Preparation of AgBiVMoO/-Al2O3 Catalysts for the Partial Oxidation of Propane

A novel mixed sol method was developed for the preparation of supported catalysts. Analyses by means of XRD and BET show that a 40%AgBiVMoO/γ-Al2O3 catalyst prepared by this method possessed high specific surface area and high dispersion of the active phase. As a result, high acrylic acid selectivity of 8.5% was obtained when the catalyst was used in the reaction of propane partial oxidation to acrylic acid in a fixed-bed reactor.     

2,2′-对苯二甲硫基二乙酸配合物的合成及表征

本文报道了配体 2 ,2′-对苯二甲硫基二乙酸和过渡金属铜 ( )、钴 ( )、镍 ( )配合物的合成。经元素分析、X射线衍射分析、红外光谱、差热 -热重分析等推算了它们的组成并指认了谱带的归属。对铜 ( )配合物进行了电子顺磁共振谱研究。     

Influence of the water molecule on cation-pi interaction: ab initio second order Møller-Plesset perturbation theory (MP2) calculations

The influence of introducing water molecules into a cation-pi complex on the interaction between the cation and the pi system was investigated using the MP2/6-311++G method to explore how a cation-pi complex changes in terms of both its geometry and its binding strength during the hydration. The calculation on the methylammonium-benzene complex showed that the cation-pi interaction is weakened by introducing H(2)O molecules into the system. For example, the optimized interaction distance between the cation and the benzene becomes longer and longer, the transferred charge between them becomes less and less, and the cation-pi binding strength becomes weaker and weaker as the water molecule is introduced one by one. Furthermore, the introduction of the third water molecule leads to a dramatic change in both the complex geometry and the binding energy, resulting in the destruction of the cation-pi interaction. The decomposition on the binding energy shows that the influence is mostly brought out through the electrostatic and induction interactions. This study also demonstrated that the basis set superposition error, thermal energy, and zero-point vibrational energy are significant and needed to be corrected for accurately predicting the binding strength in a hydrated cation-pi complex at the MP2/6-311++G level. Therefore, the results are helpful to better understand the role of water molecules in some biological processes involving cation-pi interactions.     

Mass spectrometry-based chemical mapping and profiling toward molecular understanding of diseases in precision medicine

Precision medicine has been strongly promoted in recent years. It is used in clinical management for classifying diseases at the molecular level and for selecting the most appropriate drugs or treatments to maximize efficacy and minimize adverse effects. In precision medicine, an in-depth molecular understanding of diseases is of great importance. Therefore, in the last few years, much attention has been given to translating data generated at the molecular level into clinically relevant information. However, current developments in this field lack orderly implementation. For example, high-quality chemical research is not well integrated into clinical practice, especially in the early phase, leading to a lack of understanding in the clinic of the chemistry underlying diseases. In recent years, mass spectrometry (MS) has enabled significant innovations and advances in chemical research. As reported, this technique has shown promise in chemical mapping and profiling for answering “what”, “where”, “how many” and “whose” chemicals underlie the clinical phenotypes, which are assessed by biochemical profiling, MS imaging, molecular targeting and probing, biomarker grading disease classification, etc. These features can potentially enhance the precision of disease diagnosis, monitoring and treatment and thus further transform medicine. For instance, comprehensive MS-based biochemical profiling of ovarian tumors was performed, and the results revealed a number of molecular insights into the pathways and processes that drive ovarian cancer biology and the ways that these pathways are altered in correspondence with clinical phenotypes. Another study demonstrated that quantitative biomarker mapping can be predictive of responses to immunotherapy and of survival in the supposedly homogeneous group of breast cancer patients, allowing for stratification of patients. In this context, our article attempts to provide an overview of MS-based chemical mapping and profiling, and a perspective on their clinical utility to improve the molecular understanding of diseases for advancing precision medicine.

An overview of MS-based chemical mapping and profiling, indicating its contributions to the molecular understanding of diseases in precision medicine by answering "what", "where", "how many" and "whose” chemicals underlying clinical phenotypes.     

