Chemometric methods for evaluation of chromatographic separation quality from two-way data--a review

Most traditional chromatographic separation criteria or response functions are defined on chromatograms recorded by single-channel detectors, e.g. a spectrometer measuring the absorbance at a single wavelength or a thermal conductivity detector. When the peaks are seriously overlapped, usually there is a lack of the information concerning the total number of chemical components, overlap degree of the peaks and peak purity. Such information characterizes some crucial aspects of separation process and lack of it will lead to an inaccurate and misleading evaluation of separation quality as well as some computational ambiguity for many traditional response functions. In contrast, hyphenated chromatography-(multi-channel) spectroscopy instruments together with chemometric methods will largely increase the information content available in chromatographic detection. Such information, if properly used, can cast a new light on evaluation of chromatographic separation quality. The main objective of this article is to review chemometric methods devoted to estimation of the number of chemical components, determination of elution sequence and assessment of peak purity. Some newly defined response functions or separation criteria based on extracted information by chemometric methods are also introduced. The methods reviewed are limited to those for treating two-way data obtained by hyphenation of high-performance liquid chromatography with multi-channel detectors. We prefer to provide a comprehensive view of such methods rather than present a full list of all the methods developed. Further details of some important methods are touched upon in favor of employment and understanding of them by researchers not very familiar with chemometrics.     

An approach to the spectral simulation of infrared hollow waveguide gas sensors

Optical simulations enable to model an entire chemical gas sensing platform based on hollow waveguides (HWGs) operating in the mid-infrared spectral regime using a three-dimensional representation of the sensor components and taking the spectral response to virtual analytes into account. Furthermore, a strategy for including the spectral response of dielectrically coated HWGs is demonstrated. Utilizing experimentally obtained spectroscopic data recorded at well-defined conditions, the complex refractive indices of selected target analytes (i.e., methane, butane, and isobutylene) have been derived based on a refined harmonic oscillator model. In turn, these parameters have enabled to directly assign the dielectric functions of these analytes to virtual objects representing the analyte within the modeled sensor setup. In a next step, spectroscopic sensor response functions have been simulated as absorbance spectra across selected wavelength regimes utilizing spectrally resolved ray-tracing techniques and have been compared to experimental data.     

卡尔曼滤波荧光光度法用于混合萤光染料的同时测定

根据常规与同步荧光方法的特点,提出了卡尔曼滤波荧光分光光度分析法,对多组分萤光染料混合物进行同时测定,结果良好。该法既具有优良的选择性和较高的灵敏度,又具有一定的通用性,适于推广应用。     

Multicomponent-analysis computations based on kalman filtering

A theoretical introduction to the use of Kalman filtering in analytical chemistry is based on multicomponent-analysis computations with the non-recursive least-squares estimation method as a starting point. An initial value for the computation of the error covariance matrix is given and some new practical applications (determination of number of components, estimation of constant systematic error) are derived and demonstrated. Theory and practice suggest a new possible design for experimental measurements and novel applications of on-line computation and computer control. The excellent performance of the Kalman filter algorithm is demonstrated.     

THE DETERMINATION OF PERIODICITIES IN SHORT-TERM TIME-SERIES DATA IN THE PRESENCE OF HIGH FREQUENCY NOISE AND LONG-TERM TREND

The presence of periodicities in biological data may often be obscured by superposition of a background trend or high frequency oscillations. A method of least squares curve fitting was utilized to remove the trend in signal level. Subsequent removal of higher frequency noise utilizing smoothing convolution functions allows the determination of rhythmic components. A new “noise” convolution function gives a measure of the noise in data containing both signal and noise and allows the estimation of a limit above which an observed oscillation is considered significant. The effects of treating data with these convolution functions are discussed with reference to amplitude-frequency response curves. The results of data analysis utilizing convolution functions are compared with results obtained utilizing a moving interval Fourier technique (Blume, 1965). The convolution analysis have the advantages that they are easily programmed and rapidly calculated by programmable calculators, give results relatively insensitive to the length of convolution interval, and allow estimation of the variation in period and amplitude of the rhythm investigated.     

