Method For Identification Of Components Research Articles

Chemical Profiling and Characterization of Anthraquinones from Two Bulbine Species and Dietary Supplements Using Liquid Chromatography-High Resolution Mass Spectrometry.

Bulbine natalensis Baker and Bulbine frutescens (L.) Willd., belonging to the family Asphodelaceae, are widely distributed in South Africa and traditionally used as an aphrodisiac and skin remedies. The aim of this study is to develop an analytical method for chemical profiling and identification of components in Bulbine species, which would be useful for herbal identification and understanding of the biological activity of B. natalensis in terms of safety and benefits to human health. The anthraquinone-type compounds were structurally characterized from the extracts of dried stem and roots of Bulbine species and dietary supplements using liquid chromatography-quadrupole time-of-flight mass spectrometry (LC-QToF) with negative and positive ion electrospray. The calculated accurate masses of the protonated and deprotonated molecules and fragment ions were used for identification of the components from two Bulbine species. A total of 55 anthraquinone-type compounds, including 11 standard compounds, were identified in the crude extracts of two Bulbine species. Two Bulbine species and dietary supplements were clustered into different groups and possible chemical markers were identified. The developed analytical method provided a fast and economic method for quality assessment of Bulbine species in dietary supplements based on anthraquinone-type compounds. This study reports holistic chemical profiling of Bulbine species using LC-QToF. The developed analytical method enabled non-targeted analysis of components in B. natalensis and B. frutescens, and is recommended for commercial and regulatory purposes.

Component identification for Raman spectra with deep learning network

Raman spectroscopy is widely used in the research of the molecular structure of substances because of the advantages of no invasion, no damage and no interference from water. Meanwhile, component identification for mixtures is still challenging in Raman spectra. In this paper, a graphics-based sample-generating method and a model based on deep-learning for component identification was proposed. Convolution neural network (CNN) model is an important part of deep learning network and CNN models was utilized to assess the possibility of the presence of components in samples. As is shown in the comparative studies, the model was sensitive to the relative position of the characteristic peaks and could learn spectra features in mixtures. The deep-learning based component identification method showed more robustness than conventional linear fitting methods. Therefore, the method provided a valid approach to component identification for mixtures and has the potential in spectra component analysis.

Validation of SOBI-DANS method for automatic identification of horizontal and vertical eye movement components from EEG.

Neurophysiological investigation of neural processes are hindered by the presence of large artifacts associated with eye movement. Although blind source separation (BSS)-based hybrid algorithms are useful for separating, identifying, and removing these artifacts from EEG, it remains unexamined to what extent neural signals can remain mixed with these artifact components, potentially resulting in unintended removal of critical neural signals. Here, we present a novel validation approach to quantitatively evaluate to what extent horizontal and vertical saccadic eye movement-related artifact components (H and V Comps) are indeed ocular in origin. To automate the identification of the H and V Comps recovered by the second-order blind identification (SOBI), we introduced a novel Discriminant ANd Similarity (DANS)-based method. Through source localization, we showed that over 95% of variance in the SOBI-DANS identified H and V Comps' scalp projections were ocular in origin. Through the analysis of saccade-related potentials (SRPs), we found that the H and V Comps' SRP amplitudes were finely modulated by eye movement direction and distance jointly. SOBI-DANS' component selection was in 100% agreement with human experts' selection and was 100% successful in component identification across all participants indicating a high cross-individual consistency or robustness. These results set the stage for future work to transform the to-be-thrown-away artifacts into signals indicative of gaze position, thereby providing readily co-registered eye movement and neural signal without using a separate eye tracker.

Rapid classification and identification of chemical constituents in Epimedium koreanum Nakai by UPLC-Q-TOF-MS combined with data post-processing techniques.