Enzymatic Synthesis of a Diastereomer of Neoabyssomicin Derivative Using the Diels-Alderase AbyU

Main observation and conclusion The enzyme AbyU catalyses a Diels-Alder (DA) reaction during abyssomicin C biosynthesis.In this study,AbyU is shown to convert t...     

Turing structures and stability for the 1-D Lengyel–Epstein system

This paper continues the analysis on the Lengyel–Epstein reaction- diffusion system of the chlorite-iodide-malonic acid-starch (CIMA) reaction for the rich Turing structures. The steady state structures, especially the double bifurcation one, and their stability and multiplicity are studied by the use of Lyapunov–Schmidt reduction technique and singularity theory. Numerical simulations are presented to support our theoretical studies. The results show that the richer stationary Turing patterns heavily rely both on the size of the reactor and on the effective diffusion rate in the CIMA reaction.     

Convenient One‐Pot Procedures for the Synthesis of 2,2′:6′,2″‐Terpyridine

One‐pot reactions to produce 2,2′:6′,2″‐terpyridine (tpy) under mild conditions are described under both solventless and solvent‐assisted conditions. Tpy can be obtained in 32% yield in a simple one‐pot reaction, which can readily be scaled‐up to give large quantities of tpy. These new approaches are superior to those previously described because of the fast and efficient synthesis and purification of tpy.     

Molecular Weight Distribution Control for Polymerization Processes Based on the Moment-Generating Function

The molecular weight distribution is an important factor that affects the properties of polymers. A control algorithm based on the moment-generating function was proposed to regulate the molecular weight distribution for polymerization processes in this work. The B-spline model was used to approximate the molecular weight distribution, and the weight state space equation of the system was identified by the subspace state space system identification method based on the paired date of B-spline weights and control inputs. Then, a new performance criterion mainly consisting of the moment-generating function was constructed to obtain the optimal control input. The effectiveness of the proposed control method was tested in a styrene polymerization process. The molecular weight distribution of the styrene polymers can be approximated by the B-spline model effectively, and it can also be regulated towards the desired one under the proposed control method.     

Identification of Pharmacophoric Fragments of DYRK1A Inhibitors Using Machine Learning Classification Models

Dual-specific tyrosine phosphorylation regulated kinase 1 (DYRK1A) has been regarded as a potential therapeutic target of neurodegenerative diseases, and considerable progress has been made in the discovery of DYRK1A inhibitors. Identification of pharmacophoric fragments provides valuable information for structure- and fragment-based design of potent and selective DYRK1A inhibitors. In this study, seven machine learning methods along with five molecular fingerprints were employed to develop qualitative classification models of DYRK1A inhibitors, which were evaluated by cross-validation, test set, and external validation set with four performance indicators of predictive classification accuracy (CA), the area under receiver operating characteristic (AUC), Matthews correlation coefficient (MCC), and balanced accuracy (BA). The PubChem fingerprint-support vector machine model (CA = 0.909, AUC = 0.933, MCC = 0.717, BA = 0.855) and PubChem fingerprint along with the artificial neural model (CA = 0.862, AUC = 0.911, MCC = 0.705, BA = 0.870) were considered as the optimal modes for training set and test set, respectively. A hybrid data balancing method SMOTETL, a combination of synthetic minority over-sampling technique (SMOTE) and Tomek link (TL) algorithms, was applied to explore the impact of balanced learning on the performance of models. Based on the frequency analysis and information gain, pharmacophoric fragments related to DYRK1A inhibition were also identified. All the results will provide theoretical supports and clues for the screening and design of novel DYRK1A inhibitors.     

Fractional damping enhances chaos in the nonlinear Helmholtz oscillator

Nonlinear Dynamics - The main purpose of this paper is to study both the underdamped and the overdamped dynamics of the nonlinear Helmholtz oscillator with a fractional-order damping. For that...