722 research outputs found
Correction: pH and reduction-activated polymeric prodrug nanoparticles based on a 6-thioguanine-dialdehyde sodium alginate conjugate for enhanced intracellular drug release in leukemia
No full textCorrection for ‘pH and reduction-activated polymeric prodrug nanoparticles based on a 6-thioguanine-dialdehyde sodium alginate conjugate for enhanced intracellular drug release in leukemia’ by Yanming Wan et al., Polym. Chem., 2018, DOI: 10.1039/c8py00577j.</p
Crystallization Kinetics of Concurrent Liquid-Metastable and Metastable-Stable Transitions, and Ostwald's Step Rule
No full textExperimental measurements of colloidal crystallization in a wide range of volume fractions of charged particles were performed to investigate the liquid-metastable-stable transition process. To fit the obtained experimental data, we developed a theoretical model to formulate the kinetics of the concurrent liquid metastable and metastable stable transitions. This model is well-supported by our observations. We found that when the ratio of the metastable stable transition rate to the liquid metastable rate is very large; the metastable state can become undetectable, although it still exists, offering a possible explanation for very few exceptions to Ostwald's step rule.Experimental measurements of colloidal crystallization in a wide range of volume fractions of charged particles were performed to investigate the liquid-metastable-stable transition process. To fit the obtained experimental data, we developed a theoretical model to formulate the kinetics of the concurrent liquid metastable and metastable stable transitions. This model is well-supported by our observations. We found that when the ratio of the metastable stable transition rate to the liquid metastable rate is very large; the metastable state can become undetectable, although it still exists, offering a possible explanation for very few exceptions to Ostwald's step rule
Health risks of exposure to wildfire-toxic air: Air pollution impacts
No full textEvaluating the short-term exposure to wildfire-specific fine particulate matter (PM(2.5)) showed greater risks of hospitalization for all major respiratory diseases than non-wildfire PM(2.5). When developing air quality guidelines, it is also important to consider that PM(2.5) from varying sources can have different health effects, which require targeted health and environmental policy approaches.Yiwen Zhang, Rongbin Xu, Wenzhong Huang, Tingting Ye, Pei Yu, Wenhua Yu, Yao Wu, Yanming Liu, Zhengyu Yang, Bo Wen, Ke Ju, Jiangning Song, Michael J. Abramson, Amanda Johnson, Anthony Capon, Bin Jalaludin, Donna Green, Eric Lavigne, Fay H. Johnston, Geoffrey G. Morgan, Luke D. Knibbs, Ying Zhang, Guy Marks, Jane Heyworth, Julie Arblaster, Yue Leon Guo, Lidia Morawska, Micheline S. Z. S. Coelho, Paulo H. N. Saldiva, Patricia Matus, Peng Bi, Simon Hales, Wenbiao Hu, Dung Phung, Yuming Guo, Shanshan L
中药研发色谱方法与技术
No full textChromatographic Methodology and Technology for Developing TCM
Xinmiao Liang*, Xiuli Zhang, Xingya Xue, Jiatao Feng
Dalian Institute of Chemical Physics, Chinese Academy of Sciences
457 Zhongshan Road, Dalian 116023, P. R. China
E-mail: [email protected]
Presently, Traditional Chinese Medicine (TCM) is an important resource to treat many diseases, which has a long history in China, and is very complicated in chemical composition and pharmacology. Therefore, modernization of TCM is very necessary, including standardization, resourcing, quality control and research on chemical composition and pharmacology.
“Multi-Component Chinese Medicine (MCCM)” represents a specific group of chemical composition produced from TCM according to a standardized separation process, which is hopefully to discover the chemical fundament of TCM’s pharmacology. The systematic research on MCCM is basing on modern separation and analytical techniques.
In our group, a high-throughput preparation method has been developed in order to accelerate the pharmacological study of MCCM. The whole of TCMs were separated into a serious of small fractions containing several compounds by high-throughput preparative chromatography. Then these small fractions, what is called screening multi-components (SMCs), were screened by high-throughput screening (HTS). After biological screening, active multi-components were obtained and could be studied deeply further. This method will accelerate the process of explanation of the curative mechanism of TCMs, which simplifies the complex TCMs by concentrating on those interesting compounds from hundreds of thousands of compounds in TCMs. Standard libraries of SMCs are constructed, which will not only satisfy the need of TCMs, but also will speed up drug discovery by supplying persistent source for HTS.
Two-dimensional liquid chromatography (2DLC) systems have developed for the characterization of SMCs and preparation of active compounds. Orthogonality and compatibility between the two-dimensional separations are key factors affecting the development of 2DLC. Based on the development of novel stationary phases, RP-HILIC 2DLC system and RP-RP 2DLC system were established.
