The Classification of Rice Blast Resistant Seed Based on Ranman Spectroscopy and SVM

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.     

用光学多道分析器测定RH的不确定度评定

本文使用光学多道分析器测定了氢原子光谱的计Hα、Hβ、Hγ、Hδ,谱线波长,利用最小二乘法处理测量数据：建立了测量数学模型,得到氢原子光谱里德伯常量RH,按国家计量技术规范JJF1059—1999标准对测量的不确定度进行了分析与评定。     

Luminescence interference-free lifetime nanothermometry pinpoints in vivo temperature

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.     

埋地热油管道正常运行的数值模拟研究

对高凝原油管道输送的水力热力问题进行分析研究,掌握管线运行规律,保证管线安全经济运行有着重要意义.本文建立了埋地热油管道正常运行的数学模型,采用非结构化网格和有限容积法对该问题进行了研究,计算结果与实验测量值吻合良好.     

Surfactant-Assisted Assembly of Dipeptide Forming a Broom-like Structure

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.     

NIR TADF emitters and OLEDs: challenges,progress, and perspectives

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.     

Synthesis of N-Heterocyclic Carbine Silver(Ⅰ) and Palladium(Ⅱ) Complexes with Acylated Piperazine Linker and Catalytic Activity in Three Types of C-C Coupling Reactions

Two bis-imidazolium salts LH2·Cl2 and LH2·(PF6)2 with acylated piperazine linker and two N-heterocyclic carbene (NHC) silver(Ⅰ) and palladium(Ⅱ) complexes[L2Ag2...     

Structural Investigation of the Interaction Mechanism between Chlorogenic Acid and AMPA Receptor via In Silico Approaches

Chlorogenic acid (CGA), an important metabolite in natural plant medicines such as honeysuckle and eucommia, has been shown to have potent antinociceptive effects. Nevertheless, the mechanism by which CGA relieves chronic pain remains unclear. α-amino-3-hydroxy-5-methyl-4-isooxazolpropionic acid receptor (AMPAR) is a major ionotropic glutamate receptor that mediates rapid excitatory synaptic transmission and its glutamate ionotropic receptor AMPA type subunit 1 (GluA1) plays a key role in nociceptive transmission. In this study, we used Western blot, surface plasmon resonance (SPR) assay, and the molecular simulation technologies to investigate the mechanism of interaction between CGA and AMPAR to relieve chronic pain. Our results indicate that the protein expression level of GluA1 showed a dependent decrease as the concentration of CGA increased (0, 50, 100, and 200 μM). The SPR assay demonstrates that CGA can directly bind to GluA1 (K_D = 496 μM). Furthermore, CGA forms a stable binding interaction with GluA1, which is validated by molecular dynamics (MD) simulation. The binding free energy between CGA and GluA1 is −39.803 ± 14.772 kJ/mol, where van der Waals interaction and electrostatic interaction are the major contributors to the GluA1–CGA binding, and the key residues are identified (Val-32, Glu-33, Ala-36, Glu-37, Leu-48), which play a crucial role in the binding interaction. This study first reveals the structural basis of the stable interaction between CGA and GluA1 to form a binding complex for the relief of chronic pain. The research provides the structural basis to understand the treatment of chronic pain and is valuable to the design of novel drug molecules in the future.