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Journal articles on the topic 'Kernelized multivariate fisher discriminant'
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1
Nakkiran, Arunadevi, and Vidyaa Thulasiraman. "Elastic net feature selected multivariate discriminant mapreduce classification." Indonesian Journal of Electrical Engineering and Computer Science 26, no. 1 (2022): 587. http://dx.doi.org/10.11591/ijeecs.v26.i1.pp587-596.
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Analyzing the <span>big stream data and other valuable information is a significant task. Several conventional methods are designed to analyze the big stream data. But the scheduling accuracy and time complexity is a significant issue. To resolve, an elastic-net kernelized multivariate discriminant map reduce classification (EKMDMC) is introduced with the novelty of elastic-net regularization-based feature selection and kernelized multivariate fisher Discriminant MapReduce classifier. Initially, the EKMDMC technique executes the feature selection to improve the prediction accuracy using the Elastic-Net regularization method. Elastic-Net regularization method selects relevant features such as central processing unit (CPU) time, memory and bandwidth, energy based on regression function. After selecting relevant features, kernelized multivariate fisher discriminant mapr classifier is used to schedule the tasks to optimize the processing unit. Kernel function is used to find higher similarity of stream data tasks and mean of available classes. Experimental evaluation of proposed EKMDMC technique provides better performance in terms of resource aware predictive scheduling efficiency, false positive rate, scheduling time and memory consumption.</span>
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Nakkiran, Arunadevi, and Vidyaa Thulasiraman. "Elastic net feature selected multivariate discriminant mapreduce classification." Indonesian Journal of Electrical Engineering and Computer Science 26, no. 1 (2022): 587–96. https://doi.org/10.11591/ijeecs.v26.i1.pp587-596.
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Analyzing the big stream data and other valuable information is a significant task. Several conventional methods are designed to analyze the big stream data. But the scheduling accuracy and time complexity is a significant issue. To resolve, an elastic-net kernelized multivariate discriminant map reduce classification (EKMDMC) is introduced with the novelty of elastic-net regularization-based feature selection and kernelized multivariate fisher Discriminant MapReduce classifier. Initially, the EKMDMC technique executes the feature selection to improve the prediction accuracy using the Elastic-Net regularization method. Elastic-Net regularization method selects relevant features such as central processing unit (CPU) time, memory and bandwidth, energy based on regression function. After selecting relevant features, kernelized multivariate fisher discriminant mapr classifier is used to schedule the tasks to optimize the processing unit. Kernel function is used to find higher similarity of stream data tasks and mean of available classes. Experimental evaluation of proposed EKMDMC technique provides better performance in terms of resource aware predictive scheduling efficiency, false positive rate, scheduling time and memory consumption.
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Saeroni, Amanah, Memi Nor Hayati, and Rito Goejantoro. "KLASIFIKASI TINGKAT KELANCARAN NASABAH DALAM MEMBAYAR PREMI DENGAN MENGGUNAKAN METODE K-NEAREST NEIGHBOR DAN ANALISIS DISKRIMINAN FISHER (Studi kasus: Data Nasabah PT. Prudential Life Samarinda Tahun 2019)." Jurnal Statistika Universitas Muhammadiyah Semarang 8, no. 2 (2020): 88. http://dx.doi.org/10.26714/jsunimus.8.2.2020.88-94.
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Classification is a technique to form a model of data that is already known to its classification group. The model that was formed will be used to classify new objects. The K-Nearest Neighbor (K-NN) algorithm is a method for classifying new objects based on their K nearest neighbor. Fisher discriminant analysis is a multivariate technique for separating objects in different groups to form a discriminant function for allocate new objects in groups. This research has a goal to determine the results of classifying customer premium payment status using the K-NN method and Fisher discriminant analysis and comparing the accuracy of the K-NN method classification and Fisher discriminant analysis on the insurance customer premium payment status. The data used is the insurance customer data of PT. Prudential Life Samarinda in 2019 with current premium payment status or non-current premium payment status and four independent variables are age, duration of premium payment, income and premium payment amount. The results of the comparative measurement of accuracy from the two analyzes show that the K-NN method has a higher level of accuracy than Fisher discriminant analysis for the classification of insurance customers premium payment status. The results of misclassification using the APER (Apparent Error Rate) in K-NN method is 15% while in Fisher discriminant analysis is 30%.
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Liu, Xingye, Jingye Li, Xiaohong Chen, Lin Zhou, and Kangkang Guo. "Bayesian discriminant analysis of lithofacies integrate the Fisher transformation and the kernel function estimation." Interpretation 5, no. 2 (2017): SE1—SE10. http://dx.doi.org/10.1190/int-2016-0025.1.
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The accurate identification of lithofacies is indispensable for reservoir parameter prediction. In recent years, the application of multivariate statistical methods has gained more and more attention in petroleum geology. In terms of the identification for lithofacies, the commonly used multivariate statistical methods include discriminant analysis and cluster analysis. Fisher and Bayesian discriminant analyses are two different discriminant analysis methods, which include intrinsic advantages and disadvantages. Given the discriminant efficiency of different methods, calculation cost, difficulty in the degree of determining the parameters, and the ability to analyze statistical characteristics of data, we put forward a new method combined with seismic information to classify reservoir lithologies and pore fluids. This method integrates the advantages of Fisher discrimination, the kernel function, and Bayesian discrimination. First, we analyze training data and search a projection direction. Then, data are transformed through Fisher transformation according to this direction and different kinds of facies can be distinguished more efficiently by exploiting transformed data than by using primitive data. Subsequently, using the kernel function estimates the conditional probability density function of the transformed variable. A classifier is constructed based on Bayesian theory. Then, the pending data are input to the classifier and the solution whose posteriori probability reaches the maximum is extracted as the predicted result at each grid node. An a posteriori probability distribution of predicted lithofacies can be acquired as well, from which interpreters can evaluate the uncertainty of the results. The ultimate goal of this study is to provide a novel and efficient lithofacies discriminant method. Tests on model and field data indicate that our method can obtain more accurate identification results with less uncertainty compared with conventional Fisher approaches and Bayesian methods.
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Imran, Sajida, and Young-Bae Ko. "A Novel Indoor Positioning System Using Kernel Local Discriminant Analysis in Internet-of-Things." Wireless Communications and Mobile Computing 2018 (2018): 1–9. http://dx.doi.org/10.1155/2018/2976751.
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WLAN based localization is a key technique of location-based services (LBS) indoors. However, the indoor environment is complex; received signal strength (RSS) is highly uncertain, multimodal, and nonlinear. The traditional location estimation methods fail to provide fair estimation accuracy under the said environment. We proposed a novel indoor positioning system that considers the nonlinear discriminative feature extraction of RSS using kernel local Fisher discriminant analysis (KLFDA). KLFDA extracts location features in a well-preserved kernelized space. In the new kernel featured space, nonlinear RSS features are characterized effectively. Along with handling of nonlinearity, KLFDA also copes well with the multimodality in the RSS data. By performing KLFDA, the discriminating information contained in RSS is reorganized and maximally extracted. Prior to feature extraction, we performed outlier detection on RSS data to remove any anomalies present in the data. Experimental results show that the proposed approach obtains higher positioning accuracy by extracting maximal discriminate location features and discarding outlying information present in the RSS data.
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Fan, Bing Chen. "Application of Progressively Statistical Discriminant Models." Applied Mechanics and Materials 55-57 (May 2011): 1922–25. http://dx.doi.org/10.4028/www.scientific.net/amm.55-57.1922.
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Discriminant analysis is an important multivariate statistical analysis, and plays an important part in pattern classification, data mining, machine learning et al. In this paper, based on principle of progressively statistical discriminant analysis under Fisher rule, a progressively statistical discriminant model is set up. The authors analyzed the data about the occurrence of the second generation of the corn borer in 21 years from 1985 to 2006 (except 1990) at Linyi, Shandong Province, and then set up three graded recognition pattern. The results tested the pest data showed that the fitting rate is 95.24%, 92.31% and 100% respectively, and that accuracy of forecast is satisfactory.
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Jing, CHEN, and GAO Caixia. "Sparse Linear Discriminant Analysis Based on lq Regularization." Frontiers of Chinese Pure Mathematics 1, no. 2 (2023): 31–38. http://dx.doi.org/10.48014/fcpm.20230529001.
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Linear discriminant analysis plays an important role in feature extraction, data dimensionality reduction, and classification. With the progress of science and technology, the data that need to be processed are becoming increasingly large. However, in high-dimensional situations, linear discriminant analysis faces two problems: the lack of interpretability of the projected data since they all involve all p features, which are linear combinations of all features, as well as the singularity problem of the within-class covariance matrix. There are three different arguments for linear discriminant analysis: multivariate Gaussian model, Fisher discrimination problem, and optimal scoring problem. To solve these two problems, this article establishes a model for solving the kth discriminant component, which first transforms the original model of Fisher discriminant problem in linear discriminant analysis by using a diagonal estimated matrix for the within-class variance in place of the original within-class covariance matrix, which overcomes the singularity problem of the matrix and projects it to an orthogonal projection space to remove its orthogonal constraints, and subsequently an lq norm regularization term is added to enhance its interpretability for the purpose of dimensionality reduction and classification. Finally, an iterative algorithm for solving the model and a convergence analysis are given, and it is proved that the sequence generated by the algorithm is descended and converges to a local minimum of the problem for any initial value.
