SVM-based Partial Discharge Pattern Classification for GIS

Science.gov (United States)

Ling, Yin; Bai, Demeng; Wang, Menglin; Gong, Xiaojin; Gu, Chao

2018-01-01

Partial discharges (PD) occur when there are localized dielectric breakdowns in small regions of gas insulated substations (GIS). It is of high importance to recognize the PD patterns, through which we can diagnose the defects caused by different sources so that predictive maintenance can be conducted to prevent from unplanned power outage. In this paper, we propose an approach to perform partial discharge pattern classification. It first recovers the PRPD matrices from the PRPD2D images; then statistical features are extracted from the recovered PRPD matrix and fed into SVM for classification. Experiments conducted on a dataset containing thousands of images demonstrates the high effectiveness of the method.

Pattern Classification in Kampo Medicine

Directory of Open Access Journals (Sweden)

S. Yakubo

2014-01-01

Full Text Available Pattern classification is very unique in traditional medicine. Kampo medical patterns have transformed over time during Japan’s history. In the 17th to 18th centuries, Japanese doctors advocated elimination of the Ming medical theory and followed the basic concepts put forth by Shang Han Lun and Jin Gui Yao Lue in the later Han dynasty (25–220 AD. The physician Todo Yoshimasu (1702–1773 emphasized that an appropriate treatment could be administered if a set of patterns could be identified. This principle is still referred to as “matching of pattern and formula” and is the basic concept underlying Kampo medicine today. In 1868, the Meiji restoration occurred, and the new government changed its policies to follow that of the European countries, adopting only Western medicine. Physicians trained in Western medicine played an important role in the revival of Kampo medicine, modernizing Kampo patterns to avoid confusion with Western biomedical terminology. In order to understand the Japanese version of traditional disorders and patterns, background information on the history of Kampo and its role in the current health care system in Japan is important. In this paper we overviewed the formation of Kampo patterns.

Qualitative pattern classification of shear wave elastography for breast masses: How it correlates to quantitative measurements

International Nuclear Information System (INIS)

Yoon, Jung Hyun; Ko, Kyung Hee; Jung, Hae Kyoung; Lee, Jong Tae

2013-01-01

Objective: To determine the correlation of qualitative shear wave elastography (SWE) pattern classification to quantitative SWE measurements and whether it is representative of quantitative SWE values with similar performances. Methods: From October 2012 to January 2013, 267 breast masses of 236 women (mean age: 45.12 ± 10.54 years, range: 21–88 years) who had undergone ultrasonography (US), SWE, and subsequent biopsy were included. US BI-RADS final assessment and qualitative and quantitative SWE measurements were recorded. Correlation between pattern classification and mean elasticity, maximum elasticity, elasticity ratio and standard deviation were evaluated. Diagnostic performances of grayscale US, SWE parameters, and US combined to SWE values were calculated and compared. Results: Of the 267 breast masses, 208 (77.9%) were benign and 59 (22.1%) were malignant. Pattern classifications significantly correlated with all quantitative SWE measurements, showing highest correlation with maximum elasticity, r = 0.721 (P < 0.001). Sensitivity was significantly decreased in US combined to SWE measurements to grayscale US: 69.5–89.8% to 100.0%, while specificity was significantly improved: 62.5–81.7% to 13.9% (P < 0.001). Area under the ROC curve (A z ) did not show significant differences between grayscale US to US combined to SWE (P > 0.05). Conclusion: Pattern classification shows high correlation to maximum stiffness and may be representative of quantitative SWE values. When combined to grayscale US, SWE improves specificity of US

Qualitative pattern classification of shear wave elastography for breast masses: How it correlates to quantitative measurements

Energy Technology Data Exchange (ETDEWEB)

Yoon, Jung Hyun, E-mail: lvjenny0417@gmail.com [Department of Radiology, CHA Bundang Medical Center, CHA University, School of Medicine (Korea, Republic of); Department of Radiology, Research Institute of Radiological Science, Yonsei University, College of Medicine (Korea, Republic of); Ko, Kyung Hee, E-mail: yourheeya@cha.ac.kr [Department of Radiology, CHA Bundang Medical Center, CHA University, School of Medicine (Korea, Republic of); Jung, Hae Kyoung, E-mail: AA40501@cha.ac.kr [Department of Radiology, CHA Bundang Medical Center, CHA University, School of Medicine (Korea, Republic of); Lee, Jong Tae, E-mail: jtlee@cha.ac.kr [Department of Radiology, CHA Bundang Medical Center, CHA University, School of Medicine (Korea, Republic of)

2013-12-01

Objective: To determine the correlation of qualitative shear wave elastography (SWE) pattern classification to quantitative SWE measurements and whether it is representative of quantitative SWE values with similar performances. Methods: From October 2012 to January 2013, 267 breast masses of 236 women (mean age: 45.12 ± 10.54 years, range: 21–88 years) who had undergone ultrasonography (US), SWE, and subsequent biopsy were included. US BI-RADS final assessment and qualitative and quantitative SWE measurements were recorded. Correlation between pattern classification and mean elasticity, maximum elasticity, elasticity ratio and standard deviation were evaluated. Diagnostic performances of grayscale US, SWE parameters, and US combined to SWE values were calculated and compared. Results: Of the 267 breast masses, 208 (77.9%) were benign and 59 (22.1%) were malignant. Pattern classifications significantly correlated with all quantitative SWE measurements, showing highest correlation with maximum elasticity, r = 0.721 (P < 0.001). Sensitivity was significantly decreased in US combined to SWE measurements to grayscale US: 69.5–89.8% to 100.0%, while specificity was significantly improved: 62.5–81.7% to 13.9% (P < 0.001). Area under the ROC curve (A{sub z}) did not show significant differences between grayscale US to US combined to SWE (P > 0.05). Conclusion: Pattern classification shows high correlation to maximum stiffness and may be representative of quantitative SWE values. When combined to grayscale US, SWE improves specificity of US.

Image Classification Using Biomimetic Pattern Recognition with Convolutional Neural Networks Features

Science.gov (United States)

Huo, Guanying

2017-01-01

As a typical deep-learning model, Convolutional Neural Networks (CNNs) can be exploited to automatically extract features from images using the hierarchical structure inspired by mammalian visual system. For image classification tasks, traditional CNN models employ the softmax function for classification. However, owing to the limited capacity of the softmax function, there are some shortcomings of traditional CNN models in image classification. To deal with this problem, a new method combining Biomimetic Pattern Recognition (BPR) with CNNs is proposed for image classification. BPR performs class recognition by a union of geometrical cover sets in a high-dimensional feature space and therefore can overcome some disadvantages of traditional pattern recognition. The proposed method is evaluated on three famous image classification benchmarks, that is, MNIST, AR, and CIFAR-10. The classification accuracies of the proposed method for the three datasets are 99.01%, 98.40%, and 87.11%, respectively, which are much higher in comparison with the other four methods in most cases. PMID:28316614

Classification and Target Group Selection Based Upon Frequent Patterns

NARCIS (Netherlands)

W.H.L.M. Pijls (Wim); R. Potharst (Rob)

2000-01-01

textabstractIn this technical report , two new algorithms based upon frequent patterns are proposed. One algorithm is a classification method. The other one is an algorithm for target group selection. In both algorithms, first of all, the collection of frequent patterns in the training set is

Neuroimaging classification of progression patterns in glioblastoma: a systematic review.

Science.gov (United States)

Piper, Rory J; Senthil, Keerthi K; Yan, Jiun-Lin; Price, Stephen J

2018-03-30

Our primary objective was to report the current neuroimaging classification systems of spatial patterns of progression in glioblastoma. In addition, we aimed to report the terminology used to describe 'progression' and to assess the compliance with the Response Assessment in Neuro-Oncology (RANO) Criteria. We conducted a systematic review to identify all neuroimaging studies of glioblastoma that have employed a categorical classification system of spatial progression patterns. Our review was registered with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) registry. From the included 157 results, we identified 129 studies that used labels of spatial progression patterns that were not based on radiation volumes (Group 1) and 50 studies that used labels that were based on radiation volumes (Group 2). In Group 1, we found 113 individual labels and the most frequent were: local/localised (58%), distant/distal (51%), diffuse (20%), multifocal (15%) and subependymal/subventricular zone (15%). We identified 13 different labels used to refer to 'progression', of which the most frequent were 'recurrence' (99%) and 'progression' (92%). We identified that 37% (n = 33/90) of the studies published following the release of the RANO classification were adherent compliant with the RANO criteria. Our review reports significant heterogeneity in the published systems used to classify glioblastoma spatial progression patterns. Standardization of terminology and classification systems used in studying progression would increase the efficiency of our research in our attempts to more successfully treat glioblastoma.

A new qualitative pattern classification of shear wave elastograghy for solid breast mass evaluation.

Science.gov (United States)

Cong, Rui; Li, Jing; Guo, Song

2017-02-01

To examine the efficacy of qualitative shear wave elastography (SWE) in the classification and evaluation of solid breast masses, and to compare this method with conventional ultrasonograghy (US), quantitative SWE parameters and qualitative SWE classification proposed before. From April 2015 to March 2016, 314 consecutive females with 325 breast masses who decided to undergo core needle biopsy and/or surgical biopsy were enrolled. Conventional US and SWE were previously performed in all enrolled subjects. Each mass was classified by two different qualitative classifications. One was established in our study, herein named the Qual1. Qual1 could classify the SWE images into five color patterns by the visual evaluations: Color pattern 1 (homogeneous pattern); Color pattern 2 (comparative homogeneous pattern); Color pattern 3 (irregularly heterogeneous pattern); Color pattern 4 (intralesional echo pattern); and Color pattern 5 (the stiff rim sign pattern). The second qualitative classification was named Qual2 here, and included a four-color overlay pattern classification (Tozaki and Fukuma, Acta Radiologica, 2011). The Breast Imaging Reporting and Data System (BI-RADS) assessment and quantitative SWE parameters were recorded. Diagnostic performances of conventional US, SWE parameters, and combinations of US and SWE parameters were compared. With pathological results as the gold standard, of the 325 examined breast masses, 139 (42.77%) samples were malignant and 186 (57.23%) were benign. The Qual1 showed a higher Az value than the Qual2 and quantitative SWE parameters (all Pbreast mass diagnoses. Copyright © 2016. Published by Elsevier B.V.

Combining multiple classifiers for age classification

CSIR Research Space (South Africa)

Van Heerden, C

2009-11-01

Full Text Available The authors compare several different classifier combination methods on a single task, namely speaker age classification. This task is well suited to combination strategies, since significantly different feature classes are employed. Support vector...

Walking pattern classification and walking distance estimation algorithms using gait phase information.

Science.gov (United States)

Wang, Jeen-Shing; Lin, Che-Wei; Yang, Ya-Ting C; Ho, Yu-Jen

2012-10-01

This paper presents a walking pattern classification and a walking distance estimation algorithm using gait phase information. A gait phase information retrieval algorithm was developed to analyze the duration of the phases in a gait cycle (i.e., stance, push-off, swing, and heel-strike phases). Based on the gait phase information, a decision tree based on the relations between gait phases was constructed for classifying three different walking patterns (level walking, walking upstairs, and walking downstairs). Gait phase information was also used for developing a walking distance estimation algorithm. The walking distance estimation algorithm consists of the processes of step count and step length estimation. The proposed walking pattern classification and walking distance estimation algorithm have been validated by a series of experiments. The accuracy of the proposed walking pattern classification was 98.87%, 95.45%, and 95.00% for level walking, walking upstairs, and walking downstairs, respectively. The accuracy of the proposed walking distance estimation algorithm was 96.42% over a walking distance.

An intelligent temporal pattern classification system using fuzzy ...

Indian Academy of Sciences (India)

In this paper, we propose a new pattern classification system by combining Temporal features with Fuzzy Min–Max (TFMM) neural network based classifier for effective decision support in medical diagnosis. Moreover, a Particle Swarm Optimization (PSO) algorithm based rule extractor is also proposed in this work for ...

Lateralization patterns of covert but not overt movements change with age: An EEG neurofeedback study.

Science.gov (United States)

Zich, Catharina; Debener, Stefan; De Vos, Maarten; Frerichs, Stella; Maurer, Stefanie; Kranczioch, Cornelia

2015-08-01

The mental practice of movements has been suggested as a promising add-on therapy to facilitate motor recovery after stroke. In the case of mentally practised movements, electroencephalogram (EEG) can be utilized to provide feedback about an otherwise covert act. The main target group for such an intervention are elderly patients, though research so far is largely focused on young populations (study therefore aimed to examine the influence of age on the neural correlates of covert movements (CMs) in a real-time EEG neurofeedback framework. CM-induced event-related desynchronization (ERD) was studied in young (mean age: 23.6 years) and elderly (mean age: 62.7 years) healthy adults. Participants performed covert and overt hand movements. CMs were based on kinesthetic motor imagery (MI) or quasi-movements (QM). Based on previous studies investigating QM in the mu frequency range (8-13Hz) QM were expected to result in more lateralized ERD% patterns and accordingly higher classification accuracies. Independent of CM strategy the elderly were characterized by a significantly reduced lateralization of ERD%, due to stronger ipsilateral ERD%, and in consequence, reduced classification accuracies. QM were generally perceived as more vivid, but no differences were evident between MI and QM in ERD% or classification accuracies. EEG feedback enhanced task-related activity independently of strategy and age. ERD% measures of overt and covert movements were strongly related in young adults, whereas in the elderly ERD% lateralization is dissociated. In summary, we did not find evidence in support of more pronounced ERD% lateralization patterns in QM. Our finding of a less lateralized activation pattern in the elderly is in accordance to previous research and with the idea that compensatory processes help to overcome neurodegenerative changes related to normal ageing. Importantly, it indicates that EEG neurofeedback studies should place more emphasis on the age of the potential end

Application of Classification Methods for Forecasting Mid-Term Power Load Patterns

Science.gov (United States)

Piao, Minghao; Lee, Heon Gyu; Park, Jin Hyoung; Ryu, Keun Ho

Currently an automated methodology based on data mining techniques is presented for the prediction of customer load patterns in long duration load profiles. The proposed approach in this paper consists of three stages: (i) data preprocessing: noise or outlier is removed and the continuous attribute-valued features are transformed to discrete values, (ii) cluster analysis: k-means clustering is used to create load pattern classes and the representative load profiles for each class and (iii) classification: we evaluated several supervised learning methods in order to select a suitable prediction method. According to the proposed methodology, power load measured from AMR (automatic meter reading) system, as well as customer indexes, were used as inputs for clustering. The output of clustering was the classification of representative load profiles (or classes). In order to evaluate the result of forecasting load patterns, the several classification methods were applied on a set of high voltage customers of the Korea power system and derived class labels from clustering and other features are used as input to produce classifiers. Lastly, the result of our experiments was presented.

General method of pattern classification using the two-domain theory

Science.gov (United States)

Rorvig, Mark E. (Inventor)

1993-01-01

Human beings judge patterns (such as images) by complex mental processes, some of which may not be known, while computing machines extract features. By representing the human judgements with simple measurements and reducing them and the machine extracted features to a common metric space and fitting them by regression, the judgements of human experts rendered on a sample of patterns may be imposed on a pattern population to provide automatic classification.

Fabric Weave Pattern and Yarn Color Recognition and Classification Using a Deep ELM Network

Directory of Open Access Journals (Sweden)

Babar Khan

2017-10-01

Full Text Available Usually, a fabric weave pattern is recognized using methods which identify the warp floats and weft floats. Although these methods perform well for uniform or repetitive weave patterns, in the case of complex weave patterns, these methods become computationally complex and the classification error rates are comparatively higher. Furthermore, the fault-tolerance (invariance and stability (selectivity of the existing methods are still to be enhanced. We present a novel biologically-inspired method to invariantly recognize the fabric weave pattern (fabric texture and yarn color from the color image input. We proposed a model in which the fabric weave pattern descriptor is based on the HMAX model for computer vision inspired by the hierarchy in the visual cortex, the color descriptor is based on the opponent color channel inspired by the classical opponent color theory of human vision, and the classification stage is composed of a multi-layer (deep extreme learning machine. Since the weave pattern descriptor, yarn color descriptor, and the classification stage are all biologically inspired, we propose a method which is completely biologically plausible. The classification performance of the proposed algorithm indicates that the biologically-inspired computer-aided-vision models might provide accurate, fast, reliable and cost-effective solution to industrial automation.

[Analysis of dietary pattern and diabetes mellitus influencing factors identified by classification tree model in adults of Fujian].

Science.gov (United States)

Yu, F L; Ye, Y; Yan, Y S

2017-05-10

Objective: To find out the dietary patterns and explore the relationship between environmental factors (especially dietary patterns) and diabetes mellitus in the adults of Fujian. Methods: Multi-stage sampling method were used to survey residents aged ≥18 years by questionnaire, physical examination and laboratory detection in 10 disease surveillance points in Fujian. Factor analysis was used to identify the dietary patterns, while logistic regression model was applied to analyze relationship between dietary patterns and diabetes mellitus, and classification tree model was adopted to identify the influencing factors for diabetes mellitus. Results: There were four dietary patterns in the population, including meat, plant, high-quality protein, and fried food and beverages patterns. The result of logistic analysis showed that plant pattern, which has higher factor loading of fresh fruit-vegetables and cereal-tubers, was a protective factor for non-diabetes mellitus. The risk of diabetes mellitus in the population at T2 and T3 levels of factor score were 0.727 (95 %CI: 0.561-0.943) times and 0.736 (95 %CI : 0.573-0.944) times higher, respectively, than those whose factor score was in lowest quartile. Thirteen influencing factors and eleven group at high-risk for diabetes mellitus were identified by classification tree model. The influencing factors were dyslipidemia, age, family history of diabetes, hypertension, physical activity, career, sex, sedentary time, abdominal adiposity, BMI, marital status, sleep time and high-quality protein pattern. Conclusion: There is a close association between dietary patterns and diabetes mellitus. It is necessary to promote healthy and reasonable diet, strengthen the monitoring and control of blood lipids, blood pressure and body weight, and have good lifestyle for the prevention and control of diabetes mellitus.

An Ultrasonic Pattern Recognition Approach to Welding Defect Classification

International Nuclear Information System (INIS)

Song, Sung Jin

1995-01-01

Classification of flaws in weldments from their ultrasonic scattering signals is very important in quantitative nondestructive evaluation. This problem is ideally suited to a modern ultrasonic pattern recognition technique. Here brief discussion on systematic approach to this methodology is presented including ultrasonic feature extraction, feature selection and classification. A stronger emphasis is placed on probabilistic neural networks as efficient classifiers for many practical classification problems. In an example probabilistic neural networks are applied to classify flaws in weldments into 3 classes such as cracks, porosity and slag inclusions. Probabilistic nets are shown to be able to exhibit high performance of other classifiers without any training time overhead. In addition, forward selection scheme for sensitive features is addressed to enhance network performance

In Vivo Pattern Classification of Ingestive Behavior in Ruminants Using FBG Sensors and Machine Learning

Directory of Open Access Journals (Sweden)

Vinicius Pegorini

2015-11-01

Full Text Available Pattern classification of ingestive behavior in grazing animals has extreme importance in studies related to animal nutrition, growth and health. In this paper, a system to classify chewing patterns of ruminants in in vivo experiments is developed. The proposal is based on data collected by optical fiber Bragg grating sensors (FBG that are processed by machine learning techniques. The FBG sensors measure the biomechanical strain during jaw movements, and a decision tree is responsible for the classification of the associated chewing pattern. In this study, patterns associated with food intake of dietary supplement, hay and ryegrass were considered. Additionally, two other important events for ingestive behavior were monitored: rumination and idleness. Experimental results show that the proposed approach for pattern classification is capable of differentiating the five patterns involved in the chewing process with an overall accuracy of 94%.

In Vivo Pattern Classification of Ingestive Behavior in Ruminants Using FBG Sensors and Machine Learning.

Science.gov (United States)

Pegorini, Vinicius; Karam, Leandro Zen; Pitta, Christiano Santos Rocha; Cardoso, Rafael; da Silva, Jean Carlos Cardozo; Kalinowski, Hypolito José; Ribeiro, Richardson; Bertotti, Fábio Luiz; Assmann, Tangriani Simioni

2015-11-11

Pattern classification of ingestive behavior in grazing animals has extreme importance in studies related to animal nutrition, growth and health. In this paper, a system to classify chewing patterns of ruminants in in vivo experiments is developed. The proposal is based on data collected by optical fiber Bragg grating sensors (FBG) that are processed by machine learning techniques. The FBG sensors measure the biomechanical strain during jaw movements, and a decision tree is responsible for the classification of the associated chewing pattern. In this study, patterns associated with food intake of dietary supplement, hay and ryegrass were considered. Additionally, two other important events for ingestive behavior were monitored: rumination and idleness. Experimental results show that the proposed approach for pattern classification is capable of differentiating the five patterns involved in the chewing process with an overall accuracy of 94%.

Using Pattern Classification and Recognition Techniques for Diagnostic and Prediction

Directory of Open Access Journals (Sweden)

MORARIU, N.

2007-04-01

Full Text Available The paper presents some aspects regarding the joint use of classification and recognition techniques for the activity evolution diagnostication and prediction by means of a set of indexes. Starting from the indexes set there is defined a measure on the patterns set, measure representing a scalar value that characterizes the activity analyzed at each time moment. A pattern is defined by the values of the indexes set at a given time. Over the classes set obtained by means of the classification and recognition techniques is defined a relation that allows the representation of the evolution from negative evolution towards positive evolution. For the diagnostication and prediction the following tools are used: pattern recognition and multilayer perceptron. The data set used in experiments describes the pollution due to CO2 emission from the consumption of fuels in Europe. The paper also presents the REFORME software written by the authors and the results of the experiment obtained with this software.

Electromyographic Pattern Analysis and Classification for a Robotic Prosthetic Arm

Directory of Open Access Journals (Sweden)

M. José H. Erazo Macias

2006-01-01

Full Text Available This paper deals with the statistical analysis and pattern classification of electromyographic signals from the biceps of a person with amputation below the humerus. Such signals collected from an amputation simulator are synergistically generated to produce discrete elbow movements. The purpose of this study is to utilise these signals to control an electrically driven prosthetic or orthotic elbow with minimum extra mental effort on the part of the subject. The results show very good separability of classes of movements when a learning pattern classification scheme is used, and a superposition of any composite motion to the three basic primitive motions—humeral rotation in and out, flexion and extension, and pronation and supination. Since no synergy was detected for the wrist movement, different inputs have to be provided for a grip. In addition, the method described is not limited by the location of the electrodes. For amputees with shorter stumps, synergistic signals could be obtained from the shoulder muscles. However, the presentation in this paper is limited to biceps signal classification only.

Using a Classification of Occupations to Describe Age, Sex, and Time Differences in Employment Patterns. Report No. 223.

Science.gov (United States)

Gottfredson, Gary D.; Daiger, Denise C.

Employment data from the 1960 and 1970 censuses were organized using the occupational classification system of John Holland to examine age, sex, and level differences in employment and to detect changes over the 10-year period. Data were organized by both kind and level of work in an attempt to answer the following questions: What are the relative…

On the integrity of functional brain networks in schizophrenia, Parkinson's disease, and advanced age: Evidence from connectivity-based single-subject classification.

Science.gov (United States)

Pläschke, Rachel N; Cieslik, Edna C; Müller, Veronika I; Hoffstaedter, Felix; Plachti, Anna; Varikuti, Deepthi P; Goosses, Mareike; Latz, Anne; Caspers, Svenja; Jockwitz, Christiane; Moebus, Susanne; Gruber, Oliver; Eickhoff, Claudia R; Reetz, Kathrin; Heller, Julia; Südmeyer, Martin; Mathys, Christian; Caspers, Julian; Grefkes, Christian; Kalenscher, Tobias; Langner, Robert; Eickhoff, Simon B

2017-12-01

Previous whole-brain functional connectivity studies achieved successful classifications of patients and healthy controls but only offered limited specificity as to affected brain systems. Here, we examined whether the connectivity patterns of functional systems affected in schizophrenia (SCZ), Parkinson's disease (PD), or normal aging equally translate into high classification accuracies for these conditions. We compared classification performance between pre-defined networks for each group and, for any given network, between groups. Separate support vector machine classifications of 86 SCZ patients, 80 PD patients, and 95 older adults relative to their matched healthy/young controls, respectively, were performed on functional connectivity in 12 task-based, meta-analytically defined networks using 25 replications of a nested 10-fold cross-validation scheme. Classification performance of the various networks clearly differed between conditions, as those networks that best classified one disease were usually non-informative for the other. For SCZ, but not PD, emotion-processing, empathy, and cognitive action control networks distinguished patients most accurately from controls. For PD, but not SCZ, networks subserving autobiographical or semantic memory, motor execution, and theory-of-mind cognition yielded the best classifications. In contrast, young-old classification was excellent based on all networks and outperformed both clinical classifications. Our pattern-classification approach captured associations between clinical and developmental conditions and functional network integrity with a higher level of specificity than did previous whole-brain analyses. Taken together, our results support resting-state connectivity as a marker of functional dysregulation in specific networks known to be affected by SCZ and PD, while suggesting that aging affects network integrity in a more global way. Hum Brain Mapp 38:5845-5858, 2017. © 2017 Wiley Periodicals, Inc. © 2017

Hyperspectral image classification based on local binary patterns and PCANet

Science.gov (United States)

Yang, Huizhen; Gao, Feng; Dong, Junyu; Yang, Yang

2018-04-01

Hyperspectral image classification has been well acknowledged as one of the challenging tasks of hyperspectral data processing. In this paper, we propose a novel hyperspectral image classification framework based on local binary pattern (LBP) features and PCANet. In the proposed method, linear prediction error (LPE) is first employed to select a subset of informative bands, and LBP is utilized to extract texture features. Then, spectral and texture features are stacked into a high dimensional vectors. Next, the extracted features of a specified position are transformed to a 2-D image. The obtained images of all pixels are fed into PCANet for classification. Experimental results on real hyperspectral dataset demonstrate the effectiveness of the proposed method.

A supervised learning rule for classification of spatiotemporal spike patterns.

Science.gov (United States)

Lilin Guo; Zhenzhong Wang; Adjouadi, Malek

2016-08-01

This study introduces a novel supervised algorithm for spiking neurons that take into consideration synapse delays and axonal delays associated with weights. It can be utilized for both classification and association and uses several biologically influenced properties, such as axonal and synaptic delays. This algorithm also takes into consideration spike-timing-dependent plasticity as in Remote Supervised Method (ReSuMe). This paper focuses on the classification aspect alone. Spiked neurons trained according to this proposed learning rule are capable of classifying different categories by the associated sequences of precisely timed spikes. Simulation results have shown that the proposed learning method greatly improves classification accuracy when compared to the Spike Pattern Association Neuron (SPAN) and the Tempotron learning rule.

BIOCAT: a pattern recognition platform for customizable biological image classification and annotation.

Science.gov (United States)

Zhou, Jie; Lamichhane, Santosh; Sterne, Gabriella; Ye, Bing; Peng, Hanchuan

2013-10-04

Pattern recognition algorithms are useful in bioimage informatics applications such as quantifying cellular and subcellular objects, annotating gene expressions, and classifying phenotypes. To provide effective and efficient image classification and annotation for the ever-increasing microscopic images, it is desirable to have tools that can combine and compare various algorithms, and build customizable solution for different biological problems. However, current tools often offer a limited solution in generating user-friendly and extensible tools for annotating higher dimensional images that correspond to multiple complicated categories. We develop the BIOimage Classification and Annotation Tool (BIOCAT). It is able to apply pattern recognition algorithms to two- and three-dimensional biological image sets as well as regions of interest (ROIs) in individual images for automatic classification and annotation. We also propose a 3D anisotropic wavelet feature extractor for extracting textural features from 3D images with xy-z resolution disparity. The extractor is one of the about 20 built-in algorithms of feature extractors, selectors and classifiers in BIOCAT. The algorithms are modularized so that they can be "chained" in a customizable way to form adaptive solution for various problems, and the plugin-based extensibility gives the tool an open architecture to incorporate future algorithms. We have applied BIOCAT to classification and annotation of images and ROIs of different properties with applications in cell biology and neuroscience. BIOCAT provides a user-friendly, portable platform for pattern recognition based biological image classification of two- and three- dimensional images and ROIs. We show, via diverse case studies, that different algorithms and their combinations have different suitability for various problems. The customizability of BIOCAT is thus expected to be useful for providing effective and efficient solutions for a variety of biological

Unsupervised classification of neocortical activity patterns in neonatal and pre-juvenile rodents

Directory of Open Access Journals (Sweden)

Nicole eCichon

2014-05-01

Full Text Available Flexible communication within the brain, which relies on oscillatory activity, is not confined to adult neuronal networks. Experimental evidence has documented the presence of discontinuous patterns of oscillatory activity already during early development. Their highly variable spatial and time-frequency organization has been related to region specificity. However, it might be equally due to the absence of unitary criteria for classifying the early activity patterns, since they have been mainly characterized by visual inspection. Therefore, robust and unbiased methods for categorizing these discontinuous oscillations are needed for increasingly complex data sets from different labs. Here, we introduce an unsupervised detection and classification algorithm for the discontinuous activity patterns of rodents during early development. For this, firstly time windows with discontinuous oscillations vs. epochs of network silence were identified. In a second step, the major features of detected events were identified and processed by principal component analysis for deciding on their contribution to the classification of different oscillatory patterns. Finally, these patterns were categorized using an unsupervised cluster algorithm. The results were validated on manually characterized neonatal spindle bursts, which ubiquitously entrain neocortical areas of rats and mice, and prelimbic nested gamma spindle bursts. Moreover, the algorithm led to satisfactory results for oscillatory events that, due to increased similarity of their features, were more difficult to classify, e.g. during the pre-juvenile developmental period. Based on a linear classification, the optimal number of features to consider increased with the difficulty of detection. This algorithm allows the comparison of neonatal and pre-juvenile oscillatory patterns in their spatial and temporal organization. It might represent a first step for the unbiased elucidation of activity patterns

Supervised and Unsupervised Classification for Pattern Recognition Purposes

Directory of Open Access Journals (Sweden)

Catalina COCIANU

2006-01-01

Full Text Available A cluster analysis task has to identify the grouping trends of data, to decide on the sound clusters as well as to validate somehow the resulted structure. The identification of the grouping tendency existing in a data collection assumes the selection of a framework stated in terms of a mathematical model allowing to express the similarity degree between couples of particular objects, quasi-metrics expressing the similarity between an object an a cluster and between clusters, respectively. In supervised classification, we are provided with a collection of preclassified patterns, and the problem is to label a newly encountered pattern. Typically, the given training patterns are used to learn the descriptions of classes which in turn are used to label a new pattern. The final section of the paper presents a new methodology for supervised learning based on PCA. The classes are represented in the measurement/feature space by a continuous repartitions

Hierarchical classification of dynamically varying radar pulse repetition interval modulation patterns.

Science.gov (United States)

Kauppi, Jukka-Pekka; Martikainen, Kalle; Ruotsalainen, Ulla

2010-12-01

The central purpose of passive signal intercept receivers is to perform automatic categorization of unknown radar signals. Currently, there is an urgent need to develop intelligent classification algorithms for these devices due to emerging complexity of radar waveforms. Especially multifunction radars (MFRs) capable of performing several simultaneous tasks by utilizing complex, dynamically varying scheduled waveforms are a major challenge for automatic pattern classification systems. To assist recognition of complex radar emissions in modern intercept receivers, we have developed a novel method to recognize dynamically varying pulse repetition interval (PRI) modulation patterns emitted by MFRs. We use robust feature extraction and classifier design techniques to assist recognition in unpredictable real-world signal environments. We classify received pulse trains hierarchically which allows unambiguous detection of the subpatterns using a sliding window. Accuracy, robustness and reliability of the technique are demonstrated with extensive simulations using both static and dynamically varying PRI modulation patterns. Copyright © 2010 Elsevier Ltd. All rights reserved.

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

Science.gov (United States)

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.

A Framework to Support Automated Classification and Labeling of Brain Electromagnetic Patterns

Directory of Open Access Journals (Sweden)

Gwen A. Frishkoff

2007-01-01

Full Text Available This paper describes a framework for automated classification and labeling of patterns in electroencephalographic (EEG and magnetoencephalographic (MEG data. We describe recent progress on four goals: 1 specification of rules and concepts that capture expert knowledge of event-related potentials (ERP patterns in visual word recognition; 2 implementation of rules in an automated data processing and labeling stream; 3 data mining techniques that lead to refinement of rules; and 4 iterative steps towards system evaluation and optimization. This process combines top-down, or knowledge-driven, methods with bottom-up, or data-driven, methods. As illustrated here, these methods are complementary and can lead to development of tools for pattern classification and labeling that are robust and conceptually transparent to researchers. The present application focuses on patterns in averaged EEG (ERP data. We also describe efforts to extend our methods to represent patterns in MEG data, as well as EM patterns in source (anatomical space. The broader aim of this work is to design an ontology-based system to support cross-laboratory, cross-paradigm, and cross-modal integration of brain functional data. Tools developed for this project are implemented in MATLAB and are freely available on request.

Heterogeneous patterns enhancing static and dynamic texture classification

International Nuclear Information System (INIS)

Silva, Núbia Rosa da; Martinez Bruno, Odemir

2013-01-01

Some mixtures, such as colloids like milk, blood, and gelatin, have homogeneous appearance when viewed with the naked eye, however, to observe them at the nanoscale is possible to understand the heterogeneity of its components. The same phenomenon can occur in pattern recognition in which it is possible to see heterogeneous patterns in texture images. However, current methods of texture analysis can not adequately describe such heterogeneous patterns. Common methods used by researchers analyse the image information in a global way, taking all its features in an integrated manner. Furthermore, multi-scale analysis verifies the patterns at different scales, but still preserving the homogeneous analysis. On the other hand various methods use textons to represent the texture, breaking texture down into its smallest unit. To tackle this problem, we propose a method to identify texture patterns not small as textons at distinct scales enhancing the separability among different types of texture. We find sub patterns of texture according to the scale and then group similar patterns for a more refined analysis. Tests were performed in four static texture databases and one dynamical one. Results show that our method provide better classification rate compared with conventional approaches both in static and in dynamic texture.

Age group classification and gender detection based on forced expiratory spirometry.

Science.gov (United States)

Cosgun, Sema; Ozbek, I Yucel

2015-08-01

This paper investigates the utility of forced expiratory spirometry (FES) test with efficient machine learning algorithms for the purpose of gender detection and age group classification. The proposed method has three main stages: feature extraction, training of the models and detection. In the first stage, some features are extracted from volume-time curve and expiratory flow-volume loop obtained from FES test. In the second stage, the probabilistic models for each gender and age group are constructed by training Gaussian mixture models (GMMs) and Support vector machine (SVM) algorithm. In the final stage, the gender (or age group) of test subject is estimated by using the trained GMM (or SVM) model. Experiments have been evaluated on a large database from 4571 subjects. The experimental results show that average correct classification rate performance of both GMM and SVM methods based on the FES test is more than 99.3 % and 96.8 % for gender and age group classification, respectively.

Multi-q pattern classification of polarization curves

Science.gov (United States)

Fabbri, Ricardo; Bastos, Ivan N.; Neto, Francisco D. Moura; Lopes, Francisco J. P.; Gonçalves, Wesley N.; Bruno, Odemir M.