Epimedium koreanum Nakai (EKN), is a well-known Chinese herbal medicine for the treatment of osteoporosis, immunosuppression, tumours and cardiovascular diseases. Comprehensive component identification is essential for elucidation of its pharmacological mechanism and quality control. However, its complex chemical composition has caused certain difficulties in the analysis of this traditional Chinese medicine (TCM). Therefore, there is an urgent need to establish a method for rapid classification and identification of EKN chemical components. To establish a method for rapid classification and identification of the main components of flavonoids, organic acids and alkaloids in EKN. The samples were analysed by ultra-high-performance liquid chromatography quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS) and data post-processing techniques. The UPLC system used a BEH C18 column to separate the total extract of EKN. The mobile phase consisted of 0.1% formic acid in water and acetonitrile, and the EKN extract was analysed by gradient elution at a flow rate of 0.4 mL/min. In both the positive and negative ion modes, the fragment information was obtained and compared with those of the characteristic fragmentations and neutral losses described in the literature to quickly identify the target compounds. Finally, we successfully screened out 51 chemical components, including 40 flavonoids, nine organic acids, and two alkaloids. The established method not only comprehensively analysed the chemical compositions of EKN, solved the difficult problems of analysis and identification of the complex chemical compositions of the TCM, but also further promoted the development of the application of chemical compositions of TCM.

SYSTEM BASED COMPONENT IDENTIFICATION USING COORDINATE DETERMINATION IN MIXED REALITY

AbstractThe maintenance of an industrial plant is mainly based on analogue and manual methods and processes. In the future, the maintenance data of the real object should be determined digital and transferred to a PDM system in real time. This is made possible with a digital method of component identification. This publication presents a system-based component identification using Mixed Reality. Coordinates are determined by the terminal device and compared with a reference structure. After successful identification, maintenance data can be determined and automatically transferred to a PDM System.

Vulnerability Identification and Evaluation of Interdependent Natural Gas-Electricity Systems

The intensified interdependency of natural gas and electricity systems poses urgent needs and new challenges in identifying vulnerable components of the interdependent system, which could be significantly different from those of individual systems because of the interdependency. This paper proposes a method to identify and rank vulnerable components of interdependent natural gas-electricity systems. Specially, a topological model and the vulnerability index for the interdependent system are constructed, and vulnerable component identification method is put forward to select and rank components by simultaneously considering topological and functional vulnerabilities. The proposed vulnerable component identification method is quantitatively assessed via a security evaluation approach, which calculates electricity and gas supply-demand imbalance when the vulnerable components are out of service. Numerical results of a 6-bus electricity/7-node gas system and a modified IEEE 118-bus electricity/20-node gas system illustrate effectiveness of the proposed method.

Efficient power component identification with long short-term memory and deep neural network

This paper tackles a recent challenge in patrol image processing on how to improve the identification accuracy for power component, especially for the scenarios including many interference objects. Our proposed method can fully use the patrol image information from live work, and it is thus different from traditional power component identification methods. Firstly, we use long short-term memory networks to synthesize the context information in a convolutional neural network. Then, we constructed the Mask LSTM-CNN model by combining the existing Mask R-CNN method and the context information. Further, by extracting the specific features belonging to the power components, we design an optimization algorithm to optimize the parameters of Mask LSTM-CNN model. Our solution is competitive in the sense that the power component is still identified accurately even if the patrol images contain much interference information. Extensive experiments show that the proposed scheme can improve the accuracy of component recognition and has an excellent anti-interference ability. Comparing with the existing R-FCN model and Faster R-CNN model, the proposed method demonstrates a significantly superior detection performance, and the average recognition accuracy is improved from 8 to 11%.

Software component identification and selection: A research review

SummaryNowadays, with the development of software reuse, software developers are paying more attention to component‐related technologies, which have been mostly applied in the development of large‐scale complex applications to enhance the productivity of software development and accelerate time to market. Component‐based software development is well acknowledged as a methodology, which establishes the reusability of software and reduces the development cost effectively. Two crucial problems in component‐based software development are component identification and component selection. The main purpose of this paper is to provide a reference point for future research by categorizing and classifying different component identification and component selection methods and emphasizing their respective strengths and weaknesses. We hope that it can help researchers find the current status of this issue and serve as a basis for future activities.