References
[1] Guo ZM, Lei AW, Liang XM & Xu Q. Chem Comm, 4512-4514 (2006)..
[2] Yanming Liu, Xingya Xue, Zhimou Guo, Qing Xu, Feifang Zhang, Xinmiao Liang. J. Chromatogr. A, 1208, 133–140 (2008)
[3] Yanming Liu, Zhimou Guo, Yu Jin, Xingya Xue, Qing Xu, Feifang Zhang, Xinmiao Liang. J Chromatogr. A, 1206 153–159 (2008)Chromatographic Methodology and Technology for Developing TCM
Xinmiao Liang*, Xiuli Zhang, Xingya Xue, Jiatao Feng
Dalian Institute of Chemical Physics, Chinese Academy of Sciences
457 Zhongshan Road, Dalian 116023, P. R. China
E-mail: [email protected]
Presently, Traditional Chinese Medicine (TCM) is an important resource to treat many diseases, which has a long history in China, and is very complicated in chemical composition and pharmacology. Therefore, modernization of TCM is very necessary, including standardization, resourcing, quality control and research on chemical composition and pharmacology.
“Multi-Component Chinese Medicine (MCCM)” represents a specific group of chemical composition produced from TCM according to a standardized separation process, which is hopefully to discover the chemical fundament of TCM’s pharmacology. The systematic research on MCCM is basing on modern separation and analytical techniques.
In our group, a high-throughput preparation method has been developed in order to accelerate the pharmacological study of MCCM. The whole of TCMs were separated into a serious of small fractions containing several compounds by high-throughput preparative chromatography. Then these small fractions, what is called screening multi-components (SMCs), were screened by high-throughput screening (HTS). After biological screening, active multi-components were obtained and could be studied deeply further. This method will accelerate the process of explanation of the curative mechanism of TCMs, which simplifies the complex TCMs by concentrating on those interesting compounds from hundreds of thousands of compounds in TCMs. Standard libraries of SMCs are constructed, which will not only satisfy the need of TCMs, but also will speed up drug discovery by supplying persistent source for HTS.
Two-dimensional liquid chromatography (2DLC) systems have developed for the characterization of SMCs and preparation of active compounds. Orthogonality and compatibility between the two-dimensional separations are key factors affecting the development of 2DLC. Based on the development of novel stationary phases, RP-HILIC 2DLC system and RP-RP 2DLC system were established.
References
[1] Guo ZM, Lei AW, Liang XM & Xu Q. Chem Comm, 4512-4514 (2006)..
[2] Yanming Liu, Xingya Xue, Zhimou Guo, Qing Xu, Feifang Zhang, Xinmiao Liang. J. Chromatogr. A, 1208, 133–140 (2008)
[3] Yanming Liu, Zhimou Guo, Yu Jin, Xingya Xue, Qing Xu, Feifang Zhang, Xinmiao Liang. J Chromatogr. A, 1206 153–159 (2008
Research on modern power semiconductor modelling methodology for efficiency evaluation of power electronic systems in electromagnetic transient simulation
No full textPower electronics technology has rapidly developed during the past decades. Power electronics systems aim to achieve high efficiency as power conversion interfaces while fulfilling the performance and reliability requirements. The key to achieving these objectives is power semiconductors, which dictate the power electronics system's efficiency, power density, and reliability. In recent years, traditional Silicon (Si) devices are reaching their material limits. Meanwhile, new Wide-Bandgap (WBG) devices such as Silicon Carbide (SiC) and Gallium Nitride (GaN) devices have been commercialized, featuring high breakdown voltage, fast switching speed, and high thermal capability. On the other hand, semiconductor devices are typically exposed to repetitive heat pulses and are often the most critical components affecting system reliability. Consequently, a comprehensive modelling method for modern power semiconductors that can describe various devices’ switching behaviors is highly desirable by power electronics engineers and manufacturers.
This research focuses on developing a simulation-based modelling methodology for modern power semiconductors to evaluate the power electronics system’s efficiency. A multi-level simulation strategy has been proposed and implemented in PSCAD/EMTDC. A generalized transient semiconductor model has been developed, which can reproduce the device’s switching behaviors. Subsequently, the power losses are obtained to form a multi-dimensional power loss look-up table under a wide range of operating conditions.
