Study of USGS/NASA land use classification system. [computer analysis from LANDSAT data

NASA Technical Reports Server (NTRS)

Spann, G. W.

1975-01-01

The results of a computer mapping project using LANDSAT data and the USGS/NASA land use classification system are summarized. During the computer mapping portion of the project, accuracies of 67 percent to 79 percent were achieved using Level II of the classification system and a 4,000 acre test site centered on Douglasville, Georgia. Analysis of response to a questionaire circulated to actual and potential LANDSAT data users reveals several important findings: (1) there is a substantial desire for additional information related to LANDSAT capabilities; (2) a majority of the respondents feel computer mapping from LANDSAT data could aid present or future projects; and (3) the costs of computer mapping are substantially less than those of other methods.

A hybrid sensing approach for pure and adulterated honey classification.

PubMed

Subari, Norazian; Mohamad Saleh, Junita; Md Shakaff, Ali Yeon; Zakaria, Ammar

2012-10-17

This paper presents a comparison between data from single modality and fusion methods to classify Tualang honey as pure or adulterated using Linear Discriminant Analysis (LDA) and Principal Component Analysis (PCA) statistical classification approaches. Ten different brands of certified pure Tualang honey were obtained throughout peninsular Malaysia and Sumatera, Indonesia. Various concentrations of two types of sugar solution (beet and cane sugar) were used in this investigation to create honey samples of 20%, 40%, 60% and 80% adulteration concentrations. Honey data extracted from an electronic nose (e-nose) and Fourier Transform Infrared Spectroscopy (FTIR) were gathered, analyzed and compared based on fusion methods. Visual observation of classification plots revealed that the PCA approach able to distinct pure and adulterated honey samples better than the LDA technique. Overall, the validated classification results based on FTIR data (88.0%) gave higher classification accuracy than e-nose data (76.5%) using the LDA technique. Honey classification based on normalized low-level and intermediate-level FTIR and e-nose fusion data scored classification accuracies of 92.2% and 88.7%, respectively using the Stepwise LDA method. The results suggested that pure and adulterated honey samples were better classified using FTIR and e-nose fusion data than single modality data.

Impact of FAB classification on predicting outcome in acute myeloid leukemia, not otherwise specified, patients undergoing allogeneic stem cell transplantation in CR1: An analysis of 1690 patients from the acute leukemia working party of EBMT.

PubMed

Canaani, Jonathan; Beohou, Eric; Labopin, Myriam; Socié, Gerard; Huynh, Anne; Volin, Liisa; Cornelissen, Jan; Milpied, Noel; Gedde-Dahl, Tobias; Deconinck, Eric; Fegueux, Nathalie; Blaise, Didier; Mohty, Mohamad; Nagler, Arnon

2017-04-01

The French, American, and British (FAB) classification system for acute myeloid leukemia (AML) is extensively used and is incorporated into the AML, not otherwise specified (NOS) category in the 2016 WHO edition of myeloid neoplasm classification. While recent data proposes that FAB classification does not provide additional prognostic information for patients for whom NPM1 status is available, it is unknown whether FAB still retains a current prognostic role in predicting outcome of AML patients undergoing allogeneic stem cell transplantation. Using the European Society of Blood and Bone Marrow Transplantation registry we analyzed outcome of 1690 patients transplanted in CR1 to determine if FAB classification provides additional prognostic value. Multivariate analysis revealed that M6/M7 patients had decreased leukemia free survival (hazard ratio (HR) of 1.41, 95% confidence interval (CI), 1.01-1.99; P = .046) in addition to increased nonrelapse mortality (NRM) rates (HR, 1.79; 95% CI, 1.06-3.01; P = .028) compared with other FAB types. In the NPM1 wt AML, NOS cohort, FAB M6/M7 was also associated with increased NRM (HR, 2.17; 95% CI, 1.14-4.16; P = .019). Finally, in FLT3-ITD + patients, multivariate analyses revealed that specific FAB types were tightly associated with adverse outcome. In conclusion, FAB classification may predict outcome following transplantation in AML, NOS patients. © 2017 Wiley Periodicals, Inc.

Proposition of a Classification of Adult Patients with Hemiparesis in Chronic Phase.

PubMed

Chantraine, Frédéric; Filipetti, Paul; Schreiber, Céline; Remacle, Angélique; Kolanowski, Elisabeth; Moissenet, Florent

2016-01-01

Patients who have developed hemiparesis as a result of a central nervous system lesion, often experience reduced walking capacity and worse gait quality. Although clinically, similar gait patterns have been observed, presently, no clinically driven classification has been validated to group these patients' gait abnormalities at the level of the hip, knee and ankle joints. This study has thus intended to put forward a new gait classification for adult patients with hemiparesis in chronic phase, and to validate its discriminatory capacity. Twenty-six patients with hemiparesis were included in this observational study. Following a clinical examination, a clinical gait analysis, complemented by a video analysis, was performed whereby participants were requested to walk spontaneously on a 10m walkway. A patient's classification was established from clinical examination data and video analysis. This classification was made up of three groups, including two sub-groups, defined with key abnormalities observed whilst walking. Statistical analysis was achieved on the basis of 25 parameters resulting from the clinical gait analysis in order to assess the discriminatory characteristic of the classification as displayed by the walking speed and kinematic parameters. Results revealed that the parameters related to the discriminant criteria of the proposed classification were all significantly different between groups and subgroups. More generally, nearly two thirds of the 25 parameters showed significant differences (p<0.05) between the groups and sub-groups. However, prior to being fully validated, this classification must still be tested on a larger number of patients, and the repeatability of inter-operator measures must be assessed. This classification enables patients to be grouped on the basis of key abnormalities observed whilst walking and has the advantage of being able to be used in clinical routines without necessitating complex apparatus. In the midterm, this classification may allow a decision-tree of therapies to be developed on the basis of the group in which the patient has been categorised.

Proposition of a Classification of Adult Patients with Hemiparesis in Chronic Phase

PubMed Central

Filipetti, Paul; Remacle, Angélique; Kolanowski, Elisabeth

2016-01-01

Background Patients who have developed hemiparesis as a result of a central nervous system lesion, often experience reduced walking capacity and worse gait quality. Although clinically, similar gait patterns have been observed, presently, no clinically driven classification has been validated to group these patients’ gait abnormalities at the level of the hip, knee and ankle joints. This study has thus intended to put forward a new gait classification for adult patients with hemiparesis in chronic phase, and to validate its discriminatory capacity. Methods and Findings Twenty-six patients with hemiparesis were included in this observational study. Following a clinical examination, a clinical gait analysis, complemented by a video analysis, was performed whereby participants were requested to walk spontaneously on a 10m walkway. A patient’s classification was established from clinical examination data and video analysis. This classification was made up of three groups, including two sub-groups, defined with key abnormalities observed whilst walking. Statistical analysis was achieved on the basis of 25 parameters resulting from the clinical gait analysis in order to assess the discriminatory characteristic of the classification as displayed by the walking speed and kinematic parameters. Results revealed that the parameters related to the discriminant criteria of the proposed classification were all significantly different between groups and subgroups. More generally, nearly two thirds of the 25 parameters showed significant differences (p<0.05) between the groups and sub-groups. However, prior to being fully validated, this classification must still be tested on a larger number of patients, and the repeatability of inter-operator measures must be assessed. Conclusions This classification enables patients to be grouped on the basis of key abnormalities observed whilst walking and has the advantage of being able to be used in clinical routines without necessitating complex apparatus. In the midterm, this classification may allow a decision-tree of therapies to be developed on the basis of the group in which the patient has been categorised. PMID:27271533

Classification of multiple sclerosis patients by latent class analysis of magnetic resonance imaging characteristics.

PubMed

Zwemmer, J N P; Berkhof, J; Castelijns, J A; Barkhof, F; Polman, C H; Uitdehaag, B M J

2006-10-01

Disease heterogeneity is a major issue in multiple sclerosis (MS). Classification of MS patients is usually based on clinical characteristics. More recently, a pathological classification has been presented. While clinical subtypes differ by magnetic resonance imaging (MRI) signature on a group level, a classification of individual MS patients based purely on MRI characteristics has not been presented so far. To investigate whether a restricted classification of MS patients can be made based on a combination of quantitative and qualitative MRI characteristics and to test whether the resulting subgroups are associated with clinical and laboratory characteristics. MRI examinations of the brain and spinal cord of 50 patients were scored for 21 quantitative and qualitative characteristics. Using latent class analysis, subgroups were identified, for whom disease characteristics and laboratory measures were compared. Latent class analysis revealed two subgroups that mainly differed in the extent of lesion confluency and MRI correlates of neuronal loss in the brain. Demographics and disease characteristics were comparable except for cognitive deficits. No correlations with laboratory measures were found. Latent class analysis offers a feasible approach for classifying subgroups of MS patients based on the presence of MRI characteristics. The reproducibility, longitudinal evolution and further clinical or prognostic relevance of the observed classification will have to be explored in a larger and independent sample of patients.

Deep learning for tumor classification in imaging mass spectrometry.

PubMed

Behrmann, Jens; Etmann, Christian; Boskamp, Tobias; Casadonte, Rita; Kriegsmann, Jörg; Maaß, Peter

2018-04-01

Tumor classification using imaging mass spectrometry (IMS) data has a high potential for future applications in pathology. Due to the complexity and size of the data, automated feature extraction and classification steps are required to fully process the data. Since mass spectra exhibit certain structural similarities to image data, deep learning may offer a promising strategy for classification of IMS data as it has been successfully applied to image classification. Methodologically, we propose an adapted architecture based on deep convolutional networks to handle the characteristics of mass spectrometry data, as well as a strategy to interpret the learned model in the spectral domain based on a sensitivity analysis. The proposed methods are evaluated on two algorithmically challenging tumor classification tasks and compared to a baseline approach. Competitiveness of the proposed methods is shown on both tasks by studying the performance via cross-validation. Moreover, the learned models are analyzed by the proposed sensitivity analysis revealing biologically plausible effects as well as confounding factors of the considered tasks. Thus, this study may serve as a starting point for further development of deep learning approaches in IMS classification tasks. https://gitlab.informatik.uni-bremen.de/digipath/Deep_Learning_for_Tumor_Classification_in_IMS. jbehrmann@uni-bremen.de or christianetmann@uni-bremen.de. Supplementary data are available at Bioinformatics online.

The classification of gunshot residue using laser electrospray mass spectrometry and offline multivariate statistical analysis

USDA-ARS?s Scientific Manuscript database

Nonresonant laser vaporization combined with high-resolution electrospray time-of-flight mass spectrometry enables analysis of a casing after discharge of a firearm revealing organic signature molecules including methyl centralite (MC), diphenylamine (DPA), N-nitrosodiphenylamine (N-NO-DPA), 4-nitro...

Multiplex Quantitative Histologic Analysis of Human Breast Cancer Cell Signaling and Cell Fate

DTIC Science & Technology

2010-05-01

Breast cancer, cell signaling, cell proliferation, histology, image analysis 15. NUMBER OF PAGES - 51 16. PRICE CODE 17. SECURITY CLASSIFICATION...revealed by individual stains in multiplex combinations; and (3) software (FARSIGHT) for automated multispectral image analysis that (i) segments...Task 3. Develop computational algorithms for multispectral immunohistological image analysis FARSIGHT software was developed to quantify intrinsic

Exploring objective climate classification for the Himalayan arc and adjacent regions using gridded data sources

NASA Astrophysics Data System (ADS)

Forsythe, N.; Blenkinsop, S.; Fowler, H. J.

2015-05-01

A three-step climate classification was applied to a spatial domain covering the Himalayan arc and adjacent plains regions using input data from four global meteorological reanalyses. Input variables were selected based on an understanding of the climatic drivers of regional water resource variability and crop yields. Principal component analysis (PCA) of those variables and k-means clustering on the PCA outputs revealed a reanalysis ensemble consensus for eight macro-climate zones. Spatial statistics of input variables for each zone revealed consistent, distinct climatologies. This climate classification approach has potential for enhancing assessment of climatic influences on water resources and food security as well as for characterising the skill and bias of gridded data sets, both meteorological reanalyses and climate models, for reproducing subregional climatologies. Through their spatial descriptors (area, geographic centroid, elevation mean range), climate classifications also provide metrics, beyond simple changes in individual variables, with which to assess the magnitude of projected climate change. Such sophisticated metrics are of particular interest for regions, including mountainous areas, where natural and anthropogenic systems are expected to be sensitive to incremental climate shifts.

A Hybrid Sensing Approach for Pure and Adulterated Honey Classification

PubMed Central

Subari, Norazian; Saleh, Junita Mohamad; Shakaff, Ali Yeon Md; Zakaria, Ammar

2012-01-01

This paper presents a comparison between data from single modality and fusion methods to classify Tualang honey as pure or adulterated using Linear Discriminant Analysis (LDA) and Principal Component Analysis (PCA) statistical classification approaches. Ten different brands of certified pure Tualang honey were obtained throughout peninsular Malaysia and Sumatera, Indonesia. Various concentrations of two types of sugar solution (beet and cane sugar) were used in this investigation to create honey samples of 20%, 40%, 60% and 80% adulteration concentrations. Honey data extracted from an electronic nose (e-nose) and Fourier Transform Infrared Spectroscopy (FTIR) were gathered, analyzed and compared based on fusion methods. Visual observation of classification plots revealed that the PCA approach able to distinct pure and adulterated honey samples better than the LDA technique. Overall, the validated classification results based on FTIR data (88.0%) gave higher classification accuracy than e-nose data (76.5%) using the LDA technique. Honey classification based on normalized low-level and intermediate-level FTIR and e-nose fusion data scored classification accuracies of 92.2% and 88.7%, respectively using the Stepwise LDA method. The results suggested that pure and adulterated honey samples were better classified using FTIR and e-nose fusion data than single modality data. PMID:23202033

[Proposal of new trace elements classification to be used in nutrition, oligotherapy and other therapeutics strategies].

PubMed

Ramírez Hernández, Javier; Bonete Pérez, María José; Martínez Espinosa, Rosa María

2014-12-17

1) to propose a new classification of the trace elements based on a study of the recently reported research; 2) to offer detailed and actualized information about trace elements. the analysis of the research results recently reported reveals that the advances of the molecular analysis techniques point out the importance of certain trace elements in human health. A detailed analysis of the catalytic function related to several elements not considered essential o probably essentials up to now is also offered. To perform the integral analysis of the enzymes containing trace elements informatics tools have been used. Actualized information about physiological role, kinetics, metabolism, dietetic sources and factors promoting trace elements scarcity or toxicity is also presented. Oligotherapy uses catalytic active trace elements with therapeutic proposals. The new trace element classification here presented will be of high interest for different professional sectors: doctors and other professions related to medicine; nutritionist, pharmaceutics, etc. Using this new classification and approaches, new therapeutic strategies could be designed to mitigate symptomatology related to several pathologies, particularly carential and metabolic diseases. Copyright AULA MEDICA EDICIONES 2014. Published by AULA MEDICA. All rights reserved.

Mediterranean Land Use and Land Cover Classification Assessment Using High Spatial Resolution Data

NASA Astrophysics Data System (ADS)

Elhag, Mohamed; Boteva, Silvena

2016-10-01

Landscape fragmentation is noticeably practiced in Mediterranean regions and imposes substantial complications in several satellite image classification methods. To some extent, high spatial resolution data were able to overcome such complications. For better classification performances in Land Use Land Cover (LULC) mapping, the current research adopts different classification methods comparison for LULC mapping using Sentinel-2 satellite as a source of high spatial resolution. Both of pixel-based and an object-based classification algorithms were assessed; the pixel-based approach employs Maximum Likelihood (ML), Artificial Neural Network (ANN) algorithms, Support Vector Machine (SVM), and, the object-based classification uses the Nearest Neighbour (NN) classifier. Stratified Masking Process (SMP) that integrates a ranking process within the classes based on spectral fluctuation of the sum of the training and testing sites was implemented. An analysis of the overall and individual accuracy of the classification results of all four methods reveals that the SVM classifier was the most efficient overall by distinguishing most of the classes with the highest accuracy. NN succeeded to deal with artificial surface classes in general while agriculture area classes, and forest and semi-natural area classes were segregated successfully with SVM. Furthermore, a comparative analysis indicates that the conventional classification method yielded better accuracy results than the SMP method overall with both classifiers used, ML and SVM.

Visualizing the Structure of Medical Informatics Using Term Co-Occurrence Analysis: II. INSPEC Perspective.

ERIC Educational Resources Information Center

Morris, Theodore

2001-01-01

Term co-occurrence analysis of INSPEC classification codes and thesaurus terms used to index Medical Informatics literature reveals an information science and technology perspective on the field, to accompany the biomedical perspective previously reported. This study continues the search for a better understanding of the structure of Medical…

A study of the utilization of ERTS-1 data from the Wabash River Basin

NASA Technical Reports Server (NTRS)

Landgrebe, D. A. (Principal Investigator)

1973-01-01

The author has identified the following significant results. Nine projects are defined, five ERTS data applications experiments and four supporting technology tasks. The most significant applications results were achieved in the soil association mapping, earth surface feature identification, and urban land use mapping efforts. Four soil association boundaries were accurately delineated from ERTS-1 imagery. A data bank has been developed to test surface feature classifications obtained from ERTS-1 data. Preliminary forest cover classifications indicated that the number of acres estimated tended to be greater than actually existed by 25%. Urban land use analysis of ERTS-1 data indicated highly accurate classification could be obtained for many urban catagories. The wooded residential category tended to be misclassified as woods or agricultural land. Further statistical analysis revealed that these classes could be separated using sample variance.

Single-accelerometer-based daily physical activity classification.

PubMed

Long, Xi; Yin, Bin; Aarts, Ronald M

2009-01-01

In this study, a single tri-axial accelerometer placed on the waist was used to record the acceleration data for human physical activity classification. The data collection involved 24 subjects performing daily real-life activities in a naturalistic environment without researchers' intervention. For the purpose of assessing customers' daily energy expenditure, walking, running, cycling, driving, and sports were chosen as target activities for classification. This study compared a Bayesian classification with that of a Decision Tree based approach. A Bayes classifier has the advantage to be more extensible, requiring little effort in classifier retraining and software update upon further expansion or modification of the target activities. Principal components analysis was applied to remove the correlation among features and to reduce the feature vector dimension. Experiments using leave-one-subject-out and 10-fold cross validation protocols revealed a classification accuracy of approximately 80%, which was comparable with that obtained by a Decision Tree classifier.

Laser-induced breakdown spectroscopy-based investigation and classification of pharmaceutical tablets using multivariate chemometric analysis

PubMed Central

Myakalwar, Ashwin Kumar; Sreedhar, S.; Barman, Ishan; Dingari, Narahara Chari; Rao, S. Venugopal; Kiran, P. Prem; Tewari, Surya P.; Kumar, G. Manoj

2012-01-01

We report the effectiveness of laser-induced breakdown spectroscopy (LIBS) in probing the content of pharmaceutical tablets and also investigate its feasibility for routine classification. This method is particularly beneficial in applications where its exquisite chemical specificity and suitability for remote and on site characterization significantly improves the speed and accuracy of quality control and assurance process. Our experiments reveal that in addition to the presence of carbon, hydrogen, nitrogen and oxygen, which can be primarily attributed to the active pharmaceutical ingredients, specific inorganic atoms were also present in all the tablets. Initial attempts at classification by a ratiometric approach using oxygen to nitrogen compositional values yielded an optimal value (at 746.83 nm) with the least relative standard deviation but nevertheless failed to provide an acceptable classification. To overcome this bottleneck in the detection process, two chemometric algorithms, i.e. principal component analysis (PCA) and soft independent modeling of class analogy (SIMCA), were implemented to exploit the multivariate nature of the LIBS data demonstrating that LIBS has the potential to differentiate and discriminate among pharmaceutical tablets. We report excellent prospective classification accuracy using supervised classification via the SIMCA algorithm, demonstrating its potential for future applications in process analytical technology, especially for fast on-line process control monitoring applications in the pharmaceutical industry. PMID:22099648

Bacterial community composition in the gut content of Lampetra japonica revealed by 16S rRNA gene pyrosequencing.

PubMed

Zuo, Yu; Xie, Wenfang; Pang, Yue; Li, Tiesong; Li, Qingwei; Li, Yingying

2017-01-01

The composition of the bacterial communities in the hindgut contents of Lampetrs japonica was surveyed by Illumina MiSeq of the 16S rRNA gene. An average of 32385 optimized reads was obtained from three samples. The rarefaction curve based on the operational taxonomic units tended to approach the asymptote. The rank abundance curve representing the species richness and evenness was calculated. The composition of microbe in six classification levels was also analyzed. Top 20 members in genera level were displayed as the classification tree. The abundance of microorganisms in different individuals was displayed as the pie charts at the branch nodes in the classification tree. The differences of top 50 genera in abundance between individuals of lamprey are displayed as a heatmap. The pairwise comparison of bacterial taxa abundance revealed that there are no significant differences of gut microbiota between three individuals of lamprey at a given rarefied depth. Also, the gut microbiota derived from L. japonica displays little similarity with other aquatic organism of Vertebrata after UPGMA analysis. The metabolic function of the bacterial communities was predicted through KEGG analysis. This study represents the first analysis of the bacterial community composition in the gut content of L. japonica. The investigation of the gut microbiota associated with L. japonica will broaden our understanding of this unique organism.

An examination of the identity development of African American undergraduate engineering students attending an HBCU

NASA Astrophysics Data System (ADS)

Taylor, Kenneth J.

This study examined the identity development for a sample of 90 African American undergraduate engineering male and female students attending an HBCU. Using the Student Development Task and Lifestyle Assessment (SDTLA), which is based on Chickering and Reisser's identity development theory, differences in identity development were examined with respect to gender, academic classification, and grade point average. Previous research has shown the need to look beyond academic factors to understand and influence the persistence of African American engineering students. Non-cognitive factors, including identity development have proven to be influential in predicting persistence, especially for African American engineering students. Results from the analysis revealed significant means for academic classification and five of the dependent variables to include career planning peer relations, emotional autonomy, educational involvement, and establishing and clarifying purpose. Post hoc analysis confirmed significant differences for four of those dependent variables. However, the analysis failed to confirm statistical significant differences in peer relations due to academic classification. The significant decline in the mean scores for development in these four areas, as students progressed from sophomore to senior year revealed strong implications for the need to provide programming and guidance for those students. Institutions of higher education should provide more attention to the non-cognitive areas of development as a means of understanding identity development and working toward creating support systems for students.

Feasibility of laser-induced breakdown spectroscopy (LIBS) for classification of sea salts.

PubMed

Tan, Man Minh; Cui, Sheng; Yoo, Jonghyun; Han, Song-Hee; Ham, Kyung-Sik; Nam, Sang-Ho; Lee, Yonghoon

2012-03-01

We have investigated the feasibility of laser-induced breakdown spectroscopy (LIBS) as a fast, reliable classification tool for sea salts. For 11 kinds of sea salts, potassium (K), magnesium (Mg), calcium (Ca), and aluminum (Al), concentrations were measured by inductively coupled plasma-atomic emission spectroscopy (ICP-AES), and the LIBS spectra were recorded in the narrow wavelength region between 760 and 800 nm where K (I), Mg (I), Ca (II), Al (I), and cyanide (CN) band emissions are observed. The ICP-AES measurements revealed that the K, Mg, Ca, and Al concentrations varied significantly with the provenance of each salt. The relative intensities of the K (I), Mg (I), Ca (II), and Al (I) peaks observed in the LIBS spectra are consistent with the results using ICP-AES. The principal component analysis of the LIBS spectra provided the score plot with quite a high degree of clustering. This indicates that classification of sea salts by chemometric analysis of LIBS spectra is very promising. Classification models were developed by partial least squares discriminant analysis (PLS-DA) and evaluated. In addition, the Al (I) peaks enabled us to discriminate between different production methods of the salts. © 2012 Society for Applied Spectroscopy

Prognostic value of the new International Association for the Study of Lung Cancer/American Thoracic Society/European Respiratory Society classification in stage IB lung adenocarcinoma.

PubMed

Xu, C-h; Wang, W; Wei, Y; Hu, H-d; Zou, J; Yan, J; Yu, L-k; Yang, R-s; Wang, Y

2015-10-01

Patients with pathological stage IB lung adenocarcinoma have a variable prognosis, even if received the same treatment. This study investigated the prognostic value of the new International Association for the Study of Lung Cancer, American Thoracic Society, and European Respiratory Society (IASLC/ATS/ERS) lung adenocarcinoma classification in resected stage IB lung adenocarcinoma. We identified 276 patients with pathological stage IB adenocarcinoma who had undergone surgical resection at the Nanjing Chest Hospital between 2005 and 2010. The histological subtypes of all patients were classified according to the 2011 IASLC/ATS/ERS international multidisciplinary lung adenocarcinoma classification. Kaplan-Meier and Cox regression analyses were used to analyze the correlation between the IASLC/ATS/ERS classification and patients' prognosis. Two hundred and seventy-six patients with pathological stage IB adenocarcinoma had an 86.2% 5-year overall survival (OS) and 80.4% 5-year disease-free survival (DFS). Patients with micropapillary and solid predominant tumors had a significantly worse OS and DFS as compared to those with other subtypes predominant tumors (p = 0.003 and 0.001). Multivariate analysis revealed that the new classification was an independent prognostic factor for both OS and DFS of pathological stage IB adenocarcinoma (p = 0.009 and 0.003). Our study revealed that the new IASLC/ATS/ERS classification was an independent prognostic factor of pathological stage IB adenocarcinoma. This new classification is valuable of screening out high risk patients to receive postoperative adjuvant therapy. Copyright © 2015. Published by Elsevier Ltd.

Classification and phylogenetic analysis of Chinese hawthorn assessed by plant and pollen morphology.

PubMed

Ma, S L Y; Lu, Y M

2016-09-19

The Chinese hawthorn (Crataegus pinnatifida Bge. var. major N.E.Br.) is uniquely originated in northern China. The ecological and horticultural importance of Chinese hawthorn is considerable and some varieties are valued for their fruit or medicine extracts. Its taxonomy and phylogeny remain poorly understood. Apart from general plant morphological traits, pollen is an important trait for the classification of plants and their evolutionary origin. However, few studies have investigated the pollen of Chinese hawthorn. Here, an analysis of plant and pollen morphological characteristics was conducted in 57 cultivars from the Shenyang region. Thirty plant morphological characters and nine pollen grain characters were investigated. The plant morphological analysis revealed that the coefficient of variation for 13 traits was >20%, which indicates a high degree of variability. We also found that the pollen grains varied greatly in size, shape (from prolate to perprolate), and exine pattern (striate-perforate predominantly). The number of apertures was typically three. Based on these findings, we suggest that pollen morphology associated with plant morphological traits can be used for classification and phylogenetic analysis of Chinese hawthorn cultivars. In sum, our results provide new insights and constitute a scientific basis for future studies on the classification and evolution of Chinese hawthorn.

Genome-wide classification, evolutionary analysis and gene expression patterns of the kinome in Gossypium

PubMed Central

Yan, Jun; Li, Guilin; Guo, Xingqi; Li, Yang; Cao, Xuecheng

2018-01-01

The protein kinase (PK, kinome) family is one of the largest families in plants and regulates almost all aspects of plant processes, including plant development and stress responses. Despite their important functions, comprehensive functional classification, evolutionary analysis and expression patterns of the cotton PK gene family has yet to be performed on PK genes. In this study, we identified the cotton kinomes in the Gossypium raimondii, Gossypium arboretum, Gossypium hirsutum and Gossypium barbadense genomes and classified them into 7 groups and 122–24 subfamilies using software HMMER v3.0 scanning and neighbor-joining (NJ) phylogenetic analysis. Some conserved exon-intron structures were identified not only in cotton species but also in primitive plants, ferns and moss, suggesting the significant function and ancient origination of these PK genes. Collinearity analysis revealed that 16.6 million years ago (Mya) cotton-specific whole genome duplication (WGD) events may have played a partial role in the expansion of the cotton kinomes, whereas tandem duplication (TD) events mainly contributed to the expansion of the cotton RLK group. Synteny analysis revealed that tetraploidization of G. hirsutum and G. barbadense contributed to the expansion of G. hirsutum and G. barbadense PKs. Global expression analysis of cotton PKs revealed stress-specific and fiber development-related expression patterns, suggesting that many cotton PKs might be involved in the regulation of the stress response and fiber development processes. This study provides foundational information for further studies on the evolution and molecular function of cotton PKs. PMID:29768506

Prediction of performance on the RCMP physical ability requirement evaluation.

PubMed

Stanish, H I; Wood, T M; Campagna, P

1999-08-01

The Royal Canadian Mounted Police use the Physical Ability Requirement Evaluation (PARE) for screening applicants. The purposes of this investigation were to identify those field tests of physical fitness that were associated with PARE performance and determine which most accurately classified successful and unsuccessful PARE performers. The participants were 27 female and 21 male volunteers. Testing included measures of aerobic power, anaerobic power, agility, muscular strength, muscular endurance, and body composition. Multiple regression analysis revealed a three-variable model for males (70-lb bench press, standing long jump, and agility) explaining 79% of the variability in PARE time, whereas a one-variable model (agility) explained 43% of the variability for females. Analysis of the classification accuracy of the males' data was prohibited because 91% of the males passed the PARE. Classification accuracy of the females' data, using logistic regression, produced a two-variable model (agility, 1.5-mile endurance run) with 93% overall classification accuracy.

Quantifying tolerance indicator values for common stream fish species of the United States

USGS Publications Warehouse

Meador, M.R.; Carlisle, D.M.

2007-01-01

The classification of fish species tolerance to environmental disturbance is often used as a means to assess ecosystem conditions. Its use, however, may be problematic because the approach to tolerance classification is based on subjective judgment. We analyzed fish and physicochemical data from 773 stream sites collected as part of the U.S. Geological Survey's National Water-Quality Assessment Program to calculate tolerance indicator values for 10 physicochemical variables using weighted averaging. Tolerance indicator values (TIVs) for ammonia, chloride, dissolved oxygen, nitrite plus nitrate, pH, phosphorus, specific conductance, sulfate, suspended sediment, and water temperature were calculated for 105 common fish species of the United States. Tolerance indicator values for specific conductance and sulfate were correlated (rho = 0.87), and thus, fish species may be co-tolerant to these water-quality variables. We integrated TIVs for each species into an overall tolerance classification for comparisons with judgment-based tolerance classifications. Principal components analysis indicated that the distinction between tolerant and intolerant classifications was determined largely by tolerance to suspended sediment, specific conductance, chloride, and total phosphorus. Factors such as water temperature, dissolved oxygen, and pH may not be as important in distinguishing between tolerant and intolerant classifications, but may help to segregate species classified as moderate. Empirically derived tolerance classifications were 58.8% in agreement with judgment-derived tolerance classifications. Canonical discriminant analysis revealed that few TIVs, primarily chloride, could discriminate among judgment-derived tolerance classifications of tolerant, moderate, and intolerant. To our knowledge, this is the first empirically based understanding of fish species tolerance for stream fishes in the United States.

Rapid differentiation of Ghana cocoa beans by FT-NIR spectroscopy coupled with multivariate classification

NASA Astrophysics Data System (ADS)

Teye, Ernest; Huang, Xingyi; Dai, Huang; Chen, Quansheng

2013-10-01

Quick, accurate and reliable technique for discrimination of cocoa beans according to geographical origin is essential for quality control and traceability management. This current study presents the application of Near Infrared Spectroscopy technique and multivariate classification for the differentiation of Ghana cocoa beans. A total of 194 cocoa bean samples from seven cocoa growing regions were used. Principal component analysis (PCA) was used to extract relevant information from the spectral data and this gave visible cluster trends. The performance of four multivariate classification methods: Linear discriminant analysis (LDA), K-nearest neighbors (KNN), Back propagation artificial neural network (BPANN) and Support vector machine (SVM) were compared. The performances of the models were optimized by cross validation. The results revealed that; SVM model was superior to all the mathematical methods with a discrimination rate of 100% in both the training and prediction set after preprocessing with Mean centering (MC). BPANN had a discrimination rate of 99.23% for the training set and 96.88% for prediction set. While LDA model had 96.15% and 90.63% for the training and prediction sets respectively. KNN model had 75.01% for the training set and 72.31% for prediction set. The non-linear classification methods used were superior to the linear ones. Generally, the results revealed that NIR Spectroscopy coupled with SVM model could be used successfully to discriminate cocoa beans according to their geographical origins for effective quality assurance.

Clinical significance of erythropoietin receptor expression in oral squamous cell carcinoma

PubMed Central

2012-01-01

Background Hypoxic tumors are refractory to radiation and chemotherapy. High expression of biomarkers related to hypoxia in head and neck cancer is associated with a poorer prognosis. The present study aimed to evaluate the clinicopathological significance of erythropoietin receptor (EPOR) expression in oral squamous cell carcinoma (OSCC). Methods The study included 256 patients who underwent primary surgical resection between October 1996 and August 2005 for treatment of OSCC without previous radiotherapy and/or chemotherapy. Clinicopathological information including gender, age, T classification, N classification, and TNM stage was obtained from clinical records and pathology reports. The mRNA and protein expression levels of EPOR in OSCC specimens were evaluated by Q-RT-PCR, Western blotting and immunohistochemistry assays. Results We found that EPOR were overexpressed in OSCC tissues. The study included 17 women and 239 men with an average age of 50.9 years (range, 26–87 years). The mean follow-up period was 67 months (range, 2–171 months). High EPOR expression was significantly correlated with advanced T classification (p < 0.001), advanced TNM stage (p < 0.001), and positive N classification (p = 0.001). Furthermore, the univariate analysis revealed that patients with high tumor EPOR expression had a lower 5-year overall survival rate (p = 0.0011) and 5-year disease-specific survival rate (p = 0.0017) than patients who had low tumor levels of EPOR. However, the multivariate analysis using Cox’s regression model revealed that only the T and N classifications were independent prognostic factors for the 5-year overall survival and 5-year disease-specific survival rates. Conclusions High EPOR expression in OSCC is associated with an aggressive tumor behavior and poorer prognosis in the univariate analysis among patients with OSCC. Thus, EPOR expression may serve as a treatment target for OSCC in the future. PMID:22639817

Multi-gene phylogenetic analysis reveals that shochu-fermenting Saccharomyces cerevisiae strains form a distinct sub-clade of the Japanese sake cluster.

PubMed

Futagami, Taiki; Kadooka, Chihiro; Ando, Yoshinori; Okutsu, Kayu; Yoshizaki, Yumiko; Setoguchi, Shinji; Takamine, Kazunori; Kawai, Mikihiko; Tamaki, Hisanori

2017-10-01

Shochu is a traditional Japanese distilled spirit. The formation of the distinguishing flavour of shochu produced in individual distilleries is attributed to putative indigenous yeast strains. In this study, we performed the first (to our knowledge) phylogenetic classification of shochu strains based on nucleotide gene sequences. We performed phylogenetic classification of 21 putative indigenous shochu yeast strains isolated from 11 distilleries. All of these strains were shown or confirmed to be Saccharomyces cerevisiae, sharing species identification with 34 known S. cerevisiae strains (including commonly used shochu, sake, ale, whisky, bakery, bioethanol and laboratory yeast strains and clinical isolate) that were tested in parallel. Our analysis used five genes that reflect genome-level phylogeny for the strain-level classification. In a first step, we demonstrated that partial regions of the ZAP1, THI7, PXL1, YRR1 and GLG1 genes were sufficient to reproduce previous sub-species classifications. In a second step, these five analysed regions from each of 25 strains (four commonly used shochu strains and the 21 putative indigenous shochu strains) were concatenated and used to generate a phylogenetic tree. Further analysis revealed that the putative indigenous shochu yeast strains form a monophyletic group that includes both the shochu yeasts and a subset of the sake group strains; this cluster is a sister group to other sake yeast strains, together comprising a sake-shochu group. Differences among shochu strains were small, suggesting that it may be possible to correlate subtle phenotypic differences among shochu flavours with specific differences in genome sequences. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

Highly Accurate Classification of Watson-Crick Basepairs on Termini of Single DNA Molecules

PubMed Central

Winters-Hilt, Stephen; Vercoutere, Wenonah; DeGuzman, Veronica S.; Deamer, David; Akeson, Mark; Haussler, David

2003-01-01

We introduce a computational method for classification of individual DNA molecules measured by an α-hemolysin channel detector. We show classification with better than 99% accuracy for DNA hairpin molecules that differ only in their terminal Watson-Crick basepairs. Signal classification was done in silico to establish performance metrics (i.e., where train and test data were of known type, via single-species data files). It was then performed in solution to assay real mixtures of DNA hairpins. Hidden Markov Models (HMMs) were used with Expectation/Maximization for denoising and for associating a feature vector with the ionic current blockade of the DNA molecule. Support Vector Machines (SVMs) were used as discriminators, and were the focus of off-line training. A multiclass SVM architecture was designed to place less discriminatory load on weaker discriminators, and novel SVM kernels were used to boost discrimination strength. The tuning on HMMs and SVMs enabled biophysical analysis of the captured molecule states and state transitions; structure revealed in the biophysical analysis was used for better feature selection. PMID:12547778

Using complex networks for text classification: Discriminating informative and imaginative documents

NASA Astrophysics Data System (ADS)

de Arruda, Henrique F.; Costa, Luciano da F.; Amancio, Diego R.

2016-01-01

Statistical methods have been widely employed in recent years to grasp many language properties. The application of such techniques have allowed an improvement of several linguistic applications, such as machine translation and document classification. In the latter, many approaches have emphasised the semantical content of texts, as is the case of bag-of-word language models. These approaches have certainly yielded reasonable performance. However, some potential features such as the structural organization of texts have been used only in a few studies. In this context, we probe how features derived from textual structure analysis can be effectively employed in a classification task. More specifically, we performed a supervised classification aiming at discriminating informative from imaginative documents. Using a networked model that describes the local topological/dynamical properties of function words, we achieved an accuracy rate of up to 95%, which is much higher than similar networked approaches. A systematic analysis of feature relevance revealed that symmetry and accessibility measurements are among the most prominent network measurements. Our results suggest that these measurements could be used in related language applications, as they play a complementary role in characterising texts.

Durability of classification and action learning: differences revealed using ex-Gaussian distribution analysis.

PubMed

Moutsopoulou, Karolina; Waszak, Florian

2013-05-01

It has been shown that in associative learning it is possible to disentangle the effects caused on behaviour by the associations between a stimulus and a classification (S-C) and the associations between a stimulus and the action performed towards it (S-A). Such evidence has been provided using ex-Gaussian distribution analysis to show that different parameters of the reaction time distribution reflect the different processes. Here, using this method, we investigate another difference between these two types of associations: What is the relative durability of these associations across time? Using a task-switching paradigm and by manipulating the lag between the point of the creation of the associations and the test phase, we show that S-A associations have stronger effects on behaviour when the lag between the two repetitions of a stimulus is short. However, classification learning affects behaviour not only in short-term lags but also (and equally so) when the lag between prime and probe is long and the same stimuli are repeatedly presented within a different classification task, demonstrating a remarkable durability of S-C associations.

Random forests-based differential analysis of gene sets for gene expression data.

PubMed

Hsueh, Huey-Miin; Zhou, Da-Wei; Tsai, Chen-An

2013-04-10

In DNA microarray studies, gene-set analysis (GSA) has become the focus of gene expression data analysis. GSA utilizes the gene expression profiles of functionally related gene sets in Gene Ontology (GO) categories or priori-defined biological classes to assess the significance of gene sets associated with clinical outcomes or phenotypes. Many statistical approaches have been proposed to determine whether such functionally related gene sets express differentially (enrichment and/or deletion) in variations of phenotypes. However, little attention has been given to the discriminatory power of gene sets and classification of patients. In this study, we propose a method of gene set analysis, in which gene sets are used to develop classifications of patients based on the Random Forest (RF) algorithm. The corresponding empirical p-value of an observed out-of-bag (OOB) error rate of the classifier is introduced to identify differentially expressed gene sets using an adequate resampling method. In addition, we discuss the impacts and correlations of genes within each gene set based on the measures of variable importance in the RF algorithm. Significant classifications are reported and visualized together with the underlying gene sets and their contribution to the phenotypes of interest. Numerical studies using both synthesized data and a series of publicly available gene expression data sets are conducted to evaluate the performance of the proposed methods. Compared with other hypothesis testing approaches, our proposed methods are reliable and successful in identifying enriched gene sets and in discovering the contributions of genes within a gene set. The classification results of identified gene sets can provide an valuable alternative to gene set testing to reveal the unknown, biologically relevant classes of samples or patients. In summary, our proposed method allows one to simultaneously assess the discriminatory ability of gene sets and the importance of genes for interpretation of data in complex biological systems. The classifications of biologically defined gene sets can reveal the underlying interactions of gene sets associated with the phenotypes, and provide an insightful complement to conventional gene set analyses. Copyright © 2012 Elsevier B.V. All rights reserved.

Assessment of sexual orientation using the hemodynamic brain response to visual sexual stimuli.

PubMed

Ponseti, Jorge; Granert, Oliver; Jansen, Olav; Wolff, Stephan; Mehdorn, Hubertus; Bosinski, Hartmut; Siebner, Hartwig

2009-06-01

The assessment of sexual orientation is of importance to the diagnosis and treatment of sex offenders and paraphilic disorders. Phallometry is considered gold standard in objectifying sexual orientation, yet this measurement has been criticized because of its intrusiveness and limited reliability. To evaluate whether the spatial response pattern to sexual stimuli as revealed by a change in blood oxygen level-dependent (BOLD) signal can be used for individual classification of sexual orientation. We used a preexisting functional MRI (fMRI) data set that had been acquired in a nonclinical sample of 12 heterosexual men and 14 homosexual men. During fMRI, participants were briefly exposed to pictures of same-sex and opposite-sex genitals. Data analysis involved four steps: (i) differences in the BOLD response to female and male sexual stimuli were calculated for each subject; (ii) these contrast images were entered into a group analysis to calculate whole-brain difference maps between homosexual and heterosexual participants; (iii) a single expression value was computed for each subject expressing its correspondence to the group result; and (iv) based on these expression values, Fisher's linear discriminant analysis and the kappa-nearest neighbor classification method were used to predict the sexual orientation of each subject. Sensitivity and specificity of the two classification methods in predicting individual sexual orientation. Both classification methods performed well in predicting individual sexual orientation with a mean accuracy of >85% (Fisher's linear discriminant analysis: 92% sensitivity, 85% specificity; kappa-nearest neighbor classification: 88% sensitivity, 92% specificity). Despite the small sample size, the functional response patterns of the brain to sexual stimuli contained sufficient information to predict individual sexual orientation with high accuracy. These results suggest that fMRI-based classification methods hold promise for the diagnosis of paraphilic disorders (e.g., pedophilia).

Forest land management by satellite: LANDSAT-derived information as input to a forest inventory system. [North Carolina

NASA Technical Reports Server (NTRS)

Williams, D. L.; Haver, G. F. (Principal Investigator)

1976-01-01

The author has identified the following significant results. Analysis of LANDSAT temporal data, specifically the digitally merged winter and summer scenes, provided the best overall classification results. Comparison of temporal classification results with available ground truth reveal a 94% agreement in the delineation of hardwood categories, a 96% agreement for the combined pine category, and a greater than 50% agreement for each individual pine subcategory. For nearly 1000 acres, compared clearcut acreage estimated with LANDSAT digital data differed from company inventory records by only 3%. Through analysis of summer data, pine stands were successfully classified into subcategories based upon the extent of crown closure. Maximum spectral separability of hardwood and pine stands was obtained from the analysis of winter data.

Automatic Classification of Medical Text: The Influence of Publication Form1

PubMed Central

Cole, William G.; Michael, Patricia A.; Stewart, James G.; Blois, Marsden S.

1988-01-01

Previous research has shown that within the domain of medical journal abstracts the statistical distribution of words is neither random nor uniform, but is highly characteristic. Many words are used mainly or solely by one medical specialty or when writing about one particular level of description. Due to this regularity of usage, automatic classification within journal abstracts has proved quite successful. The present research asks two further questions. It investigates whether this statistical regularity and automatic classification success can also be achieved in medical textbook chapters. It then goes on to see whether the statistical distribution found in textbooks is sufficiently similar to that found in abstracts to permit accurate classification of abstracts based solely on previous knowledge of textbooks. 14 textbook chapters and 45 MEDLINE abstracts were submitted to an automatic classification program that had been trained only on chapters drawn from a standard textbook series. Statistical analysis of the properties of abstracts vs. chapters revealed important differences in word use. Automatic classification performance was good for chapters, but poor for abstracts.

Gene features selection for three-class disease classification via multiple orthogonal partial least square discriminant analysis and S-plot using microarray data.

PubMed

Yang, Mingxing; Li, Xiumin; Li, Zhibin; Ou, Zhimin; Liu, Ming; Liu, Suhuan; Li, Xuejun; Yang, Shuyu

2013-01-01

DNA microarray analysis is characterized by obtaining a large number of gene variables from a small number of observations. Cluster analysis is widely used to analyze DNA microarray data to make classification and diagnosis of disease. Because there are so many irrelevant and insignificant genes in a dataset, a feature selection approach must be employed in data analysis. The performance of cluster analysis of this high-throughput data depends on whether the feature selection approach chooses the most relevant genes associated with disease classes. Here we proposed a new method using multiple Orthogonal Partial Least Squares-Discriminant Analysis (mOPLS-DA) models and S-plots to select the most relevant genes to conduct three-class disease classification and prediction. We tested our method using Golub's leukemia microarray data. For three classes with subtypes, we proposed hierarchical orthogonal partial least squares-discriminant analysis (OPLS-DA) models and S-plots to select features for two main classes and their subtypes. For three classes in parallel, we employed three OPLS-DA models and S-plots to choose marker genes for each class. The power of feature selection to classify and predict three-class disease was evaluated using cluster analysis. Further, the general performance of our method was tested using four public datasets and compared with those of four other feature selection methods. The results revealed that our method effectively selected the most relevant features for disease classification and prediction, and its performance was better than that of the other methods.

EEG source space analysis of the supervised factor analytic approach for the classification of multi-directional arm movement

NASA Astrophysics Data System (ADS)

Shenoy Handiru, Vikram; Vinod, A. P.; Guan, Cuntai

2017-08-01

Objective. In electroencephalography (EEG)-based brain-computer interface (BCI) systems for motor control tasks the conventional practice is to decode motor intentions by using scalp EEG. However, scalp EEG only reveals certain limited information about the complex tasks of movement with a higher degree of freedom. Therefore, our objective is to investigate the effectiveness of source-space EEG in extracting relevant features that discriminate arm movement in multiple directions. Approach. We have proposed a novel feature extraction algorithm based on supervised factor analysis that models the data from source-space EEG. To this end, we computed the features from the source dipoles confined to Brodmann areas of interest (BA4a, BA4p and BA6). Further, we embedded class-wise labels of multi-direction (multi-class) source-space EEG to an unsupervised factor analysis to make it into a supervised learning method. Main Results. Our approach provided an average decoding accuracy of 71% for the classification of hand movement in four orthogonal directions, that is significantly higher (>10%) than the classification accuracy obtained using state-of-the-art spatial pattern features in sensor space. Also, the group analysis on the spectral characteristics of source-space EEG indicates that the slow cortical potentials from a set of cortical source dipoles reveal discriminative information regarding the movement parameter, direction. Significance. This study presents evidence that low-frequency components in the source space play an important role in movement kinematics, and thus it may lead to new strategies for BCI-based neurorehabilitation.

Which Disability Classifications Are Not Particularly Litigious under the IDEA?

ERIC Educational Resources Information Center

Zirkel, Perry A.

2011-01-01

A previous exploratory analysis revealed that students with autism were notably overrepresented in published court decisions concerning the IDEA's core concepts of "free appropriate public education" (FAPE) and "least restrictive environment" (LRE). More specifically, for the period 1993 to 2006, the proportion of this…

Research on evaluating water resource resilience based on projection pursuit classification model

NASA Astrophysics Data System (ADS)

Liu, Dong; Zhao, Dan; Liang, Xu; Wu, Qiuchen

2016-03-01

Water is a fundamental natural resource while agriculture water guarantees the grain output, which shows that the utilization and management of water resource have a significant practical meaning. Regional agricultural water resource system features with unpredictable, self-organization, and non-linear which lays a certain difficulty on the evaluation of regional agriculture water resource resilience. The current research on water resource resilience remains to focus on qualitative analysis and the quantitative analysis is still in the primary stage, thus, according to the above issues, projection pursuit classification model is brought forward. With the help of artificial fish-swarm algorithm (AFSA), it optimizes the projection index function, seeks for the optimal projection direction, and improves AFSA with the application of self-adaptive artificial fish step and crowding factor. Taking Hongxinglong Administration of Heilongjiang as the research base and on the basis of improving AFSA, it established the evaluation of projection pursuit classification model to agriculture water resource system resilience besides the proceeding analysis of projection pursuit classification model on accelerating genetic algorithm. The research shows that the water resource resilience of Hongxinglong is the best than Raohe Farm, and the last 597 Farm. And the further analysis shows that the key driving factors influencing agricultural water resource resilience are precipitation and agriculture water consumption. The research result reveals the restoring situation of the local water resource system, providing foundation for agriculture water resource management.

Barrier island morphodynamic classification based on lidar metrics for north Assateague Island, Maryland

USGS Publications Warehouse

Brock, John C.; Krabill, William; Sallenger, Asbury H.

2004-01-01

In order to reap the potential of airborne lidar surveys to provide geological information useful in understanding coastal sedimentary processes acting on various time scales, a new set of analysis methods are needed. This paper presents a multi-temporal lidar analysis of north Assateague Island, Maryland, and demonstrates the calculation of lidar metrics that condense barrier island morphology and morphological change into attributed linear features that may be used to analyze trends in coastal evolution. The new methods proposed in this paper are also of significant practical value, because lidar metric analysis reduces large volumes of point elevations into linear features attributed with essential morphological variables that are ideally suited for inclusion in Geographic Information Systems. A morphodynamic classification of north Assategue Island for a recent 10 month time period that is based on the recognition of simple patterns described by lidar change metrics is presented. Such morphodynamic classification reveals the relative magnitude and the fine scale alongshore variation in the importance of coastal changes over the study area during a defined time period. More generally, through the presentation of this morphodynamic classification of north Assateague Island, the value of lidar metrics in both examining large lidar data sets for coherent trends and in building hypotheses regarding processes driving barrier evolution is demonstrated

FHR patterns that become significant in connection with ST waveform changes and metabolic acidosis at birth.

PubMed

Rosén, Karl G; Norén, Håkan; Carlsson, Ann

2018-04-18

Recent developments have produced new CTG classification systems and the question is to what extent these may affect the model of FHR + ST interpretation? The two new systems (FIGO2015 and SSOG2017) classify FHR + ST events differently from the current CTG classification system used in the STAN interpretation algorithm (STAN2007). Identify the predominant FHR patterns in connection with ST events in cases of cord artery metabolic acidosis missed by the different CTG classification systems. Indicate to what extent STAN clinical guidelines could be modified enhancing the sensitivity. Provide a pathophysiological rationale. Forty-four cases with umbilical cord artery metabolic acidosis were retrieved from a European multicenter database. Significant FHR + ST events were evaluated post hoc in consensus by an expert panel. Eighteen cases were not identified as in need of intervention and regarded as negative in the sensitivity analysis. In 12 cases, ST changes occurred but the CTG was regarded as reassuring. Visual analysis of the FHR + ST tracings revealed specific FHR patterns: Conclusion: These findings indicate FHR + ST analysis may be undertaken regardless of CTG classification system provided there is a more physiologically oriented approach to FHR assessment in connection with an ST event.

Structural Analysis of Biodiversity

PubMed Central

Sirovich, Lawrence; Stoeckle, Mark Y.; Zhang, Yu

2010-01-01

Large, recently-available genomic databases cover a wide range of life forms, suggesting opportunity for insights into genetic structure of biodiversity. In this study we refine our recently-described technique using indicator vectors to analyze and visualize nucleotide sequences. The indicator vector approach generates correlation matrices, dubbed Klee diagrams, which represent a novel way of assembling and viewing large genomic datasets. To explore its potential utility, here we apply the improved algorithm to a collection of almost 17000 DNA barcode sequences covering 12 widely-separated animal taxa, demonstrating that indicator vectors for classification gave correct assignment in all 11000 test cases. Indicator vector analysis revealed discontinuities corresponding to species- and higher-level taxonomic divisions, suggesting an efficient approach to classification of organisms from poorly-studied groups. As compared to standard distance metrics, indicator vectors preserve diagnostic character probabilities, enable automated classification of test sequences, and generate high-information density single-page displays. These results support application of indicator vectors for comparative analysis of large nucleotide data sets and raise prospect of gaining insight into broad-scale patterns in the genetic structure of biodiversity. PMID:20195371

Identification of sexually abused female adolescents at risk for suicidal ideations: a classification and regression tree analysis.

PubMed

Brabant, Marie-Eve; Hébert, Martine; Chagnon, François

2013-01-01

This study explored the clinical profiles of 77 female teenager survivors of sexual abuse and examined the association of abuse-related and personal variables with suicidal ideations. Analyses revealed that 64% of participants experienced suicidal ideations. Findings from classification and regression tree analysis indicated that depression, posttraumatic stress symptoms, and hopelessness discriminated profiles of suicidal and nonsuicidal survivors. The elevated prevalence of suicidal ideations among adolescent survivors of sexual abuse underscores the importance of investigating the presence of suicidal ideations in sexual abuse survivors. However, suicidal ideation is not the sole variable that needs to be investigated; depression, hopelessness and posttraumatic stress symptoms are also related to suicidal ideations in survivors and could therefore guide interventions.

A cross-cultural investigation of college student alcohol consumption: a classification tree analysis.

PubMed

Kitsantas, Panagiota; Kitsantas, Anastasia; Anagnostopoulou, Tanya

2008-01-01

In this cross-cultural study, the authors attempted to identify high-risk subgroups for alcohol consumption among college students. American and Greek students (N = 132) answered questions about alcohol consumption, religious beliefs, attitudes toward drinking, advertisement influences, parental monitoring, and drinking consequences. Heavy drinkers in the American group were younger and less religious than were infrequent drinkers. In the Greek group, heavy drinkers tended to deny the negative results of drinking alcohol and use a permissive attitude to justify it, whereas infrequent drinkers were more likely to be monitored by their parents. These results suggest that parental monitoring and an emphasis on informing students about the negative effects of alcohol on their health and social and academic lives may be effective methods of reducing alcohol consumption. Classification tree analysis revealed that student attitudes toward drinking were important in the classification of American and Greek drinkers, indicating that this is a powerful predictor of alcohol consumption regardless of ethnic background.

Molecular Profiling Reveals Biologically Discrete Subsets and Pathways of Progression in Diffuse Glioma.

PubMed

Ceccarelli, Michele; Barthel, Floris P; Malta, Tathiane M; Sabedot, Thais S; Salama, Sofie R; Murray, Bradley A; Morozova, Olena; Newton, Yulia; Radenbaugh, Amie; Pagnotta, Stefano M; Anjum, Samreen; Wang, Jiguang; Manyam, Ganiraju; Zoppoli, Pietro; Ling, Shiyun; Rao, Arjun A; Grifford, Mia; Cherniack, Andrew D; Zhang, Hailei; Poisson, Laila; Carlotti, Carlos Gilberto; Tirapelli, Daniela Pretti da Cunha; Rao, Arvind; Mikkelsen, Tom; Lau, Ching C; Yung, W K Alfred; Rabadan, Raul; Huse, Jason; Brat, Daniel J; Lehman, Norman L; Barnholtz-Sloan, Jill S; Zheng, Siyuan; Hess, Kenneth; Rao, Ganesh; Meyerson, Matthew; Beroukhim, Rameen; Cooper, Lee; Akbani, Rehan; Wrensch, Margaret; Haussler, David; Aldape, Kenneth D; Laird, Peter W; Gutmann, David H; Noushmehr, Houtan; Iavarone, Antonio; Verhaak, Roel G W

2016-01-28

Therapy development for adult diffuse glioma is hindered by incomplete knowledge of somatic glioma driving alterations and suboptimal disease classification. We defined the complete set of genes associated with 1,122 diffuse grade II-III-IV gliomas from The Cancer Genome Atlas and used molecular profiles to improve disease classification, identify molecular correlations, and provide insights into the progression from low- to high-grade disease. Whole-genome sequencing data analysis determined that ATRX but not TERT promoter mutations are associated with increased telomere length. Recent advances in glioma classification based on IDH mutation and 1p/19q co-deletion status were recapitulated through analysis of DNA methylation profiles, which identified clinically relevant molecular subsets. A subtype of IDH mutant glioma was associated with DNA demethylation and poor outcome; a group of IDH-wild-type diffuse glioma showed molecular similarity to pilocytic astrocytoma and relatively favorable survival. Understanding of cohesive disease groups may aid improved clinical outcomes. Copyright © 2016 Elsevier Inc. All rights reserved.

Lake water quality mapping from LANDSAT

NASA Technical Reports Server (NTRS)

Scherz, J. P.

1977-01-01

The lakes in three LANDSAT scenes were mapped by the Bendix MDAS multispectral analysis system. Field checking the maps by three separate individuals revealed approximately 90-95% correct classification for the lake categories selected. Variations between observers was about 5%. From the MDAS color coded maps the lake with the worst algae problem was easily located. This lake was closely checked and a pollution source of 100 cows was found in the springs which fed this lake. The theory, lab work and field work which made it possible for this demonstration project to be a practical lake classification procedure are presented.

An Analysis of Mission Critical Computer Software in Naval Aviation

DTIC Science & Technology

1991-03-01

No. Task No. Work Unit Accesion Number 11. TITLE (Include Security Classification) AN ANALYSIS OF MISSION CRITICAL COMPUTER SOFTWARE IN NAVAL AVIATION...software development schedules were sustained without a milestone change being made. Also, software that was released to the fleet had no major...fleet contain any major defects? This research has revealed that only about half of the original software development schedules were sustained without a

Information spreading by a combination of MEG source estimation and multivariate pattern classification.

PubMed

Sato, Masashi; Yamashita, Okito; Sato, Masa-Aki; Miyawaki, Yoichi

2018-01-01

To understand information representation in human brain activity, it is important to investigate its fine spatial patterns at high temporal resolution. One possible approach is to use source estimation of magnetoencephalography (MEG) signals. Previous studies have mainly quantified accuracy of this technique according to positional deviations and dispersion of estimated sources, but it remains unclear how accurately MEG source estimation restores information content represented by spatial patterns of brain activity. In this study, using simulated MEG signals representing artificial experimental conditions, we performed MEG source estimation and multivariate pattern analysis to examine whether MEG source estimation can restore information content represented by patterns of cortical current in source brain areas. Classification analysis revealed that the corresponding artificial experimental conditions were predicted accurately from patterns of cortical current estimated in the source brain areas. However, accurate predictions were also possible from brain areas whose original sources were not defined. Searchlight decoding further revealed that this unexpected prediction was possible across wide brain areas beyond the original source locations, indicating that information contained in the original sources can spread through MEG source estimation. This phenomenon of "information spreading" may easily lead to false-positive interpretations when MEG source estimation and classification analysis are combined to identify brain areas that represent target information. Real MEG data analyses also showed that presented stimuli were able to be predicted in the higher visual cortex at the same latency as in the primary visual cortex, also suggesting that information spreading took place. These results indicate that careful inspection is necessary to avoid false-positive interpretations when MEG source estimation and multivariate pattern analysis are combined.

Information spreading by a combination of MEG source estimation and multivariate pattern classification

PubMed Central

Sato, Masashi; Yamashita, Okito; Sato, Masa-aki

2018-01-01

To understand information representation in human brain activity, it is important to investigate its fine spatial patterns at high temporal resolution. One possible approach is to use source estimation of magnetoencephalography (MEG) signals. Previous studies have mainly quantified accuracy of this technique according to positional deviations and dispersion of estimated sources, but it remains unclear how accurately MEG source estimation restores information content represented by spatial patterns of brain activity. In this study, using simulated MEG signals representing artificial experimental conditions, we performed MEG source estimation and multivariate pattern analysis to examine whether MEG source estimation can restore information content represented by patterns of cortical current in source brain areas. Classification analysis revealed that the corresponding artificial experimental conditions were predicted accurately from patterns of cortical current estimated in the source brain areas. However, accurate predictions were also possible from brain areas whose original sources were not defined. Searchlight decoding further revealed that this unexpected prediction was possible across wide brain areas beyond the original source locations, indicating that information contained in the original sources can spread through MEG source estimation. This phenomenon of “information spreading” may easily lead to false-positive interpretations when MEG source estimation and classification analysis are combined to identify brain areas that represent target information. Real MEG data analyses also showed that presented stimuli were able to be predicted in the higher visual cortex at the same latency as in the primary visual cortex, also suggesting that information spreading took place. These results indicate that careful inspection is necessary to avoid false-positive interpretations when MEG source estimation and multivariate pattern analysis are combined. PMID:29912968

A hybrid clustering and classification approach for predicting crash injury severity on rural roads.

PubMed

Hasheminejad, Seyed Hessam-Allah; Zahedi, Mohsen; Hasheminejad, Seyed Mohammad Hossein

2018-03-01

As a threat for transportation system, traffic crashes have a wide range of social consequences for governments. Traffic crashes are increasing in developing countries and Iran as a developing country is not immune from this risk. There are several researches in the literature to predict traffic crash severity based on artificial neural networks (ANNs), support vector machines and decision trees. This paper attempts to investigate the crash injury severity of rural roads by using a hybrid clustering and classification approach to compare the performance of classification algorithms before and after applying the clustering. In this paper, a novel rule-based genetic algorithm (GA) is proposed to predict crash injury severity, which is evaluated by performance criteria in comparison with classification algorithms like ANN. The results obtained from analysis of 13,673 crashes (5600 property damage, 778 fatal crashes, 4690 slight injuries and 2605 severe injuries) on rural roads in Tehran Province of Iran during 2011-2013 revealed that the proposed GA method outperforms other classification algorithms based on classification metrics like precision (86%), recall (88%) and accuracy (87%). Moreover, the proposed GA method has the highest level of interpretation, is easy to understand and provides feedback to analysts.

Masked and unmasked error-related potentials during continuous control and feedback

NASA Astrophysics Data System (ADS)

Lopes Dias, Catarina; Sburlea, Andreea I.; Müller-Putz, Gernot R.

2018-06-01

The detection of error-related potentials (ErrPs) in tasks with discrete feedback is well established in the brain–computer interface (BCI) field. However, the decoding of ErrPs in tasks with continuous feedback is still in its early stages. Objective. We developed a task in which subjects have continuous control of a cursor’s position by means of a joystick. The cursor’s position was shown to the participants in two different modalities of continuous feedback: normal and jittered. The jittered feedback was created to mimic the instability that could exist if participants controlled the trajectory directly with brain signals. Approach. This paper studies the electroencephalographic (EEG)—measurable signatures caused by a loss of control over the cursor’s trajectory, causing a target miss. Main results. In both feedback modalities, time-locked potentials revealed the typical frontal-central components of error-related potentials. Errors occurring during the jittered feedback (masked errors) were delayed in comparison to errors occurring during normal feedback (unmasked errors). Masked errors displayed lower peak amplitudes than unmasked errors. Time-locked classification analysis allowed a good distinction between correct and error classes (average Cohen-, average TPR  =  81.8% and average TNR  =  96.4%). Time-locked classification analysis between masked error and unmasked error classes revealed results at chance level (average Cohen-, average TPR  =  60.9% and average TNR  =  58.3%). Afterwards, we performed asynchronous detection of ErrPs, combining both masked and unmasked trials. The asynchronous detection of ErrPs in a simulated online scenario resulted in an average TNR of 84.0% and in an average TPR of 64.9%. Significance. The time-locked classification results suggest that the masked and unmasked errors were indistinguishable in terms of classification. The asynchronous classification results suggest that the feedback modality did not hinder the asynchronous detection of ErrPs.

Do Psychology Department Mission Statements Reflect the American Psychological Association Undergraduate Learning Goals?

ERIC Educational Resources Information Center

Warchal, Judith R.; Ruiz, Ana I.; You, Di

2017-01-01

This study focuses on the inclusion of the American Psychological Association's learning goals in the mission statements of undergraduate psychology programs across the US. We reviewed the mission statements available on websites for 1336 psychology programs listed in the Carnegie classification. Results of a content analysis revealed that of the…

The Questionable Impact of Gender on Job Loss Reactions: Implications for Employment Counseling

ERIC Educational Resources Information Center

Wooten, Kevin C.; Valenti, Alix M.

2008-01-01

A heterogeneous sample of 301 men and 118 women who experienced job loss was surveyed on 4 affective and 14 attributional variables. Analysis revealed that approximately 90% of the reported gender differences involving job loss reactions disappeared when age, ethnic background, marital status, education, tenure, salary, job classification, and…

Assessment of pedophilia using hemodynamic brain response to sexual stimuli.

PubMed

Ponseti, Jorge; Granert, Oliver; Jansen, Olav; Wolff, Stephan; Beier, Klaus; Neutze, Janina; Deuschl, Günther; Mehdorn, Hubertus; Siebner, Hartwig; Bosinski, Hartmut

2012-02-01

Accurately assessing sexual preference is important in the treatment of child sex offenders. Phallometry is the standard method to identify sexual preference; however, this measure has been criticized for its intrusiveness and limited reliability. To evaluate whether spatial response pattern to sexual stimuli as revealed by a change in the blood oxygen level-dependent signal facilitates the identification of pedophiles. During functional magnetic resonance imaging, pedophilic and nonpedophilic participants were briefly exposed to same- and opposite-sex images of nude children and adults. We calculated differences in blood oxygen level-dependent signals to child and adult sexual stimuli for each participant. The corresponding contrast images were entered into a group analysis to calculate whole-brain difference maps between groups. We calculated an expression value that corresponded to the group result for each participant. These expression values were submitted to 2 different classification algorithms: Fisher linear discriminant analysis and κ -nearest neighbor analysis. This classification procedure was cross-validated using the leave-one-out method. Section of Sexual Medicine, Medical School, Christian Albrechts University of Kiel, Kiel, Germany. We recruited 24 participants with pedophilia who were sexually attracted to either prepubescent girls (n = 11) or prepubescent boys (n = 13) and 32 healthy male controls who were sexually attracted to either adult women (n = 18) or adult men (n = 14). Sensitivity and specificity scores of the 2 classification algorithms. The highest classification accuracy was achieved by Fisher linear discriminant analysis, which showed a mean accuracy of 95% (100% specificity, 88% sensitivity). Functional brain response patterns to sexual stimuli contain sufficient information to identify pedophiles with high accuracy. The automatic classification of these patterns is a promising objective tool to clinically diagnose pedophilia.

Basis of Criminalistic Classification of a Person in Republic Kazakhstan and Republic Mongolia

ERIC Educational Resources Information Center

Abdilov, Kanat S.; Zusbaev, Baurzan T.; Naurysbaev, Erlan A.; Nukiev, Berik A.; Nurkina, Zanar B.; Myrzahanov, Erlan N.; Urazalin, Galym T.

2016-01-01

In this article reviewed problems of the criminalistic classification building of a person. In the work were used legal formal, logical, comparative legal methods. The author describes classification kinds. Reveal the meaning of classification in criminalistic systematics. Shows types of grounds of criminalistic classification of a person.…

Robust Classification of Small-Molecule Mechanism of Action Using a Minimalist High-Content Microscopy Screen and Multidimensional Phenotypic Trajectory Analysis

PubMed Central

Twarog, Nathaniel R.; Low, Jonathan A.; Currier, Duane G.; Miller, Greg; Chen, Taosheng; Shelat, Anang A.

2016-01-01

Phenotypic screening through high-content automated microscopy is a powerful tool for evaluating the mechanism of action of candidate therapeutics. Despite more than a decade of development, however, high content assays have yielded mixed results, identifying robust phenotypes in only a small subset of compound classes. This has led to a combinatorial explosion of assay techniques, analyzing cellular phenotypes across dozens of assays with hundreds of measurements. Here, using a minimalist three-stain assay and only 23 basic cellular measurements, we developed an analytical approach that leverages informative dimensions extracted by linear discriminant analysis to evaluate similarity between the phenotypic trajectories of different compounds in response to a range of doses. This method enabled us to visualize biologically-interpretable phenotypic tracks populated by compounds of similar mechanism of action, cluster compounds according to phenotypic similarity, and classify novel compounds by comparing them to phenotypically active exemplars. Hierarchical clustering applied to 154 compounds from over a dozen different mechanistic classes demonstrated tight agreement with published compound mechanism classification. Using 11 phenotypically active mechanism classes, classification was performed on all 154 compounds: 78% were correctly identified as belonging to one of the 11 exemplar classes or to a different unspecified class, with accuracy increasing to 89% when less phenotypically active compounds were excluded. Importantly, several apparent clustering and classification failures, including rigosertib and 5-fluoro-2’-deoxycytidine, instead revealed more complex mechanisms or off-target effects verified by more recent publications. These results show that a simple, easily replicated, minimalist high-content assay can reveal subtle variations in the cellular phenotype induced by compounds and can correctly predict mechanism of action, as long as the appropriate analytical tools are used. PMID:26886014

Robust Classification of Small-Molecule Mechanism of Action Using a Minimalist High-Content Microscopy Screen and Multidimensional Phenotypic Trajectory Analysis.

PubMed

Twarog, Nathaniel R; Low, Jonathan A; Currier, Duane G; Miller, Greg; Chen, Taosheng; Shelat, Anang A

2016-01-01

Phenotypic screening through high-content automated microscopy is a powerful tool for evaluating the mechanism of action of candidate therapeutics. Despite more than a decade of development, however, high content assays have yielded mixed results, identifying robust phenotypes in only a small subset of compound classes. This has led to a combinatorial explosion of assay techniques, analyzing cellular phenotypes across dozens of assays with hundreds of measurements. Here, using a minimalist three-stain assay and only 23 basic cellular measurements, we developed an analytical approach that leverages informative dimensions extracted by linear discriminant analysis to evaluate similarity between the phenotypic trajectories of different compounds in response to a range of doses. This method enabled us to visualize biologically-interpretable phenotypic tracks populated by compounds of similar mechanism of action, cluster compounds according to phenotypic similarity, and classify novel compounds by comparing them to phenotypically active exemplars. Hierarchical clustering applied to 154 compounds from over a dozen different mechanistic classes demonstrated tight agreement with published compound mechanism classification. Using 11 phenotypically active mechanism classes, classification was performed on all 154 compounds: 78% were correctly identified as belonging to one of the 11 exemplar classes or to a different unspecified class, with accuracy increasing to 89% when less phenotypically active compounds were excluded. Importantly, several apparent clustering and classification failures, including rigosertib and 5-fluoro-2'-deoxycytidine, instead revealed more complex mechanisms or off-target effects verified by more recent publications. These results show that a simple, easily replicated, minimalist high-content assay can reveal subtle variations in the cellular phenotype induced by compounds and can correctly predict mechanism of action, as long as the appropriate analytical tools are used.

Classification of a large microarray data set: Algorithm comparison and analysis of drug signatures

PubMed Central

Natsoulis, Georges; El Ghaoui, Laurent; Lanckriet, Gert R.G.; Tolley, Alexander M.; Leroy, Fabrice; Dunlea, Shane; Eynon, Barrett P.; Pearson, Cecelia I.; Tugendreich, Stuart; Jarnagin, Kurt

2005-01-01

A large gene expression database has been produced that characterizes the gene expression and physiological effects of hundreds of approved and withdrawn drugs, toxicants, and biochemical standards in various organs of live rats. In order to derive useful biological knowledge from this large database, a variety of supervised classification algorithms were compared using a 597-microarray subset of the data. Our studies show that several types of linear classifiers based on Support Vector Machines (SVMs) and Logistic Regression can be used to derive readily interpretable drug signatures with high classification performance. Both methods can be tuned to produce classifiers of drug treatments in the form of short, weighted gene lists which upon analysis reveal that some of the signature genes have a positive contribution (act as “rewards” for the class-of-interest) while others have a negative contribution (act as “penalties”) to the classification decision. The combination of reward and penalty genes enhances performance by keeping the number of false positive treatments low. The results of these algorithms are combined with feature selection techniques that further reduce the length of the drug signatures, an important step towards the development of useful diagnostic biomarkers and low-cost assays. Multiple signatures with no genes in common can be generated for the same classification end-point. Comparison of these gene lists identifies biological processes characteristic of a given class. PMID:15867433

Gait phenotypes in paediatric hereditary spastic paraplegia revealed by dynamic time warping analysis and random forests

PubMed Central

Martín-Gonzalo, Juan Andrés; Rodríguez-Andonaegui, Irene; López-López, Javier; Pascual-Pascual, Samuel Ignacio

2018-01-01

The Hereditary Spastic Paraplegias (HSP) are a group of heterogeneous disorders with a wide spectrum of underlying neural pathology, and hence HSP patients express a variety of gait abnormalities. Classification of these phenotypes may help in monitoring disease progression and personalizing therapies. This is currently managed by measuring values of some kinematic and spatio-temporal parameters at certain moments during the gait cycle, either in the doctor´s surgery room or after very precise measurements produced by instrumental gait analysis (IGA). These methods, however, do not provide information about the whole structure of the gait cycle. Classification of the similarities among time series of IGA measured values of sagittal joint positions throughout the whole gait cycle can be achieved by hierarchical clustering analysis based on multivariate dynamic time warping (DTW). Random forests can estimate which are the most important isolated parameters to predict the classification revealed by DTW, since clinicians need to refer to them in their daily practice. We acquired time series of pelvic, hip, knee, ankle and forefoot sagittal angular positions from 26 HSP and 33 healthy children with an optokinetic IGA system. DTW revealed six gait patterns with different degrees of impairment of walking speed, cadence and gait cycle distribution and related with patient’s age, sex, GMFCS stage, concurrence of polyneuropathy and abnormal visual evoked potentials or corpus callosum. The most important parameters to differentiate patterns were mean pelvic tilt and hip flexion at initial contact. Longer time of support, decreased values of hip extension and increased knee flexion at initial contact can differentiate the mildest, near to normal HSP gait phenotype and the normal healthy one. Increased values of knee flexion at initial contact and delayed peak of knee flexion are important factors to distinguish GMFCS stages I from II-III and concurrence of polyneuropathy. PMID:29518090

A Spiking Neural Network Methodology and System for Learning and Comparative Analysis of EEG Data From Healthy Versus Addiction Treated Versus Addiction Not Treated Subjects.

PubMed

Doborjeh, Maryam Gholami; Wang, Grace Y; Kasabov, Nikola K; Kydd, Robert; Russell, Bruce

2016-09-01

This paper introduces a method utilizing spiking neural networks (SNN) for learning, classification, and comparative analysis of brain data. As a case study, the method was applied to electroencephalography (EEG) data collected during a GO/NOGO cognitive task performed by untreated opiate addicts, those undergoing methadone maintenance treatment (MMT) for opiate dependence and a healthy control group. the method is based on an SNN architecture called NeuCube, trained on spatiotemporal EEG data. NeuCube was used to classify EEG data across subject groups and across GO versus NOGO trials, but also facilitated a deeper comparative analysis of the dynamic brain processes. This analysis results in a better understanding of human brain functioning across subject groups when performing a cognitive task. In terms of the EEG data classification, a NeuCube model obtained better results (the maximum obtained accuracy: 90.91%) when compared with traditional statistical and artificial intelligence methods (the maximum obtained accuracy: 50.55%). more importantly, new information about the effects of MMT on cognitive brain functions is revealed through the analysis of the SNN model connectivity and its dynamics. this paper presented a new method for EEG data modeling and revealed new knowledge on brain functions associated with mental activity which is different from the brain activity observed in a resting state of the same subjects.

MRI histogram analysis enables objective and continuous classification of intervertebral disc degeneration.