Novel method for rapid identification of micropapillary or solid components in early-stage lung adenocarcinoma

ObjectiveSublobar resection may be insufficient for early-stage lung adenocarcinoma with micropapillary or solid components because of the associated higher incidence of locoregional recurrence. This study sought to establish a novel method for rapidly identifying their presence to facilitate decision making for sublobar resection. MethodsAntibody arrays of adhesion and apoptosis molecules were applied for adenocarcinomas with or without micropapillary/solid components to identify differentially expressed proteins. A semi-dry dot-blot system that visualizes the presence of target proteins was used to determine the presence of micropapillary or solid components in a prospective cohort of patients with clinical stage I who underwent operation. Sensitivity and specificity were calculated by comparing semi-dry dot-blot results with pathologic examinations. ResultsInsulin-like growth factor-binding protein 2 and P-cadherin were found more frequently in the micropapillary or solid positive group, and these were used as the target proteins in the semi-dry dot-blot system for detection of micropapillary or solid components. A total of 68 nodules with a mean size of 2.3 ± 0.7 cm, including 13 (19.1%) with a micropapillary and 20 (29.4%) with a solid pattern, were recruited. Micropapillary or solid (+) lesions were more likely to have lymph node upstaging, greater diameter, and higher maximum standardized uptake value. The specificity and sensitivity for detecting the minor presence of micropapillary or solid component using the semi-dry dot-blot method were 94.4% (95% confidence interval, 81.3-99.3) and 65.6% (95% confidence interval, 46.8-81.4), respectively. The average test duration was 26.9 ± 2.5 minutes. ConclusionsDetecting insulin-like growth factor-binding protein 2 and P-cadherin via the semi-dry dot-blot method could identify micropapillary or solid components in early-stage lung adenocarcinoma in a short processing time.

Identification and High-Resolution Imaging of \u03b1-Tocopherol from Human Cells to Whole Animals by TOF-SIMS Tandem Mass Spectrometry

A unique method for identification of biomolecular components in different biological specimens, while preserving the capability for high speed 2D and 3D molecular imaging, is employed to investigate cellular response to oxidative stress. The employed method enables observing the distribution of the antioxidant α-tocopherol and other molecules in cellular structures via time-of-flight secondary ion mass spectrometry (TOF-SIMS (MS1)) imaging in parallel with tandem mass spectrometry (MS2) imaging, collected simultaneously. The described method is employed to examine a network formed by neuronal cells differentiated from human induced pluripotent stem cells (iPSCs), a model for investigating human neurons in vitro. The antioxidant α-tocopherol is identified in situ within different cellular layers utilizing a 3D TOF-SIMS tandem MS imaging analysis. As oxidative stress also plays an important role in mediating inflammation, the study was expanded to whole body tissue sections of M. marinum-infected zebrafish, a model organism for tuberculosis. The TOF-SIMS tandem MS imaging results reveal an increased presence of α-tocopherol in response to the pathogen.Graphical ᅟ

Rapid characterization of TCM Qianjinteng by UPLC-QTOF-MS and its application in the evaluation of three species of Stephania

A method for rapid identification of bioactive components in herbals from Stephania species was developed by utilizing UPLC-QTOF-MS/MS in this study. Sixty-three alkaloids including the types of proaporphine, aporphine, protoberberine, morphine, hasubanan, benzylisoquinoline and bisbenzylisoquinoline were characterized. Among them, 29 components were selected for semi-quantitative analyses to characterize the difference of chemical compositions among three Stephania species. The results revealed that bisbenzylisoquinoline-type of alkaloids are the representative components of SB (Subgen. Botryodiscia). The content of aporphine-type of alkaloids was much higher in ST (Subgen. Tuberiphania) than that in SS (Subgen. Stephania), while the content of protoberberine-type of alkaloids was higher than that in ST. The present study offered an efficient approach for quality control of Qianjinteng based on the rapid identification and semi-quantification of alkaloids in Stephania species by using UPLC-QTOF-MS.