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Ле Ван Хыонг, Нгуен Нгок Киенг, Нгуен Данг Хой, and Данг Хунг Куонг. "Applying Multivariate Statistical Methods for Predicting Pinus Forest Fire Danger at Bidoup-Nui Ba National Park." Труды Карадагской научной станции им. Т.И. Вяземского - природного заповедника РАН, no. 1 (13) (April 21, 2021): 45–53. http://dx.doi.org/10.21072/eco.2021.13.05.
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The paper presents results of applying multivariate statistical methods (CCA: canonical correlation analysis and DFA: discriminant function analysis) for determining canonical correlation between a set of variables {T, H, m1, K} and a set of variables {Pc, Tc} (T: temperature, H: relative humidity, m1: mass of dry fuels, K: burning coefficient, K = m1/M, with M: total mass of fire fuels, Pc: % burned fuels and Tc: burningtime) as well as through results of discriminant function analysis DFA to set up models of predicting forest fire danger at Bidoup - Nui Ba National Park. From research data in November, December, January, February and March in the period of 2015-2017 from 340 sampling plots (each 2mx2m), at Bidoup - Nui Ba National Park, we carry on data processing on Excel (calculating) and Statgraphics (multivariate statistical methods: CCA&DFA). Three results were revealed from our analysis: (i) Canonical correlation between a set of variables {T, H, m1, K} and a set of variables {Pc, Tc} is highly significant (R = 0.675581 & P = 3.17*10-58<< 0.05); therefore, we can use a set of variables {T, H, m1, K} in models of predicting forest fire danger, (ii) Coefficients of standardized & unstandardized canonical discriminant functions (SCDF &UCDF) and Fisher classification function (FCF) are determined, (iii) Setting up two models of predicting forest fire danger (Mahalanobis distance model & Fisher classification function model).
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Li, Hanqi, Mingxing Jia, and Zhizhong Mao. "Dynamic Feature Extraction-Based Quadratic Discriminant Analysis for Industrial Process Fault Classification and Diagnosis." Entropy 25, no. 12 (2023): 1664. http://dx.doi.org/10.3390/e25121664.
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This paper introduces a novel method for enhancing fault classification and diagnosis in dynamic nonlinear processes. The method focuses on dynamic feature extraction within multivariate time series data and utilizes dynamic reconstruction errors to augment the feature set. A fault classification procedure is then developed, using the weighted maximum scatter difference (WMSD) dimensionality reduction criterion and quadratic discriminant analysis (QDA) classifier. This method addresses the challenge of high-dimensional, sample-limited fault classification, offering early diagnosis capabilities for online samples with smaller amplitudes than the training set. Validation is conducted using a cold rolling mill simulation model, with performance compared to classical methods like linear discriminant analysis (LDA) and kernel Fisher discriminant analysis (KFD). The results demonstrate the superiority of the proposed method for reliable industrial process monitoring and fault diagnosis.
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Ainurrochmah, Alifta, Memi Nor Hayati, and Andi M. Ade Satriya. "Alifta Ainurrochmah, Perbandinga." Jurnal Aplikasi Statistika & Komputasi Statistik 11, no. 2 (2020): 37. http://dx.doi.org/10.34123/jurnalasks.v11i2.156.
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Classification is a technique to form a model of data that is already known to its classification group. The model was formed will be used to classify new objects. Fisher discriminant analysis is multivariate technique to separate objects in different groups. Naive Bayes is a classification technique based on probability and Bayes theorem with assumption of independence. This research has a goal to compare the level of classification accuracy between Fisher's discriminant analysis and Naive Bayes method on the insurance premium payment status customer. The data used four independent variables that is income, age, premium payment period and premium payment amount. The results of misclassification using the APER (Apparent Rate Error) indicate that the naive Bayes method has a higher level of accuracy is 15,38% than Fisher’s discriminant analysis is 46,15% on the insurance premium payment status customer.
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Hu, Fei, Fan Yang, Huiqun Xie, et al. "Predicting the Occurrence of Advanced Schistosomiasis Based on FISHER Discriminant Analysis of Hematological Biomarkers." Pathogens 11, no. 9 (2022): 1004. http://dx.doi.org/10.3390/pathogens11091004.
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We established a model that predicts the possibility of chronic schistosomiasis (CS) patients developing into advanced schistosomiasis (AS) patients using special biomarkers that were detected in human peripheral blood. Blood biomarkers from two cohorts (132 CS cases and 139 AS cases) were examined and data were collected and analyzed by univariate and multivariate logistic regression analysis. Fisher discriminant analysis (FDA) for advanced schistosomiasis was established based on specific predictive diagnostic indicators and its accuracy was assessed using data of 109 CS. The results showed that seven indicators including HGB, MON, GLB, GGT, APTT, VIII, and Fbg match the model. The accuracy of the FDA was assessed by cross-validation, and 86.7% of the participants were correctly classified into AS and CS groups. Blood biomarker data from 109 CS patients were converted into the discriminant function to determine the possibility of occurrence of AS. The results demonstrated that the possibility of occurrence of AS and CS was 62.1% and 89.0%, respectively, and the accuracy of the established model was 81.4%. Evidence displayed that Fisher discriminant analysis is a reliable predictive model in the clinical field. It’s an important guide to effectively control the occurrence of AS and lay a solid foundation for achieving the goal of schistosomiasis elimination.
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Chen, Song, Xue Hai Fu, He Rong Gui, and Lin Hua Sun. "Multivariate statistical analysis of the hydro-geochemical characteristics for Mining groundwater: a case study from Baishan mining, northern Anhui Province, China." Water Practice and Technology 8, no. 1 (2013): 131–41. http://dx.doi.org/10.2166/wpt.2013.014.
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Major ions were analyzed for twenty five groundwater samples collected from diverse aquifer in Baishan mining, northern Anhui province, China. Conventional graphical and multivariate statistical approach were completed to identify the hydro-geochemical process and water-rock interaction, that be combined with the Cluster Analysis (CA) and Fisher discriminant analysis to recognize the sealed samples, the result showed: the diverse samples have vary ions inheriting from aquifer, samples collected from Sandstone aquifer (SA) is characterized by the high concentration of Na+ + K+, for the feldspar weathering is dominant; Ordovician limestone aquifer (OA) waters have abundance Ca2+ and Mg2+, for the dissolution of calcite and dolomite obviously; the dissolution of calcite and other calcareous concretions are dominant in Taiyuan formation water (TA) and Quaternary aquifer (QA) for the high ratio of Ca2+/Mg2+, otherwise the varied content of SO42– and HCO3− could distinguish the two aquifer water. Twenty five groundwater samples, containing six sealed samples, had been subdivided in to four groups by the CA, what are corresponded with four aquifers. Fisher discriminant functions were obtained and the efficiency was acceptable for the error rate 4% in all twenty five samples.
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Jiang, Jian-Hui, Roumiana Tsenkova, Yuqing Wu, Ru-Qin Yu, and Yukihiro Ozaki. "Principal Discriminant Variate Method for Classification of Multicollinear Data: Applications to Near-Infrared Spectra of Cow Blood Samples." Applied Spectroscopy 56, no. 4 (2002): 488–501. http://dx.doi.org/10.1366/0003702021954944.
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A new regularized discriminant analysis technique, the principal discriminant variate (PDV) method, has been developed for effectively handling multicollinear data commonly encountered in multivariate spectroscopy-based classification. The motivation behind this method is to seek a sequence of discriminant directions that not only optimize the separability between different classes, but also account for a maximized variation present in the data. This motivation furnishes the PDV method with improved stability in prediction without significant loss of separability. Different formulations for the PDV methods are suggested, and an effective computing procedure is proposed for a PDV method. Two sets of near-infrared (NIR) spectra data, one corresponding to the blood plasma samples from two cows and the other associated with the whole blood samples from mastitic and healthy cows, have been used to evaluate the behavior of the PDV method in comparison with principal component analysis (PCA), discriminant partial least-squares (DPLS), soft independent modeling of class analogies (SIMCA), and Fisher linear discriminant analysis (FLDA). Results obtained demonstrate that the NIR spectra of blood plasma samples from different classes are clearly discriminated by the PDV method, and the proposed method provides superior performance to PCA, DPLS, SIMCA, and FLDA, indicating that PDV is a promising tool in discriminant analysis of spectra-characterized samples with only small compositional differences.
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Han, F., X. Huang, E. Teye, H. Gu, H. Dai, and L. Yao. "A nondestructive method for fish freshness determination with electronic tongue combined with linear and non-linear multivariate algorithms." Czech Journal of Food Sciences 32, No. 6 (2014): 532–37. http://dx.doi.org/10.17221/88/2014-cjfs.
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Electronic tongue coupled with linear and non-linear multivariate algorithms was attempted to address the drawbacks of fish freshness detection. Parabramis pekinensis fish samples stored at 4&deg;C were used. Total volatile basic nitrogen (TVB-N) and total viable count (TVC) of the samples were measured. Fisher liner discriminant analysis (Fisher LDA) and support vector machine (SVM) were applied comparatively to classify the samples stored at different days. The results revealed that SVM model was better than Fisher LDA model with a higher identification rate of 97.22% in the prediction set. Partial least square (PLS) and support vector regression (SVR) were applied comparatively to predict the TVB-N and TVC values. The quantitative models were evaluated by the root mean square error of prediction (RMSEP) and the correlation coefficient in the prediction set (R<sub>pre</sub>). The results revealed that SVR model was superior to PLS model with RMSEP = 5.65 mg/100 g, R<sub>pre</sub> = 0.9491 for TVB-N prediction and RMSEP = 0.73 log CFU/g, R<sub>pre</sub>&nbsp;= 0.904 for TVC prediction. This study demonstrated that the electronic tongue together with SVM and SVR has a great potential for a convenient and nondestructive detection of fish freshness. &nbsp;
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Zhang, Jie, Wenna Guo, Qiao Li, Faxin Sun, Xiaomeng Xu, and Hui Xu. "Discriminant Analysis of Traditional Chinese Medicinal Properties Based on Holistic Chemical Profiling by 1H-NMR Spectrometry." Evidence-Based Complementary and Alternative Medicine 2020 (March 9, 2020): 1–7. http://dx.doi.org/10.1155/2020/3141340.