PubMed

Waldenberg, Christian; Hebelka, Hanna; Brisby, Helena; Lagerstrand, Kerstin Magdalena

2018-05-01

Magnetic resonance imaging (MRI) is the best diagnostic imaging method for low back pain. However, the technique is currently not utilized in its full capacity, often failing to depict painful intervertebral discs (IVDs), potentially due to the rough degeneration classification system used clinically today. MR image histograms, which reflect the IVD heterogeneity, may offer sensitive imaging biomarkers for IVD degeneration classification. This study investigates the feasibility of using histogram analysis as means of objective and continuous grading of IVD degeneration. Forty-nine IVDs in ten low back pain patients (six males, 25-69 years) were examined with MRI (T2-weighted images and T2-maps). Each IVD was semi-automatically segmented on three mid-sagittal slices. Histogram features of the IVD were extracted from the defined regions of interest and correlated to Pfirrmann grade. Both T2-weighted images and T2-maps displayed similar histogram features. Histograms of well-hydrated IVDs displayed two separate peaks, representing annulus fibrosus and nucleus pulposus. Degenerated IVDs displayed decreased peak separation, where the separation was shown to correlate strongly with Pfirrmann grade (P < 0.05). In addition, some degenerated IVDs within the same Pfirrmann grade displayed diametrically different histogram appearances. Histogram features correlated well with IVD degeneration, suggesting that IVD histogram analysis is a suitable tool for objective and continuous IVD degeneration classification. As histogram analysis revealed IVD heterogeneity, it may be a clinical tool for characterization of regional IVD degeneration effects. To elucidate the usefulness of histogram analysis in patient management, IVD histogram features between asymptomatic and symptomatic individuals needs to be compared.

Multivariate pattern analysis of fMRI data reveals deficits in distributed representations in schizophrenia

PubMed Central

Yoon, Jong H.; Tamir, Diana; Minzenberg, Michael J.; Ragland, J. Daniel; Ursu, Stefan; Carter, Cameron S.

2009-01-01

Background Multivariate pattern analysis is an alternative method of analyzing fMRI data, which is capable of decoding distributed neural representations. We applied this method to test the hypothesis of the impairment in distributed representations in schizophrenia. We also compared the results of this method with traditional GLM-based univariate analysis. Methods 19 schizophrenia and 15 control subjects viewed two runs of stimuli--exemplars of faces, scenes, objects, and scrambled images. To verify engagement with stimuli, subjects completed a 1-back matching task. A multi-voxel pattern classifier was trained to identify category-specific activity patterns on one run of fMRI data. Classification testing was conducted on the remaining run. Correlation of voxel-wise activity across runs evaluated variance over time in activity patterns. Results Patients performed the task less accurately. This group difference was reflected in the pattern analysis results with diminished classification accuracy in patients compared to controls, 59% and 72% respectively. In contrast, there was no group difference in GLM-based univariate measures. In both groups, classification accuracy was significantly correlated with behavioral measures. Both groups showed highly significant correlation between inter-run correlations and classification accuracy. Conclusions Distributed representations of visual objects are impaired in schizophrenia. This impairment is correlated with diminished task performance, suggesting that decreased integrity of cortical activity patterns is reflected in impaired behavior. Comparisons with univariate results suggest greater sensitivity of pattern analysis in detecting group differences in neural activity and reduced likelihood of non-specific factors driving these results. PMID:18822407

Improving oil classification quality from oil spill fingerprint beyond six sigma approach.

PubMed

Juahir, Hafizan; Ismail, Azimah; Mohamed, Saiful Bahri; Toriman, Mohd Ekhwan; Kassim, Azlina Md; Zain, Sharifuddin Md; Ahmad, Wan Kamaruzaman Wan; Wah, Wong Kok; Zali, Munirah Abdul; Retnam, Ananthy; Taib, Mohd Zaki Mohd; Mokhtar, Mazlin

2017-07-15

This study involves the use of quality engineering in oil spill classification based on oil spill fingerprinting from GC-FID and GC-MS employing the six-sigma approach. The oil spills are recovered from various water areas of Peninsular Malaysia and Sabah (East Malaysia). The study approach used six sigma methodologies that effectively serve as the problem solving in oil classification extracted from the complex mixtures of oil spilled dataset. The analysis of six sigma link with the quality engineering improved the organizational performance to achieve its objectivity of the environmental forensics. The study reveals that oil spills are discriminated into four groups' viz. diesel, hydrocarbon fuel oil (HFO), mixture oil lubricant and fuel oil (MOLFO) and waste oil (WO) according to the similarity of the intrinsic chemical properties. Through the validation, it confirmed that four discriminant component, diesel, hydrocarbon fuel oil (HFO), mixture oil lubricant and fuel oil (MOLFO) and waste oil (WO) dominate the oil types with a total variance of 99.51% with ANOVA giving F stat >F critical at 95% confidence level and a Chi Square goodness test of 74.87. Results obtained from this study reveals that by employing six-sigma approach in a data-driven problem such as in the case of oil spill classification, good decision making can be expedited. Copyright © 2017. Published by Elsevier Ltd.

Diagnosis of oral lichen planus from analysis of saliva samples using terahertz time-domain spectroscopy and chemometrics

NASA Astrophysics Data System (ADS)

Kistenev, Yury V.; Borisov, Alexey V.; Titarenko, Maria A.; Baydik, Olga D.; Shapovalov, Alexander V.

2018-04-01

The ability to diagnose oral lichen planus (OLP) based on saliva analysis using THz time-domain spectroscopy and chemometrics is discussed. The study involved 30 patients (2 male and 28 female) with OLP. This group consisted of two subgroups with the erosive form of OLP (n = 15) and with the reticular and papular forms of OLP (n = 15). The control group consisted of six healthy volunteers (one male and five females) without inflammation in the mucous membrane in the oral cavity and without periodontitis. Principal component analysis was used to reveal informative features in the experimental data. The one-versus-one multiclass classifier using support vector machine binary classifiers was used. The two-stage classification approach using several absorption spectra scans for an individual saliva sample provided 100% accuracy of differential classification between OLP subgroups and control group.

Highly efficient classification and identification of human pathogenic bacteria by MALDI-TOF MS.

PubMed

Hsieh, Sen-Yung; Tseng, Chiao-Li; Lee, Yun-Shien; Kuo, An-Jing; Sun, Chien-Feng; Lin, Yen-Hsiu; Chen, Jen-Kun

2008-02-01

Accurate and rapid identification of pathogenic microorganisms is of critical importance in disease treatment and public health. Conventional work flows are time-consuming, and procedures are multifaceted. MS can be an alternative but is limited by low efficiency for amino acid sequencing as well as low reproducibility for spectrum fingerprinting. We systematically analyzed the feasibility of applying MS for rapid and accurate bacterial identification. Directly applying bacterial colonies without further protein extraction to MALDI-TOF MS analysis revealed rich peak contents and high reproducibility. The MS spectra derived from 57 isolates comprising six human pathogenic bacterial species were analyzed using both unsupervised hierarchical clustering and supervised model construction via the Genetic Algorithm. Hierarchical clustering analysis categorized the spectra into six groups precisely corresponding to the six bacterial species. Precise classification was also maintained in an independently prepared set of bacteria even when the numbers of m/z values were reduced to six. In parallel, classification models were constructed via Genetic Algorithm analysis. A model containing 18 m/z values accurately classified independently prepared bacteria and identified those species originally not used for model construction. Moreover bacteria fewer than 10(4) cells and different species in bacterial mixtures were identified using the classification model approach. In conclusion, the application of MALDI-TOF MS in combination with a suitable model construction provides a highly accurate method for bacterial classification and identification. The approach can identify bacteria with low abundance even in mixed flora, suggesting that a rapid and accurate bacterial identification using MS techniques even before culture can be attained in the near future.

Microglia Morphological Categorization in a Rat Model of Neuroinflammation by Hierarchical Cluster and Principal Components Analysis.

PubMed

Fernández-Arjona, María Del Mar; Grondona, Jesús M; Granados-Durán, Pablo; Fernández-Llebrez, Pedro; López-Ávalos, María D

2017-01-01

It is known that microglia morphology and function are closely related, but only few studies have objectively described different morphological subtypes. To address this issue, morphological parameters of microglial cells were analyzed in a rat model of aseptic neuroinflammation. After the injection of a single dose of the enzyme neuraminidase (NA) within the lateral ventricle (LV) an acute inflammatory process occurs. Sections from NA-injected animals and sham controls were immunolabeled with the microglial marker IBA1, which highlights ramifications and features of the cell shape. Using images obtained by section scanning, individual microglial cells were sampled from various regions (septofimbrial nucleus, hippocampus and hypothalamus) at different times post-injection (2, 4 and 12 h). Each cell yielded a set of 15 morphological parameters by means of image analysis software. Five initial parameters (including fractal measures) were statistically different in cells from NA-injected rats (most of them IL-1β positive, i.e., M1-state) compared to those from control animals (none of them IL-1β positive, i.e., surveillant state). However, additional multimodal parameters were revealed more suitable for hierarchical cluster analysis (HCA). This method pointed out the classification of microglia population in four clusters. Furthermore, a linear discriminant analysis (LDA) suggested three specific parameters to objectively classify any microglia by a decision tree. In addition, a principal components analysis (PCA) revealed two extra valuable variables that allowed to further classifying microglia in a total of eight sub-clusters or types. The spatio-temporal distribution of these different morphotypes in our rat inflammation model allowed to relate specific morphotypes with microglial activation status and brain location. An objective method for microglia classification based on morphological parameters is proposed. Main points Microglia undergo a quantifiable morphological change upon neuraminidase induced inflammation.Hierarchical cluster and principal components analysis allow morphological classification of microglia.Brain location of microglia is a relevant factor.

Microglia Morphological Categorization in a Rat Model of Neuroinflammation by Hierarchical Cluster and Principal Components Analysis

PubMed Central

Fernández-Arjona, María del Mar; Grondona, Jesús M.; Granados-Durán, Pablo; Fernández-Llebrez, Pedro; López-Ávalos, María D.

2017-01-01

It is known that microglia morphology and function are closely related, but only few studies have objectively described different morphological subtypes. To address this issue, morphological parameters of microglial cells were analyzed in a rat model of aseptic neuroinflammation. After the injection of a single dose of the enzyme neuraminidase (NA) within the lateral ventricle (LV) an acute inflammatory process occurs. Sections from NA-injected animals and sham controls were immunolabeled with the microglial marker IBA1, which highlights ramifications and features of the cell shape. Using images obtained by section scanning, individual microglial cells were sampled from various regions (septofimbrial nucleus, hippocampus and hypothalamus) at different times post-injection (2, 4 and 12 h). Each cell yielded a set of 15 morphological parameters by means of image analysis software. Five initial parameters (including fractal measures) were statistically different in cells from NA-injected rats (most of them IL-1β positive, i.e., M1-state) compared to those from control animals (none of them IL-1β positive, i.e., surveillant state). However, additional multimodal parameters were revealed more suitable for hierarchical cluster analysis (HCA). This method pointed out the classification of microglia population in four clusters. Furthermore, a linear discriminant analysis (LDA) suggested three specific parameters to objectively classify any microglia by a decision tree. In addition, a principal components analysis (PCA) revealed two extra valuable variables that allowed to further classifying microglia in a total of eight sub-clusters or types. The spatio-temporal distribution of these different morphotypes in our rat inflammation model allowed to relate specific morphotypes with microglial activation status and brain location. An objective method for microglia classification based on morphological parameters is proposed. Main points Microglia undergo a quantifiable morphological change upon neuraminidase induced inflammation.Hierarchical cluster and principal components analysis allow morphological classification of microglia.Brain location of microglia is a relevant factor. PMID:28848398

Toward Biological Subtyping of Papillary Renal Cell Carcinoma With Clinical Implications Through Histologic, Immunohistochemical, and Molecular Analysis.

PubMed

Saleeb, Rola M; Brimo, Fadi; Farag, Mina; Rompré-Brodeur, Alexis; Rotondo, Fabio; Beharry, Vidya; Wala, Samantha; Plant, Pamela; Downes, Michelle R; Pace, Kenneth; Evans, Andrew; Bjarnason, Georg; Bartlett, John M S; Yousef, George M

2017-12-01

Papillary renal cell carcinoma (PRCC) has 2 histologic subtypes. Almost half of the cases fail to meet all morphologic criteria for either type, hence are characterized as PRCC not otherwise specified (NOS). There are yet no markers to resolve the PRCC NOS category. Accurate classification can better guide the management of these patients. In our previous PRCC study we identified markers that can distinguish between the subtypes. A PRCC patient cohort of 108 cases was selected for the current study. A panel of potentially distinguishing markers was chosen from our previous genomic analysis, and assessed by immunohistochemistry. The panel exhibited distinct staining patterns between the 2 classic PRCC subtypes; and successfully reclassified the NOS (45%) cases. Moreover, these immunomarkers revealed a third subtype, PRCC3 (35% of the cohort). Molecular testing using miRNA expression and copy number variation analysis confirmed the presence of 3 distinct molecular signatures corresponding to the 3 subtypes. Disease-free survival was significantly enhanced in PRCC1 versus 2 and 3 (P=0.047) on univariate analysis. The subtypes stratification was also significant on multivariate analysis (P=0.025; hazard ratio, 6; 95% confidence interval, 1.25-32.2). We propose a new classification system of PRCC integrating morphologic, immunophenotypical, and molecular analysis. The newly described PRCC3 has overlapping morphology between PRCC1 and PRCC2, hence would be subtyped as NOS in the current classification. Molecularly PRCC3 has a distinct signature and clinically it behaves similar to PRCC2. The new classification stratifies PRCC patients into clinically relevant subgroups and has significant implications on the management of PRCC.

A Quantitative Analysis of Pulsed Signals Emitted by Wild Bottlenose Dolphins.

PubMed

Luís, Ana Rita; Couchinho, Miguel N; Dos Santos, Manuel E

2016-01-01

Common bottlenose dolphins (Tursiops truncatus), produce a wide variety of vocal emissions for communication and echolocation, of which the pulsed repertoire has been the most difficult to categorize. Packets of high repetition, broadband pulses are still largely reported under a general designation of burst-pulses, and traditional attempts to classify these emissions rely mainly in their aural characteristics and in graphical aspects of spectrograms. Here, we present a quantitative analysis of pulsed signals emitted by wild bottlenose dolphins, in the Sado estuary, Portugal (2011-2014), and test the reliability of a traditional classification approach. Acoustic parameters (minimum frequency, maximum frequency, peak frequency, duration, repetition rate and inter-click-interval) were extracted from 930 pulsed signals, previously categorized using a traditional approach. Discriminant function analysis revealed a high reliability of the traditional classification approach (93.5% of pulsed signals were consistently assigned to their aurally based categories). According to the discriminant function analysis (Wilk's Λ = 0.11, F3, 2.41 = 282.75, P < 0.001), repetition rate is the feature that best enables the discrimination of different pulsed signals (structure coefficient = 0.98). Classification using hierarchical cluster analysis led to a similar categorization pattern: two main signal types with distinct magnitudes of repetition rate were clustered into five groups. The pulsed signals, here described, present significant differences in their time-frequency features, especially repetition rate (P < 0.001), inter-click-interval (P < 0.001) and duration (P < 0.001). We document the occurrence of a distinct signal type-short burst-pulses, and highlight the existence of a diverse repertoire of pulsed vocalizations emitted in graded sequences. The use of quantitative analysis of pulsed signals is essential to improve classifications and to better assess the contexts of emission, geographic variation and the functional significance of pulsed signals.

Unsupervised seismic facies analysis with spatial constraints using regularized fuzzy c-means

NASA Astrophysics Data System (ADS)

Song, Chengyun; Liu, Zhining; Cai, Hanpeng; Wang, Yaojun; Li, Xingming; Hu, Guangmin

2017-12-01

Seismic facies analysis techniques combine classification algorithms and seismic attributes to generate a map that describes main reservoir heterogeneities. However, most of the current classification algorithms only view the seismic attributes as isolated data regardless of their spatial locations, and the resulting map is generally sensitive to noise. In this paper, a regularized fuzzy c-means (RegFCM) algorithm is used for unsupervised seismic facies analysis. Due to the regularized term of the RegFCM algorithm, the data whose adjacent locations belong to same classification will play a more important role in the iterative process than other data. Therefore, this method can reduce the effect of seismic data noise presented in discontinuous regions. The synthetic data with different signal/noise values are used to demonstrate the noise tolerance ability of the RegFCM algorithm. Meanwhile, the fuzzy factor, the neighbour window size and the regularized weight are tested using various values, to provide a reference of how to set these parameters. The new approach is also applied to a real seismic data set from the F3 block of the Netherlands. The results show improved spatial continuity, with clear facies boundaries and channel morphology, which reveals that the method is an effective seismic facies analysis tool.

Acoustic target detection and classification using neural networks

NASA Technical Reports Server (NTRS)

Robertson, James A.; Conlon, Mark

1993-01-01

A neural network approach to the classification of acoustic emissions of ground vehicles and helicopters is demonstrated. Data collected during the Joint Acoustic Propagation Experiment conducted in July of l991 at White Sands Missile Range, New Mexico was used to train a classifier to distinguish between the spectrums of a UH-1, M60, M1 and M114. An output node was also included that would recognize background (i.e. no target) data. Analysis revealed specific hidden nodes responding to the features input into the classifier. Initial results using the neural network were encouraging with high correct identification rates accompanied by high levels of confidence.

Fast detection of tobacco mosaic virus infected tobacco using laser-induced breakdown spectroscopy

NASA Astrophysics Data System (ADS)

Peng, Jiyu; Song, Kunlin; Zhu, Hongyan; Kong, Wenwen; Liu, Fei; Shen, Tingting; He, Yong

2017-03-01

Tobacco mosaic virus (TMV) is one of the most devastating viruses to crops, which can cause severe production loss and affect the quality of products. In this study, we have proposed a novel approach to discriminate TMV-infected tobacco based on laser-induced breakdown spectroscopy (LIBS). Two different kinds of tobacco samples (fresh leaves and dried leaf pellets) were collected for spectral acquisition, and partial least squared discrimination analysis (PLS-DA) was used to establish classification models based on full spectrum and observed emission lines. The influences of moisture content on spectral profile, signal stability and plasma parameters (temperature and electron density) were also analysed. The results revealed that moisture content in fresh tobacco leaves would worsen the stability of analysis, and have a detrimental effect on the classification results. Good classification results were achieved based on the data from both full spectrum and observed emission lines of dried leaves, approaching 97.2% and 88.9% in the prediction set, respectively. In addition, support vector machine (SVM) could improve the classification results and eliminate influences of moisture content. The preliminary results indicate that LIBS coupled with chemometrics could provide a fast, efficient and low-cost approach for TMV-infected disease detection in tobacco leaves.

Classification of complementary and alternative medical practices: Family physicians' ratings of effectiveness.

PubMed

Fries, Christopher J

2008-11-01

ABSTRACTOBJECTIVETo develop a classification of complementary and alternative medicine (CAM) practices widely available in Canada based on physicians' effectiveness ratings of the therapies.DESIGNA self-administered postal questionnaire asking family physicians to rate their "belief in the degree of therapeutic effectiveness" of 15 CAM therapies.SETTINGProvince of Alberta.PARTICIPANTSA total of 875 family physicians.MAIN OUTCOME MEASURESDescriptive statistics of physicians' awareness of and effectiveness ratings for each of the therapies; factor analysis was applied to the ratings of the 15 therapies in order to explore whether or not the data support the proposed classification of CAM practices into categories of accepted and rejected.RESULTSPhysicians believed that acupuncture, massage therapy, chiropractic care, relaxation therapy, biofeedback, and spiritual or religious healing were effective when used in conjunction with biomedicine to treat chronic or psychosomatic indications. Physicians attributed little effectiveness to homeopathy or naturopathy, Feldenkrais or Alexander technique, Rolfing, herbal medicine, traditional Chinese medicine, and reflexology. The factor analysis revealed an underlying dimensionality to physicians' effectiveness ratings of the CAM therapies that supports the classification of these practices as either accepted or rejected.CONCLUSIONThis study provides Canadian family physicians with information concerning which CAM therapies are generally accepted by their peers as effective and which are not.

Anterior Chamber Angle Shape Analysis and Classification of Glaucoma in SS-OCT Images.

PubMed

Ni Ni, Soe; Tian, J; Marziliano, Pina; Wong, Hong-Tym

2014-01-01

Optical coherence tomography is a high resolution, rapid, and noninvasive diagnostic tool for angle closure glaucoma. In this paper, we present a new strategy for the classification of the angle closure glaucoma using morphological shape analysis of the iridocorneal angle. The angle structure configuration is quantified by the following six features: (1) mean of the continuous measurement of the angle opening distance; (2) area of the trapezoidal profile of the iridocorneal angle centered at Schwalbe's line; (3) mean of the iris curvature from the extracted iris image; (4) complex shape descriptor, fractal dimension, to quantify the complexity, or changes of iridocorneal angle; (5) ellipticity moment shape descriptor; and (6) triangularity moment shape descriptor. Then, the fuzzy k nearest neighbor (fkNN) classifier is utilized for classification of angle closure glaucoma. Two hundred and sixty-four swept source optical coherence tomography (SS-OCT) images from 148 patients were analyzed in this study. From the experimental results, the fkNN reveals the best classification accuracy (99.11 ± 0.76%) and AUC (0.98 ± 0.012) with the combination of fractal dimension and biometric parameters. It showed that the proposed approach has promising potential to become a computer aided diagnostic tool for angle closure glaucoma (ACG) disease.

Cancer Biomarkers from Genome-Scale DNA Methylation: Comparison of Evolutionary and Semantic Analysis Methods

PubMed Central

Valavanis, Ioannis; Pilalis, Eleftherios; Georgiadis, Panagiotis; Kyrtopoulos, Soterios; Chatziioannou, Aristotelis

2015-01-01

DNA methylation profiling exploits microarray technologies, thus yielding a wealth of high-volume data. Here, an intelligent framework is applied, encompassing epidemiological genome-scale DNA methylation data produced from the Illumina’s Infinium Human Methylation 450K Bead Chip platform, in an effort to correlate interesting methylation patterns with cancer predisposition and, in particular, breast cancer and B-cell lymphoma. Feature selection and classification are employed in order to select, from an initial set of ~480,000 methylation measurements at CpG sites, predictive cancer epigenetic biomarkers and assess their classification power for discriminating healthy versus cancer related classes. Feature selection exploits evolutionary algorithms or a graph-theoretic methodology which makes use of the semantics information included in the Gene Ontology (GO) tree. The selected features, corresponding to methylation of CpG sites, attained moderate-to-high classification accuracies when imported to a series of classifiers evaluated by resampling or blindfold validation. The semantics-driven selection revealed sets of CpG sites performing similarly with evolutionary selection in the classification tasks. However, gene enrichment and pathway analysis showed that it additionally provides more descriptive sets of GO terms and KEGG pathways regarding the cancer phenotypes studied here. Results support the expediency of this methodology regarding its application in epidemiological studies. PMID:27600245

Fast detection of tobacco mosaic virus infected tobacco using laser-induced breakdown spectroscopy

PubMed Central

Peng, Jiyu; Song, Kunlin; Zhu, Hongyan; Kong, Wenwen; Liu, Fei; Shen, Tingting; He, Yong

2017-01-01

Tobacco mosaic virus (TMV) is one of the most devastating viruses to crops, which can cause severe production loss and affect the quality of products. In this study, we have proposed a novel approach to discriminate TMV-infected tobacco based on laser-induced breakdown spectroscopy (LIBS). Two different kinds of tobacco samples (fresh leaves and dried leaf pellets) were collected for spectral acquisition, and partial least squared discrimination analysis (PLS-DA) was used to establish classification models based on full spectrum and observed emission lines. The influences of moisture content on spectral profile, signal stability and plasma parameters (temperature and electron density) were also analysed. The results revealed that moisture content in fresh tobacco leaves would worsen the stability of analysis, and have a detrimental effect on the classification results. Good classification results were achieved based on the data from both full spectrum and observed emission lines of dried leaves, approaching 97.2% and 88.9% in the prediction set, respectively. In addition, support vector machine (SVM) could improve the classification results and eliminate influences of moisture content. The preliminary results indicate that LIBS coupled with chemometrics could provide a fast, efficient and low-cost approach for TMV-infected disease detection in tobacco leaves. PMID:28300144

Phylogenetic analysis of a spontaneous cocoa bean fermentation metagenome reveals new insights into its bacterial and fungal community diversity.

PubMed

Illeghems, Koen; De Vuyst, Luc; Papalexandratou, Zoi; Weckx, Stefan

2012-01-01

This is the first report on the phylogenetic analysis of the community diversity of a single spontaneous cocoa bean box fermentation sample through a metagenomic approach involving 454 pyrosequencing. Several sequence-based and composition-based taxonomic profiling tools were used and evaluated to avoid software-dependent results and their outcome was validated by comparison with previously obtained culture-dependent and culture-independent data. Overall, this approach revealed a wider bacterial (mainly γ-Proteobacteria) and fungal diversity than previously found. Further, the use of a combination of different classification methods, in a software-independent way, helped to understand the actual composition of the microbial ecosystem under study. In addition, bacteriophage-related sequences were found. The bacterial diversity depended partially on the methods used, as composition-based methods predicted a wider diversity than sequence-based methods, and as classification methods based solely on phylogenetic marker genes predicted a more restricted diversity compared with methods that took all reads into account. The metagenomic sequencing analysis identified Hanseniaspora uvarum, Hanseniaspora opuntiae, Saccharomyces cerevisiae, Lactobacillus fermentum, and Acetobacter pasteurianus as the prevailing species. Also, the presence of occasional members of the cocoa bean fermentation process was revealed (such as Erwinia tasmaniensis, Lactobacillus brevis, Lactobacillus casei, Lactobacillus rhamnosus, Lactococcus lactis, Leuconostoc mesenteroides, and Oenococcus oeni). Furthermore, the sequence reads associated with viral communities were of a restricted diversity, dominated by Myoviridae and Siphoviridae, and reflecting Lactobacillus as the dominant host. To conclude, an accurate overview of all members of a cocoa bean fermentation process sample was revealed, indicating the superiority of metagenomic sequencing over previously used techniques.

Assessment of repeatability of composition of perfumed waters by high-performance liquid chromatography combined with numerical data analysis based on cluster analysis (HPLC UV/VIS - CA).

PubMed

Ruzik, L; Obarski, N; Papierz, A; Mojski, M

2015-06-01

High-performance liquid chromatography (HPLC) with UV/VIS spectrophotometric detection combined with the chemometric method of cluster analysis (CA) was used for the assessment of repeatability of composition of nine types of perfumed waters. In addition, the chromatographic method of separating components of the perfume waters under analysis was subjected to an optimization procedure. The chromatograms thus obtained were used as sources of data for the chemometric method of cluster analysis (CA). The result was a classification of a set comprising 39 perfumed water samples with a similar composition at a specified level of probability (level of agglomeration). A comparison of the classification with the manufacturer's declarations reveals a good degree of consistency and demonstrates similarity between samples in different classes. A combination of the chromatographic method with cluster analysis (HPLC UV/VIS - CA) makes it possible to quickly assess the repeatability of composition of perfumed waters at selected levels of probability. © 2014 Society of Cosmetic Scientists and the Société Française de Cosmétologie.

Spatiotemporal Change Detection Using Landsat Imagery: the Case Study of Karacabey Flooded Forest, Bursa, Turkey

NASA Astrophysics Data System (ADS)

Akay, A. E.; Gencal, B.; Taş, İ.

2017-11-01

This short paper aims to detect spatiotemporal detection of land use/land cover change within Karacabey Flooded Forest region. Change detection analysis applied to Landsat 5 TM images representing July 2000 and a Landsat 8 OLI representing June 2017. Various image processing tools were implemented using ERDAS 9.2, ArcGIS 10.4.1, and ENVI programs to conduct spatiotemporal change detection over these two images such as band selection, corrections, subset, classification, recoding, accuracy assessment, and change detection analysis. Image classification revealed that there are five significant land use/land cover types, including forest, flooded forest, swamp, water, and other lands (i.e. agriculture, sand, roads, settlement, and open areas). The results indicated that there was increase in flooded forest, water, and other lands, while the cover of forest and swamp decreased.

Prediction of Depression in Cancer Patients With Different Classification Criteria, Linear Discriminant Analysis versus Logistic Regression.

PubMed

Shayan, Zahra; Mohammad Gholi Mezerji, Naser; Shayan, Leila; Naseri, Parisa

2015-11-03

Logistic regression (LR) and linear discriminant analysis (LDA) are two popular statistical models for prediction of group membership. Although they are very similar, the LDA makes more assumptions about the data. When categorical and continuous variables used simultaneously, the optimal choice between the two models is questionable. In most studies, classification error (CE) is used to discriminate between subjects in several groups, but this index is not suitable to predict the accuracy of the outcome. The present study compared LR and LDA models using classification indices. This cross-sectional study selected 243 cancer patients. Sample sets of different sizes (n = 50, 100, 150, 200, 220) were randomly selected and the CE, B, and Q classification indices were calculated by the LR and LDA models. CE revealed the a lack of superiority for one model over the other, but the results showed that LR performed better than LDA for the B and Q indices in all situations. No significant effect for sample size on CE was noted for selection of an optimal model. Assessment of the accuracy of prediction of real data indicated that the B and Q indices are appropriate for selection of an optimal model. The results of this study showed that LR performs better in some cases and LDA in others when based on CE. The CE index is not appropriate for classification, although the B and Q indices performed better and offered more efficient criteria for comparison and discrimination between groups.

10 CFR 1045.17 - Classification levels.

Code of Federal Regulations, 2014 CFR

2014-01-01

... classification include detailed technical descriptions of critical features of a nuclear explosive design that... classification include designs for specific weapon components (not revealing critical features), key features of uranium enrichment technologies, or specifications of weapon materials. (3) Confidential. The Director of...

10 CFR 1045.17 - Classification levels.

Code of Federal Regulations, 2013 CFR

2013-01-01

... classification include detailed technical descriptions of critical features of a nuclear explosive design that... classification include designs for specific weapon components (not revealing critical features), key features of uranium enrichment technologies, or specifications of weapon materials. (3) Confidential. The Director of...

10 CFR 1045.17 - Classification levels.

Code of Federal Regulations, 2011 CFR

2011-01-01

... classification include detailed technical descriptions of critical features of a nuclear explosive design that... classification include designs for specific weapon components (not revealing critical features), key features of uranium enrichment technologies, or specifications of weapon materials. (3) Confidential. The Director of...

10 CFR 1045.17 - Classification levels.

Code of Federal Regulations, 2012 CFR

2012-01-01

... classification include detailed technical descriptions of critical features of a nuclear explosive design that... classification include designs for specific weapon components (not revealing critical features), key features of uranium enrichment technologies, or specifications of weapon materials. (3) Confidential. The Director of...

Impact of Information based Classification on Network Epidemics

PubMed Central

Mishra, Bimal Kumar; Haldar, Kaushik; Sinha, Durgesh Nandini

2016-01-01

Formulating mathematical models for accurate approximation of malicious propagation in a network is a difficult process because of our inherent lack of understanding of several underlying physical processes that intrinsically characterize the broader picture. The aim of this paper is to understand the impact of available information in the control of malicious network epidemics. A 1-n-n-1 type differential epidemic model is proposed, where the differentiality allows a symptom based classification. This is the first such attempt to add such a classification into the existing epidemic framework. The model is incorporated into a five class system called the DifEpGoss architecture. Analysis reveals an epidemic threshold, based on which the long-term behavior of the system is analyzed. In this work three real network datasets with 22002, 22469 and 22607 undirected edges respectively, are used. The datasets show that classification based prevention given in the model can have a good role in containing network epidemics. Further simulation based experiments are used with a three category classification of attack and defense strengths, which allows us to consider 27 different possibilities. These experiments further corroborate the utility of the proposed model. The paper concludes with several interesting results. PMID:27329348

A comprehensive simulation study on classification of RNA-Seq data.

PubMed

Zararsız, Gökmen; Goksuluk, Dincer; Korkmaz, Selcuk; Eldem, Vahap; Zararsiz, Gozde Erturk; Duru, Izzet Parug; Ozturk, Ahmet

2017-01-01

RNA sequencing (RNA-Seq) is a powerful technique for the gene-expression profiling of organisms that uses the capabilities of next-generation sequencing technologies. Developing gene-expression-based classification algorithms is an emerging powerful method for diagnosis, disease classification and monitoring at molecular level, as well as providing potential markers of diseases. Most of the statistical methods proposed for the classification of gene-expression data are either based on a continuous scale (eg. microarray data) or require a normal distribution assumption. Hence, these methods cannot be directly applied to RNA-Seq data since they violate both data structure and distributional assumptions. However, it is possible to apply these algorithms with appropriate modifications to RNA-Seq data. One way is to develop count-based classifiers, such as Poisson linear discriminant analysis and negative binomial linear discriminant analysis. Another way is to bring the data closer to microarrays and apply microarray-based classifiers. In this study, we compared several classifiers including PLDA with and without power transformation, NBLDA, single SVM, bagging SVM (bagSVM), classification and regression trees (CART), and random forests (RF). We also examined the effect of several parameters such as overdispersion, sample size, number of genes, number of classes, differential-expression rate, and the transformation method on model performances. A comprehensive simulation study is conducted and the results are compared with the results of two miRNA and two mRNA experimental datasets. The results revealed that increasing the sample size, differential-expression rate and decreasing the dispersion parameter and number of groups lead to an increase in classification accuracy. Similar with differential-expression studies, the classification of RNA-Seq data requires careful attention when handling data overdispersion. We conclude that, as a count-based classifier, the power transformed PLDA and, as a microarray-based classifier, vst or rlog transformed RF and SVM classifiers may be a good choice for classification. An R/BIOCONDUCTOR package, MLSeq, is freely available at https://www.bioconductor.org/packages/release/bioc/html/MLSeq.html.

New FIGO and Swedish intrapartum cardiotocography classification systems incorporated in the fetal ECG ST analysis (STAN) interpretation algorithm: agreements and discrepancies in cardiotocography classification and evaluation of significant ST events.

PubMed

Olofsson, Per; Norén, Håkan; Carlsson, Ann

2018-02-01

The updated intrapartum cardiotocography (CTG) classification system by FIGO in 2015 (FIGO2015) and the FIGO2015-approached classification by the Swedish Society of Obstetricians and Gynecologist in 2017 (SSOG2017) are not harmonized with the fetal ECG ST analysis (STAN) algorithm from 2007 (STAN2007). The study aimed to reveal homogeneity and agreement between the systems in classifying CTG and ST events, and relate them to maternal and perinatal outcomes. Among CTG traces with ST events, 100 traces originally classified as normal, 100 as suspicious and 100 as pathological were randomly selected from a STAN database and classified by two experts in consensus. Homogeneity and agreement statistics between the CTG classifications were performed. Maternal and perinatal outcomes were evaluated in cases with clinically hidden ST data (n = 151). A two-tailed p < 0.05 was regarded as significant. For CTG classes, the heterogeneity was significant between the old and new systems, and agreements were moderate to strong (proportion of agreement, kappa index 0.70-0.86). Between the new classifications, heterogeneity was significant and agreements strong (0.90, 0.92). For significant ST events, heterogeneities were significant and agreements moderate to almost perfect (STAN2007 vs. FIGO2015 0.86, 0.72; STAN2007 vs. SSOG2017 0.92, 0.84; FIGO2015 vs. SSOG2017 0.94, 0.87). Significant ST events occurred more often combined with STAN2007 than with FIGO2015 classification, but not with SSOG2017; correct identification of adverse outcomes was not significantly different between the systems. There are discrepancies in the classification of CTG patterns and significant ST events between the old and new systems. The clinical relevance of the findings remains to be shown. © 2017 The Authors. Acta Obstetricia et Gynecologica Scandinavica published by John Wiley & Sons Ltd on behalf of Nordic Federation of Societies of Obstetrics and Gynecology (NFOG).

Analysis and classification of commercial ham slice images using directional fractal dimension features.

PubMed

Mendoza, Fernando; Valous, Nektarios A; Allen, Paul; Kenny, Tony A; Ward, Paddy; Sun, Da-Wen

2009-02-01

This paper presents a novel and non-destructive approach to the appearance characterization and classification of commercial pork, turkey and chicken ham slices. Ham slice images were modelled using directional fractal (DF(0°;45°;90°;135°)) dimensions and a minimum distance classifier was adopted to perform the classification task. Also, the role of different colour spaces and the resolution level of the images on DF analysis were investigated. This approach was applied to 480 wafer thin ham slices from four types of hams (120 slices per type): i.e., pork (cooked and smoked), turkey (smoked) and chicken (roasted). DF features were extracted from digitalized intensity images in greyscale, and R, G, B, L(∗), a(∗), b(∗), H, S, and V colour components for three image resolution levels (100%, 50%, and 25%). Simulation results show that in spite of the complexity and high variability in colour and texture appearance, the modelling of ham slice images with DF dimensions allows the capture of differentiating textural features between the four commercial ham types. Independent DF features entail better discrimination than that using the average of four directions. However, DF dimensions reveal a high sensitivity to colour channel, orientation and image resolution for the fractal analysis. The classification accuracy using six DF dimension features (a(90°)(∗),a(135°)(∗),H(0°),H(45°),S(0°),H(90°)) was 93.9% for training data and 82.2% for testing data.

What are the most fire-dangerous atmospheric circulations in the Eastern-Mediterranean? Analysis of the synoptic wildfire climatology.

PubMed

Paschalidou, A K; Kassomenos, P A

2016-01-01

Wildfire management is closely linked to robust forecasts of changes in wildfire risk related to meteorological conditions. This link can be bridged either through fire weather indices or through statistical techniques that directly relate atmospheric patterns to wildfire activity. In the present work the COST-733 classification schemes are applied in order to link wildfires in Greece with synoptic circulation patterns. The analysis reveals that the majority of wildfire events can be explained by a small number of specific synoptic circulations, hence reflecting the synoptic climatology of wildfires. All 8 classification schemes used, prove that the most fire-dangerous conditions in Greece are characterized by a combination of high atmospheric pressure systems located N to NW of Greece, coupled with lower pressures located over the very Eastern part of the Mediterranean, an atmospheric pressure pattern closely linked to the local Etesian winds over the Aegean Sea. During these events, the atmospheric pressure has been reported to be anomalously high, while anomalously low 500hPa geopotential heights and negative total water column anomalies were also observed. Among the various classification schemes used, the 2 Principal Component Analysis-based classifications, namely the PCT and the PXE, as well as the Leader Algorithm classification LND proved to be the best options, in terms of being capable to isolate the vast amount of fire events in a small number of classes with increased frequency of occurrence. It is estimated that these 3 schemes, in combination with medium-range to seasonal climate forecasts, could be used by wildfire risk managers to provide increased wildfire prediction accuracy. Copyright © 2015 Elsevier B.V. All rights reserved.

A new blood vessel extraction technique using edge enhancement and object classification.

PubMed

Badsha, Shahriar; Reza, Ahmed Wasif; Tan, Kim Geok; Dimyati, Kaharudin

2013-12-01

Diabetic retinopathy (DR) is increasing progressively pushing the demand of automatic extraction and classification of severity of diseases. Blood vessel extraction from the fundus image is a vital and challenging task. Therefore, this paper presents a new, computationally simple, and automatic method to extract the retinal blood vessel. The proposed method comprises several basic image processing techniques, namely edge enhancement by standard template, noise removal, thresholding, morphological operation, and object classification. The proposed method has been tested on a set of retinal images. The retinal images were collected from the DRIVE database and we have employed robust performance analysis to evaluate the accuracy. The results obtained from this study reveal that the proposed method offers an average accuracy of about 97 %, sensitivity of 99 %, specificity of 86 %, and predictive value of 98 %, which is superior to various well-known techniques.

The prediction of swimming performance in competition from behavioral information.

PubMed

Rushall, B S; Leet, D

1979-06-01

The swimming performances of the Canadian Team at the 1976 Olympic Games were categorized as being improved or worse than previous best times in the events contested. The two groups had been previously assessed on the Psychological Inventories for Competitive Swimmers. A stepwise multiple-discriminant analysis of the inventory responses revealed that 13 test questions produced a perfect discrimination of group membership. The resultant discriminant functions for predicting performance classification were applied to the test responses of 157 swimmers at the 1977 Canadian Winter National Swimming Championships. Using the same performance classification criteria the accuracy of prediction was not better than chance in three of four sex by performance classifications. This yielded a failure to locate a set of behavioral factors which determine swimming performance improvements in elite competitive circumstances. The possibility of sets of factors which do not discriminate between performances in similar environments or between similar groups of swimmers was raised.

Science, Technology, Engineering and Math (STEM) Academic Librarian Positions during 2013: What Carnegie Classifications Reveal about Desired STEM Skills

ERIC Educational Resources Information Center

Trei, Kelli

2015-01-01

This study analyzes the requirements and preferences of 171 science, technology, engineering, and math (STEM) academic librarian positions in the United States as advertised in 2013. This analysis compares the STEM background experience preferences with the Carnegie rankings of the employing institution. The research examines the extent to which…

Object-based delineation and classification of alluvial fans by application of mean-shift segmentation and support vector machines

NASA Astrophysics Data System (ADS)

Pipaud, Isabel; Lehmkuhl, Frank

2017-09-01

In the field of geomorphology, automated extraction and classification of landforms is one of the most active research areas. Until the late 2000s, this task has primarily been tackled using pixel-based approaches. As these methods consider pixels and pixel neighborhoods as the sole basic entities for analysis, they cannot account for the irregular boundaries of real-world objects. Object-based analysis frameworks emerging from the field of remote sensing have been proposed as an alternative approach, and were successfully applied in case studies falling in the domains of both general and specific geomorphology. In this context, the a-priori selection of scale parameters or bandwidths is crucial for the segmentation result, because inappropriate parametrization will either result in over-segmentation or insufficient segmentation. In this study, we describe a novel supervised method for delineation and classification of alluvial fans, and assess its applicability using a SRTM 1‧‧ DEM scene depicting a section of the north-eastern Mongolian Altai, located in northwest Mongolia. The approach is premised on the application of mean-shift segmentation and the use of a one-class support vector machine (SVM) for classification. To consider variability in terms of alluvial fan dimension and shape, segmentation is performed repeatedly for different weightings of the incorporated morphometric parameters as well as different segmentation bandwidths. The final classification layer is obtained by selecting, for each real-world object, the most appropriate segmentation result according to fuzzy membership values derived from the SVM classification. Our results show that mean-shift segmentation and SVM-based classification provide an effective framework for delineation and classification of a particular landform. Variable bandwidths and terrain parameter weightings were identified as being crucial for consideration of intra-class variability, and, in turn, for a constantly high segmentation quality. Our analysis further reveals that incorporation of morphometric parameters quantifying specific morphological aspects of a landform is indispensable for developing an accurate classification scheme. Alluvial fans exhibiting accentuated composite morphologies were identified as a major challenge for automatic delineation, as they cannot be fully captured by a single segmentation run. There is, however, a high probability that this shortcoming can be overcome by enhancing the presented approach with a routine merging fan sub-entities based on their spatial relationships.

Revealing Significant Relations between Chemical/Biological Features and Activity: Associative Classification Mining for Drug Discovery

ERIC Educational Resources Information Center

Yu, Pulan

2012-01-01

Classification, clustering and association mining are major tasks of data mining and have been widely used for knowledge discovery. Associative classification mining, the combination of both association rule mining and classification, has emerged as an indispensable way to support decision making and scientific research. In particular, it offers a…

JDINAC: joint density-based non-parametric differential interaction network analysis and classification using high-dimensional sparse omics data.

PubMed

Ji, Jiadong; He, Di; Feng, Yang; He, Yong; Xue, Fuzhong; Xie, Lei

2017-10-01

A complex disease is usually driven by a number of genes interwoven into networks, rather than a single gene product. Network comparison or differential network analysis has become an important means of revealing the underlying mechanism of pathogenesis and identifying clinical biomarkers for disease classification. Most studies, however, are limited to network correlations that mainly capture the linear relationship among genes, or rely on the assumption of a parametric probability distribution of gene measurements. They are restrictive in real application. We propose a new Joint density based non-parametric Differential Interaction Network Analysis and Classification (JDINAC) method to identify differential interaction patterns of network activation between two groups. At the same time, JDINAC uses the network biomarkers to build a classification model. The novelty of JDINAC lies in its potential to capture non-linear relations between molecular interactions using high-dimensional sparse data as well as to adjust confounding factors, without the need of the assumption of a parametric probability distribution of gene measurements. Simulation studies demonstrate that JDINAC provides more accurate differential network estimation and lower classification error than that achieved by other state-of-the-art methods. We apply JDINAC to a Breast Invasive Carcinoma dataset, which includes 114 patients who have both tumor and matched normal samples. The hub genes and differential interaction patterns identified were consistent with existing experimental studies. Furthermore, JDINAC discriminated the tumor and normal sample with high accuracy by virtue of the identified biomarkers. JDINAC provides a general framework for feature selection and classification using high-dimensional sparse omics data. R scripts available at https://github.com/jijiadong/JDINAC. lxie@iscb.org. Supplementary data are available at Bioinformatics online. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Rapid Identification of Candida Species by Using Nuclear Magnetic Resonance Spectroscopy and a Statistical Classification Strategy

PubMed Central

Himmelreich, Uwe; Somorjai, Ray L.; Dolenko, Brion; Lee, Ok Cha; Daniel, Heide-Marie; Murray, Ronan; Mountford, Carolyn E.; Sorrell, Tania C.

2003-01-01

Nuclear magnetic resonance (NMR) spectra were acquired from suspensions of clinically important yeast species of the genus Candida to characterize the relationship between metabolite profiles and species identification. Major metabolites were identified by using two-dimensional correlation NMR spectroscopy. One-dimensional proton NMR spectra were analyzed by using a staged statistical classification strategy. Analysis of NMR spectra from 442 isolates of Candida albicans, C. glabrata, C. krusei, C. parapsilosis, and C. tropicalis resulted in rapid, accurate identification when compared with conventional and DNA-based identification. Spectral regions used for the classification of the five yeast species revealed species-specific differences in relative amounts of lipids, trehalose, polyols, and other metabolites. Isolates of C. parapsilosis and C. glabrata with unusual PCR fingerprinting patterns also generated atypical NMR spectra, suggesting the possibility of intraspecies discontinuity. We conclude that NMR spectroscopy combined with a statistical classification strategy is a rapid, nondestructive, and potentially valuable method for identification and chemotaxonomic characterization that may be broadly applicable to fungi and other microorganisms. PMID:12902244

Statistical analysis of spectral data: a methodology for designing an intelligent monitoring system for the diabetic foot

NASA Astrophysics Data System (ADS)

Liu, Chanjuan; van Netten, Jaap J.; Klein, Marvin E.; van Baal, Jeff G.; Bus, Sicco A.; van der Heijden, Ferdi

2013-12-01

Early detection of (pre-)signs of ulceration on a diabetic foot is valuable for clinical practice. Hyperspectral imaging is a promising technique for detection and classification of such (pre-)signs. However, the number of the spectral bands should be limited to avoid overfitting, which is critical for pixel classification with hyperspectral image data. The goal was to design a detector/classifier based on spectral imaging (SI) with a small number of optical bandpass filters. The performance and stability of the design were also investigated. The selection of the bandpass filters boils down to a feature selection problem. A dataset was built, containing reflectance spectra of 227 skin spots from 64 patients, measured with a spectrometer. Each skin spot was annotated manually by clinicians as "healthy" or a specific (pre-)sign of ulceration. Statistical analysis on the data set showed the number of required filters is between 3 and 7, depending on additional constraints on the filter set. The stability analysis revealed that shot noise was the most critical factor affecting the classification performance. It indicated that this impact could be avoided in future SI systems with a camera sensor whose saturation level is higher than 106, or by postimage processing.

Quantitative Assessment of Spatio-Temporal Desertification Rates in Azerbaijan during Using Timeseries Landsat-8 Satellite Images

NASA Astrophysics Data System (ADS)

Bayramov, Emil; Mammadov, Ramiz

2016-07-01

The main goals of this research are the object-based landcover classification of LANDSAT-8 multi-spectral satellite images in 2014 and 2015, quantification of Normalized Difference Vegetation Indices (NDVI) rates within the land-cover classes, change detection analysis between the NDVIs derived from multi-temporal LANDSAT-8 satellite images and the quantification of those changes within the land-cover classes and detection of changes between land-cover classes. The object-based classification accuracy of the land-cover classes was validated through the standard confusion matrix which revealed 80 % of land-cover classification accuracy for both years. The analysis revealed that the area of agricultural lands increased from 30911 sq. km. in 2014 to 31999 sq. km. in 2015. The area of barelands increased from 3933 sq. km. in 2014 to 4187 sq. km. in 2015. The area of forests increased from 8211 sq. km. in 2014 to 9175 sq. km. in 2015. The area of grasslands decreased from 27176 sq. km. in 2014 to 23294 sq. km. in 2015. The area of urban areas increased from 12479 sq. km. in 2014 to 12956 sq. km. in 2015. The decrease in the area of grasslands was mainly explained by the landuse shifts of grasslands to agricultural and urban lands. The quantification of low and medium NDVI rates revealed the increase within the agricultural, urban and forest land-cover classes in 2015. However, the high NDVI rates within agricultural, urban and forest land-cover classes in 2015 revealed to be lower relative to 2014. The change detection analysis between landscover types of 2014 and 2015 allowed to determine that 7740 sq. km. of grasslands shifted to agricultural landcover type whereas 5442sq. km. of agricultural lands shifted to rangelands. This means that the spatio-temporal patters of agricultural activities occurred in Azerbaijan because some of the areas reduced agricultural activities whereas some of them changed their landuse type to agricultural. Based on the achieved results, it is possible to conclude that the area of agricultural lands in Azerbaijan increased from 2014 to 2015. The crop productivity also increased in the croplands, however some of the areas showed lower productivity in 2015 relative to 2014.

Comprehensive Chemical Fingerprinting of High-Quality Cocoa at Early Stages of Processing: Effectiveness of Combined Untargeted and Targeted Approaches for Classification and Discrimination.

PubMed

Magagna, Federico; Guglielmetti, Alessandro; Liberto, Erica; Reichenbach, Stephen E; Allegrucci, Elena; Gobino, Guido; Bicchi, Carlo; Cordero, Chiara

2017-08-02

This study investigates chemical information of volatile fractions of high-quality cocoa (Theobroma cacao L. Malvaceae) from different origins (Mexico, Ecuador, Venezuela, Columbia, Java, Trinidad, and Sao Tomè) produced for fine chocolate. This study explores the evolution of the entire pattern of volatiles in relation to cocoa processing (raw, roasted, steamed, and ground beans). Advanced chemical fingerprinting (e.g., combined untargeted and targeted fingerprinting) with comprehensive two-dimensional gas chromatography coupled with mass spectrometry allows advanced pattern recognition for classification, discrimination, and sensory-quality characterization. The entire data set is analyzed for 595 reliable two-dimensional peak regions, including 130 known analytes and 13 potent odorants. Multivariate analysis with unsupervised exploration (principal component analysis) and simple supervised discrimination methods (Fisher ratios and linear regression trees) reveal informative patterns of similarities and differences and identify characteristic compounds related to sample origin and manufacturing step.

Site effect classification based on microtremor data analysis using a concentration-area fractal model

NASA Astrophysics Data System (ADS)

Adib, A.; Afzal, P.; Heydarzadeh, K.

2015-01-01

The aim of this study is to classify the site effect using concentration-area (C-A) fractal model in Meybod city, central Iran, based on microtremor data analysis. Log-log plots of the frequency, amplification and vulnerability index (k-g) indicate a multifractal nature for the parameters in the area. The results obtained from the C-A fractal modelling reveal that proper soil types are located around the central city. The results derived via the fractal modelling were utilized to improve the Nogoshi and Igarashi (1970, 1971) classification results in the Meybod city. The resulting categories are: (1) hard soil and weak rock with frequency of 6.2 to 8 Hz, (2) stiff soil with frequency of about 4.9 to 6.2 Hz, (3) moderately soft soil with the frequency of 2.4 to 4.9 Hz, and (4) soft soil with the frequency lower than 2.4 Hz.

Site effect classification based on microtremor data analysis using concentration-area fractal model

NASA Astrophysics Data System (ADS)

Adib, A.; Afzal, P.; Heydarzadeh, K.

2014-07-01

The aim of this study is to classify the site effect using concentration-area (C-A) fractal model in Meybod city, Central Iran, based on microtremor data analysis. Log-log plots of the frequency, amplification and vulnerability index (k-g) indicate a multifractal nature for the parameters in the area. The results obtained from the C-A fractal modeling reveal that proper soil types are located around the central city. The results derived via the fractal modeling were utilized to improve the Nogoshi's classification results in the Meybod city. The resulted categories are: (1) hard soil and weak rock with frequency of 6.2 to 8 Hz, (2) stiff soil with frequency of about 4.9 to 6.2 Hz, (3) moderately soft soil with the frequency of 2.4 to 4.9 Hz, and (4) soft soil with the frequency lower than 2.4 Hz.

Correlations between the modelled potato crop yield and the general atmospheric circulation

NASA Astrophysics Data System (ADS)

Sepp, Mait; Saue, Triin

2012-07-01

Biology-related indicators do not usually depend on just one meteorological element but on a combination of several weather indicators. One way to establish such integral indicators is to classify the general atmospheric circulation into a small number of circulation types. The aim of present study is to analyse connections between general atmospheric circulation and potato crop yield in Estonia. Meteorologically possible yield (MPY), calculated by the model POMOD, is used to characterise potato crop yield. Data of three meteorological stations and the biological parameters of two potato sorts were applied to the model, and 73 different classifications of atmospheric circulation from catalogue 1.2 of COST 733, domain 05 are used to qualify circulation conditions. Correlation analysis showed that there is at least one circulation type in each of the classifications with at least one statistically significant (99%) correlation with potato crop yield, whether in Kuressaare, Tallinn or Tartu. However, no classifications with circulation types correlating with MPY in all three stations at the same time were revealed. Circulation types inducing a decrease in the potato crop yield are more clearly represented. Clear differences occurred between the observed geographical locations as well as between the seasons: derived from the number of significant circulation types, summer and Kuressaare stand out. Of potato varieties, late 'Anti' is more influenced by circulation. Analysis of MSLP maps of circulation types revealed that the seaside stations (Tallinn, Kuressaare) suffer from negative effects of anti-cyclonic conditions (drought), while Tartu suffers from the cyclonic activity (excessive water).

Using the Landsat Archive to Monitor Gully Erosion Development, in SE Nigeria, as a Response to Land-use Classification and Environmental Variability.

NASA Astrophysics Data System (ADS)

Brolly, M.; Iro, S.

2016-12-01

This study presents novel low budget methodologies for mapping and monitoring gully erosion development in South-East Nigeria. The unabated way gullies develop, and the lack of control measures in the SE Nigeria study area, motivates this work. The Landsat archive is used to determine change in land-use/cover classification over a 30-year period (1986-2015) in a region measuring 70km x 70km. Multi-resolution segmentation is enabled through Object Based Image Analysis (OBIA) and Pixel based classification techniques (supervised/unsupervised) using an initial dataset including 40 ground validated gully sites within the region. Detected increases in gully area are positively correlated with land clearance, manifested by associated vegetation reduction and anthropogenic encroachment with r values reported of -0.94 (p<0.05) and -0.97 (p<0.05) for the Pixel and OBIA classification approaches respectively. Within the study region 14 specific gullies are further vectorised and quantified in terms of extent and rates of change. Local and regional results are then examined in regard to land-use and environmental variables, such as meteorology, soil and rock geology, and topographical/landscape parameters. Of the 14 specific sites, the maximum reported erosion rates are 232010m2 per year for the largest gully (4123765m2) and -501m2 per year for the smallest (2749m2), representing year on year % increases of 9% and -0.15% respectively. These erosion rates were exhibited in 1988 and 2007. Analysis of topography across the region at 30m resolution reveals 90% of the 40 observed gullies develop on concave slopes with high values of 4 plan curvatures and greater than 15° inclines with highest erosion rates exhibited on ferralsols soil type. Principal Component Analysis reveals inter-variable similarities, via component 1, between Slope (58%), Elevation (50%) and Gully Area (62%), while, Vegetation loss (14%), Soil structure (8%) and Rate of gully change (3%) are better defined by the second component, showing their similarities.

Analysis of classifiers performance for classification of potential microcalcification

NASA Astrophysics Data System (ADS)

M. N., Arun K.; Sheshadri, H. S.

2013-07-01

Breast cancer is a significant public health problem in the world. According to the literature early detection improve breast cancer prognosis. Mammography is a screening tool used for early detection of breast cancer. About 10-30% cases are missed during the routine check as it is difficult for the radiologists to make accurate analysis due to large amount of data. The Microcalcifications (MCs) are considered to be important signs of breast cancer. It has been reported in literature that 30% - 50% of breast cancer detected radio graphically show MCs on mammograms. Histologic examinations report 62% to 79% of breast carcinomas reveals MCs. MC are tiny, vary in size, shape, and distribution, and MC may be closely connected to surrounding tissues. There is a major challenge using the traditional classifiers in the classification of individual potential MCs as the processing of mammograms in appropriate stage generates data sets with an unequal amount of information for both classes (i.e., MC, and Not-MC). Most of the existing state-of-the-art classification approaches are well developed by assuming the underlying training set is evenly distributed. However, they are faced with a severe bias problem when the training set is highly imbalanced in distribution. This paper addresses this issue by using classifiers which handle the imbalanced data sets. In this paper, we also compare the performance of classifiers which are used in the classification of potential MC.

The Effects of Workload Transitions in a Multitasking Environment

DTIC Science & Technology

2016-09-13

completed the NASA Task Load Index to assess subjective workload, and the shortened Dundee Stress State Questionnaire to measure subjective task-related...and the analysis of both the NASA Task Load Index and of the shortened Dundee State Questionnaire did not reveal any significant differences related...Electroencepholography (EEG), NASA Task Load Index ( NASA TLX), Dundee Stress State Questionnaire (DSSQ), Hysteresis 16. SECURITY CLASSIFICATION OF

Removal of introduced inorganic content from chipped forest residues via air classification

DOE PAGES

Lacey, Jeffrey A.; Aston, John E.; Westover, Tyler L.; ...

2015-08-04

Inorganic content in biomass decreases the efficiency of conversion processes, especially thermochemical conversions. The combined concentrations of specific ash forming elements are the primary attributes that cause pine residues to be considered a degraded energy conversion feedstock, as compared to clean pine. Air classification is a potentially effective and economical tool to isolate high inorganic content biomass fractions away from primary feedstock sources to reduce their ash content. In this work, loblolly pine forest residues were air classified into 10 fractions whose ash content and composition were measured. Ash concentrations were highest in the lightest fractions (5.8–8.5 wt%), and inmore » a heavy fraction of the fines (8.9–15.1 wt%). The removal of fractions with high inorganic content resulted in a substantial reduction in the ash content of the remaining biomass in forest thinnings (1.69–1.07 wt%) and logging residues (1.09–0.68 wt%). These high inorganic content fractions from both forest residue types represented less than 7.0 wt% of the total biomass, yet they contained greater than 40% of the ash content by mass. Elemental analysis of the air classified fractions revealed the lightest fractions were comprised of high concentrations of soil elements (silicon, aluminum, iron, sodium, and titanium). However, the elements of biological origin including calcium, potassium, magnesium, sulfur, manganese, and phosphorous were evenly distributed throughout all air classified fractions, making them more difficult to isolate into fractions with high mineral concentrations. Under the conditions reported in this study, an economic analysis revealed air classification could be used for ash removal for as little as $2.23 per ton of product biomass. As a result, this study suggests air classification is a potentially attractive technology for the removal of introduced soil minerals from pine forest residues.« less

An ontology-based exploration of the concepts and relationships in the activities and participation component of the international classification of functioning, disability and health.

PubMed

Della Mea, Vincenzo; Simoncello, Andrea

2012-02-28

The International Classification of Functioning, Disability and Health (ICF) is a classification of health and health-related issues, aimed at describing and measuring health and disability at both individual and population levels. Here we discuss a preliminary qualitative and quantitative analysis of the relationships used in the Activities and Participation component of ICF, and a preliminary mapping to SUMO (Suggested Upper Merged Ontology) concepts. The aim of the analysis is to identify potential logical problems within this component of ICF, and to understand whether activities and participation might be defined more formally than in the current version of ICF. In the relationship analysis, we used four predicates among those available in SUMO for processes (Patient, Instrument, Agent, and subProcess). While at the top level subsumption was used in most cases (90%), at the lower levels the percentage of other relationships rose to 41%. Chapters were heterogeneous in the relationships used and some of the leaves of the tree seemed to represent properties or parts of the parent concept rather than subclasses. Mapping of ICF to SUMO proved partially feasible, with the activity concepts being mapped mostly (but not totally) under the IntentionalProcess concept in SUMO. On the other hand, the participation concept has not been mapped to any upper level concept. Our analysis of the relationships within ICF revealed issues related to confusion between classes and their properties, incorrect classifications, and overemphasis on subsumption, confirming what already observed by other researchers. However, it also suggested some properties for Activities that could be included in a more formal model: number of agents involved, the instrument used to carry out the activity, the object of the activity, complexity of the task, and an enumeration of relevant subtasks.

Incorporation of support vector machines in the LIBS toolbox for sensitive and robust classification amidst unexpected sample and system variability

PubMed Central

ChariDingari, Narahara; Barman, Ishan; Myakalwar, Ashwin Kumar; Tewari, Surya P.; Kumar, G. Manoj

2012-01-01

Despite the intrinsic elemental analysis capability and lack of sample preparation requirements, laser-induced breakdown spectroscopy (LIBS) has not been extensively used for real world applications, e.g. quality assurance and process monitoring. Specifically, variability in sample, system and experimental parameters in LIBS studies present a substantive hurdle for robust classification, even when standard multivariate chemometric techniques are used for analysis. Considering pharmaceutical sample investigation as an example, we propose the use of support vector machines (SVM) as a non-linear classification method over conventional linear techniques such as soft independent modeling of class analogy (SIMCA) and partial least-squares discriminant analysis (PLS-DA) for discrimination based on LIBS measurements. Using over-the-counter pharmaceutical samples, we demonstrate that application of SVM enables statistically significant improvements in prospective classification accuracy (sensitivity), due to its ability to address variability in LIBS sample ablation and plasma self-absorption behavior. Furthermore, our results reveal that SVM provides nearly 10% improvement in correct allocation rate and a concomitant reduction in misclassification rates of 75% (cf. PLS-DA) and 80% (cf. SIMCA)-when measurements from samples not included in the training set are incorporated in the test data – highlighting its robustness. While further studies on a wider matrix of sample types performed using different LIBS systems is needed to fully characterize the capability of SVM to provide superior predictions, we anticipate that the improved sensitivity and robustness observed here will facilitate application of the proposed LIBS-SVM toolbox for screening drugs and detecting counterfeit samples as well as in related areas of forensic and biological sample analysis. PMID:22292496

Analysis of EEG-fMRI data in focal epilepsy based on automated spike classification and Signal Space Projection.

PubMed

Liston, Adam D; De Munck, Jan C; Hamandi, Khalid; Laufs, Helmut; Ossenblok, Pauly; Duncan, John S; Lemieux, Louis

2006-07-01

Simultaneous acquisition of EEG and fMRI data enables the investigation of the hemodynamic correlates of interictal epileptiform discharges (IEDs) during the resting state in patients with epilepsy. This paper addresses two issues: (1) the semi-automation of IED classification in statistical modelling for fMRI analysis and (2) the improvement of IED detection to increase experimental fMRI efficiency. For patients with multiple IED generators, sensitivity to IED-correlated BOLD signal changes can be improved when the fMRI analysis model distinguishes between IEDs of differing morphology and field. In an attempt to reduce the subjectivity of visual IED classification, we implemented a semi-automated system, based on the spatio-temporal clustering of EEG events. We illustrate the technique's usefulness using EEG-fMRI data from a subject with focal epilepsy in whom 202 IEDs were visually identified and then clustered semi-automatically into four clusters. Each cluster of IEDs was modelled separately for the purpose of fMRI analysis. This revealed IED-correlated BOLD activations in distinct regions corresponding to three different IED categories. In a second step, Signal Space Projection (SSP) was used to project the scalp EEG onto the dipoles corresponding to each IED cluster. This resulted in 123 previously unrecognised IEDs, the inclusion of which, in the General Linear Model (GLM), increased the experimental efficiency as reflected by significant BOLD activations. We have also shown that the detection of extra IEDs is robust in the face of fluctuations in the set of visually detected IEDs. We conclude that automated IED classification can result in more objective fMRI models of IEDs and significantly increased sensitivity.

Incorporation of support vector machines in the LIBS toolbox for sensitive and robust classification amidst unexpected sample and system variability.

PubMed

Dingari, Narahara Chari; Barman, Ishan; Myakalwar, Ashwin Kumar; Tewari, Surya P; Kumar Gundawar, Manoj

2012-03-20

Despite the intrinsic elemental analysis capability and lack of sample preparation requirements, laser-induced breakdown spectroscopy (LIBS) has not been extensively used for real-world applications, e.g., quality assurance and process monitoring. Specifically, variability in sample, system, and experimental parameters in LIBS studies present a substantive hurdle for robust classification, even when standard multivariate chemometric techniques are used for analysis. Considering pharmaceutical sample investigation as an example, we propose the use of support vector machines (SVM) as a nonlinear classification method over conventional linear techniques such as soft independent modeling of class analogy (SIMCA) and partial least-squares discriminant analysis (PLS-DA) for discrimination based on LIBS measurements. Using over-the-counter pharmaceutical samples, we demonstrate that the application of SVM enables statistically significant improvements in prospective classification accuracy (sensitivity), because of its ability to address variability in LIBS sample ablation and plasma self-absorption behavior. Furthermore, our results reveal that SVM provides nearly 10% improvement in correct allocation rate and a concomitant reduction in misclassification rates of 75% (cf. PLS-DA) and 80% (cf. SIMCA)-when measurements from samples not included in the training set are incorporated in the test data-highlighting its robustness. While further studies on a wider matrix of sample types performed using different LIBS systems is needed to fully characterize the capability of SVM to provide superior predictions, we anticipate that the improved sensitivity and robustness observed here will facilitate application of the proposed LIBS-SVM toolbox for screening drugs and detecting counterfeit samples, as well as in related areas of forensic and biological sample analysis.

Food Composition Database Format and Structure: A User Focused Approach

PubMed Central

Clancy, Annabel K.; Woods, Kaitlyn; McMahon, Anne; Probst, Yasmine

2015-01-01

This study aimed to investigate the needs of Australian food composition database user’s regarding database format and relate this to the format of databases available globally. Three semi structured synchronous online focus groups (M = 3, F = 11) and n = 6 female key informant interviews were recorded. Beliefs surrounding the use, training, understanding, benefits and limitations of food composition data and databases were explored. Verbatim transcriptions underwent preliminary coding followed by thematic analysis with NVivo qualitative analysis software to extract the final themes. Schematic analysis was applied to the final themes related to database format. Desktop analysis also examined the format of six key globally available databases. 24 dominant themes were established, of which five related to format; database use, food classification, framework, accessibility and availability, and data derivation. Desktop analysis revealed that food classification systems varied considerably between databases. Microsoft Excel was a common file format used in all databases, and available software varied between countries. User’s also recognised that food composition databases format should ideally be designed specifically for the intended use, have a user-friendly food classification system, incorporate accurate data with clear explanation of data derivation and feature user input. However, such databases are limited by data availability and resources. Further exploration of data sharing options should be considered. Furthermore, user’s understanding of food composition data and databases limitations is inherent to the correct application of non-specific databases. Therefore, further exploration of user FCDB training should also be considered. PMID:26554836

Ensemble support vector machine classification of dementia using structural MRI and mini-mental state examination.

PubMed

Sørensen, Lauge; Nielsen, Mads

2018-05-15

The International Challenge for Automated Prediction of MCI from MRI data offered independent, standardized comparison of machine learning algorithms for multi-class classification of normal control (NC), mild cognitive impairment (MCI), converting MCI (cMCI), and Alzheimer's disease (AD) using brain imaging and general cognition. We proposed to use an ensemble of support vector machines (SVMs) that combined bagging without replacement and feature selection. SVM is the most commonly used algorithm in multivariate classification of dementia, and it was therefore valuable to evaluate the potential benefit of ensembling this type of classifier. The ensemble SVM, using either a linear or a radial basis function (RBF) kernel, achieved multi-class classification accuracies of 55.6% and 55.0% in the challenge test set (60 NC, 60 MCI, 60 cMCI, 60 AD), resulting in a third place in the challenge. Similar feature subset sizes were obtained for both kernels, and the most frequently selected MRI features were the volumes of the two hippocampal subregions left presubiculum and right subiculum. Post-challenge analysis revealed that enforcing a minimum number of selected features and increasing the number of ensemble classifiers improved classification accuracy up to 59.1%. The ensemble SVM outperformed single SVM classifications consistently in the challenge test set. Ensemble methods using bagging and feature selection can improve the performance of the commonly applied SVM classifier in dementia classification. This resulted in competitive classification accuracies in the International Challenge for Automated Prediction of MCI from MRI data. Copyright © 2018 Elsevier B.V. All rights reserved.

The Future of Classification in Wheelchair Sports; Can Data Science and Technological Advancement Offer an Alternative Point of View?

PubMed

van der Slikke, Rienk M A; Bregman, Daan J J; Berger, Monique A M; de Witte, Annemarie M H; Veeger, Dirk-Jan H E J

2017-11-01

Classification is a defining factor for competition in wheelchair sports, but it is a delicate and time-consuming process with often questionable validity. 1 New inertial sensor based measurement methods applied in match play and field tests, allow for more precise and objective estimates of the impairment effect on wheelchair mobility performance. It was evaluated if these measures could offer an alternative point of view for classification. Six standard wheelchair mobility performance outcomes of different classification groups were measured in match play (n=29), as well as best possible performance in a field test (n=47). In match-results a clear relationship between classification and performance level is shown, with increased performance outcomes in each adjacent higher classification group. Three outcomes differed significantly between the low and mid-class groups, and one between the mid and high-class groups. In best performance (field test), a split between the low and mid-class groups shows (5 out of 6 outcomes differed significantly) but hardly any difference between the mid and high-class groups. This observed split was confirmed by cluster analysis, revealing the existence of only two performance based clusters. The use of inertial sensor technology to get objective measures of wheelchair mobility performance, combined with a standardized field-test, brought alternative views for evidence based classification. The results of this approach provided arguments for a reduced number of classes in wheelchair basketball. Future use of inertial sensors in match play and in field testing could enhance evaluation of classification guidelines as well as individual athlete performance.

Psychometric properties of the painDETECT questionnaire in rheumatoid arthritis, psoriatic arthritis and spondyloarthritis: Rasch analysis and test-retest reliability.

PubMed

Rifbjerg-Madsen, Signe; Wæhrens, Eva Ejlersen; Danneskiold-Samsøe, Bente; Amris, Kirstine

2017-05-22

Pain is inherent in rheumatoid arthritis (RA), psoriatic arthritis (PsA) and spondyloarthritis (SpA) and traditionally considered to be of nociceptive origin. Emerging data suggest a potential role of augmented central pain mechanisms in subsets of patients, thus, valid instruments that can identify underlying pain mechanisms are needed. The painDETECT questionnaire (PDQ) was originally designed to differentiate between pain phenotypes. The objectives were to evaluate the psychometric properties of the PDQ in patients with inflammatory arthritis by applying Rasch analysis and to explore the reliability of pain classification by test-retest. For the Rasch analysis 900 questionnaires from patients with RA, PsA and SpA (300 per diagnosis) were extracted from 'the DANBIO painDETECT study'. The analysis was directed at the seven items assessing somatosensory symptoms and included: 1) the performance of the six-category Likert scale; 2) whether a unidimensional construct was defined; 3) the reliability and precision of estimates. Another group of 30 patients diagnosed with RA, PsA or SpA participated in a test-retest study. Intraclass Correlation Coefficients (ICC) and classification consistency were calculated. The Rasch analysis revealed: (1) Acceptable psychometric rating scale properties; the frequency distribution peaked in category 0 except for item 5, threshold calibration >10 observations per category, no disorder in the category measures for all items, scale category outfit Mnsq <2.0, small distances (<1.4 logits) between thresholds for category 1, 2 and 3 for all items. (2) The principal component analysis supported unidimensionality; the standardized residuals showed that 53.7% of total variance was explained by the measure and the magnitude of first contrast had an eigenvalue of 1.5, no misfitting items, clinical insignificant different item hierarchies across diagnoses (DIF < 0.5 logits). (3) A targeted item-person map, person and item separation indices of 1.88(reliability = 0.78), and 13.04 (reliability = 0.99). The test-retest revealed: ICC: RA 0.86(0.56-0.96), PsA 0.96(0.74-0.99), SpA 0.93(0.76-98), overall 0.94(0.84-0.98). Classification consistency was: RA 70%, PsA 80%, SpA 90%, overall 80%. The results support that the PDQ can be used as a classification instrument and assist identification of underlying pain-mechanisms in patients suffering from inflammatory arthritis.

Free classification of regional dialects of American English.

PubMed

Clopper, Cynthia G; Pisoni, David B

2007-07-01

Recent studies have found that naïve listeners perform poorly in forced-choice dialect categorization tasks. However, the listeners' error patterns in these tasks reveal systematic confusions between phonologically similar dialects. In the present study, a free classification procedure was used to measure the perceptual similarity structure of regional dialect variation in the United States. In two experiments, participants listened to a set of short English sentences produced by male talkers only (Experiment 1) and by male and female talkers (Experiment 2). The listeners were instructed to group the talkers by regional dialect into as many groups as they wanted with as many talkers in each group as they wished. Multidimensional scaling analyses of the data revealed three primary dimensions of perceptual similarity (linguistic markedness, geography, and gender). In addition, a comparison of the results obtained from the free classification task to previous results using the same stimulus materials in six-alternative forced-choice categorization tasks revealed that response biases in the six-alternative task were reduced or eliminated in the free classification task. Thus, the results obtained with the free classification task in the current study provided further evidence that the underlying structure of perceptual dialect category representations reflects important linguistic and sociolinguistic factors.

Feasibility of opportunistic osteoporosis screening in routine contrast-enhanced multi detector computed tomography (MDCT) using texture analysis.

PubMed

Mookiah, M R K; Rohrmeier, A; Dieckmeyer, M; Mei, K; Kopp, F K; Noel, P B; Kirschke, J S; Baum, T; Subburaj, K

2018-04-01

This study investigated the feasibility of opportunistic osteoporosis screening in routine contrast-enhanced MDCT exams using texture analysis. The results showed an acceptable reproducibility of texture features, and these features could discriminate healthy/osteoporotic fracture cohort with an accuracy of 83%. This aim of this study is to investigate the feasibility of opportunistic osteoporosis screening in routine contrast-enhanced MDCT exams using texture analysis. We performed texture analysis at the spine in routine MDCT exams and investigated the effect of intravenous contrast medium (IVCM) (n = 7), slice thickness (n = 7), the long-term reproducibility (n = 9), and the ability to differentiate healthy/osteoporotic fracture cohort (n = 9 age and gender matched pairs). Eight texture features were extracted using gray level co-occurrence matrix (GLCM). The independent sample t test was used to rank the features of healthy/fracture cohort and classification was performed using support vector machine (SVM). The results revealed significant correlations between texture parameters derived from MDCT scans with and without IVCM (r up to 0.91) slice thickness of 1 mm versus 2 and 3 mm (r up to 0.96) and scan-rescan (r up to 0.59). The performance of the SVM classifier was evaluated using 10-fold cross-validation and revealed an average classification accuracy of 83%. Opportunistic osteoporosis screening at the spine using specific texture parameters (energy, entropy, and homogeneity) and SVM can be performed in routine contrast-enhanced MDCT exams.

Soybean kinome: functional classification and gene expression patterns

PubMed Central

Liu, Jinyi; Chen, Nana; Grant, Joshua N.; Cheng, Zong-Ming (Max); Stewart, C. Neal; Hewezi, Tarek

2015-01-01

The protein kinase (PK) gene family is one of the largest and most highly conserved gene families in plants and plays a role in nearly all biological functions. While a large number of genes have been predicted to encode PKs in soybean, a comprehensive functional classification and global analysis of expression patterns of this large gene family is lacking. In this study, we identified the entire soybean PK repertoire or kinome, which comprised 2166 putative PK genes, representing 4.67% of all soybean protein-coding genes. The soybean kinome was classified into 19 groups, 81 families, and 122 subfamilies. The receptor-like kinase (RLK) group was remarkably large, containing 1418 genes. Collinearity analysis indicated that whole-genome segmental duplication events may have played a key role in the expansion of the soybean kinome, whereas tandem duplications might have contributed to the expansion of specific subfamilies. Gene structure, subcellular localization prediction, and gene expression patterns indicated extensive functional divergence of PK subfamilies. Global gene expression analysis of soybean PK subfamilies revealed tissue- and stress-specific expression patterns, implying regulatory functions over a wide range of developmental and physiological processes. In addition, tissue and stress co-expression network analysis uncovered specific subfamilies with narrow or wide interconnected relationships, indicative of their association with particular or broad signalling pathways, respectively. Taken together, our analyses provide a foundation for further functional studies to reveal the biological and molecular functions of PKs in soybean. PMID:25614662

Phylogeny and classification of bacteria in the genera Clavibacter and Rathayibacter on the basis of 16s rRNA gene sequence analyses.

