, respectively. The emissions of HCl are substantial, with major emissions from biomass burning and coal combustion, together accounting for 68% of the total HCl emissions. Fine particulate Cl- is mainly emitted from industrial processing, biomass burning and waste incineration. The emissions of Cl2 and HClO are mainly associated with usage of chlorine-containing disinfectants, for example, water treatment, wastewater treatment, and swimming pools. Emissions of each chlorine precursor are spatially allocated based on the characteristics of individual source category. This study provides important basic dataset for further studies with respect to the effects of chlorine chemistry on the formation of air pollution complex in the YRD region.
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