This dataset contains the digitized treatments in Plazi based on the original journal article Zhong, Qian-Qian, Wang, Ze-Huan, Xu, Jia-Ju, Sun, Qin-Wen (2023): Melanoseris kangdingensis (Lactucinae, Cichorieae, Asteraceae), a new species reported from western Sichuan, China. PhytoKeys 236: 29-37, DOI: http://dx.doi.org/10.3897/phytokeys.236.113401, URL: http://dx.doi.org/10.3897/phytokeys.236.113401
Purpose: The purpose of this study is to explore the independent-influencing factors from normal people to prediabetes and from prediabetes to diabetes and use different prediction models to build diabetes prediction models. Methods: The original data in this retrospective study are collected from the participants who took physical examinations in the Health Management Center of Peking University Shenzhen Hospital. Regression analysis is individually applied between the populations of normal and prediabetes, as well as the populations of prediabetes and diabetes, for feature selection. Afterward,the independent influencing factors mentioned above are used as predictive factors to construct a prediction model. Results: Selecting physical examination indicators for training different ML models through univariate and multivariate logistic regression, the study finds Age, PRO, TP, and ALT are four independent risk factors for normal people to develop prediabetes, and GLB and HDL.C are two independent protective factors, while logistic regression performs best on the testing set (Acc: 0.76, F-measure: 0.74, AUC: 0.78). We also find Age, Gender, BMI, SBP, U.GLU, PRO, ALT, and TG are independent risk factors for prediabetes people to diabetes, and AST is an independent protective factor, while logistic regression performs best on the testing set (Acc: 0.86, F-measure: 0.84, AUC: 0.74). Conclusion: The discussion of the clinical relationships between these indicators and diabetes supports the interpretability of our feature selection. Among four prediction models, the logistic regression model achieved the best performance on the testing set. Keywords: prediabetes, prediction model, physical examination, machine learning, regression analysis
Through mechanochemical syntheses, hybrid manganese halides were prepared displaying green emissions with highest PLQY of 79.5%. A relationship between structure and PLQY was established as a method to optimize the PLQY of hybrid metal halides.
Abstract Indium selenide (InSe) has attracted tremendous research interest due to its excellent optical and electronic properties. The direct bandgap of bulk InSe promises efficient carrier recombination in the near‐infrared (NIR) spectral range, holding great potential for NIR‐based optoelectronic device applications. However, the lowest energy transition in InSe involves out‐of‐plane optical dipoles, with resulting photoluminescence (PL) transmitted mainly along the layer plane. This limits both optical excitation and detection efficiency along the surface normal for practical device operation. To circumvent this issue, here, bulk InSe flake is coupled with circular Bragg grating (CBG) fabricated on silicon substrate by focused ion beam milling. A maximal 60‐fold PL enhancement is achieved, with Purcell effect‐facilitated carrier recombination and improved light out‐coupling via CBG contributing jointly. The same device architecture is further demonstrated to boost the second harmonic generation by a factor of 34. The results provide an effective way to enhance the optical performance of III–VI layered nanomaterials for both linear and nonlinear optoelectronic device applications.
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