Presurgical localization and spatial shift of resting state networks in patients with brain metastases.

Brain metastases are the most prevalent cerebral tumors. Resting state networks (RSNs) are involved in multiple perceptual and cognitive functions. Therefore, precisely localizing multiple RSNs may be extremely valuable before surgical resection of metastases, to minimize neurocognitive impairments. Here we aimed to investigate the reliability of independent component analysis (ICA) for localizing multiple RSNs from resting-state functional MRI (rs-fMRI) data in individual patients, and further evaluate lesion-related spatial shifts of the RSNs. Twelve patients with brain metastases and 14 healthy controls were recruited. Using an improved automatic component identification method, we successfully identified seven common RSNs, including: the default mode network (DMN), executive control network (ECN), dorsal attention network (DAN), language network (LN), sensorimotor network (SMN), auditory network (AN) and visual network (VN), in both individual patients and controls. Moreover, the RSNs in the patients showed a visible spatial shift compared to those in the controls, and the spatial shift of some regions was related to the tumor location, which may reflect a complicated functional mechanism - functional disruptions and reorganizations - caused by metastases. Besides, higher cognitive networks (DMN, ECN, DAN and LN) showed significantly larger spatial shifts than perceptual networks (SMN, AN and VN), supporting a functional dichotomy between the two network groups even in pathologic alterations associated with metastases. Overall, our findings provide evidence that ICA is a promising approach for presurgical localization of multiple RSNs from rs-fMRI data in individual patients. More attention should be paid to the spatial shifts of the RSNs before surgical resection.

An automated method for identifying an independent component analysis-based language-related resting-state network in brain tumor subjects for surgical planning

As a noninvasive and “task-free” technique, resting-state functional magnetic resonance imaging (rs-fMRI) has been gradually applied to pre-surgical functional mapping. Independent component analysis (ICA)-based mapping has shown advantage, as no a priori information is required. We developed an automated method for identifying language network in brain tumor subjects using ICA on rs-fMRI. In addition to standard processing strategies, we applied a discriminability-index-based component identification algorithm to identify language networks in three different groups. The results from the training group were validated in an independent group of healthy human subjects. For the testing group, ICA and seed-based correlation were separately computed and the detected language networks were assessed by intra-operative stimulation mapping to verify reliability of application in the clinical setting. Individualized language network mapping could be automatically achieved for all subjects from the two healthy groups except one (19/20, success rate = 95.0%). In the testing group (brain tumor patients), the sensitivity of the language mapping result was 60.9%, which increased to 87.0% (superior to that of conventional seed-based correlation [47.8%]) after extending to a radius of 1 cm. We established an automatic and practical component identification method for rs-fMRI-based pre-surgical mapping and successfully applied it to brain tumor patients.

Rapid Classification and Identification of Chemical Components of Schisandra Chinensis by UPLC-Q-TOF/MS Combined with Data Post-Processing.

Schisandra chinensis (known in Chinese as WuWeiZi, WWZ) has observable effects such as astringing the lung to stop coughs, arresting sweating, preserving semen and preventing diarrhea. The major components of WWZ include lignans, triterpenoids, organic acids and fatty acids. In this paper, a reliable method for the rapid identification of multiple components in WWZ by their characteristic fragments and neutral losses using UPLC-Q-TOF/MS technology was developed. After review of the literature and some reference experiments, the fragmentation pattern of several compounds were studied and summarized. Then, according to the corresponding characteristic fragments coupled with neutral losses in the positive or negative ion mode produced by different types of substances a rapid identification of target compounds was achieved. Finally, a total of 30 constituents of WWZ were successfully identified, including 15 lignans, nine triterpenoids, three organic acids and three fatty acids. The method established in this study not only provides a comprehensive analysis of the chemical ingredients of WWZ, providing a basis for further phytochemical studies on WWZ but also provides a more efficient way to solve the problem of identification of complex chemical constituents in traditional Chinese medicines.