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Medicinal property, which is closely related to drug chemical profiling, is the essence of traditional Chinese medicine (TCM) theory and has always been the focus of modern Chinese medicine. Based on dozens of classic and commonly used TCM herbs with recognized medicinal properties, the present study just aimed to investigate the feasibility and reliability of medicinal property discriminant by using 1H-NMR spectrometry, which provided a mass of spectral data showing holistic chemical profile for multivariate analysis and data mining, including principal component analysis (PCA), Fisher linear discriminant analysis (FLDA), and canonical discriminant analysis (CDA). By using FLDA for two-class recognition, a large majority of test herbs (59/61) were properly discriminated as cold or hot group, and the only two exceptions were Chuanbeimu (Fritillariae Cirrhosae Bulbus) and Rougui (Cinnamomi Cortex), suggesting that medicinal properties interrelate with flavor and body tropism, and all these factors together bring up medicinal property and efficacy. While by performing CDA, 98.4% of the original grouped herbs and 77.0% of the leave-one-out cross-validated grouped cases were correctly classified. The findings demonstrated that discriminant analysis based on holistic chemical profiling data by 1H-NMR spectrometry may provide a powerful alternative to have a deeper understanding of TCM medicinal property.
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Lim, Jong Gwan, Mi-hye Kim, and Sahngwoon Lee. "Empirical Validation of Objective Functions in Feature Selection Based on Acceleration Motion Segmentation Data." Mathematical Problems in Engineering 2015 (2015): 1–12. http://dx.doi.org/10.1155/2015/280140.
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Recent change in evaluation criteria from accuracy alone to trade-off with time delay has inspired multivariate energy-based approaches in motion segmentation using acceleration. The essence of multivariate approaches lies in the construction of highly dimensional energy and requires feature subset selection in machine learning. Due to fast process, filter methods are preferred; however, their poorer estimate is of the main concerns. This paper aims at empirical validation of three objective functions for filter approaches, Fisher discriminant ratio, multiple correlation (MC), and mutual information (MI), through two subsequent experiments. With respect to 63 possible subsets out of 6 variables for acceleration motion segmentation, three functions in addition to a theoretical measure are compared with two wrappers,k-nearest neighbor and Bayes classifiers in general statistics and strongly relevant variable identification by social network analysis. Then four kinds of new proposed multivariate energy are compared with a conventional univariate approach in terms of accuracy and time delay. Finally it appears that MC and MI are acceptable enough to match the estimate of two wrappers, and multivariate approaches are justified with our analytic procedures.
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Hamidu, Aliyu Chamalwa, Rann Bakari Harun, and Sambo Uba Emmanuel. "Multivariate Modeling of Student Placement in Nigeria Higher Institutions in University of Maiduguri: An Application of Fisher's Linear Discriminant Analysis." Continental J. Applied Sciences 14, no. 1 (2019): 8–24. https://doi.org/10.5281/zenodo.2612919.
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Students’ placement into courses of study in most (if not all) tertiary institutions in Nigeria had long been a problematic exercise. Most students get placed into courses of study they are ill suited to and then for which they are ill prepared. This research work titled “Multivariate Modeling of Student Placement in Nigeria Higher Institutions in University of Maiduguri: An Application of Fisher’s Linear Discriminant Analysis”. Focused on the Statistical Analysis of classifying students into various department based on their scores on Unified Tertiary Matriculation Examination (UTME). The study only covered the Department of Chemistry and Department of Biological science, Faculty of Science, University of Maiduguri. Discriminant Analysis technique was used and results obtained indicated that many students were wrongly admitted in to both Departments (Department of Chemistry and Department of Biological Science). 45% of students were wrongly admitted into Chemistry Department and 41% were also wrongly admitted into Biological Science Department based on the Apparent Error Rate (APER) calculated. Based on the result; it is however recommended that proper statistical measures (statistical application) should be introduced into the admission system of various higher institutions so as to minimized the problems highlighted.
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Pei, Mochao, Hongru Li, and He Yu. "A Novel Three-stage Feature Fusion Methodology and its Application in Degradation State Identification for Hydraulic Pumps." Measurement Science Review 21, no. 5 (2021): 123–35. http://dx.doi.org/10.2478/msr-2021-0018.
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Abstract The performance of feature is essential to the degradation state identification for hydraulic pumps. The initial feature set extracted from the vibration signal of the hydraulic pump is often high-dimensional and contains redundant information, which undermines the effectiveness of the feature set. The novel three-stage feature fusion scheme proposed in this paper aims to enhance the performance of the original features extracted from the vibration signal. First, sparse local Fisher discriminant analysis (SLFDA) performs intra-set fusion within the two original feature sets, respectively. SLFDA has a good effect on samples with intra-class multimodality, and the feature set fused by it has obvious multivariate normal distribution characteristics, which is conducive to the next fusion. Second, our modified intra-class correlation analysis (MICA) is used to fuse two feature sets in the second stage. MICA is a CCA (Canonical correlation analysis) -based method. A new class matrix is used to modify the covariance matrix between two feature sets, which allows MICA to conveniently inherit the discriminating structure while fusing features. Finally, we propose a feature selection algorithm based on kernel local Fisher discriminant analysis (KLFDA) and kernel canonical correlation analysis (KCCA) to select the desired features. This algorithm based on Max-Relevance and Min-Redundancy (mRMR) framework solves the problem that CCA cannot properly evaluate the correlation between features and the class variable, as well as accurately evaluates the correlation among features. Based on the experimental data, the proposed method is compared with several popular methods, and the feature fusion methods used in some previous studies related to the fault diagnosis of rotating machinery are compared with it as well. The results show that the fusion effectiveness of our method is better than other methods, which obtains higher recognition accuracy.
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ZHU, YE, TIANZI JIANG, YUAN ZHOU, and LISHA ZHAO. "DISCRIMINATIVE ANALYSIS OF FUNCTIONAL NEAR-INFRARED SPECTROSCOPY SIGNALS FOR DEVELOPMENT OF NEUROIMAGING BIOMARKERS OF ELDERLY DEPRESSION." Journal of Innovative Optical Health Sciences 03, no. 01 (2010): 69–74. http://dx.doi.org/10.1142/s1793545810000848.
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Functional near-infrared spectroscopy (fNIRS) is a neuroimaging technology which is suitable for psychiatric patients. Several fNIRS studies have found abnormal brain activations during cognitive tasks in elderly depression. In this paper, we proposed a discriminative model of multivariate pattern classification based on fNIRS signals to distinguish elderly depressed patients from healthy controls. This model used the brain activation patterns during a verbal fluency task as features of classification. Then Pseudo-Fisher Linear Discriminant Analysis was performed on the feature space to generate discriminative model. Using leave-one-out (LOO) cross-validation, our results showed a correct classification rate of 88%. The discriminative model showed its ability to identify people with elderly depression and suggested that fNIRS may be an efficient clinical tool for diagnosis of depression. This study may provide the first step for the development of neuroimaging biomarkers based on fNIRS in psychiatric disorders.
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Yang, Bo Ming, Zong Han Yang, Jong Kang Liu, Hui Yu Lee, and Chih Ming Kao. "Evaluation of Groundwater Quality at an Industrial Park Site Zone Using Statistical Analyses: A Case Study in Taiwan." Applied Mechanics and Materials 457-458 (October 2013): 1581–84. http://dx.doi.org/10.4028/www.scientific.net/amm.457-458.1581.
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Multivariate statistical analysis explains the huge and complicated current situation of the original data efficiently, concisely, and explicitly. It simplifies the original data into representative factors, or bases on the similarity between data to cluster and identify clustering outcome. In this study, the statistical software SPSS 12.0 was used to perform the multivariate statistical analysis to evaluate characteristics of groundwater quality at an industrial park site located in Kaohsiung, Taiwan. Results from the principal component analysis (PCA) and factor analyses (FA) show that seven principal components could be compiled from 20 groundwater quality indicators obtained from groundwater analyses, which included background factor, salt residua factor, hardness factor, ethylene chloride factor, alkalinity factor, organic pollutant factor, and chloroform factor. Among the seven principal components, the major influencing components were salinization factor and acid-base factor. Results show that the seven principal component factors were able to represent 89.6% of the total variability for 20 different groundwater quality indicators. Groundwater monitoring wells were classified into seven groups according to the partition of homogeneity and similarity using the two-phase cluster analysis (CA). The clustering results indicate that chlorides such as 1,1-dichloroethylene, 1,1-dichloroethane, and cis-1,2-dichloroethylene had the highest concentrations among the clusters. This indicates that groundwater at nearby areas may be polluted by chlorinated organic compounds. Results from the correlation analysis by Fisher coefficient formula show that the cluster results of seven groups of groundwater wells had 100 and 80% accuracies using discriminant and cross-validation analyses, respectively. This implies that high accuracy can be obtained when discriminant and cluster analyses are applied for data evaluation.