PubMed

Lee, I M; Bartoszyk, I M; Gundersen-Rindal, D E; Davis, R E

1997-07-01

A phylogenetic analysis by parsimony of 16S rRNA gene sequences (16S rDNA) revealed that species and subspecies of Clavibacter and Rathayibacter form a discrete monophyletic clade, paraphyletic to Corynebacterium species. Within the Clavibacter-Rathayibacter clade, four major phylogenetic groups (subclades) with a total of 10 distinct taxa were recognized: (I) species C. michiganensis; (II) species C. xyli; (III) species R. iranicus and R. tritici; and (IV) species R. rathayi. The first three groups form a monophyletic cluster, paraphyletic to R. rathayi. On the basis of the phylogeny inferred, reclassification of members of Clavibacter-Rathayibacter group is proposed. A system for classification of taxa in Clavibacter and Rathayibacter was developed based on restriction fragment length polymorphism (RFLP) analysis of the PCR-amplified 16S rDNA sequences. The groups delineated on the basis of RFLP patterns of 16S rDNA coincided well with the subclades delineated on the basis of phylogeny. In contrast to previous classification systems, which are based primarily on phenotypic properties and are laborious, the RFLP analyses allow for rapid differentiation among species and subspecies in the two genera.

Hyperspectral image analysis for rapid and accurate discrimination of bacterial infections: A benchmark study.

PubMed

Arrigoni, Simone; Turra, Giovanni; Signoroni, Alberto

2017-09-01

With the rapid diffusion of Full Laboratory Automation systems, Clinical Microbiology is currently experiencing a new digital revolution. The ability to capture and process large amounts of visual data from microbiological specimen processing enables the definition of completely new objectives. These include the direct identification of pathogens growing on culturing plates, with expected improvements in rapid definition of the right treatment for patients affected by bacterial infections. In this framework, the synergies between light spectroscopy and image analysis, offered by hyperspectral imaging, are of prominent interest. This leads us to assess the feasibility of a reliable and rapid discrimination of pathogens through the classification of their spectral signatures extracted from hyperspectral image acquisitions of bacteria colonies growing on blood agar plates. We designed and implemented the whole data acquisition and processing pipeline and performed a comprehensive comparison among 40 combinations of different data preprocessing and classification techniques. High discrimination performance has been achieved also thanks to improved colony segmentation and spectral signature extraction. Experimental results reveal the high accuracy and suitability of the proposed approach, driving the selection of most suitable and scalable classification pipelines and stimulating clinical validations. Copyright © 2017 Elsevier Ltd. All rights reserved.

The Influence of Hindu Epistemology on Ranganathan's Colon Classification.

ERIC Educational Resources Information Center

Maurer, Bradley Gerald

This study attempted to determine the influence of Hindu epistemology on Ranganathan's Colon Classification. Only the epistemological schools of Hindu philosophy and the Idea Plane element of Colon Classification were included. A literature search revealed that, although there is significant literature on each side of the problem, no bridges exist…

Foot-strike pattern and performance in a marathon.

PubMed

Kasmer, Mark E; Liu, Xue-Cheng; Roberts, Kyle G; Valadao, Jason M

2013-05-01

To determine prevalence of heel strike in a midsize city marathon, if there is an association between foot-strike classification and race performance, and if there is an association between foot-strike classification and gender. Foot-strike classification (forefoot, midfoot, heel, or split strike), gender, and rank (position in race) were recorded at the 8.1-km mark for 2112 runners at the 2011 Milwaukee Lakefront Marathon. 1991 runners were classified by foot-strike pattern, revealing a heel-strike prevalence of 93.67% (n = 1865). A significant difference between foot-strike classification and performance was found using a Kruskal-Wallis test (P < .0001), with more elite performers being less likely to heel strike. No significant difference between foot-strike classification and gender was found using a Fisher exact test. In addition, subgroup analysis of the 126 non-heel strikers found no significant difference between shoe wear and performance using a Kruskal-Wallis test. The high prevalence of heel striking observed in this study reflects the foot-strike pattern of most mid-distance to long-distance runners and, more important, may predict their injury profile based on the biomechanics of a heel-strike running pattern. This knowledge can help clinicians appropriately diagnose, manage, and train modifications of injured runners.

The family structure of the Mucorales: a synoptic revision based on comprehensive multigene-genealogies.

PubMed

Hoffmann, K; Pawłowska, J; Walther, G; Wrzosek, M; de Hoog, G S; Benny, G L; Kirk, P M; Voigt, K

2013-06-01

The Mucorales (Mucoromycotina) are one of the most ancient groups of fungi comprising ubiquitous, mostly saprotrophic organisms. The first comprehensive molecular studies 11 yr ago revealed the traditional classification scheme, mainly based on morphology, as highly artificial. Since then only single clades have been investigated in detail but a robust classification of the higher levels based on DNA data has not been published yet. Therefore we provide a classification based on a phylogenetic analysis of four molecular markers including the large and the small subunit of the ribosomal DNA, the partial actin gene and the partial gene for the translation elongation factor 1-alpha. The dataset comprises 201 isolates in 103 species and represents about one half of the currently accepted species in this order. Previous family concepts are reviewed and the family structure inferred from the multilocus phylogeny is introduced and discussed. Main differences between the current classification and preceding concepts affects the existing families Lichtheimiaceae and Cunninghamellaceae, as well as the genera Backusella and Lentamyces which recently obtained the status of families along with the Rhizopodaceae comprising Rhizopus, Sporodiniella and Syzygites. Compensatory base change analyses in the Lichtheimiaceae confirmed the lower level classification of Lichtheimia and Rhizomucor while genera such as Circinella or Syncephalastrum completely lacked compensatory base changes.

Similarity-dissimilarity plot for visualization of high dimensional data in biomedical pattern classification.

PubMed

Arif, Muhammad

2012-06-01

In pattern classification problems, feature extraction is an important step. Quality of features in discriminating different classes plays an important role in pattern classification problems. In real life, pattern classification may require high dimensional feature space and it is impossible to visualize the feature space if the dimension of feature space is greater than four. In this paper, we have proposed a Similarity-Dissimilarity plot which can project high dimensional space to a two dimensional space while retaining important characteristics required to assess the discrimination quality of the features. Similarity-dissimilarity plot can reveal information about the amount of overlap of features of different classes. Separable data points of different classes will also be visible on the plot which can be classified correctly using appropriate classifier. Hence, approximate classification accuracy can be predicted. Moreover, it is possible to know about whom class the misclassified data points will be confused by the classifier. Outlier data points can also be located on the similarity-dissimilarity plot. Various examples of synthetic data are used to highlight important characteristics of the proposed plot. Some real life examples from biomedical data are also used for the analysis. The proposed plot is independent of number of dimensions of the feature space.

[Application of target restoration space quantity and quantitative relation in precise esthetic prosthodontics].

PubMed

Haiyang, Yu; Tian, Luo

2016-06-01

Target restoration space (TRS) is the most precise space required for designing optimal prosthesis. TRS consists of an internal or external tooth space to confirm the esthetics and function of the final restoration. Therefore, assisted with quantitive analysis transfer, TRS quantitative analysis is a significant improvement for minimum tooth preparation. This article presents TRS quantity-related measurement, analysis, transfer, and internal relevance of three TR. classifications. Results reveal the close bond between precision and minimally invasive treatment. This study can be used to improve the comprehension and execution of precise esthetic prosthodontics.

Classification and regression tree (CART) analyses of genomic signatures reveal sets of tetramers that discriminate temperature optima of archaea and bacteria

PubMed Central

Dyer, Betsey D.; Kahn, Michael J.; LeBlanc, Mark D.

2008-01-01

Classification and regression tree (CART) analysis was applied to genome-wide tetranucleotide frequencies (genomic signatures) of 195 archaea and bacteria. Although genomic signatures have typically been used to classify evolutionary divergence, in this study, convergent evolution was the focus. Temperature optima for most of the organisms examined could be distinguished by CART analyses of tetranucleotide frequencies. This suggests that pervasive (nonlinear) qualities of genomes may reflect certain environmental conditions (such as temperature) in which those genomes evolved. The predominant use of GAGA and AGGA as the discriminating tetramers in CART models suggests that purine-loading and codon biases of thermophiles may explain some of the results. PMID:19054742

High-Reproducibility and High-Accuracy Method for Automated Topic Classification

NASA Astrophysics Data System (ADS)

Lancichinetti, Andrea; Sirer, M. Irmak; Wang, Jane X.; Acuna, Daniel; Körding, Konrad; Amaral, Luís A. Nunes

2015-01-01

Much of human knowledge sits in large databases of unstructured text. Leveraging this knowledge requires algorithms that extract and record metadata on unstructured text documents. Assigning topics to documents will enable intelligent searching, statistical characterization, and meaningful classification. Latent Dirichlet allocation (LDA) is the state of the art in topic modeling. Here, we perform a systematic theoretical and numerical analysis that demonstrates that current optimization techniques for LDA often yield results that are not accurate in inferring the most suitable model parameters. Adapting approaches from community detection in networks, we propose a new algorithm that displays high reproducibility and high accuracy and also has high computational efficiency. We apply it to a large set of documents in the English Wikipedia and reveal its hierarchical structure.

Comparing the MRI-based Goutallier Classification to an experimental quantitative MR spectroscopic fat measurement of the supraspinatus muscle.

PubMed

Gilbert, Fabian; Böhm, Dirk; Eden, Lars; Schmalzl, Jonas; Meffert, Rainer H; Köstler, Herbert; Weng, Andreas M; Ziegler, Dirk

2016-08-22

The Goutallier Classification is a semi quantitative classification system to determine the amount of fatty degeneration in rotator cuff muscles. Although initially proposed for axial computer tomography scans it is currently applied to magnet-resonance-imaging-scans. The role for its clinical use is controversial, as the reliability of the classification has been shown to be inconsistent. The purpose of this study was to compare the semi quantitative MRI-based Goutallier Classification applied by 5 different raters to experimental MR spectroscopic quantitative fat measurement in order to determine the correlation between this classification system and the true extent of fatty degeneration shown by spectroscopy. MRI-scans of 42 patients with rotator cuff tears were examined by 5 shoulder surgeons and were graduated according to the MRI-based Goutallier Classification proposed by Fuchs et al. Additionally the fat/water ratio was measured with MR spectroscopy using the experimental SPLASH technique. The semi quantitative grading according to the Goutallier Classification was statistically correlated with the quantitative measured fat/water ratio using Spearman's rank correlation. Statistical analysis of the data revealed only fair correlation of the Goutallier Classification system and the quantitative fat/water ratio with R = 0.35 (p < 0.05). By dichotomizing the scale the correlation was 0.72. The interobserver and intraobserver reliabilities were substantial with R = 0.62 and R = 0.74 (p < 0.01). The correlation between the semi quantitative MRI based Goutallier Classification system and MR spectroscopic fat measurement is weak. As an adequate estimation of fatty degeneration based on standard MRI may not be possible, quantitative methods need to be considered in order to increase diagnostic safety and thus provide patients with ideal care in regard to the amount of fatty degeneration. Spectroscopic MR measurement may increase the accuracy of the Goutallier classification and thus improve the prediction of clinical results after rotator cuff repair. However, these techniques are currently only available in an experimental setting.

A Novel Hybrid Classification Model of Genetic Algorithms, Modified k-Nearest Neighbor and Developed Backpropagation Neural Network

PubMed Central

Salari, Nader; Shohaimi, Shamarina; Najafi, Farid; Nallappan, Meenakshii; Karishnarajah, Isthrinayagy

2014-01-01

Among numerous artificial intelligence approaches, k-Nearest Neighbor algorithms, genetic algorithms, and artificial neural networks are considered as the most common and effective methods in classification problems in numerous studies. In the present study, the results of the implementation of a novel hybrid feature selection-classification model using the above mentioned methods are presented. The purpose is benefitting from the synergies obtained from combining these technologies for the development of classification models. Such a combination creates an opportunity to invest in the strength of each algorithm, and is an approach to make up for their deficiencies. To develop proposed model, with the aim of obtaining the best array of features, first, feature ranking techniques such as the Fisher's discriminant ratio and class separability criteria were used to prioritize features. Second, the obtained results that included arrays of the top-ranked features were used as the initial population of a genetic algorithm to produce optimum arrays of features. Third, using a modified k-Nearest Neighbor method as well as an improved method of backpropagation neural networks, the classification process was advanced based on optimum arrays of the features selected by genetic algorithms. The performance of the proposed model was compared with thirteen well-known classification models based on seven datasets. Furthermore, the statistical analysis was performed using the Friedman test followed by post-hoc tests. The experimental findings indicated that the novel proposed hybrid model resulted in significantly better classification performance compared with all 13 classification methods. Finally, the performance results of the proposed model was benchmarked against the best ones reported as the state-of-the-art classifiers in terms of classification accuracy for the same data sets. The substantial findings of the comprehensive comparative study revealed that performance of the proposed model in terms of classification accuracy is desirable, promising, and competitive to the existing state-of-the-art classification models. PMID:25419659

Phylogenetic Status of an Unrecorded Species of Curvularia, C. spicifera, Based on Current Classification System of Curvularia and Bipolaris Group Using Multi Loci.

PubMed

Jeon, Sun Jeong; Nguyen, Thi Thuong Thuong; Lee, Hyang Burm

2015-09-01

A seed-borne fungus, Curvularia sp. EML-KWD01, was isolated from an indigenous wheat seed by standard blotter method. This fungus was characterized based on the morphological characteristics and molecular phylogenetic analysis. Phylogenetic status of the fungus was determined using sequences of three loci: rDNA internal transcribed spacer, large ribosomal subunit, and glyceraldehyde 3-phosphate dehydrogenase gene. Multi loci sequencing analysis revealed that this fungus was Curvularia spicifera within Curvularia group 2 of family Pleosporaceae.

Relation between financial market structure and the real economy: comparison between clustering methods.

PubMed

Musmeci, Nicoló; Aste, Tomaso; Di Matteo, T

2015-01-01

We quantify the amount of information filtered by different hierarchical clustering methods on correlations between stock returns comparing the clustering structure with the underlying industrial activity classification. We apply, for the first time to financial data, a novel hierarchical clustering approach, the Directed Bubble Hierarchical Tree and we compare it with other methods including the Linkage and k-medoids. By taking the industrial sector classification of stocks as a benchmark partition, we evaluate how the different methods retrieve this classification. The results show that the Directed Bubble Hierarchical Tree can outperform other methods, being able to retrieve more information with fewer clusters. Moreover,we show that the economic information is hidden at different levels of the hierarchical structures depending on the clustering method. The dynamical analysis on a rolling window also reveals that the different methods show different degrees of sensitivity to events affecting financial markets, like crises. These results can be of interest for all the applications of clustering methods to portfolio optimization and risk hedging [corrected].

Efficient estimation of three-dimensional covariance and its application in the analysis of heterogeneous samples in cryo-electron microscopy

PubMed Central

Liao, Hstau Y.; Hashem, Yaser; Frank, Joachim

2015-01-01

Summary Single-particle cryogenic electron microscopy (cryo-EM) is a powerful tool for the study of macromolecular structures at high resolution. Classification allows multiple structural states to be extracted and reconstructed from the same sample. One classification approach is via the covariance matrix, which captures the correlation between every pair of voxels. Earlier approaches employ computing-intensive resampling and estimate only the eigenvectors of the matrix, which are then used in a separate fast classification step. We propose an iterative scheme to explicitly estimate the covariance matrix in its entirety. In our approach, the flexibility in choosing the solution domain allows us to examine a part of the molecule in greater detail. 3D covariance maps obtained in this way from experimental data (cryo-EM images of the eukaryotic pre-initiation complex) prove to be in excellent agreement with conclusions derived by using traditional approaches, revealing in addition the interdependencies of ligand bindings and structural changes. PMID:25982529

Efficient estimation of three-dimensional covariance and its application in the analysis of heterogeneous samples in cryo-electron microscopy.

PubMed

Liao, Hstau Y; Hashem, Yaser; Frank, Joachim

2015-06-02

Single-particle cryogenic electron microscopy (cryo-EM) is a powerful tool for the study of macromolecular structures at high resolution. Classification allows multiple structural states to be extracted and reconstructed from the same sample. One classification approach is via the covariance matrix, which captures the correlation between every pair of voxels. Earlier approaches employ computing-intensive resampling and estimate only the eigenvectors of the matrix, which are then used in a separate fast classification step. We propose an iterative scheme to explicitly estimate the covariance matrix in its entirety. In our approach, the flexibility in choosing the solution domain allows us to examine a part of the molecule in greater detail. Three-dimensional covariance maps obtained in this way from experimental data (cryo-EM images of the eukaryotic pre-initiation complex) prove to be in excellent agreement with conclusions derived by using traditional approaches, revealing in addition the interdependencies of ligand bindings and structural changes. Copyright © 2015 Elsevier Ltd. All rights reserved.

Relation between Financial Market Structure and the Real Economy: Comparison between Clustering Methods

PubMed Central

Musmeci, Nicoló; Aste, Tomaso; Di Matteo, T.

2015-01-01

We quantify the amount of information filtered by different hierarchical clustering methods on correlations between stock returns comparing the clustering structure with the underlying industrial activity classification. We apply, for the first time to financial data, a novel hierarchical clustering approach, the Directed Bubble Hierarchical Tree and we compare it with other methods including the Linkage and k-medoids. By taking the industrial sector classification of stocks as a benchmark partition, we evaluate how the different methods retrieve this classification. The results show that the Directed Bubble Hierarchical Tree can outperform other methods, being able to retrieve more information with fewer clusters. Moreover, we show that the economic information is hidden at different levels of the hierarchical structures depending on the clustering method. The dynamical analysis on a rolling window also reveals that the different methods show different degrees of sensitivity to events affecting financial markets, like crises. These results can be of interest for all the applications of clustering methods to portfolio optimization and risk hedging. PMID:25786703

Probabilistic grammatical model for helix‐helix contact site classification

PubMed Central

2013-01-01

Background Hidden Markov Models power many state‐of‐the‐art tools in the field of protein bioinformatics. While excelling in their tasks, these methods of protein analysis do not convey directly information on medium‐ and long‐range residue‐residue interactions. This requires an expressive power of at least context‐free grammars. However, application of more powerful grammar formalisms to protein analysis has been surprisingly limited. Results In this work, we present a probabilistic grammatical framework for problem‐specific protein languages and apply it to classification of transmembrane helix‐helix pairs configurations. The core of the model consists of a probabilistic context‐free grammar, automatically inferred by a genetic algorithm from only a generic set of expert‐based rules and positive training samples. The model was applied to produce sequence based descriptors of four classes of transmembrane helix‐helix contact site configurations. The highest performance of the classifiers reached AUCROC of 0.70. The analysis of grammar parse trees revealed the ability of representing structural features of helix‐helix contact sites. Conclusions We demonstrated that our probabilistic context‐free framework for analysis of protein sequences outperforms the state of the art in the task of helix‐helix contact site classification. However, this is achieved without necessarily requiring modeling long range dependencies between interacting residues. A significant feature of our approach is that grammar rules and parse trees are human‐readable. Thus they could provide biologically meaningful information for molecular biologists. PMID:24350601

Can the new RCP R0/R1 classification predict the clinical outcome in ductal adenocarcinoma of the pancreatic head?

PubMed

Janot, M S; Kersting, S; Belyaev, O; Matuschek, A; Chromik, A M; Suelberg, D; Uhl, W; Tannapfel, A; Bergmann, U

2012-08-01

According to the International Union Against Cancer (UICC), R1 is defined as the microscopic presence of tumor cells at the surface of the resection margin (RM). In contrast, the Royal College of Pathologists (RCP) suggested to declare R1 already when tumor cells are found within 1 mm of the RM. The aim of this study was to determine the significance of the RM concerning the prognosis of pancreatic ductal adenocarcinoma (PDAC). From 2007 to 2009, 62 patients underwent a curative operation for PDAC of the pancreatic head. The relevance of R status on cumulative overall survival (OS) was assessed on univariate and multivariate analysis for both the classic R classification (UICC) and the suggestion of the RCP. Following the UICC criteria, a positive RM was detected in 8 %. Along with grading and lymph node ratio, R status revealed a significant impact on OS on univariate and multivariate analysis. Applying the suggestion of the RCP, R1 rate rose to 26 % resulting in no significant impact on OS in univariate analysis. Our study has shown that the RCP suggestion for R status has no impact on the prognosis of PDAC. In contrast, our data confirmed the UICC R classification of RM as well as N category, grading, and lymph node ratio as significant prognostic factors.

Comparison of locus-specific databases for BRCA1 and BRCA2 variants reveals disparity in variant classification within and among databases.

PubMed

Vail, Paris J; Morris, Brian; van Kan, Aric; Burdett, Brianna C; Moyes, Kelsey; Theisen, Aaron; Kerr, Iain D; Wenstrup, Richard J; Eggington, Julie M

2015-10-01

Genetic variants of uncertain clinical significance (VUSs) are a common outcome of clinical genetic testing. Locus-specific variant databases (LSDBs) have been established for numerous disease-associated genes as a research tool for the interpretation of genetic sequence variants to facilitate variant interpretation via aggregated data. If LSDBs are to be used for clinical practice, consistent and transparent criteria regarding the deposition and interpretation of variants are vital, as variant classifications are often used to make important and irreversible clinical decisions. In this study, we performed a retrospective analysis of 2017 consecutive BRCA1 and BRCA2 genetic variants identified from 24,650 consecutive patient samples referred to our laboratory to establish an unbiased dataset representative of the types of variants seen in the US patient population, submitted by clinicians and researchers for BRCA1 and BRCA2 testing. We compared the clinical classifications of these variants among five publicly accessible BRCA1 and BRCA2 variant databases: BIC, ClinVar, HGMD (paid version), LOVD, and the UMD databases. Our results show substantial disparity of variant classifications among publicly accessible databases. Furthermore, it appears that discrepant classifications are not the result of a single outlier but widespread disagreement among databases. This study also shows that databases sometimes favor a clinical classification when current best practice guidelines (ACMG/AMP/CAP) would suggest an uncertain classification. Although LSDBs have been well established for research applications, our results suggest several challenges preclude their wider use in clinical practice.

Classification methods to detect sleep apnea in adults based on respiratory and oximetry signals: a systematic review.

PubMed

Uddin, M B; Chow, C M; Su, S W

2018-03-26

Sleep apnea (SA), a common sleep disorder, can significantly decrease the quality of life, and is closely associated with major health risks such as cardiovascular disease, sudden death, depression, and hypertension. The normal diagnostic process of SA using polysomnography is costly and time consuming. In addition, the accuracy of different classification methods to detect SA varies with the use of different physiological signals. If an effective, reliable, and accurate classification method is developed, then the diagnosis of SA and its associated treatment will be time-efficient and economical. This study aims to systematically review the literature and present an overview of classification methods to detect SA using respiratory and oximetry signals and address the automated detection approach. Sixty-two included studies revealed the application of single and multiple signals (respiratory and oximetry) for the diagnosis of SA. Both airflow and oxygen saturation signals alone were effective in detecting SA in the case of binary decision-making, whereas multiple signals were good for multi-class detection. In addition, some machine learning methods were superior to the other classification methods for SA detection using respiratory and oximetry signals. To deal with the respiratory and oximetry signals, a good choice of classification method as well as the consideration of associated factors would result in high accuracy in the detection of SA. An accurate classification method should provide a high detection rate with an automated (independent of human action) analysis of respiratory and oximetry signals. Future high-quality automated studies using large samples of data from multiple patient groups or record batches are recommended.

Land use classification using texture information in ERTS-A MSS imagery

NASA Technical Reports Server (NTRS)

Haralick, R. M. (Principal Investigator); Shanmugam, K. S.; Bosley, R.

1973-01-01

The author has identified the following significant results. Preliminary digital analysis of ERTS-1 MSS imagery reveals that the textural features of the imagery are very useful for land use classification. A procedure for extracting the textural features of ERTS-1 imagery is presented and the results of a land use classification scheme based on the textural features are also presented. The land use classification algorithm using textural features was tested on a 5100 square mile area covered by part of an ERTS-1 MSS band 5 image over the California coastline. The image covering this area was blocked into 648 subimages of size 8.9 square miles each. Based on a color composite of the image set, a total of 7 land use categories were identified. These land use categories are: coastal forest, woodlands, annual grasslands, urban areas, large irrigated fields, small irrigated fields, and water. The automatic classifier was trained to identify the land use categories using only the textural characteristics of the subimages; 75 percent of the subimages were assigned correct identifications. Since texture and spectral features provide completely different kinds of information, a significant increase in identification accuracy will take place when both features are used together.

Classification of Pelteobagrus fish in Poyang Lake based on mitochondrial COI gene sequence.

PubMed

Zhong, Bin; Chen, Ting-Ting; Gong, Rui-Yue; Zhao, Zhe-Xia; Wang, Binhua; Fang, Chunlin; Mao, Hui-Ling

2016-11-01

We use DNA molecular marker technology to correct the deficiency of traditional morphological taxonomy. Totality 770 Pelteobagrus fish from Poyang Lake were collected. After preliminary morphological classification, random selected eight samples in each species for DNA extraction. Mitochondrial COI gene sequence was cloned with universal primers and sequenced. The results showed that there are four species of Pelteobagrus living in Poyang Lake. The average of intraspecific genetic distance value was 0.003, while the average interspecific genetic distance was 0.128. The interspecific genetic distance is far more than intraspecific genetic distance. Besides, phylogenetic tree analysis revealed that molecular systematics was in accord with morphological classification. It indicated that COI gene is an effective DNA molecular marker in Pelteobagrus classification. Surprisingly, the intraspecific difference of some individuals (P. e6, P. n6, P. e5, and P. v4) from their original named exceeded species threshold (2%), which should be renewedly classified into Pelteobagrus fulvidraco. However, another individual P. v3 was very different, because its genetic distance was over 8.4% difference from original named Pelteobagrus vachelli. Its taxonomic status remained to be further studied.

A psychophysical imaging method evidencing auditory cue extraction during speech perception: a group analysis of auditory classification images.

PubMed

Varnet, Léo; Knoblauch, Kenneth; Serniclaes, Willy; Meunier, Fanny; Hoen, Michel

2015-01-01

Although there is a large consensus regarding the involvement of specific acoustic cues in speech perception, the precise mechanisms underlying the transformation from continuous acoustical properties into discrete perceptual units remains undetermined. This gap in knowledge is partially due to the lack of a turnkey solution for isolating critical speech cues from natural stimuli. In this paper, we describe a psychoacoustic imaging method known as the Auditory Classification Image technique that allows experimenters to estimate the relative importance of time-frequency regions in categorizing natural speech utterances in noise. Importantly, this technique enables the testing of hypotheses on the listening strategies of participants at the group level. We exemplify this approach by identifying the acoustic cues involved in da/ga categorization with two phonetic contexts, Al- or Ar-. The application of Auditory Classification Images to our group of 16 participants revealed significant critical regions on the second and third formant onsets, as predicted by the literature, as well as an unexpected temporal cue on the first formant. Finally, through a cluster-based nonparametric test, we demonstrate that this method is sufficiently sensitive to detect fine modifications of the classification strategies between different utterances of the same phoneme.

Comparison and critical appraisal of dengue clinical guidelines and their use in Asia and Latin America.

PubMed

Santamaria, R; Martinez, E; Kratochwill, S; Soria, C; Tan, L H; Nuñez, A; Dimaano, E; Villegas, E; Bendezú, H; Kroeger, A; Castelobranco, I; Siqueira, J B; Jaenisch, T; Horstick, O; Lum, L C S

2009-12-01

The World Health Organization (WHO) dengue classification scheme for dengue fever (DF) and dengue haemorrhagic fever (DHF)/dengue shock syndrome (DSS) has been adopted as the standard for diagnosis, clinical management and reporting. In recent years, difficulties in applying the WHO case classification have been reported in several countries. A multicenter study was carried out in Asia and Latin America to analyze the variation and utility of dengue clinical guidelines (DCGs) taking as reference the WHO/PAHO guidelines (1994) and the WHO/SEARO guidelines (1998). A document analysis of 13 dengue guidelines was followed by a questionnaire and Focus Group discussions (FGDs) with 858 health care providers in seven countries. Differences in DCGs of the 13 countries were identified including the concept of warning signs, case classification, use of treatment algorithms and grading into levels of severity. The questionnaires and FGDs revealed (1) inaccessibility of DCGs, (2) lack of training, (3) insufficient number of staff to correctly apply the DCGs at the frontline and (4) the unavailability of diagnostic tests. The differences of the DCGs and the inconsistency in their application suggest a need to re-evaluate and standardise DCGs. This applies especially to case classification and case management.

Free classification of regional dialects of American English

PubMed Central

Clopper, Cynthia G.; Pisoni, David B.

2011-01-01

Recent studies have found that naïve listeners perform poorly in forced-choice dialect categorization tasks. However, the listeners' error patterns in these tasks reveal systematic confusions between phonologically similar dialects. In the present study, a free classification procedure was used to measure the perceptual similarity structure of regional dialect variation in the United States. In two experiments, participants listened to a set of short English sentences produced by male talkers only (Experiment 1) and by male and female talkers (Experiment 2). The listeners were instructed to group the talkers by regional dialect into as many groups as they wanted with as many talkers in each group as they wished. Multidimensional scaling analyses of the data revealed three primary dimensions of perceptual similarity (linguistic markedness, geography, and gender). In addition, a comparison of the results obtained from the free classification task to previous results using the same stimulus materials in six-alternative forced-choice categorization tasks revealed that response biases in the six-alternative task were reduced or eliminated in the free classification task. Thus, the results obtained with the free classification task in the current study provided further evidence that the underlying structure of perceptual dialect category representations reflects important linguistic and sociolinguistic factors. PMID:21423862

Reducing uncertainty on satellite image classification through spatiotemporal reasoning

NASA Astrophysics Data System (ADS)

Partsinevelos, Panagiotis; Nikolakaki, Natassa; Psillakis, Periklis; Miliaresis, George; Xanthakis, Michail

2014-05-01

The natural habitat constantly endures both inherent natural and human-induced influences. Remote sensing has been providing monitoring oriented solutions regarding the natural Earth surface, by offering a series of tools and methodologies which contribute to prudent environmental management. Processing and analysis of multi-temporal satellite images for the observation of the land changes include often classification and change-detection techniques. These error prone procedures are influenced mainly by the distinctive characteristics of the study areas, the remote sensing systems limitations and the image analysis processes. The present study takes advantage of the temporal continuity of multi-temporal classified images, in order to reduce classification uncertainty, based on reasoning rules. More specifically, pixel groups that temporally oscillate between classes are liable to misclassification or indicate problematic areas. On the other hand, constant pixel group growth indicates a pressure prone area. Computational tools are developed in order to disclose the alterations in land use dynamics and offer a spatial reference to the pressures that land use classes endure and impose between them. Moreover, by revealing areas that are susceptible to misclassification, we propose specific target site selection for training during the process of supervised classification. The underlying objective is to contribute to the understanding and analysis of anthropogenic and environmental factors that influence land use changes. The developed algorithms have been tested upon Landsat satellite image time series, depicting the National Park of Ainos in Kefallinia, Greece, where the unique in the world Abies cephalonica grows. Along with the minor changes and pressures indicated in the test area due to harvesting and other human interventions, the developed algorithms successfully captured fire incidents that have been historically confirmed. Overall, the results have shown that the use of the suggested procedures can contribute to the reduction of the classification uncertainty and support the existing knowledge regarding the pressure among land-use changes.

Metabolomic analysis applied to chemosystematics and evolution of megadiverse Brazilian Vernonieae (Asteraceae).

PubMed

Gallon, Marília Elias; Monge, Marcelo; Casoti, Rosana; Da Costa, Fernando Batista; Semir, João; Gobbo-Neto, Leonardo

2018-06-01

Vernonia sensu lato is the largest and most complex genus of the tribe Vernonieae (Asteraceae). The tribe is chemically characterized by the presence of sesquiterpene lactones and flavonoids. Over the years, several taxonomic classifications have been proposed for Vernonia s.l. and for the tribe; however, there has been no consensus among the researches. According to traditional classification, Vernonia s.l. comprises more than 1000 species divided into sections, subsections and series (sensu Bentham). In a more recent classification, these species have been segregated into other genera and some subtribes were proposed, while the genus Vernonia sensu stricto was restricted to 22 species distributed mainly in North America (sensu Robinson). In this study, species from the subtribes Vernoniinae, Lepidaploinae and Rolandrinae were analyzed by UHPLC-UV-HRMS followed by multivariate statistical analysis. Data mining was performed using unsupervised (HCA and PCA) and supervised methods (OPLS-DA). The HCA showed the segregation of the species into four main groups. Comparing the HCA with taxonomical classifications of Vernonieae, we observed that the groups of the dendogram, based on metabolic profiling, were in accordance with the generic classification proposed by Robinson and with previous phylogenetic studies. The species of the genera Stenocephalum, Stilpnopappus, Strophopappus and Rolandra (Group 1) were revealed to be more related to the species of the genus Vernonanthura (Group 2), while the genera Cyrtocymura, Chrysolaena and Echinocoryne (Group 3) were chemically more similar to the genera Lessingianthus and Lepidaploa (Group 4). These findings indicated that the subtribes Vernoniinae and Lepidaploinae are non-chemically homogeneous groups and highlighted the application of untargeted metabolomic tools for taxonomy and as indicators of species evolution. Discriminant compounds for the groups obtained by OPLS-DA were determined. Groups 1 and 2 were characterized by the presence of 3',4'-dimethoxyluteolin, glaucolide A and 8-tigloyloxyglaucolide A. The species of Groups 3 and 4 were characterized by the presence of putative acacetin 7-O-rutinoside and glaucolide B. Therefore, untargeted metabolomic approach combined with multivariate statistical analysis, as proposed herein, allowed the identification of potential chemotaxonomic markers, helping in the taxonomic classifications. Copyright © 2018 Elsevier Ltd. All rights reserved.

Round-robin test for the cell-of-origin classification of diffuse large B-cell lymphoma-a feasibility study using full slide staining.

PubMed

Reinke, Sarah; Richter, Julia; Fend, Falko; Feller, Alfred; Hansmann, Martin-Leo; Hüttl, Katrin; Oschlies, Ilske; Ott, German; Möller, Peter; Rosenwald, Andreas; Stein, Harald; Altenbuchinger, Michael; Spang, Rainer; Klapper, Wolfram

2018-05-05

Diffuse large B-cell lymphoma (DLBCL) is subdivided by gene expression analysis (GEP) into two molecular subtypes named germinal center B-cell-like (GCB) and activated B-cell-like (ABC) after their putative cell-of-origin (COO). Determination of the COO is considered mandatory in any new-diagnosed DLBCL, not otherwise specified according to the updated WHO classification. Despite the fact that pathologists are free to choose the method for COO classification, immunohistochemical (IHC) assays are most widely used. However, to the best of our knowledge, no round-robin test to evaluate the interlaboratory variability has been published so far. Eight hematopathology laboratories participated in an interlaboratory test for COO classification of 10 DLBCL tumors using the IHC classifier comprising the expression of CD10, BCL6, and MUM1 (so-called Hans classifier). The results were compared with GEP for COO signature and, in a subset, with results obtained by image analysis. In 7/10 cases (70%), at least seven laboratories assigned a given case to the same COO subtype (one center assessed one sample as not analyzable), which was in agreement with the COO subtype determined by GEP. The results in 3/10 cases (30%) revealed discrepancies between centers and/or between IHC and GEP subtype. Whereas the CD10 staining results were highly reproducible, staining for MUM1 was inconsistent in 50% and for BCL6 in 40% of cases. Image analysis of 16 slides stained for BCL6 (N = 8) and MUM1 (N = 8) of the two cases with the highest disagreement in COO classification were in line with the score of the pathologists in 14/16 stainings analyzed (87.5%). This study describes the first round-robin test for COO subtyping in DLBCL using IHC and demonstrates that COO classification using the Hans classifier yields consistent results among experienced hematopathologists, even when variable staining protocols are used. Data from this small feasibility study need to be validated in larger cohorts.

Taxonomy of breast cancer based on normal cell phenotype predicts outcome

PubMed Central

Santagata, Sandro; Thakkar, Ankita; Ergonul, Ayse; Wang, Bin; Woo, Terri; Hu, Rong; Harrell, J. Chuck; McNamara, George; Schwede, Matthew; Culhane, Aedin C.; Kindelberger, David; Rodig, Scott; Richardson, Andrea; Schnitt, Stuart J.; Tamimi, Rulla M.; Ince, Tan A.

2014-01-01

Accurate classification is essential for understanding the pathophysiology of a disease and can inform therapeutic choices. For hematopoietic malignancies, a classification scheme based on the phenotypic similarity between tumor cells and normal cells has been successfully used to define tumor subtypes; however, use of normal cell types as a reference by which to classify solid tumors has not been widely emulated, in part due to more limited understanding of epithelial cell differentiation compared with hematopoiesis. To provide a better definition of the subtypes of epithelial cells comprising the breast epithelium, we performed a systematic analysis of a large set of breast epithelial markers in more than 15,000 normal breast cells, which identified 11 differentiation states for normal luminal cells. We then applied information from this analysis to classify human breast tumors based on normal cell types into 4 major subtypes, HR0–HR3, which were differentiated by vitamin D, androgen, and estrogen hormone receptor (HR) expression. Examination of 3,157 human breast tumors revealed that these HR subtypes were distinct from the current classification scheme, which is based on estrogen receptor, progesterone receptor, and human epidermal growth factor receptor 2. Patient outcomes were best when tumors expressed all 3 hormone receptors (subtype HR3) and worst when they expressed none of the receptors (subtype HR0). Together, these data provide an ontological classification scheme associated with patient survival differences and provides actionable insights for treating breast tumors. PMID:24463450

A Comprehensive Study of Retinal Vessel Classification Methods in Fundus Images

PubMed Central

Miri, Maliheh; Amini, Zahra; Rabbani, Hossein; Kafieh, Raheleh

2017-01-01

Nowadays, it is obvious that there is a relationship between changes in the retinal vessel structure and diseases such as diabetic, hypertension, stroke, and the other cardiovascular diseases in adults as well as retinopathy of prematurity in infants. Retinal fundus images provide non-invasive visualization of the retinal vessel structure. Applying image processing techniques in the study of digital color fundus photographs and analyzing their vasculature is a reliable approach for early diagnosis of the aforementioned diseases. Reduction in the arteriolar–venular ratio of retina is one of the primary signs of hypertension, diabetic, and cardiovascular diseases which can be calculated by analyzing the fundus images. To achieve a precise measuring of this parameter and meaningful diagnostic results, accurate classification of arteries and veins is necessary. Classification of vessels in fundus images faces with some challenges that make it difficult. In this paper, a comprehensive study of the proposed methods for classification of arteries and veins in fundus images is presented. Considering that these methods are evaluated on different datasets and use different evaluation criteria, it is not possible to conduct a fair comparison of their performance. Therefore, we evaluate the classification methods from modeling perspective. This analysis reveals that most of the proposed approaches have focused on statistics, and geometric models in spatial domain and transform domain models have received less attention. This could suggest the possibility of using transform models, especially data adaptive ones, for modeling of the fundus images in future classification approaches. PMID:28553578

A General Framework for Discovery and Classification in Astronomy

NASA Astrophysics Data System (ADS)

Dick, Steven J.

2012-09-01

An analysis of the discovery of 82 classes of astronomical objects reveals an extended structure of discovery, consisting of detection, interpretation and understanding, each with its own nuances and a microstructure including conceptual, technological and social roles. This is true with a remarkable degree of consistency over the last 400 years of telescopic astronomy, ranging from Galileo's discovery of satellites, planetary rings and star clusters, to the discovery of quasars and pulsars. Telescopes have served as ``engines of discovery'' in several ways, ranging from telescope size and sensitivity (planetary nebulae and spiral nebulae), to specialized detectors (TNOs) and the opening of the electromagnetic spectrum for astronomy (pulsars, pulsar planets, and most active galaxies). A few classes (radiation belts, the solar wind and cosmic rays) were initially discovered without the telescope. Classification also plays an important role in discovery. While it might seem that classification marks the end of discovery, or a post-discovery phase, in fact it often marks the beginning, even a pre-discovery phase. Nowhere is this more clearly seen than in the classification of stellar spectra, long before dwarfs, giants and supergiants were known, or their evolutionary sequence recognized. Classification may also be part of a post-discovery phase, as in the MK system of stellar classification, constructed after the discovery of stellar luminosity classes. Some classes are declared rather than detected, as in the case of gas and ice giant planets, and, infamously, Pluto as a dwarf planet. Others are inferred rather than detected, including most classes of stars.

Reflectance measurements for the detection and mapping of soil limitations

NASA Technical Reports Server (NTRS)

Benson, L. A.; Frazee, C. J.

1973-01-01

During 1971 and 1972 research was conducted on two fallow fields in the proposed Oahe Irrigation Project to investigate the relationship between the tonal variations observed on aerial photographs and the principal soil limitations of the area. A grid sampling procedure was used to collected detailed field data during the 1972 growing season. The field data was compared to imagery collected on May 14, 1971 at 3050 meters altitude. The imagery and field data were initially evaluated by a visual analysis. Correlation and regression analysis revealed a highly significant correlation and regression analysis revealed a highly significant correlation between the digitized color infrared film data and soil properties such as organic matter content, color, depth to carbonates, bulk density and reflectivity. Computer classification of the multiemulsion film data resulted in maps delineating the areas containing claypan and erosion limitations. Reflectance data from the red spectral band provided the best results.

Identification and classification of traditional Chinese medicine syndrome types among senior patients with vascular mild cognitive impairment using latent tree analysis.

PubMed

Fu, Chen; Zhang, Nevin Lianwen; Chen, Bao-Xin; Chen, Zhou Rong; Jin, Xiang Lan; Guo, Rong-Juan; Chen, Zhi-Gang; Zhang, Yun-Ling

2017-05-01

To treat patients with vascular mild cognitive impairment (VMCI) using traditional Chinese medicine (TCM), it is necessary to classify the patients into TCM syndrome types and to apply different treatments to different types. In this paper, we investigate how to properly carry out the classification for patients with VMCI aged 50 or above using a novel data-driven method known as latent tree analysis (LTA). A cross-sectional survey on VMCI was carried out in several regions in Northern China between February 2008 and February 2012 which resulted in a data set that involves 803 patients and 93 symptoms. LTA was performed on the data to reveal symptom co-occurrence patterns, and the patients were partitioned into clusters in multiple ways based on the patterns. The patient clusters were matched up with syndrome types, and population statistics of the clusters are used to quantify the syndrome types and to establish classification rules. Eight syndrome types are identified: Qi deficiency, Qi stagnation, Blood deficiency, Blood stasis, Phlegm-dampness, Fire-heat, Yang deficiency, and Yin deficiency. The prevalence and symptom occurrence characteristics of each syndrome type are determined. Quantitative classification rules are established for determining whether a patient belongs to each of the syndrome types. A solution for the TCM syndrome classification problem for patients with VMCI and aged 50 or above is established based on the LTA of unlabeled symptom survey data. The results can be used as a reference in clinic practice to improve the quality of syndrome differentiation and to reduce diagnosis variances across physicians. They can also be used for patient selection in research projects aimed at finding biomarkers for the syndrome types and in randomized control trials aimed at determining the efficacy of TCM treatments of VMCI.

Classification and mensuration of LACIE segments

NASA Technical Reports Server (NTRS)

Heydorn, R. P.; Bizzell, R. M.; Quirein, J. A.; Abotteen, K. M.; Sumner, C. A. (Principal Investigator)

1979-01-01

The theory of classification methods and the functional steps in the manual training process used in the three phases of LACIE are discussed. The major problems that arose in using a procedure for manually training a classifier and a method of machine classification are discussed to reveal the motivation that led to a redesign for the third LACIE phase.

Prioritization of reproductive toxicants in unconventional oil and gas operations using a multi-country regulatory data-driven hazard assessment.

PubMed

Inayat-Hussain, Salmaan H; Fukumura, Masao; Muiz Aziz, A; Jin, Chai Meng; Jin, Low Wei; Garcia-Milian, Rolando; Vasiliou, Vasilis; Deziel, Nicole C

2018-08-01

Recent trends have witnessed the global growth of unconventional oil and gas (UOG) production. Epidemiologic studies have suggested associations between proximity to UOG operations with increased adverse birth outcomes and cancer, though specific potential etiologic agents have not yet been identified. To perform effective risk assessment of chemicals used in UOG production, the first step of hazard identification followed by prioritization specifically for reproductive toxicity, carcinogenicity and mutagenicity is crucial in an evidence-based risk assessment approach. To date, there is no single hazard classification list based on the United Nations Globally Harmonized System (GHS), with countries applying the GHS standards to generate their own chemical hazard classification lists. A current challenge for chemical prioritization, particularly for a multi-national industry, is inconsistent hazard classification which may result in misjudgment of the potential public health risks. We present a novel approach for hazard identification followed by prioritization of reproductive toxicants found in UOG operations using publicly available regulatory databases. GHS classification for reproductive toxicity of 157 UOG-related chemicals identified as potential reproductive or developmental toxicants in a previous publication was assessed using eleven governmental regulatory agency databases. If there was discordance in classifications across agencies, the most stringent classification was assigned. Chemicals in the category of known or presumed human reproductive toxicants were further evaluated for carcinogenicity and germ cell mutagenicity based on government classifications. A scoring system was utilized to assign numerical values for reproductive health, cancer and germ cell mutation hazard endpoints. Using a Cytoscape analysis, both qualitative and quantitative results were presented visually to readily identify high priority UOG chemicals with evidence of multiple adverse effects. We observed substantial inconsistencies in classification among the 11 databases. By adopting the most stringent classification within and across countries, 43 chemicals were classified as known or presumed human reproductive toxicants (GHS Category 1), while 31 chemicals were classified as suspected human reproductive toxicants (GHS Category 2). The 43 reproductive toxicants were further subjected to analysis for carcinogenic and mutagenic properties. Calculated hazard scores and Cytoscape visualization yielded several high priority chemicals including potassium dichromate, cadmium, benzene and ethylene oxide. Our findings reveal diverging GHS classification outcomes for UOG chemicals across regulatory agencies. Adoption of the most stringent classification with application of hazard scores provides a useful approach to prioritize reproductive toxicants in UOG and other industries for exposure assessments and selection of safer alternatives. Copyright © 2018 Elsevier Ltd. All rights reserved.

Structural organization and classification of cytochrome P450 genes in flax (Linum usitatissimum L.).

PubMed

Babu, Peram Ravindra; Rao, Khareedu Venkateswara; Reddy, Vudem Dashavantha

2013-01-15

Flax CYPome analysis resulted in the identification of 334 putative cytochrome P450 (CYP450) genes in the cultivated flax genome. Classification of flax CYP450 genes based on the sequence similarity with Arabidopsis orthologs and CYP450 nomenclature, revealed 10 clans representing 44 families and 98 subfamilies. CYP80, CYP83, CYP92, CYP702, CYP705, CYP708, CYP728, CYP729, CYP733 and CYP736 families are absent in the flax genome. The subfamily members exhibited conserved sequences, length of exons and phasing of introns. Similarity search of the genomic resources of wild flax species Linum bienne with CYP450 coding sequences of the cultivated flax, revealed the presence of 127 CYP450 gene orthologs, indicating amplification of novel CYP450 genes in the cultivated flax. Seven families CYP73, 74, 75, 76, 77, 84 and 709, coding for enzymes associated with phenylpropanoid/fatty acid metabolism, showed extensive gene amplification in the flax. About 59% of the flax CYP450 genes were present in the EST libraries. Copyright © 2012 Elsevier B.V. All rights reserved.

Predicting primary progressive aphasias with support vector machine approaches in structural MRI data.

PubMed

Bisenius, Sandrine; Mueller, Karsten; Diehl-Schmid, Janine; Fassbender, Klaus; Grimmer, Timo; Jessen, Frank; Kassubek, Jan; Kornhuber, Johannes; Landwehrmeyer, Bernhard; Ludolph, Albert; Schneider, Anja; Anderl-Straub, Sarah; Stuke, Katharina; Danek, Adrian; Otto, Markus; Schroeter, Matthias L

2017-01-01

Primary progressive aphasia (PPA) encompasses the three subtypes nonfluent/agrammatic variant PPA, semantic variant PPA, and the logopenic variant PPA, which are characterized by distinct patterns of language difficulties and regional brain atrophy. To validate the potential of structural magnetic resonance imaging data for early individual diagnosis, we used support vector machine classification on grey matter density maps obtained by voxel-based morphometry analysis to discriminate PPA subtypes (44 patients: 16 nonfluent/agrammatic variant PPA, 17 semantic variant PPA, 11 logopenic variant PPA) from 20 healthy controls (matched for sample size, age, and gender) in the cohort of the multi-center study of the German consortium for frontotemporal lobar degeneration. Here, we compared a whole-brain with a meta-analysis-based disease-specific regions-of-interest approach for support vector machine classification. We also used support vector machine classification to discriminate the three PPA subtypes from each other. Whole brain support vector machine classification enabled a very high accuracy between 91 and 97% for identifying specific PPA subtypes vs. healthy controls, and 78/95% for the discrimination between semantic variant vs. nonfluent/agrammatic or logopenic PPA variants. Only for the discrimination between nonfluent/agrammatic and logopenic PPA variants accuracy was low with 55%. Interestingly, the regions that contributed the most to the support vector machine classification of patients corresponded largely to the regions that were atrophic in these patients as revealed by group comparisons. Although the whole brain approach took also into account regions that were not covered in the regions-of-interest approach, both approaches showed similar accuracies due to the disease-specificity of the selected networks. Conclusion, support vector machine classification of multi-center structural magnetic resonance imaging data enables prediction of PPA subtypes with a very high accuracy paving the road for its application in clinical settings.

Application of remote sensing in South Dakota to provide accurate inventories of agricultural crops, enhance contrast in photographic products, monitor rangeland habitat loss, map Aspen, and prepare hydrogeologic surveys

NASA Technical Reports Server (NTRS)

Myers, V. I. (Principal Investigator); Dalsted, K. J.; Best, R. G.; Smith, J. R.; Eidenshink, J. C.; Schmer, F. A.; Andrawis, A. S.; Rahn, P. H.

1977-01-01

The author has identified the following significant results. Digital analysis of LANDSAT CCT's indicated that two discrete spectral background zones occurred among the five soil zone. K-CLASS classification of corn revealed that accuracy increased when two background zones were used, compared to the classification of corn stratified by five soil zones. Selectively varying film type developer and development time produces higher contract in reprocessed imagery. Interpretation of rangeland and cropped land data from 1968 aerial photography and 1976 LANDSAT imagery indicated losses in rangeland habitat. Thermal imagery was useful in locating potential sources of sub-surface water and geothermal energy, estimating evapotranspiration, and inventorying the land.

Evidence for the Existing American Nurses Association-Recognized Standardized Nursing Terminologies: A Systematic Review

PubMed Central

Tastan, Sevinc; Linch, Graciele C. F.; Keenan, Gail M.; Stifter, Janet; McKinney, Dawn; Fahey, Linda; Dunn Lopez, Karen; Yao, Yingwei; Wilkie, Diana J.

2014-01-01

Objective To determine the state of the science for the five standardized nursing terminology sets in terms of level of evidence and study focus. Design Systematic Review. Data sources Keyword search of PubMed, CINAHL, and EMBASE databases from 1960s to March 19, 2012 revealed 1,257 publications. Review Methods From abstract review we removed duplicate articles, those not in English or with no identifiable standardized nursing terminology, and those with a low-level of evidence. From full text review of the remaining 312 articles, eight trained raters used a coding system to record standardized nursing terminology names, publication year, country, and study focus. Inter-rater reliability confirmed the level of evidence. We analyzed coded results. Results On average there were 4 studies per year between 1985 and 1995. The yearly number increased to 14 for the decade between 1996–2005, 21 between 2006–2010, and 25 in 2011. Investigators conducted the research in 27 countries. By evidence level for the 312 studies 72.4% were descriptive, 18.9% were observational, and 8.7% were intervention studies. Of the 312 reports, 72.1% focused on North American Nursing Diagnosis-International, Nursing Interventions Classification, Nursing Outcome Classification, or some combination of those three standardized nursing terminologies; 9.6% on Omaha System; 7.1% on International Classification for Nursing Practice; 1.6% on Clinical Care Classification/Home Health Care Classification; 1.6% on Perioperative Nursing Data Set; and 8.0% on two or more standardized nursing terminology sets. There were studies in all 10 foci categories including those focused on concept analysis/classification infrastructure (n = 43), the identification of the standardized nursing terminology concepts applicable to a health setting from registered nurses’ documentation (n = 54), mapping one terminology to another (n = 58), implementation of standardized nursing terminologies into electronic health records (n = 12), and secondary use of electronic health record data (n = 19). Conclusions Findings reveal that the number of standardized nursing terminology publications increased primarily since 2000 with most focusing on North American Nursing Diagnosis-International, Nursing Interventions Classification, and Nursing Outcome Classification. The majority of the studies were descriptive, qualitative, or correlational designs that provide a strong base for understanding the validity and reliability of the concepts underlying the standardized nursing terminologies. There is evidence supporting the successful integration and use in electronic health records for two standardized nursing terminology sets: (1) the North American Nursing Diagnosis-International, Nursing Interventions Classification, and Nursing Outcome Classification set; and (2) the Omaha System set. Researchers, however, should continue to strengthen standardized nursing terminology study designs to promote continuous improvement of the standardized nursing terminologies and use in clinical practice. PMID:24412062

Foot-strike pattern and performance in a marathon

PubMed Central

Kasmer, Mark E.; Liu, Xue-cheng; Roberts, Kyle G.; Valadao, Jason M.

2016-01-01

Purpose To: 1) determine prevalence of heel-strike in a mid-size city marathon, 2) determine if there is an association between foot-strike classification and race performance, and 3) determine if there is an association between foot-strike classification and gender. Methods Foot-strike classification (fore-foot strike, mid-foot strike, heel strike, or split-strike), gender, and rank (position in race) were recorded at the 8.1 kilometer (km) mark for 2,112 runners at the 2011 Milwaukee Lakefront Marathon. Results 1,991 runners were classified by foot-strike pattern, revealing a heel-strike prevalence of 93.67% (n=1,865). A significant difference between foot-strike classification and performance was found using a Kruskal-Wallis test (p < 0.0001), with more elite performers being less likely to heel-strike. No significant difference between foot-strike classification and gender was found using a Fisher’s exact test. Additionally, subgroup analysis of the 126 non-heel strikers found no significant difference between shoe wear and performance using a Kruskal-Wallis test. Conclusions The high prevalence of heel-striking observed in this study reflects the foot-strike pattern of the majority of mid- to long-distance runners and more importantly, may predict their injury profile based on the biomechanics of a heel strike running pattern. This knowledge can aid the clinician in the appropriate diagnosis, management, and training modifications of the injured runner. PMID:23006790

Posterior medial meniscus root ligament lesions: MRI classification and associated findings.

PubMed

Choi, Ja-Young; Chang, Eric Y; Cunha, Guilherme M; Tafur, Monica; Statum, Sheronda; Chung, Christine B

2014-12-01

The purposes of this study were to determine the prevalence of altered MRI appearances of "posterior medial meniscus root ligament (PMMRL)" lesions, introduce a classification of lesion types, and report associated findings. We retrospectively reviewed 419 knee MRI studies to identify the presence of PMMRL lesions. Classification was established on the basis of lesions encountered. The medial compartment was assessed for medial meniscal tears in the meniscus proper, medial meniscal extrusion, insertional PMMRL osseous changes, regional synovitis, osteoarthritis, insufficiency fracture, and cruciate ligament abnormality. PMMRL abnormalities occurred in 28.6% (120/419) of the studies: degeneration, 14.3% (60/419) and tear, 14.3% (60/419). Our classification system included degeneration and tearing. Tearing was categorized as partial or complete with delineation of the point of failure as entheseal, midsubstance, or junction to meniscus. Of all tears, 93.3% (56/60) occurred at the meniscal junction. Univariate analysis revealed significant differences between the knees with and without PMMRL lesions in age, medial meniscal tear, medial meniscal extrusion, insertional PMMRL osseous change, regional synovitis, osteoarthritis, insufficiency fracture (p=0.017), and cruciate ligament degeneration (p<0.001). PMMRL lesions are commonly detected in symptomatic patients. We have introduced an MRI classification system. PMMRL lesions are significantly associated with age, medial meniscal tears, medial meniscal extrusion, insertional PMMRL osseous change, regional synovitis, osteoarthritis, insufficiency fracture, and cruciate ligament degeneration.

Classification and discrimination of pediatric patients undergoing open heart surgery with and without methylprednisolone treatment by cytomics

NASA Astrophysics Data System (ADS)

Bocsi, Jozsef; Mittag, Anja; Pierzchalski, Arkadiusz; Osmancik, Pavel; Dähnert, Ingo; Tárnok, Attila

2011-02-01

Introduction: Methylprednisolone (MP) is frequently preoperatively administered in children undergoing open heart surgery. The aim of this medication is to inhibit overshooting immune responses. Earlier studies demonstrated cellular and humoral immunological changes in pediatric patients undergoing heart surgeries with and without MP administration. Here in a retrospective study we investigated the modulation of the cellular immune response by MP. The aim was to identify suitable parameters characterizing MP effects by cluster analysis. Methods: Blood samples were analysed from two aged matched groups with surgical correction of septum defects. Group without MP treatment consisted of 10 patients; MP was administered on 21 patients (median dose: 11mg/kg) before cardiopulmonary bypass (CPB). EDTA anticoagulated blood was obtained 24 h preoperatively, after anesthesia, at CPB begin and end (CPB2), 4h, 24h, 48h after surgery, at discharge and at out-patient followup (8.2; 3.3-12.2 month after surgery; median and IQR). Flow cytometry showed the biggest MP relevant changes at CPB2 and 4h postoperatively. They were used for clustering analysis. Classification was made by discriminant analysis and cluster analysis by means of Genes@work software. Results & conclusion: 146 parameters were obtained from analysis. Cross-validation revealed several parameters being able to discriminate between MP groups and to identify immune modulation. MP administration resulted in a delayed activation of monocytes, increased ratio of neutrophils, reduced T-lymphocytes counts. Cluster analysis demonstrated that classification of patients is possible based on the identified cytomics parameters. Further investigation of these parameters might help to understand the MP effects in pediatric open heart surgery.

Integrative Genomic Analysis of Cholangiocarcinoma Identifies Distinct IDH-Mutant Molecular Profiles.

PubMed

Farshidfar, Farshad; Zheng, Siyuan; Gingras, Marie-Claude; Newton, Yulia; Shih, Juliann; Robertson, A Gordon; Hinoue, Toshinori; Hoadley, Katherine A; Gibb, Ewan A; Roszik, Jason; Covington, Kyle R; Wu, Chia-Chin; Shinbrot, Eve; Stransky, Nicolas; Hegde, Apurva; Yang, Ju Dong; Reznik, Ed; Sadeghi, Sara; Pedamallu, Chandra Sekhar; Ojesina, Akinyemi I; Hess, Julian M; Auman, J Todd; Rhie, Suhn K; Bowlby, Reanne; Borad, Mitesh J; Zhu, Andrew X; Stuart, Josh M; Sander, Chris; Akbani, Rehan; Cherniack, Andrew D; Deshpande, Vikram; Mounajjed, Taofic; Foo, Wai Chin; Torbenson, Michael S; Kleiner, David E; Laird, Peter W; Wheeler, David A; McRee, Autumn J; Bathe, Oliver F; Andersen, Jesper B; Bardeesy, Nabeel; Roberts, Lewis R; Kwong, Lawrence N

2017-03-14

Cholangiocarcinoma (CCA) is an aggressive malignancy of the bile ducts, with poor prognosis and limited treatment options. Here, we describe the integrated analysis of somatic mutations, RNA expression, copy number, and DNA methylation by The Cancer Genome Atlas of a set of predominantly intrahepatic CCA cases and propose a molecular classification scheme. We identified an IDH mutant-enriched subtype with distinct molecular features including low expression of chromatin modifiers, elevated expression of mitochondrial genes, and increased mitochondrial DNA copy number. Leveraging the multi-platform data, we observed that ARID1A exhibited DNA hypermethylation and decreased expression in the IDH mutant subtype. More broadly, we found that IDH mutations are associated with an expanded histological spectrum of liver tumors with molecular features that stratify with CCA. Our studies reveal insights into the molecular pathogenesis and heterogeneity of cholangiocarcinoma and provide classification information of potential therapeutic significance. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Noninvasive diagnosis of intraamniotic infection: proteomic biomarkers in vaginal fluid.

PubMed

Hitti, Jane; Lapidus, Jodi A; Lu, Xinfang; Reddy, Ashok P; Jacob, Thomas; Dasari, Surendra; Eschenbach, David A; Gravett, Michael G; Nagalla, Srinivasa R

2010-07-01

We analyzed the vaginal fluid proteome to identify biomarkers of intraamniotic infection among women in preterm labor. Proteome analysis was performed on vaginal fluid specimens from women with preterm labor, using multidimensional liquid chromatography, tandem mass spectrometry, and label-free quantification. Enzyme immunoassays were used to quantify candidate proteins. Classification accuracy for intraamniotic infection (positive amniotic fluid bacterial culture and/or interleukin-6 >2 ng/mL) was evaluated using receiver-operator characteristic curves obtained by logistic regression. Of 170 subjects, 30 (18%) had intraamniotic infection. Vaginal fluid proteome analysis revealed 338 unique proteins. Label-free quantification identified 15 proteins differentially expressed in intraamniotic infection, including acute-phase reactants, immune modulators, high-abundance amniotic fluid proteins and extracellular matrix-signaling factors; these findings were confirmed by enzyme immunoassay. A multi-analyte algorithm showed accurate classification of intraamniotic infection. Vaginal fluid proteome analyses identified proteins capable of discriminating between patients with and without intraamniotic infection. Copyright (c) 2010 Mosby, Inc. All rights reserved.

Analysis of genetic diversity in banana cultivars (Musa cvs.) from the South of Oman using AFLP markers and classification by phylogenetic, hierarchical clustering and principal component analyses*

PubMed Central

Opara, Umezuruike Linus; Jacobson, Dan; Al-Saady, Nadiya Abubakar

2010-01-01

Banana is an important crop grown in Oman and there is a dearth of information on its genetic diversity to assist in crop breeding and improvement programs. This study employed amplified fragment length polymorphism (AFLP) to investigate the genetic variation in local banana cultivars from the southern region of Oman. Using 12 primer combinations, a total of 1094 bands were scored, of which 1012 were polymorphic. Eighty-two unique markers were identified, which revealed the distinct separation of the seven cultivars. The results obtained show that AFLP can be used to differentiate the banana cultivars. Further classification by phylogenetic, hierarchical clustering and principal component analyses showed significant differences between the clusters found with molecular markers and those clusters created by previous studies using morphological analysis. Based on the analytical results, a consensus dendrogram of the banana cultivars is presented. PMID:20443211

Delay Differential Equation Models of Normal and Diseased Electrocardiograms

NASA Astrophysics Data System (ADS)

Lainscsek, Claudia; Sejnowski, Terrence J.

Time series analysis with nonlinear delay differential equations (DDEs) is a powerful tool since it reveals spectral as well as nonlinear properties of the underlying dynamical system. Here global DDE models are used to analyze electrocardiography recordings (ECGs) in order to capture distinguishing features for different heart conditions such as normal heart beat, congestive heart failure, and atrial fibrillation. To capture distinguishing features of the different data types the number of terms and delays in the model as well as the order of nonlinearity of the DDE model have to be selected. The DDE structure selection is done in a supervised way by selecting the DDE that best separates different data types. We analyzed 24 h of data from 15 young healthy subjects in normal sinus rhythm (NSR) of 15 congestive heart failure (CHF) patients as well as of 15 subjects suffering from atrial fibrillation (AF) selected from the Physionet database. For the analysis presented here we used 5 min non-overlapping data windows on the raw data without any artifact removal. For classification performance we used the Cohen Kappa coefficient computed directly from the confusion matrix. The overall classification performance of the three groups was around 72-99 % on the 5 min windows for the different approaches. For 2 h data windows the classification for all three groups was above 95%.

Biometrics from the carbon isotope ratio analysis of amino acids in human hair.

PubMed

Jackson, Glen P; An, Yan; Konstantynova, Kateryna I; Rashaid, Ayat H B

2015-01-01

This study compares and contrasts the ability to classify individuals into different grouping factors through either bulk isotope ratio analysis or amino-acid-specific isotope ratio analysis of human hair. Using LC-IRMS, we measured the isotope ratios of 14 amino acids in hair proteins independently, and leucine/isoleucine as a co-eluting pair, to provide 15 variables for classification. Multivariate analysis confirmed that the essential amino acids and non-essential amino acids were mostly independent variables in the classification rules, thereby enabling the separation of dietary factors of isotope intake from intrinsic or phenotypic factors of isotope fractionation. Multivariate analysis revealed at least two potential sources of non-dietary factors influencing the carbon isotope ratio values of the amino acids in human hair: body mass index (BMI) and age. These results provide evidence that compound-specific isotope ratio analysis has the potential to go beyond region-of-origin or geospatial movements of individuals-obtainable through bulk isotope measurements-to the provision of physical and characteristic traits about the individuals, such as age and BMI. Further development and refinement, for example to genetic, metabolic, disease and hormonal factors could ultimately be of great assistance in forensic and clinical casework. Copyright © 2014 Forensic Science Society. Published by Elsevier Ireland Ltd. All rights reserved.

Exploring Children's Thinking. Part 1: The Development of Classification (Preschool - Third Grade).

ERIC Educational Resources Information Center

Alward, Keith R.

This unit of the Flexible Learning System (FLS), the first of a 3-volume series on children's thinking, discusses the development of classification in children between 3 and 8 years of age. The series is based on the application of Jean Piaget's work to early childhood education. The development of classification is revealed in the way children…

Sequence analysis of serum albumins reveals the molecular evolution of ligand recognition properties.

PubMed

Fanali, Gabriella; Ascenzi, Paolo; Bernardi, Giorgio; Fasano, Mauro

2012-01-01

Serum albumin (SA) is a circulating protein providing a depot and carrier for many endogenous and exogenous compounds. At least seven major binding sites have been identified by structural and functional investigations mainly in human SA. SA is conserved in vertebrates, with at least 49 entries in protein sequence databases. The multiple sequence analysis of this set of entries leads to the definition of a cladistic tree for the molecular evolution of SA orthologs in vertebrates, thus showing the clustering of the considered species, with lamprey SAs (Lethenteron japonicum and Petromyzon marinus) in a separate outgroup. Sequence analysis aimed at searching conserved domains revealed that most SA sequences are made up by three repeated domains (about 600 residues), as extensively characterized for human SA. On the contrary, lamprey SAs are giant proteins (about 1400 residues) comprising seven repeated domains. The phylogenetic analysis of the SA family reveals a stringent correlation with the taxonomic classification of the species available in sequence databases. A focused inspection of the sequences of ligand binding sites in SA revealed that in all sites most residues involved in ligand binding are conserved, although the versatility towards different ligands could be peculiar of higher organisms. Moreover, the analysis of molecular links between the different sites suggests that allosteric modulation mechanisms could be restricted to higher vertebrates.

Automatic classification of diseases from free-text death certificates for real-time surveillance.

PubMed

Koopman, Bevan; Karimi, Sarvnaz; Nguyen, Anthony; McGuire, Rhydwyn; Muscatello, David; Kemp, Madonna; Truran, Donna; Zhang, Ming; Thackway, Sarah

2015-07-15

Death certificates provide an invaluable source for mortality statistics which can be used for surveillance and early warnings of increases in disease activity and to support the development and monitoring of prevention or response strategies. However, their value can be realised only if accurate, quantitative data can be extracted from death certificates, an aim hampered by both the volume and variable nature of certificates written in natural language. This study aims to develop a set of machine learning and rule-based methods to automatically classify death certificates according to four high impact diseases of interest: diabetes, influenza, pneumonia and HIV. Two classification methods are presented: i) a machine learning approach, where detailed features (terms, term n-grams and SNOMED CT concepts) are extracted from death certificates and used to train a set of supervised machine learning models (Support Vector Machines); and ii) a set of keyword-matching rules. These methods were used to identify the presence of diabetes, influenza, pneumonia and HIV in a death certificate. An empirical evaluation was conducted using 340,142 death certificates, divided between training and test sets, covering deaths from 2000-2007 in New South Wales, Australia. Precision and recall (positive predictive value and sensitivity) were used as evaluation measures, with F-measure providing a single, overall measure of effectiveness. A detailed error analysis was performed on classification errors. Classification of diabetes, influenza, pneumonia and HIV was highly accurate (F-measure 0.96). More fine-grained ICD-10 classification effectiveness was more variable but still high (F-measure 0.80). The error analysis revealed that word variations as well as certain word combinations adversely affected classification. In addition, anomalies in the ground truth likely led to an underestimation of the effectiveness. The high accuracy and low cost of the classification methods allow for an effective means for automatic and real-time surveillance of diabetes, influenza, pneumonia and HIV deaths. In addition, the methods are generally applicable to other diseases of interest and to other sources of medical free-text besides death certificates.

Genetic Markers Analyses and Bioinformatic Approaches to Distinguish Between Olive Tree (Olea europaea L.) Cultivars.

PubMed

Ben Ayed, Rayda; Ben Hassen, Hanen; Ennouri, Karim; Rebai, Ahmed

2016-12-01

The genetic diversity of 22 olive tree cultivars (Olea europaea L.) sampled from different Mediterranean countries was assessed using 5 SNP markers (FAD2.1; FAD2.3; CALC; SOD and ANTHO3) located in four different genes. The genotyping analysis of the 22 cultivars with 5 SNP loci revealed 11 alleles (average 2.2 per allele). The dendrogram based on cultivar genotypes revealed three clusters consistent with the cultivars classification. Besides, the results obtained with the five SNPs were compared to those obtained with the SSR markers using bioinformatic analyses and by computing a cophenetic correlation coefficient, indicating the usefulness of the UPGMA method for clustering plant genotypes. Based on principal coordinate analysis using a similarity matrix, the first two coordinates, revealed 54.94 % of the total variance. This work provides a more comprehensive explanation of the diversity available in Tunisia olive cultivars, and an important contribution for olive breeding and olive oil authenticity.

Exploring diversity in ensemble classification: Applications in large area land cover mapping

NASA Astrophysics Data System (ADS)

Mellor, Andrew; Boukir, Samia

2017-07-01

Ensemble classifiers, such as random forests, are now commonly applied in the field of remote sensing, and have been shown to perform better than single classifier systems, resulting in reduced generalisation error. Diversity across the members of ensemble classifiers is known to have a strong influence on classification performance - whereby classifier errors are uncorrelated and more uniformly distributed across ensemble members. The relationship between ensemble diversity and classification performance has not yet been fully explored in the fields of information science and machine learning and has never been examined in the field of remote sensing. This study is a novel exploration of ensemble diversity and its link to classification performance, applied to a multi-class canopy cover classification problem using random forests and multisource remote sensing and ancillary GIS data, across seven million hectares of diverse dry-sclerophyll dominated public forests in Victoria Australia. A particular emphasis is placed on analysing the relationship between ensemble diversity and ensemble margin - two key concepts in ensemble learning. The main novelty of our work is on boosting diversity by emphasizing the contribution of lower margin instances used in the learning process. Exploring the influence of tree pruning on diversity is also a new empirical analysis that contributes to a better understanding of ensemble performance. Results reveal insights into the trade-off between ensemble classification accuracy and diversity, and through the ensemble margin, demonstrate how inducing diversity by targeting lower margin training samples is a means of achieving better classifier performance for more difficult or rarer classes and reducing information redundancy in classification problems. Our findings inform strategies for collecting training data and designing and parameterising ensemble classifiers, such as random forests. This is particularly important in large area remote sensing applications, for which training data is costly and resource intensive to collect.

Evaluation of Urinary Tract Dilation Classification System for Grading Postnatal Hydronephrosis.

PubMed

Hodhod, Amr; Capolicchio, John-Paul; Jednak, Roman; El-Sherif, Eid; El-Doray, Abd El-Alim; El-Sherbiny, Mohamed

2016-03-01

We assessed the reliability and validity of the Urinary Tract Dilation classification system as a new grading system for postnatal hydronephrosis. We retrospectively reviewed charts of patients who presented with hydronephrosis from 2008 to 2013. We included patients diagnosed prenatally and those with hydronephrosis discovered incidentally during the first year of life. We excluded cases involving urinary tract infection, neurogenic bladder and chromosomal anomalies, those associated with extraurinary congenital malformations and those with followup of less than 24 months without resolution. Hydronephrosis was graded postnatally using the Society for Fetal Urology system, and then the management protocol was chosen. All units were regraded using the Urinary Tract Dilation classification system and compared to the Society for Fetal Urology system to assess reliability. Univariate and multivariate analyses were performed to assess the validity of the Urinary Tract Dilation classification system in predicting hydronephrosis resolution and surgical intervention. A total of 490 patients (730 renal units) were eligible to participate. The Urinary Tract Dilation classification system was reliable in the assessment of hydronephrosis (parallel forms 0.92). Hydronephrosis resolved in 357 units (49%), and 86 units (12%) were managed by surgical intervention. The remainder of renal units demonstrated stable or improved hydronephrosis. Multivariate analysis revealed that the likelihood of surgical intervention was predicted independently by Urinary Tract Dilation classification system risk group, while Society for Fetal Urology grades were predictive of likelihood of resolution. The Urinary Tract Dilation classification system is reliable for evaluation of postnatal hydronephrosis and is valid in predicting surgical intervention. Copyright © 2016 American Urological Association Education and Research, Inc. Published by Elsevier Inc. All rights reserved.

Classification of Dust Days by Satellite Remotely Sensed Aerosol Products

NASA Technical Reports Server (NTRS)

Sorek-Hammer, M.; Cohen, A.; Levy, Robert C.; Ziv, B.; Broday, D. M.

2013-01-01

Considerable progress in satellite remote sensing (SRS) of dust particles has been seen in the last decade. From an environmental health perspective, such an event detection, after linking it to ground particulate matter (PM) concentrations, can proxy acute exposure to respirable particles of certain properties (i.e. size, composition, and toxicity). Being affected considerably by atmospheric dust, previous studies in the Eastern Mediterranean, and in Israel in particular, have focused on mechanistic and synoptic prediction, classification, and characterization of dust events. In particular, a scheme for identifying dust days (DD) in Israel based on ground PM10 (particulate matter of size smaller than 10 nm) measurements has been suggested, which has been validated by compositional analysis. This scheme requires information regarding ground PM10 levels, which is naturally limited in places with sparse ground-monitoring coverage. In such cases, SRS may be an efficient and cost-effective alternative to ground measurements. This work demonstrates a new model for identifying DD and non-DD (NDD) over Israel based on an integration of aerosol products from different satellite platforms (Moderate Resolution Imaging Spectroradiometer (MODIS) and Ozone Monitoring Instrument (OMI)). Analysis of ground-monitoring data from 2007 to 2008 in southern Israel revealed 67 DD, with more than 88 percent occurring during winter and spring. A Classification and Regression Tree (CART) model that was applied to a database containing ground monitoring (the dependent variable) and SRS aerosol product (the independent variables) records revealed an optimal set of binary variables for the identification of DD. These variables are combinations of the following primary variables: the calendar month, ground-level relative humidity (RH), the aerosol optical depth (AOD) from MODIS, and the aerosol absorbing index (AAI) from OMI. A logistic regression that uses these variables, coded as binary variables, demonstrated 93.2 percent correct classifications of DD and NDD. Evaluation of the combined CART-logistic regression scheme in an adjacent geographical region (Gush Dan) demonstrated good results. Using SRS aerosol products for DD and NDD, identification may enable us to distinguish between health, ecological, and environmental effects that result from exposure to these distinct particle populations.

Comparing quantitative analysis on revealed comparative advantages of aquatic products trade of china and ASEAN based on 21st century maritime silk road

NASA Astrophysics Data System (ADS)

Luo, X. F.; Han, Y. H.; Li, Z. W.

