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From One Strand Dyed/Undyed Hair With/Without Root to Fast and Successful STR Profiling and Evaluation With principle component analysis

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ELECTROPHORESIS 2025年 2025 May 2页

作者： Rayimoglu, Gulten Yonar, Fatma Cavus Anilanmert, Beril Istanbul Univ Cerrahpasa Inst Forens Sci & Legal Med Istanbul Turkiye

Fallen/shed single hair shafts with/without roots can be encountered as evidence in crime scenes. Identification from hair exhibits serious difficulties, due to the low amount of DNA, PCR inhibitors, and the existence of DNA mostly in degraded forms. Exposure of hair to chemical oxidation, sunlight, etc. brings extra difficulties to DNA analysis. In single hair shafts, no satisfactory nuDNA test method that can be used both for dyed/undyed single hair shafts with/without root exists in the literature. Next-generation systems are expensive, have a labor-intensive workflow, and are not available in many forensic laboratories. Here an STR profiling method has been developed modifying an isolation kit method for an easy, successful DNA for (1) dyed/undyed single hairs with root and (2) without root from 36 individuals and compared with the unmodified method (total 432 analyzed samples). Modified silica-based isolation method, yielded 0.65-1.05 ng/mu L DNA. The success of STR typing after capillary electrophoresis was 96%-98% for dyed/undyed hair with roots and 89%-93% for rootless ones, while the results for the unmodified method were 67%-82%. The results were investigated using PCA, t-test, and F-test. This protocol is simple, inexpensive, does not require automation, and can be performed using kits/equipment available in every forensic laboratory for forensic genotyping.

关键词： degraded DNA in hair strand dyed/undyed hair hair with root principle component analysis rootless hair STR profiling

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principle component analysis for radiotracer signal separation

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APPLIED RADIATION AND ISOTOPES 2016年 112卷 20-26页

作者： Kasban, H. Arafa, H. Elaraby, S. M. S. Atom Energy Author Nucl Res Ctr Dept Engn Cairo Egypt

Radiotracers can be used in several industrial applications by injecting the radiotracer into the industrial system and monitoring the radiation using radiation detectors for obtaining signals. These signals are analyzed to obtain indications about what is happening within the system or to determine the problems that may be present in the system. For multi-phase system analysis, more than one radiotracer is used and the result is a mixture of radiotracers signals. The problem is in such cases is how to separate these signals from each other. The paper presents a proposed method based on principle component analysis (PCA) for separating mixed two radiotracer signals from each other. Two different radiotracers (Technetium-99m (Tc-99m) and Barium-137m (Ba-137m)) were injected into a physical model for simulation of chemical reactor (PMSCR-MK2) for obtaining the radiotracer signals using radiation detectors and Data Acquisition System (DAS). The radiotracer signals are mixed and signal processing steps are performed include background correction and signal de-noising, then applying the signal separation algorithms. Three separation algorithms have been carried out;time domain based separation algorithm, Independent component analysis (ICA) based separation algorithm, and Principal components analysis (PCA) based separation algorithm. The results proved the superiority of the PCA based separation algorithm to the other based separation algorithm, and PCA based separation algorithm and the signal processing steps gives a considerable improvement of the separation process. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Radiotracer Tc-99m Ba-137m Signal separation principle component analysis

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principle component analysis in Conjuction with Data Driven Methods for Sediment Load Prediction

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WATER RESOURCES MANAGEMENT 2013年第7期27卷 2541-2554页

作者： Tayfur, Gokmen Karimi, Yashar Singh, Vijay P. Izmir Inst Technol Dept Civil Engn Izmir Turkey Ege Univ Dept Civil Engn Izmir Turkey Texas A&M Univ Dept Civil Engn College Stn TX 77843 USA

This study investigates sediment load prediction and generalization from laboratory scale to field scale using principle component analysis (PCA) in conjunction with data driven methods of artificial neural networks (ANNs) and genetic algorithms (GAs). Five main dimensionless parameters for total load are identified by using PCA. These parameters are used in the input vector of ANN for predicting total sediment loads. In addition, nonlinear equations are constructed, based upon the same identified dimensionless parameters. The optimal values of exponents and constants of the equations are obtained by the GA method. The performance of the so-developed ANN and GA based methods is evaluated using laboratory and field data. Results show that the expert methods (ANN and GA), calibrated with laboratory data, are capable of predicting total sediment load in field, thus showing their transferability. In addition, this study shows that the expert methods are not transferable for suspended load, perhaps due to insufficient laboratory data. Yet, these methods are able to predict suspended load in field, when trained with respective field data.