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Hino, Hideitsu, Nima Reyhani, and Noboru Murata. "Multiple Kernel Learning with Gaussianity Measures." Neural Computation 24, no. 7 (2012): 1853–81. http://dx.doi.org/10.1162/neco_a_00299.
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Kernel methods are known to be effective for nonlinear multivariate analysis. One of the main issues in the practical use of kernel methods is the selection of kernel. There have been a lot of studies on kernel selection and kernel learning. Multiple kernel learning (MKL) is one of the promising kernel optimization approaches. Kernel methods are applied to various classifiers including Fisher discriminant analysis (FDA). FDA gives the Bayes optimal classification axis if the data distribution of each class in the feature space is a gaussian with a shared covariance structure. Based on this fact, an MKL framework based on the notion of gaussianity is proposed. As a concrete implementation, an empirical characteristic function is adopted to measure gaussianity in the feature space associated with a convex combination of kernel functions, and two MKL algorithms are derived. From experimental results on some data sets, we show that the proposed kernel learning followed by FDA offers strong classification power.
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Qureshi, Fatir, James Adams, Kathryn Hanagan, Dae-Wook Kang, Rosa Krajmalnik-Brown, and Juergen Hahn. "Multivariate Analysis of Fecal Metabolites from Children with Autism Spectrum Disorder and Gastrointestinal Symptoms before and after Microbiota Transfer Therapy." Journal of Personalized Medicine 10, no. 4 (2020): 152. http://dx.doi.org/10.3390/jpm10040152.
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Fecal microbiota transplant (FMT) holds significant promise for patients with Autism Spectrum Disorder (ASD) and gastrointestinal (GI) symptoms. Prior work has demonstrated that plasma metabolite profiles of children with ASD become more similar to those of their typically developing (TD) peers following this treatment. This work measures the concentration of 669 biochemical compounds in feces of a cohort of 18 ASD and 20 TD children using ultrahigh performance liquid chromatography-tandem mass spectroscopy. Subsequent measurements were taken from the ASD cohort over the course of 10-week Microbiota Transfer Therapy (MTT) and 8 weeks after completion of this treatment. Univariate and multivariate statistical analysis techniques were used to characterize differences in metabolites before, during, and after treatment. Using Fisher Discriminant Analysis (FDA), it was possible to attain multivariate metabolite models capable of achieving a sensitivity of 94% and a specificity of 95% after cross-validation. Observations made following MTT indicate that the fecal metabolite profiles become more like those of the TD cohort. There was an 82–88% decrease in the median difference of the ASD and TD group for the panel metabolites, and among the top fifty most discriminating individual metabolites, 96% report more comparable values following treatment. Thus, these findings are similar, although less pronounced, as those determined using plasma metabolites.
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Merhof, Dorit, Pawel J. Markiewicz, Günther Platsch, et al. "Optimized Data Preprocessing for Multivariate Analysis Applied to 99mTc-ECD SPECT Data Sets of Alzheimer's Patients and Asymptomatic Controls." Journal of Cerebral Blood Flow & Metabolism 31, no. 1 (2010): 371–83. http://dx.doi.org/10.1038/jcbfm.2010.112.
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Multivariate image analysis has shown potential for classification between Alzheimer's disease (AD) patients and healthy controls with a high-diagnostic performance. As image analysis of positron emission tomography (PET) and single photon emission computed tomography (SPECT) data critically depends on appropriate data preprocessing, the focus of this work is to investigate the impact of data preprocessing on the outcome of the analysis, and to identify an optimal data preprocessing method. In this work, technetium-99methylcysteinatedimer (99mTc-ECD) SPECT data sets of 28 AD patients and 28 asymptomatic controls were used for the analysis. For a series of different data preprocessing methods, which includes methods for spatial normalization, smoothing, and intensity normalization, multivariate image analysis based on principal component analysis (PCA) and Fisher discriminant analysis (FDA) was applied. Bootstrap resampling was used to investigate the robustness of the analysis and the classification accuracy, depending on the data preprocessing method. Depending on the combination of preprocessing methods, significant differences regarding the classification accuracy were observed. For 99mTc-ECD SPECT data, the optimal data preprocessing method in terms of robustness and classification accuracy is based on affine registration, smoothing with a Gaussian of 12 mm full width half maximum, and intensity normalization based on the 25% brightest voxels within the whole-brain region.
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Oliveira, Leonardo Bernardes Taverny de, Antonio Clementino dos Santos, Hugo Mariano Rodrigues de Oliveira, Tiago Barbalho André, Durval Nolasco das Neves Neto, and Otacílio Silveira Júnior. "Characteristics and classification of the quality and productive standards of the mombaça grass under a livestock-forest system or full sun." Semina: Ciências Agrárias 39, no. 4 (2018): 1447. http://dx.doi.org/10.5433/1679-0359.2018v39n4p1447.
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The objective of this study is to identify qualitative and productive changes in Mombasa grass under a livestock-forest system or full sun and to classify the variables that are most relevant for evaluating the qualitative performance of these systems. The experiments were conducted in shade areas, with four replications for each of the 12 treatments, including four growth cycles and three levels of shading in Mombasa grass, totaling 48 experimental units. Calcium (Ca), magnesium (Mg), potassium (K), phosphorus (P), nitrogen (N) and dry mass production (DMP) (kg ha-1) were used as discriminatory variables for the shading groups. The levels of nutrients and shading in Mombaça grass were classified using Fisher multivariate discriminant analysis (FMDA). The FDMA indicated that Mg, K, P, N, and DMP formed a discriminant function. However, DMP was the least important variable for identifying the groups. Five groups were pre-defined before FDMA: MFS 2 (Mombasa grass in full sun in the second cycle), MFS 6 (Mombasa grass in full sun in the sixth cycle), MFS 7 (Mombasa grass in full sun in the seventh cycle), M25 4 (Mombasa grass at 25% shading in the fourth cycle), and M25 7 (Mombasa grass at 25% shading in the seventh cycle). The results indicate that Mg, P, K, and N are helpful for identifying new genotypes of plants grown on shading conditions because of the responsiveness and stability of these elements to environmental changes.
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Adams, James B., Troy Vargason, Dae-Wook Kang, Rosa Krajmalnik-Brown, and Juergen Hahn. "Multivariate Analysis of Plasma Metabolites in Children with Autism Spectrum Disorder and Gastrointestinal Symptoms Before and After Microbiota Transfer Therapy." Processes 7, no. 11 (2019): 806. http://dx.doi.org/10.3390/pr7110806.
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Current diagnosis of autism spectrum disorder (ASD) is based on assessment of behavioral symptoms, although there is strong evidence that ASD affects multiple organ systems including the gastrointestinal (GI) tract. This study used Fisher discriminant analysis (FDA) to evaluate plasma metabolites from 18 children with ASD and chronic GI problems (ASD + GI cohort) and 20 typically developing (TD) children without GI problems (TD − GI cohort). Using three plasma metabolites that may represent three general groups of metabolic abnormalities, it was possible to distinguish the ASD + GI cohort from the TD − GI cohort with 94% sensitivity and 100% specificity after leave-one-out cross-validation. After the ASD + GI participants underwent Microbiota Transfer Therapy with significant improvement in GI and ASD-related symptoms, their metabolic profiles shifted significantly to become more similar to the TD − GI group, indicating potential utility of this combination of plasma metabolites as a biomarker for treatment efficacy. Two of the metabolites, sarcosine and inosine 5′-monophosphate, improved greatly after treatment. The third metabolite, tyramine O-sulfate, showed no change in median value, suggesting it and correlated metabolites to be a possible target for future therapies. Since it is unclear whether the observed differences are due to metabolic abnormalities associated with ASD or with GI symptoms (or contributions from both), future studies aiming to classify ASD should feature TD participants with GI symptoms and have larger sample sizes to improve confidence in the results.
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Cárdenas Parra, Leidy Yurany, Ana Elisa Rojas Rodríguez, Jorge Enrique Pérez Cárdenas, and Juan Manuel Pérez-Agudelo. "Molecular Evaluation of the mRNA Expression of the ERG11, ERG3, CgCDR1, and CgSNQ2 Genes Linked to Fluconazole Resistance in Candida glabrata in a Colombian Population." Journal of Fungi 10, no. 7 (2024): 509. http://dx.doi.org/10.3390/jof10070509.
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Introduction: The study of Candida glabrata genes associated with fluconazole resistance, from a molecular perspective, increases the understanding of the phenomenon with a view to its clinical applicability. Objective: We sought to establish the predictive molecular profile of fluconazole resistance in Candida glabrata by analyzing the ERG11, ERG3, CgCDR1, and CgSNQ2 genes. Method: Expression was quantified using RT-qPCR. Metrics were obtained through molecular docking and Fisher discriminant functions. Additionally, a predictive classification was made against the susceptibility of C. glabrata to fluconazole. Results: The relative expression of the ERG3, CgCDR1, and CgSNQ2 genes was higher in the fluconazole-resistant strains than in the fluconazole-susceptible, dose-dependent strains. The gene with the highest relative expression in the fluconazole-exposed strains was CgCDR1, and in both the resistant and susceptible, dose-dependent strains exposed to fluconazole, this was also the case. The molecular docking model generated a median number of contacts between fluconazole and ERG11 that was lower than the median number of contacts between fluconazole and ERG3, -CgCDR1, and -CgSNQ2. The predicted classification through the multivariate model for fluconazole susceptibility achieved an accuracy of 73.5%. Conclusion: The resistant strains had significant expression levels of genes encoding efflux pumps and the ERG3 gene. Molecular analysis makes the identification of a low affinity between fluconazole and its pharmacological target possible, which may explain the lower intrinsic susceptibility of the fungus to fluconazole.