A dynamic thermal model for temperature estimation, and a typical electrical network using simple switch models for semiconductor devices have been implemented. The junction temperature is updated every switching cycle by the power loss with a thermal model and influence back to the electrical simulation. In this way, a closed-loop electro-thermal simulation is formed to evaluate both electrical and thermal performances in a single simulator with a range of acceptable accuracy. A double pulse test platform has been designed and built for device characterizations and power loss verifications. Moreover, a single-phase grid-tied buck-boost type inverter application has been selected as a case study and built to study the proposed method. The measured results indicate that the proposed approach is highly promising for power electronics engineers to evaluate and optimize a system during the early design stage.October 202
An improved mean-shift moving object detection and tracking algorithm based on segmentation and fusion mechanism
No full textRecommended from our members
Network Analysis and Change-point Detection
No full textAnalysis of observations on sequential events over time is common in real life. Sequential measurements over time describing the behavior of systems are usually called time series data, which have been collected in a wide range of disciplines. Over the years there have been multiple research areas in studying stochastic properties and developing statistical inference methods for time series data. The study of change-point detection in time series data has a long history in the statistics literature, aiming at the detection of time points where specific characteristics of the time series change. Network data sets, representing relationships between elements in a system, are being collected with a vast increase in size and complexity with the rapid advances in data generation and collection technologies. In recent years, the study of multiple network data sets has also gained a larger audience with the advent of multilayer and temporal network data. Statistical analysis of single-layer and multiple-layer networks has now become a popular field, including developments of statistical inference methods and studies on theoretical properties of the statistical methods.
In this dissertation, we research several aspects of statistical analysis for time series data and network data. In Chapter \ref{chap1}, we review some methods for time series analysis and network analysis. We discuss several models for univariate time series, models for single-layer networks, models for multiple-layer networks, and graph operators for network community structure recoveries. Based on the reviewed models, we develop methods for problems of community number detection in networks, change-point detection in multiple-layer networks, as well as applications of change-point detection methods to COVID-19 time series data.
In Chapter \ref{chap2} we discuss the community number estimation in sparse network data. In most clustering algorithms, the number of communities, , is a required input. Among the various approaches that have been proposed to estimate , the non-parametric methods based on the spectrum of the associated Bethe Hessian matrices (\vH_{\zeta} with parameter ) have garnered much popularity for their simplicity, computational efficiency, and robustness to the sparsity of data, which have been demonstrated in several empirical studies that have ensued. For certain heuristic choices of , such methods have been recently shown to be consistent if the input network with nodes is generated from the (semi-dense) stochastic block model, where all nodes have equal expected degrees and the common expected degree is . In this chapter, we obtain several finite sample results to show that if the input network is generated from either stochastic block models (SBM) or degree-corrected block models (DCBM) having possible heterogeneity both in terms of the expected degrees of the nodes and the sizes of the communities, and if is chosen from a certain interval, then the associated spectral methods based on \vH_{\zeta} is consistent for estimating not only for the semi-dense regime but also for the sub-logarithmic sparse regime, when the maximum () of the expected degrees of all nodes satisfies , under some mild condition on the extent of heterogeneity. We also propose a method to estimate the aforementioned interval empirically, which enables us to develop a consistent estimation procedure. We evaluate the performance of the resulting estimation procedure theoretically. The efficacy of our proposed method is demonstrated via extensive simulation studies and the application of our approach to a comprehensive collection of real-world network data arising in diverse areas of interest.
In Chapter \ref{chap3} we discuss the detection of change-points of community structure in a sequence of temporal network data. Sequences of networks are currently a common form of network data sets. Identification of structural change-points in a network data sequence is a natural problem. The problem of change-point detection can be classified into two main types - \emph{offline} change-point detection and \emph{online} or \emph{sequential} change-point detection. In this paper, we propose three different algorithms for \emph{online} change-point detection based on certain CUSUM statistics for network data with community structures. For two of the proposed algorithms, we use information-theoretic measures to construct the statistic for the estimation of a change-point. In the third algorithm, we use eigenvalues of the Bethe Hessian matrix to construct the statistic for the estimation of a change-point. We show the consistency property of the estimated change-point theoretically under networks generated from the multi-layer stochastic block model and the multi-layer degree-corrected block model. We also conduct an extensive simulation study to demonstrate the key properties of the algorithms as well as their efficacy.
Tremendous work has been done in reply to questions related to the dynamics of the COVID-19 pandemic and it has been shown there is a multiple-wave pattern of COVID-19 cases and death count data. In Chapter \ref{chap4}, we propose several algorithms for change-point detection and anomaly detection in univariate time series data based on sequential tests on target time points. In change-point detection algorithms, test statistics are likelihood-ratio-based which measure the dissimilarity between probability distributions of neighboring sub-sequences around the target time point. In the anomaly detection algorithms, test statistics are likelihood-based which measures the unlikelihood of the target observation based on the historical probability distribution. We apply the proposed algorithms to county-level COVID-19 daily death increase data and detect slope change-points, scale change-points, and anomaly reportings in waves of COVID-19 daily death increase counts. We also discuss potential associations between detected scale change-points and state-level mask orders
Discussion on the Reform of the Bilingual Teaching of Marine Engineering in Higher Vocational Colleges
No full text
- …