关键词： principle component analysis Sediment load Artificial neural network Genetic algorithm Transferability

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principle component analysis (PCA) and second-order global hard-modelling for the complete resolution of transition metal ions complex formation with 1,10-phenantroline

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ANALYTICA CHIMICA ACTA 2009年第1期648卷 60-70页

作者： Shariati-Rad, Masoud Hasani, Masoumeh Bu Ali Sina Univ Fac Chem Hamadan 65174 Iran

Second-order global hard-modelling was applied to resolve the complex formation between Co2+, Ni2+, and Cd2+ cations and 1,10-phenantroline. The highly correlated spectral and concentration profiles of the species in these systems and low concentration of some species in the individual collected data matrices prevent the well-resolution of the profiles. Therefore, a collection of six equilibrium data matrices including series of absorption spectra taken with pH changes at different reactant ratios were analyzed. Firstly, a precise principle component analysis (PCA) of different augmented arrangements of the individual data matrices was used to distinguish the number of species involved in the equilibria. Based on the results of PCA, the equilibria included in the data were specified and second-order global hard-modelling of the appropriate arrangement of six collected equilibrium data matrices resulted in well-resolved profiles and equilibrium constants. The protonation constant of the ligand (1,10-phenantroline) and spectral profiles of its protonated and unprotonated forms are the additional information obtained by global analysis. For comparison, multivariate curve resolution-alternating least squares (MCR-ALS) was applied to the same data. The results showed that second-order global hard-modelling is more convenient compared with MCR-ALS especially for systems with completely known model. It can completely resolve the system and the concentration profiles which are closer to correct ones. Moreover, parameters showing the goodness of fit are better with second-order global hard-modelling. (C) 2009 Elsevier B.V. All rights reserved.

关键词： Second-order global hard-modelling Multivariate curve resolution-alternating least squares principle component analysis Complex formation 1,10-phenantroline

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principle component analysis combined with matrix-assisted laser desorption ionization mass spectrometry for rapid diagnosing the sera of patients with major depression

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CLINICA CHIMICA ACTA 2013年 424卷 175-181页

作者： Huang, Tiao-Lai Cho, Yi-Tzu Su, Hong Shiea, Jentaie Kaohsiung Chang Gung Mem Dept Psychiat Kaohsiung 833 Taiwan Chang Gung Univ Coll Med Kaohsiung 833 Taiwan Yuh Ing Jr Coll Hlth Care & Management Dept Cosmet Applicat & Management Kaohsiung Taiwan Natl Sun Yat Sen Univ Dept Chem Kaohsiung 80424 Taiwan Kaohsiung Med Univ Dept Med & Appl Chem Kaohsiung Taiwan

Background: Previously, we demonstrated that 6 M HCl hydrolysis followed by matrix-assisted laser desorption/ionization (MALDI-TOF) mass spectrometry is a useful technique to detect potential protein biomarkers in the sera collected from major depression (MD) patients and from healthy controls. Methods: In this study, the effects of various organic acids in hydrolyzing proteins in serum were first examined. The organic matrixes commonly used in MALDI analysis were also examined for characterizing the hydrolyzed peptides. Finally, principle component analysis (PCA) was used to analyze the MALDI mass spectra of acid-hydrolyzed serum samples. Results: It was found that 20% TFA and sinapinic acid were the optimal reagents for hydrolysis and MALDI matrix. Samples collected from MD patients and healthy controls were readily classified through PCA analysis. A receiver operating characteristic (ROC) curve based on the ratio of the intensities of the two fragment ions (m/z 8606 and 9287) indicated by PCA plot was also constructed. The area under the curve was 0.845;the sensitivity and specificity were both 80%. Conclusions: An analytical platform employing trifluoroacetic acid to hydrolyze serum proteins followed by MALDI-TOF/MS and PCA analysis was developed to rapidly differentiate the sera between MD patients and healthy controls. (c) 2013 Elsevier B.V. All rights reserved.