2014-02-01

Several experimental measurements are expressed in the form of one-dimensional profiles, for which there is a scarcity of methodologies able to classify the pertinence of a given result to a specific group. The polarization curves that evaluate the corrosion kinetics of electrodes in corrosive media are applications where the behavior is chiefly analyzed from profiles. Polarization curves are indeed a classic method to determine the global kinetics of metallic electrodes, but the strong nonlinearity from different metals and alloys can overlap and the discrimination becomes a challenging problem. Moreover, even finding a typical curve from replicated tests requires subjective judgment. In this paper, we used the so-called multi-q approach based on the Tsallis statistics in a classification engine to separate the multiple polarization curve profiles of two stainless steels. We collected 48 experimental polarization curves in an aqueous chloride medium of two stainless steel types, with different resistance against localized corrosion. Multi-q pattern analysis was then carried out on a wide potential range, from cathodic up to anodic regions. An excellent classification rate was obtained, at a success rate of 90%, 80%, and 83% for low (cathodic), high (anodic), and both potential ranges, respectively, using only 2% of the original profile data. These results show the potential of the proposed approach towards efficient, robust, systematic and automatic classification of highly nonlinear profile curves.

Staining pattern classification of antinuclear autoantibodies based on block segmentation in indirect immunofluorescence images.

Directory of Open Access Journals (Sweden)

Jiaqian Li

Full Text Available Indirect immunofluorescence based on HEp-2 cell substrate is the most commonly used staining method for antinuclear autoantibodies associated with different types of autoimmune pathologies. The aim of this paper is to design an automatic system to identify the staining patterns based on block segmentation compared to the cell segmentation most used in previous research. Various feature descriptors and classifiers are tested and compared in the classification of the staining pattern of blocks and it is found that the technique of the combination of the local binary pattern and the k-nearest neighbor algorithm achieve the best performance. Relying on the results of block pattern classification, experiments on the whole images show that classifier fusion rules are able to identify the staining patterns of the whole well (specimen image with a total accuracy of about 94.62%.

Motor Oil Classification using Color Histograms and Pattern Recognition Techniques.

Science.gov (United States)

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.

Classification of topographical pattern of spasticity in cerebral palsy: a registry perspective.

Science.gov (United States)

Reid, Susan M; Carlin, John B; Reddihough, Dinah S

2011-01-01

This study used data from a population-based cerebral palsy (CP) registry and systematic review to assess the amount of heterogeneity between registries in topographical patterns when dichotomised into unilateral (USCP) and bilateral spastic CP (BSCP), and whether the terms diplegia and quadriplegia provide useful additional epidemiological information. From the Victorian CP Register, 2956 individuals (1658 males, 1298 females), born 1970-2003, with spastic CP were identified. The proportions with each topographical pattern were analysed overall and by gestational age. Binary logistic regression analysis was used to assess temporal trends. For the review, data were systematically collected on topographical patterns from 27 registries. Estimates of heterogeneity were obtained, overall and by region, reporting period and definition of quadriplegia. Among individuals born <32 weeks, 48% had diplegia, whereas the proportion for children born ≥ 32 weeks was 24% (p < 0.001). Evidence was weak for a temporal trend in the relative proportions of USCP and BSCP (p = 0.038), but much clearer for an increase in the proportion of spastic diplegia relative to quadriplegia (p < 0.001). The review revealed wide variations across studies in the proportion of diplegia (range 34-90%) and BSCP (range 51-86%). These findings argue against a topographical classification based solely on laterality. Copyright © 2011 Elsevier Ltd. All rights reserved.

Territorial pattern and classification of soils of Kryvyi Rih Iron-Ore Basin

OpenAIRE

О. О. Dolina; О. М. Smetana

2014-01-01

The authors developed the classification of soils and adapted it to the conditions of Krivyi Rih industrial region. It became the basis for determining the degree of soil cover transformation in the iron-ore basin under technogenesis. The classification represents the system of hierarchical objects of different taxonomic levels. It allows determination of relationships between objects and their properties. Researched patterns of soil cover structures’ distribution were the basis for the relev...

Pattern classification and recognition of invertebrate functional groups using self-organizing neural networks.

Science.gov (United States)

Zhang, WenJun

2007-07-01

Self-organizing neural networks can be used to mimic non-linear systems. The main objective of this study is to make pattern classification and recognition on sampling information using two self-organizing neural network models. Invertebrate functional groups sampled in the irrigated rice field were classified and recognized using one-dimensional self-organizing map and self-organizing competitive learning neural networks. Comparisons between neural network models, distance (similarity) measures, and number of neurons were conducted. The results showed that self-organizing map and self-organizing competitive learning neural network models were effective in pattern classification and recognition of sampling information. Overall the performance of one-dimensional self-organizing map neural network was better than self-organizing competitive learning neural network. The number of neurons could determine the number of classes in the classification. Different neural network models with various distance (similarity) measures yielded similar classifications. Some differences, dependent upon the specific network structure, would be found. The pattern of an unrecognized functional group was recognized with the self-organizing neural network. A relative consistent classification indicated that the following invertebrate functional groups, terrestrial blood sucker; terrestrial flyer; tourist (nonpredatory species with no known functional role other than as prey in ecosystem); gall former; collector (gather, deposit feeder); predator and parasitoid; leaf miner; idiobiont (acarine ectoparasitoid), were classified into the same group, and the following invertebrate functional groups, external plant feeder; terrestrial crawler, walker, jumper or hunter; neustonic (water surface) swimmer (semi-aquatic), were classified into another group. It was concluded that reliable conclusions could be drawn from comparisons of different neural network models that use different distance

Supervised Learning for Visual Pattern Classification

Science.gov (United States)

Zheng, Nanning; Xue, Jianru

This chapter presents an overview of the topics and major ideas of supervised learning for visual pattern classification. Two prevalent algorithms, i.e., the support vector machine (SVM) and the boosting algorithm, are briefly introduced. SVMs and boosting algorithms are two hot topics of recent research in supervised learning. SVMs improve the generalization of the learning machine by implementing the rule of structural risk minimization (SRM). It exhibits good generalization even when little training data are available for machine training. The boosting algorithm can boost a weak classifier to a strong classifier by means of the so-called classifier combination. This algorithm provides a general way for producing a classifier with high generalization capability from a great number of weak classifiers.

Classification of time series patterns from complex dynamic systems

Energy Technology Data Exchange (ETDEWEB)

Schryver, J.C.; Rao, N.

1998-07-01

An increasing availability of high-performance computing and data storage media at decreasing cost is making possible the proliferation of large-scale numerical databases and data warehouses. Numeric warehousing enterprises on the order of hundreds of gigabytes to terabytes are a reality in many fields such as finance, retail sales, process systems monitoring, biomedical monitoring, surveillance and transportation. Large-scale databases are becoming more accessible to larger user communities through the internet, web-based applications and database connectivity. Consequently, most researchers now have access to a variety of massive datasets. This trend will probably only continue to grow over the next several years. Unfortunately, the availability of integrated tools to explore, analyze and understand the data warehoused in these archives is lagging far behind the ability to gain access to the same data. In particular, locating and identifying patterns of interest in numerical time series data is an increasingly important problem for which there are few available techniques. Temporal pattern recognition poses many interesting problems in classification, segmentation, prediction, diagnosis and anomaly detection. This research focuses on the problem of classification or characterization of numerical time series data. Highway vehicles and their drivers are examples of complex dynamic systems (CDS) which are being used by transportation agencies for field testing to generate large-scale time series datasets. Tools for effective analysis of numerical time series in databases generated by highway vehicle systems are not yet available, or have not been adapted to the target problem domain. However, analysis tools from similar domains may be adapted to the problem of classification of numerical time series data.

A Study of Hand Back Skin Texture Patterns for Personal Identification and Gender Classification

Directory of Open Access Journals (Sweden)

Jin Xie

2012-06-01

Full Text Available Human hand back skin texture (HBST is often consistent for a person and distinctive from person to person. In this paper, we study the HBST pattern recognition problem with applications to personal identification and gender classification. A specially designed system is developed to capture HBST images, and an HBST image database was established, which consists of 1,920 images from 80 persons (160 hands. An efficient texton learning based method is then presented to classify the HBST patterns. First, textons are learned in the space of filter bank responses from a set of training images using the -minimization based sparse representation (SR technique. Then, under the SR framework, we represent the feature vector at each pixel over the learned dictionary to construct a representation coefficient histogram. Finally, the coefficient histogram is used as skin texture feature for classification. Experiments on personal identification and gender classification are performed by using the established HBST database. The results show that HBST can be used to assist human identification and gender classification.

Proposal of new classification of femoral trochanteric fracture by three-dimensional computed tomography and relationship to usual plain X-ray classification.

Science.gov (United States)

Shoda, Etsuo; Kitada, Shimpei; Sasaki, Yu; Hirase, Hitoshi; Niikura, Takahiro; Lee, Sang Yang; Sakurai, Atsushi; Oe, Keisuke; Sasaki, Takeharu

2017-01-01

Classification of femoral trochanteric fractures is usually based on plain X-ray findings using the Evans, Jensen, or AO/OTA classification. However, complications such as nonunion and cut out of the lag screw or blade are seen even in stable fracture. This may be due to the difficulty of exact diagnosis of fracture pattern in plain X-ray. Computed tomography (CT) may provide more information about the fracture pattern, but such data are scarce. In the present study, it was performed to propose a classification system for femoral trochanteric fractures using three-dimensional CT (3D-CT) and investigate the relationship between this classification and conventional plain X-ray classification. Using three-dimensional (3D)-CT, fractures were classified as two, three, or four parts using combinations of the head, greater trochanter, lesser trochanter, and shaft. We identified five subgroups of three-part fractures according to the fracture pattern involving the greater and lesser trochanters. In total, 239 femoral trochanteric fractures (45 men, 194 women; average age, 84.4 years) treated in four hospitals were classified using our 3D-CT classification. The relationship between this 3D-CT classification and the AO/OTA, Evans, and Jensen X-ray classifications was investigated. In the 3D-CT classification, many fractures exhibited a large oblique fragment of the greater trochanter including the lesser trochanter. This fracture type was recognized as unstable in the 3D-CT classification but was often classified as stable in each X-ray classification. It is difficult to evaluate fracture patterns involving the greater trochanter, especially large oblique fragments including the lesser trochanter, using plain X-rays. The 3D-CT shows the fracture line very clearly, making it easy to classify the fracture pattern.

Multivariate pattern classification reveals autonomic and experiential representations of discrete emotions.

Science.gov (United States)

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.

Inattention in primary school is not good for your future school achievement-A pattern classification study.

Directory of Open Access Journals (Sweden)

Astri J Lundervold

Full Text Available Inattention in childhood is associated with academic problems later in life. The contribution of specific aspects of inattentive behaviour is, however, less known. We investigated feature importance of primary school teachers' reports on nine aspects of inattentive behaviour, gender and age in predicting future academic achievement. Primary school teachers of n = 2491 children (7-9 years rated nine items reflecting different aspects of inattentive behaviour in 2002. A mean academic achievement score from the previous semester in high school (2012 was available for each youth from an official school register. All scores were at a categorical level. Feature importances were assessed by using multinominal logistic regression, classification and regression trees analysis, and a random forest algorithm. Finally, a comprehensive pattern classification procedure using k-fold cross-validation was implemented. Overall, inattention was rated as more severe in boys, who also obtained lower academic achievement scores in high school than girls. Problems related to sustained attention and distractibility were together with age and gender defined as the most important features to predict future achievement scores. Using these four features as input to a collection of classifiers employing k-fold cross-validation for prediction of academic achievement level, we obtained classification accuracy, precision and recall that were clearly better than chance levels. Primary school teachers' reports of problems related to sustained attention and distractibility were identified as the two most important features of inattentive behaviour predicting academic achievement in high school. Identification and follow-up procedures of primary school children showing these characteristics should be prioritised to prevent future academic failure.

Inattention in primary school is not good for your future school achievement-A pattern classification study.

Science.gov (United States)

Lundervold, Astri J; Bøe, Tormod; Lundervold, Arvid

2017-01-01

Inattention in childhood is associated with academic problems later in life. The contribution of specific aspects of inattentive behaviour is, however, less known. We investigated feature importance of primary school teachers' reports on nine aspects of inattentive behaviour, gender and age in predicting future academic achievement. Primary school teachers of n = 2491 children (7-9 years) rated nine items reflecting different aspects of inattentive behaviour in 2002. A mean academic achievement score from the previous semester in high school (2012) was available for each youth from an official school register. All scores were at a categorical level. Feature importances were assessed by using multinominal logistic regression, classification and regression trees analysis, and a random forest algorithm. Finally, a comprehensive pattern classification procedure using k-fold cross-validation was implemented. Overall, inattention was rated as more severe in boys, who also obtained lower academic achievement scores in high school than girls. Problems related to sustained attention and distractibility were together with age and gender defined as the most important features to predict future achievement scores. Using these four features as input to a collection of classifiers employing k-fold cross-validation for prediction of academic achievement level, we obtained classification accuracy, precision and recall that were clearly better than chance levels. Primary school teachers' reports of problems related to sustained attention and distractibility were identified as the two most important features of inattentive behaviour predicting academic achievement in high school. Identification and follow-up procedures of primary school children showing these characteristics should be prioritised to prevent future academic failure.

Classification

Science.gov (United States)

Clary, Renee; Wandersee, James

2013-01-01

In this article, Renee Clary and James Wandersee describe the beginnings of "Classification," which lies at the very heart of science and depends upon pattern recognition. Clary and Wandersee approach patterns by first telling the story of the "Linnaean classification system," introduced by Carl Linnacus (1707-1778), who is…

The influence of different classification standards of age groups on prognosis in high-grade hemispheric glioma patients.

Science.gov (United States)

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.

Rare idiopathic intestinal pneumonias (IIPs) and histologic patterns in new ATS/ERS multidisciplinary classification of the IIPs

International Nuclear Information System (INIS)

Johkoh, Takeshi; Fukuoka, Junya; Tanaka, Tomonori

2015-01-01

Highlights: •The new (ATS/ERS) update to the multidisciplinary classification of idiopathic interstitial pneumonias (IIPs) defines both rare IIPs and rare histologic patterns of IIPs. •Rare IIPs; lymphoid interstitial pneumonia, pleuroparenchymal fibroelastosis. •Rare histologic pattern, acute fibrionous organizing pneumonia, bronchocentric pattern of interstitial pneumonia. -- Abstract: The new American Thoracic Society/European Respiratory Society (ATS/ERS) update to the multidisciplinary classification of idiopathic interstitial pneumonias (IIPs) defines both rare IIPs and rare histologic patterns of IIPs. Although these diseases are rare, each has some distinguishing imaging and pathologic characteristics. Common findings for IIPs in computed tomography (CT) include cysts in lymphoid interstitial pneumonia (LIP), upper lobe subpleural consolidation in pleuropulmonary fibroelastosis (PPFE), symmetrical consolidation in acute fibrinous organizing pneumonia (AFOP), and peribronchovascular consolidation or centrilobular nodules in bronchiolocentric pattern of interstitial pneumonia

Rare idiopathic intestinal pneumonias (IIPs) and histologic patterns in new ATS/ERS multidisciplinary classification of the IIPs

Energy Technology Data Exchange (ETDEWEB)

Johkoh, Takeshi, E-mail: johkoht@aol.com [Department of Radiology, Kinki Central Hospital of Mutual Aid Association of Public School Teachers (Japan); Fukuoka, Junya, E-mail: fukuokaj@nagasaki-u.ac.jp [Department of Pathology, Nagasaki University Graduate School of Biomedical Sciences (Japan); Tanaka, Tomonori, E-mail: yotsudukayama@yahoo.com [Department of Pathology, Nagasaki University Graduate School of Biomedical Sciences (Japan)

2015-03-15

Highlights: •The new (ATS/ERS) update to the multidisciplinary classification of idiopathic interstitial pneumonias (IIPs) defines both rare IIPs and rare histologic patterns of IIPs. •Rare IIPs; lymphoid interstitial pneumonia, pleuroparenchymal fibroelastosis. •Rare histologic pattern, acute fibrionous organizing pneumonia, bronchocentric pattern of interstitial pneumonia. -- Abstract: The new American Thoracic Society/European Respiratory Society (ATS/ERS) update to the multidisciplinary classification of idiopathic interstitial pneumonias (IIPs) defines both rare IIPs and rare histologic patterns of IIPs. Although these diseases are rare, each has some distinguishing imaging and pathologic characteristics. Common findings for IIPs in computed tomography (CT) include cysts in lymphoid interstitial pneumonia (LIP), upper lobe subpleural consolidation in pleuropulmonary fibroelastosis (PPFE), symmetrical consolidation in acute fibrinous organizing pneumonia (AFOP), and peribronchovascular consolidation or centrilobular nodules in bronchiolocentric pattern of interstitial pneumonia.

Pattern Classification Using an Olfactory Model with PCA Feature Selection in Electronic Noses: Study and Application

Directory of Open Access Journals (Sweden)

Junbao Zheng

2012-03-01

Full Text Available Biologically-inspired models and algorithms are considered as promising sensor array signal processing methods for electronic noses. Feature selection is one of the most important issues for developing robust pattern recognition models in machine learning. This paper describes an investigation into the classification performance of a bionic olfactory model with the increase of the dimensions of input feature vector (outer factor as well as its parallel channels (inner factor. The principal component analysis technique was applied for feature selection and dimension reduction. Two data sets of three classes of wine derived from different cultivars and five classes of green tea derived from five different provinces of China were used for experiments. In the former case the results showed that the average correct classification rate increased as more principal components were put in to feature vector. In the latter case the results showed that sufficient parallel channels should be reserved in the model to avoid pattern space crowding. We concluded that 6~8 channels of the model with principal component feature vector values of at least 90% cumulative variance is adequate for a classification task of 3~5 pattern classes considering the trade-off between time consumption and classification rate.

Real-Time Subject-Independent Pattern Classification of Overt and Covert Movements from fNIRS Signals.

Directory of Open Access Journals (Sweden)

Neethu Robinson

Full Text Available Recently, studies have reported the use of Near Infrared Spectroscopy (NIRS for developing Brain-Computer Interface (BCI by applying online pattern classification of brain states from subject-specific fNIRS signals. The purpose of the present study was to develop and test a real-time method for subject-specific and subject-independent classification of multi-channel fNIRS signals using support-vector machines (SVM, so as to determine its feasibility as an online neurofeedback system. Towards this goal, we used left versus right hand movement execution and movement imagery as study paradigms in a series of experiments. In the first two experiments, activations in the motor cortex during movement execution and movement imagery were used to develop subject-dependent models that obtained high classification accuracies thereby indicating the robustness of our classification method. In the third experiment, a generalized classifier-model was developed from the first two experimental data, which was then applied for subject-independent neurofeedback training. Application of this method in new participants showed mean classification accuracy of 63% for movement imagery tasks and 80% for movement execution tasks. These results, and their corresponding offline analysis reported in this study demonstrate that SVM based real-time subject-independent classification of fNIRS signals is feasible. This method has important applications in the field of hemodynamic BCIs, and neuro-rehabilitation where patients can be trained to learn spatio-temporal patterns of healthy brain activity.

Classification of Porcine Cranial Fracture Patterns Using a Fracture Printing Interface,.

Science.gov (United States)

Wei, Feng; Bucak, Serhat Selçuk; Vollner, Jennifer M; Fenton, Todd W; Jain, Anil K; Haut, Roger C

2017-01-01

Distinguishing between accidental and abusive head trauma in children can be difficult, as there is a lack of baseline data for pediatric cranial fracture patterns. A porcine head model has recently been developed and utilized in a series of studies to investigate the effects of impact energy level, surface type, and constraint condition on cranial fracture patterns. In the current study, an automated pattern recognition method, or a fracture printing interface (FPI), was developed to classify cranial fracture patterns that were associated with different impact scenarios documented in previous experiments. The FPI accurately predicted the energy level when the impact surface type was rigid. Additionally, the FPI was exceedingly successful in determining fractures caused by skulls being dropped with a high-level energy (97% accuracy). The FPI, currently developed on the porcine data, may in the future be transformed to the task of cranial fracture pattern classification for human infant skulls. © 2016 American Academy of Forensic Sciences.

Ethnicity prediction and classification from iris texture patterns: A survey on recent advances

CSIR Research Space (South Africa)

Mabuza-Hocquet, Gugulethu

2017-03-01

Full Text Available The prediction and classification of ethnicity based on iris texture patterns using image processing, artificial intelligence and computer vision techniques is still a recent topic in iris biometrics. While the large body of knowledge and research...

Classification of hydrocephalus: critical analysis of classification categories and advantages of "Multi-categorical Hydrocephalus Classification" (Mc HC).

Science.gov (United States)

Oi, Shizuo

2011-10-01

Hydrocephalus is a complex pathophysiology with disturbed cerebrospinal fluid (CSF) circulation. There are numerous numbers of classification trials published focusing on various criteria, such as associated anomalies/underlying lesions, CSF circulation/intracranial pressure patterns, clinical features, and other categories. However, no definitive classification exists comprehensively to cover the variety of these aspects. The new classification of hydrocephalus, "Multi-categorical Hydrocephalus Classification" (Mc HC), was invented and developed to cover the entire aspects of hydrocephalus with all considerable classification items and categories. Ten categories include "Mc HC" category I: onset (age, phase), II: cause, III: underlying lesion, IV: symptomatology, V: pathophysiology 1-CSF circulation, VI: pathophysiology 2-ICP dynamics, VII: chronology, VII: post-shunt, VIII: post-endoscopic third ventriculostomy, and X: others. From a 100-year search of publication related to the classification of hydrocephalus, 14 representative publications were reviewed and divided into the 10 categories. The Baumkuchen classification graph made from the round o'clock classification demonstrated the historical tendency of deviation to the categories in pathophysiology, either CSF or ICP dynamics. In the preliminary clinical application, it was concluded that "Mc HC" is extremely effective in expressing the individual state with various categories in the past and present condition or among the compatible cases of hydrocephalus along with the possible chronological change in the future.

Age Patterns in Risk Taking Across the World.

Science.gov (United States)

Duell, Natasha; Steinberg, Laurence; Icenogle, Grace; Chein, Jason; Chaudhary, Nandita; Di Giunta, Laura; Dodge, Kenneth A; Fanti, Kostas A; Lansford, Jennifer E; Oburu, Paul; Pastorelli, Concetta; Skinner, Ann T; Sorbring, Emma; Tapanya, Sombat; Uribe Tirado, Liliana Maria; Alampay, Liane Peña; Al-Hassan, Suha M; Takash, Hanan M S; Bacchini, Dario; Chang, Lei

2018-05-01

Epidemiological data indicate that risk behaviors are among the leading causes of adolescent morbidity and mortality worldwide. Consistent with this, laboratory-based studies of age differences in risk behavior allude to a peak in adolescence, suggesting that adolescents demonstrate a heightened propensity, or inherent inclination, to take risks. Unlike epidemiological reports, studies of risk taking propensity have been limited to Western samples, leaving questions about the extent to which heightened risk taking propensity is an inherent or culturally constructed aspect of adolescence. In the present study, age patterns in risk-taking propensity (using two laboratory tasks: the Stoplight and the BART) and real-world risk taking (using self-reports of health and antisocial risk taking) were examined in a sample of 5227 individuals (50.7% female) ages 10-30 (M = 17.05 years, SD = 5.91) from 11 Western and non-Western countries (China, Colombia, Cyprus, India, Italy, Jordan, Kenya, the Philippines, Sweden, Thailand, and the US). Two hypotheses were tested: (1) risk taking follows an inverted-U pattern across age groups, peaking earlier on measures of risk taking propensity than on measures of real-world risk taking, and (2) age patterns in risk taking propensity are more consistent across countries than age patterns in real-world risk taking. Overall, risk taking followed the hypothesized inverted-U pattern across age groups, with health risk taking evincing the latest peak. Age patterns in risk taking propensity were more consistent across countries than age patterns in real-world risk taking. Results suggest that although the association between age and risk taking is sensitive to measurement and culture, around the world, risk taking is generally highest among late adolescents.

An artificial intelligence based improved classification of two-phase flow patterns with feature extracted from acquired images.

Science.gov (United States)

Shanthi, C; Pappa, N

2017-05-01

Flow pattern recognition is necessary to select design equations for finding operating details of the process and to perform computational simulations. Visual image processing can be used to automate the interpretation of patterns in two-phase flow. In this paper, an attempt has been made to improve the classification accuracy of the flow pattern of gas/ liquid two- phase flow using fuzzy logic and Support Vector Machine (SVM) with Principal Component Analysis (PCA). The videos of six different types of flow patterns namely, annular flow, bubble flow, churn flow, plug flow, slug flow and stratified flow are recorded for a period and converted to 2D images for processing. The textural and shape features extracted using image processing are applied as inputs to various classification schemes namely fuzzy logic, SVM and SVM with PCA in order to identify the type of flow pattern. The results obtained are compared and it is observed that SVM with features reduced using PCA gives the better classification accuracy and computationally less intensive than other two existing schemes. This study results cover industrial application needs including oil and gas and any other gas-liquid two-phase flows. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

The classification of frontal sinus pneumatization patterns by CT-based volumetry.

Science.gov (United States)

Yüksel Aslier, Nesibe Gül; Karabay, Nuri; Zeybek, Gülşah; Keskinoğlu, Pembe; Kiray, Amaç; Sütay, Semih; Ecevit, Mustafa Cenk

2016-10-01

We aimed to define the classification of frontal sinus pneumatization patterns according to three-dimensional volume measurements. Datasets of 148 sides of 74 dry skulls were generated by the computerized tomography-based volumetry to measure frontal sinus volumes. The cutoff points for frontal sinus hypoplasia and hyperplasia were tested by ROC curve analysis and the validity of the diagnostic points was measured. The overall frequencies were 4.1, 14.2, 37.2 and 44.5 % for frontal sinus aplasia, hypoplasia, medium size and hyperplasia, respectively. The aplasia was bilateral in all three skulls. Hypoplasia was seen 76 % at the right side and hyperplasia was seen 56 % at the left side. The cutoff points for diagnosing frontal sinus hypoplasia and hyperplasia were '1131.25 mm(3)' (95.2 % sensitivity and 100 % specificity) and '3328.50 mm(3)' (88 % sensitivity and 86 % specificity), respectively. The findings provided in the present study, which define frontal sinus pneumatization patterns by CT-based volumetry, proved that two opposite sides of the frontal sinuses are asymmetric and three-dimensional classification should be developed by CT-based volumetry, because two-dimensional evaluations lack depth measurement.

Classification of natural circulation two-phase flow patterns using fuzzy inference on image analysis

International Nuclear Information System (INIS)

Mesquita, R.N. de; Masotti, P.H.F.; Penha, R.M.L.; Andrade, D.A.; Sabundjian, G.; Torres, W.M.

2012-01-01

Highlights: ► A fuzzy classification system for two-phase flow instability patterns is developed. ► Flow patterns are classified based on images of natural circulation experiments. ► Fuzzy inference is optimized to use single grayscale profiles as input. - Abstract: Two-phase flow on natural circulation phenomenon has been an important theme on recent studies related to nuclear reactor designs. The accuracy of heat transfer estimation has been improved with new models that require precise prediction of pattern transitions of flow. In this work, visualization of natural circulation cycles is used to study two-phase flow patterns associated with phase transients and static instabilities of flow. A Fuzzy Flow-type Classification System (FFCS) was developed to classify these patterns based only on image extracted features. Image acquisition and temperature measurements were simultaneously done. Experiments in natural circulation facility were adjusted to generate a series of characteristic two-phase flow instability periodic cycles. The facility is composed of a loop of glass tubes, a heat source using electrical heaters, a cold source using a helicoidal heat exchanger, a visualization section and thermocouples positioned over different loop sections. The instability cyclic period is estimated based on temperature measurements associated with the detection of a flow transition image pattern. FFCS shows good results provided that adequate image acquisition parameters and pre-processing adjustments are used.

AGE GROUP CLASSIFICATION USING MACHINE LEARNING TECHNIQUES

OpenAIRE

Arshdeep Singh Syal*1 & Abhinav Gupta2

2017-01-01

A human face provides a lot of information that allows another person to identify characteristics such as age, sex, etc. Therefore, the challenge is to develop an age group prediction system using the automatic learning method. The task of estimating the age group of the human from their frontal facial images is very captivating, but also challenging because of the pattern of personalized and non-linear aging that differs from one person to another. This paper examines the problem of predicti...

Fetal functional brain age assessed from universal developmental indices obtained from neuro-vegetative activity patterns.

Directory of Open Access Journals (Sweden)

Dirk Hoyer

Full Text Available Fetal brain development involves the development of the neuro-vegetative (autonomic control that is mediated by the autonomic nervous system (ANS. Disturbances of the fetal brain development have implications for diseases in later postnatal life. In that context, the fetal functional brain age can be altered. Universal principles of developmental biology applied to patterns of autonomic control may allow a functional age assessment. The work aims at the development of a fetal autonomic brain age score (fABAS based on heart rate patterns. We analysed n = 113 recordings in quiet sleep, n = 286 in active sleep, and n = 29 in active awakeness from normals. We estimated fABAS from magnetocardiographic recordings (21.4-40.3 weeks of gestation preclassified in quiet sleep (n = 113, 63 females and active sleep (n = 286, 145 females state by cross-validated multivariate linear regression models in a cross-sectional study. According to universal system developmental principles, we included indices that address increasing fluctuation range, increasing complexity, and pattern formation (skewness, power spectral ratio VLF/LF, pNN5. The resulting models constituted fABAS. fABAS explained 66/63% (coefficient of determination R(2 of training and validation set of the variance by age in quiet, while 51/50% in active sleep. By means of a logistic regression model using fluctuation range and fetal age, quiet and active sleep were automatically reclassified (94.3/93.1% correct classifications. We did not find relevant gender differences. We conclude that functional brain age can be assessed based on universal developmental indices obtained from autonomic control patterns. fABAS reflect normal complex functional brain maturation. The presented normative data are supplemented by an explorative study of 19 fetuses compromised by intrauterine growth restriction. We observed a shift in the state distribution towards active awakeness. The lower WGA

Median Robust Extended Local Binary Pattern for Texture Classification.

Science.gov (United States)

Liu, Li; Lao, Songyang; Fieguth, Paul W; Guo, Yulan; Wang, Xiaogang; Pietikäinen, Matti

2016-03-01

Local binary patterns (LBP) are considered among the most computationally efficient high-performance texture features. However, the LBP method is very sensitive to image noise and is unable to capture macrostructure information. To best address these disadvantages, in this paper, we introduce a novel descriptor for texture classification, the median robust extended LBP (MRELBP). Different from the traditional LBP and many LBP variants, MRELBP compares regional image medians rather than raw image intensities. A multiscale LBP type descriptor is computed by efficiently comparing image medians over a novel sampling scheme, which can capture both microstructure and macrostructure texture information. A comprehensive evaluation on benchmark data sets reveals MRELBP's high performance-robust to gray scale variations, rotation changes and noise-but at a low computational cost. MRELBP produces the best classification scores of 99.82%, 99.38%, and 99.77% on three popular Outex test suites. More importantly, MRELBP is shown to be highly robust to image noise, including Gaussian noise, Gaussian blur, salt-and-pepper noise, and random pixel corruption.

Fingerprint classification using a simplified rule-set based on directional patterns and singularity features

CSIR Research Space (South Africa)

Dorasamy, K

2015-07-01

Full Text Available The use of directional patterns has recently received more attention in fingerprint classification. It provides a global representation of a fingerprint, by dividing it into homogeneous orientation partitions. With this technique, the challenge...

How Transferable are CNN-based Features for Age and Gender Classification?

OpenAIRE

Özbulak, Gökhan; Aytar, Yusuf; Ekenel, Hazım Kemal

2016-01-01

Age and gender are complementary soft biometric traits for face recognition. Successful estimation of age and gender from facial images taken under real-world conditions can contribute improving the identification results in the wild. In this study, in order to achieve robust age and gender classification in the wild, we have benefited from Deep Convolutional Neural Networks based representation. We have explored transferability of existing deep convolutional neural network (CNN) models for a...

Parametric classification of handvein patterns based on texture features

Science.gov (United States)

Al Mahafzah, Harbi; Imran, Mohammad; Supreetha Gowda H., D.

2018-04-01

In this paper, we have developed Biometric recognition system adopting hand based modality Handvein,which has the unique pattern for each individual and it is impossible to counterfeit and fabricate as it is an internal feature. We have opted in choosing feature extraction algorithms such as LBP-visual descriptor, LPQ-blur insensitive texture operator, Log-Gabor-Texture descriptor. We have chosen well known classifiers such as KNN and SVM for classification. We have experimented and tabulated results of single algorithm recognition rate for Handvein under different distance measures and kernel options. The feature level fusion is carried out which increased the performance level.

Territorial pattern and classification of soils of Kryvyi Rih Iron-Ore Basin

Directory of Open Access Journals (Sweden)

О. О. Dolina

2014-10-01

Full Text Available The authors developed the classification of soils and adapted it to the conditions of Krivyi Rih industrial region. It became the basis for determining the degree of soil cover transformation in the iron-ore basin under technogenesis. The classification represents the system of hierarchical objects of different taxonomic levels. It allows determination of relationships between objects and their properties. Researched patterns of soil cover structures’ distribution were the basis for the relevant mapping and classification of soils. The classification is adapted to highly-influential industrial conditions of soils formation in the region. The adaptation measures were specific classification levels and units, which provided more detailed differentiation of soils. The authors proposed to separate the soils by the degree of soil formation potential realization for super-divisions. The potential determination allowed predicting the outcome of soil formation and identification of transformation degree of soil cover structures in the region. The results indicated that the main type of soil structures in the industrial region was represented by primitive soils (indicated as a separate type. These soils were determined as dynamic elements in the structure of industrial region soil cover. The article indicated that presence of soil cover structures with the domination of technogenic soils, particularly post-technogenic soils, was the marker of the soil cover in Krivyi Rih Iron-Ore Basin

A novel classification system for aging theories

Directory of Open Access Journals (Sweden)

Lucas Siqueira Trindade

2013-03-01

Full Text Available Theories of lifespan evolution are a source of confusion amongst aging researchers. After a century of aging research the dispute over whether the aging process is active or passive persists and a comprehensive and universally accepted theoretical model remains elusive. Evolutionary aging theories primarily dispute whether the aging process is exclusively adapted to favor the kin or exclusively non-adapted to favor the individual. Interestingly, contradictory data and theories supporting both exclusively programmed and exclusively non-programmed theories continue to grow. However, this is a false dichotomy; natural selection favors traits resulting in efficient reproduction whether they benefit the individual or the kin. Thus, to understand the evolution of aging, first we must understand the environment-dependent balance between the advantages and disadvantages of extended lifespan in the process of spreading genes. As described by distinct theories, different niches and environmental conditions confer on extended lifespan a range of fitness values varying from highly beneficial to highly detrimental. Here, we considered the range of fitness values for extended lifespan and develop a fitness-based framework for categorizing existing theories. We show that all theories can be classified into four basic types: secondary (beneficial, maladaptive (neutral, assisted death (detrimental and senemorphic aging (varying between beneficial to detrimental. We anticipate that this classification system will assist with understanding and interpreting aging/death by providing a way of considering theories as members of one of these classes rather than consideration of their individual details.

Applying a Machine Learning Technique to Classification of Japanese Pressure Patterns

Directory of Open Access Journals (Sweden)

H Kimura

2009-04-01

Full Text Available In climate research, pressure patterns are often very important. When a climatologists need to know the days of a specific pressure pattern, for example "low pressure in Western areas of Japan and high pressure in Eastern areas of Japan (Japanese winter-type weather," they have to visually check a huge number of surface weather charts. To overcome this problem, we propose an automatic classification system using a support vector machine (SVM, which is a machine-learning method. We attempted to classify pressure patterns into two classes: "winter type" and "non-winter type". For both training datasets and test datasets, we used the JRA-25 dataset from 1981 to 2000. An experimental evaluation showed that our method obtained a greater than 0.8 F-measure. We noted that variations in results were based on differences in training datasets.

Enhancement of force patterns classification based on Gaussian distributions.