Quantum dot photoluminescence lifetime-based pH nanosensor

The first CdSe/ZnS quantum dot photoluminescence lifetime-based pH nanosensor has been developed. The average lifetime of mercaptopropionic acid-capped QD nanosensors showed a linear response in the pH range of 5.2-6.9. These nanosensors have been satisfactorily applied for pH estimation in simulated intracellular media, with high sensitivity and high selectivity toward most of the intracellular components.     

Recurrent neural network based hybrid model for reconstructing gene regulatory network

One of the exciting problems in systems biology research is to decipher how genome controls the development of complex biological system. The gene regulatory networks (GRNs) help in the identification of regulatory interactions between genes and offer fruitful information related to functional role of individual gene in a cellular system. Discovering GRNs lead to a wide range of applications, including identification of disease related pathways providing novel tentative drug targets, helps to predict disease response, and also assists in diagnosing various diseases including cancer. Reconstruction of GRNs from available biological data is still an open problem. This paper proposes a recurrent neural network (RNN) based model of GRN, hybridized with generalized extended Kalman filter for weight update in backpropagation through time training algorithm. The RNN is a complex neural network that gives a better settlement between biological closeness and mathematical flexibility to model GRN; and is also able to capture complex, non-linear and dynamic relationships among variables. Gene expression data are inherently noisy and Kalman filter performs well for estimation problem even in noisy data. Hence, we applied non-linear version of Kalman filter, known as generalized extended Kalman filter, for weight update during RNN training. The developed model has been tested on four benchmark networks such as DNA SOS repair network, IRMA network, and two synthetic networks from DREAM Challenge. We performed a comparison of our results with other state-of-the-art techniques which shows superiority of our proposed model. Further, 5% Gaussian noise has been induced in the dataset and result of the proposed model shows negligible effect of noise on results, demonstrating the noise tolerance capability of the model.     

Computational approaches for predicting mutant protein stability

Mutations in the protein affect not only the structure of protein, but also its function and stability. Prediction of mutant protein stability with accuracy is desired for uncovering the molecular aspects of diseases and design of novel proteins. Many advanced computational approaches have been developed over the years, to predict the stability and function of a mutated protein. These approaches based on structure, sequence features and combined features (both structure and sequence features) provide reasonably accurate estimation of the impact of amino acid substitution on stability and function of protein. Recently, consensus tools have been developed by incorporating many tools together, which provide single window results for comparison purpose. In this review, a useful guide for the selection of tools that can be employed in predicting mutated proteins’ stability and disease causing capability is provided.     

荧光光度法中Kalman滤波法指示未知组分的研究

以苯丙氨酸、酪氨酸和色氨酸三组分体系的荧光光度分析为例, 研究了Kalman滤波法指示未知组分的潜力。根据新息序列的白噪音特性, 建立指示函数, 并确定指示函数的临界值, 指示未知组分存在; 并根据相对新息序列的形状可大致估计未知组分的荧光光谱的形状和峰位。     

Roll-to-Roll Production of Spider Silk Nanofiber Nonwoven Meshes Using Centrifugal Electrospinning for Filtration Applications

Filtration systems used in technical and medical applications require components for fine particle deep filtration to be highly efficient and at the same time air permeable. In high efficiency filters, nonwoven meshes, which show increased performance based on small fiber diameters (e.g., using nanofibers), can be used as fine particle filter layers. Nanofiber nonwoven meshes made by electrospinning of spider silk proteins have been recently shown to exhibit required filter properties. Needle-based electrospinning, however, is limited regarding its productivity and scalability. Centrifugal electrospinning, in contrast, has been shown to allow manufacturing of ultrathin polymer nonwoven meshes in an efficient and scalable manner. Here, continuous roll-to-roll production of nonwoven meshes made of recombinant spider silk proteins is established using centrifugal electrospinning. The produced spider silk nanofiber meshes show high filter efficiency in the case of fine particulate matter below 2.5 µm (PM2.5) and a low pressure drop, resulting in excellent filter quality.     