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Md Nor, Norazwan, Che Rosmani Che Hassan, and Mohd Azlan Hussain. "A review of data-driven fault detection and diagnosis methods: applications in chemical process systems." Reviews in Chemical Engineering 36, no. 4 (2020): 513–53. http://dx.doi.org/10.1515/revce-2017-0069.
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AbstractFault detection and diagnosis (FDD) systems are developed to characterize normal variations and detect abnormal changes in a process plant. It is always important for early detection and diagnosis, especially in chemical process systems to prevent process disruptions, shutdowns, or even process failures. However, there have been only limited reviews of data-driven FDD methods published in the literature. Therefore, the aim of this review is to provide the state-of-the-art reference for chemical engineers and to promote the application of data-driven FDD methods in chemical process systems. In general, there are two different groups of data-driven FDD methods: the multivariate statistical analysis and the machine learning approaches, which are widely accepted and applied in various industrial processes, including chemicals, pharmaceuticals, and polymers. Many different multivariate statistical analysis methods have been proposed in the literature, such as principal component analysis, partial least squares, independent component analysis, and Fisher discriminant analysis, while the machine learning approaches include artificial neural networks, neuro-fuzzy methods, support vector machine, Gaussian mixture model, K-nearest neighbor, and Bayesian network. In the first part, this review intends to provide a comprehensive literature review on applications of data-driven methods in FDD systems for chemical process systems. In addition, the hybrid FDD frameworks have also been reviewed by discussing the distinct advantages and various constraints, with some applications as examples. However, the choice for the data-driven FDD methods is not a straightforward issue. Thus, in the second part, this paper provides a guideline for selecting the best possible data-driven method for FDD systems based on their faults. Finally, future directions of data-driven FDD methods are summarized with the intent to expand the use for the process monitoring community.
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Mugo, Robinson, and Sei-Ichi Saitoh. "Ensemble Modelling of Skipjack Tuna (Katsuwonus pelamis) Habitats in the Western North Pacific Using Satellite Remotely Sensed Data; a Comparative Analysis Using Machine-Learning Models." Remote Sensing 12, no. 16 (2020): 2591. http://dx.doi.org/10.3390/rs12162591.
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To examine skipjack tuna’s habitat utilization in the western North Pacific (WNP) we used an ensemble modelling approach, which applied a fisher- derived presence-only dataset and three satellite remote-sensing predictor variables. The skipjack tuna data were compiled from daily point fishing data into monthly composites and re-gridded into a quarter degree resolution to match the environmental predictor variables, the sea surface temperature (SST), sea surface chlorophyll-a (SSC) and sea surface height anomalies (SSHA), which were also processed at quarter degree spatial resolution. Using the sdm package operated in RStudio software, we constructed habitat models over a 9-month period, from March to November 2004, using 17 algorithms, with a 70:30 split of training and test data, with bootstrapping and 10 runs as parameter settings for our models. Model performance evaluation was conducted using the area under the curve (AUC) of the receiver operating characteristic (ROC), the point biserial correlation coefficient (COR), the true skill statistic (TSS) and Cohen’s kappa (k) metrics. We analyzed the response curves for each predictor variable per algorithm, the variable importance information and the ROC plots. Ensemble predictions of habitats were weighted with the TSS metric. Model performance varied across various algorithms, with the Support Vector Machines (SVM), Boosted Regression Trees (BRT), Random Forests (RF), Multivariate Adaptive Regression Splines (MARS), Generalized Additive Models (GAM), Classification and Regression Trees (CART), Multi-Layer Perceptron (MLP), Recursive Partitioning and Regression Trees (RPART), and Maximum Entropy (MAXENT), showing consistently high performance than other algorithms, while the Flexible Discriminant Analysis (FDA), Mixture Discriminant Analysis (MDA), Bioclim (BIOC), Domain (DOM), Maxlike (MAXL), Mahalanobis Distance (MAHA) and Radial Basis Function (RBF) had lower performance. We found inter-algorithm variations in predictor variable responses. We conclude that the multi-algorithm modelling approach enabled us to assess the variability in algorithm performance, hence a data driven basis for building the ensemble model. Given the inter-algorithm variations observed, the ensemble prediction maps indicated a better habitat utilization map of skipjack tuna than would have been achieved by a single algorithm.
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Guo, Meijun, Jie Shen, Xi-e. Song, et al. "Comprehensive evaluation of fluroxypyr herbicide on physiological parameters of spring hybrid millet." PeerJ 7 (September 26, 2019): e7794. http://dx.doi.org/10.7717/peerj.7794.
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Foxtail millet (Setaria italic L.) is an important food and fodder crop that is cultivated worldwide. Quantifying the effects of herbicides on foxtail millet is critical for safe herbicide application. In this study, we analyzed the effects of different fluroxypyr dosages on the growth parameters and physiological parametric of foxtail millet, that is, peroxidation characteristics, photosynthetic characteristics, and endogenous hormone production, by using multivariate statistical analysis. Indicators were screened via Fisher discriminant analysis, and the growth parameters, peroxidation characteristics, photosynthesis characteristics and endogenous hormones of foxtail millet at different fluroxypyr dosages were comprehensively evaluated by principal component analysis. On the basis of the results of principal component analysis, the cumulative contribution rate of the first two principal component factors was 93.72%. The first principal component, which explained 59.23% of total variance, was selected to represent the photosynthetic characteristics and endogenous hormones of foxtail millet. The second principal component, which explained 34.49% of total variance, represented the growth parameters of foxtail millet. According to the principal component analysis, the indexes were simplified into comprehensive index Z, and the mathematical model of comprehensive index Z was set as F = 0.592Z1 + 0.345Z2. The results showed that the comprehensive evaluation score of fluroxypyr at moderate concentrations was higher than at high concentrations. Consequently, one L (active ingredient, ai) ha−1 fluroxypyr exerted minimal effects on growth parameters, oxidase activity, photosynthetic activity, and endogenous hormones, and had highest value of comprehensive evaluation, which had efficient and safe benefits in foxtail millet field.
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Pan, Shixin, Chong Liu, Jiarui Chen, et al. "Age and flexors as risk factors for cervical radiculopathy: A new machine learning method." Medicine 103, no. 4 (2024): e36939. http://dx.doi.org/10.1097/md.0000000000036939.
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This study aimed to investigate the risk factors for cervical radiculopathy (CR) along with identifying the relationships between age, cervical flexors, and CR. This was a retrospective cohort study, including 60 patients with CR enrolled between December 2018 and June 2020. In this study, we measured C2 to C7 Cobb angle, disc degeneration, endplate degeneration, and morphology of paraspinal muscles and evaluated the value of predictive methods using receiver operating characteristic curves. Next, we established a diagnostic model for CR using Fisher discriminant model and compared different models by calculating the kappa value. Age and cervical flexor factors were used to construct clinical predictive models, which were further evaluated by C-index, receiver operating characteristic curve, calibration curve, and decision curve analysis. Multivariate analysis showed that age and cervical flexors were potential risk factors for CR, while the diagnostic model indicated that both exerted the best diagnostic effect. The obtained diagnostic equation was as follows: y1 = 0.33 × 1 + 10.302 × 2–24.139; y2 = 0.259 × 1 + 13.605 × 2–32.579. Both the C-index and AUC in the training set reached 0.939. Moreover, the C-index and AUC values in the external validation set reached 0.961. We developed 2 models for predicting CR and also confirmed their validity. Age and cervical flexors were considered potential risk factors for CR. Our noninvasive inspection method could provide clinicians with a more potential diagnostic value to detect CR accurately.
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Efimenko, O. O., I. M. Retunska, and T. O. Marturosova. "Forecasting early climax on taking into account of reproductive and biological age indicators." Biomedical and Biosocial Anthropology, no. 35 (May 5, 2019): 23–28. http://dx.doi.org/10.31393/bba35-2019-04.
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A great asset of our time is a significant increase in life expectancy. This is especially true for women’s health issues, as women live longer than men and are more stubbornly opposed to age-related changes and aging, trying to preserve not only beauty and youth, but also reproductive function. The use of algorithms and mathematical models for predicting the occurrence of pathology in medical practice makes it possible to predict in advance not only the fact of the occurrence of this complication, but also to determine the likelihood of its occurrence, which is very important for the subsequent identification of risk groups in order to develop individualized preventive and treatment measures. Namely, the timely appointment of preventive measures and the development of individual treatment programs will improve the quality of life of every woman. The purpose of the work is to develop an algorithm and a mathematical model for predicting the risk of developing early climax (EC) against the background of a woman’s biological aging by studying various factors with the subsequent development of individualized preventive and treatment measures. In order to study the possibilities of predicting the occurrence of RK against the background of a woman’s biological aging, a retrospective analysis of the frequency of the studied factors in patients with EC was carried out in comparison with women with preserved menstrual function and timely onset of menopause. The method of step-by-step discriminant analysis was used as a mathematical model, which made it possible to identify the probability of a difference between the comparison groups by the F value of Fisher statistics, to develop a forecast algorithm and conduct mathematical modeling. 12 out of 145 factors were identified by discriminant analysis, which most influenced the occurrence of this pathology, including the following: early menopause in relatives, smoking, history of artificial abortion (more than 3), extragenital pathology; the presence of stressful situations at home, at work; surgery on the uterus and appendages; inadequate physical and mental activity; adiposity; low serum estradiol concentrations; high levels of follicle-stimulating hormone in serum; anti-Mullerian serum hormone levels below normal and more than three in vitro fertilization attempts. It is the method of multivariate mathematical analysis, considering all the most informative factors and variants of their expression, made it possible to create this prognostic model. The algorithm and mathematical model developed by the authors to predict the occurrence of this pathology, considering certain factors, have a high sensitivity and informativeness, which makes it possible to identify the risk groups of patients of reproductive age in the occurrence of this pathology in order to prevent and prescribe individual treatment in a timely manner.