Science.gov (United States)

Ertelt, Thomas; Solomonovs, Ilja; Gronwald, Thomas

2018-01-23

Description of the patterns of ground reaction force is a standard method in areas such as medicine, biomechanics and robotics. The fundamental parameter is the time course of the force, which is classified visually in particular in the field of clinical diagnostics. Here, the knowledge and experience of the diagnostician is relevant for its assessment. For an objective and valid discrimination of the ground reaction force pattern, a generic method, especially in the medical field, is absolutely necessary to describe the qualities of the time-course. The aim of the presented method was to combine the approaches of two existing procedures from the fields of machine learning and the Gauss approximation in order to take advantages of both methods for the classification of ground reaction force patterns. The current limitations of both methods could be eliminated by an overarching method. Twenty-nine male athletes from different sports were examined. Each participant was given the task of performing a one-legged stopping maneuver on a force plate from the maximum possible starting speed. The individual time course of the ground reaction force of each subject was registered and approximated on the basis of eight Gaussian distributions. The descriptive coefficients were then classified using Bayesian regulated neural networks. The different sports served as the distinguishing feature. Although the athletes were all given the same task, all sports referred to a different quality in the time course of ground reaction force. Meanwhile within each sport, the athletes were homogeneous. With an overall prediction (R = 0.938) all subjects/sports were classified correctly with 94.29% accuracy. The combination of the two methods: the mathematical description of the time course of ground reaction forces on the basis of Gaussian distributions and their classification by means of Bayesian regulated neural networks, seems an adequate and promising method to discriminate the

Pediatric Biopharmaceutical Classification System: Using Age-Appropriate Initial Gastric Volume.

Science.gov (United States)

Shawahna, Ramzi

2016-05-01

Development of optimized pediatric formulations for oral administration can be challenging, time consuming, and financially intensive process. Since its inception, the biopharmaceutical classification system (BCS) has facilitated the development of oral drug formulations destined for adults. At least theoretically, the BCS principles are applied also to pediatrics. A comprehensive age-appropriate BCS has not been fully developed. The objective of this work was to provisionally classify oral drugs listed on the latest World Health Organization's Essential Medicines List for Children into an age-appropriate BCS. A total of 38 orally administered drugs were included in this classification. Dose numbers were calculated using age-appropriate initial gastric volume for neonates, 6-month-old infants, and children aging 1 year through adulthood. Using age-appropriate initial gastric volume and British National Formulary age-specific dosing recommendations in the calculation of dose numbers, the solubility classes shifted from low to high in pediatric subpopulations of 12 years and older for amoxicillin, 5 years, 12 years and older for cephalexin, 9 years and older for chloramphenicol, 3-4 years, 9-11 and 15 years and older for diazepam, 18 years and older (adult) for doxycycline and erythromycin, 8 years and older for phenobarbital, 10 years and older for prednisolone, and 15 years and older for trimethoprim. Pediatric biopharmaceutics are not fully understood where several knowledge gaps have been recently emphasized. The current biowaiver criteria are not suitable for safe application in all pediatric populations.

Fusion of fuzzy statistical distributions for classification of thyroid ultrasound patterns.

Science.gov (United States)

Iakovidis, Dimitris K; Keramidas, Eystratios G; Maroulis, Dimitris

2010-09-01

This paper proposes a novel approach for thyroid ultrasound pattern representation. Considering that texture and echogenicity are correlated with thyroid malignancy, the proposed approach encodes these sonographic features via a noise-resistant representation. This representation is suitable for the discrimination of nodules of high malignancy risk from normal thyroid parenchyma. The material used in this study includes a total of 250 thyroid ultrasound patterns obtained from 75 patients in Greece. The patterns are represented by fused vectors of fuzzy features. Ultrasound texture is represented by fuzzy local binary patterns, whereas echogenicity is represented by fuzzy intensity histograms. The encoded thyroid ultrasound patterns are discriminated by support vector classifiers. The proposed approach was comprehensively evaluated using receiver operating characteristics (ROCs). The results show that the proposed fusion scheme outperforms previous thyroid ultrasound pattern representation methods proposed in the literature. The best classification accuracy was obtained with a polynomial kernel support vector machine, and reached 97.5% as estimated by the area under the ROC curve. The fusion of fuzzy local binary patterns and fuzzy grey-level histogram features is more effective than the state of the art approaches for the representation of thyroid ultrasound patterns and can be effectively utilized for the detection of nodules of high malignancy risk in the context of an intelligent medical system. Copyright (c) 2010 Elsevier B.V. All rights reserved.

Transfer Kernel Common Spatial Patterns for Motor Imagery Brain-Computer Interface Classification

Science.gov (United States)

Dai, Mengxi; Liu, Shucong; Zhang, Pengju

2018-01-01

Motor-imagery-based brain-computer interfaces (BCIs) commonly use the common spatial pattern (CSP) as preprocessing step before classification. The CSP method is a supervised algorithm. Therefore a lot of time-consuming training data is needed to build the model. To address this issue, one promising approach is transfer learning, which generalizes a learning model can extract discriminative information from other subjects for target classification task. To this end, we propose a transfer kernel CSP (TKCSP) approach to learn a domain-invariant kernel by directly matching distributions of source subjects and target subjects. The dataset IVa of BCI Competition III is used to demonstrate the validity by our proposed methods. In the experiment, we compare the classification performance of the TKCSP against CSP, CSP for subject-to-subject transfer (CSP SJ-to-SJ), regularizing CSP (RCSP), stationary subspace CSP (ssCSP), multitask CSP (mtCSP), and the combined mtCSP and ssCSP (ss + mtCSP) method. The results indicate that the superior mean classification performance of TKCSP can achieve 81.14%, especially in case of source subjects with fewer number of training samples. Comprehensive experimental evidence on the dataset verifies the effectiveness and efficiency of the proposed TKCSP approach over several state-of-the-art methods. PMID:29743934

Index finger motor imagery EEG pattern recognition in BCI applications using dictionary cleaned sparse representation-based classification for healthy people

Science.gov (United States)

Miao, Minmin; Zeng, Hong; Wang, Aimin; Zhao, Fengkui; Liu, Feixiang

2017-09-01

Electroencephalogram (EEG)-based motor imagery (MI) brain-computer interface (BCI) has shown its effectiveness for the control of rehabilitation devices designed for large body parts of the patients with neurologic impairments. In order to validate the feasibility of using EEG to decode the MI of a single index finger and constructing a BCI-enhanced finger rehabilitation system, we collected EEG data during right hand index finger MI and rest state for five healthy subjects and proposed a pattern recognition approach for classifying these two mental states. First, Fisher's linear discriminant criteria and power spectral density analysis were used to analyze the event-related desynchronization patterns. Second, both band power and approximate entropy were extracted as features. Third, aiming to eliminate the abnormal samples in the dictionary and improve the classification performance of the conventional sparse representation-based classification (SRC) method, we proposed a novel dictionary cleaned sparse representation-based classification (DCSRC) method for final classification. The experimental results show that the proposed DCSRC method gives better classification accuracies than SRC and an average classification accuracy of 81.32% is obtained for five subjects. Thus, it is demonstrated that single right hand index finger MI can be decoded from the sensorimotor rhythms, and the feature patterns of index finger MI and rest state can be well recognized for robotic exoskeleton initiation.

High-speed classification of coherent X-ray diffraction patterns on the K computer for high-resolution single biomolecule imaging

Energy Technology Data Exchange (ETDEWEB)

Tokuhisa, Atsushi [RIKEN SPring-8 Center, 1-1-1 Kouto, Sayo-cho, Sayo-gun, Hyogo 679-5148 (Japan); Arai, Junya [The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033 (Japan); Joti, Yasumasa [JASRI, 1-1-1 Kouto, Sayo-cho, Sayo-gun, Hyogo 679-5198 (Japan); Ohno, Yoshiyuki; Kameyama, Toyohisa; Yamamoto, Keiji; Hatanaka, Masayuki; Gerofi, Balazs; Shimada, Akio; Kurokawa, Motoyoshi; Shoji, Fumiyoshi [RIKEN Advanced Institute for Computational Science, 7-1-26 Minatojima-minami-machi, Chuo-ku, Kobe, Hyogo 650-0047 (Japan); Okada, Kensuke [RIKEN SPring-8 Center, 1-1-1 Kouto, Sayo-cho, Sayo-gun, Hyogo 679-5148 (Japan); Sugimoto, Takashi [JASRI, 1-1-1 Kouto, Sayo-cho, Sayo-gun, Hyogo 679-5198 (Japan); Yamaga, Mitsuhiro; Tanaka, Ryotaro [RIKEN SPring-8 Center, 1-1-1 Kouto, Sayo-cho, Sayo-gun, Hyogo 679-5148 (Japan); Yokokawa, Mitsuo; Hori, Atsushi [RIKEN Advanced Institute for Computational Science, 7-1-26 Minatojima-minami-machi, Chuo-ku, Kobe, Hyogo 650-0047 (Japan); Ishikawa, Yutaka, E-mail: ishikawa@is.s.u-tokyo.ac.jp [The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033 (Japan); Hatsui, Takaki, E-mail: ishikawa@is.s.u-tokyo.ac.jp [RIKEN SPring-8 Center, 1-1-1 Kouto, Sayo-cho, Sayo-gun, Hyogo 679-5148 (Japan); Go, Nobuhiro [Japan Atomic Energy Agency, 8-1-7 Umemidai, Kizugawa, Kyoto 619-0215 (Japan)

2013-11-01

A code with an algorithm for high-speed classification of X-ray diffraction patterns has been developed. Results obtained for a set of 1 × 10{sup 6} simulated diffraction patterns are also reported. Single-particle coherent X-ray diffraction imaging using an X-ray free-electron laser has the potential to reveal the three-dimensional structure of a biological supra-molecule at sub-nanometer resolution. In order to realise this method, it is necessary to analyze as many as 1 × 10{sup 6} noisy X-ray diffraction patterns, each for an unknown random target orientation. To cope with the severe quantum noise, patterns need to be classified according to their similarities and average similar patterns to improve the signal-to-noise ratio. A high-speed scalable scheme has been developed to carry out classification on the K computer, a 10PFLOPS supercomputer at RIKEN Advanced Institute for Computational Science. It is designed to work on the real-time basis with the experimental diffraction pattern collection at the X-ray free-electron laser facility SACLA so that the result of classification can be feedback for optimizing experimental parameters during the experiment. The present status of our effort developing the system and also a result of application to a set of simulated diffraction patterns is reported. About 1 × 10{sup 6} diffraction patterns were successfully classificatied by running 255 separate 1 h jobs in 385-node mode.

Potential risk for healthy siblings to develop schizophrenia: evidence from pattern classification with whole-brain connectivity.

Science.gov (United States)

Liu, Meijie; Zeng, Ling-Li; Shen, Hui; Liu, Zhening; Hu, Dewen

2012-03-28

Recent resting-state functional connectivity MRI studies using group-level statistical analysis have demonstrated the inheritable characters of schizophrenia. The objective of the present study was to use pattern classification as a means to investigate schizophrenia inheritance based on the whole-brain resting-state functional connectivity at the individual subject level. One-against-one pattern classifications were made amongst three groups (i.e. patients diagnosed with schizophrenia, healthy siblings, and healthy controls after preprocessing), resulting in an 80.4% separation between patients with schizophrenia and healthy controls, a 77.6% separation between schizophrenia patients and their healthy siblings, and a 78.7% separation between healthy siblings and healthy controls, respectively. These results suggest that the healthy siblings of schizophrenia patients have an altered resting-state functional connectivity pattern compared with healthy controls. Thus, healthy siblings may have a potential higher risk for developing schizophrenia compared with the general population. Moreover, this pattern differed from that of schizophrenia patients and may contribute to the normal behavior exhibition of healthy siblings in daily life.

Added value of prone CT in the assessment of honeycombing and classification of usual interstitial pneumonia pattern.

Science.gov (United States)

Kim, Minjae; Lee, Sang Min; Song, Jae-Woo; Do, Kyung-Hyun; Lee, Hyun Joo; Lim, Soyeoun; Choe, Jooae; Park, Kye Jin; Park, Hyo Jung; Kim, Hwa Jung; Seo, Joon Beom

2017-06-01

To retrospectively investigate whether prone CT improves identification of honeycombing and classification of UIP patterns in terms of interobserver agreement and accuracy using pathological results as a reference standard. Institutional review board approval with waiver of patients' informed consent requirement was obtained. HRCTs of 86 patients with pathologically proven UIP, NSIP and chronic HP between January 2011 and April 2015 were evaluated by 8 observers. Observers were asked to review supine only set and supine and prone combined set and determine the presence of honeycombing and UIP classification (UIP, possible UIP, inconsistent with UIP). The diagnosis was regarded as correct when UIP pattern on CT corresponded to pathological UIP. Interobserver agreement of honeycombing identification among radiologists was only fair on the supine and combined set (weighted κ=0.31 and 0.34). Additional review of prone images demonstrated a significant improvement in interobserver agreement (weighted κ) of UIP classification from 0.25 to 0.33. Prone CT conferred a significant improvement in interobserver agreement of UIP classification for trainee radiologists (from 0.10 to 0.34) while no improvement was found for board-certified radiologists (from 0.35 to 0.31). There were no significant differences in the accuracy of UIP pattern with reference to pathological results between the supine and combined set (78.8% (145/184) and 81.3% (179/220), P=0.612). Additional review of prone CT can improve overall interobserver agreement of UIP classification among radiologists with variable experiences, particularly for less experienced radiologists, while no improvement was found in honeycombing identification. Copyright © 2017 Elsevier B.V. All rights reserved.

A data mining approach for classifying DNA repair genes into ageing-related or non-ageing-related

Directory of Open Access Journals (Sweden)

Vasieva Olga

2011-01-01

Full Text Available Abstract Background The ageing of the worldwide population means there is a growing need for research on the biology of ageing. DNA damage is likely a key contributor to the ageing process and elucidating the role of different DNA repair systems in ageing is of great interest. In this paper we propose a data mining approach, based on classification methods (decision trees and Naive Bayes, for analysing data about human DNA repair genes. The goal is to build classification models that allow us to discriminate between ageing-related and non-ageing-related DNA repair genes, in order to better understand their different properties. Results The main patterns discovered by the classification methods are as follows: (a the number of protein-protein interactions was a predictor of DNA repair proteins being ageing-related; (b the use of predictor attributes based on protein-protein interactions considerably increased predictive accuracy of attributes based on Gene Ontology (GO annotations; (c GO terms related to "response to stimulus" seem reasonably good predictors of ageing-relatedness for DNA repair genes; (d interaction with the XRCC5 (Ku80 protein is a strong predictor of ageing-relatedness for DNA repair genes; and (e DNA repair genes with a high expression in T lymphocytes are more likely to be ageing-related. Conclusions The above patterns are broadly integrated in an analysis discussing relations between Ku, the non-homologous end joining DNA repair pathway, ageing and lymphocyte development. These patterns and their analysis support non-homologous end joining double strand break repair as central to the ageing-relatedness of DNA repair genes. Our work also showcases the use of protein interaction partners to improve accuracy in data mining methods and our approach could be applied to other ageing-related pathways.

Forecasting Changes in Stock Prices on the Basis of Patterns Identified with the Use of Data Classification Methods

Directory of Open Access Journals (Sweden)

Szanduła Jacek

2014-06-01

Full Text Available The paper develops the concept of harnessing data classification methods to recognize patterns in stock prices. The author defines a formation as a pattern vector describing the financial instrument. Elements of such a vector can be related to the stock price as well as sales volume and other characteristics of the financial instrument. The study uses data concerning selected companies listed on the stock exchange in New York. It takes into account a number of variables that describe the behavior of prices and volume, both in the short and long term. Partitioning around medoids method has been used for data classification (for pattern recognition. An evaluation of the possibility of using certain formations for practical purposes has also been presented.

Predicting decisions in human social interactions using real-time fMRI and pattern classification.

Science.gov (United States)

Hollmann, Maurice; Rieger, Jochem W; Baecke, Sebastian; Lützkendorf, Ralf; Müller, Charles; Adolf, Daniela; Bernarding, Johannes

2011-01-01

Negotiation and trade typically require a mutual interaction while simultaneously resting in uncertainty which decision the partner ultimately will make at the end of the process. Assessing already during the negotiation in which direction one's counterpart tends would provide a tremendous advantage. Recently, neuroimaging techniques combined with multivariate pattern classification of the acquired data have made it possible to discriminate subjective states of mind on the basis of their neuronal activation signature. However, to enable an online-assessment of the participant's mind state both approaches need to be extended to a real-time technique. By combining real-time functional magnetic resonance imaging (fMRI) and online pattern classification techniques, we show that it is possible to predict human behavior during social interaction before the interacting partner communicates a specific decision. Average accuracy reached approximately 70% when we predicted online the decisions of volunteers playing the ultimatum game, a well-known paradigm in economic game theory. Our results demonstrate the successful online analysis of complex emotional and cognitive states using real-time fMRI, which will enable a major breakthrough for social fMRI by providing information about mental states of partners already during the mutual interaction. Interestingly, an additional whole brain classification across subjects confirmed the online results: anterior insula, ventral striatum, and lateral orbitofrontal cortex, known to act in emotional self-regulation and reward processing for adjustment of behavior, appeared to be strong determinants of later overt behavior in the ultimatum game. Using whole brain classification we were also able to discriminate between brain processes related to subjective emotional and motivational states and brain processes related to the evaluation of objective financial incentives.

Predicting decisions in human social interactions using real-time fMRI and pattern classification.

Directory of Open Access Journals (Sweden)

Maurice Hollmann

Full Text Available Negotiation and trade typically require a mutual interaction while simultaneously resting in uncertainty which decision the partner ultimately will make at the end of the process. Assessing already during the negotiation in which direction one's counterpart tends would provide a tremendous advantage. Recently, neuroimaging techniques combined with multivariate pattern classification of the acquired data have made it possible to discriminate subjective states of mind on the basis of their neuronal activation signature. However, to enable an online-assessment of the participant's mind state both approaches need to be extended to a real-time technique. By combining real-time functional magnetic resonance imaging (fMRI and online pattern classification techniques, we show that it is possible to predict human behavior during social interaction before the interacting partner communicates a specific decision. Average accuracy reached approximately 70% when we predicted online the decisions of volunteers playing the ultimatum game, a well-known paradigm in economic game theory. Our results demonstrate the successful online analysis of complex emotional and cognitive states using real-time fMRI, which will enable a major breakthrough for social fMRI by providing information about mental states of partners already during the mutual interaction. Interestingly, an additional whole brain classification across subjects confirmed the online results: anterior insula, ventral striatum, and lateral orbitofrontal cortex, known to act in emotional self-regulation and reward processing for adjustment of behavior, appeared to be strong determinants of later overt behavior in the ultimatum game. Using whole brain classification we were also able to discriminate between brain processes related to subjective emotional and motivational states and brain processes related to the evaluation of objective financial incentives.

Differential Classification of Dementia

Directory of Open Access Journals (Sweden)

E. Mohr

1995-01-01

Full Text Available In the absence of biological markers, dementia classification remains complex both in terms of characterization as well as early detection of the presence or absence of dementing symptoms, particularly in diseases with possible secondary dementia. An empirical, statistical approach using neuropsychological measures was therefore developed to distinguish demented from non-demented patients and to identify differential patterns of cognitive dysfunction in neurodegenerative disease. Age-scaled neurobehavioral test results (Wechsler Adult Intelligence Scale—Revised and Wechsler Memory Scale from Alzheimer's (AD and Huntington's (HD patients, matched for intellectual disability, as well as normal controls were used to derive a classification formula. Stepwise discriminant analysis accurately (99% correct distinguished controls from demented patients, and separated the two patient groups (79% correct. Variables discriminating between HD and AD patient groups consisted of complex psychomotor tasks, visuospatial function, attention and memory. The reliability of the classification formula was demonstrated with a new, independent sample of AD and HD patients which yielded virtually identical results (classification accuracy for dementia: 96%; AD versus HD: 78%. To validate the formula, the discriminant function was applied to Parkinson's (PD patients, 38% of whom were classified as demented. The validity of the classification was demonstrated by significant PD subgroup differences on measures of dementia not included in the discriminant function. Moreover, a majority of demented PD patients (65% were classified as having an HD-like pattern of cognitive deficits, in line with previous reports of the subcortical nature of PD dementia. This approach may thus be useful in classifying presence or absence of dementia and in discriminating between dementia subtypes in cases of secondary or coincidental dementia.

In Vivo Pattern Classification of Ingestive Behavior in Ruminants Using FBG Sensors and Machine Learning

OpenAIRE

Pegorini, Vinicius; Karam, Leandro Zen; Pitta, Christiano Santos Rocha; Cardoso, Rafael; da Silva, Jean Carlos Cardozo; Kalinowski, Hypolito Jos?; Ribeiro, Richardson; Bertotti, F?bio Luiz; Assmann, Tangriani Simioni

2015-01-01

Pattern classification of ingestive behavior in grazing animals has extreme importance in studies related to animal nutrition, growth and health. In this paper, a system to classify chewing patterns of ruminants in in vivo experiments is developed. The proposal is based on data collected by optical fiber Bragg grating sensors (FBG) that are processed by machine learning techniques. The FBG sensors measure the biomechanical strain during jaw movements, and a decision tree is responsible for th...

Prediction of pediatric unipolar depression using multiple neuromorphometric measurements: a pattern classification approach.

Science.gov (United States)

Wu, Mon-Ju; Wu, Hanjing Emily; Mwangi, Benson; Sanches, Marsal; Selvaraj, Sudhakar; Zunta-Soares, Giovana B; Soares, Jair C

2015-03-01

Diagnosis of pediatric neuropsychiatric disorders such as unipolar depression is largely based on clinical judgment - without objective biomarkers to guide diagnostic process and subsequent therapeutic interventions. Neuroimaging studies have previously reported average group-level neuroanatomical differences between patients with pediatric unipolar depression and healthy controls. In the present study, we investigated the utility of multiple neuromorphometric indices in distinguishing pediatric unipolar depression patients from healthy controls at an individual subject level. We acquired structural T1-weighted scans from 25 pediatric unipolar depression patients and 26 demographically matched healthy controls. Multiple neuromorphometric indices such as cortical thickness, volume, and cortical folding patterns were obtained. A support vector machine pattern classification model was 'trained' to distinguish individual subjects with pediatric unipolar depression from healthy controls based on multiple neuromorphometric indices and model predictive validity (sensitivity and specificity) calculated. The model correctly identified 40 out of 51 subjects translating to 78.4% accuracy, 76.0% sensitivity and 80.8% specificity, chi-square p-value = 0.000049. Volumetric and cortical folding abnormalities in the right thalamus and right temporal pole respectively were most central in distinguishing individual patients with pediatric unipolar depression from healthy controls. These findings provide evidence that a support vector machine pattern classification model using multiple neuromorphometric indices may qualify as diagnostic marker for pediatric unipolar depression. In addition, our results identified the most relevant neuromorphometric features in distinguishing PUD patients from healthy controls. Copyright © 2015 Elsevier Ltd. All rights reserved.

A novel method for human age group classification based on

Directory of Open Access Journals (Sweden)

Anuradha Yarlagadda

2015-10-01

Full Text Available In the computer vision community, easy categorization of a person’s facial image into various age groups is often quite precise and is not pursued effectively. To address this problem, which is an important area of research, the present paper proposes an innovative method of age group classification system based on the Correlation Fractal Dimension of complex facial image. Wrinkles appear on the face with aging thereby changing the facial edges of the image. The proposed method is rotation and poses invariant. The present paper concentrates on developing an innovative technique that classifies facial images into four categories i.e. child image (0–15, young adult image (15–30, middle-aged adult image (31–50, and senior adult image (>50 based on correlation FD value of a facial edge image.

Pattern classification of brain activation during emotional processing in subclinical depression: psychosis proneness as potential confounding factor.

Science.gov (United States)

Modinos, Gemma; Mechelli, Andrea; Pettersson-Yeo, William; Allen, Paul; McGuire, Philip; Aleman, Andre

2013-01-01

We used Support Vector Machine (SVM) to perform multivariate pattern classification based on brain activation during emotional processing in healthy participants with subclinical depressive symptoms. Six-hundred undergraduate students completed the Beck Depression Inventory II (BDI-II). Two groups were subsequently formed: (i) subclinical (mild) mood disturbance (n = 17) and (ii) no mood disturbance (n = 17). Participants also completed a self-report questionnaire on subclinical psychotic symptoms, the Community Assessment of Psychic Experiences Questionnaire (CAPE) positive subscale. The functional magnetic resonance imaging (fMRI) paradigm entailed passive viewing of negative emotional and neutral scenes. The pattern of brain activity during emotional processing allowed correct group classification with an overall accuracy of 77% (p = 0.002), within a network of regions including the amygdala, insula, anterior cingulate cortex and medial prefrontal cortex. However, further analysis suggested that the classification accuracy could also be explained by subclinical psychotic symptom scores (correlation with SVM weights r = 0.459, p = 0.006). Psychosis proneness may thus be a confounding factor for neuroimaging studies in subclinical depression.

Binary patterns encoded convolutional neural networks for texture recognition and remote sensing scene classification

Science.gov (United States)

Anwer, Rao Muhammad; Khan, Fahad Shahbaz; van de Weijer, Joost; Molinier, Matthieu; Laaksonen, Jorma

2018-04-01

Designing discriminative powerful texture features robust to realistic imaging conditions is a challenging computer vision problem with many applications, including material recognition and analysis of satellite or aerial imagery. In the past, most texture description approaches were based on dense orderless statistical distribution of local features. However, most recent approaches to texture recognition and remote sensing scene classification are based on Convolutional Neural Networks (CNNs). The de facto practice when learning these CNN models is to use RGB patches as input with training performed on large amounts of labeled data (ImageNet). In this paper, we show that Local Binary Patterns (LBP) encoded CNN models, codenamed TEX-Nets, trained using mapped coded images with explicit LBP based texture information provide complementary information to the standard RGB deep models. Additionally, two deep architectures, namely early and late fusion, are investigated to combine the texture and color information. To the best of our knowledge, we are the first to investigate Binary Patterns encoded CNNs and different deep network fusion architectures for texture recognition and remote sensing scene classification. We perform comprehensive experiments on four texture recognition datasets and four remote sensing scene classification benchmarks: UC-Merced with 21 scene categories, WHU-RS19 with 19 scene classes, RSSCN7 with 7 categories and the recently introduced large scale aerial image dataset (AID) with 30 aerial scene types. We demonstrate that TEX-Nets provide complementary information to standard RGB deep model of the same network architecture. Our late fusion TEX-Net architecture always improves the overall performance compared to the standard RGB network on both recognition problems. Furthermore, our final combination leads to consistent improvement over the state-of-the-art for remote sensing scene classification.

White matter tract covariance patterns predict age-declining cognitive abilities.

Science.gov (United States)

Gazes, Yunglin; Bowman, F DuBois; Razlighi, Qolamreza R; O'Shea, Deirdre; Stern, Yaakov; Habeck, Christian

2016-01-15

Previous studies investigating the relationship of white matter (WM) integrity to cognitive abilities and aging have either focused on a global measure or a few selected WM tracts. Ideally, contribution from all of the WM tracts should be evaluated at the same time. However, the high collinearity among WM tracts precludes systematic examination of WM tracts simultaneously without sacrificing statistical power due to stringent multiple-comparison corrections. Multivariate covariance techniques enable comprehensive simultaneous examination of all WM tracts without being penalized for high collinearity among observations. In this study, Scaled Subprofile Modeling (SSM) was applied to the mean integrity of 18 major WM tracts to extract covariance patterns that optimally predicted four cognitive abilities (perceptual speed, episodic memory, fluid reasoning, and vocabulary) in 346 participants across ages 20 to 79years old. Using expression of the covariance patterns, age-independent effects of white matter integrity on cognition and the indirect effect of WM integrity on age-related differences in cognition were tested separately, but inferences from the indirect analyses were cautiously made given that cross-sectional data set was used in the analysis. A separate covariance pattern was identified that significantly predicted each cognitive ability after controlling for age except for vocabulary, but the age by WM covariance pattern interaction was not significant for any of the three abilities. Furthermore, each of the patterns mediated the effect of age on the respective cognitive ability. A distinct set of WM tracts was most influential in each of the three patterns. The WM covariance pattern accounting for fluid reasoning showed the most number of influential WM tracts whereas the episodic memory pattern showed the least number. Specific patterns of WM tracts make significant contributions to the age-related differences in perceptual speed, episodic memory, and

Intelligent feature selection techniques for pattern classification of Lamb wave signals

International Nuclear Information System (INIS)

Hinders, Mark K.; Miller, Corey A.

2014-01-01

Lamb wave interaction with flaws is a complex, three-dimensional phenomenon, which often frustrates signal interpretation schemes based on mode arrival time shifts predicted by dispersion curves. As the flaw severity increases, scattering and mode conversion effects will often dominate the time-domain signals, obscuring available information about flaws because multiple modes may arrive on top of each other. Even for idealized flaw geometries the scattering and mode conversion behavior of Lamb waves is very complex. Here, multi-mode Lamb waves in a metal plate are propagated across a rectangular flat-bottom hole in a sequence of pitch-catch measurements corresponding to the double crosshole tomography geometry. The flaw is sequentially deepened, with the Lamb wave measurements repeated at each flaw depth. Lamb wave tomography reconstructions are used to identify which waveforms have interacted with the flaw and thereby carry information about its depth. Multiple features are extracted from each of the Lamb wave signals using wavelets, which are then fed to statistical pattern classification algorithms that identify flaw severity. In order to achieve the highest classification accuracy, an optimal feature space is required but it’s never known a priori which features are going to be best. For structural health monitoring we make use of the fact that physical flaws, such as corrosion, will only increase over time. This allows us to identify feature vectors which are topologically well-behaved by requiring that sequential classes “line up” in feature vector space. An intelligent feature selection routine is illustrated that identifies favorable class distributions in multi-dimensional feature spaces using computational homology theory. Betti numbers and formal classification accuracies are calculated for each feature space subset to establish a correlation between the topology of the class distribution and the corresponding classification accuracy

PATTERN CLASSIFICATION APPROACHES TO MATCHING BUILDING POLYGONS AT MULTIPLE SCALES

Directory of Open Access Journals (Sweden)

X. Zhang

2012-07-01

Full Text Available Matching of building polygons with different levels of detail is crucial in the maintenance and quality assessment of multi-representation databases. Two general problems need to be addressed in the matching process: (1 Which criteria are suitable? (2 How to effectively combine different criteria to make decisions? This paper mainly focuses on the second issue and views data matching as a supervised pattern classification. Several classifiers (i.e. decision trees, Naive Bayes and support vector machines are evaluated for the matching task. Four criteria (i.e. position, size, shape and orientation are used to extract information for these classifiers. Evidence shows that these classifiers outperformed the weighted average approach.

Classification of interstitial lung disease patterns with topological texture features

Science.gov (United States)

Huber, Markus B.; Nagarajan, Mahesh; Leinsinger, Gerda; Ray, Lawrence A.; Wismüller, Axel

2010-03-01

Topological texture features were compared in their ability to classify morphological patterns known as 'honeycombing' that are considered indicative for the presence of fibrotic interstitial lung diseases in high-resolution computed tomography (HRCT) images. For 14 patients with known occurrence of honey-combing, a stack of 70 axial, lung kernel reconstructed images were acquired from HRCT chest exams. A set of 241 regions of interest of both healthy and pathological (89) lung tissue were identified by an experienced radiologist. Texture features were extracted using six properties calculated from gray-level co-occurrence matrices (GLCM), Minkowski Dimensions (MDs), and three Minkowski Functionals (MFs, e.g. MF.euler). A k-nearest-neighbor (k-NN) classifier and a Multilayer Radial Basis Functions Network (RBFN) were optimized in a 10-fold cross-validation for each texture vector, and the classification accuracy was calculated on independent test sets as a quantitative measure of automated tissue characterization. A Wilcoxon signed-rank test was used to compare two accuracy distributions and the significance thresholds were adjusted for multiple comparisons by the Bonferroni correction. The best classification results were obtained by the MF features, which performed significantly better than all the standard GLCM and MD features (p < 0.005) for both classifiers. The highest accuracy was found for MF.euler (97.5%, 96.6%; for the k-NN and RBFN classifier, respectively). The best standard texture features were the GLCM features 'homogeneity' (91.8%, 87.2%) and 'absolute value' (90.2%, 88.5%). The results indicate that advanced topological texture features can provide superior classification performance in computer-assisted diagnosis of interstitial lung diseases when compared to standard texture analysis methods.

Peptide YY induces characteristic meal patterns of aged mice.

Science.gov (United States)

Mogami, Sachiko; Yamada, Chihiro; Fujitsuka, Naoki; Hattori, Tomohisa

2017-11-01

Changes in eating behavior occur in the elderly due to oral and swallowing dysfunctions. We aimed to clarify the difference between basal meal patterns of young and aged mice in relation to appetite regulating hormones. Thirty two of young (7-week-old) and aged (23-25-month-old) C57BL/6 male mice were acclimated to a single housing and then transferred to a highly sensitive automated feeding monitoring device. Feeding behavior was monitored from the onset of the dark phase after habituation to the device. Plasma peptide YY (PYY) levels were assessed under the several feeding status or after treatment of PYY. PYY and its receptor (NPY Y2 receptor, Y2R) antagonist were intraperitoneally administered 30min before the monitoring. Although the basal 24-h meal amounts did not differ by age, the total meal time and frequency of minimum feeding activity (bout) were significantly increased and the average bout size and time per bout were significantly decreased in aged mice. PYY dynamics were abnormal and the temporal reduction in food intake by exogenous PYY was more prominent in aged mice than in young mice. PYY administration to young mice induced aged-like meal patterns, and Y2R antagonist administration to aged mice induced young-like meal patterns. Aged mice exhibited characteristic meal patterns probably due to PYY metabolism dysfunction and/or enhanced PYY-Y2R signaling, suggesting a novel method for assessing eating difficulties in aged animals and a potential target for the remedy. Copyright © 2017 Elsevier Inc. All rights reserved.

Targeted Local Support Vector Machine for Age-Dependent Classification.

Science.gov (United States)

Chen, Tianle; Wang, Yuanjia; Chen, Huaihou; Marder, Karen; Zeng, Donglin

2014-09-01

We develop methods to accurately predict whether pre-symptomatic individuals are at risk of a disease based on their various marker profiles, which offers an opportunity for early intervention well before definitive clinical diagnosis. For many diseases, existing clinical literature may suggest the risk of disease varies with some markers of biological and etiological importance, for example age. To identify effective prediction rules using nonparametric decision functions, standard statistical learning approaches treat markers with clear biological importance (e.g., age) and other markers without prior knowledge on disease etiology interchangeably as input variables. Therefore, these approaches may be inadequate in singling out and preserving the effects from the biologically important variables, especially in the presence of potential noise markers. Using age as an example of a salient marker to receive special care in the analysis, we propose a local smoothing large margin classifier implemented with support vector machine (SVM) to construct effective age-dependent classification rules. The method adaptively adjusts age effect and separately tunes age and other markers to achieve optimal performance. We derive the asymptotic risk bound of the local smoothing SVM, and perform extensive simulation studies to compare with standard approaches. We apply the proposed method to two studies of premanifest Huntington's disease (HD) subjects and controls to construct age-sensitive predictive scores for the risk of HD and risk of receiving HD diagnosis during the study period.