PCI-PSO: Preference-Based Component Identification Using Particle Swarm Optimization

Abstract Nowadays, component identification is one of the main challenges of software analysis and design. The component identification process aims at clustering classes into components and subcomponents. There are a number of methods to identify components in the literature; however, most of them cannot be customized to software architect’s preferences. To address this limitation, in this paper, we propose a preference-based method by the name of preference-based component identification using particle swarm optimization (PCI-PSO) to identify logical components. PCI-PSO provides a novel method to handle the software architect’s preferences using an interactive (i.e. human in the loop) search. PCI-PSO employs a customized PSO to automatically classify classes into suitable logical components and avoid the problem of identifying the proper number of components. We evaluated the effectiveness of PCI-PSO with four real-world cases. Results revealed that PCI-PSO has an ability to identify more cohesive and independent components with respect to the software architect’s preferences in comparison to the existing component identification methods.

Long-term intensive gymnastic training induced changes in intra- and inter-network functional connectivity: an independent component analysis.

Long-term intensive gymnastic training can induce brain structural and functional reorganization. Previous studies have identified structural and functional network differences between world class gymnasts (WCGs) and non-athletes at the whole-brain level. However, it is still unclear how interactions within and between functional networks are affected by long-term intensive gymnastic training. We examined both intra- and inter-network functional connectivity of gymnasts relative to non-athletes using resting-state fMRI (R-fMRI). R-fMRI data were acquired from 13 WCGs and 14 non-athlete controls. Group-independent component analysis (ICA) was adopted to decompose the R-fMRI data into spatial independent components and associated time courses. An automatic component identification method was used to identify components of interest associated with resting-state networks (RSNs). We identified nine RSNs, the basal ganglia network (BG), sensorimotor network (SMN), cerebellum (CB), anterior and posterior default mode networks (aDMN/pDMN), left and right fronto-parietal networks (lFPN/rFPN), primary visual network (PVN), and extrastriate visual network (EVN). Statistical analyses revealed that the intra-network functional connectivity was significantly decreased within the BG, aDMN, lFPN, and rFPN, but increased within the EVN in the WCGs compared to the controls. In addition, the WCGs showed uniformly decreased inter-network functional connectivity between SMN and BG, CB, and PVN, BG and PVN, and pDMN and rFPN compared to the controls. We interpret this generally weaker intra- and inter-network functional connectivity in WCGs during the resting state as a result of greater efficiency in the WCGs' brain associated with long-term motor skill training.

Rapid screening, separation, and detection of hydroxyl radical scavengers from total flavonoids of Ginkgo biloba leaves by chromatography combined with molecular devices.

Hydroxyl radicals are the most reactive free radical of human body, a strong contributor to tissue damage. In this study, liquid chromatography coupled to electrospray ionization mass spectrometry was applied to screen and identify hydroxyl radical scavengers from the total flavonoids of Ginkgo biloba leaves, and high-performance counter current chromatography was used to separate and isolate the active compounds. Furthermore, molecular devices were used to determine hydroxyl radical scavenging activities of the obtained hydroxyl radical scavengers and other flavonoids from G. biloba leaves. As a result, six compounds were screened as hydroxyl radical scavengers, but only three flavonoids, namely, rutin, cosmos glycosides and apigenin-7-O-Glu-4'-O-Rha, were isolated successfully from total flavonoids by high-performance counter current chromatography. The purities of the three obtained compounds were over 90%, respectively, as determined by liquid chromatography. Molecular devices with 96-well microplates evaluation indicated that the 50% scavenging concentration values of screened compounds were lower than that of other flavonoids, they performed greater hydroxyl radical scavenging activity, and the evaluation effects were consistent with the liquid chromatography with mass spectrometry screening results. Therefore, chromatography combined with molecular devices is a feasible and an efficient method for systematic screening, identification, isolation, and evaluation of bioactive components in mixture of botanical medicines.

