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111.
Introduction Rapid adaptation to environmental challenges is vitally important for bacterial survival and growth. One way in which bacteria control their response to changing environmental conditions is through the mechanism of two-component signal transd… 相似文献
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Rice blast is a serious threat to rice yield. Breeding disease-resistant varieties is one of the most economical and effective ways to prevent damage from rice blast. The traditional identification of resistant rice seeds has some shortcoming, such as long possession time, high cost and complex operation. The purpose of this study was to develop an optimal prediction model for determining resistant rice seeds using Ranman spectroscopy. First, the support vector machine (SVM), BP neural network (BP) and probabilistic neural network (PNN) models were initially established on the original spectral data. Second, due to the recognition accuracy of the Raw-SVM model, the running time was fast. The support vector machine model was selected for optimization, and four improved support vector machine models (ABC-SVM (artificial bee colony algorithm, ABC), IABC-SVM (improving the artificial bee colony algorithm, IABC), GSA-SVM (gravity search algorithm, GSA) and GWO-SVM (gray wolf algorithm, GWO)) were used to identify resistant rice seeds. The difference in modeling accuracy and running time between the improved support vector machine model established in feature wavelengths and full wavelengths (200–3202 cm−1) was compared. Finally, five spectral preproccessing algorithms, Savitzky–Golay 1-Der (SGD), Savitzky–Golay Smoothing (SGS), baseline (Base), multivariate scatter correction (MSC) and standard normal variable (SNV), were used to preprocess the original spectra. The random forest algorithm (RF) was used to extract the characteristic wavelengths. After different spectral preproccessing algorithms and the RF feature extraction, the improved support vector machine models were established. The results show that the recognition accuracy of the optimal IABC-SVM model based on the original data was 71%. Among the five spectral preproccessing algorithms, the SNV algorithm’s accuracy was the best. The accuracy of the test set in the IABC-SVM model was 100%, and the running time was 13 s. After SNV algorithms and the RF feature extraction, the classification accuracy of the IABC-SVM model did not decrease, and the running time was shortened to 9 s. This demonstrates the feasibility and effectiveness of IABC in SVM parameter optimization, with higher prediction accuracy and better stability. Therefore, the improved support vector machine model based on Ranman spectroscopy can be applied to the fast and non-destructive identification of resistant rice seeds. 相似文献
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为更好地推动制造业智能化转型,智能制造与创新的关系亟待厘清.运用扎根理论方法对双环传动和海尔集团开展探索性研究,剖析智能制造与创新之间的耦合关系.研究发现,智能制造促进产品创新和流程创新;同时创新是智能制造发展的动力,它促进智能产品形成,为智能生产提供重要支撑,推动智能制造基础设施建设以及智能制造模式转变.两者之间相互... 相似文献
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Mengya Kong Yuyang Gu Yingjie Chai Jiaming Ke Yulai Liu Xincheng Xu Zhanxian Li Wei Feng Fuyou Li 《中国科学:化学(英文版)》2021,(6):974-984
Luminescence nanothermometry makes non-invasive and real-time temperature readings possible in living animals. However,the spectral fluctuation in tissues and fluids, as well as the interaction between fluorophores and environment hinders accuracy of the thermometry. Here, we report a luminescence lifetime-based nanothermometry which specifically addresses this problem. A temporal based calibration(lifetime sensing) in the NIR range, an endogenous thermal response as well as a polymer encapsulation evading environmental factors, altogether help to pinpoint temperature in vivo. Thanks to the highly condensed NdYb ions in a well-protected tiny core-shell nanocrystal(overall 11 nm), a temperature sensitivity about 2.07% K~(-1)(with 5% Yb~(3+) doped nanoparticles) and an accuracy of 0.27 K(with 25% Yb~(3+) doped nanoparticles) in biological fluids are achieved.Hopefully, combining thermally activated energy transfer nanothermometer with anti-interference lifetime thermometry would provide a more accurate temperature measurement for biological and preclinical studies. 相似文献
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Understanding the influence of surfactants on the assembly of peptides has a considerable practical motivation. In this paper, we systematically study the anionic surfactant-assisted assembly of diphenylalanine (FF). FF forms broom-like structures in a concentration of sodium cholate (NaC) around the CMC, and assembles into linear and unidirectional rods in the presence of low and high surfactant concentrations. FF’s improved hydrogen bonding and controlled assembly rates are appropriate for other anionic surfactants. At this stage, the use of FF as the simplest protein consequence can be helpful in the investigation of further protein–surfactant interactions. 相似文献
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Yuxin Xiao Hailan Wang Zongliang Xie Mingyao Shen Rongjuan Huang Yuchen Miao Guanyu Liu Tao Yu Wei Huang 《Chemical science》2022,13(31):8906
Near-infrared (NIR) light-emitting materials show excellent potential applications in the fields of military technology, bioimaging, optical communication, organic light-emitting diodes (OLEDs), etc. Recently, thermally activated delayed fluorescence (TADF) emitters have made historic developments in the field of OLEDs. These metal-free materials are more attractive because of efficient reverse intersystem crossing processes which result in promising high efficiencies in OLEDs. However, the development of NIR TADF emitters has progressed at a relatively slower pace which could be ascribed to the difficult promotion of external quantum efficiencies. Thus, increasing attention has been paid to NIR TADF emitters. In this review, the recent progress of NIR TADF emitters has been summarized along with their molecular design strategies and photophysical properties, as well as electroluminescence performance data of their OLEDs, respectively.This review presents the recent progress of NIR TADF emitters along with their molecular design strategies and photophysical properties, as well as the electroluminescence performance data of the emitters and their OLEDs. 相似文献