Characterization of Roasting Time on Sensory Quality,Color, Taste,and Nonvolatile Compounds of Yuan An Yellow Tea

Roasting is crucial for producing Yuan An yellow tea (YAYT) as it substantially affects sensory quality. However, the effect of roasting time on YAYT flavor quality is not clear. To investigate the effect of roasting time on the sensory qualities, chemical components, odor profiles, and metabolic profile of YAYTs produced with 13 min roasting, 16 min roasting, 19 min roasting, 22 min roasting, and 25 min roasting were determined. The YAYTs roasted for 22 min got higher sensory scores and better chemical qualities, such as the content of gallocatechin (GC), gallocatechin gallate (GCG), free amino acids, solutable sugar, meanwhile the lightness decreased, the hue of tea brew color (b) increased, which meant the tea brew got darker and yellower. YAYTs roasted for 22 min also increased the contents of key odorants, such as benzaldehyde, nonanal, β-cyclocitral, linalool, nerol, α-cedrol, β-ionone, limonene, 2-methylfuran, indole, and longiborneol. Moreover, non-targeted metabolomics identified up to 14 differentially expressed metabolites through pair-wise comparisons, such as flavonoids, phenolic acids, sucrose, and critical metabolites, which were the main components corresponding to YAYT roasted for 22 min. In summary, the current results provide scientific guidance for the production of high quality YAYT.     

Cloning, Expression and Purification of Wheat Acetyl-CoA Carboxylases CT Domain in E. coil

The entire gene of carboxyltransferase（CT） domain of acetyl-CoA carboxylase（ACCase） from Chinese Spring wheat（CSW） plastid was cloned firstly, and the 2.3 kb gene was inserted into PET28a^＋ vector and expressed in E. coil in a soluble state. The （His）6 fusion protein was identified by SDS-PAGE and Western blot. The recombinant protein was purified by affinity chromatography, and the calculated molecular mass（Mr） was 88000. The results of the sequence analysis indicate that the cloned gene（GeneBank accession No. EU124675） was a supplement and revision of the reported ACCase CT partial cDNA from Chinese Spring wheat plastid. The recombinant protein will be significant for us to investigate the recognizing mechanism between ACCase and herbicides, and further to screen new herbicides.     

光敏感三元共聚物的合成及溶液自组装

以一端为肉桂酸酯光交联基元、中间为聚己内酯长柔性链的甲基丙烯酸酯类大单体FM5C为第三单体,与马来酸酐(MAH)及苯乙烯(St)单体以AIBN引发共聚合,制备了光敏感三元共聚物P(FM5C-co-MAH-co-St).该三元共聚物可在选择性溶剂中形成纳米胶体粒子,并可先利用肉桂酸酯基元的光照交联作用使胶体粒子内聚合物交联,进而利用羧酸酐基元与2-氨基吡啶的室温氨解反应改变胶体粒子形态.用FTIR、GPC、1H-NMR等对该聚合物及其氨解产物进行了结构表征.用动态激光光散射(DLS)、透射电镜(TEM)、芘探针等技术研究了该聚合物胶体粒子微观形态、粒径与微环境在光照交联后及氨解后的变化.实验结果表明,P(FM5C-co-MAH-co-St)在选择性溶剂中可自组装成球形胶体粒子,肉桂酸酯光照交联作用使胶体粒子中的聚合物团聚得更加紧密,从而使其粒径更小、疏水区域更加集中;而进一步的室温氨解反应则使聚合物胶体粒子更松散、粒径变大.     

Nickel-catalyzed cross-electrophile allylation of vinyl bromides and the modification of anti-tumour natural medicine β-elemene

Herein, we present a facile and efficient allylation method via Ni-catalyzed cross-electrophile coupling of readily available allylic acetates with a variety of substituted alkenyl bromides using zinc as the terminal reductant. This Ni-catalyzed modular approach displays excellent functional group tolerance and a broad substrate scope, which the creation of a series of 1,4-dienes including several structurally complex natural products and pharmaceutical motifs. Moreover, the coupling strategy has the potential to realize enantiomeric control. The practicality of this transformation is demonstrated through the potent modification of the naturally antitumor active molecule β-elemene.

Herein, we present a facile and efficient allylation method via Ni-catalyzed cross-electrophile coupling of readily available allylic acetates with a variety of substituted alkenyl bromides using zinc as the terminal reductant.     

2,2-双(4-氯-2-氟苄基)丙二酸二乙酯的晶体结构和密度泛函理论研究

标题化合物是一个重要的精细化工中间体,可用于制备嘧啶类、吡唑类等产品.本文利用红外光谱(IR)、质谱(MS)、核磁共振氢谱(1 H NMR)、核磁共振碳谱(13 C NMR)和X-射线单晶衍射对此化合物进行了表征,并在B3LYP/6-311G(d,p)模式下使用密度泛函理论(DFT)计算了此化合物的最稳定晶体结构以及最...     