关键词： Major depression Acid hydrolysis principle component analysis Protein biomarker Matrix-assisted laser desorption ionization/time-of-flight mass spectrometry

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principle component analysis in F/10 and G/11 xylanase

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BIOCHEMICAL AND BIOPHYSICAL RESEARCH COMMUNICATIONS 2004年第1期322卷 277-280页

作者： Liu, LW Zhang, J Chen, B Shao, WL So Yangtze Univ Minist Educ Key Lab Ind Biotechnol Jiangsu 214036 Peoples R China

A bioinformatics method was used to analyze F/10 and G/11 xylanase basing on principle component analysis, and a model was made to classify between these two folds with an ideal result. The principle components were predicated to be secondary structures, the components were analyzed with the architecture of each family, and found comparable with (beta/alpha)(8)-barrel of F/10 xylanase and right-hand structure of G/11 xylanase. Compared with sequence similarities, this method gave discriminating features a clear meaning. The largest component did not appear in the model, which revealed no difference between these two families. (C) 2004 Elsevier Inc. All rights reserved.

关键词： classification computation principle component analysis structure xylanase

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principle component analysis (PCA) for investigation of relationship between population dynamics of microbial pathogenesis, chemical and sensory characteristics in beef slices containing Tarragon essential oil

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MICROBIAL PATHOGENESIS 2017年第0期105卷 37-50页

作者： Behbahani, Behrooz Alizadeh Yazdi, Farideh Tabatabaei Shahidi, Fakhri Mortazavi, Seyed Ali Mohebbi, Mohebbat Ferdowsi Univ Mashhad Dept Food Sci & Technol POB 91775-1163 Mashhad Iran

principle component analysis (PCA) was employed to examine the effect of the exerted treatments on the beef shelf life as well as discovering the correlations between the studied responses. Considering the variability of the dimensions of the responses, correlation coefficients were applied to form the matrix and extract the eigenvalue. Antimicrobial effect was evaluated on 10 pathogenic microorganisms through the methods of hole plate diffusion method, disk diffusion method, pour plate method, minimum inhibitory concentration and minimum bactericidal/fungicidal concentration. Antioxidant potential and total phenolic content were examined through the method of 2,2-diphenyl-1-picrylhydrazyl (DPPH) and Folin-Ciocalteu method, respectively. The components were identified through gas chromatography and gas chromatography/mass spectrometry. Barhang seed mucilage (BSM) based edible coating containing 0, 0.5, 1 and 1.5% (w/w) Tarragon (T) essential oil mix were applied on beef slices to control the growth of pathogenic microorganisms. Microbiological (total viable count, psychrotrophic count, Escherichia coli, Staphylococcus aureus and fungi), chemical (thiobarbituric acid, peroxide value and pH) and sensory characteristics (odor, color and overall acceptability) analysis measurements were made during the storage periodically. PCA was employed to examine the effect of the exerted treatments on the beef shelf life as well as discovering the correlations between the studied responses. Considering the variability of the dimensions of the responses, correlation coefficients were applied to form the matrix and extract the eigenvalue. The PCA showed that the properties of the uncoated meat samples on the 9th, 12th, 15th and 18th days of storage are continuously changing independent of the exerted treatments on the other samples. This reveals the effect of the exerted treatments on the samples. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： principle component analysis Antimicrobial effect Tarragon essential oil Phytochemical analysis Beef

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principle component analysis on Photoplethysmograms: Blood Oxygen Saturation Estimation and Signal Segmentation

Principle Component Analysis on Photoplethysmograms: Blood O...