Pattern classification of brain activation during emotional processing in subclinical depression: psychosis proneness as potential confounding factor

Directory of Open Access Journals (Sweden)

Gemma Modinos

2013-02-01

Full Text Available We used Support Vector Machine (SVM to perform multivariate pattern classification based on brain activation during emotional processing in healthy participants with subclinical depressive symptoms. Six-hundred undergraduate students completed the Beck Depression Inventory II (BDI-II. Two groups were subsequently formed: (i subclinical (mild mood disturbance (n = 17 and (ii no mood disturbance (n = 17. Participants also completed a self-report questionnaire on subclinical psychotic symptoms, the Community Assessment of Psychic Experiences Questionnaire (CAPE positive subscale. The functional magnetic resonance imaging (fMRI paradigm entailed passive viewing of negative emotional and neutral scenes. The pattern of brain activity during emotional processing allowed correct group classification with an overall accuracy of 77% (p = 0.002, within a network of regions including the amygdala, insula, anterior cingulate cortex and medial prefrontal cortex. However, further analysis suggested that the classification accuracy could also be explained by subclinical psychotic symptom scores (correlation with SVM weights r = 0.459, p = 0.006. Psychosis proneness may thus be a confounding factor for neuroimaging studies in subclinical depression.

Alternative Polyadenylation Patterns for Novel Gene Discovery and Classification in Cancer

Directory of Open Access Journals (Sweden)

Oguzhan Begik

2017-07-01

Full Text Available Certain aspects of diagnosis, prognosis, and treatment of cancer patients are still important challenges to be addressed. Therefore, we propose a pipeline to uncover patterns of alternative polyadenylation (APA, a hidden complexity in cancer transcriptomes, to further accelerate efforts to discover novel cancer genes and pathways. Here, we analyzed expression data for 1045 cancer patients and found a significant shift in usage of poly(A signals in common tumor types (breast, colon, lung, prostate, gastric, and ovarian compared to normal tissues. Using machine-learning techniques, we further defined specific subsets of APA events to efficiently classify cancer types. Furthermore, APA patterns were associated with altered protein levels in patients, revealed by antibody-based profiling data, suggesting functional significance. Overall, our study offers a computational approach for use of APA in novel gene discovery and classification in common tumor types, with important implications in basic research, biomarker discovery, and precision medicine approaches.

Overweight and Obesity Prevalence Among School-Aged Nunavik Inuit Children According to Three Body Mass Index Classification Systems.

Science.gov (United States)

Medehouenou, Thierry Comlan Marc; Ayotte, Pierre; St-Jean, Audray; Meziou, Salma; Roy, Cynthia; Muckle, Gina; Lucas, Michel

2015-07-01

Little is known about the suitability of three commonly used body mass index (BMI) classification system for Indigenous children. This study aims to estimate overweight and obesity prevalence among school-aged Nunavik Inuit children according to International Obesity Task Force (IOTF), Centers for Disease Control and Prevention (CDC), and World Health Organization (WHO) BMI classification systems, to measure agreement between those classification systems, and to investigate whether BMI status as defined by these classification systems is associated with levels of metabolic and inflammatory biomarkers. Data were collected on 290 school-aged children (aged 8-14 years; 50.7% girls) from the Nunavik Child Development Study with data collected in 2005-2010. Anthropometric parameters were measured and blood sampled. Participants were classified as normal weight, overweight, and obese according to BMI classification systems. Weighted kappa (κw) statistics assessed agreement between different BMI classification systems, and multivariate analysis of variance ascertained their relationship with metabolic and inflammatory biomarkers. The combined prevalence rate of overweight/obesity was 26.9% (with 6.6% obesity) with IOTF, 24.1% (11.0%) with CDC, and 40.4% (12.8%) with WHO classification systems. Agreement was the highest between IOTF and CDC (κw = .87) classifications, and substantial for IOTF and WHO (κw = .69) and for CDC and WHO (κw = .73). Insulin and high-sensitivity C-reactive protein plasma levels were significantly higher from normal weight to obesity, regardless of classification system. Among obese subjects, higher insulin level was observed with IOTF. Compared with other systems, IOTF classification appears to be more specific to identify overweight and obesity in Inuit children. Copyright © 2015 Society for Adolescent Health and Medicine. Published by Elsevier Inc. All rights reserved.

Pattern classification of brain activation during emotional processing in subclinical depression : psychosis proneness as potential confounding factor

NARCIS (Netherlands)

Modinos, Gemma; Mechelli, Andrea; Pettersson-Yeo, William; Allen, Paul; McGuire, Philip; Aleman, Andre

2013-01-01

We used Support Vector Machine (SVM) to perform multivariate pattern classification based on brain activation during emotional processing in healthy participants with subclinical depressive symptoms. Six-hundred undergraduate students completed the Beck Depression Inventory II (BDI-II). Two groups

Automated, high accuracy classification of Parkinsonian disorders: a pattern recognition approach.

Directory of Open Access Journals (Sweden)

Andre F Marquand

Full Text Available Progressive supranuclear palsy (PSP, multiple system atrophy (MSA and idiopathic Parkinson's disease (IPD can be clinically indistinguishable, especially in the early stages, despite distinct patterns of molecular pathology. Structural neuroimaging holds promise for providing objective biomarkers for discriminating these diseases at the single subject level but all studies to date have reported incomplete separation of disease groups. In this study, we employed multi-class pattern recognition to assess the value of anatomical patterns derived from a widely available structural neuroimaging sequence for automated classification of these disorders. To achieve this, 17 patients with PSP, 14 with IPD and 19 with MSA were scanned using structural MRI along with 19 healthy controls (HCs. An advanced probabilistic pattern recognition approach was employed to evaluate the diagnostic value of several pre-defined anatomical patterns for discriminating the disorders, including: (i a subcortical motor network; (ii each of its component regions and (iii the whole brain. All disease groups could be discriminated simultaneously with high accuracy using the subcortical motor network. The region providing the most accurate predictions overall was the midbrain/brainstem, which discriminated all disease groups from one another and from HCs. The subcortical network also produced more accurate predictions than the whole brain and all of its constituent regions. PSP was accurately predicted from the midbrain/brainstem, cerebellum and all basal ganglia compartments; MSA from the midbrain/brainstem and cerebellum and IPD from the midbrain/brainstem only. This study demonstrates that automated analysis of structural MRI can accurately predict diagnosis in individual patients with Parkinsonian disorders, and identifies distinct patterns of regional atrophy particularly useful for this process.

ANALYSIS OF RAILWAY USER TRAVEL BEHAVIOUR PATTERNS OF DIFFERENT AGE GROUPS

Directory of Open Access Journals (Sweden)

Takamasa AKIYAMA

2009-01-01

Full Text Available In recent years, there have been requirments for a transport environment that will foster the development of safe, comfortable townships. The study of urban activities amid an aging society and effective use of public transport modes in addressing environmental problems have become particularly important issues. This study analyzes travel behaviour patterns of varying age groups using urban railways in order to examine the relationship between urban public transport use and urban activities. specifically, it analyzes the composition of urban activity and travel behaviour patterns among urban railway users in the Keihanshin (Kyoto-Osaka-Kobe metropolitan area. This paper looks at urban activities within aging societies and identifies the differences in travel behaviour of railway users by separating them into young, middle aged and senior citizen age groups. Analysis makes particular use of the Railway station Database, which is a compilation of existing studies into attributes of railway stations and their surroundings, and results of person trip surveys. Rail use behaviour characteristics have been sorted by age group because mobility via urban railway systems is varied by age group. As a result, differences in railway usage patterns (travel objectives, distance and time, and number of transfers, etc. have been identified and so too have differences in urban activity patterns related to free activities (shopping, recreation. Furthermore, the study developed a travel behaviour pattern estimation model which is capable of categorizing specific transport behaviour patterns and estimating rail users and transport behaviour patterns from the relationship with areas surrounding railway stations to ensure future mobility by public transport for older age groups. The results make it possible to put forward proposals for urban rail services that will facilitate urban activities for the different age groups. Eventually, it will be possible to understand

Multivariate Pattern Classification of Facial Expressions Based on Large-Scale Functional Connectivity.

Science.gov (United States)

Liang, Yin; Liu, Baolin; Li, Xianglin; Wang, Peiyuan

2018-01-01

It is an important question how human beings achieve efficient recognition of others' facial expressions in cognitive neuroscience, and it has been identified that specific cortical regions show preferential activation to facial expressions in previous studies. However, the potential contributions of the connectivity patterns in the processing of facial expressions remained unclear. The present functional magnetic resonance imaging (fMRI) study explored whether facial expressions could be decoded from the functional connectivity (FC) patterns using multivariate pattern analysis combined with machine learning algorithms (fcMVPA). We employed a block design experiment and collected neural activities while participants viewed facial expressions of six basic emotions (anger, disgust, fear, joy, sadness, and surprise). Both static and dynamic expression stimuli were included in our study. A behavioral experiment after scanning confirmed the validity of the facial stimuli presented during the fMRI experiment with classification accuracies and emotional intensities. We obtained whole-brain FC patterns for each facial expression and found that both static and dynamic facial expressions could be successfully decoded from the FC patterns. Moreover, we identified the expression-discriminative networks for the static and dynamic facial expressions, which span beyond the conventional face-selective areas. Overall, these results reveal that large-scale FC patterns may also contain rich expression information to accurately decode facial expressions, suggesting a novel mechanism, which includes general interactions between distributed brain regions, and that contributes to the human facial expression recognition.

Acromioclavicular joint dislocations: radiological correlation between Rockwood classification system and injury patterns in human cadaver species.

Science.gov (United States)

Eschler, Anica; Rösler, Klaus; Rotter, Robert; Gradl, Georg; Mittlmeier, Thomas; Gierer, Philip

2014-09-01

The classification system of Rockwood and Young is a commonly used classification for acromioclavicular joint separations subdividing types I-VI. This classification hypothesizes specific lesions to anatomical structures (acromioclavicular and coracoclavicular ligaments, capsule, attached muscles) leading to the injury. In recent literature, our understanding for anatomical correlates leading to the radiological-based Rockwood classification is questioned. The goal of this experimental-based investigation was to approve the correlation between the anatomical injury pattern and the Rockwood classification. In four human cadavers (seven shoulders), the acromioclavicular and coracoclavicular ligaments were transected stepwise. Radiological correlates were recorded (Zanca view) with 15-kg longitudinal tension applied at the wrist. The resulting acromio- and coracoclavicular distances were measured. Radiographs after acromioclavicular ligament transection showed joint space enlargement (8.6 ± 0.3 vs. 3.1 ± 0.5 mm, p acromioclavicular joint space width increased to 16.7 ± 2.7 vs. 8.6 ± 0.3 mm, p acromioclavicular joint lesions higher than Rockwood type I and II. The clinical consequence for reconstruction of low-grade injuries might be a solely surgical approach for the acromioclavicular ligaments or conservative treatment. High-grade injuries were always based on additional structural damage to the coracoclavicular ligaments. Rockwood type V lesions occurred while muscle attachments were intact.

Artificial neural network classification using a minimal training set - Comparison to conventional supervised classification

Science.gov (United States)

Hepner, George F.; Logan, Thomas; Ritter, Niles; Bryant, Nevin

1990-01-01

Recent research has shown an artificial neural network (ANN) to be capable of pattern recognition and the classification of image data. This paper examines the potential for the application of neural network computing to satellite image processing. A second objective is to provide a preliminary comparison and ANN classification. An artificial neural network can be trained to do land-cover classification of satellite imagery using selected sites representative of each class in a manner similar to conventional supervised classification. One of the major problems associated with recognition and classifications of pattern from remotely sensed data is the time and cost of developing a set of training sites. This reseach compares the use of an ANN back propagation classification procedure with a conventional supervised maximum likelihood classification procedure using a minimal training set. When using a minimal training set, the neural network is able to provide a land-cover classification superior to the classification derived from the conventional classification procedure. This research is the foundation for developing application parameters for further prototyping of software and hardware implementations for artificial neural networks in satellite image and geographic information processing.

Pattern classification

CERN Document Server

Duda, Richard O; Stork, David G

2001-01-01

The first edition, published in 1973, has become a classic reference in the field. Now with the second edition, readers will find information on key new topics such as neural networks and statistical pattern recognition, the theory of machine learning, and the theory of invariances. Also included are worked examples, comparisons between different methods, extensive graphics, expanded exercises and computer project topics. An Instructor's Manual presenting detailed solutions to all the problems in the book is available from the Wiley editorial department.

A 2-d classification of diseases based on age-specific death rates

Science.gov (United States)

Richmond, Peter; Roehner, Bertrand M.

2018-02-01

Age specific mortality curves exhibit an age tc (about 10 years) which plays a crucial role in that the mortality curve decreases hyperbolically in the age interval A before tc and increases exponentially in the interval B following tc. For those familiar with reliability theory, region A is called the "burn in" phase and B is the "wear out" phase. Using the exponents of the hyperbolic and exponential phases, we introduce a new 2 dimensional map of diseases. This permits the classification of diseases into three broad classes: AS1, AS2 and S. Class AS1 includes all diseases arising from congenital malformations which dominate infant and child mortality; class AS2 includes degenerative diseases such as dementia and Alzheimer's which dominate old age mortality. In class S, which includes most infectious and metabolic diseases, the exponents from both aging phases contribute to positions on the map. Cancer is one of these mixed cases but is closer to class AS2 than AS1. A second line classification is needed to resolve S cases and to this end we introduce a 3rd dimension, namely (calendar) time. Using historical data we show that in their response to treatment (particularly vaccination), S diseases fall into three sub-classes. (i) Class E diseases (e.g. measles or meningococcal disease) which have been almost eliminated at all ages (ii) class C diseases (e.g. tuberculosis) which can be cured but whose cure becomes less effective at old age. (iii) Class U diseases for which radical cures are still unknown. Regarding the future, the fact that the wear-out process of numerous diseases already starts around the age of 25 means that a major extension of the human lifespan beyond 120 certainly also requires to uncover the secret of the "elixir of eternal youth" which has driven timeless human efforts and still seems unlikely in the foreseeable future.

Modeling and Classification of Kinetic Patterns of Dynamic Metabolic Biomarkers in Physical Activity.

Directory of Open Access Journals (Sweden)

Marc Breit

2015-08-01

Full Text Available The objectives of this work were the classification of dynamic metabolic biomarker candidates and the modeling and characterization of kinetic regulatory mechanisms in human metabolism with response to external perturbations by physical activity. Longitudinal metabolic concentration data of 47 individuals from 4 different groups were examined, obtained from a cycle ergometry cohort study. In total, 110 metabolites (within the classes of acylcarnitines, amino acids, and sugars were measured through a targeted metabolomics approach, combining tandem mass spectrometry (MS/MS with the concept of stable isotope dilution (SID for metabolite quantitation. Biomarker candidates were selected by combined analysis of maximum fold changes (MFCs in concentrations and P-values resulting from statistical hypothesis testing. Characteristic kinetic signatures were identified through a mathematical modeling approach utilizing polynomial fitting. Modeled kinetic signatures were analyzed for groups with similar behavior by applying hierarchical cluster analysis. Kinetic shape templates were characterized, defining different forms of basic kinetic response patterns, such as sustained, early, late, and other forms, that can be used for metabolite classification. Acetylcarnitine (C2, showing a late response pattern and having the highest values in MFC and statistical significance, was classified as late marker and ranked as strong predictor (MFC = 1.97, P < 0.001. In the class of amino acids, highest values were shown for alanine (MFC = 1.42, P < 0.001, classified as late marker and strong predictor. Glucose yields a delayed response pattern, similar to a hockey stick function, being classified as delayed marker and ranked as moderate predictor (MFC = 1.32, P < 0.001. These findings coincide with existing knowledge on central metabolic pathways affected in exercise physiology, such as β-oxidation of fatty acids, glycolysis, and glycogenolysis. The presented modeling

Maximum mutual information regularized classification

KAUST Repository

Wang, Jim Jing-Yan

2014-09-07

In this paper, a novel pattern classification approach is proposed by regularizing the classifier learning to maximize mutual information between the classification response and the true class label. We argue that, with the learned classifier, the uncertainty of the true class label of a data sample should be reduced by knowing its classification response as much as possible. The reduced uncertainty is measured by the mutual information between the classification response and the true class label. To this end, when learning a linear classifier, we propose to maximize the mutual information between classification responses and true class labels of training samples, besides minimizing the classification error and reducing the classifier complexity. An objective function is constructed by modeling mutual information with entropy estimation, and it is optimized by a gradient descend method in an iterative algorithm. Experiments on two real world pattern classification problems show the significant improvements achieved by maximum mutual information regularization.

Maximum mutual information regularized classification

KAUST Repository

Wang, Jim Jing-Yan; Wang, Yi; Zhao, Shiguang; Gao, Xin

2014-01-01

In this paper, a novel pattern classification approach is proposed by regularizing the classifier learning to maximize mutual information between the classification response and the true class label. We argue that, with the learned classifier, the uncertainty of the true class label of a data sample should be reduced by knowing its classification response as much as possible. The reduced uncertainty is measured by the mutual information between the classification response and the true class label. To this end, when learning a linear classifier, we propose to maximize the mutual information between classification responses and true class labels of training samples, besides minimizing the classification error and reducing the classifier complexity. An objective function is constructed by modeling mutual information with entropy estimation, and it is optimized by a gradient descend method in an iterative algorithm. Experiments on two real world pattern classification problems show the significant improvements achieved by maximum mutual information regularization.

Morphological patterns of lip prints in Mangaloreans based on Suzuki and Tsuchihashi classification

Science.gov (United States)

Jeergal, Prabhakar A; Pandit, Siddharth; Desai, Dinkar; Surekha, R; Jeergal, Vasanti A

2016-01-01

Introduction: Cheiloscopy is the study of the furrows or grooves present on the red part or vermilion border of the human lips. The present study aims to classify the characteristics of lip prints and to know the most common morphological pattern specific to Mangalorean people of Southern India. For the first time, this study also assesses the association between gender and different lip segments within a population. Materials and Methods: A total of 200 residents of Mangalore (100 males and 100 females) were included of age ranging from 18 years to 60 years. Materials used to take the impression of lips included red lipstick, A4 size white bond paper and cellophane tape. The prints obtained were scanned using a Canon Image Scanner and stored in a folder on a personal computer. The images were cropped and inverted in gray scale using Adobe Photoshop software. Each lip print was divided into eight segments and was examined. Suzuki and Tsuchihashi's classification (1970) was used to classify the types of grooves, and the results were statistically analyzed. Six types of grooves were recorded in the Mangalorean's lips. Statistical Analysis: Association between gender and different lip segments was tested using Chi-square analysis in the given population. Results: In males, the groove Type I' was the highest recorded followed by Type III, Type II, Type I, Type IV and Type V in descending order. In females, Type I' was the highest recorded followed by Type II, Type III, Type IV, Type I and Type V in descending order. Conclusion: Males and females displayed statistically significant differences in lip print patterns for different lip sites: lower medial lip, as well as upper and lower lateral segments. Only the upper medial lip segment displayed no statistically significant difference in lip print pattern between males and females. This shows that the distribution of lip prints is generally dissimilar for males and females, with varying predominance according to lip

Biometric Authentication for Gender Classification Techniques: A Review

Science.gov (United States)

Mathivanan, P.; Poornima, K.

2017-12-01

One of the challenging biometric authentication applications is gender identification and age classification, which captures gait from far distance and analyze physical information of the subject such as gender, race and emotional state of the subject. It is found that most of the gender identification techniques have focused only with frontal pose of different human subject, image size and type of database used in the process. The study also classifies different feature extraction process such as, Principal Component Analysis (PCA) and Local Directional Pattern (LDP) that are used to extract the authentication features of a person. This paper aims to analyze different gender classification techniques that help in evaluating strength and weakness of existing gender identification algorithm. Therefore, it helps in developing a novel gender classification algorithm with less computation cost and more accuracy. In this paper, an overview and classification of different gender identification techniques are first presented and it is compared with other existing human identification system by means of their performance.

Dietary patterns as predictors of successful ageing.

Science.gov (United States)

Hodge, A M; O'Dea, K; English, D R; Giles, G G; Flicker, L

2014-03-01

To examine associations between dietary patterns identified by factor analysis, and successful ageing. Prospective cohort study with diet measured in 1990-4, and successful ageing in 2003-7. Ordered logistic regression with outcome determined as dead/usual ageing/successful ageing was used to examine associations with quintile groups of dietary factor scores. Men and women (n=6308), without history of major illness at baseline, and aged >70 years at follow-up, or who had died before follow-up but would have been aged >70 at the commencement of follow-up, from the Melbourne Collaborative Cohort Study. Frequencies of intake of 121 foods at baseline were collected in a food frequency questionnaire. Anthropometry and other health and lifestyle data were collected. At follow-up, questionnaire data relating to mental health, physical function and medical history were used to define successful ageing. Four dietary factors were identified, characterized by higher loadings for (1) vegetables; (2) fruit, (3) feta, legumes, salad, olive oil, and inverse loadings for tea, margarine, cake, sweet biscuits and puddings; (4) meat, white bread, savoury pastry dishes and fried foods. In models excluding body size, the second factor 'Fruit' was positively associated with successful ageing (OR in top 20% vs lowest 20% of score 1.31, 95%CI (1.05-1.63), p trend across quintile groups 0.001); while the fourth factor 'Meat/fatty foods' was inversely associated (OR in top 20% vs lowest 20% of score 0.69, 95%CI (0.55-0.86), p trend across quintile groups 0.001). Factors 1 and 3 did not show significant associations with successful ageing. The association for 'Fruit' was little altered after adjustment for body size, while for 'Meat/fatty foods' the association was somewhat attenuated. A dietary pattern including plenty of fruit while limiting meat and fried foods may improve the likelihood of ageing successfully.

Adult height, dietary patterns, and healthy aging.

Science.gov (United States)

Ma, Wenjie; Hagan, Kaitlin A; Heianza, Yoriko; Sun, Qi; Rimm, Eric B; Qi, Lu

2017-08-01

Background: Adult height has shown directionally diverse associations with several age-related disorders, including cardiovascular disease, cancer, decline in cognitive function, and mortality. Objective: We investigated the associations of adult height with healthy aging measured by a full spectrum of health outcomes, including incidence of chronic diseases, memory, physical functioning, and mental health, among populations who have survived to older age, and whether lifestyle factors modified such relations. Design: We included 52,135 women (mean age: 44.2 y) from the Nurses' Health Study without chronic diseases in 1980 and whose health status was available in 2012. Healthy aging was defined as being free of 11 major chronic diseases and having no reported impairment of subjective memory, physical impairment, or mental health limitations. Results: Of all eligible study participants, 6877 (13.2%) were classified as healthy agers. After adjustment for demographic and lifestyle factors, we observed an 8% (95% CI: 6%, 11%) decrease in the odds of healthy aging per SD (0.062 m) increase in height. Compared with the lowest category of height (≤1.57 m), the OR of achieving healthy aging in the highest category (≥1.70 m) was 0.80 (95% CI: 0.73, 0.87; P -trend healthy aging ( P -interaction = 0.005), and among the individual dietary factors characterizing the prudent dietary pattern, fruit and vegetable intake showed the strongest effect modification ( P -interaction = 0.01). The association of greater height with reduced odds of healthy aging appeared to be more evident among women with higher adherence to the prudent dietary pattern rich in vegetable and fruit intake. Conclusions: Greater height was associated with a modest decrease in the likelihood of healthy aging. A prudent diet rich in fruit and vegetables might modify the relation. © 2017 American Society for Nutrition.

Phenomenology and classification of dystonia: a consensus update.

Science.gov (United States)

Albanese, Alberto; Bhatia, Kailash; Bressman, Susan B; Delong, Mahlon R; Fahn, Stanley; Fung, Victor S C; Hallett, Mark; Jankovic, Joseph; Jinnah, Hyder A; Klein, Christine; Lang, Anthony E; Mink, Jonathan W; Teller, Jan K

2013-06-15

This report describes the consensus outcome of an international panel consisting of investigators with years of experience in this field that reviewed the definition and classification of dystonia. Agreement was obtained based on a consensus development methodology during 3 in-person meetings and manuscript review by mail. Dystonia is defined as a movement disorder characterized by sustained or intermittent muscle contractions causing abnormal, often repetitive, movements, postures, or both. Dystonic movements are typically patterned and twisting, and may be tremulous. Dystonia is often initiated or worsened by voluntary action and associated with overflow muscle activation. Dystonia is classified along 2 axes: clinical characteristics, including age at onset, body distribution, temporal pattern and associated features (additional movement disorders or neurological features); and etiology, which includes nervous system pathology and inheritance. The clinical characteristics fall into several specific dystonia syndromes that help to guide diagnosis and treatment. We provide here a new general definition of dystonia and propose a new classification. We encourage clinicians and researchers to use these innovative definition and classification and test them in the clinical setting on a variety of patients with dystonia. © 2013 Movement Disorder Society. © 2013 Movement Disorder Society.

Demographic change in Germany and reversal of spatial ageing patterns

Directory of Open Access Journals (Sweden)

Swiaczny Frank

2010-12-01

Full Text Available The paper presents the result of a spatial analysis considering the effect of demographic ageing and ageing-in-place processes in Germany according to spatially differentiated ageing patterns among urban, sub-urban and rural counties up to 2025. As to the latest official population forecast counties of urban core regions will undergo a slower ageing process than other types of counties, resulting in a reversal of ageing patterns. Urban core areas in this analysis will gain demographically from their net migration surplus while suburban housing locations of the past will be no longer able to attract enough young migrants to compensate for their now rapidly ageing baby boomer generation. The process presented is typical for the fate of (suburban housing areas with homogenous populations under conditions of ageing and shrinking if spatial mobility in ageing population groups is declining.

Demographic change in Germany and reversal of spatial ageing patterns

Directory of Open Access Journals (Sweden)

Swiaczny F.

2010-01-01

Full Text Available The paper presents the result of a spatial analysis considering the effect of demographic ageing and ageing-in-place processes in Germany according to spatially differentiated ageing patterns among urban, sub-urban and rural counties up to 2025. As to the latest official population forecast counties of urban core regions will undergo a slower ageing process than other types of counties, resulting in a reversal of ageing patterns. Urban core areas in this analysis will gain demographically from their net migration surplus while suburban housing locations of the past will be no longer able to attract enough young migrants to compensate for their now rapidly ageing baby boomer generation. The process presented is typical for the fate of (suburban housing areas with homogenous populations under conditions of ageing and shrinking if spatial mobility in ageing population groups is declining.

PATTERNS OF FUNDUS AUTOFLUORESCENCE DEFECTS IN NEOVASCULAR AGE-RELATED MACULAR DEGENERATION SUBTYPES.

Science.gov (United States)

Ozkok, Ahmet; Sigford, Douglas K; Tezel, Tongalp H

2016-11-01

To test define characteristic fundus autofluorescence patterns of different exudative age-related macular degeneration subtypes. Cross-sectional study. Fifty-two patients with choroidal neovascularization because of three different neovascular age-related macular degeneration subtypes were included in the study. Macular and peripheral fundus autofluorescence patterns of study subjects were compared in a masked fashion. Fundus autofluorescence patterns of all three neovascular age-related macular degeneration subtypes revealed similar patterns. However, peripapillary hypo-autofluorescence was more common among patients with polypoidal choroidal vasculopathy (88.2%) compared with patients with retinal angiomatous proliferation (12.5%) and patients without retinal angiomatous proliferation and polypoidal choroidal vasculopathy (21.1%) (P autofluorescence defects in neovascular age-related macular degeneration maybe suggestive of polypoidal choroidal vasculopathy as a variant of neovascular age-related macular degeneration.

Lung Nodule Image Classification Based on Local Difference Pattern and Combined Classifier.

Science.gov (United States)

Mao, Keming; Deng, Zhuofu

2016-01-01

This paper proposes a novel lung nodule classification method for low-dose CT images. The method includes two stages. First, Local Difference Pattern (LDP) is proposed to encode the feature representation, which is extracted by comparing intensity difference along circular regions centered at the lung nodule. Then, the single-center classifier is trained based on LDP. Due to the diversity of feature distribution for different class, the training images are further clustered into multiple cores and the multicenter classifier is constructed. The two classifiers are combined to make the final decision. Experimental results on public dataset show the superior performance of LDP and the combined classifier.

Lung Nodule Image Classification Based on Local Difference Pattern and Combined Classifier

Directory of Open Access Journals (Sweden)

Keming Mao

2016-01-01

Full Text Available This paper proposes a novel lung nodule classification method for low-dose CT images. The method includes two stages. First, Local Difference Pattern (LDP is proposed to encode the feature representation, which is extracted by comparing intensity difference along circular regions centered at the lung nodule. Then, the single-center classifier is trained based on LDP. Due to the diversity of feature distribution for different class, the training images are further clustered into multiple cores and the multicenter classifier is constructed. The two classifiers are combined to make the final decision. Experimental results on public dataset show the superior performance of LDP and the combined classifier.

Analysis and Classification of Stride Patterns Associated with Children Development Using Gait Signal Dynamics Parameters and Ensemble Learning Algorithms

Directory of Open Access Journals (Sweden)

Meihong Wu

2016-01-01

Full Text Available Measuring stride variability and dynamics in children is useful for the quantitative study of gait maturation and neuromotor development in childhood and adolescence. In this paper, we computed the sample entropy (SampEn and average stride interval (ASI parameters to quantify the stride series of 50 gender-matched children participants in three age groups. We also normalized the SampEn and ASI values by leg length and body mass for each participant, respectively. Results show that the original and normalized SampEn values consistently decrease over the significance level of the Mann-Whitney U test (p<0.01 in children of 3–14 years old, which indicates the stride irregularity has been significantly ameliorated with the body growth. The original and normalized ASI values are also significantly changing when comparing between any two groups of young (aged 3–5 years, middle (aged 6–8 years, and elder (aged 10–14 years children. Such results suggest that healthy children may better modulate their gait cadence rhythm with the development of their musculoskeletal and neurological systems. In addition, the AdaBoost.M2 and Bagging algorithms were used to effectively distinguish the children’s gait patterns. These ensemble learning algorithms both provided excellent gait classification results in terms of overall accuracy (≥90%, recall (≥0.8, and precision (≥0.8077.

A Ternary Hybrid EEG-NIRS Brain-Computer Interface for the Classification of Brain Activation Patterns during Mental Arithmetic, Motor Imagery, and Idle State.

Science.gov (United States)

Shin, Jaeyoung; Kwon, Jinuk; Im, Chang-Hwan

2018-01-01

The performance of a brain-computer interface (BCI) can be enhanced by simultaneously using two or more modalities to record brain activity, which is generally referred to as a hybrid BCI. To date, many BCI researchers have tried to implement a hybrid BCI system by combining electroencephalography (EEG) and functional near-infrared spectroscopy (NIRS) to improve the overall accuracy of binary classification. However, since hybrid EEG-NIRS BCI, which will be denoted by hBCI in this paper, has not been applied to ternary classification problems, paradigms and classification strategies appropriate for ternary classification using hBCI are not well investigated. Here we propose the use of an hBCI for the classification of three brain activation patterns elicited by mental arithmetic, motor imagery, and idle state, with the aim to elevate the information transfer rate (ITR) of hBCI by increasing the number of classes while minimizing the loss of accuracy. EEG electrodes were placed over the prefrontal cortex and the central cortex, and NIRS optodes were placed only on the forehead. The ternary classification problem was decomposed into three binary classification problems using the "one-versus-one" (OVO) classification strategy to apply the filter-bank common spatial patterns filter to EEG data. A 10 × 10-fold cross validation was performed using shrinkage linear discriminant analysis (sLDA) to evaluate the average classification accuracies for EEG-BCI, NIRS-BCI, and hBCI when the meta-classification method was adopted to enhance classification accuracy. The ternary classification accuracies for EEG-BCI, NIRS-BCI, and hBCI were 76.1 ± 12.8, 64.1 ± 9.7, and 82.2 ± 10.2%, respectively. The classification accuracy of the proposed hBCI was thus significantly higher than those of the other BCIs ( p < 0.005). The average ITR for the proposed hBCI was calculated to be 4.70 ± 1.92 bits/minute, which was 34.3% higher than that reported for a previous binary hBCI study.

CASAnova: a multiclass support vector machine model for the classification of human sperm motility patterns.

Science.gov (United States)

Goodson, Summer G; White, Sarah; Stevans, Alicia M; Bhat, Sanjana; Kao, Chia-Yu; Jaworski, Scott; Marlowe, Tamara R; Kohlmeier, Martin; McMillan, Leonard; Zeisel, Steven H; O'Brien, Deborah A

2017-11-01

The ability to accurately monitor alterations in sperm motility is paramount to understanding multiple genetic and biochemical perturbations impacting normal fertilization. Computer-aided sperm analysis (CASA) of human sperm typically reports motile percentage and kinematic parameters at the population level, and uses kinematic gating methods to identify subpopulations such as progressive or hyperactivated sperm. The goal of this study was to develop an automated method that classifies all patterns of human sperm motility during in vitro capacitation following the removal of seminal plasma. We visually classified CASA tracks of 2817 sperm from 18 individuals and used a support vector machine-based decision tree to compute four hyperplanes that separate five classes based on their kinematic parameters. We then developed a web-based program, CASAnova, which applies these equations sequentially to assign a single classification to each motile sperm. Vigorous sperm are classified as progressive, intermediate, or hyperactivated, and nonvigorous sperm as slow or weakly motile. This program correctly classifies sperm motility into one of five classes with an overall accuracy of 89.9%. Application of CASAnova to capacitating sperm populations showed a shift from predominantly linear patterns of motility at initial time points to more vigorous patterns, including hyperactivated motility, as capacitation proceeds. Both intermediate and hyperactivated motility patterns were largely eliminated when sperm were incubated in noncapacitating medium, demonstrating the sensitivity of this method. The five CASAnova classifications are distinctive and reflect kinetic parameters of washed human sperm, providing an accurate, quantitative, and high-throughput method for monitoring alterations in motility. © The Authors 2017. Published by Oxford University Press on behalf of Society for the Study of Reproduction. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Multi-resolution analysis using integrated microscopic configuration with local patterns for benign-malignant mass classification

Science.gov (United States)

Rabidas, Rinku; Midya, Abhishek; Chakraborty, Jayasree; Sadhu, Anup; Arif, Wasim

2018-02-01

In this paper, Curvelet based local attributes, Curvelet-Local configuration pattern (C-LCP), is introduced for the characterization of mammographic masses as benign or malignant. Amid different anomalies such as micro- calcification, bilateral asymmetry, architectural distortion, and masses, the reason for targeting the mass lesions is due to their variation in shape, size, and margin which makes the diagnosis a challenging task. Being efficient in classification, multi-resolution property of the Curvelet transform is exploited and local information is extracted from the coefficients of each subband using Local configuration pattern (LCP). The microscopic measures in concatenation with the local textural information provide more discriminating capability than individual. The measures embody the magnitude information along with the pixel-wise relationships among the neighboring pixels. The performance analysis is conducted with 200 mammograms of the DDSM database containing 100 mass cases of each benign and malignant. The optimal set of features is acquired via stepwise logistic regression method and the classification is carried out with Fisher linear discriminant analysis. The best area under the receiver operating characteristic curve and accuracy of 0.95 and 87.55% are achieved with the proposed method, which is further compared with some of the state-of-the-art competing methods.