2017-11-01

As the world’s leading aquaculture, aquatic production and trading country, China’s development of aquatic products trade with ASEAN is facing a historic opportunity in the favourable circumstances of construction of the 21st century Maritime Silk Road. In order to make guidance of the product selection and transformation for corresponding export enterprises, this article makes a quantitative analysis the Revealed Comparative Advantage of aquatic products trade from China and ASEAN respectively based on the HS classification and thoroughly compares the RCA indices. The comparison results show that the international competitiveness of aquatic products structures of China and ASEAN are quite different with few overlaps of strong competitive products, and there is a great gap between the two areas in many kinds of products.

Monitoring nanotechnology using patent classifications: an overview and comparison of nanotechnology classification schemes

NASA Astrophysics Data System (ADS)

Jürgens, Björn; Herrero-Solana, Victor

2017-04-01

Patents are an essential information source used to monitor, track, and analyze nanotechnology. When it comes to search nanotechnology-related patents, a keyword search is often incomplete and struggles to cover such an interdisciplinary discipline. Patent classification schemes can reveal far better results since they are assigned by experts who classify the patent documents according to their technology. In this paper, we present the most important classifications to search nanotechnology patents and analyze how nanotechnology is covered in the main patent classification systems used in search systems nowadays: the International Patent Classification (IPC), the United States Patent Classification (USPC), and the Cooperative Patent Classification (CPC). We conclude that nanotechnology has a significantly better patent coverage in the CPC since considerable more nanotechnology documents were retrieved than by using other classifications, and thus, recommend its use for all professionals involved in nanotechnology patent searches.

Deep Learning Accurately Predicts Estrogen Receptor Status in Breast Cancer Metabolomics Data.

PubMed

Alakwaa, Fadhl M; Chaudhary, Kumardeep; Garmire, Lana X

2018-01-05

Metabolomics holds the promise as a new technology to diagnose highly heterogeneous diseases. Conventionally, metabolomics data analysis for diagnosis is done using various statistical and machine learning based classification methods. However, it remains unknown if deep neural network, a class of increasingly popular machine learning methods, is suitable to classify metabolomics data. Here we use a cohort of 271 breast cancer tissues, 204 positive estrogen receptor (ER+), and 67 negative estrogen receptor (ER-) to test the accuracies of feed-forward networks, a deep learning (DL) framework, as well as six widely used machine learning models, namely random forest (RF), support vector machines (SVM), recursive partitioning and regression trees (RPART), linear discriminant analysis (LDA), prediction analysis for microarrays (PAM), and generalized boosted models (GBM). DL framework has the highest area under the curve (AUC) of 0.93 in classifying ER+/ER- patients, compared to the other six machine learning algorithms. Furthermore, the biological interpretation of the first hidden layer reveals eight commonly enriched significant metabolomics pathways (adjusted P-value <0.05) that cannot be discovered by other machine learning methods. Among them, protein digestion and absorption and ATP-binding cassette (ABC) transporters pathways are also confirmed in integrated analysis between metabolomics and gene expression data in these samples. In summary, deep learning method shows advantages for metabolomics based breast cancer ER status classification, with both the highest prediction accuracy (AUC = 0.93) and better revelation of disease biology. We encourage the adoption of feed-forward networks based deep learning method in the metabolomics research community for classification.

Application of SWIR hyperspectral imaging and chemometrics for identification of aflatoxin B1 contaminated maize kernels

NASA Astrophysics Data System (ADS)

Kimuli, Daniel; Wang, Wei; Wang, Wei; Jiang, Hongzhe; Zhao, Xin; Chu, Xuan

2018-03-01

A short-wave infrared (SWIR) hyperspectral imaging system (1000-2500 nm) combined with chemometric data analysis was used to detect aflatoxin B1 (AFB1) on surfaces of 600 kernels of four yellow maize varieties from different States of the USA (Georgia, Illinois, Indiana and Nebraska). For each variety, four AFB1 solutions (10, 20, 100 and 500 ppb) were artificially deposited on kernels and a control group was generated from kernels treated with methanol solution. Principal component analysis (PCA), partial least squares discriminant analysis (PLSDA) and factorial discriminant analysis (FDA) were applied to explore and classify maize kernels according to AFB1 contamination. PCA results revealed partial separation of control kernels from AFB1 contaminated kernels for each variety while no pattern of separation was observed among pooled samples. A combination of standard normal variate and first derivative pre-treatments produced the best PLSDA classification model with accuracy of 100% and 96% in calibration and validation, respectively, from Illinois variety. The best AFB1 classification results came from FDA on raw spectra with accuracy of 100% in calibration and validation for Illinois and Nebraska varieties. However, for both PLSDA and FDA models, poor AFB1 classification results were obtained for pooled samples relative to individual varieties. SWIR spectra combined with chemometrics and spectra pre-treatments showed the possibility of detecting maize kernels of different varieties coated with AFB1. The study further suggests that increase of maize kernel constituents like water, protein, starch and lipid in a pooled sample may have influence on detection accuracy of AFB1 contamination.

Maxillectomy defects: a suggested classification scheme.

PubMed

Akinmoladun, V I; Dosumu, O O; Olusanya, A A; Ikusika, O F

2013-06-01

The term "maxillectomy" has been used to describe a variety of surgical procedures for a spectrum of diseases involving a diverse anatomical site. Hence, classifications of maxillectomy defects have often made communication difficult. This article highlights this problem, emphasises the need for a uniform system of classification and suggests a classification system which is simple and comprehensive. Articles related to this subject, especially those with specified classifications of maxillary surgical defects were sourced from the internet through Google, Scopus and PubMed using the search terms maxillectomy defects classification. A manual search through available literature was also done. The review of the materials revealed many classifications and modifications of classifications from the descriptive, reconstructive and prosthodontic perspectives. No globally acceptable classification exists among practitioners involved in the management of diseases in the mid-facial region. There were over 14 classifications of maxillary defects found in the English literature. Attempts made to address the inadequacies of previous classifications have tended to result in cumbersome and relatively complex classifications. A single classification that is based on both surgical and prosthetic considerations is most desirable and is hereby proposed.

A novel chemometric classification for FTIR spectra of mycotoxin-contaminated maize and peanuts at regulatory limits.

PubMed

Kos, Gregor; Sieger, Markus; McMullin, David; Zahradnik, Celine; Sulyok, Michael; Öner, Tuba; Mizaikoff, Boris; Krska, Rudolf

2016-10-01

The rapid identification of mycotoxins such as deoxynivalenol and aflatoxin B 1 in agricultural commodities is an ongoing concern for food importers and processors. While sophisticated chromatography-based methods are well established for regulatory testing by food safety authorities, few techniques exist to provide a rapid assessment for traders. This study advances the development of a mid-infrared spectroscopic method, recording spectra with little sample preparation. Spectral data were classified using a bootstrap-aggregated (bagged) decision tree method, evaluating the protein and carbohydrate absorption regions of the spectrum. The method was able to classify 79% of 110 maize samples at the European Union regulatory limit for deoxynivalenol of 1750 µg kg -1 and, for the first time, 77% of 92 peanut samples at 8 µg kg -1 of aflatoxin B 1 . A subset model revealed a dependency on variety and type of fungal infection. The employed CRC and SBL maize varieties could be pooled in the model with a reduction of classification accuracy from 90% to 79%. Samples infected with Fusarium verticillioides were removed, leaving samples infected with F. graminearum and F. culmorum in the dataset improving classification accuracy from 73% to 79%. A 500 µg kg -1 classification threshold for deoxynivalenol in maize performed even better with 85% accuracy. This is assumed to be due to a larger number of samples around the threshold increasing representativity. Comparison with established principal component analysis classification, which consistently showed overlapping clusters, confirmed the superior performance of bagged decision tree classification.

Rapid discrimination of sea buckthorn berries from different H. rhamnoides subspecies by multi-step IR spectroscopy coupled with multivariate data analysis

NASA Astrophysics Data System (ADS)

Liu, Yue; Zhang, Ying; Zhang, Jing; Fan, Gang; Tu, Ya; Sun, Suqin; Shen, Xudong; Li, Qingzhu; Zhang, Yi

2018-03-01

As an important ethnic medicine, sea buckthorn was widely used to prevent and treat various diseases due to its nutritional and medicinal properties. According to the Chinese Pharmacopoeia, sea buckthorn was originated from H. rhamnoides, which includes five subspecies distributed in China. Confusion and misidentification usually occurred due to their similar morphology, especially in dried and powdered forms. Additionally, these five subspecies have vital differences in quality and physiological efficacy. This paper focused on the quick classification and identification method of sea buckthorn berry powders from five H. rhamnoides subspecies using multi-step IR spectroscopy coupled with multivariate data analysis. The holistic chemical compositions revealed by the FT-IR spectra demonstrated that flavonoids, fatty acids and sugars were the main chemical components. Further, the differences in FT-IR spectra regarding their peaks, positions and intensities were used to identify H. rhamnoides subspecies samples. The discrimination was achieved using principal component analysis (PCA) and partial least square-discriminant analysis (PLS-DA). The results showed that the combination of multi-step IR spectroscopy and chemometric analysis offered a simple, fast and reliable method for the classification and identification of the sea buckthorn berry powders from different H. rhamnoides subspecies.

Crisis in Cataloging Revisited: The Year's Work in Subject Analysis, 1990.

ERIC Educational Resources Information Center

Young, James Bradford

1991-01-01

Reviews the 1990 literature that concerns subject analysis. Issues addressed include subject cataloging, including Library of Congress Subject Headings (LCSH); classification, including Dewey Decimal Classification (DDC), Library of Congress Classification, and classification in online systems; subject access, including the online use of…

Application of classification trees for the qualitative differentiation of focal liver lesions suspicious for metastasis in gadolinium-EOB-DTPA-enhanced liver MR imaging.

PubMed

Schelhorn, J; Benndorf, M; Dietzel, M; Burmeister, H P; Kaiser, W A; Baltzer, P A T

2012-09-01

To evaluate the diagnostic accuracy of qualitative descriptors alone and in combination for the classification of focal liver lesions (FLLs) suspicious for metastasis in gadolinium-EOB-DTPA-enhanced liver MR imaging. Consecutive patients with clinically suspected liver metastases were eligible for this retrospective investigation. 50 patients met the inclusion criteria. All underwent Gd-EOB-DTPA-enhanced liver MRI (T2w, chemical shift T1w, dynamic T1w). Primary liver malignancies or treated lesions were excluded. All investigations were read by two blinded observers (O1, O2). Both independently identified the presence of lesions and evaluated predefined qualitative lesion descriptors (signal intensities, enhancement pattern and morphology). A reference standard was determined under consideration of all clinical and follow-up information. Statistical analysis besides contingency tables (chi square, kappa statistics) included descriptor combinations using classification trees (CHAID methodology) as well as ROC analysis. In 38 patients, 120 FLLs (52 benign, 68 malignant) were present. 115 (48 benign, 67 malignant) were identified by the observers. The enhancement pattern, relative SI upon T2w and late enhanced T1w images contributed significantly to the differentiation of FLLs. The overall classification accuracy was 91.3 % (O1) and 88.7 % (O2), kappa = 0.902. The combination of qualitative lesion descriptors proposed in this work revealed high diagnostic accuracy and interobserver agreement in the differentiation of focal liver lesions suspicious for metastases using Gd-EOB-DTPA-enhanced liver MRI. © Georg Thieme Verlag KG Stuttgart · New York.

Applying a food processing-based classification system to a food guide: a qualitative analysis of the Brazilian experience.

PubMed

Davies, Vanessa Fernandes; Moubarac, Jean-Claude; Medeiros, Kharla Janinny; Jaime, Patricia Constante

2018-01-01

The present paper aimed to identify the stakeholders, as well as their arguments and recommendations, in the debate on the application of a food processing-based classification system to the new Brazilian Food Guide. Qualitative approach; an analysis was made of documents resulting from the consultation conducted for the development of the new Brazilian Food Guide, which uses the NOVA classification for its dietary recommendations. A thematic matrix was constructed and the resulting themes represented the main points for discussion raised during the consultation. Brazil. Actors from academia, government and associations/unions/professional bodies/organizations related to the area of nutrition and food security; non-profit institutions linked to consumer interests and civil society organizations; organizations, associations and food unions linked to the food industry; and individuals. Four themes were identified: (i) conflicting paradigms; (ii) different perceptions about the role and need of individuals; (iii) we want more from the new food guide; and (iv) a sustainable guide. There was extensive participation from different sectors of society. The debate generated by the consultation revealed two main conflicting opinions: a view aligned with the interests of the food industry and a view of healthy eating which serves the interests of the population. The first group was against the adoption of a food processing-based classification system in a public policy such as the new Brazilian Food Guide. The second group, although mostly agreeing with the new food guide, argued that it failed to address some important issues related to the food and nutrition agenda in Brazil.

The Brief Illness Perceptions Questionnaire identifies 3 classes of people seeking rehabilitation for mechanical neck pain.

PubMed

Walton, David M; Lefebvre, Andy; Reynolds, Darcy

2015-06-01

Illness representations pertain to the ways in which an individual constructs and understands the experience of a health condition. The Brief Illness Perceptions Questionnaire (BIPQ) comprises 9 items intended to capture the key components of the Illness Representations Model. The purpose of this paper was to explore the utility of the BIPQ for evaluating and classifying uncomplicated mechanical neck pain in the rehabilitation setting. A convenience sample of 198 subjects presenting to physiotherapy for neck pain problems were used in this study. In the first step, 183 subjects completed the BIPQ and a series of related cognitive measures. Latent class analysis (LCA) was used to explore the number of identifiable classes amongst the sample based on BIPQ response patterns. A regression equation was created to facilitate classification. In the second step, an independent sample of 15 subjects were classified using the equation established in step 1, and they were followed over a 3 month period. The LCA revealed 3 classes of subjects with optimal fit statistics: mildly affected, moderately affected, and severely affected. Inter-group comparisons of the secondary cognitive measures supported these labels. Classification accuracy of a regression equation was high (94.5%). Applying the equation to the independent longitudinal sample revealed that it functioned equally well and that the classes may have prognostic value. The BIPQ may be a useful clinical tool for classification of neck pain. Copyright © 2014 Elsevier Ltd. All rights reserved.

Influence of chronic back pain on kinematic reactions to unpredictable arm pulls.

PubMed

Götze, Martin; Ernst, Michael; Koch, Markus; Blickhan, Reinhard

2015-03-01

There is evidence that muscle reflexes are delayed in patients with chronic low back pain in response to perturbations. It is still unrevealed whether these delays accompanied by an altered kinematic or compensated by adaption of other muscle parameters. The aim of this study was to investigate whether chronic low back pain patients show an altered kinematic reaction and if such data are reliable for the classification of chronic low back pain. In an experiment involving 30 females, sudden lateral perturbations were applied to the arm of a subject in an upright, standing position. Kinematics was used to distinguish between chronic low back pain patients and healthy controls. A calculated model of a stepwise discriminant function analysis correctly predicted 100% of patients and 80% of healthy controls. The estimation of the classification error revealed a constant rate for the classification of the healthy controls and a slightly decreased rate for the patients. Observed reflex delays and identified kinematic differences inside and outside the region of pain during impaired movement indicated that chronic low back pain patients have an altered motor control that is not restricted to the lumbo-pelvic region. This applied paradigm of external perturbations can be used to detect chronic low back pain patients and also persons without chronic low back pain but with an altered motor control. Further investigations are essential to reveal whether healthy persons with changes in motor function have an increased potential to develop chronic back pain. Copyright © 2015 Elsevier Ltd. All rights reserved.

Ecological risk assessment of cheese whey effluents along a medium-sized river in southwest Greece.

PubMed

Karadima, Constantina; Theodoropoulos, Chris; Rouvalis, Angela; Iliopoulou-Georgudaki, Joan

2010-01-01

An ecological risk assessment of cheese whey effluents was applied in three critical sampling sites located in Vouraikos river (southwest Greece), while ecological classification using Water Framework Directive 2000/60/EU criteria allowed a direct comparison of toxicological and ecological data. Two invertebrates (Daphnia magna and Thamnocephalus platyurus) and the zebra fish Danio rerio were used for toxicological analyses, while the aquatic risk was calculated on the basis of the risk quotient (RQ = PEC/PNEC). Chemical classification of sites was carried out using the Nutrient Classification System, while benthic invertebrates were collected and analyzed for biological classification. Toxicological results revealed the heavy pollution load of the two sites, nearest to the point pollution source, as the PEC/PNEC ratio exceeded 1.0, while unexpectedly, no risk was detected for the most downstream site, due to the consequent interference of the riparian flora. These toxicological results were in agreement with the ecological analysis: the ecological quality of the two heavily impacted sites ranged from moderate to bad, whereas it was found good for the most downstream site. The results of the study indicate major ecological risk for almost 15 km downstream of the point pollution source and the potentiality of the water quality remediation by the riparian vegetation, proving the significance of its maintenance.

Histopathological Image Classification using Discriminative Feature-oriented Dictionary Learning

PubMed Central

Vu, Tiep Huu; Mousavi, Hojjat Seyed; Monga, Vishal; Rao, Ganesh; Rao, UK Arvind

2016-01-01

In histopathological image analysis, feature extraction for classification is a challenging task due to the diversity of histology features suitable for each problem as well as presence of rich geometrical structures. In this paper, we propose an automatic feature discovery framework via learning class-specific dictionaries and present a low-complexity method for classification and disease grading in histopathology. Essentially, our Discriminative Feature-oriented Dictionary Learning (DFDL) method learns class-specific dictionaries such that under a sparsity constraint, the learned dictionaries allow representing a new image sample parsimoniously via the dictionary corresponding to the class identity of the sample. At the same time, the dictionary is designed to be poorly capable of representing samples from other classes. Experiments on three challenging real-world image databases: 1) histopathological images of intraductal breast lesions, 2) mammalian kidney, lung and spleen images provided by the Animal Diagnostics Lab (ADL) at Pennsylvania State University, and 3) brain tumor images from The Cancer Genome Atlas (TCGA) database, reveal the merits of our proposal over state-of-the-art alternatives. Moreover, we demonstrate that DFDL exhibits a more graceful decay in classification accuracy against the number of training images which is highly desirable in practice where generous training is often not available. PMID:26513781

Acoustic detection of biosonar activity of deep diving odontocetes at Josephine Seamount High Seas Marine Protected Area.

PubMed

Giorli, Giacomo; Au, Whitlow W L; Ou, Hui; Jarvis, Susan; Morrissey, Ronald; Moretti, David

2015-05-01

The temporal occurrence of deep diving cetaceans in the Josephine Seamount High Seas Marine Protected Area (JSHSMPA), south-west Portugal, was monitored using a passive acoustic recorder. The recorder was deployed on 13 May 2010 at a depth of 814 m during the North Atlantic Treaty Organization Centre for Maritime Research and Experimentation cruise "Sirena10" and recovered on 6 June 2010. The recorder was programmed to record 40 s of data every 2 min. Acoustic data analysis, for the detection and classification of echolocation clicks, was performed using automatic detector/classification systems: M3R (Marine Mammal Monitoring on Navy Ranges), a custom matlab program, and an operator-supervised custom matlab program to assess the classification performance of the detector/classification systems. M3R CS-SVM algorithm contains templates to detect beaked whales, sperm whales, blackfish (pilot and false killer whales), and Risso's dolphins. The detections of each group of odontocetes was monitored as a function of time. Blackfish and Risso's dolphins were detected every day, while beaked whales and sperm whales were detected almost every day. The hourly distribution of detections reveals that blackfish and Risso's dolphins were more active at night, while beaked whales and sperm whales were more active during daylight hours.

Classification of EEG abnormalities in partial epilepsy with simultaneous EEG-fMRI recordings.

PubMed

Pedreira, C; Vaudano, A E; Thornton, R C; Chaudhary, U J; Vulliemoz, S; Laufs, H; Rodionov, R; Carmichael, D W; Lhatoo, S D; Guye, M; Quian Quiroga, R; Lemieux, L

2014-10-01

Scalp EEG recordings and the classification of interictal epileptiform discharges (IED) in patients with epilepsy provide valuable information about the epileptogenic network, particularly by defining the boundaries of the "irritative zone" (IZ), and hence are helpful during pre-surgical evaluation of patients with severe refractory epilepsies. The current detection and classification of epileptiform signals essentially rely on expert observers. This is a very time-consuming procedure, which also leads to inter-observer variability. Here, we propose a novel approach to automatically classify epileptic activity and show how this method provides critical and reliable information related to the IZ localization beyond the one provided by previous approaches. We applied Wave_clus, an automatic spike sorting algorithm, for the classification of IED visually identified from pre-surgical simultaneous Electroencephalogram-functional Magnetic Resonance Imagining (EEG-fMRI) recordings in 8 patients affected by refractory partial epilepsy candidate for surgery. For each patient, two fMRI analyses were performed: one based on the visual classification and one based on the algorithmic sorting. This novel approach successfully identified a total of 29 IED classes (compared to 26 for visual identification). The general concordance between methods was good, providing a full match of EEG patterns in 2 cases, additional EEG information in 2 other cases and, in general, covering EEG patterns of the same areas as expert classification in 7 of the 8 cases. Most notably, evaluation of the method with EEG-fMRI data analysis showed hemodynamic maps related to the majority of IED classes representing improved performance than the visual IED classification-based analysis (72% versus 50%). Furthermore, the IED-related BOLD changes revealed by using the algorithm were localized within the presumed IZ for a larger number of IED classes (9) in a greater number of patients than the expert classification (7 and 5, respectively). In contrast, in only one case presented the new algorithm resulted in fewer classes and activation areas. We propose that the use of automated spike sorting algorithms to classify IED provides an efficient tool for mapping IED-related fMRI changes and increases the EEG-fMRI clinical value for the pre-surgical assessment of patients with severe epilepsy. Copyright © 2014 Elsevier Inc. All rights reserved.

Evaluation and integration of disparate classification systems for clefts of the lip

PubMed Central

Wang, Kathie H.; Heike, Carrie L.; Clarkson, Melissa D.; Mejino, Jose L. V.; Brinkley, James F.; Tse, Raymond W.; Birgfeld, Craig B.; Fitzsimons, David A.; Cox, Timothy C.

2014-01-01

Orofacial clefting is a common birth defect with wide phenotypic variability. Many systems have been developed to classify cleft patterns to facilitate diagnosis, management, surgical treatment, and research. In this review, we examine the rationale for different existing classification schemes and determine their inter-relationships, as well as strengths and deficiencies for subclassification of clefts of the lip. The various systems differ in how they describe and define attributes of cleft lip (CL) phenotypes. Application and analysis of the CL classifications reveal discrepancies that may result in errors when comparing studies that use different systems. These inconsistencies in terminology, variable levels of subclassification, and ambiguity in some descriptions may confound analyses and impede further research aimed at understanding the genetics and etiology of clefts, development of effective treatment options for patients, as well as cross-institutional comparisons of outcome measures. Identification and reconciliation of discrepancies among existing systems is the first step toward creating a common standard to allow for a more explicit interpretation that will ultimately lead to a better understanding of the causes and manifestations of phenotypic variations in clefting. PMID:24860508

Prediction of carbonate rock type from NMR responses using data mining techniques

NASA Astrophysics Data System (ADS)

Gonçalves, Eduardo Corrêa; da Silva, Pablo Nascimento; Silveira, Carla Semiramis; Carneiro, Giovanna; Domingues, Ana Beatriz; Moss, Adam; Pritchard, Tim; Plastino, Alexandre; Azeredo, Rodrigo Bagueira de Vasconcellos

2017-05-01

Recent studies have indicated that the accurate identification of carbonate rock types in a reservoir can be employed as a preliminary step to enhance the effectiveness of petrophysical property modeling. Furthermore, rock typing activity has been shown to be of key importance in several steps of formation evaluation, such as the study of sedimentary series, reservoir zonation and well-to-well correlation. In this paper, a methodology based exclusively on the analysis of 1H-NMR (Nuclear Magnetic Resonance) relaxation responses - using data mining algorithms - is evaluated to perform the automatic classification of carbonate samples according to their rock type. We analyze the effectiveness of six different classification algorithms (k-NN, Naïve Bayes, C4.5, Random Forest, SMO and Multilayer Perceptron) and two data preprocessing strategies (discretization and feature selection). The dataset used in this evaluation is formed by 78 1H-NMR T2 distributions of fully brine-saturated rock samples from six different rock type classes. The experiments reveal that the combination of preprocessing strategies with classification algorithms is able to achieve a prediction accuracy of 97.4%.

Testing and Validating Machine Learning Classifiers by Metamorphic Testing☆

PubMed Central

Xie, Xiaoyuan; Ho, Joshua W. K.; Murphy, Christian; Kaiser, Gail; Xu, Baowen; Chen, Tsong Yueh

2011-01-01

Machine Learning algorithms have provided core functionality to many application domains - such as bioinformatics, computational linguistics, etc. However, it is difficult to detect faults in such applications because often there is no “test oracle” to verify the correctness of the computed outputs. To help address the software quality, in this paper we present a technique for testing the implementations of machine learning classification algorithms which support such applications. Our approach is based on the technique “metamorphic testing”, which has been shown to be effective to alleviate the oracle problem. Also presented include a case study on a real-world machine learning application framework, and a discussion of how programmers implementing machine learning algorithms can avoid the common pitfalls discovered in our study. We also conduct mutation analysis and cross-validation, which reveal that our method has high effectiveness in killing mutants, and that observing expected cross-validation result alone is not sufficiently effective to detect faults in a supervised classification program. The effectiveness of metamorphic testing is further confirmed by the detection of real faults in a popular open-source classification program. PMID:21532969

Widespread occurrence of lysine methylation in Plasmodium falciparum proteins at asexual blood stages.

PubMed

Kaur, Inderjeet; Zeeshan, Mohammad; Saini, Ekta; Kaushik, Abhinav; Mohmmed, Asif; Gupta, Dinesh; Malhotra, Pawan

2016-10-20

Post-transcriptional and post-translational modifications play a major role in Plasmodium life cycle regulation. Lysine methylation of histone proteins is well documented in several organisms, however in recent years lysine methylation of proteins outside histone code is emerging out as an important post-translational modification (PTM). In the present study we have performed global analysis of lysine methylation of proteins in asexual blood stages of Plasmodium falciparum development. We immunoprecipitated stage specific Plasmodium lysates using anti-methyl lysine specific antibodies that immunostained the asexual blood stage parasites. Using liquid chromatography and tandem mass spectrometry analysis, 570 lysine methylated proteins at three different blood stages were identified. Analysis of the peptide sequences identified 605 methylated sites within 422 proteins. Functional classification of the methylated proteins revealed that the proteins are mainly involved in nucleotide metabolic processes, chromatin organization, transport, homeostatic processes and protein folding. The motif analysis of the methylated lysine peptides reveals novel motifs. Many of the identified lysine methylated proteins are also interacting partners/substrates of PfSET domain proteins as revealed by STRING database analysis. Our findings suggest that the protein methylation at lysine residues is widespread in Plasmodium and plays an important regulatory role in diverse set of the parasite pathways.

Visualizing the molecular sociology at the HeLa cell nuclear periphery.

PubMed

Mahamid, Julia; Pfeffer, Stefan; Schaffer, Miroslava; Villa, Elizabeth; Danev, Radostin; Cuellar, Luis Kuhn; Förster, Friedrich; Hyman, Anthony A; Plitzko, Jürgen M; Baumeister, Wolfgang

2016-02-26

The molecular organization of eukaryotic nuclear volumes remains largely unexplored. Here we combined recent developments in cryo-electron tomography (cryo-ET) to produce three-dimensional snapshots of the HeLa cell nuclear periphery. Subtomogram averaging and classification of ribosomes revealed the native structure and organization of the cytoplasmic translation machinery. Analysis of a large dynamic structure-the nuclear pore complex-revealed variations detectable at the level of individual complexes. Cryo-ET was used to visualize previously elusive structures, such as nucleosome chains and the filaments of the nuclear lamina, in situ. Elucidation of the lamina structure provides insight into its contribution to metazoan nuclear stiffness. Copyright © 2016, American Association for the Advancement of Science.

The Bellevue Classification System: nursing's voice upon the library shelves*†

PubMed Central

Mages, Keith C

2011-01-01

This article examines the inspiration, construction, and meaning of the Bellevue Classification System (BCS), created during the 1930s for use in the Bellevue School of Nursing Library. Nursing instructor Ann Doyle, with assistance from librarian Mary Casamajor, designed the BCS after consulting with library leaders and examining leading contemporary classification systems, including the Dewey Decimal Classification and Library of Congress, Ballard, and National Health Library classification systems. A close textual reading of the classes, subclasses, and subdivisions of these classification systems against those of the resulting BCS, reveals Doyle's belief that the BCS was created not only to organize the literature, but also to promote the burgeoning intellectualism and professionalism of early twentieth-century American nursing. PMID:21243054

Using text analysis to quantify the similarity and evolution of scientific disciplines

PubMed Central

Dias, Laércio; Scharloth, Joachim

2018-01-01

We use an information-theoretic measure of linguistic similarity to investigate the organization and evolution of scientific fields. An analysis of almost 20 M papers from the past three decades reveals that the linguistic similarity is related but different from experts and citation-based classifications, leading to an improved view on the organization of science. A temporal analysis of the similarity of fields shows that some fields (e.g. computer science) are becoming increasingly central, but that on average the similarity between pairs of disciplines has not changed in the last decades. This suggests that tendencies of convergence (e.g. multi-disciplinarity) and divergence (e.g. specialization) of disciplines are in balance. PMID:29410857

The Raman spectrum character of skin tumor induced by UVB

NASA Astrophysics Data System (ADS)

Wu, Shulian; Hu, Liangjun; Wang, Yunxia; Li, Yongzeng

2016-03-01

In our study, the skin canceration processes induced by UVB were analyzed from the perspective of tissue spectrum. A home-made Raman spectral system with a millimeter order excitation laser spot size combined with a multivariate statistical analysis for monitoring the skin changed irradiated by UVB was studied and the discrimination were evaluated. Raman scattering signals of the SCC and normal skin were acquired. Spectral differences in Raman spectra were revealed. Linear discriminant analysis (LDA) based on principal component analysis (PCA) were employed to generate diagnostic algorithms for the classification of skin SCC and normal. The results indicated that Raman spectroscopy combined with PCA-LDA demonstrated good potential for improving the diagnosis of skin cancers.

Using text analysis to quantify the similarity and evolution of scientific disciplines.

PubMed

Dias, Laércio; Gerlach, Martin; Scharloth, Joachim; Altmann, Eduardo G

2018-01-01

We use an information-theoretic measure of linguistic similarity to investigate the organization and evolution of scientific fields. An analysis of almost 20 M papers from the past three decades reveals that the linguistic similarity is related but different from experts and citation-based classifications, leading to an improved view on the organization of science. A temporal analysis of the similarity of fields shows that some fields (e.g. computer science) are becoming increasingly central, but that on average the similarity between pairs of disciplines has not changed in the last decades. This suggests that tendencies of convergence (e.g. multi-disciplinarity) and divergence (e.g. specialization) of disciplines are in balance.

Teaching Methods, Intelligence, and Gender Factors in Pupil Achievement on a Classification Task

ERIC Educational Resources Information Center

Ryman, Don

1977-01-01

Reports on twelve year-old students instructed in Nuffield Project and in "traditional" classrooms. A division of the subjects into two groups based on intelligence revealed significant differences on classification ability. Interaction effects were also observed. (CP)

Hyperspectral imaging with wavelet transform for classification of colon tissue biopsy samples

NASA Astrophysics Data System (ADS)

Masood, Khalid

2008-08-01

Automatic classification of medical images is a part of our computerised medical imaging programme to support the pathologists in their diagnosis. Hyperspectral data has found its applications in medical imagery. Its usage is increasing significantly in biopsy analysis of medical images. In this paper, we present a histopathological analysis for the classification of colon biopsy samples into benign and malignant classes. The proposed study is based on comparison between 3D spectral/spatial analysis and 2D spatial analysis. Wavelet textural features in the wavelet domain are used in both these approaches for classification of colon biopsy samples. Experimental results indicate that the incorporation of wavelet textural features using a support vector machine, in 2D spatial analysis, achieve best classification accuracy.

Diagnostic classification of macular ganglion cell and retinal nerve fiber layer analysis: differentiation of false-positives from glaucoma.

PubMed

Kim, Ko Eun; Jeoung, Jin Wook; Park, Ki Ho; Kim, Dong Myung; Kim, Seok Hwan

2015-03-01

To investigate the rate and associated factors of false-positive diagnostic classification of ganglion cell analysis (GCA) and retinal nerve fiber layer (RNFL) maps, and characteristic false-positive patterns on optical coherence tomography (OCT) deviation maps. Prospective, cross-sectional study. A total of 104 healthy eyes of 104 normal participants. All participants underwent peripapillary and macular spectral-domain (Cirrus-HD, Carl Zeiss Meditec Inc, Dublin, CA) OCT scans. False-positive diagnostic classification was defined as yellow or red color-coded areas for GCA and RNFL maps. Univariate and multivariate logistic regression analyses were used to determine associated factors. Eyes with abnormal OCT deviation maps were categorized on the basis of the shape and location of abnormal color-coded area. Differences in clinical characteristics among the subgroups were compared. (1) The rate and associated factors of false-positive OCT maps; (2) patterns of false-positive, color-coded areas on the GCA deviation map and associated clinical characteristics. Of the 104 healthy eyes, 42 (40.4%) and 32 (30.8%) showed abnormal diagnostic classifications on any of the GCA and RNFL maps, respectively. Multivariate analysis revealed that false-positive GCA diagnostic classification was associated with longer axial length and larger fovea-disc angle, whereas longer axial length and smaller disc area were associated with abnormal RNFL maps. Eyes with abnormal GCA deviation map were categorized as group A (donut-shaped round area around the inner annulus), group B (island-like isolated area), and group C (diffuse, circular area with an irregular inner margin in either). The axial length showed a significant increasing trend from group A to C (P=0.001), and likewise, the refractive error was more myopic in group C than in groups A (P=0.015) and B (P=0.014). Group C had thinner average ganglion cell-inner plexiform layer thickness compared with other groups (group A=B>C, P=0.004). Abnormal OCT diagnostic classification should be interpreted with caution, especially in eyes with long axial lengths, large fovea-disc angles, and small optic discs. Our findings suggest that the characteristic patterns of OCT deviation map can provide useful clues to distinguish glaucomatous changes from false-positive findings. Copyright © 2015 American Academy of Ophthalmology. Published by Elsevier Inc. All rights reserved.

Disrupted topological organization of structural networks revealed by probabilistic diffusion tractography in Tourette syndrome children.

PubMed

Wen, Hongwei; Liu, Yue; Rekik, Islem; Wang, Shengpei; Zhang, Jishui; Zhang, Yue; Peng, Yun; He, Huiguang

2017-08-01

Tourette syndrome (TS) is a childhood-onset neurobehavioral disorder. Although previous TS studies revealed structural abnormalities in distinct corticobasal ganglia circuits, the topological alterations of the whole-brain white matter (WM) structural networks remain poorly understood. Here, we used diffusion MRI probabilistic tractography and graph theoretical analysis to investigate the topological organization of WM networks in 44 drug-naive TS children and 41 age- and gender-matched healthy children. The WM networks were constructed by estimating inter-regional connectivity probability and the topological properties were characterized using graph theory. We found that both TS and control groups showed an efficient small-world organization in WM networks. However, compared to controls, TS children exhibited decreased global and local efficiency, increased shortest path length and small worldness, indicating a disrupted balance between local specialization and global integration in structural networks. Although both TS and control groups showed highly similar hub distributions, TS children exhibited significant decreased nodal efficiency, mainly distributed in the default mode, language, visual, and sensorimotor systems. Furthermore, two separate networks showing significantly decreased connectivity in TS group were identified using network-based statistical (NBS) analysis, primarily composed of the parieto-occipital cortex, precuneus, and paracentral lobule. Importantly, we combined support vector machine and multiple kernel learning frameworks to fuse multiple levels of network topological features for classification of individuals, achieving high accuracy of 86.47%. Together, our study revealed the disrupted topological organization of structural networks related to pathophysiology of TS, and the discriminative topological features for classification are potential quantitative neuroimaging biomarkers for clinical TS diagnosis. Hum Brain Mapp 38:3988-4008, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Sorting Five Human Tumor Types Reveals Specific Biomarkers and Background Classification Genes.

PubMed

Roche, Kimberly E; Weinstein, Marvin; Dunwoodie, Leland J; Poehlman, William L; Feltus, Frank A

2018-05-25

We applied two state-of-the-art, knowledge independent data-mining methods - Dynamic Quantum Clustering (DQC) and t-Distributed Stochastic Neighbor Embedding (t-SNE) - to data from The Cancer Genome Atlas (TCGA). We showed that the RNA expression patterns for a mixture of 2,016 samples from five tumor types can sort the tumors into groups enriched for relevant annotations including tumor type, gender, tumor stage, and ethnicity. DQC feature selection analysis discovered 48 core biomarker transcripts that clustered tumors by tumor type. When these transcripts were removed, the geometry of tumor relationships changed, but it was still possible to classify the tumors using the RNA expression profiles of the remaining transcripts. We continued to remove the top biomarkers for several iterations and performed cluster analysis. Even though the most informative transcripts were removed from the cluster analysis, the sorting ability of remaining transcripts remained strong after each iteration. Further, in some iterations we detected a repeating pattern of biological function that wasn't detectable with the core biomarker transcripts present. This suggests the existence of a "background classification" potential in which the pattern of gene expression after continued removal of "biomarker" transcripts could still classify tumors in agreement with the tumor type.

Delineation of sympatric morphotypes of lake trout in Lake Superior

USGS Publications Warehouse

Moore, Seth A.; Bronte, Charles R.

2001-01-01

Three morphotypes of lake trout Salvelinus namaycush are recognized in Lake Superior: lean, siscowet, and humper. Absolute morphotype assignment can be difficult. We used a size-free, whole-body morphometric analysis (truss protocol) to determine whether differences in body shape existed among lake trout morphotypes. Our results showed discrimination where traditional morphometric characters and meristic measurements failed to detect differences. Principal components analysis revealed some separation of all three morphotypes based on head and caudal peduncle shape, but it also indicated considerable overlap in score values. Humper lake trout have smaller caudal peduncle widths to head length and depth characters than do lean or siscowet lake trout. Lean lake trout had larger head measures to caudal widths, whereas siscowet had higher caudal peduncle to head measures. Backward stepwise discriminant function analysis retained two head measures, three midbody measures, and four caudal peduncle measures; correct classification rates when using these variables were 83% for leans, 80% for siscowets, and 83% for humpers, which suggests the measures we used for initial classification were consistent. Although clear ecological reasons for these differences are not readily apparent, patterns in misclassification rates may be consistent with evolutionary hypotheses for lake trout within the Laurentian Great Lakes.

A 16-Gene Signature Distinguishes Anaplastic Astrocytoma from Glioblastoma

PubMed Central

Rao, Soumya Alige Mahabala; Srinivasan, Sujaya; Patric, Irene Rosita Pia; Hegde, Alangar Sathyaranjandas; Chandramouli, Bangalore Ashwathnarayanara; Arimappamagan, Arivazhagan; Santosh, Vani; Kondaiah, Paturu; Rao, Manchanahalli R. Sathyanarayana; Somasundaram, Kumaravel

2014-01-01

Anaplastic astrocytoma (AA; Grade III) and glioblastoma (GBM; Grade IV) are diffusely infiltrating tumors and are called malignant astrocytomas. The treatment regimen and prognosis are distinctly different between anaplastic astrocytoma and glioblastoma patients. Although histopathology based current grading system is well accepted and largely reproducible, intratumoral histologic variations often lead to difficulties in classification of malignant astrocytoma samples. In order to obtain a more robust molecular classifier, we analysed RT-qPCR expression data of 175 differentially regulated genes across astrocytoma using Prediction Analysis of Microarrays (PAM) and found the most discriminatory 16-gene expression signature for the classification of anaplastic astrocytoma and glioblastoma. The 16-gene signature obtained in the training set was validated in the test set with diagnostic accuracy of 89%. Additionally, validation of the 16-gene signature in multiple independent cohorts revealed that the signature predicted anaplastic astrocytoma and glioblastoma samples with accuracy rates of 99%, 88%, and 92% in TCGA, GSE1993 and GSE4422 datasets, respectively. The protein-protein interaction network and pathway analysis suggested that the 16-genes of the signature identified epithelial-mesenchymal transition (EMT) pathway as the most differentially regulated pathway in glioblastoma compared to anaplastic astrocytoma. In addition to identifying 16 gene classification signature, we also demonstrated that genes involved in epithelial-mesenchymal transition may play an important role in distinguishing glioblastoma from anaplastic astrocytoma. PMID:24475040

Tensor-driven extraction of developmental features from varying paediatric EEG datasets.

PubMed

Kinney-Lang, Eli; Spyrou, Loukianos; Ebied, Ahmed; Chin, Richard Fm; Escudero, Javier

2018-05-21

Constant changes in developing children's brains can pose a challenge in EEG dependant technologies. Advancing signal processing methods to identify developmental differences in paediatric populations could help improve function and usability of such technologies. Taking advantage of the multi-dimensional structure of EEG data through tensor analysis may offer a framework for extracting relevant developmental features of paediatric datasets. A proof of concept is demonstrated through identifying latent developmental features in resting-state EEG. Approach. Three paediatric datasets (n = 50, 17, 44) were analyzed using a two-step constrained parallel factor (PARAFAC) tensor decomposition. Subject age was used as a proxy measure of development. Classification used support vector machines (SVM) to test if PARAFAC identified features could predict subject age. The results were cross-validated within each dataset. Classification analysis was complemented by visualization of the high-dimensional feature structures using t-distributed Stochastic Neighbour Embedding (t-SNE) maps. Main Results. Development-related features were successfully identified for the developmental conditions of each dataset. SVM classification showed the identified features could accurately predict subject at a significant level above chance for both healthy and impaired populations. t-SNE maps revealed suitable tensor factorization was key in extracting the developmental features. Significance. The described methods are a promising tool for identifying latent developmental features occurring throughout childhood EEG. © 2018 IOP Publishing Ltd.

Stakeholders' perspectives on social participation in preschool children with Autism Spectrum Disorder.

PubMed

Germani, Tamara; Zwaigenbaum, Lonnie; Magill-Evans, Joyce; Hodgetts, Sandy; Ball, Geoff

2017-11-01

To determine (a) the essential components of social participation for preschool children with Autism Spectrum Disorder (ASD) using stakeholders' perspectives and (b) the facilitators and barriers experienced in promoting social participation. A mixed-methods, web-based survey utilizing the International Classification of Functioning, Disability and Health - Child and Youth version (ICF-CY) taxonomy was circulated across Canada through purposeful snowball sampling. Frequency analysis of the combined responses of 74 stakeholders revealed the most essential components of social participation were: (a) behavior management, (b) social interactions, and (c) various types of play. Further, content analysis revealed that stakeholders used intrinsic motivation strategies and contingency management to facilitate social participation. Stakeholders reported that the purpose of social participation was to engage the child in fun, enjoyable social activities that developed relationships between the child and peers and created a sense of belonging in the community.

An Initial Analysis of LANDSAT-4 Thematic Mapper Data for the Discrimination of Agricultural, Forested Wetland, and Urban Land Covers

NASA Technical Reports Server (NTRS)

Quattrochi, D. A.

1984-01-01

An initial analysis of LANDSAT 4 Thematic Mapper (TM) data for the discrimination of agricultural, forested wetland, and urban land covers is conducted using a scene of data collected over Arkansas and Tennessee. A classification of agricultural lands derived from multitemporal LANDSAT Multispectral Scanner (MSS) data is compared with a classification of TM data for the same area. Results from this comparative analysis show that the multitemporal MSS classification produced an overall accuracy of 80.91% while the TM classification yields an overall classification accuracy of 97.06% correct.

Quality Evaluation and Chemical Markers Screening of Salvia miltiorrhiza Bge. (Danshen) Based on HPLC Fingerprints and HPLC-MSn Coupled with Chemometrics.

PubMed

Liang, Wenyi; Chen, Wenjing; Wu, Lingfang; Li, Shi; Qi, Qi; Cui, Yaping; Liang, Linjin; Ye, Ting; Zhang, Lanzhen

2017-03-17

Danshen, the dried root of Salvia miltiorrhiza Bge., is a widely used commercially available herbal drug, and unstable quality of different samples is a current issue. This study focused on a comprehensive and systematic method combining fingerprints and chemical identification with chemometrics for discrimination and quality assessment of Danshen samples. Twenty-five samples were analyzed by HPLC-PAD and HPLC-MS n . Forty-nine components were identified and characteristic fragmentation regularities were summarized for further interpretation of bioactive components. Chemometric analysis was employed to differentiate samples and clarify the quality differences of Danshen including hierarchical cluster analysis, principal component analysis, and partial least squares discriminant analysis. Consistent results were that the samples were divided into three categories which reflected the difference in quality of Danshen samples. By analyzing the reasons for sample classification, it was revealed that the processing method had a more obvious impact on sample classification than the geographical origin, it induced the different content of bioactive compounds and finally lead to different qualities. Cryptotanshinone, trijuganone B, and 15,16-dihydrotanshinone I were screened out as markers to distinguish samples by different processing methods. The developed strategy could provide a reference for evaluation and discrimination of other traditional herbal medicines.

Material quality assessment of silk nanofibers based on swarm intelligence

NASA Astrophysics Data System (ADS)

Brandoli Machado, Bruno; Nunes Gonçalves, Wesley; Martinez Bruno, Odemir

2013-02-01

In this paper, we propose a novel approach for texture analysis based on artificial crawler model. Our method assumes that each agent can interact with the environment and each other. The evolution process converges to an equilibrium state according to the set of rules. For each textured image, the feature vector is composed by signatures of the live agents curve at each time. Experimental results revealed that combining the minimum and maximum signatures into one increase the classification rate. In addition, we pioneer the use of autonomous agents for characterizing silk fibroin scaffolds. The results strongly suggest that our approach can be successfully employed for texture analysis.

Classification Based on Hierarchical Linear Models: The Need for Incorporation of Social Contexts in Classification Analysis

ERIC Educational Resources Information Center

Vaughn, Brandon K.; Wang, Qui

2009-01-01

Many areas in educational and psychological research involve the use of classification statistical analysis. For example, school districts might be interested in attaining variables that provide optimal prediction of school dropouts. In psychology, a researcher might be interested in the classification of a subject into a particular psychological…

A spectrum fractal feature classification algorithm for agriculture crops with hyper spectrum image

NASA Astrophysics Data System (ADS)

Su, Junying

2011-11-01

A fractal dimension feature analysis method in spectrum domain for hyper spectrum image is proposed for agriculture crops classification. Firstly, a fractal dimension calculation algorithm in spectrum domain is presented together with the fast fractal dimension value calculation algorithm using the step measurement method. Secondly, the hyper spectrum image classification algorithm and flowchart is presented based on fractal dimension feature analysis in spectrum domain. Finally, the experiment result of the agricultural crops classification with FCL1 hyper spectrum image set with the proposed method and SAM (spectral angle mapper). The experiment results show it can obtain better classification result than the traditional SAM feature analysis which can fulfill use the spectrum information of hyper spectrum image to realize precision agricultural crops classification.

Tumor invasiveness defined by IASLC/ATS/ERS classification of ground-glass nodules can be predicted by quantitative CT parameters.

PubMed

Zhou, Qian-Jun; Zheng, Zhi-Chun; Zhu, Yong-Qiao; Lu, Pei-Ji; Huang, Jia; Ye, Jian-Ding; Zhang, Jie; Lu, Shun; Luo, Qing-Quan

2017-05-01

To investigate the potential value of CT parameters to differentiate ground-glass nodules between noninvasive adenocarcinoma and invasive pulmonary adenocarcinoma (IPA) as defined by IASLC/ATS/ERS classification. We retrospectively reviewed 211 patients with pathologically proved stage 0-IA lung adenocarcinoma which appeared as subsolid nodules, from January 2012 to January 2013 including 137 pure ground glass nodules (pGGNs) and 74 part-solid nodules (PSNs). Pathological data was classified under the 2011 IASLC/ATS/ERS classification. Both quantitative and qualitative CT parameters were used to determine the tumor invasiveness between noninvasive adenocarcinomas and IPAs. There were 154 noninvasive adenocarcinomas and 57 IPAs. In pGGNs, CT size and area, one-dimensional mean CT value and bubble lucency were significantly different between noninvasive adenocarcinomas and IPAs on univariate analysis. Multivariate regression and ROC analysis revealed that CT size and one-dimensional mean CT value were predictive of noninvasive adenocarcinomas compared to IPAs. Optimal cutoff value was 13.60 mm (sensitivity, 75.0%; specificity, 99.6%), and -583.60 HU (sensitivity, 68.8%; specificity, 66.9%). In PSNs, there were significant differences in CT size and area, solid component area, solid proportion, one-dimensional mean and maximum CT value, three-dimensional (3D) mean CT value between noninvasive adenocarcinomas and IPAs on univariate analysis. Multivariate and ROC analysis showed that CT size and 3D mean CT value were significantly differentiators. Optimal cutoff value was 19.64 mm (sensitivity, 53.7%; specificity, 93.9%), -571.63 HU (sensitivity, 85.4%; specificity, 75.8%). For pGGNs, CT size and one-dimensional mean CT value are determinants for tumor invasiveness. For PSNs, tumor invasiveness can be predicted by CT size and 3D mean CT value.

Long-term outcome of 2920 patients with cancers of the esophagus and esophagogastric junction: evaluation of the New Union Internationale Contre le Cancer/American Joint Cancer Committee staging system.

PubMed

Gertler, Ralf; Stein, Hubert J; Langer, Rupert; Nettelmann, Marc; Schuster, Tibor; Hoefler, Heinz; Siewert, Joerg-Ruediger; Feith, Marcus

2011-04-01

We analyzed the long-term outcome of patients operated for esophageal cancer and evaluated the new seventh edition of the tumor-node-metastasis classification for cancers of the esophagus. Retrospective analysis and new classification. Data of a single-center cohort of 2920 patients operated for cancers of the esophagus according to the seventh edition are presented. Statistical methods to evaluate survival and the prognostic performance of the staging systems included Kaplan-Meier analyses and time-dependent receiver-operating-characteristic-analysis. Union Internationale Contre le Cancer stage, R-status, histologic tumor type and age were identified as independent prognostic factors for cancers of the esophagus. Grade and tumor site, additional parameters in the new American Joint Cancer Committee prognostic groupings, were not significantly correlated with survival. Esophageal adenocarcinoma showed a significantly better long-term prognosis after resection than squamous cell carcinoma (P < 0.0001). The new number-dependent N-classification proved superior to the former site-dependent classification with significantly decreasing prognosis with the increasing number of lymph node metastases (P < 0.001). The new subclassification of T1 tumors also revealed significant differences in prognosis between pT1a and pT1b patients (P < 0.001). However, the multiple new Union Internationale Contre le Cancer and American Joint Cancer Committee subgroupings did not prove distinctive for survival between stages IIA and IIB, between IIIA and IIIB, and between IIIC and IV. The new seventh edition of the tumor-node-metastasis classification improved the predictive ability for cancers of the esophagus; however, stage groups could be condensed to a clinically relevant number. Differences in patient characteristics, pathogenesis, and especially survival clearly identify adenocarcinomas and squamous cell carcinoma of the esophagus as 2 separate tumor entities requiring differentiated therapeutic concepts.

Decoding Multiple Sound Categories in the Human Temporal Cortex Using High Resolution fMRI

PubMed Central

Zhang, Fengqing; Wang, Ji-Ping; Kim, Jieun; Parrish, Todd; Wong, Patrick C. M.

2015-01-01

Perception of sound categories is an important aspect of auditory perception. The extent to which the brain’s representation of sound categories is encoded in specialized subregions or distributed across the auditory cortex remains unclear. Recent studies using multivariate pattern analysis (MVPA) of brain activations have provided important insights into how the brain decodes perceptual information. In the large existing literature on brain decoding using MVPA methods, relatively few studies have been conducted on multi-class categorization in the auditory domain. Here, we investigated the representation and processing of auditory categories within the human temporal cortex using high resolution fMRI and MVPA methods. More importantly, we considered decoding multiple sound categories simultaneously through multi-class support vector machine-recursive feature elimination (MSVM-RFE) as our MVPA tool. Results show that for all classifications the model MSVM-RFE was able to learn the functional relation between the multiple sound categories and the corresponding evoked spatial patterns and classify the unlabeled sound-evoked patterns significantly above chance. This indicates the feasibility of decoding multiple sound categories not only within but across subjects. However, the across-subject variation affects classification performance more than the within-subject variation, as the across-subject analysis has significantly lower classification accuracies. Sound category-selective brain maps were identified based on multi-class classification and revealed distributed patterns of brain activity in the superior temporal gyrus and the middle temporal gyrus. This is in accordance with previous studies, indicating that information in the spatially distributed patterns may reflect a more abstract perceptual level of representation of sound categories. Further, we show that the across-subject classification performance can be significantly improved by averaging the fMRI images over items, because the irrelevant variations between different items of the same sound category are reduced and in turn the proportion of signals relevant to sound categorization increases. PMID:25692885

Decoding multiple sound categories in the human temporal cortex using high resolution fMRI.

PubMed

Zhang, Fengqing; Wang, Ji-Ping; Kim, Jieun; Parrish, Todd; Wong, Patrick C M

2015-01-01

Perception of sound categories is an important aspect of auditory perception. The extent to which the brain's representation of sound categories is encoded in specialized subregions or distributed across the auditory cortex remains unclear. Recent studies using multivariate pattern analysis (MVPA) of brain activations have provided important insights into how the brain decodes perceptual information. In the large existing literature on brain decoding using MVPA methods, relatively few studies have been conducted on multi-class categorization in the auditory domain. Here, we investigated the representation and processing of auditory categories within the human temporal cortex using high resolution fMRI and MVPA methods. More importantly, we considered decoding multiple sound categories simultaneously through multi-class support vector machine-recursive feature elimination (MSVM-RFE) as our MVPA tool. Results show that for all classifications the model MSVM-RFE was able to learn the functional relation between the multiple sound categories and the corresponding evoked spatial patterns and classify the unlabeled sound-evoked patterns significantly above chance. This indicates the feasibility of decoding multiple sound categories not only within but across subjects. However, the across-subject variation affects classification performance more than the within-subject variation, as the across-subject analysis has significantly lower classification accuracies. Sound category-selective brain maps were identified based on multi-class classification and revealed distributed patterns of brain activity in the superior temporal gyrus and the middle temporal gyrus. This is in accordance with previous studies, indicating that information in the spatially distributed patterns may reflect a more abstract perceptual level of representation of sound categories. Further, we show that the across-subject classification performance can be significantly improved by averaging the fMRI images over items, because the irrelevant variations between different items of the same sound category are reduced and in turn the proportion of signals relevant to sound categorization increases.

Non-linear dynamical classification of short time series of the rössler system in high noise regimes.

PubMed

Lainscsek, Claudia; Weyhenmeyer, Jonathan; Hernandez, Manuel E; Poizner, Howard; Sejnowski, Terrence J

2013-01-01

Time series analysis with delay differential equations (DDEs) reveals non-linear properties of the underlying dynamical system and can serve as a non-linear time-domain classification tool. Here global DDE models were used to analyze short segments of simulated time series from a known dynamical system, the Rössler system, in high noise regimes. In a companion paper, we apply the DDE model developed here to classify short segments of encephalographic (EEG) data recorded from patients with Parkinson's disease and healthy subjects. Nine simulated subjects in each of two distinct classes were generated by varying the bifurcation parameter b and keeping the other two parameters (a and c) of the Rössler system fixed. All choices of b were in the chaotic parameter range. We diluted the simulated data using white noise ranging from 10 to -30 dB signal-to-noise ratios (SNR). Structure selection was supervised by selecting the number of terms, delays, and order of non-linearity of the model DDE model that best linearly separated the two classes of data. The distances d from the linear dividing hyperplane was then used to assess the classification performance by computing the area A' under the ROC curve. The selected model was tested on untrained data using repeated random sub-sampling validation. DDEs were able to accurately distinguish the two dynamical conditions, and moreover, to quantify the changes in the dynamics. There was a significant correlation between the dynamical bifurcation parameter b of the simulated data and the classification parameter d from our analysis. This correlation still held for new simulated subjects with new dynamical parameters selected from each of the two dynamical regimes. Furthermore, the correlation was robust to added noise, being significant even when the noise was greater than the signal. We conclude that DDE models may be used as a generalizable and reliable classification tool for even small segments of noisy data.

Non-Linear Dynamical Classification of Short Time Series of the Rössler System in High Noise Regimes

PubMed Central

Lainscsek, Claudia; Weyhenmeyer, Jonathan; Hernandez, Manuel E.; Poizner, Howard; Sejnowski, Terrence J.

2013-01-01

Time series analysis with delay differential equations (DDEs) reveals non-linear properties of the underlying dynamical system and can serve as a non-linear time-domain classification tool. Here global DDE models were used to analyze short segments of simulated time series from a known dynamical system, the Rössler system, in high noise regimes. In a companion paper, we apply the DDE model developed here to classify short segments of encephalographic (EEG) data recorded from patients with Parkinson’s disease and healthy subjects. Nine simulated subjects in each of two distinct classes were generated by varying the bifurcation parameter b and keeping the other two parameters (a and c) of the Rössler system fixed. All choices of b were in the chaotic parameter range. We diluted the simulated data using white noise ranging from 10 to −30 dB signal-to-noise ratios (SNR). Structure selection was supervised by selecting the number of terms, delays, and order of non-linearity of the model DDE model that best linearly separated the two classes of data. The distances d from the linear dividing hyperplane was then used to assess the classification performance by computing the area A′ under the ROC curve. The selected model was tested on untrained data using repeated random sub-sampling validation. DDEs were able to accurately distinguish the two dynamical conditions, and moreover, to quantify the changes in the dynamics. There was a significant correlation between the dynamical bifurcation parameter b of the simulated data and the classification parameter d from our analysis. This correlation still held for new simulated subjects with new dynamical parameters selected from each of the two dynamical regimes. Furthermore, the correlation was robust to added noise, being significant even when the noise was greater than the signal. We conclude that DDE models may be used as a generalizable and reliable classification tool for even small segments of noisy data. PMID:24379798

Contemporary survival of patients with pulmonary arterial hypertension and congenital systemic to pulmonary shunts

PubMed Central

Chungsomprasong, Paweena; Bositthipichet, Densiri; Ketsara, Salisa; Titaram, Yuttapon; Chanthong, Prakul; Kanjanauthai, Supaluck

2018-01-01

Objective To compare survival of patients with newly diagnosed pulmonary arterial hypertension associated with congenital heart disease (PAH-CHD) according to various clinical classifications with classifications of anatomical-pathophysiological systemic to pulmonary shunts in a single-center cohort. Methods All prevalent cases of PAH-CHD with hemodynamic confirmation by cardiac catheterization in 1995–2015 were retrospectively reviewed. Patients who were younger than three months of age, or with single ventricle following surgery were excluded. Baseline characteristics and clinical outcomes were retrieved from the database. The survival analysis was performed at the end of 2016. Prognostic factors were identified using multivariate analysis. Results A total of 366 consecutive patients (24.5 ± 17.6 years of age, 40% male) with PAH-CHD were analyzed. Most had simple shunts (85 pre-tricuspid, 105 post-tricuspid, 102 combined shunts). Patients with pre-tricuspid shunts were significantly older at diagnosis in comparison to post-tricuspid, combined, and complex shunts. Clinical classifications identified patients as having Eisenmenger syndrome (ES, 26.8%), prevalent left to right shunt (66.7%), PAH with small defect (3%), or PAH following defect correction (3.5%). At follow-up (median = 5.9 years; 0.1–20.7 years), no statistically significant differences in survival rate were seen among the anatomical-pathophysiological shunts (p = 0.1). Conversely, the clinical classifications revealed that patients with PAH-small defect had inferior survival compared to patients with ES, PAH post-corrective surgery, or PAH with prevalent left to right shunt (p = 0.01). Significant mortality risks were functional class III, age < 10 years, PAH-small defect, elevated right atrial pressure > 15 mmHg, and baseline PVR > 8 WU•m.2 Conclusion Patients with PAH-CHD had a modest long-term survival. Different anatomical-pathophysiological shunts affect the natural presentation, while clinical classifications indicate treatment strategies and survival. Contemporary therapy improves survival in deliberately selected patients. PMID:29664959

Classification of single-trial auditory events using dry-wireless EEG during real and motion simulated flight.

PubMed

Callan, Daniel E; Durantin, Gautier; Terzibas, Cengiz

2015-01-01

Application of neuro-augmentation technology based on dry-wireless EEG may be considerably beneficial for aviation and space operations because of the inherent dangers involved. In this study we evaluate classification performance of perceptual events using a dry-wireless EEG system during motion platform based flight simulation and actual flight in an open cockpit biplane to determine if the system can be used in the presence of considerable environmental and physiological artifacts. A passive task involving 200 random auditory presentations of a chirp sound was used for evaluation. The advantage of this auditory task is that it does not interfere with the perceptual motor processes involved with piloting the plane. Classification was based on identifying the presentation of a chirp sound vs. silent periods. Evaluation of Independent component analysis (ICA) and Kalman filtering to enhance classification performance by extracting brain activity related to the auditory event from other non-task related brain activity and artifacts was assessed. The results of permutation testing revealed that single trial classification of presence or absence of an auditory event was significantly above chance for all conditions on a novel test set. The best performance could be achieved with both ICA and Kalman filtering relative to no processing: Platform Off (83.4% vs. 78.3%), Platform On (73.1% vs. 71.6%), Biplane Engine Off (81.1% vs. 77.4%), and Biplane Engine On (79.2% vs. 66.1%). This experiment demonstrates that dry-wireless EEG can be used in environments with considerable vibration, wind, acoustic noise, and physiological artifacts and achieve good single trial classification performance that is necessary for future successful application of neuro-augmentation technology based on brain-machine interfaces.

Validation of AN Hplc-Dad Method for the Classification of Green Teas

NASA Astrophysics Data System (ADS)

Yu, Jingbo; Ye, Nengsheng; Gu, Xuexin; Liu, Ni

A reversed phase high performance liquid chromatography (RP-HPLC) separation coupled with diode array detection (DAD) and electrospray ionization mass spectrometer (ESI/MS) was developed and optimized for the classification of green teas. Five catechins [epigallocatechin (EGC), epigallocatechin gallate (EGCG), epicatechin (EC), gallocatechin gallate (GCG), epicatechin gallate (ECG)] had been identified and quantified by the HPLC-DAD-ESI/MS/MS method. The limit of detection (LOD) of five catechins was within the range of 1.25-15 ng. All the analytes exhibited good linearity up to 2500 ng. These compounds were considered as chemical descriptors to define groups of green teas. Chemometric methods including principal component analysis (PCA) and hierarchical cluster analysis (HCA) were applied for the purpose. Twelve green tea samples originating from different regions were subjected to reveal the natural groups. The results showed that the analyzed green teas were differentiated mainly by provenance; HCA afforded an excellent performance in terms of recognition and prediction abilities. This method was accurate and reproducible, providing a potential approach for authentication of green teas.

Colonic metastasis from breast carcinoma: a case report.

PubMed

Tsujimura, Kazuma; Teruya, Tsuyoshi; Kiyuna, Masaya; Higa, Kuniki; Higa, Junko; Iha, Kouji; Chinen, Kiyoshi; Asato, Masaya; Takushi, Yasukatsu; Ota, Morihito; Dakeshita, Eijirou; Nakachi, Atsushi; Gakiya, Akira; Shiroma, Hiroshi

2017-07-05

Colonic metastasis from breast carcinoma is very rare. Here, we report a case of colonic metastasis from breast carcinoma. The patient was a 51-year-old woman. She had upper abdominal pain, vomiting, and diarrhea, repeatedly. We performed abdominal contrast-enhanced computed tomography (CT) to investigate these symptoms. The CT scan revealed a tumor in the ascending colon with contrast enhancement and showed an expanded small intestine. For further investigation of this tumor, we performed whole positron emission tomography-computed tomography (PET-CT). The PET-CT scan revealed fluorodeoxyglucose uptake in the ascending colon, mesentery, left breast, and left axillary region. Analysis of biopsy samples obtained during colonoscopy revealed signet ring cell-like carcinoma. Moreover, biopsy of the breast tumor revealed invasive lobular carcinoma. Therefore, the preoperative diagnosis was colonic metastasis from breast carcinoma. Open ileocecal resection was performed. The final diagnosis was multiple metastatic breast carcinomas, and the TNM classification was T2N1M1 Stage IV. We presented a rare case of colonic metastasis from breast carcinoma. PET-CT may be useful in the diagnosis of metastatic breast cancer. When analysis of biopsy samples obtained during colonoscopy reveals signet ring cell-like carcinoma, the possibility of breast cancer as the primary tumor should be considered.

An explorative childhood pneumonia analysis based on ultrasonic imaging texture features

NASA Astrophysics Data System (ADS)

Zenteno, Omar; Diaz, Kristians; Lavarello, Roberto; Zimic, Mirko; Correa, Malena; Mayta, Holger; Anticona, Cynthia; Pajuelo, Monica; Oberhelman, Richard; Checkley, William; Gilman, Robert H.; Figueroa, Dante; Castañeda, Benjamín.

2015-12-01

According to World Health Organization, pneumonia is the respiratory disease with the highest pediatric mortality rate accounting for 15% of all deaths of children under 5 years old worldwide. The diagnosis of pneumonia is commonly made by clinical criteria with support from ancillary studies and also laboratory findings. Chest imaging is commonly done with chest X-rays and occasionally with a chest CT scan. Lung ultrasound is a promising alternative for chest imaging; however, interpretation is subjective and requires adequate training. In the present work, a two-class classification algorithm based on four Gray-level co-occurrence matrix texture features (i.e., Contrast, Correlation, Energy and Homogeneity) extracted from lung ultrasound images from children aged between six months and five years is presented. Ultrasound data was collected using a L14-5/38 linear transducer. The data consisted of 22 positive- and 68 negative-diagnosed B-mode cine-loops selected by a medical expert and captured in the facilities of the Instituto Nacional de Salud del Niño (Lima, Peru), for a total number of 90 videos obtained from twelve children diagnosed with pneumonia. The classification capacity of each feature was explored independently and the optimal threshold was selected by a receiver operator characteristic (ROC) curve analysis. In addition, a principal component analysis was performed to evaluate the combined performance of all the features. Contrast and correlation resulted the two more significant features. The classification performance of these two features by principal components was evaluated. The results revealed 82% sensitivity, 76% specificity, 78% accuracy and 0.85 area under the ROC.

Variations in the Intragene Methylation Profiles Hallmark Induced Pluripotency

PubMed Central

Druzhkov, Pavel; Zolotykh, Nikolay; Meyerov, Iosif; Alsaedi, Ahmed; Shutova, Maria; Ivanchenko, Mikhail; Zaikin, Alexey

2015-01-01

We demonstrate the potential of differentiating embryonic and induced pluripotent stem cells by the regularized linear and decision tree machine learning classification algorithms, based on a number of intragene methylation measures. The resulting average accuracy of classification has been proven to be above 95%, which overcomes the earlier achievements. We propose a constructive and transparent method of feature selection based on classifier accuracy. Enrichment analysis reveals statistically meaningful presence of stemness group and cancer discriminating genes among the selected best classifying features. These findings stimulate the further research on the functional consequences of these differences in methylation patterns. The presented approach can be broadly used to discriminate the cells of different phenotype or in different state by their methylation profiles, identify groups of genes constituting multifeature classifiers, and assess enrichment of these groups by the sets of genes with a functionality of interest. PMID:26618180

Challenges in discriminating profanity from hate speech

NASA Astrophysics Data System (ADS)

Malmasi, Shervin; Zampieri, Marcos

2018-03-01

In this study, we approach the problem of distinguishing general profanity from hate speech in social media, something which has not been widely considered. Using a new dataset annotated specifically for this task, we employ supervised classification along with a set of features that includes ?-grams, skip-grams and clustering-based word representations. We apply approaches based on single classifiers as well as more advanced ensemble classifiers and stacked generalisation, achieving the best result of ? accuracy for this 3-class classification task. Analysis of the results reveals that discriminating hate speech and profanity is not a simple task, which may require features that capture a deeper understanding of the text not always possible with surface ?-grams. The variability of gold labels in the annotated data, due to differences in the subjective adjudications of the annotators, is also an issue. Other directions for future work are discussed.

Network-based stochastic semisupervised learning.

PubMed

Silva, Thiago Christiano; Zhao, Liang

2012-03-01

Semisupervised learning is a machine learning approach that is able to employ both labeled and unlabeled samples in the training process. In this paper, we propose a semisupervised data classification model based on a combined random-preferential walk of particles in a network (graph) constructed from the input dataset. The particles of the same class cooperate among themselves, while the particles of different classes compete with each other to propagate class labels to the whole network. A rigorous model definition is provided via a nonlinear stochastic dynamical system and a mathematical analysis of its behavior is carried out. A numerical validation presented in this paper confirms the theoretical predictions. An interesting feature brought by the competitive-cooperative mechanism is that the proposed model can achieve good classification rates while exhibiting low computational complexity order in comparison to other network-based semisupervised algorithms. Computer simulations conducted on synthetic and real-world datasets reveal the effectiveness of the model.

Medulloblastoma: Tumor Biology and Relevance to Treatment and Prognosis Paradigm.

PubMed

Coluccia, Daniel; Figuereido, Carlyn; Isik, Semra; Smith, Christian; Rutka, James T

2016-05-01

Medulloblastoma is a malignant embryonic brain tumor arising in the posterior fossa and typically occurring in pediatric patients. Current multimodal treatment regimes have significantly improved the survival rates; however, a marked heterogeneity in therapy response is observed, and one third of all patients die within 5 years after diagnosis. Large-scale genetic and transcriptome analysis revealed four medulloblastoma subgroups (WNT, SHH, Group 3, and Group 4) associated with different demographic parameters, tumor manifestation, and clinical behavior. Future treatment protocols will integrate molecular classification schemes to evaluate subgroup-specific intensification or de-escalation of adjuvant therapies aimed to increase tumor control and reduce iatrogenic induced morbidity. Furthermore, the identification of genetic drivers allows assessing target therapies in order to increase the chemotherapeutic armamentarium. This review highlights the biology behind the current classification system and elucidates relevant aspects of the disease influencing forthcoming clinical trials.

Distinguishing Man from Molecules: The Distinctiveness of Medical Concepts at Different Levels of Description

PubMed Central

Cole, William G.; Michael, Patricia; Blois, Marsden S.

1987-01-01

A computer program was created to use information about the statistical distribution of words in journal abstracts to make probabilistic judgments about the level of description (e.g. molecular, cell, organ) of medical text. Statistical analysis of 7,409 journal abstracts taken from three medical journals representing distinct levels of description revealed that many medical words seem to be highly specific to one or another level of description. For example, the word adrenoreceptors occurred only in the American Journal of Physiology, never in Journal of Biological Chemistry or in Journal of American Medical Association. Such highly specific words occured so frequently that the automatic classification program was able to classify correctly 45 out of 45 test abstracts, with 100% confidence. These findings are interpreted in terms of both a theory of the structure of medical knowledge and the pragmatics of automatic classification.

Normalization of relative and incomplete temporal expressions in clinical narratives.

PubMed

Sun, Weiyi; Rumshisky, Anna; Uzuner, Ozlem

2015-09-01

To improve the normalization of relative and incomplete temporal expressions (RI-TIMEXes) in clinical narratives. We analyzed the RI-TIMEXes in temporally annotated corpora and propose two hypotheses regarding the normalization of RI-TIMEXes in the clinical narrative domain: the anchor point hypothesis and the anchor relation hypothesis. We annotated the RI-TIMEXes in three corpora to study the characteristics of RI-TMEXes in different domains. This informed the design of our RI-TIMEX normalization system for the clinical domain, which consists of an anchor point classifier, an anchor relation classifier, and a rule-based RI-TIMEX text span parser. We experimented with different feature sets and performed an error analysis for each system component. The annotation confirmed the hypotheses that we can simplify the RI-TIMEXes normalization task using two multi-label classifiers. Our system achieves anchor point classification, anchor relation classification, and rule-based parsing accuracy of 74.68%, 87.71%, and 57.2% (82.09% under relaxed matching criteria), respectively, on the held-out test set of the 2012 i2b2 temporal relation challenge. Experiments with feature sets reveal some interesting findings, such as: the verbal tense feature does not inform the anchor relation classification in clinical narratives as much as the tokens near the RI-TIMEX. Error analysis showed that underrepresented anchor point and anchor relation classes are difficult to detect. We formulate the RI-TIMEX normalization problem as a pair of multi-label classification problems. Considering only RI-TIMEX extraction and normalization, the system achieves statistically significant improvement over the RI-TIMEX results of the best systems in the 2012 i2b2 challenge. © The Author 2015. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Prospective identification of adolescent suicide ideation using classification tree analysis: Models for community-based screening.

PubMed

Hill, Ryan M; Oosterhoff, Benjamin; Kaplow, Julie B

2017-07-01

Although a large number of risk markers for suicide ideation have been identified, little guidance has been provided to prospectively identify adolescents at risk for suicide ideation within community settings. The current study addressed this gap in the literature by utilizing classification tree analysis (CTA) to provide a decision-making model for screening adolescents at risk for suicide ideation. Participants were N = 4,799 youth (Mage = 16.15 years, SD = 1.63) who completed both Waves 1 and 2 of the National Longitudinal Study of Adolescent to Adult Health. CTA was used to generate a series of decision rules for identifying adolescents at risk for reporting suicide ideation at Wave 2. Findings revealed 3 distinct solutions with varying sensitivity and specificity for identifying adolescents who reported suicide ideation. Sensitivity of the classification trees ranged from 44.6% to 77.6%. The tree with greatest specificity and lowest sensitivity was based on a history of suicide ideation. The tree with moderate sensitivity and high specificity was based on depressive symptoms, suicide attempts or suicide among family and friends, and social support. The most sensitive but least specific tree utilized these factors and gender, ethnicity, hours of sleep, school-related factors, and future orientation. These classification trees offer community organizations options for instituting large-scale screenings for suicide ideation risk depending on the available resources and modality of services to be provided. This study provides a theoretically and empirically driven model for prospectively identifying adolescents at risk for suicide ideation and has implications for preventive interventions among at-risk youth. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Comparison of rates of reported adverse events associated with i.v. iron products in the United States.

PubMed

Bailie, George R

2012-02-15

An analysis of reported adverse events (AEs) among patients using i.v. iron products, including the newer agent ferumoxytol, is presented. All AE reports to the Food and Drug Administration (FDA) citing iron sucrose, ferric gluconate, high- and low-molecular-weight iron dextran products, or ferumoxytol from October 2009 through June 2010 were evaluated. The rates of various classifications of reported AEs were calculated on a per-unit-sold basis and, for comparison of products supplied in different unit sizes, also in terms of 100-mg dose equivalents (DEq) of iron. A total of 197 reported AEs were identified (a cumulative rate of 14.1 AEs per million units sold). The rates of all AE classifications combined ranged from 5.25 to 746 per million units sold for iron sucrose and ferumoxytol, respectively; using the other method of calculation, the rates ranged from 5.24 per million DEq (iron sucrose) to 147 per million DEq (ferumoxytol). Relative to iron sucrose and sodium ferric gluconate, ferumoxytol was associated with significantly elevated risks of death (odds ratio [OR], 475 and 156, respectively; p < 0.0001), serious nonfatal AEs (OR, 263 and 121, respectively; p < 0.0001), and all evaluated AE classifications combined (OR, 142 and 109, respectively; p < 0.05). Analysis of reports submitted to FDA revealed large differences among i.v. iron products in reported deaths, serious AEs, other major AEs, and other AEs. Iron sucrose and sodium ferric gluconate were associated with much lower rates of AEs per million units sold than iron dextran or ferumoxytol, which were associated with the highest rates of all reported AE classifications.