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33rd Annual International Conference of the IEEE Engineering-in-Medicine-and-Biology-Society (EMBS)

作者： Li, Kejia Warren, Steve Kansas State Univ Dept Elect & Comp Engn Manhattan KS 66506 USA

ISBN: (纸本)9781424441228

Most pulse oximeters determine blood oxygen saturation (SpO(2)) after calculating a coefficient, R, that represents the normalized ratiometric contributions of the pulsatile red and near-infrared photoplethysmograms (PPGs) acquired by the sensor. This paper presents a new approach that uses principle component analysis (PCA) to separate the signal and noise components of unfiltered PPGs and provide the determination of R. Also, rather than use peak-to-valley time intervals to obtain R, this technique uses eigenvalue and eigenvector data obtained during PCA to optimize these time intervals and improve the R calculation. Early analyses on unfiltered PPGs from 16 subjects indicate that these R values compare to those obtained from FFT-based methods and yield SpO(2) values consistent with those reported by a commercial unit. All signal data are considered during the PCA process, so this technique shows promise to precisely segment clean versus noise-corrupted PPGs.

关键词： artifact optimization photoplethysmogram principle component analysis pulse oximetry segmentation

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principle component analysis Based Cooperative Spectrum Sensing in Cognitive Radio 16

Principle Component Analysis Based Cooperative Spectrum Sens...

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16th International Symposium on Communications and Information Technologies (ISCIT)

作者： Chen, Xin Hou, Fukang Huang, Hai Jing, Xiaojun Beijing Univ Posts & Telecommun Minist Educ Key Lab Trustworthy Distributed Comp & Serv BUPT Beijing Peoples R China Beijing Univ Posts & Telecommun Sch Informat & Commun Engn Beijing Peoples R China

ISBN: (纸本)9781509040995

This paper introduces an improved cooperative spectrum sensing (CSS) algorithm for cognitive radio (CR) networks based on the machine learning technique. In this scheme, spectrum sensing consists of two steps. The offline training is performed by K-means clustering method and the threshold is defined by the classification result towards unsupervised data. As for the online classification stage, the similarity between the received signal and the cluster in training data is exploited for channel availability decision. The features of both the received signal and the training samples are extracted by principle component analysis (PCA). The fusion center will make final decision according to the local sensing result from differently sensors sequentially. Each sensor is responsible for the analysis towards certain period of the received signal. In this way, the initial sensing result (which will be updated due to the latest input signal) is available within short period of time. If the primary signal existed, the initial sensing result can stop further detection which improves the efficiency of spectrum sensing and avoid more interference to primary users. Compare to the existed methods of spectrum sensing, the threshold of the proposed scheme is measured by feature similarity in multi-dimension rather than a fixed value. In simulation, we evaluate the performance by making comparison between different method and the result reveals that our scheme can achieve better performance and work efficiently.

关键词： cognitive radio cooperative spectrum sensing principle component analysis K-means clustering

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An intelligent diagnosis system based on principle component analysis and ANFIS for the heart valve diseases

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EXPERT SYSTEMS WITH APPLICATIONS 2009年第2期36卷 2873-2878页

作者： Avci, Engin Turkoglu, Ibrahim Firat Univ Dept Elect & Comp Sci TR-23119 Elazig Turkey

In this paper, an intelligent diagnosis system based on principle component analysis (PCA) and adaptive network based on fuzzy inference system (ANFIS) for the heart valve disease is introduced. This intelligent system deals with combination of the feature extraction and classification from measured Doppler signal waveforms at the heart valve using the Doppler ultrasound (DHS). Here, the wavelet entropy is used as features. This intelligent system has three phases. In pre-processing phase, the data acquisition and pre-processing for DHS signals are performed. In feature extraction phase, the feature vector is extracted by calculating the 12 wavelet entropy values for per DHS signal and dimension of Doppler signal dataset, which are 12 features, is reduced to 6 features using PCA. In classification phase, these reduced wavelet entropy features are given to inputs ANFIS classifier. The correct diagnosis performance of the PCA-ANFIS intelligent system is calculated in 215 samples. The classification accuracy of this PCA-ANFIS intelligent system was 96% for normal subjects and 93.1% for abnormal subjects. (c) 2008 Elsevier Ltd. All rights reserved.

关键词： principle component analysis ANFIS Doppler heart sounds Discrete wavelet decomposition Wavelet entropy

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