Bayesian Optimization for Neuroimaging Pre-processing in Brain Age Classification and Prediction

Directory of Open Access Journals (Sweden)

Jenessa Lancaster

2018-02-01

Full Text Available Neuroimaging-based age prediction using machine learning is proposed as a biomarker of brain aging, relating to cognitive performance, health outcomes and progression of neurodegenerative disease. However, even leading age-prediction algorithms contain measurement error, motivating efforts to improve experimental pipelines. T1-weighted MRI is commonly used for age prediction, and the pre-processing of these scans involves normalization to a common template and resampling to a common voxel size, followed by spatial smoothing. Resampling parameters are often selected arbitrarily. Here, we sought to improve brain-age prediction accuracy by optimizing resampling parameters using Bayesian optimization. Using data on N = 2003 healthy individuals (aged 16–90 years we trained support vector machines to (i distinguish between young (<22 years and old (>50 years brains (classification and (ii predict chronological age (regression. We also evaluated generalisability of the age-regression model to an independent dataset (CamCAN, N = 648, aged 18–88 years. Bayesian optimization was used to identify optimal voxel size and smoothing kernel size for each task. This procedure adaptively samples the parameter space to evaluate accuracy across a range of possible parameters, using independent sub-samples to iteratively assess different parameter combinations to arrive at optimal values. When distinguishing between young and old brains a classification accuracy of 88.1% was achieved, (optimal voxel size = 11.5 mm3, smoothing kernel = 2.3 mm. For predicting chronological age, a mean absolute error (MAE of 5.08 years was achieved, (optimal voxel size = 3.73 mm3, smoothing kernel = 3.68 mm. This was compared to performance using default values of 1.5 mm3 and 4mm respectively, resulting in MAE = 5.48 years, though this 7.3% improvement was not statistically significant. When assessing generalisability, best performance was achieved when applying the entire Bayesian

Comparison of models of automatic classification of textural patterns of mineral presents in Colombian coals

International Nuclear Information System (INIS)

Lopez Carvajal, Jaime; Branch Bedoya, John Willian

2005-01-01

The automatic classification of objects is a very interesting approach under several problem domains. This paper outlines some results obtained under different classification models to categorize textural patterns of minerals using real digital images. The data set used was characterized by a small size and noise presence. The implemented models were the Bayesian classifier, Neural Network (2-5-1), support vector machine, decision tree and 3-nearest neighbors. The results after applying crossed validation show that the Bayesian model (84%) proved better predictive capacity than the others, mainly due to its noise robustness behavior. The neuronal network (68%) and the SVM (67%) gave promising results, because they could be improved increasing the data amount used, while the decision tree (55%) and K-NN (54%) did not seem to be adequate for this problem, because of their sensibility to noise

Asynchronous data-driven classification of weapon systems

International Nuclear Information System (INIS)

Jin, Xin; Mukherjee, Kushal; Gupta, Shalabh; Ray, Asok; Phoha, Shashi; Damarla, Thyagaraju

2009-01-01

This communication addresses real-time weapon classification by analysis of asynchronous acoustic data, collected from microphones on a sensor network. The weapon classification algorithm consists of two parts: (i) feature extraction from time-series data using symbolic dynamic filtering (SDF), and (ii) pattern classification based on the extracted features using the language measure (LM) and support vector machine (SVM). The proposed algorithm has been tested on field data, generated by firing of two types of rifles. The results of analysis demonstrate high accuracy and fast execution of the pattern classification algorithm with low memory requirements. Potential applications include simultaneous shooter localization and weapon classification with soldier-wearable networked sensors. (rapid communication)

Property Specification Patterns for intelligence building software

Science.gov (United States)

Chun, Seungsu

2018-03-01

In this paper, through the property specification pattern research for Modal MU(μ) logical aspects present a single framework based on the pattern of intelligence building software. In this study, broken down by state property specification pattern classification of Dwyer (S) and action (A) and was subdivided into it again strong (A) and weaknesses (E). Through these means based on a hierarchical pattern classification of the property specification pattern analysis of logical aspects Mu(μ) was applied to the pattern classification of the examples used in the actual model checker. As a result, not only can a more accurate classification than the existing classification systems were easy to create and understand the attributes specified.

Gender classification from face images by using local binary pattern and gray-level co-occurrence matrix

Science.gov (United States)

Uzbaş, Betül; Arslan, Ahmet

2018-04-01

Gender is an important step for human computer interactive processes and identification. Human face image is one of the important sources to determine gender. In the present study, gender classification is performed automatically from facial images. In order to classify gender, we propose a combination of features that have been extracted face, eye and lip regions by using a hybrid method of Local Binary Pattern and Gray-Level Co-Occurrence Matrix. The features have been extracted from automatically obtained face, eye and lip regions. All of the extracted features have been combined and given as input parameters to classification methods (Support Vector Machine, Artificial Neural Networks, Naive Bayes and k-Nearest Neighbor methods) for gender classification. The Nottingham Scan face database that consists of the frontal face images of 100 people (50 male and 50 female) is used for this purpose. As the result of the experimental studies, the highest success rate has been achieved as 98% by using Support Vector Machine. The experimental results illustrate the efficacy of our proposed method.

The gestational age pattern of human mortality

DEFF Research Database (Denmark)

Schöley, Jonas; Vaupel, James W.; Jacobsen, Rune

-infant lifetable by gestational age spanning week 23 until week 100 after the last menstrual period of the mother. This joint lifetable shows a remarkable regularity in the gestational age profile of fetal- and infant mortality: Mortality rates are declining over the whole observed age range with the exception......In order to check hypotheses about the cause for "ontogenescense" -- the phenomenon of a declining force of mortality prior to maturity -- I analyse data on human mortality by gestational age. Based on extensive microdata on births, fetal- and infant deaths in the US 2009 I calculate a joint fetal...... of a "birth hump" peaking week 38. The absolute rate of decline slows down over age. The observed gestational age pattern of the force of mortality is consistent with three hypotheses concerning the causes for ontogenescense: 1) Adaptation: as the organism growths it becomes more resilient towards death, 2...

Measurement Properties and Classification Accuracy of Two Spanish Parent Surveys of Language Development for Preschool-Age Children

Science.gov (United States)

Guiberson, Mark; Rodriguez, Barbara L.

2010-01-01

Purpose: To describe the concurrent validity and classification accuracy of 2 Spanish parent surveys of language development, the Spanish Ages and Stages Questionnaire (ASQ; Squires, Potter, & Bricker, 1999) and the Pilot Inventario-III (Pilot INV-III; Guiberson, 2008a). Method: Forty-eight Spanish-speaking parents of preschool-age children…

Estimating local scaling properties for the classification of interstitial lung disease patterns

Science.gov (United States)

Huber, Markus B.; Nagarajan, Mahesh B.; Leinsinger, Gerda; Ray, Lawrence A.; Wismueller, Axel

2011-03-01

Local scaling properties of texture regions were compared in their ability to classify morphological patterns known as 'honeycombing' that are considered indicative for the presence of fibrotic interstitial lung diseases in high-resolution computed tomography (HRCT) images. For 14 patients with known occurrence of honeycombing, a stack of 70 axial, lung kernel reconstructed images were acquired from HRCT chest exams. 241 regions of interest of both healthy and pathological (89) lung tissue were identified by an experienced radiologist. Texture features were extracted using six properties calculated from gray-level co-occurrence matrices (GLCM), Minkowski Dimensions (MDs), and the estimation of local scaling properties with Scaling Index Method (SIM). A k-nearest-neighbor (k-NN) classifier and a Multilayer Radial Basis Functions Network (RBFN) were optimized in a 10-fold cross-validation for each texture vector, and the classification accuracy was calculated on independent test sets as a quantitative measure of automated tissue characterization. A Wilcoxon signed-rank test was used to compare two accuracy distributions including the Bonferroni correction. The best classification results were obtained by the set of SIM features, which performed significantly better than all the standard GLCM and MD features (p < 0.005) for both classifiers with the highest accuracy (94.1%, 93.7%; for the k-NN and RBFN classifier, respectively). The best standard texture features were the GLCM features 'homogeneity' (91.8%, 87.2%) and 'absolute value' (90.2%, 88.5%). The results indicate that advanced texture features using local scaling properties can provide superior classification performance in computer-assisted diagnosis of interstitial lung diseases when compared to standard texture analysis methods.

Validity of self reported male balding patterns in epidemiological studies

Directory of Open Access Journals (Sweden)

Leavy Justine E

2004-12-01

Full Text Available Abstract Background Several studies have investigated the association between male pattern baldness and disease such as prostate cancer and cardiovascular disease. Limitations in the lack of standardized instruments to measure male pattern baldness have resulted in researchers measuring balding patterns in a variety of ways. This paper examines the accuracy and reliability of assessment of balding patterns by both trained observers and men themselves, using the Hamilton-Norwood classification system. Methods An observational study was carried out in Western Australia with 105 male volunteers aged between 30 and 70 years. Participants completed a short questionnaire and selected a picture that best represented their balding pattern. Two trained data collectors also independently assessed the participant's balding pattern using the same system and the men's self assessment was compared with the trained observer's assessment. In a substudy, observers assessed the balding pattern in a photo of the man aged 35 years while the man independently rated his balding at that age. Results Observers were very reliable in their assessment of balding pattern (85% exact agreement, κ = 0.83. Compared to trained observers, men were moderately accurate in their self-assessment of their balding status (48–55% exact agreement, κ = 0.39–0.46. For the substudy the exact agreement between the men and the observers was 67% and the agreement within balding groups was 87%. Conclusions We recommend that male balding patterns be assessed by trained personnel using the Hamilton-Norwood classification system. Where the use of trained personnel is not feasible, men's self assessment both currently and retrospectively has been shown to be adequate.

Pattern of histological types of breast cancer among various age ...

African Journals Online (AJOL)

, there is need to define the epidemiological pattern of breast cancer patients in the Niger delta; assessing their age distribution and histological types towards improved health care planning. OBJECTIVE: To determine the pattern of ...

Semantic Document Image Classification Based on Valuable Text Pattern

Directory of Open Access Journals (Sweden)

Hossein Pourghassem

2011-01-01

Full Text Available Knowledge extraction from detected document image is a complex problem in the field of information technology. This problem becomes more intricate when we know, a negligible percentage of the detected document images are valuable. In this paper, a segmentation-based classification algorithm is used to analysis the document image. In this algorithm, using a two-stage segmentation approach, regions of the image are detected, and then classified to document and non-document (pure region regions in the hierarchical classification. In this paper, a novel valuable definition is proposed to classify document image in to valuable or invaluable categories. The proposed algorithm is evaluated on a database consisting of the document and non-document image that provide from Internet. Experimental results show the efficiency of the proposed algorithm in the semantic document image classification. The proposed algorithm provides accuracy rate of 98.8% for valuable and invaluable document image classification problem.

Patterns of Age-Associated Degeneration Differ in Shoulder Muscles

Science.gov (United States)

Raz, Yotam; Henseler, Jan F.; Kolk, Arjen; Riaz, Muhammad; van der Zwaal, Peer; Nagels, Jochem; Nelissen, Rob G. H. H.; Raz, Vered

2015-01-01

Shoulder complaints are common in the elderly and hamper daily functioning. These complaints are often caused by tears in the muscle-tendon units of the rotator cuff (RC). The four RC muscles stabilize the shoulder joint. While some RC muscles are frequently torn in shoulder complaints others remain intact. The pathological changes in RC muscles are poorly understood. We investigated changes in RC muscle pathology combining radiological and histological procedures. We measured cross sectional area (CSA) and fatty infiltration from Magnetic Resonance Imaging with Arthrography (MRA) in subjects without (N = 294) and with (N = 109) RC-tears. Normalized muscle CSA of the four RC muscles and the deltoid shoulder muscle were compared and age-associated patterns of muscle atrophy and fatty infiltration were constructed. We identified two distinct age-associated patterns: in the supraspinatus and subscapularis RC muscles CSAs continuously declined throughout adulthood, whereas in the infraspinatus and deltoid reduced CSA was prominent from midlife onwards. In the teres minor, CSA was unchanged with age. Most importantly, age-associated patterns were highly similar between subjects without RC tear and those with RC-tears. This suggests that extensive RC muscle atrophy during aging could contribute to RC pathology. We compared muscle pathology between torn infraspinatus and non-torn teres minor and the deltoid in two patients with a massive RC-tear. In the torn infraspinatus we found pronounced fatty droplets, an increase in extracellular collagen-1, a loss of myosin heavy chain-1 expression in myofibers and an increase in Pax7-positive cells. However, the adjacent intact teres minor and deltoid exhibited healthy muscle features. This suggests that satellite cells and the extracellular matrix may contribute to extensive muscle fibrosis in torn RC. We suggest that torn RC muscles display hallmarks of muscle aging whereas the teres minor could represent an aging

Patterns of age-associated degeneration differ in shoulder muscles

Directory of Open Access Journals (Sweden)

Yotam eRaz

2015-12-01

Full Text Available Shoulder complaints are common in the elderly and hamper daily functioning. These complaints are often caused by tears in the muscle-tendon units of the rotator cuff (RC. The four RC muscles stabilize the shoulder joint. While some RC muscles are frequently torn in shoulder complaints others remain intact. The pathological changes in RC muscles are poorly understood. We investigated changes in RC muscle pathology combining radiological and histological procedures. We measured cross sectional area (CSA and fatty infiltration from Magnetic Resonance Imaging with Arthrography in subjects without (N=294 and with (N=109 RC-tears. Normalized muscle CSA of the four RC muscles and the deltoid shoulder muscle were compared and age-associated patterns of muscle atrophy and fatty infiltration were constructed. We identified two distinct age-associated patterns: in the supraspinatus and subscapularis RC muscles CSAs continuously declined throughout adulthood, whereas in the infraspinatus and deltoid reduced CSA was prominent from midlife onwards. In the teres minor, CSA was unchanged with age. Most importantly, age-associated patterns were highly similar between subjects without RC tear and those with RC-tears. This suggests that extensive RC muscle atrophy during aging could contribute to RC pathology. We compared muscle pathology between torn infraspinatus and non-torn teres minor and the deltoid in two patients with a massive RC-tear. In the torn infraspinatus we found pronounced fatty droplets, an increase in extracellular collagen-1, a loss of myosin heavy chain-1 expression in myofibers and an increase in Pax7-positive cells. However, the adjacent intact teres minor and deltoid exhibited healthy muscle features. This suggests that satellite cells and the extracellular matrix may contribute to extensive muscle fibrosis in torn RC. We suggest that torn RC muscles display hallmarks of muscle aging whereas the teres minor could represent an aging

A novel method for human age group classification based on Correlation Fractal Dimension of facial edges

OpenAIRE

Yarlagadda, Anuradha; Murthy, J.V.R.; Krishna Prasad, M.H.M.

2015-01-01

In the computer vision community, easy categorization of a person’s facial image into various age groups is often quite precise and is not pursued effectively. To address this problem, which is an important area of research, the present paper proposes an innovative method of age group classification system based on the Correlation Fractal Dimension of complex facial image. Wrinkles appear on the face with aging thereby changing the facial edges of the image. The proposed method is rotation an...

Cellular image classification

CERN Document Server

Xu, Xiang; Lin, Feng

2017-01-01

This book introduces new techniques for cellular image feature extraction, pattern recognition and classification. The authors use the antinuclear antibodies (ANAs) in patient serum as the subjects and the Indirect Immunofluorescence (IIF) technique as the imaging protocol to illustrate the applications of the described methods. Throughout the book, the authors provide evaluations for the proposed methods on two publicly available human epithelial (HEp-2) cell datasets: ICPR2012 dataset from the ICPR'12 HEp-2 cell classification contest and ICIP2013 training dataset from the ICIP'13 Competition on cells classification by fluorescent image analysis. First, the reading of imaging results is significantly influenced by one’s qualification and reading systems, causing high intra- and inter-laboratory variance. The authors present a low-order LP21 fiber mode for optical single cell manipulation and imaging staining patterns of HEp-2 cells. A focused four-lobed mode distribution is stable and effective in optical...

Mammogram classification scheme using 2D-discrete wavelet and local binary pattern for detection of breast cancer

Science.gov (United States)

Adi Putra, Januar

2018-04-01

In this paper, we propose a new mammogram classification scheme to classify the breast tissues as normal or abnormal. Feature matrix is generated using Local Binary Pattern to all the detailed coefficients from 2D-DWT of the region of interest (ROI) of a mammogram. Feature selection is done by selecting the relevant features that affect the classification. Feature selection is used to reduce the dimensionality of data and features that are not relevant, in this paper the F-test and Ttest will be performed to the results of the feature extraction dataset to reduce and select the relevant feature. The best features are used in a Neural Network classifier for classification. In this research we use MIAS and DDSM database. In addition to the suggested scheme, the competent schemes are also simulated for comparative analysis. It is observed that the proposed scheme has a better say with respect to accuracy, specificity and sensitivity. Based on experiments, the performance of the proposed scheme can produce high accuracy that is 92.71%, while the lowest accuracy obtained is 77.08%.

Dietary patterns are associated with overweight and obesity in Mexican school-age children.

Science.gov (United States)

Rodríguez-Ramírez, Sonia; Mundo-Rosas, Verónica; García-Guerra, Armando; Shamah-Levy, Teresa

2011-09-01

In Mexico, about one third of school-age population is overweight or obese and the diet is one of the main determinants. The purpose of this study was to identify the dietary patterns of Mexican school-age children and to determine their association with the risk of overweight/obesity. This study included 8252 school-age children who participated in the 2006 National Health and Nutrition Survey (ENSANUT-2006). Dietary data were collected using a 7-day Food Frequency Questionnaire (FFQ). Foods were classified into 25 groups and dietary patterns were defined by cluster analysis. Body Mass Index and prevalence of overweight/obesity were calculated. Logistic regression models were used to evaluate the association between dietary patterns and overweight/obesity. Five dietary patterns were identified: Rural dietary pattern (high intake of tortilla and legumes), sweet cereal and corn dishes pattern (high intake of sugary cereals, tortilla, and maize products); diverse pattern (intake of several food groups); western pattern (high intake of sweetened beverages, fried snacks, industrial snack cakes, and sugary cereals), and whole milk and sweet pattern (high intake of whole milk and sweets). We found that children with sweet cereal and corn dishes and western dietary patterns showed an association with overweight and obesity (prevalence ratio 1.29 and 1.35, respectively, using as reference the rural dietary pattern). Patterns characterized by high intakes of sugary cereals, sweetened beverages, industrial snack, cakes, whole milk, and sweets were associated with a higher risk of overweight/obesity among in Mexican school-age children.

Activation analysis. A basis for chemical similarity and classification

Energy Technology Data Exchange (ETDEWEB)

Beeck, J OP de [Ghent Rijksuniversiteit (Belgium). Instituut voor Kernwetenschappen

1977-01-01

It is shown that activation analysis is especially suited to serve as a basis for determining the chemical similarity between samples defined by their trace-element concentration patterns. The general problem of classification and identification is discussed. The nature of possible classification structures and their appropriate clustering strategies is considered. A practical computer method is suggested and its application as well as the graphical representation of classification results are given. The possibility for classification using information theory is mentioned. Classification of chemical elements is discussed and practically realized after Hadamard transformation of the concentration variation patterns in a series of samples.

Normal cranial bone marrow MR imaging pattern with age-related ADC value distribution

International Nuclear Information System (INIS)

Li Qi; Pan Shinong; Yin Yuming; Li Wei; Chen Zhian; Liu Yunhui; Wu Zhenhua; Guo Qiyong

2011-01-01

Objective: To determine MRI appearances of normal age-related cranial bone marrow and the relationship between MRI patterns and apparent diffusion coefficient (ADC) values. Methods: Five hundred subjects were divided into seven groups based on ages. Cranial bone marrow MRI patterns were performed based on different thickness of the diploe and signal intensity distribution characteristics. ADC values of the frontal, parietal, occipital and temporal bones on DWI were measured and calculated. Correlations between ages and ADC values, between patterns and ADC values, as well as the distribution of ADC values were analyzed. Results: Normal cranial bone marrow was divided into four types and six subtypes, Type I, II, III and IV, which had positive correlation with age increasing (χ 2 = 266.36, P 0.05). In addition, there was significant negative correlation between the ADC values and MRI patterns in the normal parietal and occipital bones (r = -0.691 and -0.750, P < 0.01). Conclusion: The combination of MRI features and ADC values changes in different cranial bones showed significant correlation with age increasing. Familiar with the MRI appearance of the normal bone marrow conversion pattern in different age group and their ADC value will aid the diagnosis and differential of the cranial bone pathology.

Magnetic Resonance Enhancement Patterns at the Different Ages of Symptomatic Osteoporotic Vertebral Compression Fractures

Energy Technology Data Exchange (ETDEWEB)

You, Ja Yeon; Lee, Joon Woo; Kim, Jung Eun; Kang, Heung Sik [Dept. of Radiology, Seoul National University Bundang Hospital, Seongnam (Korea, Republic of)

2013-06-15

To investigate the magnetic resonance (MR) enhancement patterns of symptomatic osteoporotic vertebral compression fracture (VCF) according to the fracture age, based on the successful single-level percutaneous vertebroplasty (PVP) cases. The study included 135 patients who underwent contrast-enhanced MR imaging and successful PVP from 2005 to 2010 due to a single- level osteoporotic VCF. Two radiologists blinded to the fracture age evaluated the MR enhancement patterns in consensus. The MR enhancement patterns were classified according to the enhancing proportion to the vertebral height and the presence or extent of a non-enhancing cleft within the enhancing area on sagittal plane. The Fisher' exact test, Kruskal-Wallis test and Mann-Whitney U test were performed to assess the differences in the MR enhancement patterns according to the fracture age. Symptomatic VCFs show variable MR enhancement patterns in all fracture ages. A diffuse enhancing area can be seen in not only the hyperacute and acute VCFs but also the chronic symptomatic VCFs. Symptomatic VCFs having a segmental enhancing area were all included in the hyperacute or acute stage. Most symptomatic osteoporotic VCFs had a non-enhancing cleft in the enhanced vertebral body (128/135, 94.8%). There was no statistical difference of the enhancement pattern according to the fracture age. Symptomatic VCFs show variable MR enhancement patterns in all fracture ages. The most common pattern is a non-enhancing cleft within a diffuse enhanced vertebra.

Magnetic Resonance Enhancement Patterns at the Different Ages of Symptomatic Osteoporotic Vertebral Compression Fractures

International Nuclear Information System (INIS)

You, Ja Yeon; Lee, Joon Woo; Kim, Jung Eun; Kang, Heung Sik

2013-01-01

To investigate the magnetic resonance (MR) enhancement patterns of symptomatic osteoporotic vertebral compression fracture (VCF) according to the fracture age, based on the successful single-level percutaneous vertebroplasty (PVP) cases. The study included 135 patients who underwent contrast-enhanced MR imaging and successful PVP from 2005 to 2010 due to a single- level osteoporotic VCF. Two radiologists blinded to the fracture age evaluated the MR enhancement patterns in consensus. The MR enhancement patterns were classified according to the enhancing proportion to the vertebral height and the presence or extent of a non-enhancing cleft within the enhancing area on sagittal plane. The Fisher' exact test, Kruskal-Wallis test and Mann-Whitney U test were performed to assess the differences in the MR enhancement patterns according to the fracture age. Symptomatic VCFs show variable MR enhancement patterns in all fracture ages. A diffuse enhancing area can be seen in not only the hyperacute and acute VCFs but also the chronic symptomatic VCFs. Symptomatic VCFs having a segmental enhancing area were all included in the hyperacute or acute stage. Most symptomatic osteoporotic VCFs had a non-enhancing cleft in the enhanced vertebral body (128/135, 94.8%). There was no statistical difference of the enhancement pattern according to the fracture age. Symptomatic VCFs show variable MR enhancement patterns in all fracture ages. The most common pattern is a non-enhancing cleft within a diffuse enhanced vertebra.

Spatial pattern of structural ageing in eastern Croatia: evolution and explanations

Directory of Open Access Journals (Sweden)

Marijan Jukic

2015-11-01

Full Text Available This article aims to examine the ageing situation and social policy issues in the Osijek-Baranja County of eastern Croatia. Using historical evidence from census data, research suggests that the evolution of the ageing pattern has been mainly determined by such factors as development of the transport system, changes in political-territorial organisation, supply of jobs in the cities, deagrarianisation and a domestic war in the 1990s. The increased importance of urban centres, through planned industrialisation and administrative centralisation, has accelerated and intensified rural-tourban migration. Consequently, the spatial pattern of structural ageing has been substantially affected. A significant variation was found in urban and rural areas and also within sub-regional units. The findings suggest that the evolution of spatial disparities in the ageing pattern is because of unplanned migration; spatial differences in the level of socio-economic development; the influence of tradition, such as higher fertility rates historically in some areas; and suburbanisation, notably around the city of Osijek. The article concludes that ageing is affecting the country's economic growth and the formal and informal social support systems, including the provision of resources for older citizens in the endangered areas.

Deep neural network with weight sparsity control and pre-training extracts hierarchical features and enhances classification performance: Evidence from whole-brain resting-state functional connectivity patterns of schizophrenia.

Science.gov (United States)

Kim, Junghoe; Calhoun, Vince D; Shim, Eunsoo; Lee, Jong-Hwan

2016-01-01

Functional connectivity (FC) patterns obtained from resting-state functional magnetic resonance imaging data are commonly employed to study neuropsychiatric conditions by using pattern classifiers such as the support vector machine (SVM). Meanwhile, a deep neural network (DNN) with multiple hidden layers has shown its ability to systematically extract lower-to-higher level information of image and speech data from lower-to-higher hidden layers, markedly enhancing classification accuracy. The objective of this study was to adopt the DNN for whole-brain resting-state FC pattern classification of schizophrenia (SZ) patients vs. healthy controls (HCs) and identification of aberrant FC patterns associated with SZ. We hypothesized that the lower-to-higher level features learned via the DNN would significantly enhance the classification accuracy, and proposed an adaptive learning algorithm to explicitly control the weight sparsity in each hidden layer via L1-norm regularization. Furthermore, the weights were initialized via stacked autoencoder based pre-training to further improve the classification performance. Classification accuracy was systematically evaluated as a function of (1) the number of hidden layers/nodes, (2) the use of L1-norm regularization, (3) the use of the pre-training, (4) the use of framewise displacement (FD) removal, and (5) the use of anatomical/functional parcellation. Using FC patterns from anatomically parcellated regions without FD removal, an error rate of 14.2% was achieved by employing three hidden layers and 50 hidden nodes with both L1-norm regularization and pre-training, which was substantially lower than the error rate from the SVM (22.3%). Moreover, the trained DNN weights (i.e., the learned features) were found to represent the hierarchical organization of aberrant FC patterns in SZ compared with HC. Specifically, pairs of nodes extracted from the lower hidden layer represented sparse FC patterns implicated in SZ, which was

Visual Scanning Patterns and Executive Function in Relation to Facial Emotion Recognition in Aging

Science.gov (United States)

Circelli, Karishma S.; Clark, Uraina S.; Cronin-Golomb, Alice

2012-01-01

Objective The ability to perceive facial emotion varies with age. Relative to younger adults (YA), older adults (OA) are less accurate at identifying fear, anger, and sadness, and more accurate at identifying disgust. Because different emotions are conveyed by different parts of the face, changes in visual scanning patterns may account for age-related variability. We investigated the relation between scanning patterns and recognition of facial emotions. Additionally, as frontal-lobe changes with age may affect scanning patterns and emotion recognition, we examined correlations between scanning parameters and performance on executive function tests. Methods We recorded eye movements from 16 OA (mean age 68.9) and 16 YA (mean age 19.2) while they categorized facial expressions and non-face control images (landscapes), and administered standard tests of executive function. Results OA were less accurate than YA at identifying fear (precognition of sad expressions and with scanning patterns for fearful, sad, and surprised expressions. Conclusion We report significant age-related differences in visual scanning that are specific to faces. The observed relation between scanning patterns and executive function supports the hypothesis that frontal-lobe changes with age may underlie some changes in emotion recognition. PMID:22616800

Classifying Human Activity Patterns from Smartphone Collected GPS data: a Fuzzy Classification and Aggregation Approach.

Science.gov (United States)

Wan, Neng; Lin, Ge

2016-12-01

Smartphones have emerged as a promising type of equipment for monitoring human activities in environmental health studies. However, degraded location accuracy and inconsistency of smartphone-measured GPS data have limited its effectiveness for classifying human activity patterns. This study proposes a fuzzy classification scheme for differentiating human activity patterns from smartphone-collected GPS data. Specifically, a fuzzy logic reasoning was adopted to overcome the influence of location uncertainty by estimating the probability of different activity types for single GPS points. Based on that approach, a segment aggregation method was developed to infer activity patterns, while adjusting for uncertainties of point attributes. Validations of the proposed methods were carried out based on a convenient sample of three subjects with different types of smartphones. The results indicate desirable accuracy (e.g., up to 96% in activity identification) with use of this method. Two examples were provided in the appendix to illustrate how the proposed methods could be applied in environmental health studies. Researchers could tailor this scheme to fit a variety of research topics.

Age-Related Evolution Patterns in Online Handwriting

Science.gov (United States)

2016-01-01

Characterizing age from handwriting (HW) has important applications, as it is key to distinguishing normal HW evolution with age from abnormal HW change, potentially triggered by neurodegenerative decline. We propose, in this work, an original approach for online HW style characterization based on a two-level clustering scheme. The first level generates writer-independent word clusters from raw spatial-dynamic HW information. At the second level, each writer's words are converted into a Bag of Prototype Words that is augmented by an interword stability measure. This two-level HW style representation is input to an unsupervised learning technique, aiming at uncovering HW style categories and their correlation with age. To assess the effectiveness of our approach, we propose information theoretic measures to quantify the gain on age information from each clustering layer. We have carried out extensive experiments on a large public online HW database, augmented by HW samples acquired at Broca Hospital in Paris from people mostly between 60 and 85 years old. Unlike previous works claiming that there is only one pattern of HW change with age, our study reveals three major aging HW styles, one specific to aged people and the two others shared by other age groups. PMID:27752277

A support vector machine classifier reduces interscanner variation in the HRCT classification of regional disease pattern in diffuse lung disease: Comparison to a Bayesian classifier

Energy Technology Data Exchange (ETDEWEB)

Chang, Yongjun; Lim, Jonghyuck; Kim, Namkug; Seo, Joon Beom [Department of Radiology, University of Ulsan College of Medicine, 388-1 Pungnap2-dong, Songpa-gu, Seoul 138-736 (Korea, Republic of); Lynch, David A. [Department of Radiology, National Jewish Medical and Research Center, Denver, Colorado 80206 (United States)

2013-05-15

Purpose: To investigate the effect of using different computed tomography (CT) scanners on the accuracy of high-resolution CT (HRCT) images in classifying regional disease patterns in patients with diffuse lung disease, support vector machine (SVM) and Bayesian classifiers were applied to multicenter data. Methods: Two experienced radiologists marked sets of 600 rectangular 20 Multiplication-Sign 20 pixel regions of interest (ROIs) on HRCT images obtained from two scanners (GE and Siemens), including 100 ROIs for each of local patterns of lungs-normal lung and five of regional pulmonary disease patterns (ground-glass opacity, reticular opacity, honeycombing, emphysema, and consolidation). Each ROI was assessed using 22 quantitative features belonging to one of the following descriptors: histogram, gradient, run-length, gray level co-occurrence matrix, low-attenuation area cluster, and top-hat transform. For automatic classification, a Bayesian classifier and a SVM classifier were compared under three different conditions. First, classification accuracies were estimated using data from each scanner. Next, data from the GE and Siemens scanners were used for training and testing, respectively, and vice versa. Finally, all ROI data were integrated regardless of the scanner type and were then trained and tested together. All experiments were performed based on forward feature selection and fivefold cross-validation with 20 repetitions. Results: For each scanner, better classification accuracies were achieved with the SVM classifier than the Bayesian classifier (92% and 82%, respectively, for the GE scanner; and 92% and 86%, respectively, for the Siemens scanner). The classification accuracies were 82%/72% for training with GE data and testing with Siemens data, and 79%/72% for the reverse. The use of training and test data obtained from the HRCT images of different scanners lowered the classification accuracy compared to the use of HRCT images from the same scanner. For

A support vector machine classifier reduces interscanner variation in the HRCT classification of regional disease pattern in diffuse lung disease: Comparison to a Bayesian classifier

International Nuclear Information System (INIS)

Chang, Yongjun; Lim, Jonghyuck; Kim, Namkug; Seo, Joon Beom; Lynch, David A.

2013-01-01

Purpose: To investigate the effect of using different computed tomography (CT) scanners on the accuracy of high-resolution CT (HRCT) images in classifying regional disease patterns in patients with diffuse lung disease, support vector machine (SVM) and Bayesian classifiers were applied to multicenter data. Methods: Two experienced radiologists marked sets of 600 rectangular 20 × 20 pixel regions of interest (ROIs) on HRCT images obtained from two scanners (GE and Siemens), including 100 ROIs for each of local patterns of lungs—normal lung and five of regional pulmonary disease patterns (ground-glass opacity, reticular opacity, honeycombing, emphysema, and consolidation). Each ROI was assessed using 22 quantitative features belonging to one of the following descriptors: histogram, gradient, run-length, gray level co-occurrence matrix, low-attenuation area cluster, and top-hat transform. For automatic classification, a Bayesian classifier and a SVM classifier were compared under three different conditions. First, classification accuracies were estimated using data from each scanner. Next, data from the GE and Siemens scanners were used for training and testing, respectively, and vice versa. Finally, all ROI data were integrated regardless of the scanner type and were then trained and tested together. All experiments were performed based on forward feature selection and fivefold cross-validation with 20 repetitions. Results: For each scanner, better classification accuracies were achieved with the SVM classifier than the Bayesian classifier (92% and 82%, respectively, for the GE scanner; and 92% and 86%, respectively, for the Siemens scanner). The classification accuracies were 82%/72% for training with GE data and testing with Siemens data, and 79%/72% for the reverse. The use of training and test data obtained from the HRCT images of different scanners lowered the classification accuracy compared to the use of HRCT images from the same scanner. For integrated ROI

Age at menarche and menstrual pattern in secondary schoolgirls in ...

African Journals Online (AJOL)

Menarche is the first menstruation in the life of a woman. Menstrual pattern involves the length of bleeding, the length of the cycle and other associated events such as pain ( ). Dysmenorrhoea has been identified as a reason for school absenteeismin girls. To determine the mean age at menarche and pattern of ...

Forensic age assessment by 3.0T MRI of the knee: proposal of a new MRI classification of ossification stages.

Science.gov (United States)

Vieth, Volker; Schulz, Ronald; Heindel, Walter; Pfeiffer, Heidi; Buerke, Boris; Schmeling, Andreas; Ottow, Christian

2018-03-13

To explore the possibility of determining majority via a morphology-based examination of the epiphyseal-diaphyseal fusion by 3.0 T magnetic resonance imaging (MRI), a prospective cross-sectional study developing and applying a new stage classification was conducted. 344 male and 350 female volunteers of German nationality between the ages of 12-24 years were scanned between May 2013 and June 2015. A 3.0 T MRI scanner was used, acquiring a T1-weighted (T1-w) turbo spin-echo sequence (TSE) and a T2-weighted (T2-w) TSE sequence with fat suppression by spectral pre-saturation with inversion recovery (SPIR). The gathered information was sifted and a five-stage classification was formulated as a hypothesis. The images were then assessed using this classification. The relevant statistics were defined, the intra- and interobserver agreements were determined, and the differences between the sexes were analysed. The application of the new classification made it possible to correctly assess majority in both sexes by the examination of the epiphyses of the knee joint. The intra- and interobserver agreement levels were very good (κ > 0.80). The Mann-Whitney-U Test implied significant sex-related differences for most stages. Applying the presented MRI classification, it is possible to determine the completion of the 18th year of life in either sex by 3.0 T MRI of the knee joint. • Based on prospective referential data a new MRI classification was formulated. • The setting allows assessment of the age of an individual's skeletal development. • The classification scheme allows the reliable determination of majority in both sexes. • The staging shows a high reproducibility for instructed and trained professional personnel. • The proposed classification is likely to be adaptable to other long bone epiphyses.

Prevalence of rheumatoid arthritis in persons 60 years of age and older in the United States: effect of different methods of case classification.