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Lunghi, Monia, Laura Vanelli, Paolo Bernasconi, et al. "The Relationship between the Immunophenotypic Profile and Cytogenetic Abnormalities in Acute Myeloid Leukemia." Blood 106, no. 11 (2005): 4495. http://dx.doi.org/10.1182/blood.v106.11.4495.4495.
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Abstract We studied the immunophenotypic characteristics of 459 consecutive adult AML patients and their correlation with karyotype. Immunophenotype was performed using a panel of 26 directly conjugated monoclonal antibodies. Cytogenetic analysis was performed using a standard G-banding technique. Karyotype was available in 394 patients (not done in 15, failed in 50): 1) 45 (11.4%) were t(15;17) APL patients with a mature myeloid phenotype (HLA-DR-/CD13+ and/or CD33+). CD2 and CD56 were expressed in 20% and 13.3% of cases, respectively. CD11b-positivity was less frequent than in the other cytogenetic groups. The markers significantly associated with t(15;17) were: presence of CD2 (two tailed Fisher exact test: p=.003) and CD117 (p=.01), absence of CD4dim (p<.001), CD7 (p<.001), CD11b (p<.001), CD11c (p<.001), CD14 (p<.001), CD34 (p<.001), TdT (p=.03) and HLA-DR (p<.001). 2) 12 (3%) showed t(8;21) and were characterized by CD34+/CD19+/CD13+<CD33+ in more than 80% of blasts. CD56 were expressed in 87.5%. CD11b was positive only in 8.3% and CD14 was constantly negative. In univariate analysis, t(8;21) was associated with CD11b- (p=.03), CD19+ (p<.001) and CD56+(p<.001). 3) 23 (5.8%) had inv(16) or t(16;16) with CD13+/CD33+ in >90% of blasts, CD34+ in 70%, MPO+ in 95.8% and HLA-DR+ in 89.3%. The association with CD14 and TdT was of borderline statistical significance. 4) 24 (6.1%) had −5/5q- with more than 80% of blasts CD117+/CD13+>CD33+. CD34 was positive in 62.9% of cases, CD7 in 33%, CD11b in 35.7%, CD11c in 67.8% and CD14 in 7.1%. CD15-positivity was less frequent than in other AML subtypes. Univariate analysis showed a trend of positive association with CD7 and CD117. 5) 40 (10.2%) showed −7/7q-, with CD13+>CD33+ detected in more than 80%, CD7 in 33.3%, CD11b in 72.7%, CD15 in 55%, CD34 in 73.3%. Abnormal CD16/CD33 and/or CD11b/CD66b myeloid cell pattern was detected in 40% of cases (p=.001). −7/7q- were significantly associated with CD34 (p=.02) and CD7 (p=.04). 6) 41 (10.4%), had complex karyotype (≥3 abn) with a similar antigenic profile than group 4) and 5) but with a more frequent expression of CD11b, CD14, CD15 and less frequent of MPO. In univariate analysis CD19 (p=.04), CD34 (p=.02) and MPO (p<.001) retained statistical significance. 7) 20 (5.1%) with +8 were CD13+ in 95.6%, while the other markers were present in less than 80% of cases, in particular lower CD33+ blasts (p=.01). 8) 17 (4.3%) had 11q23abn, with CD13+<CD33+, frequent TdT+ (61.5%) and rare CD34+ (31.8%, p=.02). CD11c and CD14 were more frequently expressed than in other subtypes (86.4% and 36.4%, respectively). T-lymphoid markers were present in 36.4%. Univariate analysis showed a positive association between 11q23abn and presence of CD11c (p=.04) and CD14 (p=.05). 9) 115 (29.2%) with a normal karyotype had a dishomogeneous antigenic profile. CD2 (p=.002), CD11c (p=.04) and HLA-DR (p<.001) were the most discriminant markers. Using a multivariate discriminant analysis, we identified a discriminant function only for group 1) and group 2), based on CD11c/CD13/CD34/MPO/HLA-DR (sensitivity >99%, specificity 94.7%) and CD7/CD11c/CD19/CD56/MPO/HLA-DR (sensitivity 83.3%, specificity 94.5%), respectively. We conclude that in AML patients some cytogenetic abnormalities are associated with peculiar antigenic profiles. In patients with normal karyotype the heterogeneity of antigenic pattern may reflect underlined distinct molecular abnormalities.
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Rakotonirina, Jean Claude, and Brian L. Fisher. "Revision of the Malagasy Camponotus subgenus Myrmosaga (Hymenoptera, Formicidae) using qualitative and quantitative morphology." ZooKeys 1098 (May 3, 2022): 1–180. http://dx.doi.org/10.3897/zookeys.1098.73223.
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The Camponotus subgenus Myrmosagasubgen. rev. from the Malagasy region is revised based on analysis of both qualitative morphological characters and morphometric traits. The multivariate analysis used the Nest Centroid (NC)-clustering method to generate species hypotheses based on 19 continuous morphological traits of minor workers. The proposed species hypotheses were confirmed by cumulative Linear Discriminant Analysis (LDA). Morphometric ratios for the subsets of minor and major workers were used in species descriptions and redefinitions. The present study places the subgenus Myrmopytiasyn. nov. in synonymy to Myrmosaga. It recognizes 38 species, of which 19 are newly described: C. ainasp. nov., C. arosp. nov., C. asarasp. nov., C. atimosp. nov., C. bemahevasp. nov., C. bozakasp. nov., C. darainasp. nov., C. harenarumsp. nov., C. joanysp. nov., C. karstisp. nov., C. kelimasosp. nov., C. lokobesp. nov., C. mahafalysp. nov., C. niavosp. nov., C. rotraesp. nov., C. sambiranoensissp. nov., C. tapiasp. nov., C. tendryisp. nov., C. vanosp. nov. Eleven species are redescribed: C. aurosus Roger, C. cervicalis Roger, C. dufouri Forel, C. gibber Forel, C. hagensii Forel, C. hova Forel, C. hovahovoides Forel, C. immaculatus Forel, C. quadrimaculatus Forel, C. roeseli Forel, C. strangulatus Santschi. The following are raised to species and redescribed: C. becki Santschi stat. nov., C. boivini Forel stat. rev., C. cemeryi Özdikmen stat. rev., C. mixtellus Forel stat. nov., C. radamae Forel stat. nov.Camponotus maculatus st. fairmairei Santschi syn. nov., is synonymized under C. boivini. The following are synonymized under C. cervicalis: Camponotus cervicalis gaullei Santschi, syn. nov.; Camponotus perroti Forel, syn. nov.; Camponotus perroti aeschylus Forel, syn. nov.; Camponotus gerberti Donisthorpe, syn. nov.Camponotus dufouri imerinensis Forel, syn. nov. is a synonym of C. dufouri, Camponotus hova var. obscuratus Emery, syn. nov. is a synonym of C. hova, Camponotus quadrimaculatus opacata Emery, syn. nov. is a synonym of C. immaculatus, Camponotus maculatus st. legionarium Santschi, syn. nov. is a synonym of C. roeseli, Camponotus hova maculatoides Emery, syn. nov. is a synonym of C. strangulatus. The following are synonymized under C. quadrimaculatus: Camponotus kelleri Forel, syn. nov., Camponotus kelleri var. invalidus Forel, syn. nov., Camponotus quadrimaculatus sellaris Emery, syn. nov. As C. imitator Forel, C. liandia Rakotonirina &amp; Fisher, and C. lubbocki Forel have been recently described and redescribed, only diagnoses and taxonomic discussions are provided. This revision also includes an illustrated species identification key, taxonomic discussions, images, and distribution maps for each species superimposed on the ecoregions of Madagascar.
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Volkova, N. V., Ju V. Malysheva, T. N. Iureva, and S. I. Kolesnikov. "The Role of Biologically Active Aqueous Humor Molecules of the Anterior Chamber and Lacrimal Fluid in the Implementation of the Hypotensive Effect of Non-Penetrating Deep Sclerectomy." Acta Biomedica Scientifica 6, no. 2 (2021): 126–32. http://dx.doi.org/10.29413/abs.2021-6.2.14.
