Near-infrared laser spectroscopy of NiI

Laser-induced fluorescence spectrum of NiI in the near infrared region of 714-770 nm has been recorded. Seven bands belonging to three electronic transition systems were observed and analyzed: the (0,0), (1,0), and (2,0) bands of [13.3] (2)Sigma(+)-A (2)Pi(3/2) system; the (1,1) and (0,1) bands of [13.9] (2)Pi(3/2)-X (2)Delta(5/2) system; and the (0,0) and (1,0) bands of [13.9] (2)Pi(3/2)-A (2)Pi(3/2) system. Spectra of isotopic molecules confirmed the vibrational quantum number assignment of the observed bands. Least-squares fit of rotationally resolved transition lines yielded accurate molecular constants for the v=0-2 levels of the [13.3] (2)Sigma(+) state, the v=0 level of the A (2)Pi(3/2), and the v=1 level of the X (2)Delta(5/2) state. The vibrational separation, DeltaG(1/2), of the ground state was measured to be 276.674 cm(-1). With the observation of the [13.9] (2)Pi(3/2)-A (2)Pi(3/2) and [13.9] (2)Pi(3/2)-X (2)Delta(5/2) transitions, we accurately determined the energy separation between the A (2)Pi(3/2) and the X (2)Delta(5/2) to be 163.847 cm(-1). This confirms that the order of the A (2)Pi(3/2) and X (2)Delta(5/2) states in NiI is reversed when compared with other nickel monohalides.     

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.     

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.     

Development of a ~3He Gas Filling Station at the China Spallation Neutron Source

At the China Spallation Neutron Source(CSNS), we have developed a custom gas-filling station, a glassblowing workshop, and a spin-exchange optical pumping(SEOP) system for producing high-quality ~3He-based neutron spin filter(NSF) cells. The gas-filling station is capable of routinely filling ~3He cells made from GE180 glass of various dimensions, to be used as neutron polarizers and analyzers on beamlines at the CSNS. Performance tests on cells fabricated at our gas-filling station are conducted via neutron transmission and nuclear-magneticresonance measurements, revealing nominal filling pressures, and a saturated ~3He polarization in the region of 80%, with a lifetime of approximately 240 hours. These results demonstrate our ability to produce competitive NSF cells to meet the ever-increasing research needs of the polarized neutron research community.     

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.     

Mechanism Study on Nanoparticle Negative Surface Charge Modification by Ascorbyl Palmitate and Its Improvement of Tumor Targeting Ability

Surface charge polarity and density influence the immune clearance and cellular uptake of intravenously administered lipid nanoparticles (LNPs), thus determining the efficiency of their delivery to the target. Here, we modified the surface charge with ascorbyl palmitate (AsP) used as a negatively charged lipid. AsP-PC-LNPs were prepared by dispersion and ultrasonication of AsP and phosphatidylcholine (PC) composite films at various ratios. AsP inserted into the PC film with its polar head outward. The pK_a for AsP was 4.34, and its ion form conferred the LNPs with negative surface charge. Zeta potentials were correlated with the amount and distribution of AsP on the LNPs surface. DSC, Raman and FTIR spectra, and molecular dynamics simulations disclosed that AsP distributed homogeneously in PC at 1–8% (w/w), and there were strong hydrogen bonds between the polar heads of AsP and PC (PO²⁻), which favored LNPs’ stability. But at AsP:PC > 8% (w/w), the excessive AsP changed the interaction modes between AsP and PC. The AsP–PC composite films became inhomogeneous, and their phase transition behaviors and Raman and FTIR spectra were altered. Our results clarified the mechanism of surface charge modification by AsP and provided a rational use of AsP as a charged lipid to modify LNP surface properties in targeted drug delivery systems. Furthermore, AsP–PC composites were used as phospholipid-based biological membranes to prepare paclitaxel-loaded LNPs, which had stable surface negative charge, better tumor targeting and tumor inhibitory effects.     

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...     

Evaluation of Water Ammonium Ion Test Kit and Its Feasibility for the Analysis of Protein in Food

The traditional method for the determination of protein in food needs the operations of digestion, distillation, absorption, and titration; therefore, it is complicated and time-consuming and requires professional personnel. Is there a more convenient and faster detection method that can directly determine the ammonium ions in protein digestion solution to obtain the protein content of food and avoid the distillation–absorption–titration process? The feasibility of water ammonium ion test kits for food protein rapid detection was discussed here. After digestion, the protein in food transforms into ammonium ions in the digestion solution. Because of the variety of food, there are many different inorganic ions left in the food digestion solution, and at the same time, digestion agents are added in the digestion process and become potential interference factors in ammonium determination. Therefore, the detection accuracy of ammonium test kits needs to be evaluated first, including their anti-interference ability. The standard curve of ammonium was established by the test kit. When the ammonium concentration was 0.00–2.50 mg/L, the absorbance at 620 nm was linearly related to the ammonium concentration, the determination coefficient R² was 0.9995, and the detection limit of this method was 0.01 mg/L. The influences of temperature, pH value, and reaction time on the test kit method were discussed. The precision was 0.90–3.33%; the repeatability was 1.71–4.86%; and the recovery rate of tap water, river water, and sea water was controlled within 90–103%. The anti-interference ability of the evaluated test kit was better than that of the national standard detection method. The test kit, combined with sample pretreatment and protein conversion formula, was used to detect protein in different types of food (milk powder, rice flour, wheat flour, soy, banana, milk, fish food, chicken food, and dog food). The results showed that there were no significant differences (ρ > 0.05) between the national method and the test kit method. The ammonium ion test kit method shortened the determination time and had higher sensitivity, showing its potential for the rapid determination of food protein.     

Activation of Stimulation of Interferon Genes (STING) Signal and Cancer Immunotherapy

Stimulator of interferon gene (STING), an intracellular receptor in the endoplasmic reticulum, could induce the production of cytokines such as type I interferon (IFN) by activating the cGAS-STING signal pathway. In recent years, activation of STING has shown great potential to enhance anti-tumor immunity and reshape the tumor microenvironment, which is expected to be used in tumor immunotherapy. A number of STING agonists have demonstrated promising biological activity and showed excellent synergistic anti-tumor effects in combination with other cancer therapies in preclinical studies and some clinical trials. The combination of STING agonists and ICI also showed a potent effect in improving anti-tumor immunity. In this review, we introduce the cGAS-STING signaling pathway and its effect in tumor immunity and discuss the recent strategies of activation of the STING signaling pathway and its research progress in tumor immunotherapy.