Science.gov (United States)

Rasch, Elizabeth K; Hirsch, Rosemarie; Paulose-Ram, Ryne; Hochberg, Marc C

2003-04-01

To determine prevalence estimates for rheumatoid arthritis (RA) in noninstitutionalized older adults in the US. Prevalence estimates were compared using 3 different classification methods based on current classification criteria for RA. Data from the Third National Health and Nutrition Examination Survey (NHANES-III) were used to generate prevalence estimates by 3 classification methods in persons 60 years of age and older (n = 5,302). Method 1 applied the "n of k" rule, such that subjects who met 3 of 6 of the American College of Rheumatology (ACR) 1987 criteria were classified as having RA (data from hand radiographs were not available). In method 2, the ACR classification tree algorithm was applied. For method 3, medication data were used to augment case identification via method 2. Population prevalence estimates and 95% confidence intervals (95% CIs) were determined using the 3 methods on data stratified by sex, race/ethnicity, age, and education. Overall prevalence estimates using the 3 classification methods were 2.03% (95% CI 1.30-2.76), 2.15% (95% CI 1.43-2.87), and 2.34% (95% CI 1.66-3.02), respectively. The prevalence of RA was generally greater in the following groups: women, Mexican Americans, respondents with less education, and respondents who were 70 years of age and older. The prevalence of RA in persons 60 years of age and older is approximately 2%, representing the proportion of the US elderly population who will most likely require medical intervention because of disease activity. Different classification methods yielded similar prevalence estimates, although detection of RA was enhanced by incorporation of data on use of prescription medications, an important consideration in large population surveys.

An objective daily Weather Type classification for Iberia since 1850; patterns, trends, variability and impact in precipitation

Science.gov (United States)

Ramos, A. M.; Trigo, R. M.; Lorenzo, M. N.; Vaquero, J. M.; Gallego, M. C.; Valente, M. A.; Gimeno, L.

2009-04-01

In recent years a large number of automated classifications of atmospheric circulation patterns have been published covering the entire European continent or specific sub-regions (Huth et al., 2008). This generalized use of objective classifications results from their relatively straightforward computation but crucially from their capacity to provide simple description of typical synoptic conditions as well as their climatic and environmental impact. For this purpose, the vast majority of authors has employed the Reanalyses datasets, namely from either NCEP/NCAR or ECMWF projects. However, both these widely used datasets suffer from important caveats, namely their restricted temporal coverage, that is limited to the last six decades (NCEP/NCAR since 1948 and ECMWF since 1958). This limitation has been partially mitigated by the recent availability of continuous daily mean sea level pressure obtained within the European project EMULATE, that extended the historic records over the extra-tropical Atlantic and Europe (70°-25° N by 70° W-50° E), for the period 1850 to the present (Ansell, T. J. et al. 2006). Here we have used the extended EMULATE dataset to construct an automated version of the Lamb Weather type (WTs) classification scheme (Jones et al 1993) adapted for the center of the Iberian Peninsula. We have identified 10 basic WTs (Cyclonic, Anticyclonic and 8 directional types) following a similar methodology to that previously adopted by Trigo and DaCamara, 2000 (for Portugal) and Lorenzo et al. 2008 (for Galicia, northwestern Iberia). We have evaluated trends of monthly/seasonal frequency of each WT for the entire period and several shorter periods. Finally, we use the long-term precipitation time series from Lisbon (recently digitized) and Cadiz (southern Spain) to evaluate, the impact of each WT on the precipitation regime. It is shown that the Anticyclonic (A) type, although being the most frequent class in winter, gives a rather small contribution to

Sensing Urban Land-Use Patterns by Integrating Google Tensorflow and Scene-Classification Models

Science.gov (United States)

Yao, Y.; Liang, H.; Li, X.; Zhang, J.; He, J.

2017-09-01

With the rapid progress of China's urbanization, research on the automatic detection of land-use patterns in Chinese cities is of substantial importance. Deep learning is an effective method to extract image features. To take advantage of the deep-learning method in detecting urban land-use patterns, we applied a transfer-learning-based remote-sensing image approach to extract and classify features. Using the Google Tensorflow framework, a powerful convolution neural network (CNN) library was created. First, the transferred model was previously trained on ImageNet, one of the largest object-image data sets, to fully develop the model's ability to generate feature vectors of standard remote-sensing land-cover data sets (UC Merced and WHU-SIRI). Then, a random-forest-based classifier was constructed and trained on these generated vectors to classify the actual urban land-use pattern on the scale of traffic analysis zones (TAZs). To avoid the multi-scale effect of remote-sensing imagery, a large random patch (LRP) method was used. The proposed method could efficiently obtain acceptable accuracy (OA = 0.794, Kappa = 0.737) for the study area. In addition, the results show that the proposed method can effectively overcome the multi-scale effect that occurs in urban land-use classification at the irregular land-parcel level. The proposed method can help planners monitor dynamic urban land use and evaluate the impact of urban-planning schemes.

Pap-smear Benchmark Data For Pattern Classification

DEFF Research Database (Denmark)

Jantzen, Jan; Norup, Jonas; Dounias, Georgios

2005-01-01

This case study provides data and a baseline for comparing classification methods. The data consists of 917 images of Pap-smear cells, classified carefully by cyto-technicians and doctors. Each cell is described by 20 numerical features, and the cells fall into 7 classes. A basic data analysis in...

Disorder-specific predictive classification of adolescents with attention deficit hyperactivity disorder (ADHD relative to autism using structural magnetic resonance imaging.

Directory of Open Access Journals (Sweden)

Lena Lim

Full Text Available Attention Deficit Hyperactivity Disorder (ADHD is a neurodevelopmental disorder, but diagnosed by subjective clinical and rating measures. The study's aim was to apply Gaussian process classification (GPC to grey matter (GM volumetric data, to assess whether individual ADHD adolescents can be accurately differentiated from healthy controls based on objective, brain structure measures and whether this is disorder-specific relative to autism spectrum disorder (ASD.Twenty-nine adolescent ADHD boys and 29 age-matched healthy and 19 boys with ASD were scanned. GPC was applied to make disorder-specific predictions of ADHD diagnostic status based on individual brain structure patterns. In addition, voxel-based morphometry (VBM analysis tested for traditional univariate group level differences in GM.The pattern of GM correctly classified 75.9% of patients and 82.8% of controls, achieving an overall classification accuracy of 79.3%. Furthermore, classification was disorder-specific relative to ASD. The discriminating GM patterns showed higher classification weights for ADHD in earlier developing ventrolateral/premotor fronto-temporo-limbic and stronger classification weights for healthy controls in later developing dorsolateral fronto-striato-parieto-cerebellar networks. Several regions were also decreased in GM in ADHD relative to healthy controls in the univariate VBM analysis, suggesting they are GM deficit areas.The study provides evidence that pattern recognition analysis can provide significant individual diagnostic classification of ADHD patients and healthy controls based on distributed GM patterns with 79.3% accuracy and that this is disorder-specific relative to ASD. Findings are a promising first step towards finding an objective differential diagnostic tool based on brain imaging measures to aid with the subjective clinical diagnosis of ADHD.

A simple phenotypic classification for celiac disease

Directory of Open Access Journals (Sweden)

Ajit Sood

2018-04-01

Full Text Available Background/Aims : Celiac disease is a global health problem. The presentation of celiac disease has unfolded over years and it is now known that it can manifest at different ages, has varied presentations, and is prone to develop complications, if not managed properly. Although the Oslo definitions provide consensus on the various terminologies used in literature, there is no phenotypic classification providing a composite diagnosis for the disease. Methods : Various variables identified for phenotypic classification included age at diagnosis, age at onset of symptoms, clinical presentation, family history and complications. These were applied to the existing registry of 1,664 patients at Dayanand Medical College and Hospital, Ludhiana, India. In addition, age was evaluated as below 15 and below 18 years. Cross tabulations were used for the verification of the classification using the existing data. Expert opinion was sought from both international and national experts of varying fields. Results : After empirical verification, age at diagnosis was considered appropriate in between A1 (<18 and A2 (≧18. The disease presentation has been classified into 3 types–P1 (classical, P2 (non-classical and P3 (asymptomatic. Complications were considered as absent (C0 or present (C1. A single phenotypic classification based on these 3 characteristics, namely age at the diagnosis, clinical presentation, and intestinal complications (APC classification was derived. Conclusions : APC classification (age at diagnosis, presentation, complications is a simple disease explanatory classification for patients with celiac disease aimed at providing a composite diagnosis.

A Multi-layer Hybrid Framework for Dimensional Emotion Classification

NARCIS (Netherlands)

Nicolaou, Mihalis A.; Gunes, Hatice; Pantic, Maja

2011-01-01

This paper investigates dimensional emotion prediction and classification from naturalistic facial expressions. Similarly to many pattern recognition problems, dimensional emotion classification requires generating multi-dimensional outputs. To date, classification for valence and arousal dimensions

Comparison of an automated classification system with an empirical classification of circulation patterns over the Pannonian basin, Central Europe

Science.gov (United States)

Maheras, Panagiotis; Tolika, Konstantia; Tegoulias, Ioannis; Anagnostopoulou, Christina; Szpirosz, Klicász; Károssy, Csaba; Makra, László

2018-04-01

The aim of the study is to compare the performance of the two classification methods, based on the atmospheric circulation types over the Pannonian basin in Central Europe. Moreover, relationships including seasonal occurrences and correlation coefficients, as well as comparative diagrams of the seasonal occurrences of the circulation types of the two classification systems are presented. When comparing of the automated (objective) and empirical (subjective) classification methods, it was found that the frequency of the empirical anticyclonic (cyclonic) types is much higher (lower) than that of the automated anticyclonic (cyclonic) types both on an annual and seasonal basis. The highest and statistically significant correlations between the circulation types of the two classification systems, as well as those between the cumulated seasonal anticyclonic and cyclonic types occur in winter for both classifications, since the weather-influencing effect of the atmospheric circulation in this season is the most prevalent. Precipitation amounts in Budapest display a decreasing trend in accordance with the decrease in the occurrence of the automated cyclonic types. In contrast, the occurrence of the empirical cyclonic types displays an increasing trend. There occur types in a given classification that are usually accompanied by high ratios of certain types in the other classification.

Improved prognostic classification of breast cancer defined by antagonistic activation patterns of immune response pathway modules

International Nuclear Information System (INIS)

Teschendorff, Andrew E; Gomez, Sergio; Arenas, Alex; El-Ashry, Dorraya; Schmidt, Marcus; Gehrmann, Mathias; Caldas, Carlos

2010-01-01

Elucidating the activation pattern of molecular pathways across a given tumour type is a key challenge necessary for understanding the heterogeneity in clinical response and for developing novel more effective therapies. Gene expression signatures of molecular pathway activation derived from perturbation experiments in model systems as well as structural models of molecular interactions ('model signatures') constitute an important resource for estimating corresponding activation levels in tumours. However, relatively few strategies for estimating pathway activity from such model signatures exist and only few studies have used activation patterns of pathways to refine molecular classifications of cancer. Here we propose a novel network-based method for estimating pathway activation in tumours from model signatures. We find that although the pathway networks inferred from cancer expression data are highly consistent with the prior information contained in the model signatures, that they also exhibit a highly modular structure and that estimation of pathway activity is dependent on this modular structure. We apply our methodology to a panel of 438 estrogen receptor negative (ER-) and 785 estrogen receptor positive (ER+) breast cancers to infer activation patterns of important cancer related molecular pathways. We show that in ER negative basal and HER2+ breast cancer, gene expression modules reflecting T-cell helper-1 (Th1) and T-cell helper-2 (Th2) mediated immune responses play antagonistic roles as major risk factors for distant metastasis. Using Boolean interaction Cox-regression models to identify non-linear pathway combinations associated with clinical outcome, we show that simultaneous high activation of Th1 and low activation of a TGF-beta pathway module defines a subtype of particularly good prognosis and that this classification provides a better prognostic model than those based on the individual pathways. In ER+ breast cancer, we find that

Assessment and classification of psychopathology in epidemiological research of children 0-3 years of age: a review of the literature

DEFF Research Database (Denmark)

Skovgaard, A M; Houmann, T; Landorph, S L

2004-01-01

, such as the Bayley Scales, and relationship assessments, such as the Early Relational Assessment (ERA). The classification of psychopathology in young children can be approved by the Diagnostic Classification 0-3. The reliability and validity of DC 0-3 have not yet been established, but preliminary results seem...... promising. The demands made on diagnostic assessment procedures in epidemiological research of children 0-3 years of age can be met by a combination of well-established research instruments, such as the CBCL, with in-depth clinical assessment procedures, such as the Bayley Scales and the ERA, and diagnostic...... classification by DC 0-3....

Dream content of Canadian males from adolescence to old age: An exploration of ontogenetic patterns.

Science.gov (United States)

Dale, Allyson; Lafrenière, Alexandre; De Koninck, Joseph

2017-03-01

The present study was a first look at the ontogenetic pattern of dream content across the lifespan for men. The participants included 50 Canadian men in each of 5 age groups, from adolescence to old age including 12-17, 18-24, 25-39, 40-64, and 65-85. The last age group included 31 participants, totaling 231 males. One dream per participant was scored by two independent judges using content analysis. Trend analysis was used to determine the lifespan-developmental pattern of the dream content categories. Results demonstrated a predominance of aggressive dream imagery in the adolescent age group in line with social-developmental research. These patterns of dream imagery reflect the waking developmental patterns as proposed by social theories and recognized features of aging. Limitations and suggestions for future research, including the examining of the developmental pattern of gender differences across the lifespan, are discussed. Copyright © 2017 Elsevier Inc. All rights reserved.

Nutritional status and morbidity pattern in school age children in Nepal

Directory of Open Access Journals (Sweden)

N Bhandari

2012-09-01

Full Text Available School Health has been regarded as a high priority intervention in developing countries. However it has not been prioritized in Nepal for many years. The objectives of the study are to find out the nutritional status and morbidity pattern in school age children. To arouse importance of personal hygiene and healthful surrounding through information, education and communication (IEC. This cross-sectional study was administered in two schools located in Bolde phedeche and Mahure of Kavrepalanchowk. From the selected schools, a total number of 160 students studying from Grade 1 to V were enumerated in the study using census survey method. Among 160 students, the most important three problems were pediculosis 42(26.2 %, dental caries 29(18.1%, and waxy ear 27(17.1 %. Thus the school health education should put more emphasis on oral care, nutrition, personal hygiene and others. Applying classification of Indian Academy of Pediatrics: based on weight for age, 36(55.3% boys and 34(35.8% girls fall under 1st degree malnutrition and 15(23.07% boys and 44(46.3% girls fall under IInd degree malnutrition, 7(7.2 % girls fall under IIIrd degree malnutrition.The health and nutritional standards of school children in this study were found to be unsatisfactory. Among different morbidity pediculosis is found more in girls. The present study put more emphasis on the need for initiation of school health program in the school with more on improving personal hygiene, prevention of disease like parasitic infection/infestation and improvement of their nutritional status. Journal of College of Medical Sciences-Nepal,2012,Vol-8,No-2, 12-16 DOI: http://dx.doi.org/10.3126/jcmsn.v8i2.6832

The Developing, Aging Neocortex: How genetics and epigenetics influence early developmental patterning and age-related change.

Directory of Open Access Journals (Sweden)

Kelly J. Huffman

2012-10-01

Full Text Available A hallmark of mammalian development is the generation of functional subdivisions within the nervous system. In humans, this regionalization creates a complex system that regulates behavior, cognition, memory and emotion. During development, specification of neocortical tissue that leads to functional sensory and motor regions results from an interplay between cortically intrinsic, molecular processes, such as gene expression, and extrinsic processes regulated by sensory input. Cortical specification in mice occurs pre- and perinatally, when gene expression is robust and various anatomical distinctions are observed alongside an emergence of physiological function. After patterning, gene expression continues to shift and axonal connections mature into an adult form. The function of adult cortical gene expression may be to maintain neocortical subdivisions that were established during early patterning. As some changes in neocortical gene expression have been observed past early development into late adulthood, gene expression may also play a role in the altered neocortical function observed in age-related cognitive decline and brain dysfunction. This review provides a discussion of how neocortical gene expression and specific patterns of neocortical sensori-motor axonal connections develop and change throughout the lifespan of the animal. We posit that a role of neocortical gene expression in neocortex is to regulate plasticity mechanisms that impact critical periods for sensory and motor plasticity in aging. We describe results from several studies in aging brain that detail changes in gene expression that may relate to microstructural changes observed in brain anatomy. We discuss the role of altered glucocorticoid signaling in age-related cognitive and functional decline, as well as how aging in the brain may result from immune system activation. We describe how caloric restriction or reduction of oxidative stress may ameliorate effects of aging

SENSING URBAN LAND-USE PATTERNS BY INTEGRATING GOOGLE TENSORFLOW AND SCENE-CLASSIFICATION MODELS

Directory of Open Access Journals (Sweden)

Y. Yao

2017-09-01

Full Text Available With the rapid progress of China’s urbanization, research on the automatic detection of land-use patterns in Chinese cities is of substantial importance. Deep learning is an effective method to extract image features. To take advantage of the deep-learning method in detecting urban land-use patterns, we applied a transfer-learning-based remote-sensing image approach to extract and classify features. Using the Google Tensorflow framework, a powerful convolution neural network (CNN library was created. First, the transferred model was previously trained on ImageNet, one of the largest object-image data sets, to fully develop the model’s ability to generate feature vectors of standard remote-sensing land-cover data sets (UC Merced and WHU-SIRI. Then, a random-forest-based classifier was constructed and trained on these generated vectors to classify the actual urban land-use pattern on the scale of traffic analysis zones (TAZs. To avoid the multi-scale effect of remote-sensing imagery, a large random patch (LRP method was used. The proposed method could efficiently obtain acceptable accuracy (OA = 0.794, Kappa = 0.737 for the study area. In addition, the results show that the proposed method can effectively overcome the multi-scale effect that occurs in urban land-use classification at the irregular land-parcel level. The proposed method can help planners monitor dynamic urban land use and evaluate the impact of urban-planning schemes.

[Gait characteristics of women with fibromyalgia: a premature aging pattern].

Science.gov (United States)

Góes, Suelen M; Leite, Neiva; de Souza, Ricardo M; Homann, Diogo; Osiecki, Ana C V; Stefanello, Joice M F; Rodacki, André L F

2014-01-01

Fibromyalgia is a condition which involves chronic pain. Middle-aged individuals with fibromyalgia seem to exhibit changes in gait pattern, which may prematurely expose them to a gait pattern which resembles that found in the elderly population. To determine the 3D spatial (linear and angular) gait parameters of middle-aged women with fibromyalgia and compare to elderly women without this condition. 25 women (10 in the fibromyalgia group and 15 in the elderly group) volunteered to participate in the study. Kinematics was performed using an optoelectronic system, and linear and angular kinematic variables were determined. There was no difference in walking speed, stride length, cadence, hip, knee and ankle joints range of motion between groups, except the pelvic rotation, in which the fibromyalgia group showed greater rotation (P<0.05) compared to the elderly group. Also, there was a negative correlation with pelvic rotation and gluteus pain (r = -0.69; P<0.05), and between pelvic obliquity and greater trochanter pain (r = -0.69; P<0.05) in the fibromyalgia group. Middle-aged women with fibromyalgia showed gait pattern resemblances to elderly, women, which is characterized by reduced lower limb ROM, stride length and walking speed. Copyright © 2014 Elsevier Editora Ltda. All rights reserved.

Age differences in visual search for compound patterns: long- versus short-range grouping.

Science.gov (United States)

Burack, J A; Enns, J T; Iarocci, G; Randolph, B

2000-11-01

Visual search for compound patterns was examined in observers aged 6, 8, 10, and 22 years. The main question was whether age-related improvement in search rate (response time slope over number of items) was different for patterns defined by short- versus long-range spatial relations. Perceptual access to each type of relation was varied by using elements of same contrast (easy to access) or mixed contrast (hard to access). The results showed large improvements with age in search rate for long-range targets; search rate for short-range targets was fairly constant across age. This pattern held regardless of whether perceptual access to a target was easy or hard, supporting the hypothesis that different processes are involved in perceptual grouping at these two levels. The results also point to important links between ontogenic and microgenic change in perception (H. Werner, 1948, 1957).

Algorithms for adaptive nonlinear pattern recognition

Science.gov (United States)

Schmalz, Mark S.; Ritter, Gerhard X.; Hayden, Eric; Key, Gary

2011-09-01

In Bayesian pattern recognition research, static classifiers have featured prominently in the literature. A static classifier is essentially based on a static model of input statistics, thereby assuming input ergodicity that is not realistic in practice. Classical Bayesian approaches attempt to circumvent the limitations of static classifiers, which can include brittleness and narrow coverage, by training extensively on a data set that is assumed to cover more than the subtense of expected input. Such assumptions are not realistic for more complex pattern classification tasks, for example, object detection using pattern classification applied to the output of computer vision filters. In contrast, we have developed a two step process, that can render the majority of static classifiers adaptive, such that the tracking of input nonergodicities is supported. Firstly, we developed operations that dynamically insert (or resp. delete) training patterns into (resp. from) the classifier's pattern database, without requiring that the classifier's internal representation of its training database be completely recomputed. Secondly, we developed and applied a pattern replacement algorithm that uses the aforementioned pattern insertion/deletion operations. This algorithm is designed to optimize the pattern database for a given set of performance measures, thereby supporting closed-loop, performance-directed optimization. This paper presents theory and algorithmic approaches for the efficient computation of adaptive linear and nonlinear pattern recognition operators that use our pattern insertion/deletion technology - in particular, tabular nearest-neighbor encoding (TNE) and lattice associative memories (LAMs). Of particular interest is the classification of nonergodic datastreams that have noise corruption with time-varying statistics. The TNE and LAM based classifiers discussed herein have been successfully applied to the computation of object classification in hyperspectral

Role of pattern recognition receptors of the neurovascular unit in inflamm-aging.

Science.gov (United States)

Wilhelm, Imola; Nyúl-Tóth, Ádám; Kozma, Mihály; Farkas, Attila E; Krizbai, István A

2017-11-01

Aging is associated with chronic inflammation partly mediated by increased levels of damage-associated molecular patterns, which activate pattern recognition receptors (PRRs) of the innate immune system. Furthermore, many aging-related disorders are associated with inflammation. PRRs, such as Toll-like receptors (TLRs) and nucleotide-binding oligomerization domain-like receptors (NLRs), are expressed not only in cells of the innate immune system but also in other cells, including cells of the neurovascular unit and cerebral vasculature forming the blood-brain barrier. In this review, we summarize our present knowledge about the relationship between activation of PRRs expressed by cells of the neurovascular unit-blood-brain barrier, chronic inflammation, and aging-related pathologies of the brain. The most important damage-associated molecular pattern-sensing PRRs in the brain are TLR2, TLR4, and NLR family pyrin domain-containing protein-1 and pyrin domain-containing protein-3, which are activated during physiological and pathological aging in microglia, neurons, astrocytes, and possibly endothelial cells and pericytes. Copyright © 2017 the American Physiological Society.

The Ross classification for heart failure in children after 25 years: a review and an age-stratified revision.

Science.gov (United States)

Ross, Robert D

2012-12-01

Accurate grading of the presence and severity of heart failure (HF) signs and symptoms in infants and children remains challenging. It has been 25 years since the Ross classification was first used for this purpose. Since then, several modifications of the system have been used and others proposed. New evidence has shown that in addition to signs and symptoms, data from echocardiography, exercise testing, and biomarkers such as N-terminal pro-brain natriuretic peptide (NT-proBNP) all are useful in stratifying outcomes for children with HF. It also is apparent that grading of signs and symptoms in children is dependent on age because infants manifest HF differently than toddlers and older children. This review culminates in a proposed new age-based Ross classification for HF in children that incorporates the most useful data from the last two decades. Testing of this new system will be important to determine whether an age-stratified scoring system can unify the way communication of HF severity and research on HF in children is performed in the future.

Swimming level classification of young school age children and their success in a long distance swimming test

OpenAIRE

Nováková, Martina

2010-01-01

Title: Swimming level classification of young school age children and their success in a long distance swimming test Work objectives: The outcome of our work is comparison and evaluation of the initial and final swimming lenght in a test of long distance swimming. This test is taken during one swimming course. Methodology: Data which were obtained by testing a certain group of people and were statistically processed, showed the swimming level and performance of the young school age children. ...

Rhythmic EEG patterns in extremely preterm infants : Classification and association with brain injury and outcome

NARCIS (Netherlands)

Weeke, Lauren C; van Ooijen, Inge M; Groenendaal, Floris; van Huffelen, Alexander C.; van Haastert, Ingrid C; van Stam, Carolien; Benders, Manon J; Toet, Mona C; Hellström-Westas, Lena; de Vries, Linda S

2017-01-01

OBJECTIVE: Classify rhythmic EEG patterns in extremely preterm infants and relate these to brain injury and outcome. METHODS: Retrospective analysis of 77 infants born <28 weeks gestational age (GA) who had a 2-channel EEG during the first 72 h after birth. Patterns detected by the BrainZ seizure

Morphological pattern of parotid gland tumors

International Nuclear Information System (INIS)

Musani, M.A.; Zafar, A.; Malik, S.

2008-01-01

To determine the morphological pattern of parotid tumours. During this study, 204 patients with parotid tumours were registered. The patients of all ages and both gender were included in this study. All patients were evaluated by history, clinical examination, F.N.A.C. and ultrasound, C.T/MRI was done in selected cases. All patients were surgically managed and their tumour specimen was sent for histopathology. Classification of individual tumour was based on 1991 World Health Organization Classification. Discrete data was presented in percentage and proportions. Out of 204 cases, 152 (74.5%) were benign and 52 (25.5%) were malignant. Of these, 117 (57.35%) patients were females and 87 (42.65%) males. Benign tumours were more common in females whereas malignant tumours were common in males. The mean age of patients was 34 years and 42 years for benign and malignant tumours respectively. Pleomorphic adenoma was most common benign tumor (83.5%), followed by Warthins tumour. The most common malignant tumour was mucoepidermoid carcinoma (60%), followed by adenoid cystic carcinoma. Superficial lobe of parotid gland was the commonest site, 120 benign and all 52 malignant tumours arising from it while 32 benign tumours originated from deep lobe. Parotid swelling for years was main feature of benign tumours, whereas malignant tumours presented with pain, fixation to skin or underlying structure, cervical lymphadenopathy and facial palsy. Pleomorphic adenoma was the most common benign tumour and mucoepidermoid carcinoma was most common malignant tumour. The morphological patterns and distribution followed the known pattern. (author)

Fuzzy Pattern Classification Based Detection of Faulty Electronic Fuel Control (EFC Valves Used in Diesel Engines

Directory of Open Access Journals (Sweden)

Umut Tugsal

2014-05-01

Full Text Available In this paper, we develop mathematical models of a rotary Electronic Fuel Control (EFC valve used in a Diesel engine based on dynamic performance test data and system identification methodology in order to detect the faulty EFC valves. The model takes into account the dynamics of the electrical and mechanical portions of the EFC valves. A recursive least squares (RLS type system identification methodology has been utilized to determine the transfer functions of the different types of EFC valves that were investigated in this study. Both in frequency domain and time domain methods have been utilized for this purpose. Based on the characteristic patterns exhibited by the EFC valves, a fuzzy logic based pattern classification method was utilized to evaluate the residuals and identify faulty EFC valves from good ones. The developed methodology has been shown to provide robust diagnostics for a wide range of EFC valves.

Automated classification of immunostaining patterns in breast tissue from the human protein atlas.

Science.gov (United States)

Swamidoss, Issac Niwas; Kårsnäs, Andreas; Uhlmann, Virginie; Ponnusamy, Palanisamy; Kampf, Caroline; Simonsson, Martin; Wählby, Carolina; Strand, Robin

2013-01-01

The Human Protein Atlas (HPA) is an effort to map the location of all human proteins (http://www.proteinatlas.org/). It contains a large number of histological images of sections from human tissue. Tissue micro arrays (TMA) are imaged by a slide scanning microscope, and each image represents a thin slice of a tissue core with a dark brown antibody specific stain and a blue counter stain. When generating antibodies for protein profiling of the human proteome, an important step in the quality control is to compare staining patterns of different antibodies directed towards the same protein. This comparison is an ultimate control that the antibody recognizes the right protein. In this paper, we propose and evaluate different approaches for classifying sub-cellular antibody staining patterns in breast tissue samples. The proposed methods include the computation of various features including gray level co-occurrence matrix (GLCM) features, complex wavelet co-occurrence matrix (CWCM) features, and weighted neighbor distance using compound hierarchy of algorithms representing morphology (WND-CHARM)-inspired features. The extracted features are used into two different multivariate classifiers (support vector machine (SVM) and linear discriminant analysis (LDA) classifier). Before extracting features, we use color deconvolution to separate different tissue components, such as the brownly stained positive regions and the blue cellular regions, in the immuno-stained TMA images of breast tissue. We present classification results based on combinations of feature measurements. The proposed complex wavelet features and the WND-CHARM features have accuracy similar to that of a human expert. Both human experts and the proposed automated methods have difficulties discriminating between nuclear and cytoplasmic staining patterns. This is to a large extent due to mixed staining of nucleus and cytoplasm. Methods for quantification of staining patterns in histopathology have many

Automated classification of immunostaining patterns in breast tissue from the human protein Atlas

Directory of Open Access Journals (Sweden)

Issac Niwas Swamidoss

2013-01-01

Full Text Available Background: The Human Protein Atlas (HPA is an effort to map the location of all human proteins (http://www.proteinatlas.org/. It contains a large number of histological images of sections from human tissue. Tissue micro arrays (TMA are imaged by a slide scanning microscope, and each image represents a thin slice of a tissue core with a dark brown antibody specific stain and a blue counter stain. When generating antibodies for protein profiling of the human proteome, an important step in the quality control is to compare staining patterns of different antibodies directed towards the same protein. This comparison is an ultimate control that the antibody recognizes the right protein. In this paper, we propose and evaluate different approaches for classifying sub-cellular antibody staining patterns in breast tissue samples. Materials and Methods: The proposed methods include the computation of various features including gray level co-occurrence matrix (GLCM features, complex wavelet co-occurrence matrix (CWCM features, and weighted neighbor distance using compound hierarchy of algorithms representing morphology (WND-CHARM-inspired features. The extracted features are used into two different multivariate classifiers (support vector machine (SVM and linear discriminant analysis (LDA classifier. Before extracting features, we use color deconvolution to separate different tissue components, such as the brownly stained positive regions and the blue cellular regions, in the immuno-stained TMA images of breast tissue. Results: We present classification results based on combinations of feature measurements. The proposed complex wavelet features and the WND-CHARM features have accuracy similar to that of a human expert. Conclusions: Both human experts and the proposed automated methods have difficulties discriminating between nuclear and cytoplasmic staining patterns. This is to a large extent due to mixed staining of nucleus and cytoplasm. Methods for

Imaging Protocols in Clinical Studies in Advanced Age-Related Macular Degeneration: Recommendations from Classification of Atrophy Consensus Meetings

NARCIS (Netherlands)

Holz, F.G.; Sadda, S.R.; Staurenghi, G.; Lindner, M.; Bird, A.C.; Blodi, B.A.; Bottoni, F.; Chakravarthy, U.; Chew, E.Y.; Csaky, K.; Curcio, C.A.; Danis, R.; Fleckenstein, M.; Freund, K.B.; Grunwald, J.; Guymer, R.; Hoyng, C.B.; Jaffe, G.J.; Liakopoulos, S.; Mones, J.M.; Oishi, A.; Pauleikhoff, D.; Rosenfeld, P.J.; Sarraf, D.; Spaide, R.F.; Tadayoni, R.; Tufail, A.; Wolf, S.; Schmitz-Valckenberg, S.

2017-01-01

PURPOSE: To summarize the results of 2 consensus meetings (Classification of Atrophy Meeting [CAM]) on conventional and advanced imaging modalities used to detect and quantify atrophy due to late-stage non-neovascular and neovascular age-related macular degeneration (AMD) and to provide

Examining Preschoolers' Nutrition Knowledge Using a Meal Creation and Food Group Classification Task: Age and Gender Differences

Science.gov (United States)

Holub, Shayla C.; Musher-Eizenman, Dara R.

2010-01-01

Eating behaviours begin to develop during early childhood, but relatively little is known about preschoolers' nutrition knowledge. The current study examined age and gender differences in this knowledge using two tasks: food group classification and the creation of unhealthy, healthy and preferred meals. Sixty-nine three- to six-year-old children…

Relationship between BOLD amplitude and pattern classification of orientation-selective activity in the human visual cortex

Science.gov (United States)

Tong, Frank; Harrison, Stephenie A.; Dewey, John A.; Kamitani, Yukiyasu

2012-01-01

Orientation-selective responses can be decoded from fMRI activity patterns in the human visual cortex, using multivariate pattern analysis (MVPA). To what extent do these feature-selective activity patterns depend on the strength and quality of the sensory input, and might the reliability of these activity patterns be predicted by the gross amplitude of the stimulus-driven BOLD response? Observers viewed oriented gratings that varied in luminance contrast (4, 20 or 100%) or spatial frequency (0.25, 1.0 or 4.0 cpd). As predicted, activity patterns in early visual areas led to better discrimination of orientations presented at high than low contrast, with greater effects of contrast found in area V1 than in V3. A second experiment revealed generally better decoding of orientations at low or moderate as compared to high spatial frequencies. Interestingly however, V1 exhibited a relative advantage at discriminating high spatial frequency orientations, consistent with the finer scale of representation in the primary visual cortex. In both experiments, the reliability of these orientation-selective activity patterns was well predicted by the average BOLD amplitude in each region of interest, as indicated by correlation analyses, as well as decoding applied to a simple model of voxel responses to simulated orientation columns. Moreover, individual differences in decoding accuracy could be predicted by the signal-to-noise ratio of an individual's BOLD response. Our results indicate that decoding accuracy can be well predicted by incorporating the amplitude of the BOLD response into simple simulation models of cortical selectivity; such models could prove useful in future applications of fMRI pattern classification. PMID:22917989

Snacking Patterns of Preschool-Aged Children: Opportunity for Improvement.

Science.gov (United States)

Hutchinson, Joy M; Watterworth, Jessica C; Haines, Jess; Duncan, Alison M; Mirotta, Julia A; Ma, David W L; Buchholz, Andrea C

2018-03-01

Dietary patterns established in childhood track into adulthood. Despite this, little research has explored preschoolers' snacking. This study examined snacking patterns (frequency, quality, quantity) of preschool-aged boys and girls. Cross-sectional data were collected on 52 children (23 males; 3.4 ± 1.1 years of age; BMI 16.1 ± 1.4 kg/m 2 ) enrolled in the Guelph Family Health Study pilot. Parent-reported 3-day food records were analyzed for children's snacking patterns including frequency (number of snacking occasions per day), quantity (percent energy from snacks) and quality (inclusion of food groups from Eating Well with Canada's Food Guide, macronutrient distribution, sugary and salty snacks). Mann-Whitney U tests examined sex differences in snacking patterns. Ninety-six percent of children snacked daily, consuming a mean of 2.3 ± 0.7 snacks per day. Snacks accounted for one-third of daily energy. 78% of boys' versus 63% of girls' snacks contained a food group (P = 0.016). Boys consumed significantly fewer sugary snacks (0.5 ± 0.4 vs 0.9 ± 0.6 snacks per day, P = 0.016), although the percent of snack calories from sugar for both boys and girls was high (group mean 37.2 ± 6.7%). Nearly all preschoolers in this study snacked daily, and consumed a variety of snack foods. Boys' and girls' snacking preferences begin to diverge early in life. Preschool children should be encouraged to consume healthful snacks.

Contrasted patterns of age-specific reproduction in long-lived seabirds.

Science.gov (United States)

Berman, M; Gaillard, J-M; Weimerskirch, H

2009-01-22

While the number of studies providing evidence of actuarial senescence is increasing, and covers a wide range of taxa, the process of reproductive senescence remains poorly understood. In fact, quite high reproductive output until the last years of life has been reported in several vertebrate species, so that whether or not reproductive senescence is widespread remains unknown. We compared age-specific changes of reproductive parameters between two closely related species of long-lived seabirds: the small-sized snow petrel Pagodroma nivea, and the medium-sized southern fulmar Fulmarus glacialoides. Both are sympatric in Antarctica. We used an exceptional dataset collected over more than 40 years to assess age-specific variations of both breeding probability and breeding success. We found contrasted age-specific reproductive patterns between the two species. Reproductive senescence clearly occurred from 21 years of age onwards in the southern fulmar, in both breeding probability and success, whereas we did not report any decline in the breeding success of the snow petrel, although a very late decrease in the proportion of breeders occurred at 34 years. Such a contrasted age-specific reproductive pattern was rather unexpected. Differences in life history including size or migratory behaviour are the most likely candidates to account for the difference we reported in reproductive senescence between these sympatric seabird species.