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To date, the factors affecting the course of the reparative process after non-penetrating deep sclerectomy (NPDS) have not been fully determined. There is no systematic information about the regulatory role of the cytokines TGF-β, IL-6, IL-8 and MMP-9, VEGF A 121 and 165 in the formation mechanisms of the newly created pathways consistency of intraocular aqueous humor outflow.Purpose. To determine possible ways of impact of biologically active aqueous humor molecules of the anterior chamber and lacrimal fluid on the hypotensive effect of non-penetrating deep sclerectomy.Methods. A prospective study of 65 patients with open-angle glaucoma before and 12 months after NPDS and 22 patients without eye hydrodynamic disorders with the determination of the initial concentrations of biologically active molecules in the lacrimal fluid and aqueous humor of the anterior chamber. Twelve months after NPDS all patients were divided into three groups, depending on the hypotensive effect of the operation, according to the criteria.Results. Multivariate discriminant analysis showed the greatest inter-group differences, calculated by the square of the Mahalanobis distance, between group 3 with no hypotensive effect of NPDS and the control group (R2 = 8.48, p = 0.001). The most informative features that determine the differences between the 4 groups in the total population, calculated according to the Fischer F-test, were MMP-9 (F = 14.7, p = 0.001) and TGF-β (F = 7.08, p = 0.001) in the aqueous humor of the anterior chamber. In pairwise comparison of groups 1 and 2, the maximum level of significance according to the F-criterion was characteristic of the level of tear IL-6 (F = 21.25, p = 0.001), with approximately equal degree – IL-8 (F = 7.85, p = 0.001) and VEGF (F = 7.12, p = 0.001), to a lesser extent TGF of aqueous humor (F = 4.43, p = 0.001) and MMR-9 (F = 2.23, p = 0.001). Between groups 1–3, the maximum differences according to the Fisher criterion were observed in the IL-8 (F = 20.99, p = 0.001), TGF (F = 8.75, p = 0.001) and to a lesser extent – TGF (F = 5.83, p = 0.001).Conclusion. The analysis of the obtained data showed the decisive role of the imbalance of proinflammatory cytokines, signaling proteins with prolymphoangiogenic activity, and MMP-9 in the aqueous humor of the anterior chamber, as well as in the initial state of the lacrimal fluid in the postoperative healing processes after NPDS.
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Rakotonirina, Jean Claude, and Brian L. Fisher. "Revision of the Malagasy Camponotus subgenus Myrmosaga (Hymenoptera, Formicidae) using qualitative and quantitative morphology." ZooKeys 1098 (May 3, 2022): 1–180. https://doi.org/10.3897/zookeys.1098.73223.
Full textAbstract:
The Camponotus subgenus Myrmosaga subgen. rev. from the Malagasy region is revised based on analysis of both qualitative morphological characters and morphometric traits. The multivariate analysis used the Nest Centroid (NC)-clustering method to generate species hypotheses based on 19 continuous morphological traits of minor workers. The proposed species hypotheses were confirmed by cumulative Linear Discriminant Analysis (LDA). Morphometric ratios for the subsets of minor and major workers were used in species descriptions and redefinitions. The present study places the subgenus Myrmopytia syn. nov. in synonymy to Myrmosaga. It recognizes 38 species, of which 19 are newly described: C. aina sp. nov., C. aro sp. nov., C. asara sp. nov., C. atimo sp. nov., C. bemaheva sp. nov., C. bozaka sp. nov., C. daraina sp. nov., C. harenarum sp. nov., C. joany sp. nov., C. karsti sp. nov., C. kelimaso sp. nov., C. lokobe sp. nov., C. mahafaly sp. nov., C. niavo sp. nov., C. rotrae sp. nov., C. sambiranoensis sp. nov., C. tapia sp. nov., C. tendryi sp. nov., C. vano sp. nov. Eleven species are redescribed: C. aurosus Roger, C. cervicalis Roger, C. dufouri Forel, C. gibber Forel, C. hagensii Forel, C. hova Forel, C. hovahovoides Forel, C. immaculatus Forel, C. quadrimaculatus Forel, C. roeseli Forel, C. strangulatus Santschi. The following are raised to species and redescribed: C. becki Santschi stat. nov., C. boivini Forel stat. rev., C. cemeryi Özdikmen stat. rev., C. mixtellus Forel stat. nov., C. radamae Forel stat. nov. Camponotus maculatus st. fairmairei Santschi syn. nov., is synonymized under C. boivini. The following are synonymized under C. cervicalis: Camponotus cervicalis gaullei Santschi, syn. nov.; Camponotus perroti Forel, syn. nov.; Camponotus perroti aeschylus Forel, syn. nov.; Camponotus gerberti Donisthorpe, syn. nov. Camponotus dufouri imerinensis Forel, syn. nov. is a synonym of C. dufouri, Camponotus hova var. obscuratus Emery, syn. nov. is a synonym of C. hova, Camponotus quadrimaculatus opacata Emery, syn. nov. is a synonym of C. immaculatus, Camponotus maculatus st. legionarium Santschi, syn. nov. is a synonym of C. roeseli, Camponotus hova maculatoides Emery, syn. nov. is a synonym of C. strangulatus. The following are synonymized under C. quadrimaculatus: Camponotus kelleri Forel, syn. nov., Camponotus kelleri var. invalidus Forel, syn. nov., Camponotus quadrimaculatus sellaris Emery, syn. nov. As C. imitator Forel, C. liandia Rakotonirina & Fisher, and C. lubbocki Forel have been recently described and redescribed, only diagnoses and taxonomic discussions are provided. This revision also includes an illustrated species identification key, taxonomic discussions, images, and distribution maps for each species superimposed on the ecoregions of Madagascar.
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Jin, Yisen, Xin Zhang, and Aaron J. Molstad. "Kernelized discriminant analysis for joint modeling of multivariate categorical responses." Journal of Computational and Graphical Statistics, July 9, 2025, 1–22. https://doi.org/10.1080/10618600.2025.2526412.
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Doherty, Conor T., and Meagan S. Mauter. "Fisher Discriminant Analysis for Extracting Interpretable Phenological Information from Multivariate Time Series Data." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2024, 1–14. https://doi.org/10.1109/jstars.2024.3517415.
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Lee, Yeon-Hee, Jong Hyun Won, Q. Schick Auh, and Yung-Kyun Noh. "Age group prediction with panoramic radiomorphometric parameters using machine learning algorithms." Scientific Reports 12, no. 1 (2022). http://dx.doi.org/10.1038/s41598-022-15691-9.
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AbstractThe aim of this study is to investigate the relationship of 18 radiomorphometric parameters of panoramic radiographs based on age, and to estimate the age group of people with permanent dentition in a non-invasive, comprehensive, and accurate manner using five machine learning algorithms. For the study population (209 men and 262 women; mean age, 32.12 ± 18.71 years), 471 digital panoramic radiographs of Korean individuals were applied. The participants were divided into three groups (with a 20-year age gap) and six groups (with a 10-year age gap), and each age group was estimated using the following five machine learning models: a linear discriminant analysis, logistic regression, kernelized support vector machines, multilayer perceptron, and extreme gradient boosting. Finally, a Fisher discriminant analysis was used to visualize the data configuration. In the prediction of the three age-group classification, the areas under the curve (AUCs) obtained for classifying young ages (10–19 years) ranged from 0.85 to 0.88 for five different machine learning models. The AUC values of the older age group (50–69 years) ranged from 0.82 to 0.88, and those of adults (20–49 years) were approximately 0.73. In the six age-group classification, the best scores were also found in age groups 1 (10–19 years) and 6 (60–69 years), with mean AUCs ranging from 0.85 to 0.87 and 80 to 0.90, respectively. A feature analysis based on LDA weights showed that the L-Pulp Area was important for discriminating young ages (10–49 years), and L-Crown, U-Crown, L-Implant, U-Implant, and Periodontitis were used as predictors for discriminating older ages (50–69 years). We established acceptable linear and nonlinear machine learning models for a dental age group estimation using multiple maxillary and mandibular radiomorphometric parameters. Since certain radiomorphological characteristics of young and the elderly were linearly related to age, young and old groups could be easily distinguished from other age groups with automated machine learning models.
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Chen, Zailin, Xianfeng Cheng, Xingyu Wang, Shijun Ni, Qiulian Yu, and Junchun Hu. "Identification of core carcinogenic elements based on the age-standardized mortality rate of lung cancer in Xuanwei Formation coal in China." Scientific Reports 14, no. 1 (2024). http://dx.doi.org/10.1038/s41598-023-49975-5.
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AbstractIn this study, the core carcinogenic elements in Xuanwei Formation coal were identified. Thirty-one samples were collected based on the age-standardized mortality rate (ASMR) of lung cancer; Si, V, Cr, Co, Ni, As, Mo, Cd, Sb, Pb, and rare earth elements and yttrium (REYs) were analyzed and compared; multivariate statistical analyses (CA, PCA, and FDA) were performed; and comprehensive identification was carried out by combining multivariate statistical analyses with toxicology and mineralogy. The final results indicated that (1) the high-concentration Si, Ni, V, Cr, Co, and Cd in coal may have some potential carcinogenic risk. (2) The concentrations of Cr, Ni, As, Mo, Cd, and Pb meet the zoning characteristics of the ASMR, while the Si concentration is not completely consistent. (3) The REY distribution pattern in Longtan Formation coal is lower than that in Xuanwei Formation coal, indicating that the materials of these elements in coal are different. (5) The heatmap divides the sampling sites into two clusters and subtypes in accordance with carcinogenic zoning based on the ASMR. (6) PC1, PC2, and PC3 explain 62.629% of the total variance, identifying Co, Ni, As, Cd, Mo, Cr, and V. (7) Fisher discriminant analysis identifies Ni, Si, Cd, As, and Co based on the discriminant function. (8) Comprehensive identification reveals that Ni is the primary carcinogenic element, followed by Co, Cd, and Si in combination with toxicology. (9) The paragenesis of Si (nanoquartz), Ni, Co, and Cd is an interesting finding. In other words, carcinogenic elements Ni, Co, Cd, and Si and their paragenetic properties should receive more attention.