Classification of pasture habitats by Hungarian herders in a steppe landscape (Hungary)

PubMed Central

2012-01-01

Background Landscape ethnoecology focuses on the ecological features of the landscape, how the landscape is perceived, and used by people who live in it. Though studying folk classifications of species has a long history, the comparative study of habitat classifications is just beginning. I studied the habitat classification of herders in a Hungarian steppe, and compared it to classifications of botanists and laymen. Methods For a quantitative analysis the picture sort method was used. Twenty-three pictures of 7-11 habitat types were sorted by 25 herders.’Density’ of pictures along the habitat gradient of the Hortobágy salt steppe was set as equal as possible, but pictures differed in their dominant species, wetness, season, etc. Before sorts, herders were asked to describe pictures to assure proper recognition of habitats. Results Herders classified the images into three main groups: (1) fertile habitats at the higher parts of the habitat gradient (partos, lit. on the shore); (2) saline habitats (szík, lit. salt or saline place), and (3) meadows and marshes (lapos, lit. flooded) at the lower end of the habitat gradient. Sharpness of delimitation changed along the gradient. Saline habitats were the most isolated from the rest. Botanists identified 6 groups. Laymen grouped habitats in a less coherent way. As opposed to my expectations, botanical classification was not more structured than that done by herders. I expected and found high correspondence between the classifications by herders, botanists and laymen. All tended to recognize similar main groups: wetlands, ”good grass” and dry/saline habitats. Two main factors could have been responsible for similar classifications: salient features correlated (e.g. salinity recognizable by herders and botanists but not by laymen correlated with the density of grasslands or height of vegetation recognizable also for laymen), or the same salient features were used as a basis for sorting (wetness, and abiotic stress). Conclusions Despite all the difficulties of studying habitat classifications (more implicit, more variable knowledge than knowledge on species), conducting landscape ethnoecological research will inevitably reveal a deeper human understanding of biological organization at a supraspecific level, where natural discontinuities are less sharp than at the species or population level. PMID:22853549

Mapping the rehabilitation interventions of a community stroke team to the extended International Classification of Functioning, Disability and Health Core Set for Stroke.

PubMed

Evans, Melissa; Hocking, Clare; Kersten, Paula

2017-12-01

This study aim was to evaluate whether the Extended International Classification of Functioning, Disability and Health Core Set for Stroke captured the interventions of a community stroke rehabilitation team situated in a large city in New Zealand. It was proposed that the results would identify the contribution of each discipline, and the gaps and differences in service provision to Māori and non-Māori. Applying the Extended International Classification of Functioning, Disability and Health Core Set for Stroke in this way would also inform whether this core set should be adopted in New Zealand. Interventions were retrospectively extracted from 18 medical records and linked to the International Classification of Functioning, Disability and Health and the Extended International Classification of Functioning, Disability and Health Core Set for Stroke. The frequencies of linked interventions and the health discipline providing the intervention were calculated. Analysis revealed that 98.8% of interventions provided by the rehabilitation team could be linked to the Extended International Classification of Functioning, Disability and Health Core Set for Stroke, with more interventions for body function and structure than for activities and participation; no interventions for emotional concerns; and limited interventions for community, social and civic life. Results support previous recommendations for additions to the EICSS. The results support the use of the Extended International Classification of Functioning, Disability and Health Core Set for Stroke in New Zealand and demonstrates its use as a quality assurance tool that can evaluate the scope and practice of a rehabilitation service. Implications for Rehabilitation The Extended International Classification of Functioning Disability and Health Core Set for Stroke appears to represent the stroke interventions of a community stroke rehabilitation team in New Zealand. As a result, researchers and clinicians may have increased confidence to use this core set in research and clinical practice. The Extended International Classification of Functioning Disability and Health Core Set for Stroke can be used as a quality assurance tool to establish whether a community stroke rehabilitation team is meeting the functional needs of its stroke population.

Evolution, substrate specificity and subfamily classification of glycoside hydrolase family 5 (GH5).

PubMed

Aspeborg, Henrik; Coutinho, Pedro M; Wang, Yang; Brumer, Harry; Henrissat, Bernard

2012-09-20

The large Glycoside Hydrolase family 5 (GH5) groups together a wide range of enzymes acting on β-linked oligo- and polysaccharides, and glycoconjugates from a large spectrum of organisms. The long and complex evolution of this family of enzymes and its broad sequence diversity limits functional prediction. With the objective of improving the differentiation of enzyme specificities in a knowledge-based context, and to obtain new evolutionary insights, we present here a new, robust subfamily classification of family GH5. About 80% of the current sequences were assigned into 51 subfamilies in a global analysis of all publicly available GH5 sequences and associated biochemical data. Examination of subfamilies with catalytically-active members revealed that one third are monospecific (containing a single enzyme activity), although new functions may be discovered with biochemical characterization in the future. Furthermore, twenty subfamilies presently have no characterization whatsoever and many others have only limited structural and biochemical data. Mapping of functional knowledge onto the GH5 phylogenetic tree revealed that the sequence space of this historical and industrially important family is far from well dispersed, highlighting targets in need of further study. The analysis also uncovered a number of GH5 proteins which have lost their catalytic machinery, indicating evolution towards novel functions. Overall, the subfamily division of GH5 provides an actively curated resource for large-scale protein sequence annotation for glycogenomics; the subfamily assignments are openly accessible via the Carbohydrate-Active Enzyme database at http://www.cazy.org/GH5.html.

A Mapping from the Human Factors Analysis and Classification System (DOD-HFACS) to the Domains of Human Systems Integration (HSI)

DTIC Science & Technology

2009-11-01

Equation Chapter 1 Section 1 A MAPPING FROM THE HUMAN FACTORS ANALYSIS AND CLASSIFICATION SYSTEM (DOD...OMB control number. 1. REPORT DATE NOV 2009 2. REPORT TYPE 3. DATES COVERED 4. TITLE AND SUBTITLE A Mapping from the Human Factors Analysis ...7 The Human Factors Analysis and Classification System .................................................. 7 Mapping of DoD

Rural environment study for water from different sources in cluster of villages in Mehsana district of Gujarat.

PubMed

Khatri, Nitasha; Tyagi, Sanjiv; Rawtani, Deepak

2017-12-07

Water pollution and water scarcity are major environmental issues in rural and urban areas. They lead to decline in the quality of water, especially drinking water. Proper qualitative assessment of water is thus necessary to ensure that the water consumed is potable. This study aims to analyze the physicochemical parameters in different sources of water in rural areas and assess the quality of water through a classification system based on BIS and CPCB standards. The classification method has defined water quality in six categories, viz., A, B, C, D, E, and F depending on the levels of physicochemical parameters in the water samples. The proposed classification system was applied to nine villages in Kadi Taluka, Mehsana district of Gujarat. The water samples were collected from borewells, lakes, Narmada Canal, and sewerage systems and were analyzed as per APHA and IS methods. It was observed that most of the physicochemical parameters of Narmada Canal and borewell water fell under class A, thus making them most suitable for drinking. Further, a health camp conducted at Karannagar village, Mehsana revealed no incidents of any waterborne diseases. However, there were certain incidents of kidney stones and joint pain in few villages due to high levels of TDS. Toxic metal analysis in all the water sources revealed low to undetectable concentration of toxic metals such as lead, arsenic, mercury, and cadmium in all the water sources. It is also recommended that the regular treatment of the Narmada Canal water be continued to maintain its excellent quality.

Posture Detection Based on Smart Cushion for Wheelchair Users

PubMed Central

Ma, Congcong; Li, Wenfeng; Gravina, Raffaele; Fortino, Giancarlo

2017-01-01

The postures of wheelchair users can reveal their sitting habit, mood, and even predict health risks such as pressure ulcers or lower back pain. Mining the hidden information of the postures can reveal their wellness and general health conditions. In this paper, a cushion-based posture recognition system is used to process pressure sensor signals for the detection of user’s posture in the wheelchair. The proposed posture detection method is composed of three main steps: data level classification for posture detection, backward selection of sensor configuration, and recognition results compared with previous literature. Five supervised classification techniques—Decision Tree (J48), Support Vector Machines (SVM), Multilayer Perceptron (MLP), Naive Bayes, and k-Nearest Neighbor (k-NN)—are compared in terms of classification accuracy, precision, recall, and F-measure. Results indicate that the J48 classifier provides the highest accuracy compared to other techniques. The backward selection method was used to determine the best sensor deployment configuration of the wheelchair. Several kinds of pressure sensor deployments are compared and our new method of deployment is shown to better detect postures of the wheelchair users. Performance analysis also took into account the Body Mass Index (BMI), useful for evaluating the robustness of the method across individual physical differences. Results show that our proposed sensor deployment is effective, achieving 99.47% posture recognition accuracy. Our proposed method is very competitive for posture recognition and robust in comparison with other former research. Accurate posture detection represents a fundamental basic block to develop several applications, including fatigue estimation and activity level assessment. PMID:28353684

A Comparison of the Effects of Electrode Implantation and Targeting on Pattern Classification Accuracy for Prosthesis Control

PubMed Central

Farrell, Todd R.; Weir, Richard F. ff.

2011-01-01

The use of surface versus intramuscular electrodes as well as the effect of electrode targeting on pattern-recognition-based multifunctional prosthesis control was explored. Surface electrodes are touted for their ability to record activity from relatively large portions of muscle tissue. Intramuscular electromyograms (EMGs) can provide focal recordings from deep muscles of the forearm and independent signals relatively free of crosstalk. However, little work has been done to compare the two. Additionally, while previous investigations have either targeted electrodes to specific muscles or used untargeted (symmetric) electrode arrays, no work has compared these approaches to determine if one is superior. The classification accuracies of pattern-recognition-based classifiers utilizing surface and intramuscular as well as targeted and untargeted electrodes were compared across 11 subjects. A repeated-measures analysis of variance revealed that when only EMG amplitude information was used from all available EMG channels, the targeted surface, targeted intramuscular, and untargeted surface electrodes produced similar classification accuracies while the untargeted intramuscular electrodes produced significantly lower accuracies. However, no statistical differences were observed between any of the electrode conditions when additional features were extracted from the EMG signal. It was concluded that the choice of electrode should be driven by clinical factors, such as signal robustness/stability, cost, etc., instead of by classification accuracy. PMID:18713689

MALDI Mass Spectrometry Imaging: A Novel Tool for the Identification and Classification of Amyloidosis.

PubMed

Winter, Martin; Tholey, Andreas; Kristen, Arnt; Röcken, Christoph

2017-11-01

Amyloidosis is a group of diseases caused by extracellular accumulation of fibrillar polypeptide aggregates. So far, diagnosis is performed by Congo red staining of tissue sections in combination with polarization microscopy. Subsequent identification of the causative protein by immunohistochemistry harbors some difficulties regarding sensitivity and specificity. Mass spectrometry based approaches have been demonstrated to constitute a reliable method to supplement typing of amyloidosis, but still depend on Congo red staining. In the present study, we used matrix-assisted laser desorption/ionization mass spectrometry imaging coupled with ion mobility separation (MALDI-IMS MSI) to investigate amyloid deposits in formalin-fixed and paraffin-embedded tissue samples. Utilizing a novel peptide filter method, we found a universal peptide signature for amyloidoses. Furthermore, differences in the peptide composition of ALλ and ATTR amyloid were revealed and used to build a reliable classification model. Integrating the peptide filter in MALDI-IMS MSI analysis, we developed a bioinformatics workflow facilitating the identification and classification of amyloidosis in a less time and sample-consuming experimental setup. Our findings demonstrate also the feasibility to investigate the amyloid's protein composition, thus paving the way to establish classification models for the diverse types of amyloidoses and to shed further light on the complex process of amyloidogenesis. © 2017 The Authors, Proteomics Published by WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Malay sentiment analysis based on combined classification approaches and Senti-lexicon algorithm.

PubMed

Al-Saffar, Ahmed; Awang, Suryanti; Tao, Hai; Omar, Nazlia; Al-Saiagh, Wafaa; Al-Bared, Mohammed

2018-01-01

Sentiment analysis techniques are increasingly exploited to categorize the opinion text to one or more predefined sentiment classes for the creation and automated maintenance of review-aggregation websites. In this paper, a Malay sentiment analysis classification model is proposed to improve classification performances based on the semantic orientation and machine learning approaches. First, a total of 2,478 Malay sentiment-lexicon phrases and words are assigned with a synonym and stored with the help of more than one Malay native speaker, and the polarity is manually allotted with a score. In addition, the supervised machine learning approaches and lexicon knowledge method are combined for Malay sentiment classification with evaluating thirteen features. Finally, three individual classifiers and a combined classifier are used to evaluate the classification accuracy. In experimental results, a wide-range of comparative experiments is conducted on a Malay Reviews Corpus (MRC), and it demonstrates that the feature extraction improves the performance of Malay sentiment analysis based on the combined classification. However, the results depend on three factors, the features, the number of features and the classification approach.

Malay sentiment analysis based on combined classification approaches and Senti-lexicon algorithm

PubMed Central

Awang, Suryanti; Tao, Hai; Omar, Nazlia; Al-Saiagh, Wafaa; Al-bared, Mohammed

2018-01-01

Sentiment analysis techniques are increasingly exploited to categorize the opinion text to one or more predefined sentiment classes for the creation and automated maintenance of review-aggregation websites. In this paper, a Malay sentiment analysis classification model is proposed to improve classification performances based on the semantic orientation and machine learning approaches. First, a total of 2,478 Malay sentiment-lexicon phrases and words are assigned with a synonym and stored with the help of more than one Malay native speaker, and the polarity is manually allotted with a score. In addition, the supervised machine learning approaches and lexicon knowledge method are combined for Malay sentiment classification with evaluating thirteen features. Finally, three individual classifiers and a combined classifier are used to evaluate the classification accuracy. In experimental results, a wide-range of comparative experiments is conducted on a Malay Reviews Corpus (MRC), and it demonstrates that the feature extraction improves the performance of Malay sentiment analysis based on the combined classification. However, the results depend on three factors, the features, the number of features and the classification approach. PMID:29684036

Metagenomics of an Alkaline Hot Spring in Galicia (Spain): Microbial Diversity Analysis and Screening for Novel Lipolytic Enzymes.

PubMed

López-López, Olalla; Knapik, Kamila; Cerdán, Maria-Esperanza; González-Siso, María-Isabel

2015-01-01

A fosmid library was constructed with the metagenomic DNA from the water of the Lobios hot spring (76°C, pH = 8.2) located in Ourense (Spain). Metagenomic sequencing of the fosmid library allowed the assembly of 9722 contigs ranging in size from 500 to 56,677 bp and spanning ~18 Mbp. 23,207 ORFs (Open Reading Frames) were predicted from the assembly. Biodiversity was explored by taxonomic classification and it revealed that bacteria were predominant, while the archaea were less abundant. The six most abundant bacterial phyla were Deinococcus-Thermus, Proteobacteria, Firmicutes, Acidobacteria, Aquificae, and Chloroflexi. Within the archaeal superkingdom, the phylum Thaumarchaeota was predominant with the dominant species "Candidatus Caldiarchaeum subterraneum." Functional classification revealed the genes associated to one-carbon metabolism as the most abundant. Both taxonomic and functional classifications showed a mixture of different microbial metabolic patterns: aerobic and anaerobic, chemoorganotrophic and chemolithotrophic, autotrophic and heterotrophic. Remarkably, the presence of genes encoding enzymes with potential biotechnological interest, such as xylanases, galactosidases, proteases, and lipases, was also revealed in the metagenomic library. Functional screening of this library was subsequently done looking for genes encoding lipolytic enzymes. Six genes conferring lipolytic activity were identified and one was cloned and characterized. This gene was named LOB4Est and it was expressed in a yeast mesophilic host. LOB4Est codes for a novel esterase of family VIII, with sequence similarity to β-lactamases, but with unusual wide substrate specificity. When the enzyme was purified from the mesophilic host it showed half-life of 1 h and 43 min at 50°C, and maximal activity at 40°C and pH 7.5 with p-nitrophenyl-laurate as substrate. Interestingly, the enzyme retained more than 80% of maximal activity in a broad range of pH from 6.5 to 8.

A System to Automatically Classify and Name Any Individual Genome-Sequenced Organism Independently of Current Biological Classification and Nomenclature

PubMed Central

Song, Yuhyun; Leman, Scotland; Monteil, Caroline L.; Heath, Lenwood S.; Vinatzer, Boris A.

2014-01-01

A broadly accepted and stable biological classification system is a prerequisite for biological sciences. It provides the means to describe and communicate about life without ambiguity. Current biological classification and nomenclature use the species as the basic unit and require lengthy and laborious species descriptions before newly discovered organisms can be assigned to a species and be named. The current system is thus inadequate to classify and name the immense genetic diversity within species that is now being revealed by genome sequencing on a daily basis. To address this lack of a general intra-species classification and naming system adequate for today’s speed of discovery of new diversity, we propose a classification and naming system that is exclusively based on genome similarity and that is suitable for automatic assignment of codes to any genome-sequenced organism without requiring any phenotypic or phylogenetic analysis. We provide examples demonstrating that genome similarity-based codes largely align with current taxonomic groups at many different levels in bacteria, animals, humans, plants, and viruses. Importantly, the proposed approach is only slightly affected by the order of code assignment and can thus provide codes that reflect similarity between organisms and that do not need to be revised upon discovery of new diversity. We envision genome similarity-based codes to complement current biological nomenclature and to provide a universal means to communicate unambiguously about any genome-sequenced organism in fields as diverse as biodiversity research, infectious disease control, human and microbial forensics, animal breed and plant cultivar certification, and human ancestry research. PMID:24586551

Comparative analysis of image classification methods for automatic diagnosis of ophthalmic images

NASA Astrophysics Data System (ADS)

Wang, Liming; Zhang, Kai; Liu, Xiyang; Long, Erping; Jiang, Jiewei; An, Yingying; Zhang, Jia; Liu, Zhenzhen; Lin, Zhuoling; Li, Xiaoyan; Chen, Jingjing; Cao, Qianzhong; Li, Jing; Wu, Xiaohang; Wang, Dongni; Li, Wangting; Lin, Haotian

2017-01-01

There are many image classification methods, but it remains unclear which methods are most helpful for analyzing and intelligently identifying ophthalmic images. We select representative slit-lamp images which show the complexity of ocular images as research material to compare image classification algorithms for diagnosing ophthalmic diseases. To facilitate this study, some feature extraction algorithms and classifiers are combined to automatic diagnose pediatric cataract with same dataset and then their performance are compared using multiple criteria. This comparative study reveals the general characteristics of the existing methods for automatic identification of ophthalmic images and provides new insights into the strengths and shortcomings of these methods. The relevant methods (local binary pattern +SVMs, wavelet transformation +SVMs) which achieve an average accuracy of 87% and can be adopted in specific situations to aid doctors in preliminarily disease screening. Furthermore, some methods requiring fewer computational resources and less time could be applied in remote places or mobile devices to assist individuals in understanding the condition of their body. In addition, it would be helpful to accelerate the development of innovative approaches and to apply these methods to assist doctors in diagnosing ophthalmic disease.

Unraveling cognitive traits using the Morris water maze unbiased strategy classification (MUST-C) algorithm.

PubMed

Illouz, Tomer; Madar, Ravit; Louzon, Yoram; Griffioen, Kathleen J; Okun, Eitan

2016-02-01

The assessment of spatial cognitive learning in rodents is a central approach in neuroscience, as it enables one to assess and quantify the effects of treatments and genetic manipulations from a broad perspective. Although the Morris water maze (MWM) is a well-validated paradigm for testing spatial learning abilities, manual categorization of performance in the MWM into behavioral strategies is subject to individual interpretation, and thus to biases. Here we offer a support vector machine (SVM) - based, automated, MWM unbiased strategy classification (MUST-C) algorithm, as well as a cognitive score scale. This model was examined and validated by analyzing data obtained from five MWM experiments with changing platform sizes, revealing a limitation in the spatial capacity of the hippocampus. We have further employed this algorithm to extract novel mechanistic insights on the impact of members of the Toll-like receptor pathway on cognitive spatial learning and memory. The MUST-C algorithm can greatly benefit MWM users as it provides a standardized method of strategy classification as well as a cognitive scoring scale, which cannot be derived from typical analysis of MWM data. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

Classification of yeast cells from image features to evaluate pathogen conditions

NASA Astrophysics Data System (ADS)

van der Putten, Peter; Bertens, Laura; Liu, Jinshuo; Hagen, Ferry; Boekhout, Teun; Verbeek, Fons J.

2007-01-01

Morphometrics from images, image analysis, may reveal differences between classes of objects present in the images. We have performed an image-features-based classification for the pathogenic yeast Cryptococcus neoformans. Building and analyzing image collections from the yeast under different environmental or genetic conditions may help to diagnose a new "unseen" situation. Diagnosis here means that retrieval of the relevant information from the image collection is at hand each time a new "sample" is presented. The basidiomycetous yeast Cryptococcus neoformans can cause infections such as meningitis or pneumonia. The presence of an extra-cellular capsule is known to be related to virulence. This paper reports on the approach towards developing classifiers for detecting potentially more or less virulent cells in a sample, i.e. an image, by using a range of features derived from the shape or density distribution. The classifier can henceforth be used for automating screening and annotating existing image collections. In addition we will present our methods for creating samples, collecting images, image preprocessing, identifying "yeast cells" and creating feature extraction from the images. We compare various expertise based and fully automated methods of feature selection and benchmark a range of classification algorithms and illustrate successful application to this particular domain.

Computer-aided diagnosis in phase contrast imaging X-ray computed tomography for quantitative characterization of ex vivo human patellar cartilage.

PubMed

Nagarajan, Mahesh B; Coan, Paola; Huber, Markus B; Diemoz, Paul C; Glaser, Christian; Wismuller, Axel

2013-10-01

Visualization of ex vivo human patellar cartilage matrix through the phase contrast imaging X-ray computed tomography (PCI-CT) has been previously demonstrated. Such studies revealed osteoarthritis-induced changes to chondrocyte organization in the radial zone. This study investigates the application of texture analysis to characterizing such chondrocyte patterns in the presence and absence of osteoarthritic damage. Texture features derived from Minkowski functionals (MF) and gray-level co-occurrence matrices (GLCM) were extracted from 842 regions of interest (ROI) annotated on PCI-CT images of ex vivo human patellar cartilage specimens. These texture features were subsequently used in a machine learning task with support vector regression to classify ROIs as healthy or osteoarthritic; classification performance was evaluated using the area under the receiver operating characteristic curve (AUC). The best classification performance was observed with the MF features perimeter (AUC: 0.94 ±0.08 ) and "Euler characteristic" (AUC: 0.94 ±0.07 ), and GLCM-derived feature "Correlation" (AUC: 0.93 ±0.07). These results suggest that such texture features can provide a detailed characterization of the chondrocyte organization in the cartilage matrix, enabling classification of cartilage as healthy or osteoarthritic with high accuracy.

What Images Reveal: a Comparative Study of Science Images between Australian and Taiwanese Junior High School Textbooks

NASA Astrophysics Data System (ADS)

Ge, Yun-Ping; Unsworth, Len; Wang, Kuo-Hua; Chang, Huey-Por

2017-07-01

From a social semiotic perspective, image designs in science textbooks are inevitably influenced by the sociocultural context in which the books are produced. The learning environments of Australia and Taiwan vary greatly. Drawing on social semiotics and cognitive science, this study compares classificational images in Australian and Taiwanese junior high school science textbooks. Classificational images are important kinds of images, which can represent taxonomic relations among objects as reported by Kress and van Leeuwen (Reading images: the grammar of visual design, 2006). An analysis of the images from sample chapters in Australian and Taiwanese high school science textbooks showed that the majority of the Taiwanese images are covert taxonomies, which represent hierarchical relations implicitly. In contrast, Australian classificational images included diversified designs, but particularly types with a tree structure which depicted overt taxonomies, explicitly representing hierarchical super-ordinate and subordinate relations. Many of the Taiwanese images are reminiscent of the specimen images in eighteenth century science texts representing "what truly is", while more Australian images emphasize structural objectivity. Moreover, Australian images support cognitive functions which facilitate reading comprehension. The relationships between image designs and learning environments are discussed and implications for textbook research and design are addressed.

An analysis of metropolitan land-use by machine processing of earth resources technology satellite data

NASA Technical Reports Server (NTRS)

Mausel, P. W.; Todd, W. J.; Baumgardner, M. F.

1976-01-01

A successful application of state-of-the-art remote sensing technology in classifying an urban area into its broad land use classes is reported. This research proves that numerous urban features are amenable to classification using ERTS multispectral data automatically processed by computer. Furthermore, such automatic data processing (ADP) techniques permit areal analysis on an unprecedented scale with a minimum expenditure of time. Also, classification results obtained using ADP procedures are consistent, comparable, and replicable. The results of classification are compared with the proposed U. S. G. S. land use classification system in order to determine the level of classification that is feasible to obtain through ERTS analysis of metropolitan areas.

High-density marker profiling confirms ancestral genomes of Avena species and identifies D-genome chromosomes of hexaploid oat.

PubMed

Yan, Honghai; Bekele, Wubishet A; Wight, Charlene P; Peng, Yuanying; Langdon, Tim; Latta, Robert G; Fu, Yong-Bi; Diederichsen, Axel; Howarth, Catherine J; Jellen, Eric N; Boyle, Brian; Wei, Yuming; Tinker, Nicholas A

2016-11-01

Genome analysis of 27 oat species identifies ancestral groups, delineates the D genome, and identifies ancestral origin of 21 mapped chromosomes in hexaploid oat. We investigated genomic relationships among 27 species of the genus Avena using high-density genetic markers revealed by genotyping-by-sequencing (GBS). Two methods of GBS analysis were used: one based on tag-level haplotypes that were previously mapped in cultivated hexaploid oat (A. sativa), and one intended to sample and enumerate tag-level haplotypes originating from all species under investigation. Qualitatively, both methods gave similar predictions regarding the clustering of species and shared ancestral genomes. Furthermore, results were consistent with previous phylogenies of the genus obtained with conventional approaches, supporting the robustness of whole genome GBS analysis. Evidence is presented to justify the final and definitive classification of the tetraploids A. insularis, A. maroccana (=A. magna), and A. murphyi as containing D-plus-C genomes, and not A-plus-C genomes, as is most often specified in past literature. Through electronic painting of the 21 chromosome representations in the hexaploid oat consensus map, we show how the relative frequency of matches between mapped hexaploid-derived haplotypes and AC (DC)-genome tetraploids vs. A- and C-genome diploids can accurately reveal the genome origin of all hexaploid chromosomes, including the approximate positions of inter-genome translocations. Evidence is provided that supports the continued classification of a diverged B genome in AB tetraploids, and it is confirmed that no extant A-genome diploids, including A. canariensis, are similar enough to the D genome of tetraploid and hexaploid oat to warrant consideration as a D-genome diploid.

Potential impacts of robust surface roughness indexes on DTM-based segmentation

NASA Astrophysics Data System (ADS)

Trevisani, Sebastiano; Rocca, Michele

2017-04-01

In this study, we explore the impact of robust surface texture indexes based on MAD (median absolute differences), implemented by Trevisani and Rocca (2015), in the unsupervised morphological segmentation of an alpine basin. The area was already object of a geomorphometric analysis, consisting in the roughness-based segmentation of the landscape (Trevisani et al. 2012); the roughness indexes were calculated on a high resolution DTM derived by means of airborne Lidar using the variogram as estimator. The calculated roughness indexes have been then used for the fuzzy clustering (Odeh et al., 1992; Burrough et al., 2000) of the basin, revealing the high informative geomorphometric content of the roughness-based indexes. However, the fuzzy clustering revealed a high fuzziness and a high degree of mixing between textural classes; this was ascribed both to the morphological complexity of the basin and to the high sensitivity of variogram to non-stationarity and signal-noise. Accordingly, we explore how the new implemented roughness indexes based on MAD affect the morphological segmentation of the studied basin. References Burrough, P.A., Van Gaans, P.F.M., MacMillan, R.A., 2000. High-resolution landform classification using fuzzy k-means. Fuzzy Sets and Systems 113, 37-52. Odeh, I.O.A., McBratney, A.B., Chittleborough, D.J., 1992. Soil pattern recognition with fuzzy-c-means: application to classification and soil-landform interrelationships. Soil Sciences Society of America Journal 56, 505-516. Trevisani, S., Cavalli, M. & Marchi, L. 2012, "Surface texture analysis of a high-resolution DTM: Interpreting an alpine basin", Geomorphology, vol. 161-162, pp. 26-39. Trevisani, S. & Rocca, M. 2015, "MAD: Robust image texture analysis for applications in high resolution geomorphometry", Computers and Geosciences, vol. 81, pp. 78-92.

Do Reading Experts Agree with MCAT Verbal Reasoning Item Classifications?

ERIC Educational Resources Information Center

Jackson, Evelyn W.; And Others

1994-01-01

Examined whether expert raters (n=5) could agree about classification of Medical College Admission Test (MCAT) items and whether they agreed with MCAT student manual in labeling skill being measured by each test item. Results revealed difficulties in replicating authors' labeling of skills for reading items on practice test provided with 1991 MCAT…

Test-Enhanced Learning of Natural Concepts: Effects on Recognition Memory, Classification, and Metacognition

ERIC Educational Resources Information Center

Jacoby, Larry L.; Wahlheim, Christopher N.; Coane, Jennifer H.

2010-01-01

Three experiments examined testing effects on learning of natural concepts and metacognitive assessments of such learning. Results revealed that testing enhanced recognition memory and classification accuracy for studied and novel exemplars of bird families on immediate and delayed tests. These effects depended on the balance of study and test…

Asynchronous P300 classification in a reactive brain-computer interface during an outlier detection task

NASA Astrophysics Data System (ADS)

Krumpe, Tanja; Walter, Carina; Rosenstiel, Wolfgang; Spüler, Martin

2016-08-01

Objective. In this study, the feasibility of detecting a P300 via an asynchronous classification mode in a reactive EEG-based brain-computer interface (BCI) was evaluated. The P300 is one of the most popular BCI control signals and therefore used in many applications, mostly for active communication purposes (e.g. P300 speller). As the majority of all systems work with a stimulus-locked mode of classification (synchronous), the field of applications is limited. A new approach needs to be applied in a setting in which a stimulus-locked classification cannot be used due to the fact that the presented stimuli cannot be controlled or predicted by the system. Approach. A continuous observation task requiring the detection of outliers was implemented to test such an approach. The study was divided into an offline and an online part. Main results. Both parts of the study revealed that an asynchronous detection of the P300 can successfully be used to detect single events with high specificity. It also revealed that no significant difference in performance was found between the synchronous and the asynchronous approach. Significance. The results encourage the use of an asynchronous classification approach in suitable applications without a potential loss in performance.

Improved Hierarchical Optimization-Based Classification of Hyperspectral Images Using Shape Analysis

NASA Technical Reports Server (NTRS)

Tarabalka, Yuliya; Tilton, James C.

2012-01-01

A new spectral-spatial method for classification of hyperspectral images is proposed. The HSegClas method is based on the integration of probabilistic classification and shape analysis within the hierarchical step-wise optimization algorithm. First, probabilistic support vector machines classification is applied. Then, at each iteration two neighboring regions with the smallest Dissimilarity Criterion (DC) are merged, and classification probabilities are recomputed. The important contribution of this work consists in estimating a DC between regions as a function of statistical, classification and geometrical (area and rectangularity) features. Experimental results are presented on a 102-band ROSIS image of the Center of Pavia, Italy. The developed approach yields more accurate classification results when compared to previously proposed methods.

Waveform classification and statistical analysis of seismic precursors to the July 2008 Vulcanian Eruption of Soufrière Hills Volcano, Montserrat

NASA Astrophysics Data System (ADS)

Rodgers, Mel; Smith, Patrick; Pyle, David; Mather, Tamsin

2016-04-01

Understanding the transition between quiescence and eruption at dome-forming volcanoes, such as Soufrière Hills Volcano (SHV), Montserrat, is important for monitoring volcanic activity during long-lived eruptions. Statistical analysis of seismic events (e.g. spectral analysis and identification of multiplets via cross-correlation) can be useful for characterising seismicity patterns and can be a powerful tool for analysing temporal changes in behaviour. Waveform classification is crucial for volcano monitoring, but consistent classification, both during real-time analysis and for retrospective analysis of previous volcanic activity, remains a challenge. Automated classification allows consistent re-classification of events. We present a machine learning (random forest) approach to rapidly classify waveforms that requires minimal training data. We analyse the seismic precursors to the July 2008 Vulcanian explosion at SHV and show systematic changes in frequency content and multiplet behaviour that had not previously been recognised. These precursory patterns of seismicity may be interpreted as changes in pressure conditions within the conduit during magma ascent and could be linked to magma flow rates. Frequency analysis of the different waveform classes supports the growing consensus that LP and Hybrid events should be considered end members of a continuum of low-frequency source processes. By using both supervised and unsupervised machine-learning methods we investigate the nature of waveform classification and assess current classification schemes.

Characterization of Escherichia coli isolates from different fecal sources by means of classification tree analysis of fatty acid methyl ester (FAME) profiles.

PubMed

Seurinck, Sylvie; Deschepper, Ellen; Deboch, Bishaw; Verstraete, Willy; Siciliano, Steven

2006-03-01

Microbial source tracking (MST) methods need to be rapid, inexpensive and accurate. Unfortunately, many MST methods provide a wealth of information that is difficult to interpret by the regulators who use this information to make decisions. This paper describes the use of classification tree analysis to interpret the results of a MST method based on fatty acid methyl ester (FAME) profiles of Escherichia coli isolates, and to present results in a format readily interpretable by water quality managers. Raw sewage E. coli isolates and animal E. coli isolates from cow, dog, gull, and horse were isolated and their FAME profiles collected. Correct classification rates determined with leaveone-out cross-validation resulted in an overall low correct classification rate of 61%. A higher overall correct classification rate of 85% was obtained when the animal isolates were pooled together and compared to the raw sewage isolates. Bootstrap aggregation or adaptive resampling and combining of the FAME profile data increased correct classification rates substantially. Other MST methods may be better suited to differentiate between different fecal sources but classification tree analysis has enabled us to distinguish raw sewage from animal E. coli isolates, which previously had not been possible with other multivariate methods such as principal component analysis and cluster analysis.

A multilayered approach for the analysis of perinatal mortality using different classification systems.

PubMed

Gordijn, Sanne J; Korteweg, Fleurisca J; Erwich, Jan Jaap H M; Holm, Jozien P; van Diem, Mariet Th; Bergman, Klasien A; Timmer, Albertus

2009-06-01

Many classification systems for perinatal mortality are available, all with their own strengths and weaknesses: none of them has been universally accepted. We present a systematic multilayered approach for the analysis of perinatal mortality based on information related to the moment of death, the conditions associated with death and the underlying cause of death, using a combination of representatives of existing classification systems. We compared the existing classification systems regarding their definition of the perinatal period, level of complexity, inclusion of maternal, foetal and/or placental factors and whether they focus at a clinical or pathological viewpoint. Furthermore, we allocated the classification systems to one of three categories: 'when', 'what' or 'why', dependent on whether the allocation of the individual cases of perinatal mortality is based on the moment of death ('when'), the clinical conditions associated with death ('what'), or the underlying cause of death ('why'). A multilayered approach for the analysis and classification of perinatal mortality is possible by using combinations of existing systems; for example the Wigglesworth or Nordic Baltic ('when'), ReCoDe ('what') and Tulip ('why') classification systems. This approach is useful not only for in depth analysis of perinatal mortality in the developed world but also for analysis of perinatal mortality in the developing countries, where resources to investigate death are often limited.

[Analysis of an ophthalmic pathology cohort of human fetal eyes with regard to interesting findings].

PubMed

Herwig, M C; Müller, A M; Holz, F G; Loeffler, K U

2010-11-01

Information on the evaluation of prenatal ocular findings is sparse. This article provides an overview of the morphology in a cohort of human fetal eyes, with particular emphasis on interesting findings. The study investigated 216 eyes from 115 human fetuses. The majority of fetal eyes presented with a regular phenotype. Rarely, unexpected findings were discovered in fetuses with or without systemic malformations. Routine evaluation of fetal eyes reveals-albeit rarely-new aspects providing further knowledge and occasionally enabling the exact classification of syndromes.

Symmetry-protected line nodes and Majorana flat bands in nodal crystalline superconductors

NASA Astrophysics Data System (ADS)

Kobayashi, Shingo; Sumita, Shuntaro; Yanase, Youichi; Sato, Masatoshi

2018-05-01

Line nodes in the superconducting gap are known to be a source of Majorana flat bands (MFBs) on a surface. Here, we extend this relation to all symmetry-protected line nodes where an additional constraint arising from crystal symmetry destabilizes or hides the existence of MFBs. By establishing a one-to-one correspondence between group theoretical and topological classifications, we are able to classify the possible line-node-induced MFBs, including cases with (magnetic) nonsymmorphic space groups. Our theoretical analysis reveals MFBs in antiferromagnetic superconductors.

Chemotypes of essential oil of unripe galls of Pistacia atlantica Desf. from Algeria.

PubMed

Sifi, Ibrahim; Gourine, Nadhir; Gaydou, Emile M; Yousfi, Mohamed

2015-01-01

The essential oils (EOs) of unripe galls (from male and female plants) of a total number of 52 samples of Pistacia atlantica collected from different regions in Algeria were analysed by GC/MS and GC. The yields of the extraction of the EO by hydrodistillation vary from low to high values (0.08-1.89% v/w). The results of both methods of principal component analysis and hierarchical ascendant classification revealed the presence of two different chemotypes: α-pinene chemotype and α-pinene/sabinene/terpinen-4-ol chemotype.

Comparing ecoregional classifications for natural areas management in the Klamath Region, USA

USGS Publications Warehouse

Sarr, Daniel A.; Duff, Andrew; Dinger, Eric C.; Shafer, Sarah L.; Wing, Michael; Seavy, Nathaniel E.; Alexander, John D.

2015-01-01

We compared three existing ecoregional classification schemes (Bailey, Omernik, and World Wildlife Fund) with two derived schemes (Omernik Revised and Climate Zones) to explore their effectiveness in explaining species distributions and to better understand natural resource geography in the Klamath Region, USA. We analyzed presence/absence data derived from digital distribution maps for trees, amphibians, large mammals, small mammals, migrant birds, and resident birds using three statistical analyses of classification accuracy (Analysis of Similarity, Canonical Analysis of Principal Coordinates, and Classification Strength). The classifications were roughly comparable in classification accuracy, with Omernik Revised showing the best overall performance. Trees showed the strongest fidelity to the classifications, and large mammals showed the weakest fidelity. We discuss the implications for regional biogeography and describe how intermediate resolution ecoregional classifications may be appropriate for use as natural areas management domains.

Behavior Based Social Dimensions Extraction for Multi-Label Classification

PubMed Central

Li, Le; Xu, Junyi; Xiao, Weidong; Ge, Bin

2016-01-01

Classification based on social dimensions is commonly used to handle the multi-label classification task in heterogeneous networks. However, traditional methods, which mostly rely on the community detection algorithms to extract the latent social dimensions, produce unsatisfactory performance when community detection algorithms fail. In this paper, we propose a novel behavior based social dimensions extraction method to improve the classification performance in multi-label heterogeneous networks. In our method, nodes’ behavior features, instead of community memberships, are used to extract social dimensions. By introducing Latent Dirichlet Allocation (LDA) to model the network generation process, nodes’ connection behaviors with different communities can be extracted accurately, which are applied as latent social dimensions for classification. Experiments on various public datasets reveal that the proposed method can obtain satisfactory classification results in comparison to other state-of-the-art methods on smaller social dimensions. PMID:27049849

Pattern classification of kinematic and kinetic running data to distinguish gender, shod/barefoot and injury groups with feature ranking.

PubMed

Eskofier, Bjoern M; Kraus, Martin; Worobets, Jay T; Stefanyshyn, Darren J; Nigg, Benno M

2012-01-01

The identification of differences between groups is often important in biomechanics. This paper presents group classification tasks using kinetic and kinematic data from a prospective running injury study. Groups composed of gender, of shod/barefoot running and of runners who developed patellofemoral pain syndrome (PFPS) during the study, and asymptotic runners were classified. The features computed from the biomechanical data were deliberately chosen to be generic. Therefore, they were suited for different biomechanical measurements and classification tasks without adaptation to the input signals. Feature ranking was applied to reveal the relevance of each feature to the classification task. Data from 80 runners were analysed for gender and shod/barefoot classification, while 12 runners were investigated in the injury classification task. Gender groups could be differentiated with 84.7%, shod/barefoot running with 98.3%, and PFPS with 100% classification rate. For the latter group, one single variable could be identified that alone allowed discrimination.

A new method for shape and texture classification of orthopedic wear nanoparticles.

PubMed

Zhang, Dongning; Page, Janet R; Kavanaugh, Aaron E; Billi, Fabrizio

2012-09-27

Detailed morphologic analysis of particles produced during wear of orthopedic implants is important in determining a correlation among material, wear, and biological effects. However, the use of simple shape descriptors is insufficient to categorize the data and to compare the nature of wear particles generated by different implants. An approach based on Discrete Fourier Transform (DFT) is presented for describing particle shape and surface texture. Four metal-on-metal bearing couples were tested in an orbital wear simulator under standard and adverse (steep-angled cups) wear simulator conditions. Digitized Scanning Electron Microscope (SEM) images of the wear particles were imported into MATLAB to carry out Fourier descriptor calculations via a specifically developed algorithm. The descriptors were then used for studying particle characteristics (shape and texture) as well as for cluster classification. Analysis of the particles demonstrated the validity of the proposed model by showing that steep-angle Co-Cr wear particles were more asymmetric, compressed, extended, triangular, square, and roughened at 3 Mc than after 0.25 Mc. In contrast, particles from standard angle samples were only more compressed and extended after 3 Mc compared to 0.25 Mc. Cluster analysis revealed that the 0.25 Mc steep-angle particle distribution was a subset of the 3 Mc distribution.

Rapid identification of oral Actinomyces species cultivated from subgingival biofilm by MALDI-TOF-MS

PubMed Central

Stingu, Catalina S.; Borgmann, Toralf; Rodloff, Arne C.; Vielkind, Paul; Jentsch, Holger; Schellenberger, Wolfgang; Eschrich, Klaus

2015-01-01

Background Actinomyces are a common part of the residential flora of the human intestinal tract, genitourinary system and skin. Isolation and identification of Actinomyces by conventional methods is often difficult and time consuming. In recent years, matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) has become a rapid and simple method to identify bacteria. Objective The present study evaluated a new in-house algorithm using MALDI-TOF-MS for rapid identification of different species of oral Actinomyces cultivated from subgingival biofilm. Design Eleven reference strains and 674 clinical strains were used in this study. All the strains were preliminarily identified using biochemical methods and then subjected to MALDI-TOF-MS analysis using both similarity-based analysis and classification methods (support vector machine [SVM]). The genotype of the reference strains and of 232 clinical strains was identified by sequence analysis of the 16S ribosomal RNA (rRNA). Results The sequence analysis of the 16S rRNA gene of all references strains confirmed their previous identification. The MALDI-TOF-MS spectra obtained from the reference strains and the other clinical strains undoubtedly identified as Actinomyces by 16S rRNA sequencing were used to create the mass spectra reference database. Already a visual inspection of the mass spectra of different species reveals both similarities and differences. However, the differences between them are not large enough to allow a reliable differentiation by similarity analysis. Therefore, classification methods were applied as an alternative approach for differentiation and identification of Actinomyces at the species level. A cross-validation of the reference database representing 14 Actinomyces species yielded correct results for all species which were represented by more than two strains in the database. Conclusions Our results suggest that a combination of MALDI-TOF-MS with powerful classification algorithms, such as SVMs, provide a useful tool for the differentiation and identification of oral Actinomyces. PMID:25597306

Information analysis of a spatial database for ecological land classification

NASA Technical Reports Server (NTRS)

Davis, Frank W.; Dozier, Jeff

1990-01-01

An ecological land classification was developed for a complex region in southern California using geographic information system techniques of map overlay and contingency table analysis. Land classes were identified by mutual information analysis of vegetation pattern in relation to other mapped environmental variables. The analysis was weakened by map errors, especially errors in the digital elevation data. Nevertheless, the resulting land classification was ecologically reasonable and performed well when tested with higher quality data from the region.

The software application and classification algorithms for welds radiograms analysis

NASA Astrophysics Data System (ADS)

Sikora, R.; Chady, T.; Baniukiewicz, P.; Grzywacz, B.; Lopato, P.; Misztal, L.; Napierała, L.; Piekarczyk, B.; Pietrusewicz, T.; Psuj, G.

2013-01-01

The paper presents a software implementation of an Intelligent System for Radiogram Analysis (ISAR). The system has to support radiologists in welds quality inspection. The image processing part of software with a graphical user interface and a welds classification part are described with selected classification results. Classification was based on a few algorithms: an artificial neural network, a k-means clustering, a simplified k-means and a rough sets theory.

An initial analysis of LANDSAT 4 Thematic Mapper data for the classification of agricultural, forested wetland, and urban land covers

NASA Technical Reports Server (NTRS)

Quattrochi, D. A.; Anderson, J. E.; Brannon, D. P.; Hill, C. L.

1982-01-01

An initial analysis of LANDSAT 4 thematic mapper (TM) data for the delineation and classification of agricultural, forested wetland, and urban land covers was conducted. A study area in Poinsett County, Arkansas was used to evaluate a classification of agricultural lands derived from multitemporal LANDSAT multispectral scanner (MSS) data in comparison with a classification of TM data for the same area. Data over Reelfoot Lake in northwestern Tennessee were utilized to evaluate the TM for delineating forested wetland species. A classification of the study area was assessed for accuracy in discriminating five forested wetland categories. Finally, the TM data were used to identify urban features within a small city. A computer generated classification of Union City, Tennessee was analyzed for accuracy in delineating urban land covers. An evaluation of digitally enhanced TM data using principal components analysis to facilitate photointerpretation of urban features was also performed.

Supporting cognition in systems biology analysis: findings on users' processes and design implications.

PubMed

Mirel, Barbara

2009-02-13

Current usability studies of bioinformatics tools suggest that tools for exploratory analysis support some tasks related to finding relationships of interest but not the deep causal insights necessary for formulating plausible and credible hypotheses. To better understand design requirements for gaining these causal insights in systems biology analyses a longitudinal field study of 15 biomedical researchers was conducted. Researchers interacted with the same protein-protein interaction tools to discover possible disease mechanisms for further experimentation. Findings reveal patterns in scientists' exploratory and explanatory analysis and reveal that tools positively supported a number of well-structured query and analysis tasks. But for several of scientists' more complex, higher order ways of knowing and reasoning the tools did not offer adequate support. Results show that for a better fit with scientists' cognition for exploratory analysis systems biology tools need to better match scientists' processes for validating, for making a transition from classification to model-based reasoning, and for engaging in causal mental modelling. As the next great frontier in bioinformatics usability, tool designs for exploratory systems biology analysis need to move beyond the successes already achieved in supporting formulaic query and analysis tasks and now reduce current mismatches with several of scientists' higher order analytical practices. The implications of results for tool designs are discussed.

Gene selection for microarray cancer classification using a new evolutionary method employing artificial intelligence concepts.

PubMed

Dashtban, M; Balafar, Mohammadali

2017-03-01

Gene selection is a demanding task for microarray data analysis. The diverse complexity of different cancers makes this issue still challenging. In this study, a novel evolutionary method based on genetic algorithms and artificial intelligence is proposed to identify predictive genes for cancer classification. A filter method was first applied to reduce the dimensionality of feature space followed by employing an integer-coded genetic algorithm with dynamic-length genotype, intelligent parameter settings, and modified operators. The algorithmic behaviors including convergence trends, mutation and crossover rate changes, and running time were studied, conceptually discussed, and shown to be coherent with literature findings. Two well-known filter methods, Laplacian and Fisher score, were examined considering similarities, the quality of selected genes, and their influences on the evolutionary approach. Several statistical tests concerning choice of classifier, choice of dataset, and choice of filter method were performed, and they revealed some significant differences between the performance of different classifiers and filter methods over datasets. The proposed method was benchmarked upon five popular high-dimensional cancer datasets; for each, top explored genes were reported. Comparing the experimental results with several state-of-the-art methods revealed that the proposed method outperforms previous methods in DLBCL dataset. Copyright © 2017 Elsevier Inc. All rights reserved.

Gold-standard for computer-assisted morphological sperm analysis.

PubMed

Chang, Violeta; Garcia, Alejandra; Hitschfeld, Nancy; Härtel, Steffen

2017-04-01

Published algorithms for classification of human sperm heads are based on relatively small image databases that are not open to the public, and thus no direct comparison is available for competing methods. We describe a gold-standard for morphological sperm analysis (SCIAN-MorphoSpermGS), a dataset of sperm head images with expert-classification labels in one of the following classes: normal, tapered, pyriform, small or amorphous. This gold-standard is for evaluating and comparing known techniques and future improvements to present approaches for classification of human sperm heads for semen analysis. Although this paper does not provide a computational tool for morphological sperm analysis, we present a set of experiments for comparing sperm head description and classification common techniques. This classification base-line is aimed to be used as a reference for future improvements to present approaches for human sperm head classification. The gold-standard provides a label for each sperm head, which is achieved by majority voting among experts. The classification base-line compares four supervised learning methods (1- Nearest Neighbor, naive Bayes, decision trees and Support Vector Machine (SVM)) and three shape-based descriptors (Hu moments, Zernike moments and Fourier descriptors), reporting the accuracy and the true positive rate for each experiment. We used Fleiss' Kappa Coefficient to evaluate the inter-expert agreement and Fisher's exact test for inter-expert variability and statistical significant differences between descriptors and learning techniques. Our results confirm the high degree of inter-expert variability in the morphological sperm analysis. Regarding the classification base line, we show that none of the standard descriptors or classification approaches is best suitable for tackling the problem of sperm head classification. We discovered that the correct classification rate was highly variable when trying to discriminate among non-normal sperm heads. By using the Fourier descriptor and SVM, we achieved the best mean correct classification: only 49%. We conclude that the SCIAN-MorphoSpermGS will provide a standard tool for evaluation of characterization and classification approaches for human sperm heads. Indeed, there is a clear need for a specific shape-based descriptor for human sperm heads and a specific classification approach to tackle the problem of high variability within subcategories of abnormal sperm cells. Copyright © 2017 Elsevier Ltd. All rights reserved.

Spectral analysis of two-signed microarray expression data.

PubMed

Higham, Desmond J; Kalna, Gabriela; Vass, J Keith

2007-06-01

We give a simple and informative derivation of a spectral algorithm for clustering and reordering complementary DNA microarray expression data. Here, expression levels of a set of genes are recorded simultaneously across a number of samples, with a positive weight reflecting up-regulation and a negative weight reflecting down-regulation. We give theoretical support for the algorithm based on a biologically justified hypothesis about the structure of the data, and illustrate its use on public domain data in the context of unsupervised tumour classification. The algorithm is derived by considering a discrete optimization problem and then relaxing to the continuous realm. We prove that in the case where the data have an inherent 'checkerboard' sign pattern, the algorithm will automatically reveal that pattern. Further, our derivation shows that the algorithm may be regarded as imposing a random graph model on the expression levels and then clustering from a maximum likelihood perspective. This indicates that the output will be tolerant to perturbations and will reveal 'near-checkerboard' patterns when these are present in the data. It is interesting to note that the checkerboard structure is revealed by the first (dominant) singular vectors--previous work on spectral methods has focussed on the case of nonnegative edge weights, where only the second and higher singular vectors are relevant. We illustrate the algorithm on real and synthetic data, and then use it in a tumour classification context on three different cancer data sets. Our results show that respecting the two-signed nature of the data (thereby distinguishing between up-regulation and down-regulation) reveals structures that cannot be gleaned from the absolute value data (where up- and down-regulation are both regarded as 'changes').

Functional resting-state connectivity of the human motor network: differences between right- and left-handers.

PubMed

Pool, Eva-Maria; Rehme, Anne K; Eickhoff, Simon B; Fink, Gereon R; Grefkes, Christian

2015-04-01

Handedness is associated with differences in activation levels in various motor tasks performed with the dominant or non-dominant hand. Here we tested whether handedness is reflected in the functional architecture of the motor system even in the absence of an overt motor task. Using resting-state functional magnetic resonance imaging we investigated 18 right- and 18 left-handers. Whole-brain functional connectivity maps of the primary motor cortex (M1), supplementary motor area (SMA), dorsolateral premotor cortex (PMd), pre-SMA, inferior frontal junction and motor putamen were compared between right- and left-handers. We further used a multivariate linear support vector machine (SVM) classifier to reveal the specificity of brain regions for classifying handedness based on individual resting-state maps. Using left M1 as seed region, functional connectivity analysis revealed stronger interhemispheric functional connectivity between left M1 and right PMd in right-handers as compared to left-handers. This connectivity cluster contributed to the individual classification of right- and left-handers with 86.2% accuracy. Consistently, also seeding from right PMd yielded a similar handedness-dependent effect in left M1, albeit with lower classification accuracy (78.1%). Control analyses of the other resting-state networks including the speech and the visual network revealed no significant differences in functional connectivity related to handedness. In conclusion, our data revealed an intrinsically higher functional connectivity in right-handers. These results may help to explain that hand preference is more lateralized in right-handers than in left-handers. Furthermore, enhanced functional connectivity between left M1 and right PMd may serve as an individual marker of handedness. Copyright © 2015 Elsevier Inc. All rights reserved.

Functional resting-state connectivity of the human motor network: Differences between right- and left-handers

PubMed Central

Pool, Eva-Maria; Rehme, Anne K.; Eickhoff, Simon B.; Fink, Gereon R.; Grefkes, Christian

2016-01-01

Handedness is associated with differences in activation levels in various motor tasks performed with the dominant or non-dominant hand. Here we tested whether handedness is reflected in the functional architecture of the motor system even in the absence of an overt motor task. Using resting-state functional magnetic resonance imaging we investigated 18 right- and 18 left-handers. Whole-brain functional connectivity maps of the primary motor cortex (M1), supplementary motor area (SMA), dorsolateral premotor cortex (PMd), pre-SMA, inferior frontal junction and motor putamen were compared between right- and left-handers. We further used a multivariate linear support vector machine (SVM) classifier to reveal the specificity of brain regions for classifying handedness based on individual resting-state maps. Using left M1 as seed region, functional connectivity analysis revealed stronger interhemispheric functional connectivity between left M1 and right PMd in right-handers as compared to left-handers. This connectivity cluster contributed to the individual classification of right- and left-handers with 86.2% accuracy. Consistently, also seeding from right PMd yielded a similar handedness-dependent effect in left M1, albeit with lower classification accuracy (78.1%). Control analyses of the other resting-state networks including the speech and the visual network revealed no significant differences in functional connectivity related to handedness. In conclusion, our data revealed an intrinsically higher functional connectivity in right-handers. These results may help to explain that hand preference is more lateralized in right-handers than in left-handers. Furthermore, enhanced functional connectivity between left M1 and right PMd may serve as an individual marker of handedness. PMID:25613438

Genomic Diversity and Evolution of the Lyssaviruses

PubMed Central

Delmas, Olivier; Holmes, Edward C.; Talbi, Chiraz; Larrous, Florence; Dacheux, Laurent; Bouchier, Christiane; Bourhy, Hervé

2008-01-01

Lyssaviruses are RNA viruses with single-strand, negative-sense genomes responsible for rabies-like diseases in mammals. To date, genomic and evolutionary studies have most often utilized partial genome sequences, particularly of the nucleoprotein and glycoprotein genes, with little consideration of genome-scale evolution. Herein, we report the first genomic and evolutionary analysis using complete genome sequences of all recognised lyssavirus genotypes, including 14 new complete genomes of field isolates from 6 genotypes and one genotype that is completely sequenced for the first time. In doing so we significantly increase the extent of genome sequence data available for these important viruses. Our analysis of these genome sequence data reveals that all lyssaviruses have the same genomic organization. A phylogenetic analysis reveals strong geographical structuring, with the greatest genetic diversity in Africa, and an independent origin for the two known genotypes that infect European bats. We also suggest that multiple genotypes may exist within the diversity of viruses currently classified as ‘Lagos Bat’. In sum, we show that rigorous phylogenetic techniques based on full length genome sequence provide the best discriminatory power for genotype classification within the lyssaviruses. PMID:18446239

Novel methodologies for spectral classification of exon and intron sequences

NASA Astrophysics Data System (ADS)

Kwan, Hon Keung; Kwan, Benjamin Y. M.; Kwan, Jennifer Y. Y.

2012-12-01

Digital processing of a nucleotide sequence requires it to be mapped to a numerical sequence in which the choice of nucleotide to numeric mapping affects how well its biological properties can be preserved and reflected from nucleotide domain to numerical domain. Digital spectral analysis of nucleotide sequences unfolds a period-3 power spectral value which is more prominent in an exon sequence as compared to that of an intron sequence. The success of a period-3 based exon and intron classification depends on the choice of a threshold value. The main purposes of this article are to introduce novel codes for 1-sequence numerical representations for spectral analysis and compare them to existing codes to determine appropriate representation, and to introduce novel thresholding methods for more accurate period-3 based exon and intron classification of an unknown sequence. The main findings of this study are summarized as follows: Among sixteen 1-sequence numerical representations, the K-Quaternary Code I offers an attractive performance. A windowed 1-sequence numerical representation (with window length of 9, 15, and 24 bases) offers a possible speed gain over non-windowed 4-sequence Voss representation which increases as sequence length increases. A winner threshold value (chosen from the best among two defined threshold values and one other threshold value) offers a top precision for classifying an unknown sequence of specified fixed lengths. An interpolated winner threshold value applicable to an unknown and arbitrary length sequence can be estimated from the winner threshold values of fixed length sequences with a comparable performance. In general, precision increases as sequence length increases. The study contributes an effective spectral analysis of nucleotide sequences to better reveal embedded properties, and has potential applications in improved genome annotation.

Toward a definition of blueprint of virgin olive oil by comprehensive two-dimensional gas chromatography.

PubMed

Purcaro, Giorgia; Cordero, Chiara; Liberto, Erica; Bicchi, Carlo; Conte, Lanfranco S

2014-03-21

This study investigates the applicability of an iterative approach aimed at defining a chemical blueprint of virgin olive oil volatiles to be correlated to the product sensory quality. The investigation strategy proposed allows to fully exploit the informative content of a comprehensive multidimensional gas chromatography (GC×GC) coupled to a mass spectrometry (MS) data set. Olive oil samples (19), including 5 reference standards, obtained from the International Olive Oil Council, and commercial samples, were submitted to a sensory evaluation by a Panel test, before being analyzed in two laboratories using different instrumentation, column set, and software elaboration packages in view of a cross-validation of the entire methodology. A first classification of samples based on untargeted peak features information, was obtained on raw data from two different column combinations (apolar×polar and polar×apolar) by applying unsupervised multivariate analysis (i.e., principal component analysis-PCA). However, to improve effectiveness and specificity of this classification, peak features were reliably identified (261 compounds), on the basis of the MS spectrum and linear retention index matching, and subjected to successive pair-wise comparisons based on 2D patterns, which revealed peculiar distribution of chemicals correlated with samples sensory classification. The most informative compounds were thus identified and collected in a "blueprint" of specific defects (or combination of defects) successively adopted to discriminate Extra Virgin from defected oils (i.e., lampante oil) with the aid of a supervised approach, i.e., partial least squares-discriminant analysis (PLS-DA). In this last step, the principles of sensomics, which assigns higher information potential to analytes with lower odor threshold proved to be successful, and a much more powerful discrimination of samples was obtained in view of a sensory quality assessment. Copyright © 2014 Elsevier B.V. All rights reserved.

Toward literature-based feature selection for diagnostic classification: a meta-analysis of resting-state fMRI in depression.

PubMed

Sundermann, Benedikt; Olde Lütke Beverborg, Mona; Pfleiderer, Bettina

2014-01-01

Information derived from functional magnetic resonance imaging (fMRI) during wakeful rest has been introduced as a candidate diagnostic biomarker in unipolar major depressive disorder (MDD). Multiple reports of resting state fMRI in MDD describe group effects. Such prior knowledge can be adopted to pre-select potentially discriminating features for diagnostic classification models with the aim to improve diagnostic accuracy. Purpose of this analysis was to consolidate spatial information about alterations of spontaneous brain activity in MDD, primarily to serve as feature selection for multivariate pattern analysis techniques (MVPA). Thirty two studies were included in final analyses. Coordinates extracted from the original reports were assigned to two categories based on directionality of findings. Meta-analyses were calculated using the non-additive activation likelihood estimation approach with coordinates organized by subject group to account for non-independent samples. Converging evidence revealed a distributed pattern of brain regions with increased or decreased spontaneous activity in MDD. The most distinct finding was hyperactivity/hyperconnectivity presumably reflecting the interaction of cortical midline structures (posterior default mode network components including the precuneus and neighboring posterior cingulate cortices associated with self-referential processing and the subgenual anterior cingulate and neighboring medial frontal cortices) with lateral prefrontal areas related to externally-directed cognition. Other areas of hyperactivity/hyperconnectivity include the left lateral parietal cortex, right hippocampus and right cerebellum whereas hypoactivity/hypoconnectivity was observed mainly in the left temporal cortex, the insula, precuneus, superior frontal gyrus, lentiform nucleus and thalamus. Results are made available in two different data formats to be used as spatial hypotheses in future studies, particularly for diagnostic classification by MVPA.

Non-Gaussian Distributions Affect Identification of Expression Patterns, Functional Annotation, and Prospective Classification in Human Cancer Genomes

PubMed Central

Marko, Nicholas F.; Weil, Robert J.

2012-01-01

Introduction Gene expression data is often assumed to be normally-distributed, but this assumption has not been tested rigorously. We investigate the distribution of expression data in human cancer genomes and study the implications of deviations from the normal distribution for translational molecular oncology research. Methods We conducted a central moments analysis of five cancer genomes and performed empiric distribution fitting to examine the true distribution of expression data both on the complete-experiment and on the individual-gene levels. We used a variety of parametric and nonparametric methods to test the effects of deviations from normality on gene calling, functional annotation, and prospective molecular classification using a sixth cancer genome. Results Central moments analyses reveal statistically-significant deviations from normality in all of the analyzed cancer genomes. We observe as much as 37% variability in gene calling, 39% variability in functional annotation, and 30% variability in prospective, molecular tumor subclassification associated with this effect. Conclusions Cancer gene expression profiles are not normally-distributed, either on the complete-experiment or on the individual-gene level. Instead, they exhibit complex, heavy-tailed distributions characterized by statistically-significant skewness and kurtosis. The non-Gaussian distribution of this data affects identification of differentially-expressed genes, functional annotation, and prospective molecular classification. These effects may be reduced in some circumstances, although not completely eliminated, by using nonparametric analytics. This analysis highlights two unreliable assumptions of translational cancer gene expression analysis: that “small” departures from normality in the expression data distributions are analytically-insignificant and that “robust” gene-calling algorithms can fully compensate for these effects. PMID:23118863

Gene Set−Based Integrative Analysis Revealing Two Distinct Functional Regulation Patterns in Four Common Subtypes of Epithelial Ovarian Cancer

PubMed Central

Chang, Chia-Ming; Chuang, Chi-Mu; Wang, Mong-Lien; Yang, Yi-Ping; Chuang, Jen-Hua; Yang, Ming-Jie; Yen, Ming-Shyen; Chiou, Shih-Hwa; Chang, Cheng-Chang

2016-01-01

Clear cell (CCC), endometrioid (EC), mucinous (MC) and high-grade serous carcinoma (SC) are the four most common subtypes of epithelial ovarian carcinoma (EOC). The widely accepted dualistic model of ovarian carcinogenesis divided EOCs into type I and II categories based on the molecular features. However, this hypothesis has not been experimentally demonstrated. We carried out a gene set-based analysis by integrating the microarray gene expression profiles downloaded from the publicly available databases. These quantified biological functions of EOCs were defined by 1454 Gene Ontology (GO) term and 674 Reactome pathway gene sets. The pathogenesis of the four EOC subtypes was investigated by hierarchical clustering and exploratory factor analysis. The patterns of functional regulation among the four subtypes containing 1316 cases could be accurately classified by machine learning. The results revealed that the ERBB and PI3K-related pathways played important roles in the carcinogenesis of CCC, EC and MC; while deregulation of cell cycle was more predominant in SC. The study revealed that two different functional regulation patterns exist among the four EOC subtypes, which were compatible with the type I and II classifications proposed by the dualistic model of ovarian carcinogenesis. PMID:27527159

Extending a field-based Sonoran desert vegetation classification to a regional scale using optical and microwave satellite imagery

NASA Astrophysics Data System (ADS)

Shupe, Scott Marshall

2000-10-01

Vegetation mapping in and regions facilitates ecological studies, land management, and provides a record to which future land changes can be compared. Accurate and representative mapping of desert vegetation requires a sound field sampling program and a methodology to transform the data collected into a representative classification system. Time and cost constraints require that a remote sensing approach be used if such a classification system is to be applied on a regional scale. However, desert vegetation may be sparse and thus difficult to sense at typical satellite resolutions, especially given the problem of soil reflectance. This study was designed to address these concerns by conducting vegetation mapping research using field and satellite data from the US Army Yuma Proving Ground (USYPG) in Southwest Arizona. Line and belt transect data from the Army's Land Condition Trend Analysis (LCTA) Program were transformed into relative cover and relative density classification schemes using cluster analysis. Ordination analysis of the same data produced two and three-dimensional graphs on which the homogeneity of each vegetation class could be examined. It was found that the use of correspondence analysis (CA), detrended correspondence analysis (DCA), and non-metric multidimensional scaling (NMS) ordination methods was superior to the use of any single ordination method for helping to clarify between-class and within-class relationships in vegetation composition. Analysis of these between-class and within-class relationships were of key importance in examining how well relative cover and relative density schemes characterize the USYPG vegetation. Using these two classification schemes as reference data, maximum likelihood and artificial neural net classifications were then performed on a coregistered dataset consisting of a summer Landsat Thematic Mapper (TM) image, one spring and one summer ERS-1 microwave image, and elevation, slope, and aspect layers. Classifications using a combination of ERS-1 imagery and elevation, slope, and aspect data were superior to classifications carried out using Landsat TM data alone. In all classification iterations it was consistently found that the highest classification accuracy was obtained by using a combination of Landsat TM, ERS-1, and elevation, slope, and aspect data. Maximum likelihood classification accuracy was found to be higher than artificial neural net classification in all cases.

Classification accuracy on the family planning participation status using kernel discriminant analysis

NASA Astrophysics Data System (ADS)

Kurniawan, Dian; Suparti; Sugito

2018-05-01

Population growth in Indonesia has increased every year. According to the population census conducted by the Central Bureau of Statistics (BPS) in 2010, the population of Indonesia has reached 237.6 million people. Therefore, to control the population growth rate, the government hold Family Planning or Keluarga Berencana (KB) program for couples of childbearing age. The purpose of this program is to improve the health of mothers and children in order to manifest prosperous society by controlling births while ensuring control of population growth. The data used in this study is the updated family data of Semarang city in 2016 that conducted by National Family Planning Coordinating Board (BKKBN). From these data, classifiers with kernel discriminant analysis will be obtained, and also classification accuracy will be obtained from that method. The result of the analysis showed that normal kernel discriminant analysis gives 71.05 % classification accuracy with 28.95 % classification error. Whereas triweight kernel discriminant analysis gives 73.68 % classification accuracy with 26.32 % classification error. Using triweight kernel discriminant for data preprocessing of family planning participation of childbearing age couples in Semarang City of 2016 can be stated better than with normal kernel discriminant.

The process and utility of classification and regression tree methodology in nursing research

PubMed Central

Kuhn, Lisa; Page, Karen; Ward, John; Worrall-Carter, Linda

2014-01-01

Aim This paper presents a discussion of classification and regression tree analysis and its utility in nursing research. Background Classification and regression tree analysis is an exploratory research method used to illustrate associations between variables not suited to traditional regression analysis. Complex interactions are demonstrated between covariates and variables of interest in inverted tree diagrams. Design Discussion paper. Data sources English language literature was sourced from eBooks, Medline Complete and CINAHL Plus databases, Google and Google Scholar, hard copy research texts and retrieved reference lists for terms including classification and regression tree* and derivatives and recursive partitioning from 1984–2013. Discussion Classification and regression tree analysis is an important method used to identify previously unknown patterns amongst data. Whilst there are several reasons to embrace this method as a means of exploratory quantitative research, issues regarding quality of data as well as the usefulness and validity of the findings should be considered. Implications for Nursing Research Classification and regression tree analysis is a valuable tool to guide nurses to reduce gaps in the application of evidence to practice. With the ever-expanding availability of data, it is important that nurses understand the utility and limitations of the research method. Conclusion Classification and regression tree analysis is an easily interpreted method for modelling interactions between health-related variables that would otherwise remain obscured. Knowledge is presented graphically, providing insightful understanding of complex and hierarchical relationships in an accessible and useful way to nursing and other health professions. PMID:24237048

The process and utility of classification and regression tree methodology in nursing research.

PubMed

Kuhn, Lisa; Page, Karen; Ward, John; Worrall-Carter, Linda

2014-06-01

This paper presents a discussion of classification and regression tree analysis and its utility in nursing research. Classification and regression tree analysis is an exploratory research method used to illustrate associations between variables not suited to traditional regression analysis. Complex interactions are demonstrated between covariates and variables of interest in inverted tree diagrams. Discussion paper. English language literature was sourced from eBooks, Medline Complete and CINAHL Plus databases, Google and Google Scholar, hard copy research texts and retrieved reference lists for terms including classification and regression tree* and derivatives and recursive partitioning from 1984-2013. Classification and regression tree analysis is an important method used to identify previously unknown patterns amongst data. Whilst there are several reasons to embrace this method as a means of exploratory quantitative research, issues regarding quality of data as well as the usefulness and validity of the findings should be considered. Classification and regression tree analysis is a valuable tool to guide nurses to reduce gaps in the application of evidence to practice. With the ever-expanding availability of data, it is important that nurses understand the utility and limitations of the research method. Classification and regression tree analysis is an easily interpreted method for modelling interactions between health-related variables that would otherwise remain obscured. Knowledge is presented graphically, providing insightful understanding of complex and hierarchical relationships in an accessible and useful way to nursing and other health professions. © 2013 The Authors. Journal of Advanced Nursing Published by John Wiley & Sons Ltd.

Burdigalian turbid water patch reef environment revealed by larger benthic foraminifera

NASA Astrophysics Data System (ADS)

Novak, V.; Renema, W.; Throughflow-project

2012-04-01

Ancient isolated patch reefs outcropping from siliciclastic sediments are a trademark for the Miocene carbonate deposits occurring in East Kalimantan, Indonesia. They develop in transitional shelf sediments deposited between deltaic and deep marine deposits (Allen and Chambers, 1998). The Batu Putih Limestone (Wilson, 2005) and similar outcrops in adjacent areas have been characterized as shallow water carbonates influenced by high siliciclastic input, showing low relief patch reefs in turbid waters. Larger benthic foraminifera (LBF) are excellent markers for biochronology and paleoenvironmental reconstruction. This study aims to reveal age and paleoenvironment of a shallow water carbonate patch reef developed in mixed depositional system by using LBF and microfacies analysis. The studied section is located near Bontang, East Kalimantan, and is approximately 80 m long and 12 m high. It is placed within Miocene sediments in the central part of the Kutai Basin. Patch reef and capping sediments were logged through eight transects along section and divided into nine different lithological units from which samples were collected. Thin sections and isolated specimens of larger benthic foraminifera were analyzed and recognized to species level (where possible) providing age and environmental information. Microfacies analysis of thin sections included carbonate classification (textural scheme of Dunham, 1962) and assemblage composition of LBF, algae and corals relative abundance. Three environmentally indicative groups of LBF were separated based on test morphology, habitat or living relatives (Hallock and Glenn, 1986). Analysed foraminifera assemblage suggests Burdigalian age (Tf1). With use of microfacies analysis nine successive lithological units were grouped into five facies types. Paleoenvironmental reconstruction of LBF fossil assemblage indicate two cycles of possible deepening recorded in the section. Based on high muddy matrix ratio in analyzed thin-sections we still cannot conclude whether they were deeper water assemblage, or that they occurred in shallower water and influenced by turbid conditions as the result of terrigenous input. According to preliminary analysis and siliciclastic content in the sediments the later one should be more likely. Further work will include additional fossil groups analysis (corals, algae and bryozoans), detailed petrographical analysis and Strontium isotope stratigraphy. Allen, G.P., and Chambers, J.L.C. (1998): Sedimentation in the Modern and Miocene Mahakam Delta. Indonesian Petroleum Association, Jakarta, Indonesia, 236 p. Dunham, R.J. (1962): Classification of carbonate rocks according to their depositional texture. In: Ham, W.E., ed., Classification of Carbonate Rocks: American Association of Petroleum Geologists Memoir, v. 1, p. 108-121. Hallock, P. and Glenn, C.E. (1986): Larger Foraminifera: A tool for paleoenvironmental analysis of Cenozoic carbonate depositional facies. Palaios 1, 55-64. Wilson, M.E.J. (2005): Development of equatorial delta-front patch reefs during the Neogene, Borneo. - Journal of Sedimentary Research, 75(1): 114-133.

A detailed comparison of analysis processes for MCC-IMS data in disease classification—Automated methods can replace manual peak annotations

PubMed Central

Horsch, Salome; Kopczynski, Dominik; Kuthe, Elias; Baumbach, Jörg Ingo; Rahmann, Sven

2017-01-01

Motivation Disease classification from molecular measurements typically requires an analysis pipeline from raw noisy measurements to final classification results. Multi capillary column—ion mobility spectrometry (MCC-IMS) is a promising technology for the detection of volatile organic compounds in the air of exhaled breath. From raw measurements, the peak regions representing the compounds have to be identified, quantified, and clustered across different experiments. Currently, several steps of this analysis process require manual intervention of human experts. Our goal is to identify a fully automatic pipeline that yields competitive disease classification results compared to an established but subjective and tedious semi-manual process. Method We combine a large number of modern methods for peak detection, peak clustering, and multivariate classification into analysis pipelines for raw MCC-IMS data. We evaluate all combinations on three different real datasets in an unbiased cross-validation setting. We determine which specific algorithmic combinations lead to high AUC values in disease classifications across the different medical application scenarios. Results The best fully automated analysis process achieves even better classification results than the established manual process. The best algorithms for the three analysis steps are (i) SGLTR (Savitzky-Golay Laplace-operator filter thresholding regions) and LM (Local Maxima) for automated peak identification, (ii) EM clustering (Expectation Maximization) and DBSCAN (Density-Based Spatial Clustering of Applications with Noise) for the clustering step and (iii) RF (Random Forest) for multivariate classification. Thus, automated methods can replace the manual steps in the analysis process to enable an unbiased high throughput use of the technology. PMID:28910313

Supervised DNA Barcodes species classification: analysis, comparisons and results

PubMed Central

2014-01-01

Background Specific fragments, coming from short portions of DNA (e.g., mitochondrial, nuclear, and plastid sequences), have been defined as DNA Barcode and can be used as markers for organisms of the main life kingdoms. Species classification with DNA Barcode sequences has been proven effective on different organisms. Indeed, specific gene regions have been identified as Barcode: COI in animals, rbcL and matK in plants, and ITS in fungi. The classification problem assigns an unknown specimen to a known species by analyzing its Barcode. This task has to be supported with reliable methods and algorithms. Methods In this work the efficacy of supervised machine learning methods to classify species with DNA Barcode sequences is shown. The Weka software suite, which includes a collection of supervised classification methods, is adopted to address the task of DNA Barcode analysis. Classifier families are tested on synthetic and empirical datasets belonging to the animal, fungus, and plant kingdoms. In particular, the function-based method Support Vector Machines (SVM), the rule-based RIPPER, the decision tree C4.5, and the Naïve Bayes method are considered. Additionally, the classification results are compared with respect to ad-hoc and well-established DNA Barcode classification methods. Results A software that converts the DNA Barcode FASTA sequences to the Weka format is released, to adapt different input formats and to allow the execution of the classification procedure. The analysis of results on synthetic and real datasets shows that SVM and Naïve Bayes outperform on average the other considered classifiers, although they do not provide a human interpretable classification model. Rule-based methods have slightly inferior classification performances, but deliver the species specific positions and nucleotide assignments. On synthetic data the supervised machine learning methods obtain superior classification performances with respect to the traditional DNA Barcode classification methods. On empirical data their classification performances are at a comparable level to the other methods. Conclusions The classification analysis shows that supervised machine learning methods are promising candidates for handling with success the DNA Barcoding species classification problem, obtaining excellent performances. To conclude, a powerful tool to perform species identification is now available to the DNA Barcoding community. PMID:24721333

Comparative Analysis of RF Emission Based Fingerprinting Techniques for ZigBee Device Classification

DTIC Science & Technology

quantify the differences invarious RF fingerprinting techniques via comparative analysis of MDA/ML classification results. The findings herein demonstrate...correct classification rates followed by COR-DNA and then RF-DNA in most test cases and especially in low Eb/N0 ranges, where ZigBee is designed to operate.