Scalable and Transferable Reinforcement Learning for Multi-Agent Mixed Cooperative–Competitive Environments Based on Hierarchical Graph Attention

Most previous studies on multi-agent systems aim to coordinate agents to achieve a common goal, but the lack of scalability and transferability prevents them from being applied to large-scale multi-agent tasks. To deal with these limitations, we propose a deep reinforcement learning (DRL) based multi-agent coordination control method for mixed cooperative–competitive environments. To improve scalability and transferability when applying in large-scale multi-agent systems, we construct inter-agent communication and use hierarchical graph attention networks (HGAT) to process the local observations of agents and received messages from neighbors. We also adopt the gated recurrent units (GRU) to address the partial observability issue by recording historical information. The simulation results based on a cooperative task and a competitive task not only show the superiority of our method, but also indicate the scalability and transferability of our method in various scale tasks.     

An Algorithm Framework for Drug-Induced Liver Injury Prediction Based on Genetic Algorithm and Ensemble Learning

In the process of drug discovery, drug-induced liver injury (DILI) is still an active research field and is one of the most common and important issues in toxicity evaluation research. It directly leads to the high wear attrition of the drug. At present, there are a variety of computer algorithms based on molecular representations to predict DILI. It is found that a single molecular representation method is insufficient to complete the task of toxicity prediction, and multiple molecular fingerprint fusion methods have been used as model input. In order to solve the problem of high dimensional and unbalanced DILI prediction data, this paper integrates existing datasets and designs a new algorithm framework, Rotation-Ensemble-GA (R-E-GA). The main idea is to find a feature subset with better predictive performance after rotating the fusion vector of high-dimensional molecular representation in the feature space. Then, an Adaboost-type ensemble learning method is integrated into R-E-GA to improve the prediction accuracy. The experimental results show that the performance of R-E-GA is better than other state-of-art algorithms including ensemble learning-based and graph neural network-based methods. Through five-fold cross-validation, the R-E-GA obtains an ACC of 0.77, an F1 score of 0.769, and an AUC of 0.842.     

Space-Air-Ground Integrated Mobile Crowdsensing for Partially Observable Data Collection by Multi-Scale Convolutional Graph Reinforcement Learning

Mobile crowdsensing (MCS) is attracting considerable attention in the past few years as a new paradigm for large-scale information sensing. Unmanned aerial vehicles (UAVs) have played a significant role in MCS tasks and served as crucial nodes in the newly-proposed space-air-ground integrated network (SAGIN). In this paper, we incorporate SAGIN into MCS task and present a Space-Air-Ground integrated Mobile CrowdSensing (SAG-MCS) problem. Based on multi-source observations from embedded sensors and satellites, an aerial UAV swarm is required to carry out energy-efficient data collection and recharging tasks. Up to date, few studies have explored such multi-task MCS problem with the cooperation of UAV swarm and satellites. To address this multi-agent problem, we propose a novel deep reinforcement learning (DRL) based method called Multi-Scale Soft Deep Recurrent Graph Network (ms-SDRGN). Our ms-SDRGN approach incorporates a multi-scale convolutional encoder to process multi-source raw observations for better feature exploitation. We also use a graph attention mechanism to model inter-UAV communications and aggregate extra neighboring information, and utilize a gated recurrent unit for long-term performance. In addition, a stochastic policy can be learned through a maximum-entropy method with an adjustable temperature parameter. Specifically, we design a heuristic reward function to encourage the agents to achieve global cooperation under partial observability. We train the model to convergence and conduct a series of case studies. Evaluation results show statistical significance and that ms-SDRGN outperforms three state-of-the-art DRL baselines in SAG-MCS. Compared with the best-performing baseline, ms-SDRGN improves 29.0% reward and 3.8% CFE score. We also investigate the scalability and robustness of ms-SDRGN towards DRL environments with diverse observation scales or demanding communication conditions.     

Sudden Change of Quantum Discord in Qutrit-Qutrit System Under Depolarising Noise

The effects of decoherence on quantum discord of two-qutrit system under the influence of multilocal, collective and global depolarising noises are investigated. Sudden change of quantum discord with the decoherence parameter and the initial state parameter occurs for the qutrits being coupled to multilocal or global depolarising noises. On the other hand, decoherence cannot induce sudden change of quantum discord when the system is collectively coupled to the depolarising noise. In addition, the local unitary transformation can alter the region in which the phenomenon of sudden change exists.     

Ginzburg-Landau theory of a supersolid

We develop a simple Ginsburg-Landau theory to study all the possible phases and phase transitions in 4He, analyze the condition for the existence of the supersolid (SS) and map out its global phase diagram from a unified framework. If the condition favors the existence of the SS, we use the GL theory to address several experimental facts and also make some predictions that are amenable to experimental tests. A key prediction is that the x-ray scattering intensity from the SS ought to have an additional modulation over that of the normal solid. The modulation amplitude is proportional to the nonclassical rotational-inertial observed in the torsional oscillator experiments.