Recurrent Patterns in Dst Time Series

Directory of Open Access Journals (Sweden)

Hee-Jeong Kim

2003-06-01

Full Text Available This study reports one approach for the classification of magnetic storms into recurrent patterns. A storm event is defined as a local minimum of Dst index. The analysis of Dst index for the period of year 1957 through year 2000 has demonstrated that a large portion of the storm events can be classified into a set of recurrent patterns. In our approach, the classification is performed by seeking a categorization that minimizes thermodynamic free energy which is defined as the sum of classification errors and entropy. The error is calculated as the squared sum of the value differences between events. The classification depends on the noise parameter T that represents the strength of the intrinsic error in the observation and classification process. The classification results would be applicable in space weather forecasting.

Age patterns and transmission characteristics of hand, foot and mouth disease in China

Directory of Open Access Journals (Sweden)

Jijun Zhao

2016-11-01

Full Text Available Abstract Background Hand, foot and mouth disease (HFMD has circulated in China and caused yearly outbreak. To understand the transmission of the disease and to assess the spatial variation in cases reported, we examined age-specific transmission characteristics and reporting rates of HFMD for 31 provinces in mainland China. Methods We first analyzed incidence spatial patterns and age-specific incidence patterns using dataset from 2008 to 2012. Transmission characteristics were estimated based on catalytic model. Reporting rates were estimated using a simple mass action model from “Time Series Susceptible Infectious Recovered” (TSIR modeling. Results We found age-specific spatial incidence patterns: age-specific proportions of HFMD cases varied geographically in China; larger case percentage was among children of 3–5 years old in the northern part of China and was among children of 0–2 years old in the southern part of China. Our analysis results revealed that: 1 reporting rates and transmission characteristics including the average age at infection, the force of infection and the basic reproduction number varied geographically in China; 2 patterns of the age-specific force of infection for 30 provinces were similar to that of childhood infections in developed countries; the age group that had the highest infection risk was 3–5 years old in 30 provinces, and 10–14 years old in Tibet; 3 a large difference in HFMD transmission existed between northwest region and southeast region; 4 transmission characteristics determined incidence patterns: the higher the disease transmission in a province, the earlier the annual seasonality started and the more case percentage was among children 0–2 years old and less among 3–5 years old. Conclusion Because HFMD has higher transmission than most childhood infections reported, high effective vaccine coverage is needed to substantially reduce HFMD incidence. Control measures before the vaccine

Food Patterns According to Sociodemographics, Physical Activity, Sleeping and Obesity in Portuguese Children

OpenAIRE

Moreira; Santos; Padrão; Cordeiro; Bessa; Valente; Barros; Teixeira; Mitchell; Lopes; Moreira

2010-01-01

Our study aimed to describe the association between food patterns and gender, parental education, physical activity, sleeping and obesity in 1976 children aged 5−10 years old. Dietary intake was measured by a semi quantitative food frequency questionnaire; body mass index was calculated and categorized according to the IOTF classification. Factor analysis and generalized linear models were applied to identify food patterns and their associations. TV viewing and male gender were significant po...

Association of major dietary patterns with socioeconomic factors among rural school-aged children in Bijar, 2014

Directory of Open Access Journals (Sweden)

2015-12-01

Full Text Available Background: The identification of major dietary patterns using factor analysis can provide information about health status of children by obtaining an overall picture of the person's diet. The aim of this study was to determine major dietary patterns and to identify socioeconomic factors affecting them in school age children in rural areas of Bijar, Iran. Materials and Methods: In this cross sectional study, 255 rural school age children living rural areas of Bijar were selected by simple random sampling. Dietary intakes during the past year and assessment of socioeconomic information were examined. Dietary patterns were determined using factor analysis and their relation to socioeconomic factors was investigated. Results: Three major dietary patterns," traditional", "modern" and "mixed", were identified. After adjusting for age, sex, ethnic and energy intake, Age of mother (b= 0.03, CI=0.00_0.05 was positively associated and age of father (b= -0.03, CI=-0.05_-0.01, laboring Job for father (b= -0.24, CI=-0.44_-0.03 and higher education of parents (b= -0.20, CI=-0.35_-0.05 were negatively associated with traditional dietary pattern. In addition, higher education of parents (b= 0.27, CI=0.11_0.44 was positively associated and age of mother (b= -0.03, CI=-0.06_0.00 was negatively associated with mixed dietary pattern. Conclusion: Some socio-economic variables such as maternal age, parental education, parental occupation and economic conditions can have effect on major dietary patterns among rural children.

Psychological stress exposure to aged mice causes abnormal feeding patterns with changes in the bout number.

Science.gov (United States)

Yamada, Chihiro; Mogami, Sachiko; Hattori, Tomohisa

2017-11-09

Stress responses are affected by aging. However, studies on stress-related changes in feeding patterns with aging subject are minimal. We investigated feeding patterns induced by two psychological stress models, revealing characteristics of stress-induced feeding patterns as "meal" and "bout" (defined as the minimum feeding behavior parameters) in aged mice. Feeding behaviors of C57BL/6J mice were monitored for 24 h by an automatic monitoring device. Novelty stress reduced the meal amount over the 24 h in both young and aged mice, but as a result of a time course study it was persistent in aged mice. In addition, the decreased bout number was more pronounced in aged mice than in young mice. The 24-h meal and bout parameters did not change in either the young or aged mice following water avoidance stress (WAS). However, the meal amount and bout number increased in aged mice for 0-6 h after WAS exposure but remained unchanged in young mice. Our findings suggest that changes in bout number may lead to abnormal stress-related feeding patterns and may be one tool for evaluating eating abnormality in aged mice.

Videodermoscopy and doppler-ultrasound in spider naevi: towards a new classification?

Science.gov (United States)

Alegre-Sánchez, A; Bernárdez, C; Fonda-Pascual, P; Moreno-Arrones, O M; López-Gutiérrez, J C; Jaén-Olasolo, P; Boixeda, P

2018-01-01

Spider naevi (SN) are considered a subtype of telangiectasias, currently classified as low-flow vascular malformations. To describe the videodermoscopy and Doppler-ultrasound (US) features of a large group of SN. A retrospective study of cases of SN collected at our Dermatology department during the period between June 2015 and June 2017 was performed. Clinical images, dermoscopic, videodermoscopic and Doppler-US files were reviewed. For each case, the age of the patient, time since onset, size and dermoscopic pattern of the lesions were recorded. The presence of pulsatility was also evaluated visually on the videodermoscopy. Two hundred and thirty-three SN in 189 patients were included. The mean age was 39.5 years (range: 10-76 years). Mean size of the lesions was 4.1 ± 2.0 mm. We described three dermoscopic patterns: network, star and looping. Older age, longer time since onset and larger size were found associated with higher frequency of the looping and star patterns compared to that of network pattern (P US studies, a high-flow with arterial biphasic waveform was found. In the light of the results, we support that SN could be reconsidered in upcoming classifications as lesions closer to the group of high-flow arteriovenous malformations. © 2017 European Academy of Dermatology and Venereology.

The decision tree approach to classification

Science.gov (United States)

Wu, C.; Landgrebe, D. A.; Swain, P. H.

1975-01-01

A class of multistage decision tree classifiers is proposed and studied relative to the classification of multispectral remotely sensed data. The decision tree classifiers are shown to have the potential for improving both the classification accuracy and the computation efficiency. Dimensionality in pattern recognition is discussed and two theorems on the lower bound of logic computation for multiclass classification are derived. The automatic or optimization approach is emphasized. Experimental results on real data are reported, which clearly demonstrate the usefulness of decision tree classifiers.

Street-level classification of illicit heroin using inorganic elements coupled with pattern monitoring

Directory of Open Access Journals (Sweden)

Kar-Weng Chan

2016-09-01

Full Text Available A total of 96 illicit heroin samples seized in 2013–2014 were analyzed by inductively coupled plasma-mass spectrometry (ICP-MS to determine 16 inorganic elements at parts-per-billion (ppb level. Of eleven submissions, two or three samples with similar appearance were taken from the same seizure to form related samples. These samples were used to monitor the clustering outcome suggested by principal component analysis (PCA. They provided hints regarding the acceptance of within-seizure variability in-situ. The previously established data pretreatment method (N+4R did not function well with the present data probably due to the higher concentrations reported for the current samples. With the aid of the above-cited related samples for pattern monitoring, a better outcome was achieved when the pretreatment method was modified to employ solely standardization (S to optimize the necessary variability for sample classification.

Automatic classification of prostate stromal tissue in histological images using Haralick descriptors and Local Binary Patterns

International Nuclear Information System (INIS)

Oliveira, D L L; Batista, V R; Duarte, Y A S; Nascimento, M Z; Neves, L A; Godoy, M F; Jacomini, R S; Arruda, P F F; Neto, D S

2014-01-01

In this paper we presente a classification system that uses a combination of texture features from stromal regions: Haralick features and Local Binary Patterns (LBP) in wavelet domain. The system has five steps for classification of the tissues. First, the stromal regions were detected and extracted using segmentation techniques based on thresholding and RGB colour space. Second, the Wavelet decomposition was applied in the extracted regions to obtain the Wavelet coefficients. Third, the Haralick and LBP features were extracted from the coefficients. Fourth, relevant features were selected using the ANOVA statistical method. The classication (fifth step) was performed with Radial Basis Function (RBF) networks. The system was tested in 105 prostate images, which were divided into three groups of 35 images: normal, hyperplastic and cancerous. The system performance was evaluated using the area under the ROC curve and resulted in 0.98 for normal versus cancer, 0.95 for hyperplasia versus cancer and 0.96 for normal versus hyperplasia. Our results suggest that texture features can be used as discriminators for stromal tissues prostate images. Furthermore, the system was effective to classify prostate images, specially the hyperplastic class which is the most difficult type in diagnosis and prognosis

Empirically Derived Dietary Patterns in UK Adults Are Associated with Sociodemographic Characteristics, Lifestyle, and Diet Quality

Science.gov (United States)

Cade, Janet; Dawson, Jeremy; Holdsworth, Michelle

2018-01-01

The aim of this study was to examine empirical dietary patterns in UK adults and their association with sociodemographic characteristics, lifestyle factors, self-reported nutrient intake, nutrient biomarkers, and the Nutrient-based Diet Quality Score (NDQS) using National Diet and Nutrition Survey data 2008–2012 (n = 2083; mean age 49 years; 43.3% male). Four patterns explained 13.6% of the total variance: ‘Snacks, fast food, fizzy drinks’ (SFFFD), ‘Fruit, vegetables, oily fish’ (FVOF), ‘Meat, potatoes, beer’ (MPB), and ‘Sugary foods, dairy’ (SFD). ‘SFFFD’ was associated positively with: being male; smoking; body mass index (BMI); urinary sodium; intake of non-milk extrinsic sugars (NMES), fat and starch; and negatively with: age; plasma carotenoids; and NDQS. ‘FVOF’ was associated positively with: being non-white; age; income; socioeconomic classification (National Statistics Socio-economic Classifications; NSSEC); plasma carotenoids; intake of non-starch polysaccharides and polyunsaturated fatty acids. It was negatively associated with: being male, smoking, BMI, urinary sodium, intake of saturated fat; and NMES and NDQS. Whilst the patterns explained only 13.6% of the total variance, they were associated with self-reported nutrient intake, biomarkers of nutrient intake, sociodemographic and lifestyle variables, and the NDQS. These findings provide support for dietary patterns analyses as a means of exploring dietary intake in the UK population to inform public health nutrition policy and guidance. PMID:29415478

Deep learning based classification of morphological patterns in RCM to guide noninvasive diagnosis of melanocytic lesions (Conference Presentation)

Science.gov (United States)

Kose, Kivanc; Bozkurt, Alican; Ariafar, Setareh; Alessi-Fox, Christi A.; Gill, Melissa; Dy, Jennifer G.; Brooks, Dana H.; Rajadhyaksha, Milind

2017-02-01

In this study we present a deep learning based classification algorithm for discriminating morphological patterns that appear in RCM mosaics of melanocytic lesions collected at the dermal epidermal junction (DEJ). These patterns are classified into 6 distinct types in the literature: background, meshwork, ring, clod, mixed, and aspecific. Clinicians typically identify these morphological patterns by examination of their textural appearance at 10X magnification. To mimic this process we divided mosaics into smaller regions, which we call tiles, and classify each tile in a deep learning framework. We used previously acquired DEJ mosaics of lesions deemed clinically suspicious, from 20 different patients, which were then labelled according to those 6 types by 2 expert users. We tried three different approaches for classification, all starting with a publicly available convolutional neural network (CNN) trained on natural image, consisting of a series of convolutional layers followed by a series of fully connected layers: (1) We fine-tuned this network using training data from the dataset. (2) Instead, we added an additional fully connected layer before the output layer network and then re-trained only last two layers, (3) We used only the CNN convolutional layers as a feature extractor, encoded the features using a bag of words model, and trained a support vector machine (SVM) classifier. Sensitivity and specificity were generally comparable across the three methods, and in the same ranges as our previous work using SURF features with SVM . Approach (3) was less computationally intensive to train but more sensitive to unbalanced representation of the 6 classes in the training data. However we expect CNN performance to improve as we add more training data because both the features and the classifier are learned jointly from the data. *First two authors share first authorship.

Reliability of a four-column classification for tibial plateau fractures.

Science.gov (United States)

Martínez-Rondanelli, Alfredo; Escobar-González, Sara Sofía; Henao-Alzate, Alejandro; Martínez-Cano, Juan Pablo

2017-09-01

A four-column classification system offers a different way of evaluating tibial plateau fractures. The aim of this study is to compare the intra-observer and inter-observer reliability between four-column and classic classifications. This is a reliability study, which included patients presenting with tibial plateau fractures between January 2013 and September 2015 in a level-1 trauma centre. Four orthopaedic surgeons blindly classified each fracture according to four different classifications: AO, Schatzker, Duparc and four-column. Kappa, intra-observer and inter-observer concordance were calculated for the reliability analysis. Forty-nine patients were included. The mean age was 39 ± 14.2 years, with no gender predominance (men: 51%; women: 49%), and 67% of the fractures included at least one of the posterior columns. The intra-observer and inter-observer concordance were calculated for each classification: four-column (84%/79%), Schatzker (60%/71%), AO (50%/59%) and Duparc (48%/58%), with a statistically significant difference among them (p = 0.001/p = 0.003). Kappa coefficient for intr-aobserver and inter-observer evaluations: Schatzker 0.48/0.39, four-column 0.61/0.34, Duparc 0.37/0.23, and AO 0.34/0.11. The proposed four-column classification showed the highest intra and inter-observer agreement. When taking into account the agreement that occurs by chance, Schatzker classification showed the highest inter-observer kappa, but again the four-column had the highest intra-observer kappa value. The proposed classification is a more inclusive classification for the posteromedial and posterolateral fractures. We suggest, therefore, that it be used in addition to one of the classic classifications in order to better understand the fracture pattern, as it allows more attention to be paid to the posterior columns, it improves the surgical planning and allows the surgical approach to be chosen more accurately.

Automated Decision Tree Classification of Corneal Shape

Science.gov (United States)

Twa, Michael D.; Parthasarathy, Srinivasan; Roberts, Cynthia; Mahmoud, Ashraf M.; Raasch, Thomas W.; Bullimore, Mark A.

2011-01-01

Purpose The volume and complexity of data produced during videokeratography examinations present a challenge of interpretation. As a consequence, results are often analyzed qualitatively by subjective pattern recognition or reduced to comparisons of summary indices. We describe the application of decision tree induction, an automated machine learning classification method, to discriminate between normal and keratoconic corneal shapes in an objective and quantitative way. We then compared this method with other known classification methods. Methods The corneal surface was modeled with a seventh-order Zernike polynomial for 132 normal eyes of 92 subjects and 112 eyes of 71 subjects diagnosed with keratoconus. A decision tree classifier was induced using the C4.5 algorithm, and its classification performance was compared with the modified Rabinowitz–McDonnell index, Schwiegerling’s Z3 index (Z3), Keratoconus Prediction Index (KPI), KISA%, and Cone Location and Magnitude Index using recommended classification thresholds for each method. We also evaluated the area under the receiver operator characteristic (ROC) curve for each classification method. Results Our decision tree classifier performed equal to or better than the other classifiers tested: accuracy was 92% and the area under the ROC curve was 0.97. Our decision tree classifier reduced the information needed to distinguish between normal and keratoconus eyes using four of 36 Zernike polynomial coefficients. The four surface features selected as classification attributes by the decision tree method were inferior elevation, greater sagittal depth, oblique toricity, and trefoil. Conclusions Automated decision tree classification of corneal shape through Zernike polynomials is an accurate quantitative method of classification that is interpretable and can be generated from any instrument platform capable of raw elevation data output. This method of pattern classification is extendable to other classification

Radiological patterns of pulmonary tuberculosis in the paediatric age group

International Nuclear Information System (INIS)

Lamont, A.C.; Cremin, B.J.; Pelteret, R.M.; Cape Town Univ.

1986-01-01

The radiological patterns of culture-proven pulmonary tuberculosis in 154 children under the age of 14 years were studied. Good quality radiographs were an essential requirement to the study, and in cases where lymphadenopathy was in doubt, tomograms or high kV magnification films were obtained. The radiographical terms used were defined and the results of film review were analysed to show the prevalent patterns. These are summarized at the end of the article. It is felt that awareness of the radiographic patterns in paediatric pulmonary tuberculosis will be of value of those working in communities where tuberculosis is unusual or rare, in immigrant communities, and also for the investigation of children who are inadvertently exposed to the disease. (orig.)

Early growth patterns are associated with intelligence quotient scores in children born small-for-gestational age.

Science.gov (United States)

Varella, Marcia H; Moss, William J

2015-08-01

To assess whether patterns of growth trajectory during infancy are associated with intelligence quotient (IQ) scores at 4 years of age in children born small-for-gestational age (SGA). Children in the Collaborative Perinatal Project born SGA were eligible for analysis. The primary outcome was the Stanford-Binet IQ score at 4 years of age. Growth patterns were defined based on changes in weight-for-age z-scores from birth to 4 months and 4 to 12 months of age and consisted of steady, early catch-up, late catch-up, constant catch-up, early catch-down, late catch-down, constant catch-down, early catch-up & late catch-down, and early catch-down & late catch-up. Multivariate linear regression was used to assess associations between patterns of growth and IQ. We evaluated patterns of growth and IQ in 5640 children. Compared with children with steady growth, IQ scores were 2.9 [standard deviation (SD)=0.54], 1.5 (SD=0.63), and 2.2 (SD=0.9) higher in children with early catch-up, early catch-up and later catch-down, and constant catch-up growth patterns, respectively, and 4.4 (SD=1.4) and 3.9 (SD=1.5) lower in children with early catch-down & late catch-up, and early catch-down growth patterns, respectively. Patterns in weight gain before 4 months of age were associated with differences in IQ scores at 4 years of age, with children with early catch-up having slightly higher IQ scores than children with steady growth and children with early catch-down having slightly lower IQ scores. These findings have implications for early infant nutrition in children born SGA. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Pattern Recognition Approaches for Breast Cancer DCE-MRI Classification: A Systematic Review.

Science.gov (United States)

Fusco, Roberta; Sansone, Mario; Filice, Salvatore; Carone, Guglielmo; Amato, Daniela Maria; Sansone, Carlo; Petrillo, Antonella

2016-01-01

We performed a systematic review of several pattern analysis approaches for classifying breast lesions using dynamic, morphological, and textural features in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). Several machine learning approaches, namely artificial neural networks (ANN), support vector machines (SVM), linear discriminant analysis (LDA), tree-based classifiers (TC), and Bayesian classifiers (BC), and features used for classification are described. The findings of a systematic review of 26 studies are presented. The sensitivity and specificity are respectively 91 and 83 % for ANN, 85 and 82 % for SVM, 96 and 85 % for LDA, 92 and 87 % for TC, and 82 and 85 % for BC. The sensitivity and specificity are respectively 82 and 74 % for dynamic features, 93 and 60 % for morphological features, 88 and 81 % for textural features, 95 and 86 % for a combination of dynamic and morphological features, and 88 and 84 % for a combination of dynamic, morphological, and other features. LDA and TC have the best performance. A combination of dynamic and morphological features gives the best performance.

Comparison of Pixel-Based and Object-Based Classification Using Parameters and Non-Parameters Approach for the Pattern Consistency of Multi Scale Landcover

Science.gov (United States)

Juniati, E.; Arrofiqoh, E. N.

2017-09-01

Information extraction from remote sensing data especially land cover can be obtained by digital classification. In practical some people are more comfortable using visual interpretation to retrieve land cover information. However, it is highly influenced by subjectivity and knowledge of interpreter, also takes time in the process. Digital classification can be done in several ways, depend on the defined mapping approach and assumptions on data distribution. The study compared several classifiers method for some data type at the same location. The data used Landsat 8 satellite imagery, SPOT 6 and Orthophotos. In practical, the data used to produce land cover map in 1:50,000 map scale for Landsat, 1:25,000 map scale for SPOT and 1:5,000 map scale for Orthophotos, but using visual interpretation to retrieve information. Maximum likelihood Classifiers (MLC) which use pixel-based and parameters approach applied to such data, and also Artificial Neural Network classifiers which use pixel-based and non-parameters approach applied too. Moreover, this study applied object-based classifiers to the data. The classification system implemented is land cover classification on Indonesia topographic map. The classification applied to data source, which is expected to recognize the pattern and to assess consistency of the land cover map produced by each data. Furthermore, the study analyse benefits and limitations the use of methods.

Age at Menarche and the Menstrual Pattern of Igbo Women of South ...

African Journals Online (AJOL)

This study determines the age at menarche and menstrual pattern of an Igbo population in 12 randomly selected rural communities of Ebonyi State. Information on recalled ages at menarche, menstrual flow duration and cycle length was collected using a semi structured questionnaire over three months. 1209 women of ...

The relationship of major American dietary patterns to age-related macular degeneration

Science.gov (United States)

We hypothesized that major American dietary patterns are associated with age-related macular degeneration (AMD) risk. This was a cross-sectional study with 8,103 eyes from 4,088 eligible participants in the baseline Age-Related Eye Disease Study (AREDS) were classified into control (n=2,739), early ...

Optimization of Classification Strategies of Acetowhite Temporal Patterns towards Improving Diagnostic Performance of Colposcopy

Directory of Open Access Journals (Sweden)

Karina Gutiérrez-Fragoso

2017-01-01

Full Text Available Efforts have been being made to improve the diagnostic performance of colposcopy, trying to help better diagnose cervical cancer, particularly in developing countries. However, improvements in a number of areas are still necessary, such as the time it takes to process the full digital image of the cervix, the performance of the computing systems used to identify different kinds of tissues, and biopsy sampling. In this paper, we explore three different, well-known automatic classification methods (k-Nearest Neighbors, Naïve Bayes, and C4.5, in addition to different data models that take full advantage of this information and improve the diagnostic performance of colposcopy based on acetowhite temporal patterns. Based on the ROC and PRC area scores, the k-Nearest Neighbors and discrete PLA representation performed better than other methods. The values of sensitivity, specificity, and accuracy reached using this method were 60% (95% CI 50–70, 79% (95% CI 71–86, and 70% (95% CI 60–80, respectively. The acetowhitening phenomenon is not exclusive to high-grade lesions, and we have found acetowhite temporal patterns of epithelial changes that are not precancerous lesions but that are similar to positive ones. These findings need to be considered when developing more robust computing systems in the future.

Associations of gender and age groups on the knowledge and use of drug information resources by American pharmacists.

Science.gov (United States)

Carvajal, Manuel J; Clauson, Kevin A; Gershman, Jennifer; Polen, Hyla H

2013-04-01

To explore knowledge and use of drug information resources by pharmacists and identify patterns influenced by gender and age-group classification. A survey questionnaire was mailed nationwide to 1,000 practitioners working in community (n = 500) and hospital (n = 500) settings who answer drug information questions as part of their expected job responsibilities. Responses pertaining to drug information resource use and knowledge of different types of drug-related queries, resource media preferences, and perceived adequacy of resources maintained in the pharmacy were analyzed by gender and age group. The t statistic was used to test for significant differences of means and percentages between genders and between age groups. Descriptive statistics were used to characterize other findings. Gender and age group classification influenced patterns of knowledge and use of drug information resources by pharmacists. They also affected pharmacists' perceptions of the most common types of questions prompting them to consult a drug information reference, as well as the resources consulted. Micromedex, exclusively available in electronic format, was the most commonly consulted resource overall by pharmacists. Lexi-Comp Online was the leading choice by women, preferred over Micromedex, but was not one of the top two resources selected by men. This study successfully identified the influence of gender and age-group classification in assessing drug information resource knowledge and use of general and specific types of drug-related queries.

[Definition, etiology, classification and presentation forms].

Science.gov (United States)

Mas Garriga, Xavier

2014-01-01

Osteoarthritis is defined as a degenerative process affecting the joints as a result of mechanical and biological disorders that destabilize the balance between the synthesis and degradation of joint cartilage, stimulating the growth of subchondral bone; chronic synovitis is also present. Currently, the joint is considered as a functional unit that includes distinct tissues, mainly cartilage, the synovial membrane, and subchondral bone, all of which are involved in the pathogenesis of the disease. Distinct risk factors for the development of osteoarthritis have been described: general, unmodifiable risk factors (age, sex, and genetic makeup), general, modifiable risk factors (obesity and hormonal factors) and local risk factors (prior joint anomalies and joint overload). Notable among the main factors related to disease progression are joint alignment defects and generalized osteoarthritis. Several classifications of osteoarthritis have been proposed but none is particularly important for the primary care management of the disease. These classifications include etiological (primary or idiopathic forms and secondary forms) and topographical (typical and atypical localizations) classifications, the Kellgren and Lawrence classification (radiological repercussions) and that of the American College of Rheumatology for osteoarthritis of the hand, hip and knee. The prevalence of knee osteoarthritis is 10.2% in Spain and shows a marked discrepancy between clinical and radiological findings. Hand osteoarthritis, with a prevalence of symptomatic involvement of around 6.2%, has several forms of presentation (nodal osteoarthritis, generalized osteoarthritis, rhizarthrosis, and erosive osteoarthritis). Symptomatic osteoarthritis of the hip affects between 3.5% and 5.6% of persons older than 50 years and has different radiological patterns depending on femoral head migration. Copyright © 2014 Elsevier España, S.L. All rights reserved.

Pattern classification by memristive crossbar circuits using ex situ and in situ training

Science.gov (United States)

Alibart, Fabien; Zamanidoost, Elham; Strukov, Dmitri B.

2013-06-01

Memristors are memory resistors that promise the efficient implementation of synaptic weights in artificial neural networks. Whereas demonstrations of the synaptic operation of memristors already exist, the implementation of even simple networks is more challenging and has yet to be reported. Here we demonstrate pattern classification using a single-layer perceptron network implemented with a memrisitive crossbar circuit and trained using the perceptron learning rule by ex situ and in situ methods. In the first case, synaptic weights, which are realized as conductances of titanium dioxide memristors, are calculated on a precursor software-based network and then imported sequentially into the crossbar circuit. In the second case, training is implemented in situ, so the weights are adjusted in parallel. Both methods work satisfactorily despite significant variations in the switching behaviour of the memristors. These results give hope for the anticipated efficient implementation of artificial neuromorphic networks and pave the way for dense, high-performance information processing systems.

Food Patterns According to Sociodemographics, Physical Activity, Sleeping and Obesity in Portuguese Children

Science.gov (United States)

Moreira, Pedro; Santos, Susana; Padrão, Patrícia; Cordeiro, Tânia; Bessa, Mariana; Valente, Hugo; Barros, Renata; Teixeira, Vitor; Mitchell, Vanessa; Lopes, Carla; Moreira, André

2010-01-01

Our study aimed to describe the association between food patterns and gender, parental education, physical activity, sleeping and obesity in 1976 children aged 5−10 years old. Dietary intake was measured by a semi quantitative food frequency questionnaire; body mass index was calculated and categorized according to the IOTF classification. Factor analysis and generalized linear models were applied to identify food patterns and their associations. TV viewing and male gender were significant positive predictors for fast-food, sugar sweetened beverages and pastry pattern, while a higher level of maternal education and longer sleeping duration were positively associated with a dietary patterns that included fruit and vegetables. PMID:20617022

Food Patterns According to Sociodemographics, Physical Activity, Sleeping and Obesity in Portuguese Children

Directory of Open Access Journals (Sweden)

Carla Lopes

2010-03-01

Full Text Available Our study aimed to describe the association between food patterns and gender, parental education, physical activity, sleeping and obesity in 1976 children aged 5−10 years old. Dietary intake was measured by a semi quantitative food frequency questionnaire; body mass index was calculated and categorized according to the IOTF classification. Factor analysis and generalized linear models were applied to identify food patterns and their associations. TV viewing and male gender were significant positive predictors for fast-food, sugar sweetened beverages and pastry pattern, while a higher level of maternal education and longer sleeping duration were positively associated with a dietary patterns that included fruit and vegetables.

Behavioral state classification in epileptic brain using intracranial electrophysiology

Science.gov (United States)

Kremen, Vaclav; Duque, Juliano J.; Brinkmann, Benjamin H.; Berry, Brent M.; Kucewicz, Michal T.; Khadjevand, Fatemeh; Van Gompel, Jamie; Stead, Matt; St. Louis, Erik K.; Worrell, Gregory A.

2017-04-01

Objective. Automated behavioral state classification can benefit next generation implantable epilepsy devices. In this study we explored the feasibility of automated awake (AW) and slow wave sleep (SWS) classification using wide bandwidth intracranial EEG (iEEG) in patients undergoing evaluation for epilepsy surgery. Approach. Data from seven patients (age 34+/- 12 , 4 women) who underwent intracranial depth electrode implantation for iEEG monitoring were included. Spectral power features (0.1-600 Hz) spanning several frequency bands from a single electrode were used to train and test a support vector machine classifier. Main results. Classification accuracy of 97.8  ±  0.3% (normal tissue) and 89.4  ±  0.8% (epileptic tissue) across seven subjects using multiple spectral power features from a single electrode was achieved. Spectral power features from electrodes placed in normal temporal neocortex were found to be more useful (accuracy 90.8  ±  0.8%) for sleep-wake state classification than electrodes located in normal hippocampus (87.1  ±  1.6%). Spectral power in high frequency band features (Ripple (80-250 Hz), Fast Ripple (250-600 Hz)) showed comparable performance for AW and SWS classification as the best performing Berger bands (Alpha, Beta, low Gamma) with accuracy  ⩾90% using a single electrode contact and single spectral feature. Significance. Automated classification of wake and SWS should prove useful for future implantable epilepsy devices with limited computational power, memory, and number of electrodes. Applications include quantifying patient sleep patterns and behavioral state dependent detection, prediction, and electrical stimulation therapies.

Comparison of four classification methods for brain-computer interface

Czech Academy of Sciences Publication Activity Database

Frolov, A.; Húsek, Dušan; Bobrov, P.

2011-01-01

Roč. 21, č. 2 (2011), s. 101-115 ISSN 1210-0552 R&D Projects: GA MŠk(CZ) 1M0567; GA ČR GA201/05/0079; GA ČR GAP202/10/0262 Institutional research plan: CEZ:AV0Z10300504 Keywords : brain computer interface * motor imagery * visual imagery * EEG pattern classification * Bayesian classification * Common Spatial Patterns * Common Tensor Discriminant Analysis Subject RIV: IN - Informatics, Computer Science Impact factor: 0.646, year: 2011

Patterns of Parental Rearing Styles and Child Behaviour Problems among Portuguese School-Aged Children

OpenAIRE

Pereira, Ana I. F.; Canavarro, Cristina; Cardoso, Margarida F.; Mendonça, Denisa

2008-01-01

The majority of studies investigating the effects of parental behaviour on the child’s adjustment have a dimensional approach. We identified the existence of various patterns in parental rearing styles and analysed the relationship between different parenting patterns and behavioural problems in a group of school-aged children. A longitudinal, multi-informant study was conducted. The sample consisted of 519 school-aged children from the Portuguese general population. Parental rearing styles w...

Changing incidence patterns of hepatocellular carcinoma among age groups in Taiwan.

Science.gov (United States)

Hung, Giun-Yi; Horng, Jiun-Lin; Yen, Hsiu-Ju; Lee, Chih-Ying; Lin, Li-Yih

2015-12-01

This study examined and compared the incidence patterns of hepatocellular carcinoma among age groups in Taiwan, 30 years after a universal hepatitis B virus immunization program was launched. Data for hepatocellular carcinoma diagnosed in 2003-2011 were collected from the population-based Taiwan Cancer Registry. Age-standardized incidence rates were calculated to analyze and compare the changes in incidence rates and trends. More specific analyses were performed on four age groups separated by sex. A total of 82,856 patients were diagnosed with hepatocellular carcinoma in 2003-2011 in Taiwan, yielding an age-standardized incidence rate of 32.97 per 100,000 person-years. Hepatocellular carcinoma was predominantly diagnosed in middle-aged adults (50.1%) and elderly people (49.1%), in contrast to the low incidences in children (0.04%) and adolescents and young adults (0.8%). Striking variations in trends were found for children (annual percent change: -16.6%, 2003-2010) and adolescents and young adults (annual percent change: -7.9%, 2003-2011). The incidence rate of hepatocellular carcinoma in children decreased to zero in 2011; only a slight decline in trends occurred for the middle-aged group (annual percent change: -2%, 2003-2011), and a slight upward trend was observed for elderly people (1.3%), specifically in women (1.7%). In Taiwan, hepatitis B virus-related hepatocellular carcinoma was nearly eradicated in children in 2011. The findings on age-specific incidence patterns and trends of hepatocellular carcinoma suggest that different control strategies for treating this devastating disease in the future be made according to age. Copyright © 2015 European Association for the Study of the Liver. Published by Elsevier B.V. All rights reserved.

Alcohol imagery and branding, and age classification of films popular in the UK.

Science.gov (United States)

Lyons, Ailsa; McNeill, Ann; Gilmore, Ian; Britton, John

2011-10-01

Exposure to alcohol products in feature films is a risk factor for use of alcohol by young people. This study was designed to document the extent to which alcohol imagery and brand appearances occur in popular UK films, and in relation to British Board of Film Classification (BBFC) age ratings intended to protect children and young people from harmful imagery. Alcohol appearances (classified as 'alcohol use, inferred alcohol use, other alcohol reference and alcohol brand appearances') were measured using 5-min interval coding of 300 films, comprising the 15 highest grossing films at the UK Box Office each year over a period of 20 years from 1989 to 2008. At least one alcohol appearance occurred in 86% of films, at least one episode of alcohol branding in 35% and nearly a quarter (23%) of all intervals analysed contained at least one appearance of alcohol. The occurrence of 'alcohol use and branded alcohol appearances' was particularly high in 1989, but the frequency of these and all other appearance categories changed little in subsequent years. Most films containing alcohol appearances, including 90% of those including 'alcohol brand appearances', were rated as suitable for viewing by children and young people. The most frequently shown brands were American beers: Budweiser, Miller and Coors. Alcohol appearances were similarly frequent in films originating from the UK, as from the USA. Alcohol imagery is extremely common in all films popular in the UK, irrespective of BBFC age classification. Given the relationship between exposure to alcohol imagery in films and use of alcohol by young people, we suggest that alcohol imagery should be afforded greater consideration in determining the suitability of films for viewing by children and young people.