New RP-HPLC Method for Separation of Naja haje haje Venom and Studies of its Bactericidal Effect

Abstract Background: Snake venom is a complex mixture of biologically active substances. Some peptides and proteins from snake have already demonstrated their therapeutically potential. The venom of Naja haje, an Elapidae member, has been analyzed from this point of view. Understanding the fully biochemical role of its enzymes has determined the scientists to find new separation and identification methods. Objective: Our goal was to develop an optimal HPLC analytical method for separation and identification of Naja haje snake venom components, known for its neurotoxic activity. In addition, we wanted to find out if crude snake venom could inhibit the development of both Gram-positive and Gram-negative bacterial cultures. Materials and Method: Analysis of venom was performed on a HPLC system using a C16 column with UV detection at 210 nm. The analysis was done using two mobile phases, containing different concentrations of acetonitrile and trifluoroacetic acid aqueous solution at different pH values. An elution gradient was set at a flow of 0.60 mL/min. Bactericidal activity was quantified by measuring inhibition diameter around an aseptically disk filled with crude venom using Staphylococcus aureus and Escherichia coli. Results: An optimal HPLC analytical method has been developed by changing different parameters such as the pH value of mobile phase A or the elution gradient. The best resolution were obtained at a pH value of 7.4, in gradient varying from 5% to 45% in mobile phase B. Microbiological studies of the venom showed that Gram-positive bacteria growth was inhibited by crude venom, while on Gram-negative bacteria growth no effect was observed. Inhibition zone is dose-dependent and fresh crude venom is with 30% more potent than venom freeze and kept at -55°C. Conclusions: A comprehensive catalog of venom composition may serve as a starting point for studying structurefunction correlations of individual toxins for the development of new research tools and drugs of potential clinical use.

Межкультурное исследование эмоциональной составляющей воспринимаемого качества акустических событий

In the article, the results of empirical study of peculiarities of acoustical events’ perception by representatives of three cultural groups from Tuva, Almaty (Kazakhstan) and Moscow are presented. The aim of the study based on the perceived quality paradigm was to contribute to creation of the library of emotionally charged acoustical events. The results of an analysis of verbalizations produced by participants when perceiving acoustical events are given. A set of criterions are proposed to identify an emotionally charged acoustical event. The criterions were used to identify from a set of 23 sounds those 7 ones that were unambiguously attributed by all the three groups of listeners to the concrete emotions. Perspectives of further research and conditions of selecting the ecologically valid sounds for the library are outlined. Elaboration of the method and criteria for identification of emotional components of acoustical events’ perceived quality was financially supported by the Russian Foundation for Basic Research (grant 15-06-05499 “Perceived quality of acoustical environment in the course of technological changes”, under the direction of V. N. Nosulenko). Empirical data was gathered in the framework of the President grant MK-7002.2015.6 “Ethno-psychological peculiarities of perception of emotionally charged acoustical events” (under the direction of N.A. Vyskochil).

Component identification method based on semantic similarity and the cluster algorithm

Component identification is a key problem in software reuse. In order to obtain a set of business components BCs with high reuse value and good reuse performance to support reuse, a BC design method based on the cluster algorithm was proposed. Through analysing existing business models, element composite models were described to divide the domain by analysing the conception semantics of the transaction field. The hierarchical clustering analysis technique based on the similarity degree among activities was also given. In the identification process, the concept of business element similarity which can overcome the limitation of the domain platform was given. Commonality, variability, granularity, and reuse cost were taken into account in the method. Experiment results show that the valuation and performance of reusability for the transaction component are improved effectively, especially the design in the platform independent model.