Using Discrete Loss Functions and Weighted Kappa for Classification: An Illustration Based on Bayesian Network Analysis

ERIC Educational Resources Information Center

Zwick, Rebecca; Lenaburg, Lubella

2009-01-01

In certain data analyses (e.g., multiple discriminant analysis and multinomial log-linear modeling), classification decisions are made based on the estimated posterior probabilities that individuals belong to each of several distinct categories. In the Bayesian network literature, this type of classification is often accomplished by assigning…

Strength Analysis on Ship Ladder Using Finite Element Method

NASA Astrophysics Data System (ADS)

Budianto; Wahyudi, M. T.; Dinata, U.; Ruddianto; Eko P., M. M.

2018-01-01

In designing the ship’s structure, it should refer to the rules in accordance with applicable classification standards. In this case, designing Ladder (Staircase) on a Ferry Ship which is set up, it must be reviewed based on the loads during ship operations, either during sailing or at port operations. The classification rules in ship design refer to the calculation of the structure components described in Classification calculation method and can be analysed using the Finite Element Method. Classification Regulations used in the design of Ferry Ships used BKI (Bureau of Classification Indonesia). So the rules for the provision of material composition in the mechanical properties of the material should refer to the classification of the used vessel. The analysis in this structure used program structure packages based on Finite Element Method. By using structural analysis on Ladder (Ladder), it obtained strength and simulation structure that can withstand load 140 kg both in static condition, dynamic, and impact. Therefore, the result of the analysis included values of safety factors in the ship is to keep the structure safe but the strength of the structure is not excessive.

Exploring the Impact of Target Eccentricity and Task Difficulty on Covert Visual Spatial Attention and Its Implications for Brain Computer Interfacing

PubMed Central

Roijendijk, Linsey; Farquhar, Jason; van Gerven, Marcel; Jensen, Ole; Gielen, Stan

2013-01-01

Objective Covert visual spatial attention is a relatively new task used in brain computer interfaces (BCIs) and little is known about the characteristics which may affect performance in BCI tasks. We investigated whether eccentricity and task difficulty affect alpha lateralization and BCI performance. Approach We conducted a magnetoencephalography study with 14 participants who performed a covert orientation discrimination task at an easy or difficult stimulus contrast at either a near (3.5°) or far (7°) eccentricity. Task difficulty was manipulated block wise and subjects were aware of the difficulty level of each block. Main Results Grand average analyses revealed a significantly larger hemispheric lateralization of posterior alpha power in the difficult condition than in the easy condition, while surprisingly no difference was found for eccentricity. The difference between task difficulty levels was significant in the interval between 1.85 s and 2.25 s after cue onset and originated from a stronger decrease in the contralateral hemisphere. No significant effect of eccentricity was found. Additionally, single-trial classification analysis revealed a higher classification rate in the difficult (65.9%) than in the easy task condition (61.1%). No effect of eccentricity was found in classification rate. Significance Our results indicate that manipulating the difficulty of a task gives rise to variations in alpha lateralization and that using a more difficult task improves covert visual spatial attention BCI performance. The variations in the alpha lateralization could be caused by different factors such as an increased mental effort or a higher visual attentional demand. Further research is necessary to discriminate between them. We did not discover any effect of eccentricity in contrast to results of previous research. PMID:24312477

Exploring the impact of target eccentricity and task difficulty on covert visual spatial attention and its implications for brain computer interfacing.

PubMed

Roijendijk, Linsey; Farquhar, Jason; van Gerven, Marcel; Jensen, Ole; Gielen, Stan

2013-01-01

Covert visual spatial attention is a relatively new task used in brain computer interfaces (BCIs) and little is known about the characteristics which may affect performance in BCI tasks. We investigated whether eccentricity and task difficulty affect alpha lateralization and BCI performance. We conducted a magnetoencephalography study with 14 participants who performed a covert orientation discrimination task at an easy or difficult stimulus contrast at either a near (3.5°) or far (7°) eccentricity. Task difficulty was manipulated block wise and subjects were aware of the difficulty level of each block. Grand average analyses revealed a significantly larger hemispheric lateralization of posterior alpha power in the difficult condition than in the easy condition, while surprisingly no difference was found for eccentricity. The difference between task difficulty levels was significant in the interval between 1.85 s and 2.25 s after cue onset and originated from a stronger decrease in the contralateral hemisphere. No significant effect of eccentricity was found. Additionally, single-trial classification analysis revealed a higher classification rate in the difficult (65.9%) than in the easy task condition (61.1%). No effect of eccentricity was found in classification rate. Our results indicate that manipulating the difficulty of a task gives rise to variations in alpha lateralization and that using a more difficult task improves covert visual spatial attention BCI performance. The variations in the alpha lateralization could be caused by different factors such as an increased mental effort or a higher visual attentional demand. Further research is necessary to discriminate between them. We did not discover any effect of eccentricity in contrast to results of previous research.

2D-DIGE analysis of mango (Mangifera indica L.) fruit reveals major proteomic changes associated with ripening.

PubMed

Andrade, Jonathan de Magalhães; Toledo, Tatiana Torres; Nogueira, Silvia Beserra; Cordenunsi, Beatriz Rosana; Lajolo, Franco Maria; do Nascimento, João Roberto Oliveira

2012-06-18

A comparative proteomic investigation between the pre-climacteric and climacteric mango fruits (cv. Keitt) was performed to identify protein species with variable abundance during ripening. Proteins were phenol-extracted from fruits, cyanine-dye-labeled, and separated on 2D gels at pH 4-7. Total spot count of about 373 proteins spots was detected in each gel and forty-seven were consistently different between pre-climacteric and climacteric fruits and were subjected to LC-MS/MS analysis. Functional classification revealed that protein species involved in carbon fixation and hormone biosynthesis decreased during ripening, whereas those related to catabolism and the stress-response, including oxidative stress and abiotic and pathogen defense factors, accumulated. In relation to fruit quality, protein species putatively involved in color development and pulp softening were also identified. This study on mango proteomics provides an overview of the biological processes that occur during ripening. Copyright © 2012 Elsevier B.V. All rights reserved.

Driver gene classification reveals a substantial overrepresentation of tumor suppressors among very large chromatin-regulating proteins.

PubMed

Waks, Zeev; Weissbrod, Omer; Carmeli, Boaz; Norel, Raquel; Utro, Filippo; Goldschmidt, Yaara

2016-12-23

Compiling a comprehensive list of cancer driver genes is imperative for oncology diagnostics and drug development. While driver genes are typically discovered by analysis of tumor genomes, infrequently mutated driver genes often evade detection due to limited sample sizes. Here, we address sample size limitations by integrating tumor genomics data with a wide spectrum of gene-specific properties to search for rare drivers, functionally classify them, and detect features characteristic of driver genes. We show that our approach, CAnceR geNe similarity-based Annotator and Finder (CARNAF), enables detection of potentially novel drivers that eluded over a dozen pan-cancer/multi-tumor type studies. In particular, feature analysis reveals a highly concentrated pool of known and putative tumor suppressors among the <1% of genes that encode very large, chromatin-regulating proteins. Thus, our study highlights the need for deeper characterization of very large, epigenetic regulators in the context of cancer causality.

Object-Based Random Forest Classification of Land Cover from Remotely Sensed Imagery for Industrial and Mining Reclamation

NASA Astrophysics Data System (ADS)

Chen, Y.; Luo, M.; Xu, L.; Zhou, X.; Ren, J.; Zhou, J.

2018-04-01

The RF method based on grid-search parameter optimization could achieve a classification accuracy of 88.16 % in the classification of images with multiple feature variables. This classification accuracy was higher than that of SVM and ANN under the same feature variables. In terms of efficiency, the RF classification method performs better than SVM and ANN, it is more capable of handling multidimensional feature variables. The RF method combined with object-based analysis approach could highlight the classification accuracy further. The multiresolution segmentation approach on the basis of ESP scale parameter optimization was used for obtaining six scales to execute image segmentation, when the segmentation scale was 49, the classification accuracy reached the highest value of 89.58 %. The classification accuracy of object-based RF classification was 1.42 % higher than that of pixel-based classification (88.16 %), and the classification accuracy was further improved. Therefore, the RF classification method combined with object-based analysis approach could achieve relatively high accuracy in the classification and extraction of land use information for industrial and mining reclamation areas. Moreover, the interpretation of remotely sensed imagery using the proposed method could provide technical support and theoretical reference for remotely sensed monitoring land reclamation.

Comparison of detection methods for HPV status as a prognostic marker for loco-regional control after radiochemotherapy in patients with HNSCC.

PubMed

Linge, Annett; Schötz, Ulrike; Löck, Steffen; Lohaus, Fabian; von Neubeck, Cläre; Gudziol, Volker; Nowak, Alexander; Tinhofer, Inge; Budach, Volker; Sak, Ali; Stuschke, Martin; Balermpas, Panagiotis; Rödel, Claus; Bunea, Hatice; Grosu, Anca-Ligia; Abdollahi, Amir; Debus, Jürgen; Ganswindt, Ute; Lauber, Kirsten; Pigorsch, Steffi; Combs, Stephanie E; Mönnich, David; Zips, Daniel; Baretton, Gustavo B; Buchholz, Frank; Krause, Mechthild; Belka, Claus; Baumann, Michael

2018-04-01

To compare six HPV detection methods in pre-treatment FFPE tumour samples from patients with locally advanced head and neck squamous cell carcinoma (HNSCC) who received postoperative (N = 175) or primary (N = 90) radiochemotherapy. HPV analyses included detection of (i) HPV16 E6/E7 RNA, (ii) HPV16 DNA (PCR-based arrays, A-PCR), (iii) HPV DNA (GP5+/GP6+ qPCR, (GP-PCR)), (iv) p16 (immunohistochemistry, p16 IHC), (v) combining p16 IHC and the A-PCR result and (vi) combining p16 IHC and the GP-PCR result. Differences between HPV positive and negative subgroups were evaluated for the primary endpoint loco-regional control (LRC) using Cox regression. Correlation between the HPV detection methods was high (chi-squared test, p < 0.001). While p16 IHC analysis resulted in several false positive classifications, A-PCR, GP-PCR and the combination of p16 IHC and A-PCR or GP-PCR led to results comparable to RNA analysis. In both cohorts, Cox regression analyses revealed significantly prolonged LRC for patients with HPV positive tumours irrespective of the detection method. The most stringent classification was obtained by detection of HPV16 RNA, or combining p16 IHC with A-PCR or GP-PCR. This approach revealed the lowest rate of recurrence in patients with tumours classified as HPV positive and therefore appears most suited for patient stratification in HPV-based clinical studies. Copyright © 2017 Elsevier B.V. All rights reserved.

ASIST SIG/CR Classification Workshop 2000: Classification for User Support and Learning.

ERIC Educational Resources Information Center

Soergel, Dagobert

2001-01-01

Reports on papers presented at the 62nd Annual Meeting of ASIST (American Society for Information Science and Technology) for the Special Interest Group in Classification Research (SIG/CR). Topics include types of knowledge; developing user-oriented classifications, including domain analysis; classification in the user interface; and automatic…

A review of intelligent systems for heart sound signal analysis.

PubMed

Nabih-Ali, Mohammed; El-Dahshan, El-Sayed A; Yahia, Ashraf S

2017-10-01

Intelligent computer-aided diagnosis (CAD) systems can enhance the diagnostic capabilities of physicians and reduce the time required for accurate diagnosis. CAD systems could provide physicians with a suggestion about the diagnostic of heart diseases. The objective of this paper is to review the recent published preprocessing, feature extraction and classification techniques and their state of the art of phonocardiogram (PCG) signal analysis. Published literature reviewed in this paper shows the potential of machine learning techniques as a design tool in PCG CAD systems and reveals that the CAD systems for PCG signal analysis are still an open problem. Related studies are compared to their datasets, feature extraction techniques and the classifiers they used. Current achievements and limitations in developing CAD systems for PCG signal analysis using machine learning techniques are presented and discussed. In the light of this review, a number of future research directions for PCG signal analysis are provided.

Association of Ki-67 Labelling Index and IL-17A with Pituitary Adenoma.

PubMed

Glebauskiene, Brigita; Liutkeviciene, Rasa; Vilkeviciute, Alvita; Gudinaviciene, Inga; Rocyte, Aurelija; Simonaviciute, Dovile; Mazetyte, Ruta; Kriauciuniene, Loresa; Zaliuniene, Dalia

2018-01-01

The aim of the present study was to determine if the Ki-67 labelling index reflects invasiveness of pituitary adenoma and to evaluate IL-17A concentration in blood serum of pituitary adenoma patients. The study was conducted in the Hospital of Lithuanian University of Health Sciences. All pituitary adenomas were analysed based on magnetic resonance imaging findings. The suprasellar extension and sphenoid sinus invasion by pituitary adenoma were classified according to Hardy classification modified by Wilson. Knosp classification system was used to quantify the invasion of the cavernous sinus. The Ki-67 labelling index was obtained by immunohistochemical analysis with the monoclonal antibody, and serum levels of IL-17A were determined by enzyme-linked immunosorbent assay (ELISA). Sixty-nine PA tissue samples were investigated. Serum levels of IL-17A were determined in 60 patients with PA and 64 control subjects. Analysis revealed statistically significantly higher Ki-67 labelling index in invasive compared to noninvasive pituitary adenomas. Median serum IL-17A level was higher in the pituitary adenoma patients than in the control group. Conclusion . IL-17A might be a significant marker for patients with pituitary adenoma and Ki-67 labelling index in case of invasive pituitary adenomas.

Classification of communication signals of the little brown bat

NASA Astrophysics Data System (ADS)

Melendez, Karla V.; Jones, Douglas L.; Feng, Albert S.

2005-09-01

Little brown bats, Myotis lucifugus, are known for their ability to echolocate and utilize their echolocation system to navigate, locate, and identify prey. Their echolocation signals have been characterized in detail, but their communication signals are poorly understood despite their widespread use during the social interactions. The goal of this study was to characterize the communication signals of little brown bats. Sound recordings were made overnight on five individual bats (housed separately from a large group of captive bats) for 7 nights, using a Pettersson ultrasound detector D240x bat detector and Nagra ARES-BB digital recorder. The spectral and temporal characteristics of recorded sounds were first analyzed using BATSOUND software from Pettersson. Sounds were first classified by visual observation of calls' temporal pattern and spectral composition, and later using an automatic classification scheme based on multivariate statistical parameters in MATLAB. Human- and machine-based analysis revealed five discrete classes of bat's communication signals: downward frequency-modulated calls, constant frequency calls, broadband noise bursts, broadband chirps, and broadband click trains. Future studies will focus on analysis of calls' spectrotemporal modulations to discriminate any subclasses that may exist. [Research supported by Grant R01-DC-04998 from the National Institute for Deafness and Communication Disorders.

Classification Preictal and Interictal Stages via Integrating Interchannel and Time-Domain Analysis of EEG Features.

PubMed

Lin, Lung-Chang; Chen, Sharon Chia-Ju; Chiang, Ching-Tai; Wu, Hui-Chuan; Yang, Rei-Cheng; Ouyang, Chen-Sen

2017-03-01

The life quality of patients with refractory epilepsy is extremely affected by abrupt and unpredictable seizures. A reliable method for predicting seizures is important in the management of refractory epilepsy. A critical factor in seizure prediction involves the classification of the preictal and interictal stages. This study aimed to develop an efficient, automatic, quantitative, and individualized approach for preictal/interictal stage identification. Five epileptic children, who had experienced at least 2 episodes of seizures during a 24-hour video EEG recording, were included. Artifact-free preictal and interictal EEG epochs were acquired, respectively, and characterized with 216 global feature descriptors. The best subset of 5 discriminative descriptors was identified. The best subsets showed differences among the patients. Statistical analysis revealed most of the 5 descriptors in each subset were significantly different between the preictal and interictal stages for each patient. The proposed approach yielded weighted averages of 97.50% correctness, 96.92% sensitivity, 97.78% specificity, and 95.45% precision on classifying test epochs. Although the case number was limited, this study successfully integrated a new EEG analytical method to classify preictal and interictal EEG segments and might be used further in predicting the occurrence of seizures.

A mapping and monitoring assessment of the Philippines' mangrove forests from 1990 to 2010

USGS Publications Warehouse

Long, Jordan; Napton, Darrell; Giri, Chandra; Graesser, Jordan

2014-01-01

Information on the present condition and spatiotemporal dynamics of mangrove forests is needed for land-change studies and integrated natural resources planning and management. Although several national mangrove estimates for the Philippines exist, information is unavailable at sufficient spatial and thematic detail for change analysis. Historical and contemporary mangrove distribution maps of the Philippines for 1990 and 2010 were prepared at nominal 30-m spatial resolution using Landsat satellite data. Image classification was performed using a supervised decision tree classification approach. Additionally, decadal land-cover change maps from 1990 to 2010 were prepared to depict changes in mangrove area. Total mangrove area decreased 10.5% from 1990 to 2010. Comparison of estimates produced from this study with selected historical mangrove area estimates revealed that total mangrove area decreased by approximately half (51.8%) from 1918 to 2010. This study provides the most current and reliable data regarding the Philippines mangrove area and spatial distribution and delineates where and when mangrove change has occurred in recent decades. The results from this study are useful for developing conservation strategies, biodiversity loss mitigation efforts, and future monitoring and analysis.

A novel approach for detection and classification of mammographic microcalcifications using wavelet analysis and extreme learning machine.

PubMed

Malar, E; Kandaswamy, A; Chakravarthy, D; Giri Dharan, A

2012-09-01

The objective of this paper is to reveal the effectiveness of wavelet based tissue texture analysis for microcalcification detection in digitized mammograms using Extreme Learning Machine (ELM). Microcalcifications are tiny deposits of calcium in the breast tissue which are potential indicators for early detection of breast cancer. The dense nature of the breast tissue and the poor contrast of the mammogram image prohibit the effectiveness in identifying microcalcifications. Hence, a new approach to discriminate the microcalcifications from the normal tissue is done using wavelet features and is compared with different feature vectors extracted using Gray Level Spatial Dependence Matrix (GLSDM) and Gabor filter based techniques. A total of 120 Region of Interests (ROIs) extracted from 55 mammogram images of mini-Mias database, including normal and microcalcification images are used in the current research. The network is trained with the above mentioned features and the results denote that ELM produces relatively better classification accuracy (94%) with a significant reduction in training time than the other artificial neural networks like Bayesnet classifier, Naivebayes classifier, and Support Vector Machine. ELM also avoids problems like local minima, improper learning rate, and over fitting. Copyright © 2012 Elsevier Ltd. All rights reserved.

Frequency Optimization for Enhancement of Surface Defect Classification Using the Eddy Current Technique

PubMed Central

Fan, Mengbao; Wang, Qi; Cao, Binghua; Ye, Bo; Sunny, Ali Imam; Tian, Guiyun

2016-01-01

Eddy current testing is quite a popular non-contact and cost-effective method for nondestructive evaluation of product quality and structural integrity. Excitation frequency is one of the key performance factors for defect characterization. In the literature, there are many interesting papers dealing with wide spectral content and optimal frequency in terms of detection sensitivity. However, research activity on frequency optimization with respect to characterization performances is lacking. In this paper, an investigation into optimum excitation frequency has been conducted to enhance surface defect classification performance. The influences of excitation frequency for a group of defects were revealed in terms of detection sensitivity, contrast between defect features, and classification accuracy using kernel principal component analysis (KPCA) and a support vector machine (SVM). It is observed that probe signals are the most sensitive on the whole for a group of defects when excitation frequency is set near the frequency at which maximum probe signals are retrieved for the largest defect. After the use of KPCA, the margins between the defect features are optimum from the perspective of the SVM, which adopts optimal hyperplanes for structure risk minimization. As a result, the best classification accuracy is obtained. The main contribution is that the influences of excitation frequency on defect characterization are interpreted, and experiment-based procedures are proposed to determine the optimal excitation frequency for a group of defects rather than a single defect with respect to optimal characterization performances. PMID:27164112

Predicted singers' vocal fold lengths and voice classification-a study of x-ray morphological measures.

PubMed

Roers, Friederike; Mürbe, Dirk; Sundberg, Johan

2009-07-01

Students admitted to the solo singing education at the University of Music Dresden, Germany have been submitted to a detailed physical examination of a variety of factors with relevance to voice function since 1959. In the years 1959-1991, this scheme of examinations included X-ray profiles of the singers' vocal tracts. This material of 132 X-rays of voice professionals was used to investigate different laryngeal morphological measures and their relation to vocal fold length. Further, the study aimed to investigate if there are consistent anatomical differences between singers of different voice classifications. The study design used was a retrospective analysis. Vocal fold length could be measured in 29 of these singer subjects directly. These data showed a strong correlation with the anterior-posterior diameter of the subglottis and the trachea as well as with the distance from the anterior contour of the thyroid cartilage to the anterior contour of the spine. These relations were used in an attempt to predict the 132 singers' vocal fold lengths. The results revealed a clear covariation between predicted vocal fold length and voice classification. Anterior-posterior subglottic-tracheal diameter yielded mean vocal fold lengths of 14.9, 16.0, 16.6, 18.4, 19.5, and 20.9mm for sopranos, mezzo-sopranos, altos, tenors, baritones, and basses, respectively. The data support the assumption that there are consistent anatomical laryngeal differences between singers of different voice classifications, which are of relevance to pitch range and timbre of the voice.

Frequency Optimization for Enhancement of Surface Defect Classification Using the Eddy Current Technique.

PubMed

Fan, Mengbao; Wang, Qi; Cao, Binghua; Ye, Bo; Sunny, Ali Imam; Tian, Guiyun

2016-05-07

Eddy current testing is quite a popular non-contact and cost-effective method for nondestructive evaluation of product quality and structural integrity. Excitation frequency is one of the key performance factors for defect characterization. In the literature, there are many interesting papers dealing with wide spectral content and optimal frequency in terms of detection sensitivity. However, research activity on frequency optimization with respect to characterization performances is lacking. In this paper, an investigation into optimum excitation frequency has been conducted to enhance surface defect classification performance. The influences of excitation frequency for a group of defects were revealed in terms of detection sensitivity, contrast between defect features, and classification accuracy using kernel principal component analysis (KPCA) and a support vector machine (SVM). It is observed that probe signals are the most sensitive on the whole for a group of defects when excitation frequency is set near the frequency at which maximum probe signals are retrieved for the largest defect. After the use of KPCA, the margins between the defect features are optimum from the perspective of the SVM, which adopts optimal hyperplanes for structure risk minimization. As a result, the best classification accuracy is obtained. The main contribution is that the influences of excitation frequency on defect characterization are interpreted, and experiment-based procedures are proposed to determine the optimal excitation frequency for a group of defects rather than a single defect with respect to optimal characterization performances.

Using self-organizing maps to develop ambient air quality classifications: a time series example

PubMed Central

2014-01-01

Background Development of exposure metrics that capture features of the multipollutant environment are needed to investigate health effects of pollutant mixtures. This is a complex problem that requires development of new methodologies. Objective Present a self-organizing map (SOM) framework for creating ambient air quality classifications that group days with similar multipollutant profiles. Methods Eight years of day-level data from Atlanta, GA, for ten ambient air pollutants collected at a central monitor location were classified using SOM into a set of day types based on their day-level multipollutant profiles. We present strategies for using SOM to develop a multipollutant metric of air quality and compare results with more traditional techniques. Results Our analysis found that 16 types of days reasonably describe the day-level multipollutant combinations that appear most frequently in our data. Multipollutant day types ranged from conditions when all pollutants measured low to days exhibiting relatively high concentrations for either primary or secondary pollutants or both. The temporal nature of class assignments indicated substantial heterogeneity in day type frequency distributions (~1%-14%), relatively short-term durations (<2 day persistence), and long-term and seasonal trends. Meteorological summaries revealed strong day type weather dependencies and pollutant concentration summaries provided interesting scenarios for further investigation. Comparison with traditional methods found SOM produced similar classifications with added insight regarding between-class relationships. Conclusion We find SOM to be an attractive framework for developing ambient air quality classification because the approach eases interpretation of results by allowing users to visualize classifications on an organized map. The presented approach provides an appealing tool for developing multipollutant metrics of air quality that can be used to support multipollutant health studies. PMID:24990361

Provenance and depositional environment of epi-shelf lake sediment from Schirmacher Oasis, East Antarctica, vis-à-vis scanning electron microscopy of quartz grain, size distribution and chemical parameters

NASA Astrophysics Data System (ADS)

Shrivastava, Prakash K.; Asthana, Rajesh; Roy, Sandip K.; Swain, Ashit K.; Dharwadkar, Amit

2012-07-01

The scientific study of quartz grains is a powerful tool in deciphering the depositional environment and mode of transportation of sediments, and ultimately the origin and classification of sediments. Surface microfeatures, angularity, chemical features, and grain-size analysis of quartz grains, collectively reveal the sedimentary and physicochemical processes that acted on the grains during different stages of their geological history. Here, we apply scanning electron microscopic (SEM) analysis to evaluating the sedimentary provenance, modes of transport, weathering characteristics, alteration, and sedimentary environment of selected detrital quartz grains from the peripheral part of two epi-shelf lakes (ESL-1 and ESL-2) of the Schirmacher Oasis of East Antarctica. Our study reveals that different styles of physical weathering, erosive signatures, and chemical precipitation variably affected these quartz grains before final deposition as lake sediments. Statistical analysis (central tendencies, sorting, skewness, and kurtosis) indicates that these quartz-bearing sediments are poorly sorted glaciofluvial sediments. Saltation and suspension seem to have been the two dominant modes of transportation, and chemical analysis of these sediments indicates a gneissic provenance.

Alteration mapping at Goldfield, Nevada, by cluster and discriminant analysis of LANDSAT digital data

NASA Technical Reports Server (NTRS)

Ballew, G.

1977-01-01

The ability of Landsat multispectral digital data to differentiate among 62 combinations of rock and alteration types at the Goldfield mining district of Western Nevada was investigated by using statistical techniques of cluster and discriminant analysis. Multivariate discriminant analysis was not effective in classifying each of the 62 groups, with classification results essentially the same whether data of four channels alone or combined with six ratios of channels were used. Bivariate plots of group means revealed a cluster of three groups including mill tailings, basalt and all other rock and alteration types. Automatic hierarchical clustering based on the fourth dimensional Mahalanobis distance between group means of 30 groups having five or more samples was performed. The results of the cluster analysis revealed hierarchies of mill tailings vs. natural materials, basalt vs. non-basalt, highly reflectant rocks vs. other rocks and exclusively unaltered rocks vs. predominantly altered rocks. The hierarchies were used to determine the order in which sets of multiple discriminant analyses were to be performed and the resulting discriminant functions were used to produce a map of geology and alteration which has an overall accuracy of 70 percent for discriminating exclusively altered rocks from predominantly altered rocks.

Monitoring of peri-distal gastrectomy carbohydrate antigen 19-9 level in gastric juice and its significance

PubMed Central

Xu, A-Man; Huang, Lei; Han, Wen-Xiu; Wei, Zhi-Jian

2014-01-01

Gastric carcinoma is one of the most common and deadly malignancies nowadays, and carbohydrate antigen 19-9 (CA 19-9) in gastric juice has been rarely studied. To compare peri-distal gastrectomy (DG) gastric juice and serum CA 19-9 and reveal its significance, we selected 67 patients diagnosed with gastric carcinoma who underwent DG, and collected their perioperative gastric juice whose CA 19-9 was detected, with serum CA 19-9 monitored as a comparison. We found that: gastric juice CA 19-9 pre-gastrectomy was significantly correlated with tumor TNM classification, regarding tumor size, level of gastric wall invaded, differentiated grade and number of metastatic lymph nodes as influencing factors, while serum CA 19-9 revealed little information; gastric juice CA 19-9 was significantly correlated with radical degree, and regarded number of resected lymph nodes and classification of cutting edge as impact factors; thirteen patients whose gastric juice CA 19-9 rose post-DG showed features indicating poor prognosis; the difference of gastric juice CA 19-9 between pre- and post-gastrectomy was correlated with tumor TNM classification and radical degree, and regarded tumor size, number of resected metastatic and normal lymph nodes, sum of distances from tumor to cutting edges and classification of cutting edge as influential factors. We conclude that peri-DG gastric juice CA 19-9 reveals much information about tumor and radical gastrectomy, and may indicate prognosis; while serum CA 19-9 has limited significance. PMID:24482710

Transporter taxonomy - a comparison of different transport protein classification schemes.

PubMed

Viereck, Michael; Gaulton, Anna; Digles, Daniela; Ecker, Gerhard F

2014-06-01

Currently, there are more than 800 well characterized human membrane transport proteins (including channels and transporters) and there are estimates that about 10% (approx. 2000) of all human genes are related to transport. Membrane transport proteins are of interest as potential drug targets, for drug delivery, and as a cause of side effects and drug–drug interactions. In light of the development of Open PHACTS, which provides an open pharmacological space, we analyzed selected membrane transport protein classification schemes (Transporter Classification Database, ChEMBL, IUPHAR/BPS Guide to Pharmacology, and Gene Ontology) for their ability to serve as a basis for pharmacology driven protein classification. A comparison of these membrane transport protein classification schemes by using a set of clinically relevant transporters as use-case reveals the strengths and weaknesses of the different taxonomy approaches.

Motor Oil Classification using Color Histograms and Pattern Recognition Techniques.

PubMed

Ahmadi, Shiva; Mani-Varnosfaderani, Ahmad; Habibi, Biuck

2018-04-20

Motor oil classification is important for quality control and the identification of oil adulteration. In thiswork, we propose a simple, rapid, inexpensive and nondestructive approach based on image analysis and pattern recognition techniques for the classification of nine different types of motor oils according to their corresponding color histograms. For this, we applied color histogram in different color spaces such as red green blue (RGB), grayscale, and hue saturation intensity (HSI) in order to extract features that can help with the classification procedure. These color histograms and their combinations were used as input for model development and then were statistically evaluated by using linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and support vector machine (SVM) techniques. Here, two common solutions for solving a multiclass classification problem were applied: (1) transformation to binary classification problem using a one-against-all (OAA) approach and (2) extension from binary classifiers to a single globally optimized multilabel classification model. In the OAA strategy, LDA, QDA, and SVM reached up to 97% in terms of accuracy, sensitivity, and specificity for both the training and test sets. In extension from binary case, despite good performances by the SVM classification model, QDA and LDA provided better results up to 92% for RGB-grayscale-HSI color histograms and up to 93% for the HSI color map, respectively. In order to reduce the numbers of independent variables for modeling, a principle component analysis algorithm was used. Our results suggest that the proposed method is promising for the identification and classification of different types of motor oils.

The phosphoproteome of Aspergillus nidulans reveals functional association with cellular processes involved in morphology and secretion.

PubMed

Ramsubramaniam, Nikhil; Harris, Steven D; Marten, Mark R

2014-11-01

We describe the first phosphoproteome of the model filamentous fungus Aspergillus nidulans. Phosphopeptides were enriched using titanium dioxide, separated using a convenient ultra-long reverse phase gradient, and identified using a "high-high" strategy (high mass accuracy on the parent and fragment ions) with higher-energy collisional dissociation. Using this approach 1801 phosphosites, from 1637 unique phosphopeptides, were identified. Functional classification revealed phosphoproteins were overrepresented under GO categories related to fungal morphogenesis: "sites of polar growth," "vesicle mediated transport," and "cytoskeleton organization." In these same GO categories, kinase-substrate analysis of phosphoproteins revealed the majority were target substrates of CDK and CK2 kinase families, indicating these kinase families play a prominent role in fungal morphogenesis. Kinase-substrate analysis also identified 57 substrates for kinases known to regulate secretion of hydrolytic enzymes (e.g. PkaA, SchA, and An-Snf1). Altogether this data will serve as a benchmark that can be used to elucidate regulatory networks functionally associated with fungal morphogenesis and secretion. All MS data have been deposited in the ProteomeXchange with identifier PXD000715 (http://proteomecentral.proteomexchange.org/dataset/PXD000715). © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Automated identification of sleep states from EEG signals by means of ensemble empirical mode decomposition and random under sampling boosting.

PubMed

Hassan, Ahnaf Rashik; Bhuiyan, Mohammed Imamul Hassan

2017-03-01

Automatic sleep staging is essential for alleviating the burden of the physicians of analyzing a large volume of data by visual inspection. It is also a precondition for making an automated sleep monitoring system feasible. Further, computerized sleep scoring will expedite large-scale data analysis in sleep research. Nevertheless, most of the existing works on sleep staging are either multichannel or multiple physiological signal based which are uncomfortable for the user and hinder the feasibility of an in-home sleep monitoring device. So, a successful and reliable computer-assisted sleep staging scheme is yet to emerge. In this work, we propose a single channel EEG based algorithm for computerized sleep scoring. In the proposed algorithm, we decompose EEG signal segments using Ensemble Empirical Mode Decomposition (EEMD) and extract various statistical moment based features. The effectiveness of EEMD and statistical features are investigated. Statistical analysis is performed for feature selection. A newly proposed classification technique, namely - Random under sampling boosting (RUSBoost) is introduced for sleep stage classification. This is the first implementation of EEMD in conjunction with RUSBoost to the best of the authors' knowledge. The proposed feature extraction scheme's performance is investigated for various choices of classification models. The algorithmic performance of our scheme is evaluated against contemporary works in the literature. The performance of the proposed method is comparable or better than that of the state-of-the-art ones. The proposed algorithm gives 88.07%, 83.49%, 92.66%, 94.23%, and 98.15% for 6-state to 2-state classification of sleep stages on Sleep-EDF database. Our experimental outcomes reveal that RUSBoost outperforms other classification models for the feature extraction framework presented in this work. Besides, the algorithm proposed in this work demonstrates high detection accuracy for the sleep states S1 and REM. Statistical moment based features in the EEMD domain distinguish the sleep states successfully and efficaciously. The automated sleep scoring scheme propounded herein can eradicate the onus of the clinicians, contribute to the device implementation of a sleep monitoring system, and benefit sleep research. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Discriminant forest classification method and system

DOEpatents

Chen, Barry Y.; Hanley, William G.; Lemmond, Tracy D.; Hiller, Lawrence J.; Knapp, David A.; Mugge, Marshall J.

2012-11-06

A hybrid machine learning methodology and system for classification that combines classical random forest (RF) methodology with discriminant analysis (DA) techniques to provide enhanced classification capability. A DA technique which uses feature measurements of an object to predict its class membership, such as linear discriminant analysis (LDA) or Andersen-Bahadur linear discriminant technique (AB), is used to split the data at each node in each of its classification trees to train and grow the trees and the forest. When training is finished, a set of n DA-based decision trees of a discriminant forest is produced for use in predicting the classification of new samples of unknown class.

Tissue classification for laparoscopic image understanding based on multispectral texture analysis

NASA Astrophysics Data System (ADS)

Zhang, Yan; Wirkert, Sebastian J.; Iszatt, Justin; Kenngott, Hannes; Wagner, Martin; Mayer, Benjamin; Stock, Christian; Clancy, Neil T.; Elson, Daniel S.; Maier-Hein, Lena

2016-03-01

Intra-operative tissue classification is one of the prerequisites for providing context-aware visualization in computer-assisted minimally invasive surgeries. As many anatomical structures are difficult to differentiate in conventional RGB medical images, we propose a classification method based on multispectral image patches. In a comprehensive ex vivo study we show (1) that multispectral imaging data is superior to RGB data for organ tissue classification when used in conjunction with widely applied feature descriptors and (2) that combining the tissue texture with the reflectance spectrum improves the classification performance. Multispectral tissue analysis could thus evolve as a key enabling technique in computer-assisted laparoscopy.

Functional Basis of Microorganism Classification.

PubMed

Zhu, Chengsheng; Delmont, Tom O; Vogel, Timothy M; Bromberg, Yana

2015-08-01

Correctly identifying nearest "neighbors" of a given microorganism is important in industrial and clinical applications where close relationships imply similar treatment. Microbial classification based on similarity of physiological and genetic organism traits (polyphasic similarity) is experimentally difficult and, arguably, subjective. Evolutionary relatedness, inferred from phylogenetic markers, facilitates classification but does not guarantee functional identity between members of the same taxon or lack of similarity between different taxa. Using over thirteen hundred sequenced bacterial genomes, we built a novel function-based microorganism classification scheme, functional-repertoire similarity-based organism network (FuSiON; flattened to fusion). Our scheme is phenetic, based on a network of quantitatively defined organism relationships across the known prokaryotic space. It correlates significantly with the current taxonomy, but the observed discrepancies reveal both (1) the inconsistency of functional diversity levels among different taxa and (2) an (unsurprising) bias towards prioritizing, for classification purposes, relatively minor traits of particular interest to humans. Our dynamic network-based organism classification is independent of the arbitrary pairwise organism similarity cut-offs traditionally applied to establish taxonomic identity. Instead, it reveals natural, functionally defined organism groupings and is thus robust in handling organism diversity. Additionally, fusion can use organism meta-data to highlight the specific environmental factors that drive microbial diversification. Our approach provides a complementary view to cladistic assignments and holds important clues for further exploration of microbial lifestyles. Fusion is a more practical fit for biomedical, industrial, and ecological applications, as many of these rely on understanding the functional capabilities of the microbes in their environment and are less concerned with phylogenetic descent.

Functional Basis of Microorganism Classification

PubMed Central

Zhu, Chengsheng; Delmont, Tom O.; Vogel, Timothy M.; Bromberg, Yana

2015-01-01

Correctly identifying nearest “neighbors” of a given microorganism is important in industrial and clinical applications where close relationships imply similar treatment. Microbial classification based on similarity of physiological and genetic organism traits (polyphasic similarity) is experimentally difficult and, arguably, subjective. Evolutionary relatedness, inferred from phylogenetic markers, facilitates classification but does not guarantee functional identity between members of the same taxon or lack of similarity between different taxa. Using over thirteen hundred sequenced bacterial genomes, we built a novel function-based microorganism classification scheme, functional-repertoire similarity-based organism network (FuSiON; flattened to fusion). Our scheme is phenetic, based on a network of quantitatively defined organism relationships across the known prokaryotic space. It correlates significantly with the current taxonomy, but the observed discrepancies reveal both (1) the inconsistency of functional diversity levels among different taxa and (2) an (unsurprising) bias towards prioritizing, for classification purposes, relatively minor traits of particular interest to humans. Our dynamic network-based organism classification is independent of the arbitrary pairwise organism similarity cut-offs traditionally applied to establish taxonomic identity. Instead, it reveals natural, functionally defined organism groupings and is thus robust in handling organism diversity. Additionally, fusion can use organism meta-data to highlight the specific environmental factors that drive microbial diversification. Our approach provides a complementary view to cladistic assignments and holds important clues for further exploration of microbial lifestyles. Fusion is a more practical fit for biomedical, industrial, and ecological applications, as many of these rely on understanding the functional capabilities of the microbes in their environment and are less concerned with phylogenetic descent. PMID:26317871

7 CFR 160.61 - Kinds of certificates issued.

Code of Federal Regulations, 2013 CFR

2013-01-01

.... The kind of certificates issued are as follows: (a) Turpentine analysis and classification certificate. (b) Turpentine field classification certificate. (c) Rosin classification and grade certificate. (d...

7 CFR 160.61 - Kinds of certificates issued.

Code of Federal Regulations, 2014 CFR

2014-01-01

.... The kind of certificates issued are as follows: (a) Turpentine analysis and classification certificate. (b) Turpentine field classification certificate. (c) Rosin classification and grade certificate. (d...

7 CFR 160.61 - Kinds of certificates issued.

Code of Federal Regulations, 2011 CFR

2011-01-01

.... The kind of certificates issued are as follows: (a) Turpentine analysis and classification certificate. (b) Turpentine field classification certificate. (c) Rosin classification and grade certificate. (d...

7 CFR 160.61 - Kinds of certificates issued.

Code of Federal Regulations, 2012 CFR

2012-01-01

.... The kind of certificates issued are as follows: (a) Turpentine analysis and classification certificate. (b) Turpentine field classification certificate. (c) Rosin classification and grade certificate. (d...

International standards for neurological classification of spinal cord injury: impact of the revised worksheet (revision 02/13) on classification performance.

PubMed

Schuld, Christian; Franz, Steffen; Brüggemann, Karin; Heutehaus, Laura; Weidner, Norbert; Kirshblum, Steven C; Rupp, Rüdiger

2016-09-01

Prospective cohort study. Comparison of the classification performance between the worksheet revisions of 2011 and 2013 of the International Standards for Neurological Classification of Spinal Cord Injury (ISNCSCI). Ongoing ISNCSCI instructional courses of the European Multicenter Study on Human Spinal Cord Injury (EMSCI). For quality control all participants were requested to classify five ISNCSCI cases directly before (pre-test) and after (post-test) the workshop. One hundred twenty-five clinicians working in 22 SCI centers attended the instructional course between November 2011 and March 2015. Seventy-two clinicians completed the post-test with the 2011 revision of the worksheet and 53 with the 2013 revision. Not applicable. The clinicians' classification performance assessed by the percentage of correctly determined motor levels (ML) and sensory levels, neurological levels of injury (NLI), ASIA Impairment Scales and zones of partial preservations. While no group differences were found in the pre-tests, the overall performance (rev2011: 92.2% ± 6.7%, rev2013: 94.3% ± 7.7%; P = 0.010), the percentage of correct MLs (83.2% ± 14.5% vs. 88.1% ± 15.3%; P = 0.046) and NLIs (86.1% ± 16.7% vs. 90.9% ± 18.6%; P = 0.043) improved significantly in the post-tests. Detailed ML analysis revealed the largest benefit of the 2013 revision (50.0% vs. 67.0%) in a case with a high cervical injury (NLI C2). The results from the EMSCI ISNCSCI post-tests show a significantly better classification performance using the revised 2013 worksheet presumably due to the body-side based grouping of myotomes and dermatomes and their correct horizontal alignment. Even with these proven advantages of the new layout, the correct determination of MLs in the segments C2-C4 remains difficult.

Quantitative evaluation of variations in rule-based classifications of land cover in urban neighbourhoods using WorldView-2 imagery.

PubMed

Belgiu, Mariana; Dr Guţ, Lucian; Strobl, Josef

2014-01-01

The increasing availability of high resolution imagery has triggered the need for automated image analysis techniques, with reduced human intervention and reproducible analysis procedures. The knowledge gained in the past might be of use to achieving this goal, if systematically organized into libraries which would guide the image analysis procedure. In this study we aimed at evaluating the variability of digital classifications carried out by three experts who were all assigned the same interpretation task. Besides the three classifications performed by independent operators, we developed an additional rule-based classification that relied on the image classifications best practices found in the literature, and used it as a surrogate for libraries of object characteristics. The results showed statistically significant differences among all operators who classified the same reference imagery. The classifications carried out by the experts achieved satisfactory results when transferred to another area for extracting the same classes of interest, without modification of the developed rules.

Quantitative evaluation of variations in rule-based classifications of land cover in urban neighbourhoods using WorldView-2 imagery

PubMed Central

Belgiu, Mariana; Drǎguţ, Lucian; Strobl, Josef

2014-01-01

The increasing availability of high resolution imagery has triggered the need for automated image analysis techniques, with reduced human intervention and reproducible analysis procedures. The knowledge gained in the past might be of use to achieving this goal, if systematically organized into libraries which would guide the image analysis procedure. In this study we aimed at evaluating the variability of digital classifications carried out by three experts who were all assigned the same interpretation task. Besides the three classifications performed by independent operators, we developed an additional rule-based classification that relied on the image classifications best practices found in the literature, and used it as a surrogate for libraries of object characteristics. The results showed statistically significant differences among all operators who classified the same reference imagery. The classifications carried out by the experts achieved satisfactory results when transferred to another area for extracting the same classes of interest, without modification of the developed rules. PMID:24623959

Analysis of Landsat-4 Thematic Mapper data for classification of forest stands in Baldwin County, Alabama

NASA Technical Reports Server (NTRS)

Hill, C. L.

1984-01-01

A computer-implemented classification has been derived from Landsat-4 Thematic Mapper data acquired over Baldwin County, Alabama on January 15, 1983. One set of spectral signatures was developed from the data by utilizing a 3x3 pixel sliding window approach. An analysis of the classification produced from this technique identified forested areas. Additional information regarding only the forested areas. Additional information regarding only the forested areas was extracted by employing a pixel-by-pixel signature development program which derived spectral statistics only for pixels within the forested land covers. The spectral statistics from both approaches were integrated and the data classified. This classification was evaluated by comparing the spectral classes produced from the data against corresponding ground verification polygons. This iterative data analysis technique resulted in an overall classification accuracy of 88.4 percent correct for slash pine, young pine, loblolly pine, natural pine, and mixed hardwood-pine. An accuracy assessment matrix has been produced for the classification.

Quantitative evaluation of variations in rule-based classifications of land cover in urban neighbourhoods using WorldView-2 imagery

NASA Astrophysics Data System (ADS)

Belgiu, Mariana; ǎguţ, Lucian, , Dr; Strobl, Josef

2014-01-01

The increasing availability of high resolution imagery has triggered the need for automated image analysis techniques, with reduced human intervention and reproducible analysis procedures. The knowledge gained in the past might be of use to achieving this goal, if systematically organized into libraries which would guide the image analysis procedure. In this study we aimed at evaluating the variability of digital classifications carried out by three experts who were all assigned the same interpretation task. Besides the three classifications performed by independent operators, we developed an additional rule-based classification that relied on the image classifications best practices found in the literature, and used it as a surrogate for libraries of object characteristics. The results showed statistically significant differences among all operators who classified the same reference imagery. The classifications carried out by the experts achieved satisfactory results when transferred to another area for extracting the same classes of interest, without modification of the developed rules.

Electronic 12-Hour Dietary Recall (e-12HR): Comparison of a Mobile Phone App for Dietary Intake Assessment With a Food Frequency Questionnaire and Four Dietary Records.

PubMed

Béjar, Luis María; Reyes, Óscar Adrián; García-Perea, María Dolores

2018-06-15

One of the greatest challenges in nutritional epidemiology is improving upon traditional self-reporting methods for the assessment of habitual dietary intake. The aim of this study was to evaluate the relative validity of a new method known as the current-day dietary recall (or current-day recall), based on a smartphone app called 12-hour dietary recall, for determining the habitual intake of a series of key food and drink groups using a food frequency questionnaire (FFQ) and four dietary records as reference methods. University students over the age of 18 years recorded their consumption of certain groups of food and drink using 12-hour dietary recall for 28 consecutive days. During this 28-day period, they also completed four dietary records on randomly selected days. Once the monitoring period was over, subjects then completed an FFQ. The two methods were compared using the Spearman correlation coefficient (SCC), a cross-classification analysis, and weighted kappa. A total of 87 participants completed the study (64% women, 56/87; 36% men, 31/87). For e-12HR versus FFQ, for all food and drink groups, the average SCC was 0.70. Cross-classification analysis revealed that the average percentage of individuals classified in the exact agreement category was 51.5%; exact agreement + adjacent was 91.8%, and no participant (0%) was classified in the extreme disagreement category. The average weighted kappa was 0.51. For e-12HR versus the four dietary records, for all food and drink groups, the average SCC was 0.63. Cross-classification analysis revealed that the average percentage of individuals classified in the exact agreement category was 47.1%; exact agreement + adjacent was 89.2%; and no participant (0%) was classified in the extreme disagreement category. The average weighted kappa was 0.47. Current-day recall, based on the 12-hour dietary recall app, was found to be in good agreement with the two reference methods (FFQ & four dietary records), demonstrating its potential usefulness for categorizing individuals according to their habitual dietary intake of certain food and drink groups. ©Luis María Béjar, Óscar Adrián Reyes, María Dolores García-Perea. Originally published in JMIR Mhealth and Uhealth (http://mhealth.jmir.org), 15.06.2018.

Circular RNA and gene expression profiles in gastric cancer based on microarray chip technology.

PubMed

Sui, Weiguo; Shi, Zhoufang; Xue, Wen; Ou, Minglin; Zhu, Ying; Chen, Jiejing; Lin, Hua; Liu, Fuhua; Dai, Yong

2017-03-01

The aim of the present study was to screen gastric cancer (GC) tissue and adjacent tissue for differences in mRNA and circular (circRNA) expression, to analyze the differences in circRNA and mRNA expression, and to investigate the circRNA expression in gastric carcinoma and its mechanism. circRNA and mRNA differential expression profiles generated using Agilent microarray technology were analyzed in the GC tissues and adjacent tissues. qRT-PCR was used to verify the differential expression of circRNAs and mRNAs according to the interactions between circRNAs and miRNAs as well as the possible existence of miRNA and mRNA interactions. We found that: i) the circRNA expression profile revealed 1,285 significant differences in circRNA expression, with circRNA expression downregulated in 594 samples and upregulated in 691 samples via interactions with miRNAs. The qRT-PCR validation experiments showed that hsa_circRNA_400071, hsa_circRNA_000543 and hsa_circRNA_001959 expression was consistent with the microarray analysis results. ii) 29,112 genes were found in the GC tissues and adjacent tissues, including 5,460 differentially expressed genes. Among them, 2,390 differentially expressed genes were upregulated and 3,070 genes were downregulated. Gene Ontology (GO) analysis of the differentially expressed genes revealed these genes involved in biological process classification, cellular component classification and molecular function classification. Pathway analysis of the differentially expressed genes identified 83 significantly enriched genes, including 28 upregulated genes and 55 downregulated genes. iii) 69 differentially expressed circRNAs were found that might adsorb specific miRNAs to regulate the expression of their target gene mRNAs. The conclusions are: i) differentially expressed circRNAs had corresponding miRNA binding sites. These circRNAs regulated the expression of target genes through interactions with miRNAs and might become new molecular biomarkers for GC in the future. ii) Differentially expressed genes may be involved in the occurrence of GC via a variety of mechanisms. iii) CD44, CXXC5, MYH9, MALAT1 and other genes may have important implications for the occurrence and development of GC through the regulation, interaction, and mutual influence of circRNA-miRNA-mRNA via different mechanisms.

Multiple-rule bias in the comparison of classification rules

PubMed Central

Yousefi, Mohammadmahdi R.; Hua, Jianping; Dougherty, Edward R.

2011-01-01

Motivation: There is growing discussion in the bioinformatics community concerning overoptimism of reported results. Two approaches contributing to overoptimism in classification are (i) the reporting of results on datasets for which a proposed classification rule performs well and (ii) the comparison of multiple classification rules on a single dataset that purports to show the advantage of a certain rule. Results: This article provides a careful probabilistic analysis of the second issue and the ‘multiple-rule bias’, resulting from choosing a classification rule having minimum estimated error on the dataset. It quantifies this bias corresponding to estimating the expected true error of the classification rule possessing minimum estimated error and it characterizes the bias from estimating the true comparative advantage of the chosen classification rule relative to the others by the estimated comparative advantage on the dataset. The analysis is applied to both synthetic and real data using a number of classification rules and error estimators. Availability: We have implemented in C code the synthetic data distribution model, classification rules, feature selection routines and error estimation methods. The code for multiple-rule analysis is implemented in MATLAB. The source code is available at http://gsp.tamu.edu/Publications/supplementary/yousefi11a/. Supplementary simulation results are also included. Contact: edward@ece.tamu.edu Supplementary Information: Supplementary data are available at Bioinformatics online. PMID:21546390

Ecoregions and ecodistricts: Ecological regionalizations for the Netherlands' environmental policy

NASA Astrophysics Data System (ADS)

Klijn, Frans; de Waal, Rein W.; Oude Voshaar, Jan H.

1995-11-01

For communicating data on the state of the environment to policy makers, various integrative frameworks are used, including regional integration. For this kind of integration we have developed two related ecological regionalizations, ecoregions and ecodistricts, which are two levels in a series of classifications for hierarchically nested ecosystems at different spatial scale levels. We explain the compilation of the maps from existing geographical data, demonstrating the relatively holistic, a priori integrated approach. The resulting maps are submitted to discriminant analysis to test the consistancy of the use of mapping characteristics, using data on individual abiotic ecosystem components from a national database on a 1-km2 grid. This reveals that the spatial patterns of soil, groundwater, and geomorphology correspond with the ecoregion and ecodistrict maps. Differences between the original maps and maps formed by automatically reclassifying 1-km2 cells with these discriminant components are found to be few. These differences are discussed against the background of the principal dilemma between deductive, a priori integrated, and inductive, a posteriori, classification.

Using Single-trial EEG to Predict and Analyze Subsequent Memory

PubMed Central

Noh, Eunho; Herzmann, Grit; Curran, Tim; de Sa, Virginia R.

2013-01-01

We show that it is possible to successfully predict subsequent memory performance based on single-trial EEG activity before and during item presentation in the study phase. Two-class classification was conducted to predict subsequently remembered vs. forgotten trials based on subjects’ responses in the recognition phase. The overall accuracy across 18 subjects was 59.6 % by combining pre- and during-stimulus information. The single-trial classification analysis provides a dimensionality reduction method to project the high-dimensional EEG data onto a discriminative space. These projections revealed novel findings in the pre- and during-stimulus period related to levels of encoding. It was observed that the pre-stimulus information (specifically oscillatory activity between 25–35Hz) −300 to 0 ms before stimulus presentation and during-stimulus alpha (7–12 Hz) information between 1000–1400 ms after stimulus onset distinguished between recollection and familiarity while the during-stimulus alpha information and temporal information between 400–800 ms after stimulus onset mapped these two states to similar values. PMID:24064073

Impact of the revised (2008) EORTC/MSG definitions for invasive fungal disease on the rates of diagnosis of invasive aspergillosis.

PubMed

Tsitsikas, Dimitris A; Morin, Amelie; Araf, Shamzah; Murtagh, Bernadine; Johnson, Gemma; Vinnicombe, Sarah; Ellis, Stephen; Suaris, Tamara; Wilks, Mark; Doffman, Sarah; Agrawal, Samir G

2012-07-01

Diagnosis of invasive aspergillosis (IA) remains a challenge as the clinical manifestations are not specific, and a histological diagnosis is often unfeasible. The 2002 European Organization for Research and Treatment of Cancer (EORTC) and the National Institute of Allergy and Infectious Diseases Mycoses Study Group (MSG) criteria for classification of cases into possible, probable or proven were revised in 2008. Our objective was to analyze the impact of these revisions on the diagnosis of IA. A retrospective analysis of 589 high risk patient-episodes revealed that 125 of 155 'possible' (81%) and 12 of 16 'probable' (75%) cases of IA should be changed to 'non-classifiable' when the new criteria were applied. We concluded, as expected, that the 2008 EORTC/MSG revised definitions reduced the number of cases classified as 'possible' IA, but additionally, there has been a dramatic reduction in 'probable' cases. These changes have significant implications on the interpretation of clinical trial data based on EORTC/MSG classifications.

Fault tolerance in computational grids: perspectives, challenges, and issues.

PubMed

Haider, Sajjad; Nazir, Babar

2016-01-01

Computational grids are established with the intention of providing shared access to hardware and software based resources with special reference to increased computational capabilities. Fault tolerance is one of the most important issues faced by the computational grids. The main contribution of this survey is the creation of an extended classification of problems that incur in the computational grid environments. The proposed classification will help researchers, developers, and maintainers of grids to understand the types of issues to be anticipated. Moreover, different types of problems, such as omission, interaction, and timing related have been identified that need to be handled on various layers of the computational grid. In this survey, an analysis and examination is also performed pertaining to the fault tolerance and fault detection mechanisms. Our conclusion is that a dependable and reliable grid can only be established when more emphasis is on fault identification. Moreover, our survey reveals that adaptive and intelligent fault identification, and tolerance techniques can improve the dependability of grid working environments.

Changing techniques in crop plant classification: molecularization at the National Institute of Agricultural Botany during the 1980s.

PubMed

Holmes, Matthew

2017-04-01

Modern methods of analysing biological materials, including protein and DNA sequencing, are increasingly the objects of historical study. Yet twentieth-century taxonomic techniques have been overlooked in one of their most important contexts: agricultural botany. This paper addresses this omission by harnessing unexamined archival material from the National Institute of Agricultural Botany (NIAB), a British plant science organization. During the 1980s the NIAB carried out three overlapping research programmes in crop identification and analysis: electrophoresis, near infrared spectroscopy (NIRS) and machine vision systems. For each of these three programmes, contemporary economic, statutory and scientific factors behind their uptake by the NIAB are discussed. This approach reveals significant links between taxonomic practice at the NIAB and historical questions around agricultural research, intellectual property and scientific values. Such links are of further importance given that the techniques developed by researchers at the NIAB during the 1980s remain part of crop classification guidelines issued by international bodies today.

Automated classification of mouse pup isolation syllables: from cluster analysis to an Excel-based "mouse pup syllable classification calculator".

PubMed

Grimsley, Jasmine M S; Gadziola, Marie A; Wenstrup, Jeffrey J

2012-01-01

Mouse pups vocalize at high rates when they are cold or isolated from the nest. The proportions of each syllable type produced carry information about disease state and are being used as behavioral markers for the internal state of animals. Manual classifications of these vocalizations identified 10 syllable types based on their spectro-temporal features. However, manual classification of mouse syllables is time consuming and vulnerable to experimenter bias. This study uses an automated cluster analysis to identify acoustically distinct syllable types produced by CBA/CaJ mouse pups, and then compares the results to prior manual classification methods. The cluster analysis identified two syllable types, based on their frequency bands, that have continuous frequency-time structure, and two syllable types featuring abrupt frequency transitions. Although cluster analysis computed fewer syllable types than manual classification, the clusters represented well the probability distributions of the acoustic features within syllables. These probability distributions indicate that some of the manually classified syllable types are not statistically distinct. The characteristics of the four classified clusters were used to generate a Microsoft Excel-based mouse syllable classifier that rapidly categorizes syllables, with over a 90% match, into the syllable types determined by cluster analysis.

Online Learning for Classification of Alzheimer Disease based on Cortical Thickness and Hippocampal Shape Analysis.

PubMed

Lee, Ga-Young; Kim, Jeonghun; Kim, Ju Han; Kim, Kiwoong; Seong, Joon-Kyung

2014-01-01

Mobile healthcare applications are becoming a growing trend. Also, the prevalence of dementia in modern society is showing a steady growing trend. Among degenerative brain diseases that cause dementia, Alzheimer disease (AD) is the most common. The purpose of this study was to identify AD patients using magnetic resonance imaging in the mobile environment. We propose an incremental classification for mobile healthcare systems. Our classification method is based on incremental learning for AD diagnosis and AD prediction using the cortical thickness data and hippocampus shape. We constructed a classifier based on principal component analysis and linear discriminant analysis. We performed initial learning and mobile subject classification. Initial learning is the group learning part in our server. Our smartphone agent implements the mobile classification and shows various results. With use of cortical thickness data analysis alone, the discrimination accuracy was 87.33% (sensitivity 96.49% and specificity 64.33%). When cortical thickness data and hippocampal shape were analyzed together, the achieved accuracy was 87.52% (sensitivity 96.79% and specificity 63.24%). In this paper, we presented a classification method based on online learning for AD diagnosis by employing both cortical thickness data and hippocampal shape analysis data. Our method was implemented on smartphone devices and discriminated AD patients for normal group.

Perinatal mortality classification: an analysis of 112 cases of stillbirth.

PubMed

Reis, Ana Paula; Rocha, Ana; Lebre, Andrea; Ramos, Umbelina; Cunha, Ana

2017-10-01

This was a retrospective cohort analysis of stillbirths that occurred from January 2004 to December 2013 in our institution. We compared Tulip and Wigglesworth classification systems on a cohort of stillbirths and analysed the main differences between these two classifications. In this period, there were 112 stillbirths of a total of 31,758 births (stillbirth rate of 3.5 per 1000 births). There were 99 antepartum deaths and 13 intrapartum deaths. Foetal autopsy was performed in 99 cases and placental histopathological examination in all of the cases. The Wigglesworth found 'unknown' causes in 47 cases and the Tulip classification allocated 33 of these. Fourteen cases remained in the group of 'unknown' causes. Therefore, the Wigglesworth classification of stillbirths results in a higher proportion of unexplained stillbirths. We suggest that the traditional Wigglesworth classification should be substituted by a classification that manages the available information.

Gene-expression signatures can distinguish gastric cancer grades and stages.

PubMed

Cui, Juan; Li, Fan; Wang, Guoqing; Fang, Xuedong; Puett, J David; Xu, Ying

2011-03-18

Microarray gene-expression data of 54 paired gastric cancer and adjacent noncancerous gastric tissues were analyzed, with the aim to establish gene signatures for cancer grades (well-, moderately-, poorly- or un-differentiated) and stages (I, II, III and IV), which have been determined by pathologists. Our statistical analysis led to the identification of a number of gene combinations whose expression patterns serve well as signatures of different grades and different stages of gastric cancer. A 19-gene signature was found to have discerning power between high- and low-grade gastric cancers in general, with overall classification accuracy at 79.6%. An expanded 198-gene panel allows the stratification of cancers into four grades and control, giving rise to an overall classification agreement of 74.2% between each grade designated by the pathologists and our prediction. Two signatures for cancer staging, consisting of 10 genes and 9 genes, respectively, provide high classification accuracies at 90.0% and 84.0%, among early-, advanced-stage cancer and control. Functional and pathway analyses on these signature genes reveal the significant relevance of the derived signatures to cancer grades and progression. To the best of our knowledge, this represents the first study on identification of genes whose expression patterns can serve as markers for cancer grades and stages.

Characterizing the SEMG patterns with myofascial pain using a multi-scale wavelet model through machine learning approaches.

PubMed

Lin, Yu-Ching; Yu, Nan-Ying; Jiang, Ching-Fen; Chang, Shao-Hsia

2018-06-02

In this paper, we introduce a newly developed multi-scale wavelet model for the interpretation of surface electromyography (SEMG) signals and validate the model's capability to characterize changes in neuromuscular activation in cases with myofascial pain syndrome (MPS) via machine learning methods. The SEMG data collected from normal (N = 30; 27 women, 3 men) and MPS subjects (N = 26; 22 women, 4 men) were adopted for this retrospective analysis. SMEGs were measured from the taut-band loci on both sides of the trapezius muscle on the upper back while he/she conducted a cyclic bilateral backward shoulder extension movement within 1 min. Classification accuracy of the SEMG model to differentiate MPS patients from normal subjects was 77% using template matching and 60% using K-means clustering. Classification consistency between the two machine learning methods was 87% in the normal group and 93% in the MPS group. The 2D feature graphs derived from the proposed multi-scale model revealed distinct patterns between normal subjects and MPS patients. The classification consistency using template matching and K-means clustering suggests the potential of using the proposed model to characterize interference pattern changes induced by MPS. Copyright © 2018. Published by Elsevier Ltd.

Dissociating mental states related to doing nothing by means of fMRI pattern classification.

PubMed

Kühn, Simone; Bodammer, Nils Christian; Brass, Marcel

2010-12-01

Most juridical systems recognize intentional non-actions - the failure to render assistance - as intentional acts by regarding them as in principle culpable. This raises the fundamental question whether intentional non-actions can be distinguished from simply not doing anything. Classical GLM analysis on functional magnetic resonance imaging (fMRI) data reveals that not doing anything is associated with resting state brain areas whereas intentionally non-acting is associated with brain activity in left inferior parietal lobe and left dorsal premotor cortex. By means of pattern classification we quantify the accuracy with which we can distinguish these two mental states on the basis of brain activity. In order to identify brain regions that harbour a distributed, overlapping representation of voluntary non-actions and the decision not to act we performed pattern classification on brain areas that did not appear in the GLM contrasts. The prediction rate is not reduced and we show that the prediction relies mostly on brain areas that have been associated with action production and motor imagery as supplementary motor area, right inferior frontal gyrus and right middle temporal area (V5/MT). Hence our data support the implicit assumption of legal practice that voluntary non-action shares important features with overt voluntary action. Copyright © 2010 Elsevier Inc. All rights reserved.

Optimal Representation of Anuran Call Spectrum in Environmental Monitoring Systems Using Wireless Sensor Networks.

PubMed

Luque, Amalia; Gómez-Bellido, Jesús; Carrasco, Alejandro; Barbancho, Julio

2018-06-03

The analysis and classification of the sounds produced by certain animal species, notably anurans, have revealed these amphibians to be a potentially strong indicator of temperature fluctuations and therefore of the existence of climate change. Environmental monitoring systems using Wireless Sensor Networks are therefore of interest to obtain indicators of global warming. For the automatic classification of the sounds recorded on such systems, the proper representation of the sound spectrum is essential since it contains the information required for cataloguing anuran calls. The present paper focuses on this process of feature extraction by exploring three alternatives: the standardized MPEG-7, the Filter Bank Energy (FBE), and the Mel Frequency Cepstral Coefficients (MFCC). Moreover, various values for every option in the extraction of spectrum features have been considered. Throughout the paper, it is shown that representing the frame spectrum with pure FBE offers slightly worse results than using the MPEG-7 features. This performance can easily be increased, however, by rescaling the FBE in a double dimension: vertically, by taking the logarithm of the energies; and, horizontally, by applying mel scaling in the filter banks. On the other hand, representing the spectrum in the cepstral domain, as in MFCC, has shown additional marginal improvements in classification performance.

[Can informal employment be compared in South America? Analysis of its definition, measurement and classification].

PubMed

Ruiz, Marisol E; Tarafa Orpinell, Gemma; Jódar Martínez, Pere; Benach, Joan

2015-01-01

To characterize and analyze the situation of informal employment with regard to its definition, measurement and classification in South American countries. A literature review was conducted from four databases and grey literature through a scoping review, which included reports from international organizations and from the 12 countries in South America. The data were analyzed by evaluating content and establishing similarities among countries. The data reviewed showed a disparity in the definitions used, although many countries define informal employment as workers with no contract. Most countries measured informal employment through household surveys, but due to the differences in classifications, the information found was heterogeneous, with little standardization among registries. Therefore, the data could not be compared at a regional level. The definition of the International Labour Organization was not useful to study informal employment in the countries studied. The definition should include protected and unprotected workers. An appropriate and specific definition of informal employment would allow nuances to be studied within the concept, revealing the loopholes faced by most of the population working informally. The key to meaningful comparisons within the study region is to incorporate common indicators among local registration systems (measurement) in order to determine the public health impact in the informally employed population. Copyright © 2014 SESPAS. Published by Elsevier Espana. All rights reserved.

Independent components analysis to increase efficiency of discriminant analysis methods (FDA and LDA): Application to NMR fingerprinting of wine.

PubMed

Monakhova, Yulia B; Godelmann, Rolf; Kuballa, Thomas; Mushtakova, Svetlana P; Rutledge, Douglas N

2015-08-15

Discriminant analysis (DA) methods, such as linear discriminant analysis (LDA) or factorial discriminant analysis (FDA), are well-known chemometric approaches for solving classification problems in chemistry. In most applications, principle components analysis (PCA) is used as the first step to generate orthogonal eigenvectors and the corresponding sample scores are utilized to generate discriminant features for the discrimination. Independent components analysis (ICA) based on the minimization of mutual information can be used as an alternative to PCA as a preprocessing tool for LDA and FDA classification. To illustrate the performance of this ICA/DA methodology, four representative nuclear magnetic resonance (NMR) data sets of wine samples were used. The classification was performed regarding grape variety, year of vintage and geographical origin. The average increase for ICA/DA in comparison with PCA/DA in the percentage of correct classification varied between 6±1% and 8±2%. The maximum increase in classification efficiency of 11±2% was observed for discrimination of the year of vintage (ICA/FDA) and geographical origin (ICA/LDA). The procedure to determine the number of extracted features (PCs, ICs) for the optimum DA models was discussed. The use of independent components (ICs) instead of principle components (PCs) resulted in improved classification performance of DA methods. The ICA/LDA method is preferable to ICA/FDA for recognition tasks based on NMR spectroscopic measurements. Copyright © 2015 Elsevier B.V. All rights reserved.

Classification of right-hand grasp movement based on EMOTIV Epoc+

NASA Astrophysics Data System (ADS)

Tobing, T. A. M. L.; Prawito, Wijaya, S. K.

2017-07-01

Combinations of BCT elements for right-hand grasp movement have been obtained, providing the average value of their classification accuracy. The aim of this study is to find a suitable combination for best classification accuracy of right-hand grasp movement based on EEG headset, EMOTIV Epoc+. There are three movement classifications: grasping hand, relax, and opening hand. These classifications take advantage of Event-Related Desynchronization (ERD) phenomenon that makes it possible to differ relaxation, imagery, and movement state from each other. The combinations of elements are the usage of Independent Component Analysis (ICA), spectrum analysis by Fast Fourier Transform (FFT), maximum mu and beta power with their frequency as features, and also classifier Probabilistic Neural Network (PNN) and Radial Basis Function (RBF). The average values of classification accuracy are ± 83% for training and ± 57% for testing. To have a better understanding of the signal quality recorded by EMOTIV Epoc+, the result of classification accuracy of left or right-hand grasping movement EEG signal (provided by Physionet) also be given, i.e.± 85% for training and ± 70% for testing. The comparison of accuracy value from each combination, experiment condition, and external EEG data are provided for the purpose of value analysis of classification accuracy.

Spatial relations between floodplain environments and land use - land cover of a large lowland tropical river valley: Pánuco basin, México.

PubMed

Hudson, Paul F; Colditz, René R; Aguilar-Robledo, Miguel

2006-09-01

Large lowland river valleys include a variety of floodplain environments that represent opportunities and constraints for human activities. This study integrates extensive field observations and geomorphic data with analysis of satellite remote sensing data to examine spatial relations between land use/land cover (LULC) and floodplain environments in the lower Pánuco basin of eastern Mexico. The floodplain of the lower Pánuco basin was delineated by combining a digital elevation model with a satellite image of a large flood event. The LULC was classified by combining a hybrid classification strategy with image stratification, applied to 15-m-resolution ASTER data. A geomorphic classification of floodplain environments was performed using a dry-stage image (ASTER data) and a 1993 Landsat image acquired during a large flood event. Accuracy assessment was based on aerial photographs (1:38,000), global positioning satellite ground-truthing, and a Landsat 7ETM(+) image from 2000, which resulted in an overall accuracy of 82.9% and a KHAT of 79.8% for the LULC classification. The geomorphic classification yielded 83.5% overall accuracy, whereas the KHAT was 81.5%. LULC analysis was performed for the entire floodplain and individually within four valley segments. The analysis indicates that the study area is primarily utilized for grazing and farming. Agriculture is primarily associated with coarse-grained (sandy/silty) natural levee and point bar units close to the river channel, whereas cattle grazing occurs in distal and lower-lying reaches dominated by cohesive fine-grained (clayey) deposits, such as backswamps. In the Pánuco valley, wetlands and lakes occur within backswamp environments, whereas in the Moctezuma segments, wetlands and lakes are associated with relict channels. This study reveals considerable variation in LULC related to spatial differences in floodplain environments and illustrates the importance of considering older anthropogenic influences on the landscape. The research design should be applicable for other large lowland coastal plain river valleys where agriculture is a major component of the floodplain landscape.

The classification of anxiety and hysterical states. Part I. Historical review and empirical delineation.

PubMed

Sheehan, D V; Sheehan, K H

1982-08-01

The history of the classification of anxiety, hysterical, and hypochondriacal disorders is reviewed. Problems in the ability of current classification schemes to predict, control, and describe the relationship between the symptoms and other phenomena are outlined. Existing classification schemes failed the first test of a good classification model--that of providing categories that are mutually exclusive. The independence of these diagnostic categories from each other does not appear to hold up on empirical testing. In the absence of inherently mutually exclusive categories, further empirical investigation of these classes is obstructed since statistically valid analysis of the nominal data and any useful multivariate analysis would be difficult if not impossible. It is concluded that the existing classifications are unsatisfactory and require some fundamental reconceptualization.

College Chemistry and Piaget: An Analysis of Gender Difference, Cognitive Abilities, and Achievement Measures Seventeen Years Apart

NASA Astrophysics Data System (ADS)

Shibley, Ivan A., Jr.; Milakofsky, Louis M.; Bender, David S.; Patterson, Henry O.

2003-05-01

This study revisits an analysis of gender difference in the cognitive abilities of college chemistry students using scores from "Inventory of Piaget's Developmental Tasks" (IPDT), the Scholastic Assessment Test (SAT), and final grades from an introductory college chemistry course. Comparison of 1998 scores with those from 1981 showed an overall decline on most of the measures and a changing pattern among males and females. Gender differences were found in the IPDT subtests measuring imagery, classification, and proportional reasoning, but not conservation, a pattern that differs from the findings reported 17 years earlier. The generational and gender differences revealed in this study suggest that instructors should be cognizant of, and should periodically assess, the diversity of students' cognitive abilities.

Towards a robust framework for catchment classification

NASA Astrophysics Data System (ADS)

Deshmukh, A.; Samal, A.; Singh, R.

2017-12-01

Classification of catchments based on various measures of similarity has emerged as an important technique to understand regional scale hydrologic behavior. Classification of catchment characteristics and/or streamflow response has been used reveal which characteristics are more likely to explain the observed variability of hydrologic response. However, numerous algorithms for supervised or unsupervised classification are available, making it hard to identify the algorithm most suitable for the dataset at hand. Consequently, existing catchment classification studies vary significantly in the classification algorithms employed with no previous attempt at understanding the degree of uncertainty in classification due to this algorithmic choice. This hinders the generalizability of interpretations related to hydrologic behavior. Our goal is to develop a protocol that can be followed while classifying hydrologic datasets. We focus on a classification framework for unsupervised classification and provide a step-by-step classification procedure. The steps include testing the clusterabiltiy of original dataset prior to classification, feature selection, validation of clustered data, and quantification of similarity of two clusterings. We test several commonly available methods within this framework to understand the level of similarity of classification results across algorithms. We apply the proposed framework on recently developed datasets for India to analyze to what extent catchment properties can explain observed catchment response. Our testing dataset includes watershed characteristics for over 200 watersheds which comprise of both natural (physio-climatic) characteristics and socio-economic characteristics. This framework allows us to understand the controls on observed hydrologic variability across India.

Horizontal and Vertical Cultural Differences in the Content of Advertising Appeals

PubMed Central

Shavitt, Sharon; Johnson, Timothy P.; Zhang, Jing

2014-01-01

The distinction between vertical (emphasizing hierarchy) and horizontal (valuing equality) cultures yields novel predictions regarding the prevalence of advertising appeals. A content analysis of 1211 magazine advertisements in five countries (Denmark, Korea, Poland, Russia, U.S.) revealed differences in ad content that underscore the value of this distinction. Patterns in the degree to which ads emphasized status benefits and uniqueness benefits corresponded to the countries' vertical/horizontal cultural classification. These and other patterns of ad benefits are analyzed and the predictions afforded by the vertical/horizontal distinction versus the broader individualism-collectivism distinction are compared and tested. PMID:25554720

A temporal/spectral analysis of small grain crops and confusion crops. [North Dakota

NASA Technical Reports Server (NTRS)

Johnson, W. R. (Principal Investigator)

1981-01-01

Spectral data from the LANDSAT-2 satellite were used to study the growth cycles of fields of wheat, barley, alfalfa, corn, sunflowers, soybeans, rye, flax, oats, millet, grass, and hay. Signatures of pastures, trees, and idle fallow were also studied. The growth cycles were portrayed in the form of temporal plots of the greeness-brightness transformation vector applied to average channel pixel values within the fields, all of which were in three counties in North Dakota. The plots of each crop reveal characteristics which can be used in crop classification procedures.

Horizontal and Vertical Cultural Differences in the Content of Advertising Appeals.

PubMed

Shavitt, Sharon; Johnson, Timothy P; Zhang, Jing

2011-05-01

The distinction between vertical (emphasizing hierarchy) and horizontal (valuing equality) cultures yields novel predictions regarding the prevalence of advertising appeals. A content analysis of 1211 magazine advertisements in five countries (Denmark, Korea, Poland, Russia, U.S.) revealed differences in ad content that underscore the value of this distinction. Patterns in the degree to which ads emphasized status benefits and uniqueness benefits corresponded to the countries' vertical/horizontal cultural classification. These and other patterns of ad benefits are analyzed and the predictions afforded by the vertical/horizontal distinction versus the broader individualism-collectivism distinction are compared and tested.

[Analysis of binary classification repeated measurement data with GEE and GLMMs using SPSS software].