Self‐Assembling Proteins for Design of Anticancer Nanodrugs

Inspired by the diverse protein‐based structures and materials in organisms, proteins have been expected as promising biological components for constructing nanomaterials toward various applications. In numerous studies protein‐based nanomaterials have been constructed with the merits of abundant bioactivity and good biocompatibility. However, self‐assembly of proteins as a dominant approach in constructing anticancer nanodrugs has not been reviewed. Here, we provide a comprehensive account of the role of protein self‐assembly in fabrication, regulation, and application of anticancer nanodrugs. The supramolecular strategies, building blocks, and molecular interactions of protein self‐assembly as well as the properties, functions, and applications of the resulting nanodrugs are discussed. The applications in chemotherapy, radiotherapy, photodynamic therapy, photothermal therapy, gene therapy, and combination therapy are included. Especially, manipulation of molecular interactions for realizing cancer‐specific response and cancer theranostics are emphasized. By expounding the impact of molecular interactions on therapeutic activity, rational design of highly efficient protein‐based nanodrugs for precision anticancer therapy can be envisioned. Also, the challenges and perspectives in constructing nanodrugs based on protein self‐assembly are presented to advance clinical translation of protein‐based nanodrugs and next‐generation nanomedicine.     

Natural products as an inspiration in the diversity-oriented synthesis of bioactive compound libraries

The purpose of diversity-oriented synthesis is to drive the discovery of small molecules with previously unknown biological functions. Natural products necessarily populate biologically relevant chemical space, since they bind both their biosynthetic enzymes and their target macromolecules. Natural product families are, therefore, libraries of pre-validated, functionally diverse structures in which individual compounds selectively modulate unrelated macromolecular targets. This review describes examples of diversity-oriented syntheses which have, to some extent, been inspired by the structures of natural products. Particular emphasis is placed on innovations that allow the synthesis of compound libraries that, like natural products, are skeletally diverse. Mimicking the broad structural features of natural products may allow the discovery of compounds that modulate the functions of macromolecules for which ligands are not known. The ability of innovations in diversity-oriented synthesis to deliver such compounds is critically assessed.     

Analysis of partially resolved liquid chromatographic peaks using dynamic modeling with the kalman filter

Establishing a calibration model is an important part of any mathematical method for multi-component determination. Use of a calibration model based on single spectra is subject to error, because the model spectrum chosen may not be representative of the response over the full range of the calibration. Alternative calibration models require more time to establish calibration, an these may not be convenient for real-time determinations. A novel calibration method is reported for use with Kalman filters. The method, dynamic modeling, is based on the use of libraries of calibration spectra. The set of used to describe the model at any time is based on component concentrations, estimated for the multi-component mixture, as determined from the Kalman filter, so that several spectra can be used to best describe a varying response. Through application of the dynamic modelingt to simulated and real chromatograms, it is demonstrated that use of the method decreases estimation errors cause by model data mismatches, and that full benefit can be obtained from relatively small libraries.     

Use of characteristic functions derived from proficiency testing data to evaluate measurement uncertainties

Interlaboratory comparisons show that reproducibility standard deviations are dependent on the concentration of the analyte. Many attempts have been made to model this. In this paper, ??characteristic?? functions are used for modelling the concentration dependence of the reproducibility standard deviations based on data from proficiency tests for water analysis. The characteristics of the resulting functions can be used for the estimation of measurement uncertainties at different concentration levels. These functions are especially useful to determine the concentration levels below which absolute uncertainties tend to be constant and above which the relative uncertainties are more constant. By comparing the characteristic functions of different analytical procedures for the determination of the same analyte, the performance of these procedures under routine application can be compared. Finally, these functions may be used to get an indication on the average quality of analytical result in a specific field to be used by regulators in order to formulate requirements in the legislation that are in accordance with current measurement quality.     

Estimation of single solute adsorption isotherms applying the nonlinear frequency response method using non-optimal frequencies

In order to estimate single solute adsorption isotherms, the nonlinear frequency response (FR) of a chromatographic column is analyzed experimentally and evaluated using the concept of higher order frequency response functions (FRFs) based on the Volterra series and generalized Fourier transform. In this case study, it has been investigated the adsorption of ethyl benzoate on octadecyl silica from a mixture of methanol and water (60:40) as a solvent. Experiments are performed using a standard gradient HPLC unit. For estimation of adsorption isotherms by the nonlinear FR method the column inlet concentration is changed in a nearly sine waveform around several steady-state concentrations. Using this method the first three local derivatives of a single solute adsorption isotherm are estimated from the low frequency asymptotes of the corresponding functions, i.e. the phase and first order derivative of the FRFs. For an accurate estimation of isotherm coefficients periodical experiments should be preformed for frequencies below a certain critical frequency. This is the frequency needed for approaching the low frequency asymptotic behaviour of the corresponding functions close enough, so that errors due to the non-feasibility of experiments with zero frequency can be neglected. Unfortunately, depending on the properties of the system, it can happen (as for the system investigated here) that experiments for the critical frequency would be too long and cannot be realized. In order to study the loss of accuracy of the nonlinear FR method, when it is applied for non-optimal frequencies, experiments are performed for frequencies approximately one order of magnitude higher than the critical frequency required to evaluate the FRF phases. The obtained isotherm model coefficients are compared with the ones estimated using conventional frontal analysis as a reference method. The isotherms determined by two methods are similar, however a closer look reveals that peaks predicted under overloading conditions differ.     