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Csősz, Sándor, Ana C. Loss, and Brian L. Fisher. "Exploring the diversity of the Malagasy Ponera (Hymenoptera: Formicidae) fauna via integrative taxonomy." Organisms Diversity & Evolution, June 28, 2023. http://dx.doi.org/10.1007/s13127-023-00610-1.
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AbstractThe genus Ponera includes over 60 extant species worldwide. These tiny, endogeic predator ants are predominantly distributed in the Indomalaya and Australasia regions, with a few additional Holarctic species. Herein, we explore and describe the diversity of the Malagasy Ponera fauna through an integrative taxonomic approach. We obtained our morphological species hypotheses from multivariate analyses of ten continuous morphometric characters. Species boundaries and reliability of morphological clusters were tested via confirmatory Linear Discriminant Analysis (LDA), cross-validation (LOOCV), and analyses of a mitochondrial COI gene fragment. According to the combined application of the analyses, altogether, three species are inferred in the Malagasy region, Ponera petila Wilson (1957), P. swezeyi Wheeler (1933), and P. adumbrans Csősz & Fisher sp. n. Ponera petila and P. swezeyi belong to the Indo-Australian Ponera tenuis group; the third species, P. adumbrans sp. n., is morphologically similar to the Papua New Guinean P. clavicornis Emery (1900). Furthermore, Linear Discriminant Analysis classified the type specimens of P. bableti Perrault (1993), along with a P. petila cluster with posterior p = 1. Therefore, we propose the new junior synonymy of P. bableti with P. petila. Madagascar’s extant biodiversity is predominantly explained by colonization events from the African continent across the Mozambique channel via rafting. However, since no native Ponera species are known from the Afrotropical continent, and the closest congeners have an almost exclusively Indo-Australian distribution, the likelihood of an Indo-Australian origin of the Malagasy Ponera fauna is implied.
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Marmarelis, Vasilis Z., Dae C. Shin, and Rong Zhang. "The Dynamic Relationship Between Cortical Oxygenation and End-Tidal CO2 Transient Changes Is Impaired in Mild Cognitive Impairment Patients." Frontiers in Physiology 12 (December 9, 2021). http://dx.doi.org/10.3389/fphys.2021.772456.
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Background: Recent studies have utilized data-based dynamic modeling to establish strong association between dysregulation of cerebral perfusion and Mild Cognitive Impairment (MCI), expressed in terms of impaired CO2 dynamic vasomotor reactivity in the cerebral vasculature. This raises the question of whether this is due to dysregulation of central mechanisms (baroreflex and chemoreflex) or mechanisms of cortical tissue oxygenation (CTO) in MCI patients. We seek to answer this question using data-based input-output predictive dynamic models.Objective: To use subject-specific data-based multivariate input-output dynamic models to quantify the effects of systemic hemodynamic and blood CO2 changes upon CTO and to examine possible differences in CTO regulation in MCI patients versus age-matched controls, after the dynamic effects of central regulatory mechanisms have been accounted for by using cerebral flow measurements as another input.Methods: The employed model-based approach utilized the general dynamic modeling methodology of Laguerre expansions of kernels to analyze spontaneous time-series data in order to quantify the dynamic effects upon CTO (an index of relative capillary hemoglobin saturation distribution measured via near-infrared spectroscopy) of contemporaneous changes in end-tidal CO2 (proxy for arterial CO2), arterial blood pressure and cerebral blood flow velocity in the middle cerebral arteries (measured via transcranial Doppler). Model-based indices (physio-markers) were computed for these distinct dynamic relationships.Results: The obtained model-based indices revealed significant statistical differences of CO2 dynamic vasomotor reactivity in cortical tissue, combined with “perfusivity” that quantifies the dynamic relationship between flow velocity in cerebral arteries and CTO in MCI patients versus age-matched controls (p = 0.006). Significant difference between MCI patients and age-matched controls was also found in the respective model-prediction accuracy (p = 0.0001). Combination of these model-based indices via the Fisher Discriminant achieved even smaller p-value (p = 5 × 10–5) when comparing MCI patients with controls. The differences in dynamics of CTO in MCI patients are in lower frequencies (&lt;0.05 Hz), suggesting impairment in endocrine/metabolic (rather than neural) mechanisms.Conclusion: The presented model-based approach elucidates the multivariate dynamic connectivity in the regulation of cerebral perfusion and yields model-based indices that may serve as physio-markers of possible dysregulation of CTO during transient CO2 changes in MCI patients.
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Kerr, Hamish A., Eric H. Ledet, Juergen Hahn, and Kathryn Hollowood-Jones. "Quantitative Assessment of Balance for Accurate Prediction of Return to Sport From Sport-Related Concussion." Sports Health: A Multidisciplinary Approach, February 4, 2022, 194173812110688. http://dx.doi.org/10.1177/19417381211068817.
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Background: Determining when athletes are able to return to sport after sports-related concussion (SRC) can be difficult. Hypothesis: A multimodal algorithm using cognitive testing, postural stability, and clinical assessment can predict return to sports after SRC. Study Design: Prospective cohort. Level of Evidence: Level 2b. Methods: Athletes were evaluated within 2 to 3 weeks of SRC. Clinical assessment, Immediate Post Concussion and Cognitive Testing (ImPACT), and postural stability (Equilibrate) were conducted. Resulting data and machine learning techniques were used to optimize an algorithm discriminating between patients ready to return to sports versus those who are not yet recovered. A Fisher discriminant analysis with leave-one-out cross-validation assessed every combination of 2 to 5 factors to optimize the algorithm with lowest combination of type I and type II errors. Results: A total of 193 athletes returned to contact sports after SRC at a mean 84.6 days (±88.8). Twelve subjects (6.2%) sustained repeat SRC within 12 months after return to sport. The combination of (1) days since injury, (2) total symptom score, and (3) nondominant foot tandem eyes closed postural stability score created the best algorithm for discriminating those ready to return to sports after SRC with lowest type I error (13.85%) and type II error (11.25%). The model was able to discriminate between patients who were ready to successfully return to sports versus those who were not with area under the receiver operating characteristic (ROC) curve of 0.82. Conclusion: The algorithm predicts successful return to sports with an acceptable sensitivity and specificity. Tandem balance with eyes closed measured with a video-force plate discriminated athletes ready to return to sports from SRC when combined in multivariate analysis with symptom score and time since injury. The combination of these factors may pose advantages over computerized neuropsychological testing when evaluating young athletes with SRC for return to contact sports. Clinical Relevance: When assessing young athletes sustaining an SRC in a concussion clinic, measuring postural stability in tandem stance with eyes closed combined with clinical assessment and cognitive recovery is effective to determine who is ready to successfully return to sports.
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Song, Peng, Jian Shi, Xinju Liu, et al. "Comprehensive Evaluation Method of the Tight Oil Reservoir Quality in the Ordos Basin." Geofluids 2025, no. 1 (2025). https://doi.org/10.1155/gfl/7745871.
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The Wuqi area of the Ordos Basin boasts significant resource potential in the Chang 8 member of the Yanchang Formation. However, under the dual control of lithology and physical properties, reservoirs are generally dense and heterogeneous, and the quality of the oil layer changes rapidly, which brings difficulties to the optimization of favorable areas. To evaluate the reservoir quality more accurately, based on core observations, and logging and dynamic data analysis, combined with casting thin sections, scanning electron microscopy, high‐pressure mercury injection, nuclear magnetic resonance, and other related experiments, different reservoir types and characteristics were analyzed, and a comprehensive method for evaluating the reservoir quality was established. There are three types of sand body structures in the shallow‐water delta of Chang 8 in the study area, including the continuous superposition type, interval superposition type, and lateral single‐layer type. They mainly experienced diagenesis, such as compaction, cementation, and dissolution. Among these, porosity loss in the Chang 8 reservoir due to compaction and cementation reached 79.3%, consistent with trends observed in other continental tight oil plays such as the Songliao and Junggar Basins, while the improvement in physical properties due to dissolution was minimal. The main parameters influencing different reservoir types are optimized, and the comprehensive classification with the multivariate coefficient is constructed after providing coefficients with different weights. Four reservoir types are quantitatively delineated, among which the physical properties of Type I reservoirs are the best and the physical properties of Type IV reservoirs are the worst. Combined with the difference in the characteristics of sensitive logging curve responses, four logging parameters, density, neutron, resistivity, and acoustic time difference, are optimized, and different reservoir types are quantitatively identified by Fisher discriminant analysis. Comprehensively considering the change in the vertical sand body structure and reservoir type, the three key parameters of interlayer density, interlayer frequency, and reservoir thickness are selected, and the comprehensive evaluation index N of reservoir quality is innovatively constructed. The proposed evaluation index effectively decouples lithological and petrophysical variations, refining reservoir quality assessments for enhanced exploration and production strategies. The greater the N value is, the better the quality of the oil layer. The smaller the N value is, the thinner the oil layer, the more developed the interlayer, and the worse the oil layer quality. The N index exhibits a strong correlation with production characteristics, indicating that the method has effectively evaluated reservoir quality and provided a theoretical basis for targeting favorable areas.