PubMed

An, Shengli; Zhang, Yanhong; Chen, Zheng

2012-12-01

To analyze binary classification repeated measurement data with generalized estimating equations (GEE) and generalized linear mixed models (GLMMs) using SPSS19.0. GEE and GLMMs models were tested using binary classification repeated measurement data sample using SPSS19.0. Compared with SAS, SPSS19.0 allowed convenient analysis of categorical repeated measurement data using GEE and GLMMs.

Full-motion video analysis for improved gender classification

NASA Astrophysics Data System (ADS)

Flora, Jeffrey B.; Lochtefeld, Darrell F.; Iftekharuddin, Khan M.

2014-06-01

The ability of computer systems to perform gender classification using the dynamic motion of the human subject has important applications in medicine, human factors, and human-computer interface systems. Previous works in motion analysis have used data from sensors (including gyroscopes, accelerometers, and force plates), radar signatures, and video. However, full-motion video, motion capture, range data provides a higher resolution time and spatial dataset for the analysis of dynamic motion. Works using motion capture data have been limited by small datasets in a controlled environment. In this paper, we explore machine learning techniques to a new dataset that has a larger number of subjects. Additionally, these subjects move unrestricted through a capture volume, representing a more realistic, less controlled environment. We conclude that existing linear classification methods are insufficient for the gender classification for larger dataset captured in relatively uncontrolled environment. A method based on a nonlinear support vector machine classifier is proposed to obtain gender classification for the larger dataset. In experimental testing with a dataset consisting of 98 trials (49 subjects, 2 trials per subject), classification rates using leave-one-out cross-validation are improved from 73% using linear discriminant analysis to 88% using the nonlinear support vector machine classifier.

Beluga whale (Delphinapterus leucas) vocalizations and call classification from the eastern Beaufort Sea population.

PubMed

Garland, Ellen C; Castellote, Manuel; Berchok, Catherine L

2015-06-01

Beluga whales, Delphinapterus leucas, have a graded call system; call types exist on a continuum making classification challenging. A description of vocalizations from the eastern Beaufort Sea beluga population during its spring migration are presented here, using both a non-parametric classification tree analysis (CART), and a Random Forest analysis. Twelve frequency and duration measurements were made on 1019 calls recorded over 14 days off Icy Cape, Alaska, resulting in 34 identifiable call types with 83% agreement in classification for both CART and Random Forest analyses. This high level of agreement in classification, with an initial subjective classification of calls into 36 categories, demonstrates that the methods applied here provide a quantitative analysis of a graded call dataset. Further, as calls cannot be attributed to individuals using single sensor passive acoustic monitoring efforts, these methods provide a comprehensive analysis of data where the influence of pseudo-replication of calls from individuals is unknown. This study is the first to describe the vocal repertoire of a beluga population using a robust and repeatable methodology. A baseline eastern Beaufort Sea beluga population repertoire is presented here, against which the call repertoire of other seasonally sympatric Alaskan beluga populations can be compared.

Image Analysis and Classification Based on Soil Strength

DTIC Science & Technology

2016-08-01

Satellite imagery classification is useful for a variety of commonly used ap- plications, such as land use classification, agriculture , wetland...required use of a coinci- dent digital elevation model (DEM) and a high-resolution orthophoto- graph collected by the National Agriculture Imagery Program...14. ABSTRACT Satellite imagery classification is useful for a variety of commonly used applications, such as land use classification, agriculture

Novel Functional Properties of Drosophila CNS Glutamate Receptors

DOE Office of Scientific and Technical Information (OSTI.GOV)

Li, Yan; Dharkar, Poorva; Han, Tae-Hee

Phylogenetic analysis reveals AMPA, kainate, and NMDA receptor families in insect genomes, suggesting conserved functional properties corresponding to their vertebrate counterparts. However, heterologous expression of the Drosophila kainate receptor DKaiR1D and the AMPA receptor DGluR1A revealed novel ligand selectivity at odds with the classification used for vertebrate glutamate receptor ion channels (iGluRs). DKaiR1D forms a rapidly activating and desensitizing receptor that is inhibited by both NMDA and the NMDA receptor antagonist AP5; crystallization of the KaiR1D ligand-binding domain reveals that these ligands stabilize open cleft conformations, explaining their action as antagonists. Surprisingly, the AMPA receptor DGluR1A shows weak activation bymore » its namesake agonist AMPA and also by quisqualate. Crystallization of the DGluR1A ligand-binding domain reveals amino acid exchanges that interfere with binding of these ligands. The unexpected ligand-binding profiles of insect iGluRs allows classical tools to be used in novel approaches for the study of synaptic regulation.« less

Novel Functional Properties of Drosophila CNS Glutamate Receptors.

PubMed

Li, Yan; Dharkar, Poorva; Han, Tae-Hee; Serpe, Mihaela; Lee, Chi-Hon; Mayer, Mark L

2016-12-07

Phylogenetic analysis reveals AMPA, kainate, and NMDA receptor families in insect genomes, suggesting conserved functional properties corresponding to their vertebrate counterparts. However, heterologous expression of the Drosophila kainate receptor DKaiR1D and the AMPA receptor DGluR1A revealed novel ligand selectivity at odds with the classification used for vertebrate glutamate receptor ion channels (iGluRs). DKaiR1D forms a rapidly activating and desensitizing receptor that is inhibited by both NMDA and the NMDA receptor antagonist AP5; crystallization of the KaiR1D ligand-binding domain reveals that these ligands stabilize open cleft conformations, explaining their action as antagonists. Surprisingly, the AMPA receptor DGluR1A shows weak activation by its namesake agonist AMPA and also by quisqualate. Crystallization of the DGluR1A ligand-binding domain reveals amino acid exchanges that interfere with binding of these ligands. The unexpected ligand-binding profiles of insect iGluRs allows classical tools to be used in novel approaches for the study of synaptic regulation. VIDEO ABSTRACT. Published by Elsevier Inc.

Exploratory graphical models of functional and structural connectivity patterns for Alzheimer's Disease diagnosis.

PubMed

Ortiz, Andrés; Munilla, Jorge; Álvarez-Illán, Ignacio; Górriz, Juan M; Ramírez, Javier

2015-01-01

Alzheimer's Disease (AD) is the most common neurodegenerative disease in elderly people. Its development has been shown to be closely related to changes in the brain connectivity network and in the brain activation patterns along with structural changes caused by the neurodegenerative process. Methods to infer dependence between brain regions are usually derived from the analysis of covariance between activation levels in the different areas. However, these covariance-based methods are not able to estimate conditional independence between variables to factor out the influence of other regions. Conversely, models based on the inverse covariance, or precision matrix, such as Sparse Gaussian Graphical Models allow revealing conditional independence between regions by estimating the covariance between two variables given the rest as constant. This paper uses Sparse Inverse Covariance Estimation (SICE) methods to learn undirected graphs in order to derive functional and structural connectivity patterns from Fludeoxyglucose (18F-FDG) Position Emission Tomography (PET) data and segmented Magnetic Resonance images (MRI), drawn from the ADNI database, for Control, MCI (Mild Cognitive Impairment Subjects), and AD subjects. Sparse computation fits perfectly here as brain regions usually only interact with a few other areas. The models clearly show different metabolic covariation patters between subject groups, revealing the loss of strong connections in AD and MCI subjects when compared to Controls. Similarly, the variance between GM (Gray Matter) densities of different regions reveals different structural covariation patterns between the different groups. Thus, the different connectivity patterns for controls and AD are used in this paper to select regions of interest in PET and GM images with discriminative power for early AD diagnosis. Finally, functional an structural models are combined to leverage the classification accuracy. The results obtained in this work show the usefulness of the Sparse Gaussian Graphical models to reveal functional and structural connectivity patterns. This information provided by the sparse inverse covariance matrices is not only used in an exploratory way but we also propose a method to use it in a discriminative way. Regression coefficients are used to compute reconstruction errors for the different classes that are then introduced in a SVM for classification. Classification experiments performed using 68 Controls, 70 AD, and 111 MCI images and assessed by cross-validation show the effectiveness of the proposed method.

Analysis of Chi-square Automatic Interaction Detection (CHAID) and Classification and Regression Tree (CRT) for Classification of Corn Production

NASA Astrophysics Data System (ADS)

Susanti, Yuliana; Zukhronah, Etik; Pratiwi, Hasih; Respatiwulan; Sri Sulistijowati, H.

2017-11-01

To achieve food resilience in Indonesia, food diversification by exploring potentials of local food is required. Corn is one of alternating staple food of Javanese society. For that reason, corn production needs to be improved by considering the influencing factors. CHAID and CRT are methods of data mining which can be used to classify the influencing variables. The present study seeks to dig up information on the potentials of local food availability of corn in regencies and cities in Java Island. CHAID analysis yields four classifications with accuracy of 78.8%, while CRT analysis yields seven classifications with accuracy of 79.6%.

Analysis of land cover change and its driving forces in a desert oasis landscape of southern Xinjiang, China

NASA Astrophysics Data System (ADS)

Amuti, T.; Luo, G.

2014-07-01

The combined effects of drought, warming and the changes in land cover have caused severe land degradation for several decades in the extremely arid desert oases of Southern Xinjiang, Northwest China. This study examined land cover changes during 1990-2008 to characterize and quantify the transformations in the typical oasis of Hotan. Land cover classifications of these images were performed based on the supervised classification scheme integrated with conventional vegetation and soil indexes. Change-detection techniques in remote sensing (RS) and a geographic information system (GIS) were applied to quantify temporal and spatial dynamics of land cover changes. The overall accuracies, Kappa coefficients, and average annual increase rate or decrease rate of land cover classes were calculated to assess classification results and changing rate of land cover. The analysis revealed that major trends of the land cover changes were the notable growth of the oasis and the reduction of the desert-oasis ecotone, which led to accelerated soil salinization and plant deterioration within the oasis. These changes were mainly attributed to the intensified human activities. The results indicated that the newly created agricultural land along the margins of the Hotan oasis could result in more potential areas of land degradation. If no effective measures are taken against the deterioration of the oasis environment, soil erosion caused by land cover change may proceed. The trend of desert moving further inward and the shrinking of the ecotone may lead to potential risks to the eco-environment of the Hotan oasis over the next decades.

Genomewide identification and expression analysis of the ARF gene family in apple.

PubMed

Luo, Xiao-Cui; Sun, Mei-Hong; Xu, Rui-Rui; Shu, Huai-Rui; Wang, Jia-Wei; Zhang, Shi-Zhong

2014-12-01

Auxin response factors (ARF) are transcription factors that regulate auxin responses in plants. Although the genomewide analysis of this family has been performed in some species, little is known regarding ARF genes in apple (Malus domestica). In this study, 31 putative apple ARF genes have been identified and located within the apple genome. The phylogenetic analysis revealed that MdARFs could be divided into three subfamilies (groups I, II and III). The predicted MdARFs were distributed across 15 of 17 chromosomes with different densities. In addition, the analysis of exon-intron junctions and of the intron phase inside the predicted coding region of each candidate gene has revealed high levels of conservation within and between phylogenetic groups. Expression profile analyses of MdARF genes were performed in different tissues (root, stem, leaf, flower and fruit), and all the selected genes were expressed in at least one of the tissues that were tested, which indicated that MdARFs are involved in various aspects of physiological and developmental processes of apple. To our knowledge, this report is the first to provide a genomewide analysis of the apple ARF gene family. This study provides valuable information for understanding the classification and putative functions of the ARF signal in apple.

The Landscape of long non-coding RNA classification

PubMed Central

St Laurent, Georges; Wahlestedt, Claes; Kapranov, Philipp

2015-01-01

Advances in the depth and quality of transcriptome sequencing have revealed many new classes of long non-coding RNAs (lncRNAs). lncRNA classification has mushroomed to accommodate these new findings, even though the real dimensions and complexity of the non-coding transcriptome remain unknown. Although evidence of functionality of specific lncRNAs continues to accumulate, conflicting, confusing, and overlapping terminology has fostered ambiguity and lack of clarity in the field in general. The lack of fundamental conceptual un-ambiguous classification framework results in a number of challenges in the annotation and interpretation of non-coding transcriptome data. It also might undermine integration of the new genomic methods and datasets in an effort to unravel function of lncRNA. Here, we review existing lncRNA classifications, nomenclature, and terminology. Then we describe the conceptual guidelines that have emerged for their classification and functional annotation based on expanding and more comprehensive use of large systems biology-based datasets. PMID:25869999

[The establishment, development and application of classification approach of freshwater phytoplankton based on the functional group: a review].

PubMed

Yang, Wen; Zhu, Jin-Yong; Lu, Kai-Hong; Wan, Li; Mao, Xiao-Hua

2014-06-01

Appropriate schemes for classification of freshwater phytoplankton are prerequisites and important tools for revealing phytoplanktonic succession and studying freshwater ecosystems. An alternative approach, functional group of freshwater phytoplankton, has been proposed and developed due to the deficiencies of Linnaean and molecular identification in ecological applications. The functional group of phytoplankton is a classification scheme based on autoecology. In this study, the theoretical basis and classification criterion of functional group (FG), morpho-functional group (MFG) and morphology-based functional group (MBFG) were summarized, as well as their merits and demerits. FG was considered as the optimal classification approach for the aquatic ecology research and aquatic environment evaluation. The application status of FG was introduced, with the evaluation standards and problems of two approaches to assess water quality on the basis of FG, index methods of Q and QR, being briefly discussed.

Research on Optimization of GLCM Parameter in Cell Classification

NASA Astrophysics Data System (ADS)

Zhang, Xi-Kun; Hou, Jie; Hu, Xin-Hua

2016-05-01

Real-time classification of biological cells according to their 3D morphology is highly desired in a flow cytometer setting. Gray level co-occurrence matrix (GLCM) algorithm has been developed to extract feature parameters from measured diffraction images ,which are too complicated to coordinate with the real-time system for a large amount of calculation. An optimization of GLCM algorithm is provided based on correlation analysis of GLCM parameters. The results of GLCM analysis and subsequent classification demonstrate optimized method can lower the time complexity significantly without loss of classification accuracy.

Racial classification in the evolutionary sciences: a comparative analysis.

PubMed

Billinger, Michael S

2007-01-01

Human racial classification has long been a problem for the discipline of anthropology, but much of the criticism of the race concept has focused on its social and political connotations. The central argument of this paper is that race is not a specifically human problem, but one that exists in evolutionary thought in general. This paper looks at various disciplinary approaches to racial or subspecies classification, extending its focus beyond the anthropological race concept by providing a comparative analysis of the use of racial classification in evolutionary biology, genetics, and anthropology.

The State of Social Media Policies in Higher Education

PubMed Central

Pomerantz, Jeffrey; Hank, Carolyn; Sugimoto, Cassidy R.

2015-01-01

This paper presents an analysis of the current state of development of social media policies at institution of higher education. Content analysis of social media policies for all institutions listed in the Carnegie Classification Data File revealed that less than one-quarter of institutions had an accessible social media policy. Analysis was done by institution and campus unit, finding that social media policies were most likely to appear at doctorate-granting institutions and health, athletics, and library units. Policies required that those affiliated with the institution post appropriate content, represent the unit appropriately, and moderate conversations with coworkers and external agencies. This analysis may inform the development and revision of social media policies across the field of higher education, taking into consideration the rapidly changing landscape of social media, issues of academic freedom, and notions of interoperability with policies at the unit and campus levels. PMID:26017549

The state of social media policies in higher education.

PubMed

Pomerantz, Jeffrey; Hank, Carolyn; Sugimoto, Cassidy R

2015-01-01

This paper presents an analysis of the current state of development of social media policies at institution of higher education. Content analysis of social media policies for all institutions listed in the Carnegie Classification Data File revealed that less than one-quarter of institutions had an accessible social media policy. Analysis was done by institution and campus unit, finding that social media policies were most likely to appear at doctorate-granting institutions and health, athletics, and library units. Policies required that those affiliated with the institution post appropriate content, represent the unit appropriately, and moderate conversations with coworkers and external agencies. This analysis may inform the development and revision of social media policies across the field of higher education, taking into consideration the rapidly changing landscape of social media, issues of academic freedom, and notions of interoperability with policies at the unit and campus levels.

Primary tonsillar lymphomas according to the new World Health Organization classification: to report 87 cases and literature review and analysis.

PubMed

Mohammadianpanah, Mohammad; Daneshbod, Yahya; Ramzi, Mani; Hamidizadeh, Nasrin; Dehghani, Seyed Javad; Bidouei, Farzad; Khademi, Bijan; Ahmadloo, Niloofar; Ansari, Mansour; Omidvari, Shapour; Mosalaei, Ahmad; Dehghani, Mehdi

2010-10-01

The present study aimed to define the natural history, World Health Organization (WHO) classification, prognostic factors, and treatment outcome of 87 patients with primary lymphoma of the palatine tonsil and literature review and analysis. Between 1990 and March 2008, 87 consecutive patients diagnosed with primary lymphoid malignancy of the palatine tonsil. All pathologic specimens were reviewed and reclassified according to the recent WHO classification. To investigate the association of tonsillar lymphomas with Epstein-Barr virus (EBV), in situ hybridization was performed for 24 tonsillar lymphomas (23 diffuse large B-cell lymphoma (DLBC) and one classic Hodgkin's disease) and ten normal tonsils as control group. In literature review, we found 26 major related series including 1,602 patients with primary tonsillar lymphoma. The median age of our patients was 52 years (range 11-86 years). There were 39 women and 48 men with a median follow-up of 67 months for living patients. The vast majority (95%) of patients had B-cell phenotype. DLBC was the most frequent histology. In situ hybridization revealed none of 23 DLBC to be positive for EBV. The 5-year disease-free and overall survival rates were 78.9% and 86%, respectively. In the literature review and by analyzing the data collection from 26 major reported series, the median age was 55 years and male/female ratio was 1.3:1. Intermediate grade tumors consisted of 72% of all tonsillar lymphomas and B-cell lymphomas constituted 82% of all cell immunophenotypes. The 5-year disease-free and overall survival rates were 61% and 67%, respectively. The vast majority of tonsillar lymphomas are of B-cell origin and with intermediate to high-grade histology. These neoplasms tend to present in early stage disease and to have favorable outcome. WHO classification predicts more accurately treatment outcome of patients with tonsillar lymphoma. The association of DLBC in the palatine tonsil with EBV infection is infrequent.

IRIS COLOUR CLASSIFICATION SCALES – THEN AND NOW

PubMed Central

Grigore, Mariana; Avram, Alina

2015-01-01

Eye colour is one of the most obvious phenotypic traits of an individual. Since the first documented classification scale developed in 1843, there have been numerous attempts to classify the iris colour. In the past centuries, iris colour classification scales has had various colour categories and mostly relied on comparison of an individual’s eye with painted glass eyes. Once photography techniques were refined, standard iris photographs replaced painted eyes, but this did not solve the problem of painted/ printed colour variability in time. Early clinical scales were easy to use, but lacked objectivity and were not standardised or statistically tested for reproducibility. The era of automated iris colour classification systems came with the technological development. Spectrophotometry, digital analysis of high-resolution iris images, hyper spectral analysis of the human real iris and the dedicated iris colour analysis software, all accomplished an objective, accurate iris colour classification, but are quite expensive and limited in use to research environment. Iris colour classification systems evolved continuously due to their use in a wide range of studies, especially in the fields of anthropology, epidemiology and genetics. Despite the wide range of the existing scales, up until present there has been no generally accepted iris colour classification scale. PMID:27373112

IRIS COLOUR CLASSIFICATION SCALES--THEN AND NOW.

PubMed

Grigore, Mariana; Avram, Alina

2015-01-01

Eye colour is one of the most obvious phenotypic traits of an individual. Since the first documented classification scale developed in 1843, there have been numerous attempts to classify the iris colour. In the past centuries, iris colour classification scales has had various colour categories and mostly relied on comparison of an individual's eye with painted glass eyes. Once photography techniques were refined, standard iris photographs replaced painted eyes, but this did not solve the problem of painted/ printed colour variability in time. Early clinical scales were easy to use, but lacked objectivity and were not standardised or statistically tested for reproducibility. The era of automated iris colour classification systems came with the technological development. Spectrophotometry, digital analysis of high-resolution iris images, hyper spectral analysis of the human real iris and the dedicated iris colour analysis software, all accomplished an objective, accurate iris colour classification, but are quite expensive and limited in use to research environment. Iris colour classification systems evolved continuously due to their use in a wide range of studies, especially in the fields of anthropology, epidemiology and genetics. Despite the wide range of the existing scales, up until present there has been no generally accepted iris colour classification scale.

Sparse dictionary learning for resting-state fMRI analysis

NASA Astrophysics Data System (ADS)

Lee, Kangjoo; Han, Paul Kyu; Ye, Jong Chul

2011-09-01

Recently, there has been increased interest in the usage of neuroimaging techniques to investigate what happens in the brain at rest. Functional imaging studies have revealed that the default-mode network activity is disrupted in Alzheimer's disease (AD). However, there is no consensus, as yet, on the choice of analysis method for the application of resting-state analysis for disease classification. This paper proposes a novel compressed sensing based resting-state fMRI analysis tool called Sparse-SPM. As the brain's functional systems has shown to have features of complex networks according to graph theoretical analysis, we apply a graph model to represent a sparse combination of information flows in complex network perspectives. In particular, a new concept of spatially adaptive design matrix has been proposed by implementing sparse dictionary learning based on sparsity. The proposed approach shows better performance compared to other conventional methods, such as independent component analysis (ICA) and seed-based approach, in classifying the AD patients from normal using resting-state analysis.

The Australian National Sub-Acute and Non-Acute Patient casemix classification.

PubMed

Eagar, K

1999-01-01

The Australian National Sub-Acute and Non-Acute Patient (AN-SNAP) Version 1 casemix classification was completed in 1997. AN-SNAP is designed for the classification of sub-acute and non-acute care provided in both inpatient and ambulatory settings and is intended to be useful for both funding and clinical management purposes. The National Sub-Acute and Non-Acute Casemix Classification study has produced the first version of a national classification of sub-acute and non-acute care. Ongoing refinement (leading to Version 2) will be possible through further analysis of the existing data set in combination with analysis of the results of a carefully planned and phased implementation.

A comparison of autonomous techniques for multispectral image analysis and classification

NASA Astrophysics Data System (ADS)

Valdiviezo-N., Juan C.; Urcid, Gonzalo; Toxqui-Quitl, Carina; Padilla-Vivanco, Alfonso

2012-10-01

Multispectral imaging has given place to important applications related to classification and identification of objects from a scene. Because of multispectral instruments can be used to estimate the reflectance of materials in the scene, these techniques constitute fundamental tools for materials analysis and quality control. During the last years, a variety of algorithms has been developed to work with multispectral data, whose main purpose has been to perform the correct classification of the objects in the scene. The present study introduces a brief review of some classical as well as a novel technique that have been used for such purposes. The use of principal component analysis and K-means clustering techniques as important classification algorithms is here discussed. Moreover, a recent method based on the min-W and max-M lattice auto-associative memories, that was proposed for endmember determination in hyperspectral imagery, is introduced as a classification method. Besides a discussion of their mathematical foundation, we emphasize their main characteristics and the results achieved for two exemplar images conformed by objects similar in appearance, but spectrally different. The classification results state that the first components computed from principal component analysis can be used to highlight areas with different spectral characteristics. In addition, the use of lattice auto-associative memories provides good results for materials classification even in the cases where some spectral similarities appears in their spectral responses.

The influence of different classification standards of age groups on prognosis in high-grade hemispheric glioma patients.

PubMed

Chen, Jian-Wu; Zhou, Chang-Fu; Lin, Zhi-Xiong

2015-09-15

Although age is thought to correlate with the prognosis of glioma patients, the most appropriate age-group classification standard to evaluate prognosis had not been fully studied. This study aimed to investigate the influence of age-group classification standards on the prognosis of patients with high-grade hemispheric glioma (HGG). This retrospective study of 125 HGG patients used three different classification standards of age-groups (≤ 50 and >50 years old, ≤ 60 and >60 years old, ≤ 45 and 45-65 and ≥ 65 years old) to evaluate the impact of age on prognosis. The primary end-point was overall survival (OS). The Kaplan-Meier method was applied for univariate analysis and Cox proportional hazards model for multivariate analysis. Univariate analysis showed a significant correlation between OS and all three classification standards of age-groups as well as between OS and pathological grade, gender, location of glioma, and regular chemotherapy and radiotherapy treatment. Multivariate analysis showed that the only independent predictors of OS were classification standard of age-groups ≤ 50 and > 50 years old, pathological grade and regular chemotherapy. In summary, the most appropriate classification standard of age-groups as an independent prognostic factor was ≤ 50 and > 50 years old. Pathological grade and chemotherapy were also independent predictors of OS in post-operative HGG patients. Copyright © 2015. Published by Elsevier B.V.

"Rebuilding our community": hearing silenced voices on Aboriginal youth suicide.

PubMed

Walls, Melissa L; Hautala, Dane; Hurley, Jenna

2014-02-01

This paper brings forth the voices of adult Aboriginal First Nations community members who gathered in focus groups to discuss the problem of youth suicide on their reserves. Our approach emphasizes multilevel (e.g., individual, family, and broader ecological systems) factors viewed by participants as relevant to youth suicide. Wheaton's conceptualization of stressors and Evans-Campbell's multilevel classification of the impacts of historical trauma are used as theoretical and analytic guides. Thematic analysis of qualitative data transcripts revealed a highly complex intersection of stressors, traumas, and social problems seen by community members as underlying mechanisms influencing heightened levels of Aboriginal youth suicidality. Our multilevel coding approach revealed that suicidal behaviors were described by community members largely as a problem with deep historical and contemporary structural roots, as opposed to being viewed as individualized pathology.

Revealing Fundamental Physics from the Daya Bay Neutrino Experiment Using Deep Neural Networks

DOE PAGES

Racah, Evan; Ko, Seyoon; Sadowski, Peter; ...

2017-02-02

Experiments in particle physics produce enormous quantities of data that must be analyzed and interpreted by teams of physicists. This analysis is often exploratory, where scientists are unable to enumerate the possible types of signal prior to performing the experiment. Thus, tools for summarizing, clustering, visualizing and classifying high-dimensional data are essential. Here in this work, we show that meaningful physical content can be revealed by transforming the raw data into a learned high-level representation using deep neural networks, with measurements taken at the Daya Bay Neutrino Experiment as a case study. We further show how convolutional deep neural networksmore » can provide an effective classification filter with greater than 97% accuracy across different classes of physics events, significantly better than other machine learning approaches.« less

[Spatial distribution characteristics of the physical and chemical properties of water in the Kunes River after the supply of snowmelt during spring].

PubMed

Liu, Xiang; Guo, Ling-Peng; Zhang, Fei-Yun; Ma, Jie; Mu, Shu-Yong; Zhao, Xin; Li, Lan-Hai

2015-02-01

Eight physical and chemical indicators related to water quality were monitored from nineteen sampling sites along the Kunes River at the end of snowmelt season in spring. To investigate the spatial distribution characteristics of water physical and chemical properties, cluster analysis (CA), discriminant analysis (DA) and principal component analysis (PCA) are employed. The result of cluster analysis showed that the Kunes River could be divided into three reaches according to the similarities of water physical and chemical properties among sampling sites, representing the upstream, midstream and downstream of the river, respectively; The result of discriminant analysis demonstrated that the reliability of such a classification was high, and DO, Cl- and BOD5 were the significant indexes leading to this classification; Three principal components were extracted on the basis of the principal component analysis, in which accumulative variance contribution could reach 86.90%. The result of principal component analysis also indicated that water physical and chemical properties were mostly affected by EC, ORP, NO3(-) -N, NH4(+) -N, Cl- and BOD5. The sorted results of principal component scores in each sampling sites showed that the water quality was mainly influenced by DO in upstream, by pH in midstream, and by the rest of indicators in downstream. The order of comprehensive scores for principal components revealed that the water quality degraded from the upstream to downstream, i.e., the upstream had the best water quality, followed by the midstream, while the water quality at downstream was the worst. This result corresponded exactly to the three reaches classified using cluster analysis. Anthropogenic activity and the accumulation of pollutants along the river were probably the main reasons leading to this spatial difference.

The Universal Decimal Classification: Some Factors Concerning Its Origins, Development, and Influence.

ERIC Educational Resources Information Center

McIlwaine, I. C.

1997-01-01

Discusses the history and development of the Universal Decimal Classification (UDC). Topics include the relationship with Dewey Decimal Classification; revision process; structure; facet analysis; lack of standard rules for application; application in automated systems; influence of UDC on classification development; links with thesauri; and use…

A Comparison of Two-Group Classification Methods

ERIC Educational Resources Information Center

Holden, Jocelyn E.; Finch, W. Holmes; Kelley, Ken

2011-01-01

The statistical classification of "N" individuals into "G" mutually exclusive groups when the actual group membership is unknown is common in the social and behavioral sciences. The results of such classification methods often have important consequences. Among the most common methods of statistical classification are linear discriminant analysis,…

Development of Automated Image Analysis Software for Suspended Marine Particle Classification

DTIC Science & Technology

2002-09-30

Development of Automated Image Analysis Software for Suspended Marine Particle Classification Scott Samson Center for Ocean Technology...and global water column. 1 OBJECTIVES The project’s objective is to develop automated image analysis software to reduce the effort and time

Morphological and Molecular Data Reveal Three Distinct Populations of Indian Wild Rice Oryza rufipogon Griff. Species Complex.

PubMed

Singh, Balwant; Singh, Nisha; Mishra, Shefali; Tripathi, Kabita; Singh, Bikram P; Rai, Vandna; Singh, Ashok K; Singh, Nagendra K

2018-01-01

Wild relatives of crops possess adaptive mutations for agronomically important traits, which could play significant role in crop improvement for sustainable agriculture. However, global climate change and human activities pose serious threats to the natural habitats leading to erosion of genetic diversity of wild rice populations. The purpose of this study was to explore and characterize India's huge untapped wild rice diversity in Oryza rufipogon Griff. species complex from a wide range of ecological niches. We made strategic expeditions around diversity hot spots in 64 districts of nine different agro-climatic zones of the country and collected 418 wild rice accessions. Significant variation was observed among the accessions for 46 morphological descriptors, allowing classification into O. nivara, O. rufipogon , and O. sativa f. spontanea morpho-taxonomic groups. Genome-specific pSINE1 markers confirmed all the accessions having AA genome, which were further classified using ecotype-specific pSINE1 markers into annual, perennial, intermediate, and an unknown type. Principal component analysis revealed continuous variation for the morphological traits in each ecotype group. Genetic diversity analysis based on multi-allelic SSR markers clustered these accessions into three major groups and analysis of molecular variance for nine agro-climatic zones showed that 68% of the genetic variation was inherent amongst individuals while only 11% of the variation separated the zones, though there was significant correlation between genetic and spatial distances of the accessions. Model based population structure analysis using genome wide bi-allelic SNP markers revealed three sub-populations designated 'Pro-Indica,' 'Pro-Aus,' and 'Mid-Gangetic,' which showed poor correspondence with the morpho - taxonomic classification or pSINE1 ecotypes. There was Pan-India distribution of the 'Pro-Indica' and 'Pro-Aus' sub-populations across agro-climatic zones, indicating a more fundamental grouping based on the ancestry closely related to 'Indica' and 'Aus' groups of rice cultivars. The Pro-Indica population has substantial presence in the Eastern Himalayan Region and Lower Gangetic Plains, whereas 'Pro-Aus' sub-population was predominant in the Upper Gangetic Plains, Western Himalayan Region, Gujarat Plains and Hills, and Western Coastal Plains. In contrast 'Mid-Gangetic' population was largely concentrated in the Mid Gangetic Plains. The information presented here will be useful in the utilization of wild rice resources for varietal improvement.

Semi-Supervised Marginal Fisher Analysis for Hyperspectral Image Classification

NASA Astrophysics Data System (ADS)

Huang, H.; Liu, J.; Pan, Y.

2012-07-01

The problem of learning with both labeled and unlabeled examples arises frequently in Hyperspectral image (HSI) classification. While marginal Fisher analysis is a supervised method, which cannot be directly applied for Semi-supervised classification. In this paper, we proposed a novel method, called semi-supervised marginal Fisher analysis (SSMFA), to process HSI of natural scenes, which uses a combination of semi-supervised learning and manifold learning. In SSMFA, a new difference-based optimization objective function with unlabeled samples has been designed. SSMFA preserves the manifold structure of labeled and unlabeled samples in addition to separating labeled samples in different classes from each other. The semi-supervised method has an analytic form of the globally optimal solution, and it can be computed based on eigen decomposition. Classification experiments with a challenging HSI task demonstrate that this method outperforms current state-of-the-art HSI-classification methods.

Efficacy measures associated to a plantar pressure based classification system in diabetic foot medicine.

PubMed

Deschamps, Kevin; Matricali, Giovanni Arnoldo; Desmet, Dirk; Roosen, Philip; Keijsers, Noel; Nobels, Frank; Bruyninckx, Herman; Staes, Filip

2016-09-01

The concept of 'classification' has, similar to many other diseases, been found to be fundamental in the field of diabetic medicine. In the current study, we aimed at determining efficacy measures of a recently published plantar pressure based classification system. Technical efficacy of the classification system was investigated by applying a high resolution, pixel-level analysis on the normalized plantar pressure pedobarographic fields of the original experimental dataset consisting of 97 patients with diabetes and 33 persons without diabetes. Clinical efficacy was assessed by considering the occurence of foot ulcers at the plantar aspect of the forefoot in this dataset. Classification efficacy was assessed by determining the classification recognition rate as well as its sensitivity and specificity using cross-validation subsets of the experimental dataset together with a novel cohort of 12 patients with diabetes. Pixel-level comparison of the four groups associated to the classification system highlighted distinct regional differences. Retrospective analysis showed the occurence of eleven foot ulcers in the experimental dataset since their gait analysis. Eight out of the eleven ulcers developed in a region of the foot which had the highest forces. Overall classification recognition rate exceeded 90% for all cross-validation subsets. Sensitivity and specificity of the four groups associated to the classification system exceeded respectively the 0.7 and 0.8 level in all cross-validation subsets. The results of the current study support the use of the novel plantar pressure based classification system in diabetic foot medicine. It may particularly serve in communication, diagnosis and clinical decision making. Copyright © 2016 Elsevier B.V. All rights reserved.

Use of Neuroanatomical Pattern Classification to Identify Subjects in At-Risk Mental States of Psychosis and Predict Disease Transition

PubMed Central

Koutsouleris, Nikolaos; Meisenzahl, Eva M.; Davatzikos, Christos; Bottlender, Ronald; Frodl, Thomas; Scheuerecker, Johanna; Schmitt, Gisela; Zetzsche, Thomas; Decker, Petra; Reiser, Maximilian; Möller, Hans-Jürgen; Gaser, Christian

2014-01-01

Context Identification of individuals at high risk of developing psychosis has relied on prodromal symptomatology. Recently, machine learning algorithms have been successfully used for magnetic resonance imaging–based diagnostic classification of neuropsychiatric patient populations. Objective To determine whether multivariate neuroanatomical pattern classification facilitates identification of individuals in different at-risk mental states (ARMS) of psychosis and enables the prediction of disease transition at the individual level. Design Multivariate neuroanatomical pattern classification was performed on the structural magnetic resonance imaging data of individuals in early or late ARMS vs healthy controls (HCs). The predictive power of the method was then evaluated by categorizing the baseline imaging data of individuals with transition to psychosis vs those without transition vs HCs after 4 years of clinical follow-up. Classification generalizability was estimated by cross-validation and by categorizing an independent cohort of 45 new HCs. Setting Departments of Psychiatry and Psychotherapy, Ludwig-Maximilians-University, Munich, Germany. Participants The first classification analysis included 20 early and 25 late at-risk individuals and 25 matched HCs. The second analysis consisted of 15 individuals with transition, 18 without transition, and 17 matched HCs. Main Outcome Measures Specificity, sensitivity, and accuracy of classification. Results The 3-group, cross-validated classification accuracies of the first analysis were 86% (HCs vs the rest), 91% (early at-risk individuals vs the rest), and 86% (late at-risk individuals vs the rest). The accuracies in the second analysis were 90% (HCs vs the rest), 88% (individuals with transition vs the rest), and 86% (individuals without transition vs the rest). Independent HCs were correctly classified in 96% (first analysis) and 93% (second analysis) of cases. Conclusions Different ARMSs and their clinical outcomes may be reliably identified on an individual basis by assessing patterns of whole-brain neuroanatomical abnormalities. These patterns may serve as valuable biomarkers for the clinician to guide early detection in the prodromal phase of psychosis. PMID:19581561

Generalization error analysis: deep convolutional neural network in mammography

NASA Astrophysics Data System (ADS)

Richter, Caleb D.; Samala, Ravi K.; Chan, Heang-Ping; Hadjiiski, Lubomir; Cha, Kenny

2018-02-01

We conducted a study to gain understanding of the generalizability of deep convolutional neural networks (DCNNs) given their inherent capability to memorize data. We examined empirically a specific DCNN trained for classification of masses on mammograms. Using a data set of 2,454 lesions from 2,242 mammographic views, a DCNN was trained to classify masses into malignant and benign classes using transfer learning from ImageNet LSVRC-2010. We performed experiments with varying amounts of label corruption and types of pixel randomization to analyze the generalization error for the DCNN. Performance was evaluated using the area under the receiver operating characteristic curve (AUC) with an N-fold cross validation. Comparisons were made between the convergence times, the inference AUCs for both the training set and the test set of the original image patches without corruption, and the root-mean-squared difference (RMSD) in the layer weights of the DCNN trained with different amounts and methods of corruption. Our experiments observed trends which revealed that the DCNN overfitted by memorizing corrupted data. More importantly, this study improved our understanding of DCNN weight updates when learning new patterns or new labels. Although we used a specific classification task with the ImageNet as example, similar methods may be useful for analysis of the DCNN learning processes, especially those that employ transfer learning for medical image analysis where sample size is limited and overfitting risk is high.

Comparison analysis for classification algorithm in data mining and the study of model use

NASA Astrophysics Data System (ADS)

Chen, Junde; Zhang, Defu

2018-04-01

As a key technique in data mining, classification algorithm was received extensive attention. Through an experiment of classification algorithm in UCI data set, we gave a comparison analysis method for the different algorithms and the statistical test was used here. Than that, an adaptive diagnosis model for preventive electricity stealing and leakage was given as a specific case in the paper.

Inter- and intra-rater reliability of nasal auscultation in daycare children.

PubMed

Santos, Rita; Silva Alexandrino, Ana; Tomé, David; Melo, Cristina; Mesquita Montes, António; Costa, Daniel; Pinto Ferreira, João

2018-02-01

The aim of this study was to assess nasal auscultation's intra- and inter-rater reliability and to analyze ear and respiratory clinical condition according to nasal auscultation. Cross-sectional study performed in 125 children aged up to 3 years old attending daycare centers. Nasal auscultation, tympanometry and Paediatric Respiratory Severity Score (PRSS) were applied to all children. Nasal sounds were classified by an expert panel in order to determine nasal auscultation's intra and inter- rater reliability. The classification of nasal sounds was assessed against tympanometric and PRSS values. Nasal auscultation revealed substantial inter-rater (K=0.75) and intra-rater (K=0.69; K=0.61 and K=0.72) reliability. Children with a "non-obstructed" classification revealed a lower peak pressure (t=-3.599, P<0.001 in left ear; t=-2.258, P=0.026 in right ear) and a higher compliance (t=-2,728, P=0.007 in left ear; t=-3.830. P<0.001 in right ear) in both ears. There was an association between the classification of sounds and tympanogram types in both ears (X=11.437, P=0.003 in left ear; X=13.535, P=0.001 in right ear). Children with a "non-obstructed" classification had a healthier respiratory condition. Nasal auscultation revealed substantial intra- and inter-rater reliability. Nasal auscultation exhibited important differences according to ear and respiratory clinical conditions. Nasal auscultation in pediatrics seems to be an original topic as well as a simple method that can be used to identify early signs of nasopharyngeal obstruction.

Multiple Generations of Boudinage in a P-T Path: Insights from 3D Analysis of Amphibolite Boudins in Marble on Naxos, Greece

NASA Astrophysics Data System (ADS)

von Hagke, C.; Virgo, S.; Urai, J. L.

2016-12-01

Boudins are periodic structures in mechanically layered rocks deformed by layer parallel extension. At first sight, 2D sections of boudinaged layers are similar although 3D boudin patterns can be dramatically different. We aim to develop criteria to infer 3D strain from 2D outcrop observation of boudins. In marble quarries in the high grade complex on Naxos, Greece, we studied spectacular outcrops of amphibolite and pegmatite boudins, in combination with serial slicing of quarried blocks to reconstruct the 3D boudin structures. We identified multiple boudin generations, with early, high grade pinch and swell boudins followed by two generations of brittle shearband and torn boudins formed along the retrograde path under greenschist facies conditions. This shows how the rheological contract between marble and amphibolite changes from amphibolite to greenschist facies and suggests E-W shortening and N-S stretching in the footwall of the Naxos detachment. The later phases of boudinage interact with existing boudin geometries, producing complex structures in 3D. In 2D section the complexity is not directly apparent and reveals itself only in statistical analysis of long continuous sections. Our findings highlight the importance of 3D characterization of boudinage structures for boudin classification. The insights we gain from the analysis of multiphase boudinage structures on Naxos are the basis for quantitative boudin analysis to infer rheology, effective stress, vorticity and strain and establish a mechanics-based boudin classification scheme.

Analysis of vehicle classification and truck weight data of the New England states

DOT National Transportation Integrated Search

1998-09-01

This report is about a statistical analysis of 1995-96 classification and weigh in motion (WIM) data from 17 continuous traffic-monitoring sites in New England. It documents work performed by Oak Ridge National Laboratory in fulfillment of 'Analysis ...

Automated analysis and classification of melanocytic tumor on skin whole slide images.

PubMed

Xu, Hongming; Lu, Cheng; Berendt, Richard; Jha, Naresh; Mandal, Mrinal

2018-06-01

This paper presents a computer-aided technique for automated analysis and classification of melanocytic tumor on skin whole slide biopsy images. The proposed technique consists of four main modules. First, skin epidermis and dermis regions are segmented by a multi-resolution framework. Next, epidermis analysis is performed, where a set of epidermis features reflecting nuclear morphologies and spatial distributions is computed. In parallel with epidermis analysis, dermis analysis is also performed, where dermal cell nuclei are segmented and a set of textural and cytological features are computed. Finally, the skin melanocytic image is classified into different categories such as melanoma, nevus or normal tissue by using a multi-class support vector machine (mSVM) with extracted epidermis and dermis features. Experimental results on 66 skin whole slide images indicate that the proposed technique achieves more than 95% classification accuracy, which suggests that the technique has the potential to be used for assisting pathologists on skin biopsy image analysis and classification. Copyright © 2018 Elsevier Ltd. All rights reserved.

Forensic Discrimination of Latent Fingerprints Using Laser-Induced Breakdown Spectroscopy (LIBS) and Chemometric Approaches.

PubMed

Yang, Jun-Ho; Yoh, Jack J

2018-01-01

A novel technique is reported for separating overlapping latent fingerprints using chemometric approaches that combine laser-induced breakdown spectroscopy (LIBS) and multivariate analysis. The LIBS technique provides the capability of real time analysis and high frequency scanning as well as the data regarding the chemical composition of overlapping latent fingerprints. These spectra offer valuable information for the classification and reconstruction of overlapping latent fingerprints by implementing appropriate statistical multivariate analysis. The current study employs principal component analysis and partial least square methods for the classification of latent fingerprints from the LIBS spectra. This technique was successfully demonstrated through a classification study of four distinct latent fingerprints using classification methods such as soft independent modeling of class analogy (SIMCA) and partial least squares discriminant analysis (PLS-DA). The novel method yielded an accuracy of more than 85% and was proven to be sufficiently robust. Furthermore, through laser scanning analysis at a spatial interval of 125 µm, the overlapping fingerprints were reconstructed as separate two-dimensional forms.

REGIONAL-SCALE WIND FIELD CLASSIFICATION EMPLOYING CLUSTER ANALYSIS

DOE Office of Scientific and Technical Information (OSTI.GOV)

Glascoe, L G; Glaser, R E; Chin, H S

2004-06-17

The classification of time-varying multivariate regional-scale wind fields at a specific location can assist event planning as well as consequence and risk analysis. Further, wind field classification involves data transformation and inference techniques that effectively characterize stochastic wind field variation. Such a classification scheme is potentially useful for addressing overall atmospheric transport uncertainty and meteorological parameter sensitivity issues. Different methods to classify wind fields over a location include the principal component analysis of wind data (e.g., Hardy and Walton, 1978) and the use of cluster analysis for wind data (e.g., Green et al., 1992; Kaufmann and Weber, 1996). The goalmore » of this study is to use a clustering method to classify the winds of a gridded data set, i.e, from meteorological simulations generated by a forecast model.« less

In silico prediction of ROCK II inhibitors by different classification approaches.

PubMed

Cai, Chuipu; Wu, Qihui; Luo, Yunxia; Ma, Huili; Shen, Jiangang; Zhang, Yongbin; Yang, Lei; Chen, Yunbo; Wen, Zehuai; Wang, Qi

2017-11-01

ROCK II is an important pharmacological target linked to central nervous system disorders such as Alzheimer's disease. The purpose of this research is to generate ROCK II inhibitor prediction models by machine learning approaches. Firstly, four sets of descriptors were calculated with MOE 2010 and PaDEL-Descriptor, and optimized by F-score and linear forward selection methods. In addition, four classification algorithms were used to initially build 16 classifiers with k-nearest neighbors [Formula: see text], naïve Bayes, Random forest, and support vector machine. Furthermore, three sets of structural fingerprint descriptors were introduced to enhance the predictive capacity of classifiers, which were assessed with fivefold cross-validation, test set validation and external test set validation. The best two models, MFK + MACCS and MLR + SubFP, have both MCC values of 0.925 for external test set. After that, a privileged substructure analysis was performed to reveal common chemical features of ROCK II inhibitors. Finally, binding modes were analyzed to identify relationships between molecular descriptors and activity, while main interactions were revealed by comparing the docking interaction of the most potent and the weakest ROCK II inhibitors. To the best of our knowledge, this is the first report on ROCK II inhibitors utilizing machine learning approaches that provides a new method for discovering novel ROCK II inhibitors.

Automatic adventitious respiratory sound analysis: A systematic review.

PubMed

Pramono, Renard Xaviero Adhi; Bowyer, Stuart; Rodriguez-Villegas, Esther

2017-01-01

Automatic detection or classification of adventitious sounds is useful to assist physicians in diagnosing or monitoring diseases such as asthma, Chronic Obstructive Pulmonary Disease (COPD), and pneumonia. While computerised respiratory sound analysis, specifically for the detection or classification of adventitious sounds, has recently been the focus of an increasing number of studies, a standardised approach and comparison has not been well established. To provide a review of existing algorithms for the detection or classification of adventitious respiratory sounds. This systematic review provides a complete summary of methods used in the literature to give a baseline for future works. A systematic review of English articles published between 1938 and 2016, searched using the Scopus (1938-2016) and IEEExplore (1984-2016) databases. Additional articles were further obtained by references listed in the articles found. Search terms included adventitious sound detection, adventitious sound classification, abnormal respiratory sound detection, abnormal respiratory sound classification, wheeze detection, wheeze classification, crackle detection, crackle classification, rhonchi detection, rhonchi classification, stridor detection, stridor classification, pleural rub detection, pleural rub classification, squawk detection, and squawk classification. Only articles were included that focused on adventitious sound detection or classification, based on respiratory sounds, with performance reported and sufficient information provided to be approximately repeated. Investigators extracted data about the adventitious sound type analysed, approach and level of analysis, instrumentation or data source, location of sensor, amount of data obtained, data management, features, methods, and performance achieved. A total of 77 reports from the literature were included in this review. 55 (71.43%) of the studies focused on wheeze, 40 (51.95%) on crackle, 9 (11.69%) on stridor, 9 (11.69%) on rhonchi, and 18 (23.38%) on other sounds such as pleural rub, squawk, as well as the pathology. Instrumentation used to collect data included microphones, stethoscopes, and accelerometers. Several references obtained data from online repositories or book audio CD companions. Detection or classification methods used varied from empirically determined thresholds to more complex machine learning techniques. Performance reported in the surveyed works were converted to accuracy measures for data synthesis. Direct comparison of the performance of surveyed works cannot be performed as the input data used by each was different. A standard validation method has not been established, resulting in different works using different methods and performance measure definitions. A review of the literature was performed to summarise different analysis approaches, features, and methods used for the analysis. The performance of recent studies showed a high agreement with conventional non-automatic identification. This suggests that automated adventitious sound detection or classification is a promising solution to overcome the limitations of conventional auscultation and to assist in the monitoring of relevant diseases.

Automatic adventitious respiratory sound analysis: A systematic review

PubMed Central

Bowyer, Stuart; Rodriguez-Villegas, Esther

2017-01-01

Background Automatic detection or classification of adventitious sounds is useful to assist physicians in diagnosing or monitoring diseases such as asthma, Chronic Obstructive Pulmonary Disease (COPD), and pneumonia. While computerised respiratory sound analysis, specifically for the detection or classification of adventitious sounds, has recently been the focus of an increasing number of studies, a standardised approach and comparison has not been well established. Objective To provide a review of existing algorithms for the detection or classification of adventitious respiratory sounds. This systematic review provides a complete summary of methods used in the literature to give a baseline for future works. Data sources A systematic review of English articles published between 1938 and 2016, searched using the Scopus (1938-2016) and IEEExplore (1984-2016) databases. Additional articles were further obtained by references listed in the articles found. Search terms included adventitious sound detection, adventitious sound classification, abnormal respiratory sound detection, abnormal respiratory sound classification, wheeze detection, wheeze classification, crackle detection, crackle classification, rhonchi detection, rhonchi classification, stridor detection, stridor classification, pleural rub detection, pleural rub classification, squawk detection, and squawk classification. Study selection Only articles were included that focused on adventitious sound detection or classification, based on respiratory sounds, with performance reported and sufficient information provided to be approximately repeated. Data extraction Investigators extracted data about the adventitious sound type analysed, approach and level of analysis, instrumentation or data source, location of sensor, amount of data obtained, data management, features, methods, and performance achieved. Data synthesis A total of 77 reports from the literature were included in this review. 55 (71.43%) of the studies focused on wheeze, 40 (51.95%) on crackle, 9 (11.69%) on stridor, 9 (11.69%) on rhonchi, and 18 (23.38%) on other sounds such as pleural rub, squawk, as well as the pathology. Instrumentation used to collect data included microphones, stethoscopes, and accelerometers. Several references obtained data from online repositories or book audio CD companions. Detection or classification methods used varied from empirically determined thresholds to more complex machine learning techniques. Performance reported in the surveyed works were converted to accuracy measures for data synthesis. Limitations Direct comparison of the performance of surveyed works cannot be performed as the input data used by each was different. A standard validation method has not been established, resulting in different works using different methods and performance measure definitions. Conclusion A review of the literature was performed to summarise different analysis approaches, features, and methods used for the analysis. The performance of recent studies showed a high agreement with conventional non-automatic identification. This suggests that automated adventitious sound detection or classification is a promising solution to overcome the limitations of conventional auscultation and to assist in the monitoring of relevant diseases. PMID:28552969

Plus Disease in Retinopathy of Prematurity: A Continuous Spectrum of Vascular Abnormality as a Basis of Diagnostic Variability.

PubMed

Campbell, J Peter; Kalpathy-Cramer, Jayashree; Erdogmus, Deniz; Tian, Peng; Kedarisetti, Dharanish; Moleta, Chace; Reynolds, James D; Hutcheson, Kelly; Shapiro, Michael J; Repka, Michael X; Ferrone, Philip; Drenser, Kimberly; Horowitz, Jason; Sonmez, Kemal; Swan, Ryan; Ostmo, Susan; Jonas, Karyn E; Chan, R V Paul; Chiang, Michael F

2016-11-01

To identify patterns of interexpert discrepancy in plus disease diagnosis in retinopathy of prematurity (ROP). We developed 2 datasets of clinical images as part of the Imaging and Informatics in ROP study and determined a consensus reference standard diagnosis (RSD) for each image based on 3 independent image graders and the clinical examination results. We recruited 8 expert ROP clinicians to classify these images and compared the distribution of classifications between experts and the RSD. Eight participating experts with more than 10 years of clinical ROP experience and more than 5 peer-reviewed ROP publications who analyzed images obtained during routine ROP screening in neonatal intensive care units. Expert classification of images of plus disease in ROP. Interexpert agreement (weighted κ statistic) and agreement and bias on ordinal classification between experts (analysis of variance [ANOVA]) and the RSD (percent agreement). There was variable interexpert agreement on diagnostic classifications between the 8 experts and the RSD (weighted κ, 0-0.75; mean, 0.30). The RSD agreement ranged from 80% to 94% for the dataset of 100 images and from 29% to 79% for the dataset of 34 images. However, when images were ranked in order of disease severity (by average expert classification), the pattern of expert classification revealed a consistent systematic bias for each expert consistent with unique cut points for the diagnosis of plus disease and preplus disease. The 2-way ANOVA model suggested a highly significant effect of both image and user on the average score (dataset A: P < 0.05 and adjusted R 2  = 0.82; and dataset B: P < 0.05 and adjusted R 2  = 0.6615). There is wide variability in the classification of plus disease by ROP experts, which occurs because experts have different cut points for the amounts of vascular abnormality required for presence of plus and preplus disease. This has important implications for research, teaching, and patient care for ROP and suggests that a continuous ROP plus disease severity score may reflect more accurately the behavior of expert ROP clinicians and may better standardize classification in the future. Copyright © 2016 American Academy of Ophthalmology. Published by Elsevier Inc. All rights reserved.

Analyzing thematic maps and mapping for accuracy

USGS Publications Warehouse

Rosenfield, G.H.

1982-01-01

Two problems which exist while attempting to test the accuracy of thematic maps and mapping are: (1) evaluating the accuracy of thematic content, and (2) evaluating the effects of the variables on thematic mapping. Statistical analysis techniques are applicable to both these problems and include techniques for sampling the data and determining their accuracy. In addition, techniques for hypothesis testing, or inferential statistics, are used when comparing the effects of variables. A comprehensive and valid accuracy test of a classification project, such as thematic mapping from remotely sensed data, includes the following components of statistical analysis: (1) sample design, including the sample distribution, sample size, size of the sample unit, and sampling procedure; and (2) accuracy estimation, including estimation of the variance and confidence limits. Careful consideration must be given to the minimum sample size necessary to validate the accuracy of a given. classification category. The results of an accuracy test are presented in a contingency table sometimes called a classification error matrix. Usually the rows represent the interpretation, and the columns represent the verification. The diagonal elements represent the correct classifications. The remaining elements of the rows represent errors by commission, and the remaining elements of the columns represent the errors of omission. For tests of hypothesis that compare variables, the general practice has been to use only the diagonal elements from several related classification error matrices. These data are arranged in the form of another contingency table. The columns of the table represent the different variables being compared, such as different scales of mapping. The rows represent the blocking characteristics, such as the various categories of classification. The values in the cells of the tables might be the counts of correct classification or the binomial proportions of these counts divided by either the row totals or the column totals from the original classification error matrices. In hypothesis testing, when the results of tests of multiple sample cases prove to be significant, some form of statistical test must be used to separate any results that differ significantly from the others. In the past, many analyses of the data in this error matrix were made by comparing the relative magnitudes of the percentage of correct classifications, for either individual categories, the entire map or both. More rigorous analyses have used data transformations and (or) two-way classification analysis of variance. A more sophisticated step of data analysis techniques would be to use the entire classification error matrices using the methods of discrete multivariate analysis or of multiviariate analysis of variance.

Land cover classification for Puget Sound, 1974-1979

NASA Technical Reports Server (NTRS)

Eby, J. R.

1981-01-01

Digital analysis of LANDSAT data for land cover classification projects in the Puget Sound region is surveyed. Two early rural and urban land use classifications and their application are described. After acquisition of VICAR/IBIs software, another land use classification of the area was performed, and is described in more detail. Future applications are considered.

Wheeze sound analysis using computer-based techniques: a systematic review.

PubMed

Ghulam Nabi, Fizza; Sundaraj, Kenneth; Chee Kiang, Lam; Palaniappan, Rajkumar; Sundaraj, Sebastian

2017-10-31

Wheezes are high pitched continuous respiratory acoustic sounds which are produced as a result of airway obstruction. Computer-based analyses of wheeze signals have been extensively used for parametric analysis, spectral analysis, identification of airway obstruction, feature extraction and diseases or pathology classification. While this area is currently an active field of research, the available literature has not yet been reviewed. This systematic review identified articles describing wheeze analyses using computer-based techniques on the SCOPUS, IEEE Xplore, ACM, PubMed and Springer and Elsevier electronic databases. After a set of selection criteria was applied, 41 articles were selected for detailed analysis. The findings reveal that 1) computerized wheeze analysis can be used for the identification of disease severity level or pathology, 2) further research is required to achieve acceptable rates of identification on the degree of airway obstruction with normal breathing, 3) analysis using combinations of features and on subgroups of the respiratory cycle has provided a pathway to classify various diseases or pathology that stem from airway obstruction.

On the Implementation of a Land Cover Classification System for SAR Images Using Khoros

NASA Technical Reports Server (NTRS)

Medina Revera, Edwin J.; Espinosa, Ramon Vasquez

1997-01-01

The Synthetic Aperture Radar (SAR) sensor is widely used to record data about the ground under all atmospheric conditions. The SAR acquired images have very good resolution which necessitates the development of a classification system that process the SAR images to extract useful information for different applications. In this work, a complete system for the land cover classification was designed and programmed using the Khoros, a data flow visual language environment, taking full advantages of the polymorphic data services that it provides. Image analysis was applied to SAR images to improve and automate the processes of recognition and classification of the different regions like mountains and lakes. Both unsupervised and supervised classification utilities were used. The unsupervised classification routines included the use of several Classification/Clustering algorithms like the K-means, ISO2, Weighted Minimum Distance, and the Localized Receptive Field (LRF) training/classifier. Different texture analysis approaches such as Invariant Moments, Fractal Dimension and Second Order statistics were implemented for supervised classification of the images. The results and conclusions for SAR image classification using the various unsupervised and supervised procedures are presented based on their accuracy and performance.

Lean waste classification model to support the sustainable operational practice

NASA Astrophysics Data System (ADS)

Sutrisno, A.; Vanany, I.; Gunawan, I.; Asjad, M.

2018-04-01

Driven by growing pressure for a more sustainable operational practice, improvement on the classification of non-value added (waste) is one of the prerequisites to realize sustainability of a firm. While the use of the 7 (seven) types of the Ohno model now becoming a versatile tool to reveal the lean waste occurrence. In many recent investigations, the use of the Seven Waste model of Ohno is insufficient to cope with the types of waste occurred in industrial practices at various application levels. Intended to a narrowing down this limitation, this paper presented an improved waste classification model based on survey to recent studies discussing on waste at various operational stages. Implications on the waste classification model to the body of knowledge and industrial practices are provided.

Why do pathological stage IA lung adenocarcinomas vary from prognosis?: a clinicopathologic study of 176 patients with pathological stage IA lung adenocarcinoma based on the IASLC/ATS/ERS classification.

PubMed

Zhang, Jie; Wu, Jie; Tan, Qiang; Zhu, Lei; Gao, Wen

2013-09-01

Patients with pathological stage IA adenocarcinoma (AC) have a variable prognosis, even if treated in the same way. The postoperative treatment of pathological stage IA patients is also controversial. We identified 176 patients with pathological stage IA AC who had undergone a lobectomy and mediastinal lymph node dissection at the Shanghai Chest Hospital, Shanghai, China, between 2000 and 2006. No patient had preoperative treatment. The histologic subtypes of all patients were classified according to the 2011 International Association for the Study of Lung Cancer (IASLC)/American Thoracic Society (ATS)/European Respiratory Society (ERS) international multidisciplinary lung AC classification. Patients' 5-year overall survival (OS) and 5-year disease-free survival (DFS) were calculated using Kaplan-Meier and Cox regression analyses. One hundred seventy-six patients with pathological stage IA AC had an 86.6% 5-year OS and 74.6% 5-year DFS. The 10 patients with micropapillary predominant subtype had the lowest 5-year DFS (40.0%).The 12 patients with solid predominant with mucin production subtype had the lowest 5-year OS (66.7%). Univariate and multivariate analysis showed that sex and prognositic groups of the IASLC/ATS/ERS histologic classification were significantly associated with 5-year DFS of pathological stage IA AC. Our study revealed that sex was an independent prognostic factor of pathological stage IA AC. The IASLC/ATS/ERS classification of lung AC identifies histologic categories with prognostic differences that could be helpful in clinical therapy.

Evaluation of feature selection algorithms for classification in temporal lobe epilepsy based on MR images

NASA Astrophysics Data System (ADS)

Lai, Chunren; Guo, Shengwen; Cheng, Lina; Wang, Wensheng; Wu, Kai

2017-02-01

It's very important to differentiate the temporal lobe epilepsy (TLE) patients from healthy people and localize the abnormal brain regions of the TLE patients. The cortical features and changes can reveal the unique anatomical patterns of brain regions from the structural MR images. In this study, structural MR images from 28 normal controls (NC), 18 left TLE (LTLE), and 21 right TLE (RTLE) were acquired, and four types of cortical feature, namely cortical thickness (CTh), cortical surface area (CSA), gray matter volume (GMV), and mean curvature (MCu), were explored for discriminative analysis. Three feature selection methods, the independent sample t-test filtering, the sparse-constrained dimensionality reduction model (SCDRM), and the support vector machine-recursive feature elimination (SVM-RFE), were investigated to extract dominant regions with significant differences among the compared groups for classification using the SVM classifier. The results showed that the SVM-REF achieved the highest performance (most classifications with more than 92% accuracy), followed by the SCDRM, and the t-test. Especially, the surface area and gray volume matter exhibited prominent discriminative ability, and the performance of the SVM was improved significantly when the four cortical features were combined. Additionally, the dominant regions with higher classification weights were mainly located in temporal and frontal lobe, including the inferior temporal, entorhinal cortex, fusiform, parahippocampal cortex, middle frontal and frontal pole. It was demonstrated that the cortical features provided effective information to determine the abnormal anatomical pattern and the proposed method has the potential to improve the clinical diagnosis of the TLE.

Climate Classification is an Important Factor in ­Assessing Hospital Performance Metrics

NASA Astrophysics Data System (ADS)

Boland, M. R.; Parhi, P.; Gentine, P.; Tatonetti, N. P.

2017-12-01

Context/Purpose: Climate is a known modulator of disease, but its impact on hospital performance metrics remains unstudied. Methods: We assess the relationship between Köppen-Geiger climate classification and hospital performance metrics, specifically 30-day mortality, as reported in Hospital Compare, and collected for the period July 2013 through June 2014 (7/1/2013 - 06/30/2014). A hospital-level multivariate linear regression analysis was performed while controlling for known socioeconomic factors to explore the relationship between all-cause mortality and climate. Hospital performance scores were obtained from 4,524 hospitals belonging to 15 distinct Köppen-Geiger climates and 2,373 unique counties. Results: Model results revealed that hospital performance metrics for mortality showed significant climate dependence (p<0.001) after adjusting for socioeconomic factors. Interpretation: Currently, hospitals are reimbursed by Governmental agencies using 30-day mortality rates along with 30-day readmission rates. These metrics allow Government agencies to rank hospitals according to their `performance' along these metrics. Various socioeconomic factors are taken into consideration when determining individual hospitals performance. However, no climate-based adjustment is made within the existing framework. Our results indicate that climate-based variability in 30-day mortality rates does exist even after socioeconomic confounder adjustment. Use of standardized high-level climate classification systems (such as Koppen-Geiger) would be useful to incorporate in future metrics. Conclusion: Climate is a significant factor in evaluating hospital 30-day mortality rates. These results demonstrate that climate classification is an important factor when comparing hospital performance across the United States.

Combined target factor analysis and Bayesian soft-classification of interference-contaminated samples: forensic fire debris analysis.

PubMed

Williams, Mary R; Sigman, Michael E; Lewis, Jennifer; Pitan, Kelly McHugh

2012-10-10

A bayesian soft classification method combined with target factor analysis (TFA) is described and tested for the analysis of fire debris data. The method relies on analysis of the average mass spectrum across the chromatographic profile (i.e., the total ion spectrum, TIS) from multiple samples taken from a single fire scene. A library of TIS from reference ignitable liquids with assigned ASTM classification is used as the target factors in TFA. The class-conditional distributions of correlations between the target and predicted factors for each ASTM class are represented by kernel functions and analyzed by bayesian decision theory. The soft classification approach assists in assessing the probability that ignitable liquid residue from a specific ASTM E1618 class, is present in a set of samples from a single fire scene, even in the presence of unspecified background contributions from pyrolysis products. The method is demonstrated with sample data sets and then tested on laboratory-scale burn data and large-scale field test burns. The overall performance achieved in laboratory and field test of the method is approximately 80% correct classification of fire debris samples. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

Multivariate pattern classification reveals autonomic and experiential representations of discrete emotions.

PubMed

Kragel, Philip A; Labar, Kevin S

2013-08-01

Defining the structural organization of emotions is a central unresolved question in affective science. In particular, the extent to which autonomic nervous system activity signifies distinct affective states remains controversial. Most prior research on this topic has used univariate statistical approaches in attempts to classify emotions from psychophysiological data. In the present study, electrodermal, cardiac, respiratory, and gastric activity, as well as self-report measures were taken from healthy subjects during the experience of fear, anger, sadness, surprise, contentment, and amusement in response to film and music clips. Information pertaining to affective states present in these response patterns was analyzed using multivariate pattern classification techniques. Overall accuracy for classifying distinct affective states was 58.0% for autonomic measures and 88.2% for self-report measures, both of which were significantly above chance. Further, examining the error distribution of classifiers revealed that the dimensions of valence and arousal selectively contributed to decoding emotional states from self-report, whereas a categorical configuration of affective space was evident in both self-report and autonomic measures. Taken together, these findings extend recent multivariate approaches to study emotion and indicate that pattern classification tools may improve upon univariate approaches to reveal the underlying structure of emotional experience and physiological expression. PsycINFO Database Record (c) 2013 APA, all rights reserved.

Multivariate Pattern Classification Reveals Autonomic and Experiential Representations of Discrete Emotions

PubMed Central

Kragel, Philip A.; LaBar, Kevin S.

2013-01-01

Defining the structural organization of emotions is a central unresolved question in affective science. In particular, the extent to which autonomic nervous system activity signifies distinct affective states remains controversial. Most prior research on this topic has used univariate statistical approaches in attempts to classify emotions from psychophysiological data. In the present study, electrodermal, cardiac, respiratory, and gastric activity, as well as self-report measures were taken from healthy subjects during the experience of fear, anger, sadness, surprise, contentment, and amusement in response to film and music clips. Information pertaining to affective states present in these response patterns was analyzed using multivariate pattern classification techniques. Overall accuracy for classifying distinct affective states was 58.0% for autonomic measures and 88.2% for self-report measures, both of which were significantly above chance. Further, examining the error distribution of classifiers revealed that the dimensions of valence and arousal selectively contributed to decoding emotional states from self-report, whereas a categorical configuration of affective space was evident in both self-report and autonomic measures. Taken together, these findings extend recent multivariate approaches to study emotion and indicate that pattern classification tools may improve upon univariate approaches to reveal the underlying structure of emotional experience and physiological expression. PMID:23527508

Human error analysis of commercial aviation accidents using the human factors analysis and classification system (HFACS)

DOT National Transportation Integrated Search

2001-02-01

The Human Factors Analysis and Classification System (HFACS) is a general human error framework : originally developed and tested within the U.S. military as a tool for investigating and analyzing the human : causes of aviation accidents. Based upon ...

7 CFR 160.1 - Definitions of general terms.

Code of Federal Regulations, 2010 CFR

2010-01-01

...) Analysis. Any examination by physical, chemical, or sensory methods. (m) Classification. Designation as to... Administrator has sufficient and proper interest in the analysis, classification, grading, or sale of naval... provisions of the act and the provisions in this part to show the results of any examination, analysis...

Classification of river water pollution using Hyperion data

NASA Astrophysics Data System (ADS)

Kar, Soumyashree; Rathore, V. S.; Champati ray, P. K.; Sharma, Richa; Swain, S. K.

2016-06-01

A novel attempt is made to use hyperspectral remote sensing to identify the spatial variability of metal pollutants present in river water. It was also attempted to classify the hyperspectral image - Earth Observation-1 (EO-1) Hyperion data of an 8 km stretch of the river Yamuna, near Allahabad city in India depending on its chemical composition. For validating image analysis results, a total of 10 water samples were collected and chemically analyzed using Inductively Coupled Plasma-Optical Emission Spectroscopy (ICP-OES). Two different spectral libraries from field and image data were generated for the 10 sample locations. Advanced per-pixel supervised classifications such as Spectral Angle Mapper (SAM), SAM target finder using BandMax and Support Vector Machine (SVM) were carried out along with the unsupervised clustering procedure - Iterative Self-Organizing Data Analysis Technique (ISODATA). The results were compared and assessed with respect to ground data. Analytical Spectral Devices (ASD), Inc. spectroradiometer, FieldSpec 4 was used to generate the spectra of the water samples which were compiled into a spectral library and used for Spectral Absorption Depth (SAD) analysis. The spectral depth pattern of image and field spectral libraries was found to be highly correlated (correlation coefficient, R2 = 0.99) which validated the image analysis results with respect to the ground data. Further, we carried out a multivariate regression analysis to assess the varying concentrations of metal ions present in water based on the spectral depth of the corresponding absorption feature. Spectral Absorption Depth (SAD) analysis along with metal analysis of field data revealed the order in which the metals affected the river pollution, which was in conformity with the findings of Central Pollution Control Board (CPCB). Therefore, it is concluded that hyperspectral imaging provides opportunity that can be used for satellite based remote monitoring of water quality from space.

Multivariate pattern analysis of obsessive-compulsive disorder using structural neuroanatomy.

PubMed

Hu, Xinyu; Liu, Qi; Li, Bin; Tang, Wanjie; Sun, Huaiqiang; Li, Fei; Yang, Yanchun; Gong, Qiyong; Huang, Xiaoqi

2016-02-01

Magnetic resonance imaging (MRI) studies have revealed brain structural abnormalities in obsessive-compulsive disorder (OCD) patients, involving both gray matter (GM) and white matter (WM). However, the results of previous publications were based on average differences between groups, which limited their usages in clinical practice. Therefore, the aim of this study was to examine whether the application of multivariate pattern analysis (MVPA) to high-dimensional structural images would allow accurate discrimination between OCD patients and healthy control subjects (HCS). High-resolution T1-weighted images were acquired from 33 OCD patients and 33 demographically matched HCS in a 3.0 T scanner. Differences in GM and WM volume between OCD and HCS were examined using two types of well-established MVPA techniques: support vector machine (SVM) and Gaussian process classifier (GPC). We also drew a receiver operating characteristic (ROC) curve to evaluate the performance of each classifier. The classification accuracies for both classifiers using GM and WM anatomy were all above 75%. The highest classification accuracy (81.82%, P<0.001) was achieved with the SVM classifier using WM information. Regional brain anomalies with high discriminative power were based on three distributed networks including the fronto-striatal circuit, the temporo-parieto-occipital junction and the cerebellum. Our study illustrated that both GM and WM anatomical features may be useful in differentiating OCD patients from HCS. WM volume using the SVM approach showed the highest accuracy in our population for revealing group differences, which suggested its potential diagnostic role in detecting highly enriched OCD patients at the level of the individual. Copyright © 2015 Elsevier B.V. and ECNP. All rights reserved.

Multivariate pattern analysis reveals anatomical connectivity differences between the left and right mesial temporal lobe epilepsy.

PubMed

Fang, Peng; An, Jie; Zeng, Ling-Li; Shen, Hui; Chen, Fanglin; Wang, Wensheng; Qiu, Shijun; Hu, Dewen

2015-01-01

Previous studies have demonstrated differences of clinical signs and functional brain network organizations between the left and right mesial temporal lobe epilepsy (mTLE), but the anatomical connectivity differences underlying functional variance between the left and right mTLE remain uncharacterized. We examined 43 (22 left, 21 right) mTLE patients with hippocampal sclerosis and 39 healthy controls using diffusion tensor imaging. After the whole-brain anatomical networks were constructed for each subject, multivariate pattern analysis was applied to classify the left mTLE from the right mTLE and extract the anatomical connectivity differences between the left and right mTLE patients. The classification results reveal 93.0% accuracy for the left mTLE versus the right mTLE, 93.4% accuracy for the left mTLE versus controls and 90.0% accuracy for the right mTLE versus controls. Compared with the right mTLE, the left mTLE exhibited a different connectivity pattern in the cortical-limbic network and cerebellum. The majority of the most discriminating anatomical connections were located within or across the cortical-limbic network and cerebellum, thereby indicating that these disease-related anatomical network alterations may give rise to a portion of the complex of emotional and memory deficit between the left and right mTLE. Moreover, the orbitofrontal gyrus, cingulate cortex, hippocampus and parahippocampal gyrus, which exhibit high discriminative power in classification, may play critical roles in the pathophysiology of mTLE. The current study demonstrated that anatomical connectivity differences between the left mTLE and the right mTLE may have the potential to serve as a neuroimaging biomarker to guide personalized diagnosis of the left and right mTLE.

Derivation of an artificial gene to improve classification accuracy upon gene selection.

PubMed

Seo, Minseok; Oh, Sejong

2012-02-01

Classification analysis has been developed continuously since 1936. This research field has advanced as a result of development of classifiers such as KNN, ANN, and SVM, as well as through data preprocessing areas. Feature (gene) selection is required for very high dimensional data such as microarray before classification work. The goal of feature selection is to choose a subset of informative features that reduces processing time and provides higher classification accuracy. In this study, we devised a method of artificial gene making (AGM) for microarray data to improve classification accuracy. Our artificial gene was derived from a whole microarray dataset, and combined with a result of gene selection for classification analysis. We experimentally confirmed a clear improvement of classification accuracy after inserting artificial gene. Our artificial gene worked well for popular feature (gene) selection algorithms and classifiers. The proposed approach can be applied to any type of high dimensional dataset. Copyright © 2011 Elsevier Ltd. All rights reserved.

Revealing how different spinors can be: The Lounesto spinor classification

NASA Astrophysics Data System (ADS)

Hoff da Silva, J. M.; Cavalcanti, R. T.

2017-11-01

This paper aims to give a coordinate-based introduction to the so-called Lounesto spinorial classification scheme. Among other results, it has evinced classes of spinors which fail to satisfy Dirac equation. The underlying idea and the central aspects of such spinorial categorization are introduced in an argumentative basis, after which we delve into a commented account on recent results obtained from (and within) this branch of research.

The research on medical image classification algorithm based on PLSA-BOW model.

PubMed

Cao, C H; Cao, H L

2016-04-29

With the rapid development of modern medical imaging technology, medical image classification has become more important for medical diagnosis and treatment. To solve the existence of polysemous words and synonyms problem, this study combines the word bag model with PLSA (Probabilistic Latent Semantic Analysis) and proposes the PLSA-BOW (Probabilistic Latent Semantic Analysis-Bag of Words) model. In this paper we introduce the bag of words model in text field to image field, and build the model of visual bag of words model. The method enables the word bag model-based classification method to be further improved in accuracy. The experimental results show that the PLSA-BOW model for medical image classification can lead to a more accurate classification.

Pros and cons of conjoint analysis of discrete choice experiments to define classification and response criteria in rheumatology.

PubMed

Taylor, William J

2016-03-01

Conjoint analysis of choice or preference data has been used in marketing for over 40 years but has appeared in healthcare settings much more recently. It may be a useful technique for applications within the rheumatology field. Conjoint analysis in rheumatology contexts has mainly used the approaches implemented in 1000Minds Ltd, Dunedin, New Zealand, Sawtooth Software, Orem UT, USA. Examples include classification criteria, composite response criteria, service prioritization tools and utilities assessment. Limitations imposed by very many attributes can be managed using new techniques. Conjoint analysis studies of classification and response criteria suggest that the assumption of equal weighting of attributes cannot be met, which challenges traditional approaches to composite criteria construction. Weights elicited through choice experiments with experts can derive more accurate classification criteria, than unweighted criteria. Studies that find significant variation in attribute weights for composite response criteria for gout make construction of such criteria problematic. Better understanding of various multiattribute phenomena is likely to increase with increased use of conjoint analysis, especially when the attributes concern individual perceptions or opinions. In addition to classification criteria, some applications for conjoint analysis that are emerging in rheumatology include prioritization tools, remission criteria, and utilities for life areas.