Estimation of pure component properties. Part 4: Estimation of the saturated liquid viscosity of non-electrolyte organic compounds via group contributions and group interactions

A new group contribution method for the prediction of pure component saturated liquid viscosity has been developed. The method is an extension of the pure component property estimation techniques that we have developed for normal boiling points, critical property data, and vapour pressures. Predictions can be made from simply having knowledge of the molecular structure of the compound. In addition, the structural group definitions for the method are identical to those proposed for estimation of saturated vapour pressures. Structural groups were defined in a standardized form and fragmentation of the molecular structures was performed by an automatic procedure to eliminate any arbitrary assumptions. The new method is based on liquid viscosity data for more than 1600 components. Results of the new method are compared to several other estimation methods published in literature and are found to be significantly better. A relative mean deviation in viscosity of 15.3% was observed for 813 components (12,139 data points). By comparison, the Van Velzen method, the best literature method in our benchmarking exercise produced a relative mean deviation of 92.8% for 670 components (11,115 data points). Estimation results at the normal boiling temperature were also tested against an empirical rule for more than 4000 components. The range of the method is usually from the triple or melting point to a reduced temperature of 0.75–0.8. Larger than average deviations were observed in the case of molecules with higher rotational symmetry, but no specific correction of this effect was included in this method.     

An energy dissipation approach on complete loading-unloading and dynamic impact predictions with experimental verification for rubber anti-vibration component

Accurate evaluations of a completed loading-unloading cycle and dynamic impact response for rubber anti-vibration components have been very challenging for industry over many years. In this article, we have altered the classic hyperelastic models to predict complete loading-unloading response using an energy dissipation approach. In addition, we proposed NFR (Natural Frequency Region) approach to simulate a dynamic impact event instead of using the usual viscoelastic methodology, as results from different viscoelastic models may vary widely and to avoid complex parameter fitting procedures. The proposed approaches have been validated in laboratory experiments using industrial anti-vibration components. We have also detailed a procedure for engineers to implement this approach in commercial finite element software without writing intricate user subroutines, as simulation based on finite element method has been routinely used in industry to support design of new products. It is suggested that these methodologies could be used for a design stage in engineering applications.     

不定物种的多组分体系同时测定：逐步回归法解析紫外光谱矢量数据的研究

本文采用逐步回归法对不定物种的多组分体系的紫外光谱矢量数据进行解析，可对物种及物种含量同时进行定性和定量分析，通过对两个复方药物体系（复方扑热息痛与复方维生素Ｂ）的紫外光谱数据的实际解析，表明本法能有效地完成混合物中物种及物种含量的同时定性和定量测定。     

Recent Progress in Fluorescence Signal Design for DNA-Based Logic Circuits

DNA-based logic circuits, encoding algorithms in DNA and processing information, are pushing the frontiers of molecular computers forward, owing to DNA′s advantages of stability, accessibility, manipulability, and especially inherent biological significance and potential medical application. In recent years, numerous logic functions, from arithmetic to nonarithmetic, have been realized based on DNA. However, DNA can barely provide a detectable signal by itself, so that the DNA-based circuits depend on extrinsic signal actuators. The signal strategy of carrying out a response is becoming one of the design focuses in DNA-based logic circuit construction. Although work on sequence and structure design for DNA-based circuits has been well reviewed, the strategy on signal production lacks comprehensive summary. In this review, we focused on the latest designs of fluorescent output for DNA-based logic circuits. Several basic strategies are summarized and a few designs for developing multi-output systems are provided. Finally, some current difficulties and possible opportunities were also discussed.