Arsenic metabolism in high altitude modern stromatolites revealed by metagenomic analysis.

PubMed

Kurth, Daniel; Amadio, Ariel; Ordoñez, Omar F; Albarracín, Virginia H; Gärtner, Wolfgang; Farías, María E

2017-04-21

Modern stromatolites thrive only in selected locations in the world. Socompa Lake, located in the Andean plateau at 3570 masl, is one of the numerous extreme Andean microbial ecosystems described over recent years. Extreme environmental conditions include hypersalinity, high UV incidence, and high arsenic content, among others. After Socompa's stromatolite microbial communities were analysed by metagenomic DNA sequencing, taxonomic classification showed dominance of Proteobacteria, Bacteroidetes and Firmicutes, and a remarkably high number of unclassified sequences. A functional analysis indicated that carbon fixation might occur not only by the Calvin-Benson cycle, but also through alternative pathways such as the reverse TCA cycle, and the reductive acetyl-CoA pathway. Deltaproteobacteria were involved both in sulfate reduction and nitrogen fixation. Significant differences were found when comparing the Socompa stromatolite metagenome to the Shark Bay (Australia) smooth mat metagenome: namely, those involving stress related processes, particularly, arsenic resistance. An in-depth analysis revealed a surprisingly diverse metabolism comprising all known types of As resistance and energy generating pathways. While the ars operon was the main mechanism, an important abundance of arsM genes was observed in selected phyla. The data resulting from this work will prove a cornerstone for further studies on this rare microbial community.

Land use/cover classification in the Brazilian Amazon using satellite images.

PubMed

Lu, Dengsheng; Batistella, Mateus; Li, Guiying; Moran, Emilio; Hetrick, Scott; Freitas, Corina da Costa; Dutra, Luciano Vieira; Sant'anna, Sidnei João Siqueira

2012-09-01

Land use/cover classification is one of the most important applications in remote sensing. However, mapping accurate land use/cover spatial distribution is a challenge, particularly in moist tropical regions, due to the complex biophysical environment and limitations of remote sensing data per se. This paper reviews experiments related to land use/cover classification in the Brazilian Amazon for a decade. Through comprehensive analysis of the classification results, it is concluded that spatial information inherent in remote sensing data plays an essential role in improving land use/cover classification. Incorporation of suitable textural images into multispectral bands and use of segmentation-based method are valuable ways to improve land use/cover classification, especially for high spatial resolution images. Data fusion of multi-resolution images within optical sensor data is vital for visual interpretation, but may not improve classification performance. In contrast, integration of optical and radar data did improve classification performance when the proper data fusion method was used. Of the classification algorithms available, the maximum likelihood classifier is still an important method for providing reasonably good accuracy, but nonparametric algorithms, such as classification tree analysis, has the potential to provide better results. However, they often require more time to achieve parametric optimization. Proper use of hierarchical-based methods is fundamental for developing accurate land use/cover classification, mainly from historical remotely sensed data.

Land use/cover classification in the Brazilian Amazon using satellite images

PubMed Central

Lu, Dengsheng; Batistella, Mateus; Li, Guiying; Moran, Emilio; Hetrick, Scott; Freitas, Corina da Costa; Dutra, Luciano Vieira; Sant’Anna, Sidnei João Siqueira

2013-01-01

Land use/cover classification is one of the most important applications in remote sensing. However, mapping accurate land use/cover spatial distribution is a challenge, particularly in moist tropical regions, due to the complex biophysical environment and limitations of remote sensing data per se. This paper reviews experiments related to land use/cover classification in the Brazilian Amazon for a decade. Through comprehensive analysis of the classification results, it is concluded that spatial information inherent in remote sensing data plays an essential role in improving land use/cover classification. Incorporation of suitable textural images into multispectral bands and use of segmentation-based method are valuable ways to improve land use/cover classification, especially for high spatial resolution images. Data fusion of multi-resolution images within optical sensor data is vital for visual interpretation, but may not improve classification performance. In contrast, integration of optical and radar data did improve classification performance when the proper data fusion method was used. Of the classification algorithms available, the maximum likelihood classifier is still an important method for providing reasonably good accuracy, but nonparametric algorithms, such as classification tree analysis, has the potential to provide better results. However, they often require more time to achieve parametric optimization. Proper use of hierarchical-based methods is fundamental for developing accurate land use/cover classification, mainly from historical remotely sensed data. PMID:24353353

Railroad Classification Yard Technology : An Introductory Analysis of Functions and Operations

DOT National Transportation Integrated Search

1975-05-01

A review of the basic operating characteristics and functions of railroad classification yards is presented. Introductory descriptions of terms, concepts, and problems of railroad operations involving classification yards are included in an attempt t...

Site Classification using Multichannel Channel Analysis of Surface Wave (MASW) method on Soft and Hard Ground

NASA Astrophysics Data System (ADS)

Ashraf, M. A. M.; Kumar, N. S.; Yusoh, R.; Hazreek, Z. A. M.; Aziman, M.

2018-04-01

Site classification utilizing average shear wave velocity (Vs(30) up to 30 meters depth is a typical parameter. Numerous geophysical methods have been proposed for estimation of shear wave velocity by utilizing assortment of testing configuration, processing method, and inversion algorithm. Multichannel Analysis of Surface Wave (MASW) method is been rehearsed by numerous specialist and professional to geotechnical engineering for local site characterization and classification. This study aims to determine the site classification on soft and hard ground using MASW method. The subsurface classification was made utilizing National Earthquake Hazards Reduction Program (NERHP) and international Building Code (IBC) classification. Two sites are chosen to acquire the shear wave velocity which is in the state of Pulau Pinang for soft soil and Perlis for hard rock. Results recommend that MASW technique can be utilized to spatially calculate the distribution of shear wave velocity (Vs(30)) in soil and rock to characterize areas.

Modified Angle's Classification for Primary Dentition.

PubMed

Chandranee, Kaushik Narendra; Chandranee, Narendra Jayantilal; Nagpal, Devendra; Lamba, Gagandeep; Choudhari, Purva; Hotwani, Kavita

2017-01-01

This study aims to propose a modification of Angle's classification for primary dentition and to assess its applicability in children from Central India, Nagpur. Modification in Angle's classification has been proposed for application in primary dentition. Small roman numbers i/ii/iii are used for primary dentition notation to represent Angle's Class I/II/III molar relationships as in permanent dentition, respectively. To assess applicability of modified Angle's classification a cross-sectional preschool 2000 children population from central India; 3-6 years of age residing in Nagpur metropolitan city of Maharashtra state were selected randomly as per the inclusion and exclusion criteria. Majority 93.35% children were found to have bilateral Class i followed by 2.5% bilateral Class ii and 0.2% bilateral half cusp Class iii molar relationships as per the modified Angle's classification for primary dentition. About 3.75% children had various combinations of Class ii relationships and 0.2% children were having Class iii subdivision relationship. Modification of Angle's classification for application in primary dentition has been proposed. A cross-sectional investigation using new classification revealed various 6.25% Class ii and 0.4% Class iii molar relationships cases in preschool children population in a metropolitan city of Nagpur. Application of the modified Angle's classification to other population groups is warranted to validate its routine application in clinical pediatric dentistry.

Computer-based classification of bacteria species by analysis of their colonies Fresnel diffraction patterns

NASA Astrophysics Data System (ADS)

Suchwalko, Agnieszka; Buzalewicz, Igor; Podbielska, Halina

2012-01-01

In the presented paper the optical system with converging spherical wave illumination for classification of bacteria species, is proposed. It allows for compression of the observation space, observation of Fresnel patterns, diffraction pattern scaling and low level of optical aberrations, which are not possessed by other optical configurations. Obtained experimental results have shown that colonies of specific bacteria species generate unique diffraction signatures. Analysis of Fresnel diffraction patterns of bacteria colonies can be fast and reliable method for classification and recognition of bacteria species. To determine the unique features of bacteria colonies diffraction patterns the image processing analysis was proposed. Classification can be performed by analyzing the spatial structure of diffraction patterns, which can be characterized by set of concentric rings. The characteristics of such rings depends on the bacteria species. In the paper, the influence of basic features and ring partitioning number on the bacteria classification, is analyzed. It is demonstrated that Fresnel patterns can be used for classification of following species: Salmonella enteritidis, Staplyococcus aureus, Proteus mirabilis and Citrobacter freundii. Image processing is performed by free ImageJ software, for which a special macro with human interaction, was written. LDA classification, CV method, ANOVA and PCA visualizations preceded by image data extraction were conducted using the free software R.

Elevation of B-Type Natriuretic Peptide at Discharge is Associated With 2-Year Mortality After Transcatheter Aortic Valve Replacement in Patients With Severe Aortic Stenosis: Insights From a Multicenter Prospective OCEAN-TAVI (Optimized Transcatheter Valvular Intervention-Transcatheter Aortic Valve Implantation) Registry.

PubMed

Mizutani, Kazuki; Hara, Masahiko; Iwata, Shinichi; Murakami, Takashi; Shibata, Toshihiko; Yoshiyama, Minoru; Naganuma, Toru; Yamanaka, Futoshi; Higashimori, Akihiro; Tada, Norio; Takagi, Kensuke; Araki, Motoharu; Ueno, Hiroshi; Tabata, Minoru; Shirai, Shinichi; Watanabe, Yusuke; Yamamoto, Masanori; Hayashida, Kentaro

2017-07-14

In this study, we sought to investigate the 2-year prognostic impact of B-type natriuretic peptide (BNP) levels at discharge, following transcatheter aortic valve replacement. We enrolled 1094 consecutive patients who underwent transcatheter aortic valve replacement between 2013 and 2016. Study patients were stratified into 2 groups according to survival classification and regression tree analysis (high versus low BNP groups). We evaluated the impact of high BNP on 2-year mortality compared with that of low BNP using a multivariable Cox model, and assessed whether this stratification would improve predictive accuracy for determining 2-year mortality by assessing time-dependent net reclassification improvement and integrated discrimination improvement. The median age of patients was 85 years (quartile 82-88), and 29.2% of the study population were men. The median Society of Thoracic Surgeons score was 6.8 (4.7-9.5), and BNP at discharge was 186 (93-378) pg/mL. All-cause mortality following discharge was 7.9% (95% CI, 5.8-9.9%) at 1 year and 15.4% (95% CI, 11.6-19.0%) at 2 years. The survival classification and regression tree analysis revealed that the discriminating BNP level to discern 2-year mortality was 202 pg/mL, and that elevated BNP had a statistically significant impact on outcomes, with an adjusted hazard ratio of 2.28 (1.36-3.82, P =0.002). The time-dependent net reclassification improvement ( P =0.047) and integrated discrimination improvement ( P =0.029) analysis revealed that the incorporation of BNP stratification with other clinical variables significantly improved predictive accuracy for 2-year mortality. Elevation of BNP at discharge is associated with 2-year mortality after transcatheter aortic valve replacement. © 2017 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley.

Outcome Prediction in Pneumonia Induced ALI/ARDS by Clinical Features and Peptide Patterns of BALF Determined by Mass Spectrometry

PubMed Central

Frenzel, Jochen; Gessner, Christian; Sandvoss, Torsten; Hammerschmidt, Stefan; Schellenberger, Wolfgang; Sack, Ulrich; Eschrich, Klaus; Wirtz, Hubert

2011-01-01

Background Peptide patterns of bronchoalveolar lavage fluid (BALF) were assumed to reflect the complex pathology of acute lung injury (ALI)/acute respiratory distress syndrome (ARDS) better than clinical and inflammatory parameters and may be superior for outcome prediction. Methodology/Principal Findings A training group of patients suffering from ALI/ARDS was compiled from equal numbers of survivors and nonsurvivors. Clinical history, ventilation parameters, Murray's lung injury severity score (Murray's LISS) and interleukins in BALF were gathered. In addition, samples of bronchoalveolar lavage fluid were analyzed by means of hydrophobic chromatography and MALDI-ToF mass spectrometry (MALDI-ToF MS). Receiver operating characteristic (ROC) analysis for each clinical and cytokine parameter revealed interleukin-6>interleukin-8>diabetes mellitus>Murray's LISS as the best outcome predictors. Outcome predicted on the basis of BALF levels of interleukin-6 resulted in 79.4% accuracy, 82.7% sensitivity and 76.1% specificity (area under the ROC curve, AUC, 0.853). Both clinical parameters and cytokines as well as peptide patterns determined by MALDI-ToF MS were analyzed by classification and regression tree (CART) analysis and support vector machine (SVM) algorithms. CART analysis including Murray's LISS, interleukin-6 and interleukin-8 in combination was correct in 78.0%. MALDI-ToF MS of BALF peptides did not reveal a single identifiable biomarker for ARDS. However, classification of patients was successfully achieved based on the entire peptide pattern analyzed using SVM. This method resulted in 90% accuracy, 93.3% sensitivity and 86.7% specificity following a 10-fold cross validation (AUC = 0.953). Subsequent validation of the optimized SVM algorithm with a test group of patients with unknown prognosis yielded 87.5% accuracy, 83.3% sensitivity and 90.0% specificity. Conclusions/Significance MALDI-ToF MS peptide patterns of BALF, evaluated by appropriate mathematical methods can be of value in predicting outcome in pneumonia induced ALI/ARDS. PMID:21991318

Outcome prediction in pneumonia induced ALI/ARDS by clinical features and peptide patterns of BALF determined by mass spectrometry.

PubMed

Frenzel, Jochen; Gessner, Christian; Sandvoss, Torsten; Hammerschmidt, Stefan; Schellenberger, Wolfgang; Sack, Ulrich; Eschrich, Klaus; Wirtz, Hubert

2011-01-01

Peptide patterns of bronchoalveolar lavage fluid (BALF) were assumed to reflect the complex pathology of acute lung injury (ALI)/acute respiratory distress syndrome (ARDS) better than clinical and inflammatory parameters and may be superior for outcome prediction. A training group of patients suffering from ALI/ARDS was compiled from equal numbers of survivors and nonsurvivors. Clinical history, ventilation parameters, Murray's lung injury severity score (Murray's LISS) and interleukins in BALF were gathered. In addition, samples of bronchoalveolar lavage fluid were analyzed by means of hydrophobic chromatography and MALDI-ToF mass spectrometry (MALDI-ToF MS). Receiver operating characteristic (ROC) analysis for each clinical and cytokine parameter revealed interleukin-6>interleukin-8>diabetes mellitus>Murray's LISS as the best outcome predictors. Outcome predicted on the basis of BALF levels of interleukin-6 resulted in 79.4% accuracy, 82.7% sensitivity and 76.1% specificity (area under the ROC curve, AUC, 0.853). Both clinical parameters and cytokines as well as peptide patterns determined by MALDI-ToF MS were analyzed by classification and regression tree (CART) analysis and support vector machine (SVM) algorithms. CART analysis including Murray's LISS, interleukin-6 and interleukin-8 in combination was correct in 78.0%. MALDI-ToF MS of BALF peptides did not reveal a single identifiable biomarker for ARDS. However, classification of patients was successfully achieved based on the entire peptide pattern analyzed using SVM. This method resulted in 90% accuracy, 93.3% sensitivity and 86.7% specificity following a 10-fold cross validation (AUC = 0.953). Subsequent validation of the optimized SVM algorithm with a test group of patients with unknown prognosis yielded 87.5% accuracy, 83.3% sensitivity and 90.0% specificity. MALDI-ToF MS peptide patterns of BALF, evaluated by appropriate mathematical methods can be of value in predicting outcome in pneumonia induced ALI/ARDS.

[LiLa classification for paediatric long bone fractures. Intraobserver and interobserver reliability].

PubMed

Kamphaus, A; Rapp, M; Wessel, L M; Buchholz, M; Massalme, E; Schneidmüller, D; Roeder, C; Kaiser, M M

2015-04-01

There are two child-specific fracture classification systems for long bone fractures: the AO classification of pediatric long-bone fractures (PCCF) and the LiLa classification of pediatric fractures of long bones (LiLa classification). Both are still not widely established in comparison to the adult AO classification for long bone fractures. During a period of 12 months all long bone fractures in children were documented and classified according to the LiLa classification by experts and non-experts. Intraobserver and interobserver reliability were calculated according to Cohen (kappa). A total of 408 fractures were classified. The intraobserver reliability for location in the skeletal and bone segment showed an almost perfect agreement (K = 0.91-0.95) and also the morphology (joint/shaft fracture) (K = 0.87-0.93). Due to different judgment of the fracture displacement in the second classification round, the intraobserver reliability of the whole classification revealed moderate agreement (K = 0.53-0.58). Interobserver reliability showed moderate agreement (K = 0.55) often due to the low quality of the X-rays. Further differences occurred due to difficulties in assigning the precise transition from metaphysis to diaphysis. The LiLa classification is suitable and in most cases user-friendly for classifying long bone fractures in children. Reliability is higher than in established fracture specific classifications and comparable to the AO classification of pediatric long bone fractures. Some mistakes were due to a low quality of the X-rays and some due to difficulties to classify the fractures themselves. Improvements include a more precise definition of the metaphysis and the kind of displacement. Overall the LiLa classification should still be considered as an alternative for classifying pediatric long bone fractures.

Railroad Classification Yard Technology Manual. Volume I : Yard Design Methods

DOT National Transportation Integrated Search

1981-02-01

This volume documents the procedures and methods associated with the design of railroad classification yards. Subjects include: site location, economic analysis, yard capacity analysis, design of flat yards, overall configuration of hump yards, hump ...

Singular spectrum decomposition of Bouligand-Minkowski fractal descriptors: an application to the classification of texture Images

NASA Astrophysics Data System (ADS)

Florindo, João. Batista

2018-04-01

This work proposes the use of Singular Spectrum Analysis (SSA) for the classification of texture images, more specifically, to enhance the performance of the Bouligand-Minkowski fractal descriptors in this task. Fractal descriptors are known to be a powerful approach to model and particularly identify complex patterns in natural images. Nevertheless, the multiscale analysis involved in those descriptors makes them highly correlated. Although other attempts to address this point was proposed in the literature, none of them investigated the relation between the fractal correlation and the well-established analysis employed in time series. And SSA is one of the most powerful techniques for this purpose. The proposed method was employed for the classification of benchmark texture images and the results were compared with other state-of-the-art classifiers, confirming the potential of this analysis in image classification.

Steady-state visual evoked potential (SSVEP)-based communication: impact of harmonic frequency components

NASA Astrophysics Data System (ADS)

Müller-Putz, Gernot R.; Scherer, Reinhold; Brauneis, Christian; Pfurtscheller, Gert

2005-12-01

Brain-computer interfaces (BCIs) can be realized on the basis of steady-state evoked potentials (SSEPs). These types of brain signals resulting from repetitive stimulation have the same fundamental frequency as the stimulation but also include higher harmonics. This study investigated how the classification accuracy of a 4-class BCI system can be improved by incorporating visually evoked harmonic oscillations. The current study revealed that the use of three SSVEP harmonics yielded a significantly higher classification accuracy than was the case for one or two harmonics. During feedback experiments, the five subjects investigated reached a classification accuracy between 42.5% and 94.4%.

Steady-state visual evoked potential (SSVEP)-based communication: impact of harmonic frequency components.

PubMed

Müller-Putz, Gernot R; Scherer, Reinhold; Brauneis, Christian; Pfurtscheller, Gert

2005-12-01

Brain-computer interfaces (BCIs) can be realized on the basis of steady-state evoked potentials (SSEPs). These types of brain signals resulting from repetitive stimulation have the same fundamental frequency as the stimulation but also include higher harmonics. This study investigated how the classification accuracy of a 4-class BCI system can be improved by incorporating visually evoked harmonic oscillations. The current study revealed that the use of three SSVEP harmonics yielded a significantly higher classification accuracy than was the case for one or two harmonics. During feedback experiments, the five subjects investigated reached a classification accuracy between 42.5% and 94.4%.

Apocrine hidradenocarcinoma of the scalp: a classification conundrum.

PubMed

Cohen, Marc; Cassarino, David S; Shih, Hubert B; Abemayor, Elliot; St John, Maie

2009-03-01

The classification of malignant sweat gland lesions is complex. Traditionally, cutaneous sweat gland tumors have been classified by either eccrine or apocrine features. A case report of a 33-year-old Hispanic man with a left scalp mass diagnosed as a malignancy of adnexal origin preoperatively is discussed. After presentation at our multidisciplinary tumor board, excision with ipsilateral neck dissection was undertaken. Final pathology revealed an apocrine hidradenocarcinoma. The classification and behavior of this entity are discussed in this report. Apocrine hidradenocarcinoma can be viewed as an aggressive malignant lesion of cutaneous sweat glands on a spectrum that involves both eccrine and apoeccrine lesions.

Apocrine Hidradenocarcinoma of the Scalp: A Classification Conundrum

PubMed Central

Cassarino, David S.; Shih, Hubert B.; Abemayor, Elliot; John, Maie St.

2008-01-01

Introduction The classification of malignant sweat gland lesions is complex. Traditionally, cutaneous sweat gland tumors have been classified by either eccrine or apocrine features. Methods A case report of a 33-year-old Hispanic man with a left scalp mass diagnosed as a malignancy of adnexal origin preoperatively is discussed. After presentation at our multidisciplinary tumor board, excision with ipsilateral neck dissection was undertaken. Results Final pathology revealed an apocrine hidradenocarcinoma. The classification and behavior of this entity are discussed in this report. Conclusion Apocrine hidradenocarcinoma can be viewed as an aggressive malignant lesion of cutaneous sweat glands on a spectrum that involves both eccrine and apoeccrine lesions. PMID:20596988

Identifying key hospital service quality factors in online health communities.

PubMed

Jung, Yuchul; Hur, Cinyoung; Jung, Dain; Kim, Minki

2015-04-07

The volume of health-related user-created content, especially hospital-related questions and answers in online health communities, has rapidly increased. Patients and caregivers participate in online community activities to share their experiences, exchange information, and ask about recommended or discredited hospitals. However, there is little research on how to identify hospital service quality automatically from the online communities. In the past, in-depth analysis of hospitals has used random sampling surveys. However, such surveys are becoming impractical owing to the rapidly increasing volume of online data and the diverse analysis requirements of related stakeholders. As a solution for utilizing large-scale health-related information, we propose a novel approach to identify hospital service quality factors and overtime trends automatically from online health communities, especially hospital-related questions and answers. We defined social media-based key quality factors for hospitals. In addition, we developed text mining techniques to detect such factors that frequently occur in online health communities. After detecting these factors that represent qualitative aspects of hospitals, we applied a sentiment analysis to recognize the types of recommendations in messages posted within online health communities. Korea's two biggest online portals were used to test the effectiveness of detection of social media-based key quality factors for hospitals. To evaluate the proposed text mining techniques, we performed manual evaluations on the extraction and classification results, such as hospital name, service quality factors, and recommendation types using a random sample of messages (ie, 5.44% (9450/173,748) of the total messages). Service quality factor detection and hospital name extraction achieved average F1 scores of 91% and 78%, respectively. In terms of recommendation classification, performance (ie, precision) is 78% on average. Extraction and classification performance still has room for improvement, but the extraction results are applicable to more detailed analysis. Further analysis of the extracted information reveals that there are differences in the details of social media-based key quality factors for hospitals according to the regions in Korea, and the patterns of change seem to accurately reflect social events (eg, influenza epidemics). These findings could be used to provide timely information to caregivers, hospital officials, and medical officials for health care policies.

Field sampling and data analysis methods for development of ecological land classifications: an application on the Manistee National Forest.

Treesearch

George E. Host; Carl W. Ramm; Eunice A. Padley; Kurt S. Pregitzer; James B. Hart; David T. Cleland

1992-01-01

Presents technical documentation for development of an Ecological Classification System for the Manistee National Forest in northwest Lower Michigan, and suggests procedures applicable to other ecological land classification projects. Includes discussion of sampling design, field data collection, data summarization and analyses, development of classification units,...

A New Tool for Climatic Analysis Using the Koppen Climate Classification

ERIC Educational Resources Information Center

Larson, Paul R.; Lohrengel, C. Frederick, II

2011-01-01

The purpose of climate classification is to help make order of the seemingly endless spatial distribution of climates. The Koppen classification system in a modified format is the most widely applied system in use today. This system may not be the best nor most complete climate classification that can be conceived, but it has gained widespread…

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cook, E.H. Jr.; Stein, M.A.; Krasowski, M.D.

Attention-deficit hyperactivity disorder (ADHD) has been shown to be familial and heritable, in previous studies. As with most psychiatric disorders, examination of pedigrees has not revealed a consistent Mendelian mode of transmission. The response of ADHD patients to medications that inhibit the dopamine transporter, including methylphenidate, amphetamine, pemoline, and bupropion, led us to consider the dopamine transporter as a primary candidate gene for ADHD. To avoid effects of population stratification and to avoid the problem of classification of relatives with other psychiatric disorders as affected or unaffected, we used the haplotype-based haplotype relative risk (HHRR) method to test for associationmore » between a VNTR polymorphism at the dopamine transporter locus (DAT1) and DSM-III-R-diagnosed ADHD (N = 49) and undifferentiated attention-deficit disorder (UADD) (N = 8) in trios composed of father, mother, and affected offspring. HHRR analysis revealed significant association between ADHS/UADD and the 480-bp DAT1 allele (X{sup 2} 7.51, 1 df, P = .006). When cases of UADD were dropped from the analysis, similar results were found (X{sup 2} 7.29, 1 df, P = .007). If these findings are replicated, molecular analysis of the dopamine transporter gene may identify mutations that increase susceptibility to ADHD/UADD. Biochemical analysis of such mutations may lead to development of more effective therapeutic interventions. 36 refs., 4 tabs.« less

Alteration mapping at Goldfield, Nevada, by cluster and discriminant analysis of Landsat digital data. [mapping of hydrothermally altered volcanic rocks

NASA Technical Reports Server (NTRS)

Ballew, G.

1977-01-01

The ability of Landsat multispectral digital data to differentiate among 62 combinations of rock and alteration types at the Goldfield mining district of Western Nevada was investigated by using statistical techniques of cluster and discriminant analysis. Multivariate discriminant analysis was not effective in classifying each of the 62 groups, with classification results essentially the same whether data of four channels alone or combined with six ratios of channels were used. Bivariate plots of group means revealed a cluster of three groups including mill tailings, basalt and all other rock and alteration types. Automatic hierarchical clustering based on the fourth dimensional Mahalanobis distance between group means of 30 groups having five or more samples was performed using Johnson's HICLUS program. The results of the cluster analysis revealed hierarchies of mill tailings vs. natural materials, basalt vs. non-basalt, highly reflectant rocks vs. other rocks and exclusively unaltered rocks vs. predominantly altered rocks. The hierarchies were used to determine the order in which sets of multiple discriminant analyses were to be performed and the resulting discriminant functions were used to produce a map of geology and alteration which has an overall accuracy of 70 percent for discriminating exclusively altered rocks from predominantly altered rocks.

Classification of ligand molecules in PDB with graph match-based structural superposition.

PubMed

Shionyu-Mitsuyama, Clara; Hijikata, Atsushi; Tsuji, Toshiyuki; Shirai, Tsuyoshi

2016-12-01

The fast heuristic graph match algorithm for small molecules, COMPLIG, was improved by adding a structural superposition process to verify the atom-atom matching. The modified method was used to classify the small molecule ligands in the Protein Data Bank (PDB) by their three-dimensional structures, and 16,660 types of ligands in the PDB were classified into 7561 clusters. In contrast, a classification by a previous method (without structure superposition) generated 3371 clusters from the same ligand set. The characteristic feature in the current classification system is the increased number of singleton clusters, which contained only one ligand molecule in a cluster. Inspections of the singletons in the current classification system but not in the previous one implied that the major factors for the isolation were differences in chirality, cyclic conformations, separation of substructures, and bond length. Comparisons between current and previous classification systems revealed that the superposition-based classification was effective in clustering functionally related ligands, such as drugs targeted to specific biological processes, owing to the strictness of the atom-atom matching.

IARC use of oxidative stress as key mode of action characteristic for facilitating cancer classification: Glyphosate case example illustrating a lack of robustness in interpretative implementation.

PubMed

Bus, James S

2017-06-01

The International Agency for Research on Cancer (IARC) has formulated 10 key characteristics of human carcinogens to incorporate mechanistic data into cancer hazard classifications. The analysis used glyphosate as a case example to examine the robustness of IARC's determination of oxidative stress as "strong" evidence supporting a plausible cancer mechanism in humans. The IARC analysis primarily relied on 14 human/mammalian studies; 19 non-mammalian studies were uninformative of human cancer given the broad spectrum of test species and extensive use of formulations and aquatic testing. The mammalian studies had substantial experimental limitations for informing cancer mechanism including use of: single doses and time points; cytotoxic/toxic test doses; tissues not identified as potential cancer targets; glyphosate formulations or mixtures; technically limited oxidative stress biomarkers. The doses were many orders of magnitude higher than human exposures determined in human biomonitoring studies. The glyphosate case example reveals that the IARC evaluation fell substantially short of "strong" supporting evidence of oxidative stress as a plausible human cancer mechanism, and suggests that other IARC monographs relying on the 10 key characteristics approach should be similarly examined for a lack of robust data integration fundamental to reasonable mode of action evaluations. Copyright © 2017 Elsevier Inc. All rights reserved.

Global metabolomics reveals potential urinary biomarkers of esophageal squamous cell carcinoma for diagnosis and staging

NASA Astrophysics Data System (ADS)

Xu, Jing; Chen, Yanhua; Zhang, Ruiping; He, Jiuming; Song, Yongmei; Wang, Jingbo; Wang, Huiqing; Wang, Luhua; Zhan, Qimin; Abliz, Zeper

2016-10-01

We performed a metabolomics study using liquid chromatography-mass spectrometry (LC-MS) combined with multivariate data analysis (MVDA) to discriminate global urine profiles in urine samples from esophageal squamous cell carcinoma (ESCC) patients and healthy controls (NC). Our work evaluated the feasibility of employing urine metabolomics for the diagnosis and staging of ESCC. The satisfactory classification between the healthy controls and ESCC patients was obtained using the MVDA model, and obvious classification of early-stage and advanced-stage patients was also observed. The results suggest that the combination of LC-MS analysis and MVDA may have potential applications for ESCC diagnosis and staging. We then conducted LC-MS/MS experiments to identify the potential biomarkers with large contributions to the discrimination. A total of 83 potential diagnostic biomarkers for ESCC were screened out, and 19 potential biomarkers were identified; the variations between the differences in staging using these potential biomarkers were further analyzed. These biomarkers may not be unique to ESCCs, but instead result from any malignant disease. To further elucidate the pathophysiology of ESCC, we studied related metabolic pathways and found that ESCC is associated with perturbations of fatty acid β-oxidation and the metabolism of amino acids, purines, and pyrimidines.

High-Resolution, Non-Invasive Imaging of Upper Vocal Tract Articulators Compatible with Human Brain Recordings

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bouchard, Kristofer E.; Conant, David F.; Anumanchipalli, Gopala K.

A complete neurobiological understanding of speech motor control requires determination of the relationship between simultaneously recorded neural activity and the kinematics of the lips, jaw, tongue, and larynx. Many speech articulators are internal to the vocal tract, and therefore simultaneously tracking the kinematics of all articulators is nontrivial-especially in the context of human electrophysiology recordings. Here, we describe a noninvasive, multi-modal imaging system to monitor vocal tract kinematics, demonstrate this system in six speakers during production of nine American English vowels, and provide new analysis of such data. Classification and regression analysis revealed considerable variability in the articulator-to-acoustic relationship acrossmore » speakers. Non-negative matrix factorization extracted basis sets capturing vocal tract shapes allowing for higher vowel classification accuracy than traditional methods. Statistical speech synthesis generated speech from vocal tract measurements, and we demonstrate perceptual identification. We demonstrate the capacity to predict lip kinematics from ventral sensorimotor cortical activity. These results demonstrate a multi-modal system to non-invasively monitor articulator kinematics during speech production, describe novel analytic methods for relating kinematic data to speech acoustics, and provide the first decoding of speech kinematics from electrocorticography. These advances will be critical for understanding the cortical basis of speech production and the creation of vocal prosthetics.« less

Phenotype classification of single cells using SRS microscopy, RNA sequencing, and microfluidics (Conference Presentation)

NASA Astrophysics Data System (ADS)

Streets, Aaron M.; Cao, Chen; Zhang, Xiannian; Huang, Yanyi

2016-03-01

Phenotype classification of single cells reveals biological variation that is masked in ensemble measurement. This heterogeneity is found in gene and protein expression as well as in cell morphology. Many techniques are available to probe phenotypic heterogeneity at the single cell level, for example quantitative imaging and single-cell RNA sequencing, but it is difficult to perform multiple assays on the same single cell. In order to directly track correlation between morphology and gene expression at the single cell level, we developed a microfluidic platform for quantitative coherent Raman imaging and immediate RNA sequencing (RNA-Seq) of single cells. With this device we actively sort and trap cells for analysis with stimulated Raman scattering microscopy (SRS). The cells are then processed in parallel pipelines for lysis, and preparation of cDNA for high-throughput transcriptome sequencing. SRS microscopy offers three-dimensional imaging with chemical specificity for quantitative analysis of protein and lipid distribution in single cells. Meanwhile, the microfluidic platform facilitates single-cell manipulation, minimizes contamination, and furthermore, provides improved RNA-Seq detection sensitivity and measurement precision, which is necessary for differentiating biological variability from technical noise. By combining coherent Raman microscopy with RNA sequencing, we can better understand the relationship between cellular morphology and gene expression at the single-cell level.

Towards a truly mobile auditory brain-computer interface: exploring the P300 to take away.

PubMed

De Vos, Maarten; Gandras, Katharina; Debener, Stefan

2014-01-01

In a previous study we presented a low-cost, small, and wireless 14-channel EEG system suitable for field recordings (Debener et al., 2012, psychophysiology). In the present follow-up study we investigated whether a single-trial P300 response can be reliably measured with this system, while subjects freely walk outdoors. Twenty healthy participants performed a three-class auditory oddball task, which included rare target and non-target distractor stimuli presented with equal probabilities of 16%. Data were recorded in a seated (control condition) and in a walking condition, both of which were realized outdoors. A significantly larger P300 event-related potential amplitude was evident for targets compared to distractors (p<.001), but no significant interaction with recording condition emerged. P300 single-trial analysis was performed with regularized stepwise linear discriminant analysis and revealed above chance-level classification accuracies for most participants (19 out of 20 for the seated, 16 out of 20 for the walking condition), with mean classification accuracies of 71% (seated) and 64% (walking). Moreover, the resulting information transfer rates for the seated and walking conditions were comparable to a recently published laboratory auditory brain-computer interface (BCI) study. This leads us to conclude that a truly mobile auditory BCI system is feasible. © 2013.

High-Resolution, Non-Invasive Imaging of Upper Vocal Tract Articulators Compatible with Human Brain Recordings

PubMed Central

Anumanchipalli, Gopala K.; Dichter, Benjamin; Chaisanguanthum, Kris S.; Johnson, Keith; Chang, Edward F.

2016-01-01

A complete neurobiological understanding of speech motor control requires determination of the relationship between simultaneously recorded neural activity and the kinematics of the lips, jaw, tongue, and larynx. Many speech articulators are internal to the vocal tract, and therefore simultaneously tracking the kinematics of all articulators is nontrivial—especially in the context of human electrophysiology recordings. Here, we describe a noninvasive, multi-modal imaging system to monitor vocal tract kinematics, demonstrate this system in six speakers during production of nine American English vowels, and provide new analysis of such data. Classification and regression analysis revealed considerable variability in the articulator-to-acoustic relationship across speakers. Non-negative matrix factorization extracted basis sets capturing vocal tract shapes allowing for higher vowel classification accuracy than traditional methods. Statistical speech synthesis generated speech from vocal tract measurements, and we demonstrate perceptual identification. We demonstrate the capacity to predict lip kinematics from ventral sensorimotor cortical activity. These results demonstrate a multi-modal system to non-invasively monitor articulator kinematics during speech production, describe novel analytic methods for relating kinematic data to speech acoustics, and provide the first decoding of speech kinematics from electrocorticography. These advances will be critical for understanding the cortical basis of speech production and the creation of vocal prosthetics. PMID:27019106

High-Resolution, Non-Invasive Imaging of Upper Vocal Tract Articulators Compatible with Human Brain Recordings

DOE PAGES

Bouchard, Kristofer E.; Conant, David F.; Anumanchipalli, Gopala K.; ...

2016-03-28

A complete neurobiological understanding of speech motor control requires determination of the relationship between simultaneously recorded neural activity and the kinematics of the lips, jaw, tongue, and larynx. Many speech articulators are internal to the vocal tract, and therefore simultaneously tracking the kinematics of all articulators is nontrivial-especially in the context of human electrophysiology recordings. Here, we describe a noninvasive, multi-modal imaging system to monitor vocal tract kinematics, demonstrate this system in six speakers during production of nine American English vowels, and provide new analysis of such data. Classification and regression analysis revealed considerable variability in the articulator-to-acoustic relationship acrossmore » speakers. Non-negative matrix factorization extracted basis sets capturing vocal tract shapes allowing for higher vowel classification accuracy than traditional methods. Statistical speech synthesis generated speech from vocal tract measurements, and we demonstrate perceptual identification. We demonstrate the capacity to predict lip kinematics from ventral sensorimotor cortical activity. These results demonstrate a multi-modal system to non-invasively monitor articulator kinematics during speech production, describe novel analytic methods for relating kinematic data to speech acoustics, and provide the first decoding of speech kinematics from electrocorticography. These advances will be critical for understanding the cortical basis of speech production and the creation of vocal prosthetics.« less

U.S. Geological Survey ArcMap Sediment Classification tool

USGS Publications Warehouse

O'Malley, John

2007-01-01

The U.S. Geological Survey (USGS) ArcMap Sediment Classification tool is a custom toolbar that extends the Environmental Systems Research Institute, Inc. (ESRI) ArcGIS 9.2 Desktop application to aid in the analysis of seabed sediment classification. The tool uses as input either a point data layer with field attributes containing percentage of gravel, sand, silt, and clay or four raster data layers representing a percentage of sediment (0-100%) for the various sediment grain size analysis: sand, gravel, silt and clay. This tool is designed to analyze the percent of sediment at a given location and classify the sediments according to either the Folk (1954, 1974) or Shepard (1954) as modified by Schlee(1973) classification schemes. The sediment analysis tool is based upon the USGS SEDCLASS program (Poppe, et al. 2004).

Conceptual model of iCAL4LA: Proposing the components using comparative analysis

NASA Astrophysics Data System (ADS)

Ahmad, Siti Zulaiha; Mutalib, Ariffin Abdul

2016-08-01

This paper discusses an on-going study that initiates an initial process in determining the common components for a conceptual model of interactive computer-assisted learning that is specifically designed for low achieving children. This group of children needs a specific learning support that can be used as an alternative learning material in their learning environment. In order to develop the conceptual model, this study extracts the common components from 15 strongly justified computer assisted learning studies. A comparative analysis has been conducted to determine the most appropriate components by using a set of specific indication classification to prioritize the applicability. The results of the extraction process reveal 17 common components for consideration. Later, based on scientific justifications, 16 of them were selected as the proposed components for the model.

Spectral multi-energy CT texture analysis with machine learning for tissue classification: an investigation using classification of benign parotid tumours as a testing paradigm.

PubMed

Al Ajmi, Eiman; Forghani, Behzad; Reinhold, Caroline; Bayat, Maryam; Forghani, Reza

2018-06-01

There is a rich amount of quantitative information in spectral datasets generated from dual-energy CT (DECT). In this study, we compare the performance of texture analysis performed on multi-energy datasets to that of virtual monochromatic images (VMIs) at 65 keV only, using classification of the two most common benign parotid neoplasms as a testing paradigm. Forty-two patients with pathologically proven Warthin tumour (n = 25) or pleomorphic adenoma (n = 17) were evaluated. Texture analysis was performed on VMIs ranging from 40 to 140 keV in 5-keV increments (multi-energy analysis) or 65-keV VMIs only, which is typically considered equivalent to single-energy CT. Random forest (RF) models were constructed for outcome prediction using separate randomly selected training and testing sets or the entire patient set. Using multi-energy texture analysis, tumour classification in the independent testing set had accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of 92%, 86%, 100%, 100%, and 83%, compared to 75%, 57%, 100%, 100%, and 63%, respectively, for single-energy analysis. Multi-energy texture analysis demonstrates superior performance compared to single-energy texture analysis of VMIs at 65 keV for classification of benign parotid tumours. • We present and validate a paradigm for texture analysis of DECT scans. • Multi-energy dataset texture analysis is superior to single-energy dataset texture analysis. • DECT texture analysis has high accura\\cy for diagnosis of benign parotid tumours. • DECT texture analysis with machine learning can enhance non-invasive diagnostic tumour evaluation.

One input-class and two input-class classifications for differentiating olive oil from other edible vegetable oils by use of the normal-phase liquid chromatography fingerprint of the methyl-transesterified fraction.

PubMed

Jiménez-Carvelo, Ana M; Pérez-Castaño, Estefanía; González-Casado, Antonio; Cuadros-Rodríguez, Luis

2017-04-15

A new method for differentiation of olive oil (independently of the quality category) from other vegetable oils (canola, safflower, corn, peanut, seeds, grapeseed, palm, linseed, sesame and soybean) has been developed. The analytical procedure for chromatographic fingerprinting of the methyl-transesterified fraction of each vegetable oil, using normal-phase liquid chromatography, is described and the chemometric strategies applied and discussed. Some chemometric methods, such as k-nearest neighbours (kNN), partial least squared-discriminant analysis (PLS-DA), support vector machine classification analysis (SVM-C), and soft independent modelling of class analogies (SIMCA), were applied to build classification models. Performance of the classification was evaluated and ranked using several classification quality metrics. The discriminant analysis, based on the use of one input-class, (plus a dummy class) was applied for the first time in this study. Copyright © 2016 Elsevier Ltd. All rights reserved.

Singularity and Nonnormality in the Classification of Compositional Data

USGS Publications Warehouse

Bohling, Geoffrey C.; Davis, J.C.; Olea, R.A.; Harff, Jan

1998-01-01

Geologists may want to classify compositional data and express the classification as a map. Regionalized classification is a tool that can be used for this purpose, but it incorporates discriminant analysis, which requires the computation and inversion of a covariance matrix. Covariance matrices of compositional data always will be singular (noninvertible) because of the unit-sum constraint. Fortunately, discriminant analyses can be calculated using a pseudo-inverse of the singular covariance matrix; this is done automatically by some statistical packages such as SAS. Granulometric data from the Darss Sill region of the Baltic Sea is used to explore how the pseudo-inversion procedure influences discriminant analysis results, comparing the algorithm used by SAS to the more conventional Moore-Penrose algorithm. Logratio transforms have been recommended to overcome problems associated with analysis of compositional data, including singularity. A regionalized classification of the Darss Sill data after logratio transformation is different only slightly from one based on raw granulometric data, suggesting that closure problems do not influence severely regionalized classification of compositional data.

Back-and-Forth Methodology for Objective Voice Quality Assessment: From/to Expert Knowledge to/from Automatic Classification of Dysphonia

NASA Astrophysics Data System (ADS)

Fredouille, Corinne; Pouchoulin, Gilles; Ghio, Alain; Revis, Joana; Bonastre, Jean-François; Giovanni, Antoine

2009-12-01

This paper addresses voice disorder assessment. It proposes an original back-and-forth methodology involving an automatic classification system as well as knowledge of the human experts (machine learning experts, phoneticians, and pathologists). The goal of this methodology is to bring a better understanding of acoustic phenomena related to dysphonia. The automatic system was validated on a dysphonic corpus (80 female voices), rated according to the GRBAS perceptual scale by an expert jury. Firstly, focused on the frequency domain, the classification system showed the interest of 0-3000 Hz frequency band for the classification task based on the GRBAS scale. Later, an automatic phonemic analysis underlined the significance of consonants and more surprisingly of unvoiced consonants for the same classification task. Submitted to the human experts, these observations led to a manual analysis of unvoiced plosives, which highlighted a lengthening of VOT according to the dysphonia severity validated by a preliminary statistical analysis.

Tissue classification using depth-dependent ultrasound time series analysis: in-vitro animal study

NASA Astrophysics Data System (ADS)

Imani, Farhad; Daoud, Mohammad; Moradi, Mehdi; Abolmaesumi, Purang; Mousavi, Parvin

2011-03-01

Time series analysis of ultrasound radio-frequency (RF) signals has been shown to be an effective tissue classification method. Previous studies of this method for tissue differentiation at high and clinical-frequencies have been reported. In this paper, analysis of RF time series is extended to improve tissue classification at the clinical frequencies by including novel features extracted from the time series spectrum. The primary feature examined is the Mean Central Frequency (MCF) computed for regions of interest (ROIs) in the tissue extending along the axial axis of the transducer. In addition, the intercept and slope of a line fitted to the MCF-values of the RF time series as a function of depth have been included. To evaluate the accuracy of the new features, an in vitro animal study is performed using three tissue types: bovine muscle, bovine liver, and chicken breast, where perfect two-way classification is achieved. The results show statistically significant improvements over the classification accuracies with previously reported features.

Gender classification of running subjects using full-body kinematics

NASA Astrophysics Data System (ADS)

Williams, Christina M.; Flora, Jeffrey B.; Iftekharuddin, Khan M.

2016-05-01

This paper proposes novel automated gender classification of subjects while engaged in running activity. The machine learning techniques include preprocessing steps using principal component analysis followed by classification with linear discriminant analysis, and nonlinear support vector machines, and decision-stump with AdaBoost. The dataset consists of 49 subjects (25 males, 24 females, 2 trials each) all equipped with approximately 80 retroreflective markers. The trials are reflective of the subject's entire body moving unrestrained through a capture volume at a self-selected running speed, thus producing highly realistic data. The classification accuracy using leave-one-out cross validation for the 49 subjects is improved from 66.33% using linear discriminant analysis to 86.74% using the nonlinear support vector machine. Results are further improved to 87.76% by means of implementing a nonlinear decision stump with AdaBoost classifier. The experimental findings suggest that the linear classification approaches are inadequate in classifying gender for a large dataset with subjects running in a moderately uninhibited environment.

Development of neural network techniques for finger-vein pattern classification

NASA Astrophysics Data System (ADS)

Wu, Jian-Da; Liu, Chiung-Tsiung; Tsai, Yi-Jang; Liu, Jun-Ching; Chang, Ya-Wen

2010-02-01

A personal identification system using finger-vein patterns and neural network techniques is proposed in the present study. In the proposed system, the finger-vein patterns are captured by a device that can transmit near infrared through the finger and record the patterns for signal analysis and classification. The biometric system for verification consists of a combination of feature extraction using principal component analysis and pattern classification using both back-propagation network and adaptive neuro-fuzzy inference systems. Finger-vein features are first extracted by principal component analysis method to reduce the computational burden and removes noise residing in the discarded dimensions. The features are then used in pattern classification and identification. To verify the effect of the proposed adaptive neuro-fuzzy inference system in the pattern classification, the back-propagation network is compared with the proposed system. The experimental results indicated the proposed system using adaptive neuro-fuzzy inference system demonstrated a better performance than the back-propagation network for personal identification using the finger-vein patterns.

Examining the effectiveness of discriminant function analysis and cluster analysis in species identification of male field crickets based on their calling songs.

PubMed

Jaiswara, Ranjana; Nandi, Diptarup; Balakrishnan, Rohini

2013-01-01

Traditional taxonomy based on morphology has often failed in accurate species identification owing to the occurrence of cryptic species, which are reproductively isolated but morphologically identical. Molecular data have thus been used to complement morphology in species identification. The sexual advertisement calls in several groups of acoustically communicating animals are species-specific and can thus complement molecular data as non-invasive tools for identification. Several statistical tools and automated identifier algorithms have been used to investigate the efficiency of acoustic signals in species identification. Despite a plethora of such methods, there is a general lack of knowledge regarding the appropriate usage of these methods in specific taxa. In this study, we investigated the performance of two commonly used statistical methods, discriminant function analysis (DFA) and cluster analysis, in identification and classification based on acoustic signals of field cricket species belonging to the subfamily Gryllinae. Using a comparative approach we evaluated the optimal number of species and calling song characteristics for both the methods that lead to most accurate classification and identification. The accuracy of classification using DFA was high and was not affected by the number of taxa used. However, a constraint in using discriminant function analysis is the need for a priori classification of songs. Accuracy of classification using cluster analysis, which does not require a priori knowledge, was maximum for 6-7 taxa and decreased significantly when more than ten taxa were analysed together. We also investigated the efficacy of two novel derived acoustic features in improving the accuracy of identification. Our results show that DFA is a reliable statistical tool for species identification using acoustic signals. Our results also show that cluster analysis of acoustic signals in crickets works effectively for species classification and identification.

Nearest Neighbor Classification of Stationary Time Series: An Application to Anesthesia Level Classification by EEG Analysis.

DTIC Science & Technology

1980-12-05

classification procedures that are common in speech processing. The anesthesia level classification by EEG time series population screening problem example is in...formance. The use of the KL number type metric in NN rule classification, in a delete-one subj ect ’s EE-at-a-time KL-NN and KL- kNN classification of the...17 individual labeled EEG sample population using KL-NN and KL- kNN rules. The results obtained are shown in Table 1. The entries in the table indicate

Fast-HPLC Fingerprinting to Discriminate Olive Oil from Other Edible Vegetable Oils by Multivariate Classification Methods.

PubMed

Jiménez-Carvelo, Ana M; González-Casado, Antonio; Pérez-Castaño, Estefanía; Cuadros-Rodríguez, Luis

2017-03-01

A new analytical method for the differentiation of olive oil from other vegetable oils using reversed-phase LC and applying chemometric techniques was developed. A 3 cm short column was used to obtain the chromatographic fingerprint of the methyl-transesterified fraction of each vegetable oil. The chromatographic analysis took only 4 min. The multivariate classification methods used were k-nearest neighbors, partial least-squares (PLS) discriminant analysis, one-class PLS, support vector machine classification, and soft independent modeling of class analogies. The discrimination of olive oil from other vegetable edible oils was evaluated by several classification quality metrics. Several strategies for the classification of the olive oil were used: one input-class, two input-class, and pseudo two input-class.

Real-Time Classification of Exercise Exertion Levels Using Discriminant Analysis of HRV Data.

PubMed

Jeong, In Cheol; Finkelstein, Joseph

2015-01-01

Heart rate variability (HRV) was shown to reflect activation of sympathetic nervous system however it is not clear which set of HRV parameters is optimal for real-time classification of exercise exertion levels. There is no studies that compared potential of two types of HRV parameters (time-domain and frequency-domain) in predicting exercise exertion level using discriminant analysis. The main goal of this study was to compare potential of HRV time-domain parameters versus HRV frequency-domain parameters in classifying exercise exertion level. Rest, exercise, and recovery categories were used in classification models. Overall 79.5% classification agreement by the time-domain parameters as compared to overall 52.8% classification agreement by frequency-domain parameters demonstrated that the time-domain parameters had higher potential in classifying exercise exertion levels.

Cluster Method Analysis of K. S. C. Image

NASA Technical Reports Server (NTRS)

Rodriguez, Joe, Jr.; Desai, M.

1997-01-01

Information obtained from satellite-based systems has moved to the forefront as a method in the identification of many land cover types. Identification of different land features through remote sensing is an effective tool for regional and global assessment of geometric characteristics. Classification data acquired from remote sensing images have a wide variety of applications. In particular, analysis of remote sensing images have special applications in the classification of various types of vegetation. Results obtained from classification studies of a particular area or region serve towards a greater understanding of what parameters (ecological, temporal, etc.) affect the region being analyzed. In this paper, we make a distinction between both types of classification approaches although, focus is given to the unsupervised classification method using 1987 Thematic Mapped (TM) images of Kennedy Space Center.

Molecular events of apical bud formation in white spruce, Picea glauca.

PubMed

El Kayal, Walid; Allen, Carmen C G; Ju, Chelsea J-T; Adams, Eri; King-Jones, Susanne; Zaharia, L Irina; Abrams, Suzanne R; Cooke, Janice E K

2011-03-01

Bud formation is an adaptive trait that temperate forest trees have acquired to facilitate seasonal synchronization. We have characterized transcriptome-level changes that occur during bud formation of white spruce [Picea glauca (Moench) Voss], a primarily determinate species in which preformed stem units contained within the apical bud constitute most of next season's growth. Microarray analysis identified 4460 differentially expressed sequences in shoot tips during short day-induced bud formation. Cluster analysis revealed distinct temporal patterns of expression, and functional classification of genes in these clusters implied molecular processes that coincide with anatomical changes occurring in the developing bud. Comparing expression profiles in developing buds under long day and short day conditions identified possible photoperiod-responsive genes that may not be essential for bud development. Several genes putatively associated with hormone signalling were identified, and hormone quantification revealed distinct profiles for abscisic acid (ABA), cytokinins, auxin and their metabolites that can be related to morphological changes to the bud. Comparison of gene expression profiles during bud formation in different tissues revealed 108 genes that are differentially expressed only in developing buds and show greater transcript abundance in developing buds than other tissues. These findings provide a temporal roadmap of bud formation in white spruce. © 2011 Blackwell Publishing Ltd.

Assessing the statistical significance of the achieved classification error of classifiers constructed using serum peptide profiles, and a prescription for random sampling repeated studies for massive high-throughput genomic and proteomic studies.

PubMed

Lyons-Weiler, James; Pelikan, Richard; Zeh, Herbert J; Whitcomb, David C; Malehorn, David E; Bigbee, William L; Hauskrecht, Milos

2005-01-01

Peptide profiles generated using SELDI/MALDI time of flight mass spectrometry provide a promising source of patient-specific information with high potential impact on the early detection and classification of cancer and other diseases. The new profiling technology comes, however, with numerous challenges and concerns. Particularly important are concerns of reproducibility of classification results and their significance. In this work we describe a computational validation framework, called PACE (Permutation-Achieved Classification Error), that lets us assess, for a given classification model, the significance of the Achieved Classification Error (ACE) on the profile data. The framework compares the performance statistic of the classifier on true data samples and checks if these are consistent with the behavior of the classifier on the same data with randomly reassigned class labels. A statistically significant ACE increases our belief that a discriminative signal was found in the data. The advantage of PACE analysis is that it can be easily combined with any classification model and is relatively easy to interpret. PACE analysis does not protect researchers against confounding in the experimental design, or other sources of systematic or random error. We use PACE analysis to assess significance of classification results we have achieved on a number of published data sets. The results show that many of these datasets indeed possess a signal that leads to a statistically significant ACE.

Authorship Discovery in Blogs Using Bayesian Classification with Corrective Scaling

DTIC Science & Technology

2008-06-01

4 2.3 W. Fucks ’ Diagram of n-Syllable Word Frequencies . . . . . . . . . . . . . . 5 3.1 Confusion Matrix for All Test Documents of 500...of the books which scholars believed he had. • Wilhelm Fucks discriminated between authors using the average number of syllables per word and average...distance between equal-syllabled words [8]. Fucks , too, concluded that a study such as his reveals a “possibility of a quantitative classification

“Rebuilding our community”: Hearing silenced voices on Aboriginal youth suicide

PubMed Central

Walls, Melissa L.; Hautala, Dane; Hurley, Jenna

2014-01-01

This paper brings forth the voices of adult Aboriginal First Nations community members who gathered in focus groups to discuss the problem of youth suicide on their reserves. Our approach emphasizes multilevel (e.g., individual, family, and broader ecological systems) factors viewed by participants as relevant to youth suicide. Wheaton’s conceptualization of stressors (1994; 1999) and Evans-Campbell’s (2008) multilevel classification of the impacts of historical trauma are used as theoretical and analytic guides. Thematic analysis of qualitative data transcripts revealed a highly complex intersection of stressors, traumas, and social problems seen by community members as underlying mechanisms influencing heightened levels of Aboriginal youth suicidality. Our multilevel coding approach revealed that suicidal behaviors were described by community members largely as a problem with deep historical and contemporary structural roots as opposed to being viewed as individualized pathology. PMID:24097414

How Factor Analysis Can Be Used in Classification.

ERIC Educational Resources Information Center

Harman, Harry H.

This is a methodological study that suggests a taxometric technique for objective classification of yeasts. It makes use of the minres method of factor analysis and groups strains of yeast according to their factor profiles. The similarities are judged in the higher-dimensional space determined by the factor analysis, but otherwise rely on the…

A Model Comparison for Characterizing Protein Motions from Structure

NASA Astrophysics Data System (ADS)

David, Charles; Jacobs, Donald

2011-10-01

A comparative study is made using three computational models that characterize native state dynamics starting from known protein structures taken from four distinct SCOP classifications. A geometrical simulation is performed, and the results are compared to the elastic network model and molecular dynamics. The essential dynamics is quantified by a direct analysis of a mode subspace constructed from ANM and a principal component analysis on both the FRODA and MD trajectories using root mean square inner product and principal angles. Relative subspace sizes and overlaps are visualized using the projection of displacement vectors on the model modes. Additionally, a mode subspace is constructed from PCA on an exemplar set of X-ray crystal structures in order to determine similarly with respect to the generated ensembles. Quantitative analysis reveals there is significant overlap across the three model subspaces and the model independent subspace. These results indicate that structure is the key determinant for native state dynamics.

Understanding exercise behavior among Korean adults: a test of the transtheoretical model.

PubMed

Kim, YoungHo; Cardinal, Bradley J; Lee, JongYoung

2006-01-01

The purpose of this study was to examine the theorized association of Transtheoretical Model (TTM) of behavior change constructs by stage of change for exercise behavior among Korean adults. A total of 1,335 Korean adults were recruited and surveyed from the Nowon district, geographically located in northern Seoul. Four Korean-version questionnaires were used to identify the stage of exercise behavior and psychological attributes of adolescents. Data were analyzed by frequency analysis, MANOVA, correlation analysis, and discriminant analysis. Multivariate F tests indicated that behavioral and cognitive processes of change, exercise efficacy, and pros differentiated participants across the stages of exercise behavior. Furthermore, the findings revealed that adults' exercise behavior was significantly correlated with the TTM constructs and that overall classification accuracy across the stages of change was 50.6%. This study supports the internal and external validity of the TTM for explaining exercise behavior.

NMR-based metabolomic analysis of spatial variation in soft corals.

PubMed

He, Qing; Sun, Ruiqi; Liu, Huijuan; Geng, Zhufeng; Chen, Dawei; Li, Yinping; Han, Jiao; Lin, Wenhan; Du, Shushan; Deng, Zhiwei

2014-03-28

Soft corals are common marine organisms that inhabit tropical and subtropical oceans. They are shown to be rich source of secondary metabolites with biological activities. In this work, soft corals from two geographical locations were investigated using ¹H-NMR spectroscopy coupled with multivariate statistical analysis at the metabolic level. A partial least-squares discriminant analysis showed clear separation among extracts of soft corals grown in Sanya Bay and Weizhou Island. The specific markers that contributed to discrimination between soft corals in two origins belonged to terpenes, sterols and N-containing compounds. The satisfied precision of classification obtained indicates this approach using combined ¹H-NMR and chemometrics is effective to discriminate soft corals collected in different geographical locations. The results revealed that metabolites of soft corals evidently depended on living environmental condition, which would provide valuable information for further relevant coastal marine environment evaluation.

Geographical identification of saffron (Crocus sativus L.) by linear discriminant analysis applied to the UV-visible spectra of aqueous extracts.

PubMed

D'Archivio, Angelo Antonio; Maggi, Maria Anna

2017-03-15

We attempted geographical classification of saffron using UV-visible spectroscopy, conventionally adopted for quality grading according to the ISO Normative 3632. We investigated 81 saffron samples produced in L'Aquila, Città della Pieve, Cascia, and Sardinia (Italy) and commercial products purchased in various supermarkets. Exploratory principal component analysis applied to the UV-vis spectra of saffron aqueous extracts revealed a clear differentiation of the samples belonging to different quality categories, but a poor separation according to the geographical origin of the spices. On the other hand, linear discriminant analysis based on 8 selected absorbance values, concentrated near 279, 305 and 328nm, allowed a good distinction of the spices coming from different sites. Under severe validation conditions (30% and 50% of saffron samples in the evaluation set), correct predictions were 85 and 83%, respectively. Copyright © 2016 Elsevier Ltd. All rights reserved.

Classification of electroencephalograph signals using time-frequency decomposition and linear discriminant analysis

NASA Astrophysics Data System (ADS)

Szuflitowska, B.; Orlowski, P.

2017-08-01

Automated detection system consists of two key steps: extraction of features from EEG signals and classification for detection of pathology activity. The EEG sequences were analyzed using Short-Time Fourier Transform and the classification was performed using Linear Discriminant Analysis. The accuracy of the technique was tested on three sets of EEG signals: epilepsy, healthy and Alzheimer's Disease. The classification error below 10% has been considered a success. The higher accuracy are obtained for new data of unknown classes than testing data. The methodology can be helpful in differentiation epilepsy seizure and disturbances in the EEG signal in Alzheimer's Disease.

A hydrologically explicit, spatially exact, classification of landforms for Canada at 1:500,000 scale.

NASA Astrophysics Data System (ADS)

MacMillan, Robert A.; Geng, Xiaoyuan; Smith, Scott; Zawadzka, Joanna; Hengl, Tom

2016-04-01

A new approach for classifying landform types has been developed and applied to all of Canada using a 250 m DEM. The resulting LandMapR classification has been designed to provide a stable and consistent spatial fabric to act as initial proto-polygons to be used in updating the current 1:1 M scale Soil Landscapes of Canada map to 1:500,000 scale. There is a desire to make the current SLC polygon fabric more consistent across the country, more correctly aligned to observable hydrological and landscape features, more spatially exact, more detailed and more interpretable. The approach is essentially a modification of the Hammond (1954) criteria for classifying macro landform types as implemented for computerized analysis by Dikau (1989, 1991) and Brabyn (1998). The major modification is that the key input variables of local relief and relative position in the landscape are computed for specific hillslopes that occur between individual, explicitly defined, channels and divides. While most approaches, including Dikau et al., (1991) and SOTER (Dobos et al., 2005) compute relative relief and landscape position within a neighborhood analysis window (NAW) of some fixed size (9,600 m and 1 km respectively) the LandMapR method assesses these variables based on explicit analysis of flow paths between locally defined divides and channels (or lakes). We have modified the Hammond criteria by splitting the lowest relief class of 0-30 m into 4 classes of 0-0 m, 0-1 m, 1-10 m and 10-30 m) in order to be able to better differentiate subtle landform features in areas of low relief. Essentially this enables recognition of lakes and open water (0 relief and 0 slope), shorelines and littoral zones (0-1 m), nearly flat, low-relief landforms (1-10 m) and low relief undulating plains (10-30 m). We also modified the Hammond approach for separating upper versus lower landform positions used to differentiate flat areas in uplands from flat lowlands. We instead differentiate 3 relative slope positions of channel valley, toe slope and upper slope consistently and exhaustively and so can identify any flat areas that occur in any of these three landform positions. We did not find it necessary to use slope gradient as a criteria for defining and delineating classes because relief acts as a surrogate for slope and each relief class exhibits a narrow and definable range of slope gradients. Dominant slope gradient (or other attributes) can be computed, post classification, for each defined polygon, if there is a need to further classify by slope or other attribute. This simplifies classification and also reduces pixilation in the classification arising from considering too many local criteria in the class definitions. The resulting polygons provide an extremely detailed classification of relief and landform position at the level of individual hillslopes across all of Canada. The polygon boundaries explicitly follow major identifiable drainage networks and work their way upslope to delineate interfluves that occupy upslope positions at all levels of relief. The detailed LandMapR polygon classifications nest consistently within more general regions defined by the original Hammond-Dikau procedures. Initial visual analysis reveals a strong and consistent spatial relationship between observable changes in slope, vegetation and drainage regime and LandMapR landform polygon boundaries. More detailed quantitative assessment of the accuracy and utility of the LandMapR polygon classes is planned.

Retinal vasculature classification using novel multifractal features

NASA Astrophysics Data System (ADS)

Ding, Y.; Ward, W. O. C.; Duan, Jinming; Auer, D. P.; Gowland, Penny; Bai, L.

2015-11-01

Retinal blood vessels have been implicated in a large number of diseases including diabetic retinopathy and cardiovascular diseases, which cause damages to retinal blood vessels. The availability of retinal vessel imaging provides an excellent opportunity for monitoring and diagnosis of retinal diseases, and automatic analysis of retinal vessels will help with the processes. However, state of the art vascular analysis methods such as counting the number of branches or measuring the curvature and diameter of individual vessels are unsuitable for the microvasculature. There has been published research using fractal analysis to calculate fractal dimensions of retinal blood vessels, but so far there has been no systematic research extracting discriminant features from retinal vessels for classifications. This paper introduces new methods for feature extraction from multifractal spectra of retinal vessels for classification. Two publicly available retinal vascular image databases are used for the experiments, and the proposed methods have produced accuracies of 85.5% and 77% for classification of healthy and diabetic retinal vasculatures. Experiments show that classification with multiple fractal features produces better rates compared with methods using a single fractal dimension value. In addition to this, experiments also show that classification accuracy can be affected by the accuracy of vessel segmentation algorithms.

Combining multiple decisions: applications to bioinformatics

NASA Astrophysics Data System (ADS)

Yukinawa, N.; Takenouchi, T.; Oba, S.; Ishii, S.

2008-01-01

Multi-class classification is one of the fundamental tasks in bioinformatics and typically arises in cancer diagnosis studies by gene expression profiling. This article reviews two recent approaches to multi-class classification by combining multiple binary classifiers, which are formulated based on a unified framework of error-correcting output coding (ECOC). The first approach is to construct a multi-class classifier in which each binary classifier to be aggregated has a weight value to be optimally tuned based on the observed data. In the second approach, misclassification of each binary classifier is formulated as a bit inversion error with a probabilistic model by making an analogy to the context of information transmission theory. Experimental studies using various real-world datasets including cancer classification problems reveal that both of the new methods are superior or comparable to other multi-class classification methods.

Free classification of American English dialects by native and non-native listeners

PubMed Central

Clopper, Cynthia G.; Bradlow, Ann R.

2009-01-01

Most second language acquisition research focuses on linguistic structures, and less research has examined the acquisition of sociolinguistic patterns. The current study explored the perceptual classification of regional dialects of American English by native and non-native listeners using a free classification task. Results revealed similar classification strategies for the native and non-native listeners. However, the native listeners were more accurate overall than the non-native listeners. In addition, the non-native listeners were less able to make use of constellations of cues to accurately classify the talkers by dialect. However, the non-native listeners were able to attend to cues that were either phonologically or sociolinguistically relevant in their native language. These results suggest that non-native listeners can use information in the speech signal to classify talkers by regional dialect, but that their lack of signal-independent cultural knowledge about variation in the second language leads to less accurate classification performance. PMID:20161400

Turnover and organizational performance: a comparative analysis of the effects of voluntary, involuntary, and reduction-in-force turnover.

PubMed

McElroy, J C; Morrow, P C; Rude, S N

2001-12-01

Data were collected from 31 regional subunits of a national financial services company to examine differential effects of 3 types of turnover (voluntary, involuntary, and reduction-in-force) on measures of organizational subunit performance. Although each form of turnover exhibited adverse effects on subunit performance when examined separately, partial correlation results revealed greater and more pervasive adverse effects for reduction-in-force turnover (i.e., downsizing) in comparison with the effects of voluntary and involuntary turnover. The results confirm the negative effects of downsizing, suggesting the need to move beyond the traditional voluntary-involuntary classification scheme used in turnover research.

Spiral waves characterization: Implications for an automated cardiodynamic tissue characterization.

PubMed

Alagoz, Celal; Cohen, Andrew R; Frisch, Daniel R; Tunç, Birkan; Phatharodom, Saran; Guez, Allon

2018-07-01

Spiral waves are phenomena observed in cardiac tissue especially during fibrillatory activities. Spiral waves are revealed through in-vivo and in-vitro studies using high density mapping that requires special experimental setup. Also, in-silico spiral wave analysis and classification is performed using membrane potentials from entire tissue. In this study, we report a characterization approach that identifies spiral wave behaviors using intracardiac electrogram (EGM) readings obtained with commonly used multipolar diagnostic catheters that perform localized but high-resolution readings. Specifically, the algorithm is designed to distinguish between stationary, meandering, and break-up rotors. The clustering and classification algorithms are tested on simulated data produced using a phenomenological 2D model of cardiac propagation. For EGM measurements, unipolar-bipolar EGM readings from various locations on tissue using two catheter types are modeled. The distance measure between spiral behaviors are assessed using normalized compression distance (NCD), an information theoretical distance. NCD is a universal metric in the sense it is solely based on compressibility of dataset and not requiring feature extraction. We also introduce normalized FFT distance (NFFTD) where compressibility is replaced with a FFT parameter. Overall, outstanding clustering performance was achieved across varying EGM reading configurations. We found that effectiveness in distinguishing was superior in case of NCD than NFFTD. We demonstrated that distinct spiral activity identification on a behaviorally heterogeneous tissue is also possible. This report demonstrates a theoretical validation of clustering and classification approaches that provide an automated mapping from EGM signals to assessment of spiral wave behaviors and hence offers a potential mapping and analysis framework for cardiac tissue wavefront propagation patterns. Copyright © 2018 Elsevier B.V. All rights reserved.

Complementary biomarker-based methods for characterising Arctic sea ice conditions: A case study comparison between multivariate analysis and the PIP25 index

NASA Astrophysics Data System (ADS)

Köseoğlu, Denizcan; Belt, Simon T.; Smik, Lukas; Yao, Haoyi; Panieri, Giuliana; Knies, Jochen

2018-02-01

The discovery of IP25 as a qualitative biomarker proxy for Arctic sea ice and subsequent introduction of the so-called PIP25 index for semi-quantitative descriptions of sea ice conditions has significantly advanced our understanding of long-term paleo Arctic sea ice conditions over the past decade. We investigated the potential for classification tree (CT) models to provide a further approach to paleo Arctic sea ice reconstruction through analysis of a suite of highly branched isoprenoid (HBI) biomarkers in ca. 200 surface sediments from the Barents Sea. Four CT models constructed using different HBI assemblages revealed IP25 and an HBI triene as the most appropriate classifiers of sea ice conditions, achieving a >90% cross-validated classification rate. Additionally, lower model performance for locations in the Marginal Ice Zone (MIZ) highlighted difficulties in characterisation of this climatically-sensitive region. CT model classification and semi-quantitative PIP25-derived estimates of spring sea ice concentration (SpSIC) for four downcore records from the region were consistent, although agreement between proxy and satellite/observational records was weaker for a core from the west Svalbard margin, likely due to the highly variable sea ice conditions. The automatic selection of appropriate biomarkers for description of sea ice conditions, quantitative model assessment, and insensitivity to the c-factor used in the calculation of the PIP25 index are key attributes of the CT approach, and we provide an initial comparative assessment between these potentially complementary methods. The CT model should be capable of generating longer-term temporal shifts in sea ice conditions for the climatically sensitive Barents Sea.

Process Simulation and Cost Analysis for Removing Inorganics from Wood Chips using Combined Mechanical and Chemical Preprocessing

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hu, Hongqiang; Westover, Tyler L.; Cherry, Robert

Naturally occurring and introduced inorganic species (ash) in biomass feedstocks negatively impact thermochemical energy conversion processes such as pyrolysis, hydrothermal liquefaction, gasification and combustion to biopower. As such, it is desirable to better understand the cost:benefit ratios of various ash reduction processes. Here, a novel process simulation model was developed using AspenPlus to reduce the ash content of Loblolly logging residues using both air classification and a dilute-acid leaching process. For costing purposes, a throughput of 25 tons/hour was selected. At this scale, the process cost for a standalone air classification process was $3 per ton for a biomass feedstock.more » Ash reduction via dilute –acid leaching was simulated based on experimentally determined kinetics of ion diffusion at an acid concentration of 0.5% H2SO4 and temperature of 75°F. The total estimated processing cost for leaching at these conditions was approximately $14/ton of dry biomass. Sensitivity analysis of three parameters on mineral reduction in the leaching process revealed that increasing leaching temperature was not economically feasible, while it was viable to apply a longer retention time in leaching for higher ash removal or achieve a lower water content in final products with reasonable extra costs. In addition, scenarios combining air classification with leaching were examined. A whole process cost of approximately $16/ton of biomass at a biomass feedstock rate of 25 ton/hour considering a 9% of biomass classified as light fraction to be leached. The leaching operating costs constituted 75% of this amount, of which the heating costs of dryer was 44%. This suggests that the process costs would be substantially reduced if more efficient drying methods are applied in future.« less

"Sarcomatoid" carcinomas of the lung: a clinicopathological study of 86 cases with a new perspective on tumor classification.

PubMed

Weissferdt, Annikka; Kalhor, Neda; Correa, Arlene M; Moran, Cesar A

2017-05-01

Pulmonary sarcomatoid carcinoma includes a heterogenous group of tumors difficult to diagnose and treat. We report the clinicopathological features of 86 such tumors, including 74 pleomorphic and 12 spindle cell carcinomas, and propose a novel approach to the classification of these neoplasms in an attempt to better guide patient management. The patients were 47 men and 39 women aged 36 to 87 years (mean, 63 years) who primarily presented with shortness of breath, cough, and chest pain. Eighty-six percent of patients had a smoking history. Histologically, the pleomorphic carcinomas consisted of spindle and/or giant cells with varying proportions of conventional non-small cell carcinoma in the form of adenocarcinoma (n=29), squamous cell carcinoma (n=10), or large cell carcinoma (n=18); 17 cases contained a mix of spindle and giant cells only. The 12 spindle cell carcinomas consisted of spindle cells only. Based on the combined histopathologic and immunohistochemical features of these tumors, we were able to reanalyze the spectrum of these lesions and reclassify them accordingly. Statistical analysis revealed an overall survival at 3, 5, and 10 years of 42.9%, 34.6%, and 23.5%, respectively, and a median survival of 15 months. Log-rank test showed that in multivariate analysis, only pathological T stage was a factor associated with prognosis. The current classification of pulmonary sarcomatoid carcinomas precludes optimal triaging of these tumors with the risk of denying patients access to novel treatment. Our proposal for a reclassification of these tumors would more accurately guide patient management and facilitate targeted therapies. Copyright © 2016 Elsevier Inc. All rights reserved.

Distinguishing early and late brain aging from the Alzheimer's disease spectrum: consistent morphological patterns across independent samples.

PubMed

Doan, Nhat Trung; Engvig, Andreas; Zaske, Krystal; Persson, Karin; Lund, Martina Jonette; Kaufmann, Tobias; Cordova-Palomera, Aldo; Alnæs, Dag; Moberget, Torgeir; Brækhus, Anne; Barca, Maria Lage; Nordvik, Jan Egil; Engedal, Knut; Agartz, Ingrid; Selbæk, Geir; Andreassen, Ole A; Westlye, Lars T

2017-09-01

Alzheimer's disease (AD) is a debilitating age-related neurodegenerative disorder. Accurate identification of individuals at risk is complicated as AD shares cognitive and brain features with aging. We applied linked independent component analysis (LICA) on three complementary measures of gray matter structure: cortical thickness, area and gray matter density of 137 AD, 78 mild (MCI) and 38 subjective cognitive impairment patients, and 355 healthy adults aged 18-78 years to identify dissociable multivariate morphological patterns sensitive to age and diagnosis. Using the lasso classifier, we performed group classification and prediction of cognition and age at different age ranges to assess the sensitivity and diagnostic accuracy of the LICA patterns in relation to AD, as well as early and late healthy aging. Three components showed high sensitivity to the diagnosis and cognitive status of AD, with different relationships with age: one reflected an anterior-posterior gradient in thickness and gray matter density and was uniquely related to diagnosis, whereas the other two, reflecting widespread cortical thickness and medial temporal lobe volume, respectively, also correlated significantly with age. Repeating the LICA decomposition and between-subject analysis on ADNI data, including 186 AD, 395 MCI and 220 age-matched healthy controls, revealed largely consistent brain patterns and clinical associations across samples. Classification results showed that multivariate LICA-derived brain characteristics could be used to predict AD and age with high accuracy (area under ROC curve up to 0.93 for classification of AD from controls). Comparison between classifiers based on feature ranking and feature selection suggests both common and unique feature sets implicated in AD and aging, and provides evidence of distinct age-related differences in early compared to late aging. Copyright © 2017 Elsevier Inc. All rights reserved.