Alcohol imagery and branding, and age classification of films popular in the UK

Science.gov (United States)

Lyons, Ailsa; McNeill, Ann; Gilmore, Ian; Britton, John

2011-01-01

Background Exposure to alcohol products in feature films is a risk factor for use of alcohol by young people. This study was designed to document the extent to which alcohol imagery and brand appearances occur in popular UK films, and in relation to British Board of Film Classification (BBFC) age ratings intended to protect children and young people from harmful imagery. Methods Alcohol appearances (classified as ‘alcohol use, inferred alcohol use, other alcohol reference and alcohol brand appearances’) were measured using 5-min interval coding of 300 films, comprising the 15 highest grossing films at the UK Box Office each year over a period of 20 years from 1989 to 2008. Results At least one alcohol appearance occurred in 86% of films, at least one episode of alcohol branding in 35% and nearly a quarter (23%) of all intervals analysed contained at least one appearance of alcohol. The occurrence of ‘alcohol use and branded alcohol appearances’ was particularly high in 1989, but the frequency of these and all other appearance categories changed little in subsequent years. Most films containing alcohol appearances, including 90% of those including ‘alcohol brand appearances’, were rated as suitable for viewing by children and young people. The most frequently shown brands were American beers: Budweiser, Miller and Coors. Alcohol appearances were similarly frequent in films originating from the UK, as from the USA. Conclusion Alcohol imagery is extremely common in all films popular in the UK, irrespective of BBFC age classification. Given the relationship between exposure to alcohol imagery in films and use of alcohol by young people, we suggest that alcohol imagery should be afforded greater consideration in determining the suitability of films for viewing by children and young people. PMID:22039199

Patterns - "A crime solver".

Science.gov (United States)

Nagasupriya, A; Dhanapal, Raghu; Reena, K; Saraswathi, Tr; Ramachandran, Cr

2011-01-01

This study is intended to analyze the predominant pattern of lip and finger prints in males and females and to correlate lip print and finger print for gender identity. The study sample comprised of 200 students of Vishnu Dental College, Bhimavaram, Andhra Pradesh, 100 males and 100 females aged between 18 to 27 years. Brown/pink colored lip stick was applied on the lips and the subject was asked to spread it uniformly over the lips. Lip prints were traced in the normal rest position of the lips with the help of cellophane tape. The imprint of the left thumb was taken on a white chart sheet and visualized using magnifying lens. While three main types of finger prints are identified, the classification of lip prints is simplified into branched, reticular, and vertical types. Association between lip prints and finger prints was statistically tested using Chi-square test. This study showed that lip and finger patterns did not reveal statistically significant results within the gender. The correlation between lip and finger patterns for gender identification, was statistically significant. In males, branched type of lip pattern associated with arch, loop, and whorl type of finger pattern was most significant. In females, vertical lip pattern associated with arch finger pattern and reticular lip pattern associated with whorl finger patterns were most significant. We conclude that a correlative study between the lip print and finger print will be very useful in forensic science for gender identification.

Aesthetics-based classification of geological structures in outcrops for geotourism purposes: a tentative proposal

Science.gov (United States)

Mikhailenko, Anna V.; Nazarenko, Olesya V.; Ruban, Dmitry A.; Zayats, Pavel P.

2017-03-01

The current growth in geotourism requires an urgent development of classifications of geological features on the basis of criteria that are relevant to tourist perceptions. It appears that structure-related patterns are especially attractive for geotourists. Consideration of the main criteria by which tourists judge beauty and observations made in the geodiversity hotspot of the Western Caucasus allow us to propose a tentative aesthetics-based classification of geological structures in outcrops, with two classes and four subclasses. It is possible to distinguish between regular and quasi-regular patterns (i.e., striped and lined and contorted patterns) and irregular and complex patterns (paysage and sculptured patterns). Typical examples of each case are found both in the study area and on a global scale. The application of the proposed classification permits to emphasise features of interest to a broad range of tourists. Aesthetics-based (i.e., non-geological) classifications are necessary to take into account visions and attitudes of visitors.

Age-related patterns of drug use initiation among polydrug using regular psychostimulant users.

Science.gov (United States)

Darke, Shane; Kaye, Sharlene; Torok, Michelle

2012-09-01

To determine age-related patterns of drug use initiation, drug sequencing and treatment entry among regular psychostimulant users. Cross-sectional study of 269 regular psychostimulant users, administered a structured interview examining onset of use for major licit and illicit drugs. The mean age at first intoxication was not associated with age or gender. In contrast, younger age was associated with earlier ages of onset for all of the illicit drug classes. Each additional year of age was associated with a 4 month increase in onset age for methamphetamine, and 3 months for heroin. By the age of 17, those born prior to 1961 had, on average, used only tobacco and alcohol, whereas those born between 1986 and 1990 had used nine different drug classes. The period between initial use and the transition to regular use, however, was stable. Age was also negatively correlated with both age at initial injection and regular injecting. Onset sequences, however, remained stable. Consistent with the age-related patterns of drug use, each additional year of age associated with a 0.47 year increase in the age at first treatment. While the age at first intoxication appeared stable, the trajectory through illicit drug use was substantially truncated. The data indicate that, at least among those who progress to regular illicit drug use, younger users are likely to be exposed to far broader polydrug use in their teens than has previously been the case. © 2012 Australasian Professional Society on Alcohol and other Drugs.

Classification of bacterial contamination using image processing and distributed computing.

Science.gov (United States)

Ahmed, W M; Bayraktar, B; Bhunia, A; Hirleman, E D; Robinson, J P; Rajwa, B

2013-01-01

Disease outbreaks due to contaminated food are a major concern not only for the food-processing industry but also for the public at large. Techniques for automated detection and classification of microorganisms can be a great help in preventing outbreaks and maintaining the safety of the nations food supply. Identification and classification of foodborne pathogens using colony scatter patterns is a promising new label-free technique that utilizes image-analysis and machine-learning tools. However, the feature-extraction tools employed for this approach are computationally complex, and choosing the right combination of scatter-related features requires extensive testing with different feature combinations. In the presented work we used computer clusters to speed up the feature-extraction process, which enables us to analyze the contribution of different scatter-based features to the overall classification accuracy. A set of 1000 scatter patterns representing ten different bacterial strains was used. Zernike and Chebyshev moments as well as Haralick texture features were computed from the available light-scatter patterns. The most promising features were first selected using Fishers discriminant analysis, and subsequently a support-vector-machine (SVM) classifier with a linear kernel was used. With extensive testing we were able to identify a small subset of features that produced the desired results in terms of classification accuracy and execution speed. The use of distributed computing for scatter-pattern analysis, feature extraction, and selection provides a feasible mechanism for large-scale deployment of a light scatter-based approach to bacterial classification.

Emotional Faces in Context: Age Differences in Recognition Accuracy and Scanning Patterns

Science.gov (United States)

Noh, Soo Rim; Isaacowitz, Derek M.

2014-01-01

While age-related declines in facial expression recognition are well documented, previous research relied mostly on isolated faces devoid of context. We investigated the effects of context on age differences in recognition of facial emotions and in visual scanning patterns of emotional faces. While their eye movements were monitored, younger and older participants viewed facial expressions (i.e., anger, disgust) in contexts that were emotionally congruent, incongruent, or neutral to the facial expression to be identified. Both age groups had highest recognition rates of facial expressions in the congruent context, followed by the neutral context, and recognition rates in the incongruent context were worst. These context effects were more pronounced for older adults. Compared to younger adults, older adults exhibited a greater benefit from congruent contextual information, regardless of facial expression. Context also influenced the pattern of visual scanning characteristics of emotional faces in a similar manner across age groups. In addition, older adults initially attended more to context overall. Our data highlight the importance of considering the role of context in understanding emotion recognition in adulthood. PMID:23163713

Walking or dancing: patterns of physical activity by cross-sectional age among U.S. women.

Science.gov (United States)

Fan, Jessie X; Kowaleski-Jones, Lori; Wen, Ming

2013-10-01

To identify age differences in physical activity (PA) participation for women. Data from 3,952 women 25+ from the 2003-2006 National Health and Nutrition Examination Surveys (NHANES) were used to analyze participation patterns for 17 PA types. The top five leisure PAs by participation rate for all ages were walking (42%), dancing (20%), treadmill (15%), biking (11%), and yoga (10%). Participation in running, dancing, treadmill, and team sports declined around ages 35 to 44, and participation in household PA, walking, weightlifting, and hiking declined around ages 55 to 64. At age 75+ further substantial decline in most activities occurred. Nativity status was the most important moderator for age-related PA decline. Total PA declines with age but significant decline does not occur until ages 55 to 64. Major decline in leisure PA participation starts earlier at ages 35 to 44. While age-related declining patterns differ for different activities, the top five most popular leisure activities are similar for all age groups.

The Effect of Aging on Muscular Dynamics Underlying Movement Patterns Changes.

Science.gov (United States)

Vernooij, Carlijn A; Rao, Guillaume; Berton, Eric; Retornaz, Frédérique; Temprado, Jean-Jacques

2016-01-01

Introduction: Aging leads to alterations not only within the complex subsystems of the neuro-musculo-skeletal system, but also in the coupling between them. Here, we studied how aging affects functional reorganizations that occur both within and between the behavioral and muscular levels, which must be coordinated to produce goal-directed movements. Using unimanual reciprocal Fitts' task, we examined the behavioral and muscular dynamics of older adults (74.4 ± 3.7 years) and compared them to those found for younger adults (23.2 ± 2.0 years). Methods: To achieve this objective, we manipulated the target size to trigger a phase transition in the behavioral regime and searched for concomitant signatures of a phase transition in the muscular coordination. Here, muscular coordination was derived by using the method of muscular synergy extraction. With this technique, we obtained functional muscular patterns through non-negative matrix factorization of the muscular signals followed by clustering the resulting synergies. Results: Older adults showed a phase transition in behavioral regime, although, in contrast to young participants, their kinematic profiles did not show a discontinuity. In parallel, muscular coordination displayed two typical signatures of a phase transition, that is, increased variability of coordination patterns and a reorganization of muscular synergies. Both signatures confirmed the existence of muscular reorganization in older adults, which is coupled with change in dynamical regime at behavioral level. However, relative to young adults, transition occurred at lower index of difficulty (ID) in older participants and the reorganization of muscular patterns lasted longer (over multiple IDs). Discussion: This implies that consistent changes occur in coordination processes across behavior and muscle. Furthermore, the repertoire of muscular patterns was reduced and somewhat modified for older adults, relative to young participants. This suggests that

A novel Neuro-fuzzy classification technique for data mining

Directory of Open Access Journals (Sweden)

Soumadip Ghosh

2014-11-01

Full Text Available In our study, we proposed a novel Neuro-fuzzy classification technique for data mining. The inputs to the Neuro-fuzzy classification system were fuzzified by applying generalized bell-shaped membership function. The proposed method utilized a fuzzification matrix in which the input patterns were associated with a degree of membership to different classes. Based on the value of degree of membership a pattern would be attributed to a specific category or class. We applied our method to ten benchmark data sets from the UCI machine learning repository for classification. Our objective was to analyze the proposed method and, therefore compare its performance with two powerful supervised classification algorithms Radial Basis Function Neural Network (RBFNN and Adaptive Neuro-fuzzy Inference System (ANFIS. We assessed the performance of these classification methods in terms of different performance measures such as accuracy, root-mean-square error, kappa statistic, true positive rate, false positive rate, precision, recall, and f-measure. In every aspect the proposed method proved to be superior to RBFNN and ANFIS algorithms.

Comparison of Pattern Classification Methods in Crossarm Reuse Judgement System Based on Rust Images

Science.gov (United States)

Yamana, Michiko; Murata, Hiroshi; Onoda, Takashi; Ohashi, Tohru; Kato, Seiji

Japanese electric power companies currently utilize existing equipments completely and maintain facilities effectively. Human experts presently judge various hardwares whether they are be reusable or not to utilize equipments completely. Especially, this paper considers about crossarm reuse judgement. This judgement is based on rust, which attaches on crossarms, by human experts. However, this judgement depends on human expertise and it is difficult to keep constant judgement accuracy. Electric power companies want to take constant and good judgement accuracy. Therefore, we develop a crossarm reuse judgement system based on rust images using machine learning techniques. The system consists of commercial microscope and standard note PC to keep the cost. And we estimate the judgement accuracy of various pattern classification methods without the special image processing such as extracting features. The results show that support vector machine is the most suitable method for this judgement system.

ANALYSIS OF RAILWAY USER TRAVEL BEHAVIOUR PATTERNS OF DIFFERENT AGE GROUPS

OpenAIRE

AKIYAMA, Takamasa; OKUSHIMA, Masashi

2009-01-01

In recent years, there have been requirments for a transport environment that will foster the development of safe, comfortable townships. The study of urban activities amid an aging society and effective use of public transport modes in addressing environmental problems have become particularly important issues. This study analyzes travel behaviour patterns of varying age groups using urban railways in order to examine the relationship between urban public transport use and urban activities. ...

Rational kernels for Arabic Root Extraction and Text Classification

Directory of Open Access Journals (Sweden)

Attia Nehar

2016-04-01

Full Text Available In this paper, we address the problems of Arabic Text Classification and root extraction using transducers and rational kernels. We introduce a new root extraction approach on the basis of the use of Arabic patterns (Pattern Based Stemmer. Transducers are used to model these patterns and root extraction is done without relying on any dictionary. Using transducers for extracting roots, documents are transformed into finite state transducers. This document representation allows us to use and explore rational kernels as a framework for Arabic Text Classification. Root extraction experiments are conducted on three word collections and yield 75.6% of accuracy. Classification experiments are done on the Saudi Press Agency dataset and N-gram kernels are tested with different values of N. Accuracy and F1 report 90.79% and 62.93% respectively. These results show that our approach, when compared with other approaches, is promising specially in terms of accuracy and F1.

The impact of ageing and changing utilization patterns on future cardiovascular drug expenditure: a pharmacoepidemiological projection approach

DEFF Research Database (Denmark)

Kildemoes, Helle Wallach; Andersen, Morten; Støvring, Henrik

2010-01-01

To develop a method for projecting the impact of ageing and changing drug utilization patterns on future drug expenditure.......To develop a method for projecting the impact of ageing and changing drug utilization patterns on future drug expenditure....

A new precipitation and drought climatology based on weather patterns.

Science.gov (United States)

Richardson, Douglas; Fowler, Hayley J; Kilsby, Christopher G; Neal, Robert

2018-02-01

Weather-pattern, or weather-type, classifications are a valuable tool in many applications as they characterize the broad-scale atmospheric circulation over a given region. This study analyses the aspects of regional UK precipitation and meteorological drought climatology with respect to a new set of objectively defined weather patterns. These new patterns are currently being used by the Met Office in several probabilistic forecasting applications driven by ensemble forecasting systems. Weather pattern definitions and daily occurrences are mapped to Lamb weather types (LWTs), and parallels between the two classifications are drawn. Daily precipitation distributions are associated with each weather pattern and LWT. Standardized precipitation index (SPI) and drought severity index (DSI) series are calculated for a range of aggregation periods and seasons. Monthly weather-pattern frequency anomalies are calculated for SPI wet and dry periods and for the 5% most intense DSI-based drought months. The new weather-pattern definitions and daily occurrences largely agree with their respective LWTs, allowing comparison between the two classifications. There is also broad agreement between weather pattern and LWT changes in frequencies. The new data set is shown to be adequate for precipitation-based analyses in the UK, although a smaller set of clustered weather patterns is not. Furthermore, intra-pattern precipitation variability is lower in the new classification compared to the LWTs, which is an advantage in this context. Six of the new weather patterns are associated with drought over the entire UK, with several other patterns linked to regional drought. It is demonstrated that the new data set of weather patterns offers a new opportunity for classification-based analyses in the UK.

Acceleration of Age-Associated Methylation Patterns in HIV-1-Infected Adults

Science.gov (United States)

Sehl, Mary; Sinsheimer, Janet S.; Hultin, Patricia M.; Hultin, Lance E.; Quach, Austin; Martínez-Maza, Otoniel; Horvath, Steve; Vilain, Eric; Jamieson, Beth D.

2015-01-01

Patients with treated HIV-1-infection experience earlier occurrence of aging-associated diseases, raising speculation that HIV-1-infection, or antiretroviral treatment, may accelerate aging. We recently described an age-related co-methylation module comprised of hundreds of CpGs; however, it is unknown whether aging and HIV-1-infection exert negative health effects through similar, or disparate, mechanisms. We investigated whether HIV-1-infection would induce age-associated methylation changes. We evaluated DNA methylation levels at >450,000 CpG sites in peripheral blood mononuclear cells (PBMC) of young (20-35) and older (36-56) adults in two separate groups of participants. Each age group for each data set consisted of 12 HIV-1-infected and 12 age-matched HIV-1-uninfected samples for a total of 96 samples. The effects of age and HIV-1 infection on methylation at each CpG revealed a strong correlation of 0.49, pmodules; module 3 (ME3) was significantly correlated with age (cor=0.70) and HIV-1 status (cor=0.31). Older HIV-1+ individuals had a greater number of hypermethylated CpGs across ME3 (p=0.015). In a multivariate model, ME3 was significantly associated with age and HIV status (Data set 1: βage= 0.007088, p=2.08 x 10-9; βHIV= 0.099574, p=0.0011; Data set 2: βage= 0.008762, p=1.27x 10-5; βHIV= 0.128649, p= 0.0001). Using this model, we estimate that HIV-1 infection accelerates age-related methylation by approximately 13.7 years in data set 1 and 14.7 years in data set 2. The genes related to CpGs in ME3 are enriched for polycomb group target genes known to be involved in cell renewal and aging. The overlap between ME3 and an aging methylation module found in solid tissues is also highly significant (Fisher-exact p=5.6 x 10-6, odds ratio=1.91). These data demonstrate that HIV-1 infection is associated with methylation patterns that are similar to age-associated patterns and suggest that general aging and HIV-1 related aging work through some common cellular

Functional classification of pulmonary hypertension in children: Report from the PVRI pediatric taskforce, Panama 2011.

Science.gov (United States)

Lammers, Astrid E; Adatia, Ian; Cerro, Maria Jesus Del; Diaz, Gabriel; Freudenthal, Alexandra Heath; Freudenthal, Franz; Harikrishnan, S; Ivy, Dunbar; Lopes, Antonio A; Raj, J Usha; Sandoval, Julio; Stenmark, Kurt; Haworth, Sheila G

2011-08-02

The members of the Pediatric Task Force of the Pulmonary Vascular Research Institute (PVRI) were aware of the need to develop a functional classification of pulmonary hypertension in children. The proposed classification follows the same pattern and uses the same criteria as the Dana Point pulmonary hypertension specific classification for adults. Modifications were necessary for children, since age, physical growth and maturation influences the way in which the functional effects of a disease are expressed. It is essential to encapsulate a child's clinical status, to make it possible to review progress with time as he/she grows up, as consistently and as objectively as possible. Particularly in younger children we sought to include objective indicators such as thriving, need for supplemental feeds and the record of school or nursery attendance. This helps monitor the clinical course of events and response to treatment over the years. It also facilitates the development of treatment algorithms for children. We present a consensus paper on a functional classification system for children with pulmonary hypertension, discussed at the Annual Meeting of the PVRI in Panama City, February 2011.

Trends in epidemiology and patho-anatomical pattern of proximal humeral fractures

DEFF Research Database (Denmark)

Bahrs, Christian; Stojicevic, Tanja; Tanja, Stojicevic

2014-01-01

PURPOSE: Proximal humeral fractures are common and frequently associated with osteoporosis. Little is known about the association between the patho-anatomical fracture pattern of proximal humeral fractures and patient characteristics. The purpose of this six year longitudinal registry analysis...... of proximal humeral fractures was to study overall numbers, certain predefined pathoanatomical patterns and distribution compared with specific patient characteristics. METHODS: Data of patients treated between 2006 and 2011 in a country hospital that provides care >95 % of the city's hospitalised patients...... with fractures was retrospectively reviewed. Data were analysed according to patient characteristics of age, gender, comorbidity, accompanying injuries and radiological analysis of pathoanatomical fracture patterns based on Neer and Arbeitsgemeinschaft für Osteosynthesefragen (AO) classification. RESULTS: Eight...

A definition and classification of status epilepticus--Report of the ILAE Task Force on Classification of Status Epilepticus.

Science.gov (United States)

Trinka, Eugen; Cock, Hannah; Hesdorffer, Dale; Rossetti, Andrea O; Scheffer, Ingrid E; Shinnar, Shlomo; Shorvon, Simon; Lowenstein, Daniel H

2015-10-01

The Commission on Classification and Terminology and the Commission on Epidemiology of the International League Against Epilepsy (ILAE) have charged a Task Force to revise concepts, definition, and classification of status epilepticus (SE). The proposed new definition of SE is as follows: Status epilepticus is a condition resulting either from the failure of the mechanisms responsible for seizure termination or from the initiation of mechanisms, which lead to abnormally, prolonged seizures (after time point t1 ). It is a condition, which can have long-term consequences (after time point t2 ), including neuronal death, neuronal injury, and alteration of neuronal networks, depending on the type and duration of seizures. This definition is conceptual, with two operational dimensions: the first is the length of the seizure and the time point (t1 ) beyond which the seizure should be regarded as "continuous seizure activity." The second time point (t2 ) is the time of ongoing seizure activity after which there is a risk of long-term consequences. In the case of convulsive (tonic-clonic) SE, both time points (t1 at 5 min and t2 at 30 min) are based on animal experiments and clinical research. This evidence is incomplete, and there is furthermore considerable variation, so these time points should be considered as the best estimates currently available. Data are not yet available for other forms of SE, but as knowledge and understanding increase, time points can be defined for specific forms of SE based on scientific evidence and incorporated into the definition, without changing the underlying concepts. A new diagnostic classification system of SE is proposed, which will provide a framework for clinical diagnosis, investigation, and therapeutic approaches for each patient. There are four axes: (1) semiology; (2) etiology; (3) electroencephalography (EEG) correlates; and (4) age. Axis 1 (semiology) lists different forms of SE divided into those with prominent motor

Individual Differences in Spatial Pattern Separation Performance Associated with Healthy Aging in Humans

Science.gov (United States)

Stark, Shauna M.; Yassa, Michael A.; Stark, Craig E. L.

2010-01-01

Rodent studies have suggested that "pattern separation," the ability to distinguish among similar experiences, is diminished in a subset of aged rats. We extended these findings to the human using a task designed to assess spatial pattern separation behavior (determining at time of test whether pairs of pictures shown during the study were in the…

Associations of gender and age groups on the knowledge and use of drug information resources by American pharmacists

Directory of Open Access Journals (Sweden)

Carvajal MJ

2013-06-01

Full Text Available Objectives: To explore knowledge and use of drug information resources by pharmacists and identify patterns influenced by gender and age-group classification. Methods: A survey questionnaire was mailed nationwide to 1,000 practitioners working in community (n = 500 and hospital (n = 500 settings who answer drug information questions as part of their expected job responsibilities. Responses pertaining to drug information resource use and knowledge of different types of drug-related queries, resource media preferences, and perceived adequacy of resources maintained in the pharmacy were analyzed by gender and age group. The t statistic was used to test for significant differences of means and percentages between genders and between age groups. Descriptive statistics were used to characterize other findings.Results: Gender and age group classification influenced patterns of knowledge and use of drug information resources by pharmacists. They also affected pharmacists’ perceptions of the most common types of questions prompting them to consult a drug information reference, as well as the resources consulted. Micromedex, exclusively available in electronic format, was the most commonly consulted resource overall by pharmacists. Lexi-Comp Online was the leading choice by women, preferred over Micromedex, but was not one of the top two resources selected by men. Conclusion: This study successfully identified the influence of gender and age-group classification in assessing drug information resource knowledge and use of general and specific types of drug-related queries.

A subject-independent pattern-based Brain-Computer Interface

Directory of Open Access Journals (Sweden)

Andreas Markus Ray

2015-10-01

Full Text Available While earlier Brain-Computer Interface (BCI studies have mostly focused on modulating specific brain regions or signals, new developments in pattern classification of brain states are enabling real-time decoding and modulation of an entire functional network. The present study proposes a new method for real-time pattern classification and neurofeedback of brain states from electroencephalographic (EEG signals. It involves the creation of a fused classification model based on the method of Common Spatial Patterns (CSPs from data of several healthy individuals. The subject-independent model is then used to classify EEG data in real-time and provide feedback to new individuals. In a series of offline experiments involving training and testing of the classifier with individual data from 27 healthy subjects, a mean classification accuracy of 75.30% was achieved, demonstrating that the classification system at hand can reliably decode two types of imagery used in our experiments, i.e. happy emotional imagery and motor imagery. In a subsequent experiment it is shown that the classifier can be used to provide neurofeedback to new subjects, and that these subjects learn to match their brain pattern to that of the fused classification model in a few days of neurofeedback training. This finding can have important implications for future studies on neurofeedback and its clinical applications on neuropsychiatric disorders.

Filter Bank Regularized Common Spatial Pattern Ensemble for Small Sample Motor Imagery Classification.

Science.gov (United States)

Park, Sang-Hoon; Lee, David; Lee, Sang-Goog

2018-02-01

For the last few years, many feature extraction methods have been proposed based on biological signals. Among these, the brain signals have the advantage that they can be obtained, even by people with peripheral nervous system damage. Motor imagery electroencephalograms (EEG) are inexpensive to measure, offer a high temporal resolution, and are intuitive. Therefore, these have received a significant amount of attention in various fields, including signal processing, cognitive science, and medicine. The common spatial pattern (CSP) algorithm is a useful method for feature extraction from motor imagery EEG. However, performance degradation occurs in a small-sample setting (SSS), because the CSP depends on sample-based covariance. Since the active frequency range is different for each subject, it is also inconvenient to set the frequency range to be different every time. In this paper, we propose the feature extraction method based on a filter bank to solve these problems. The proposed method consists of five steps. First, motor imagery EEG is divided by a using filter bank. Second, the regularized CSP (R-CSP) is applied to the divided EEG. Third, we select the features according to mutual information based on the individual feature algorithm. Fourth, parameter sets are selected for the ensemble. Finally, we classify using ensemble based on features. The brain-computer interface competition III data set IVa is used to evaluate the performance of the proposed method. The proposed method improves the mean classification accuracy by 12.34%, 11.57%, 9%, 4.95%, and 4.47% compared with CSP, SR-CSP, R-CSP, filter bank CSP (FBCSP), and SR-FBCSP. Compared with the filter bank R-CSP ( , ), which is a parameter selection version of the proposed method, the classification accuracy is improved by 3.49%. In particular, the proposed method shows a large improvement in performance in the SSS.

FEEDING PATTERN TOWARD THE INCREASING OF NUTRITIONAL STATUS IN CHILDREN AGED 1–3 YEARS

Directory of Open Access Journals (Sweden)

Toni Subarkah

2017-02-01

Full Text Available Introduction: The prevalence of nutritional status problems with underweight in Indonesia at the moments is (19,6%. Data showed that children with less nutritional status aged 1-3 years in Kalijudan, Surabaya are existed. Provide feeding pattern properly is one effort to improve the nutritional status by fulfilling the needs of the child nutrition. The purpose of this study was to explain the relationship of feeding pattern and nutritional status in children aged 1-3 years in the Kalijudan district, Surabaya. Methods: The research design used was cross-sectional study with dietary habit as the independent variable and nutritional status as dependent variable. The sample was taken from 154 mothers and children. Consecutive sampling was deployed. Data collection by questionnaires, and then data analysis using the Spearman’s Rho in level  of significance α≤0.05. Result and Analysis: There was strong relationship between feeding pattern and nutritional status (r=0.640. The result showed that inappropriate feeding patterns with nutritional status is very thin (44.4% a proper feeding patterns with normal nutritional status (89.7%.  Discussion and Conclussion: The efforts to improve nutritional status of children aged 1-3 years related to feeding patterns should be improved in order to achieve a normal nutritional status. Further research may explore on the feeding patterns based on dietary allowances. Keywords: feeding pattern, nutritional status, 1-3 years old children

[Guidelines for hygienic classification of learning technologies].

Science.gov (United States)

Kuchma, V R; Teksheva, L M; Milushkina, O Iu

2008-01-01

Optimization of the educational environment under the present-day conditions has been in progress, by using learning techwares (LTW) without fail. To organize and regulate an academic process in terms of the safety of applied LTW, there is a need for their classification. The currently existing attempts to structure LTW disregard hygienically significant aspects. The task of the present study was to substantiate a LTW safety criterion ensuring a universal approach to working out regulations. This criterion may be the exposure intensity determined by the form of organization of education and its pattern, by the procedure of information presentation, and the age-related peculiarities of a pupil, i.e. by the actual load that is presented by the product of the intensity exposure and its time. The hygienic classification of LTW may be used to evaluate their negative effect in an educational process on the health status of children and adolescents, to regulate hazardous factors and training modes, to design and introduce new learning complexes. The structuring of a LTW system allows one to define possible deleterious actions and the possibilities of preventing this action on the basis of strictly established regulations.

Indicators of dietary patterns in Danish infants at 9 months of age

DEFF Research Database (Denmark)

Andersen, Louise B B; Mølgaard, Christian; Michaelsen, Kim F

2015-01-01

responsibilities and fathers in the labour market compared to being a student, A lower Health-Conscious Food pattern score indicates a less healthy diet. A lower infant Health-Conscious Food pattern score was associated with a higher maternal BMI, a greater number of children in the household, a higher BMI z...... indicators and adherence to dietary patterns for infants aged 9 months. DESIGN: The two dietary patterns 'Family Food' and 'Health-Conscious Food' were displayed by principal component analysis, and associations with possible indicators were analysed by multiple linear regressions in a pooled sample (n=374......) of two comparable observational cohorts, SKOT I and SKOT II. These cohorts comprised infants with mainly non-obese mothers versus infants with obese mothers, respectively. RESULTS: A lower Family Food score indicates a higher intake of liquid baby food, as this pattern shows transition from baby food...

Indicators of dietary patterns in Danish infants at 9 months of age

DEFF Research Database (Denmark)

Andersen, Louise Beltoft Borup; Mølgaard, Christian; Michaelsen, Kim F.

2015-01-01

BACKGROUND: It is important to increase the awareness of indicators associated with adverse infant dietary patterns to be able to prevent or to improve dietary patterns early on. OBJECTIVE: The aim of this study was to investigate the association between a wide range of possible family and child...... indicators and adherence to dietary patterns for infants aged 9 months. DESIGN: The two dietary patterns 'Family Food' and 'Health-Conscious Food' were displayed by principal component analysis, and associations with possible indicators were analysed by multiple linear regressions in a pooled sample (n=374......) of two comparable observational cohorts, SKOT I and SKOT II. These cohorts comprised infants with mainly non-obese mothers versus infants with obese mothers, respectively. RESULTS: A lower Family Food score indicates a higher intake of liquid baby food, as this pattern shows transition from baby food...

Advanced brain aging: relationship with epidemiologic and genetic risk factors, and overlap with Alzheimer disease atrophy patterns.

Science.gov (United States)

Habes, M; Janowitz, D; Erus, G; Toledo, J B; Resnick, S M; Doshi, J; Van der Auwera, S; Wittfeld, K; Hegenscheid, K; Hosten, N; Biffar, R; Homuth, G; Völzke, H; Grabe, H J; Hoffmann, W; Davatzikos, C

2016-04-05

We systematically compared structural imaging patterns of advanced brain aging (ABA) in the general-population, herein defined as significant deviation from typical BA to those found in Alzheimer disease (AD). The hypothesis that ABA would show different patterns of structural change compared with those found in AD was tested via advanced pattern analysis methods. In particular, magnetic resonance images of 2705 participants from the Study of Health in Pomerania (aged 20-90 years) were analyzed using an index that captures aging atrophy patterns (Spatial Pattern of Atrophy for Recognition of BA (SPARE-BA)), and an index previously shown to capture atrophy patterns found in clinical AD (Spatial Patterns of Abnormality for Recognition of Early Alzheimer's Disease (SPARE-AD)). We studied the association between these indices and risk factors, including an AD polygenic risk score. Finally, we compared the ABA-associated atrophy with typical AD-like patterns. We observed that SPARE-BA had significant association with: smoking (Prisk score was significantly associated with SPARE-AD but not with SPARE-BA. Our findings suggest that ABA is likely characterized by pathophysiologic mechanisms that are distinct from, or only partially overlapping with those of AD.

Beverage Consumption Patterns at Age 13 to 17 Years Are Associated with Weight, Height, and Body Mass Index at Age 17 Years.

Science.gov (United States)

Marshall, Teresa A; Van Buren, John M; Warren, John J; Cavanaugh, Joseph E; Levy, Steven M

2017-05-01

Sugar-sweetened beverages (SSBs) have been associated with obesity in children and adults; however, associations between beverage patterns and obesity are not understood. Our aim was to describe beverage patterns during adolescence and associations between adolescent beverage patterns and anthropometric measures at age 17 years. We conducted a cross-sectional analyses of longitudinally collected data. Data from participants in the longitudinal Iowa Fluoride Study having at least one beverage questionnaire completed between ages 13.0 and 14.0 years, having a second questionnaire completed between 16.0 and 17.0 years, and attending clinic examination for weight and height measurements at age 17 years (n=369) were included. Beverages were collapsed into four categories (ie, 100% juice, milk, water and other sugar-free beverages, and SSBs) for the purpose of clustering. Five beverage clusters were identified from standardized age 13 to 17 years mean daily beverage intakes and named by the authors for the dominant beverage: juice, milk, water/sugar-free beverages, neutral, and SSB. Weight, height, and body mass index (BMI; calculated as kg/m 2 ) at age 17 years were analyzed. We used Ward's method for clustering of beverage variables, one-way analysis of variance and χ 2 tests for bivariable associations, and γ-regression for associations of weight or BMI (outcomes) with beverage clusters and demographic variables. Linear regression was used for associations of height (outcome) with beverage clusters and demographic variables. Participants with family incomes beverage cluster membership. For example, on average, male and female members of the neutral cluster were 4.5 cm (P=0.010) and 4.2 cm (P=0.034) shorter, respectively, than members of the milk cluster. For members of the juice cluster, mean BMI was lower than for members of the milk cluster (by 2.4 units), water/sugar-free beverage cluster (3.5 units), neutral cluster (2.2 units), and SSB cluster (3.2 units) (all

Patterns and correlates of co-occurrence among multiple types of child maltreatment

Science.gov (United States)

Kim, Kihyun; Mennen, Ferol E.; Trickett, Penelope K.

2017-01-01

This study examined the patterns and correlates of the types of maltreatment experienced by adolescents aged 9–12, participating in an ongoing longitudinal study on the impact of neglect on children’s development. Using case record abstraction, the study compared the child protection classification and findings from the case record abstraction with regard to the rates of four types of maltreatment (i.e. physical, sexual, emotional abuse and neglect) as well as co-occurrence across multiple types of maltreatment. Next, the study examined the frequently observed patterns of child maltreatment. Finally, the study investigated whether aspects of caretaker functioning and the detailed incident characteristics in the cases of neglect differed by the number of different types of maltreatment the children experienced. Results showed significant discrepancies between the Child Protective Service classification and case record abstraction. Child Protective Service classification considerably underestimated the rate of co-occurrence across multiple types of maltreatment. Neglect accompanied by physical and emotional abuse was the most common form. Some of the caretaker functioning variables distinguished the number of types of maltreatment. Based on the findings, future-research directions and practice implication were discussed. PMID:29225485

Segmentation of turbo generator and reactor coolant pump vibratory patterns: a syntactic pattern recognition approach

International Nuclear Information System (INIS)

Tira, Z.

1993-02-01

This study was undertaken in the context of turbogenerator and reactor coolant pump vibration surveillance. Vibration meters are used to monitor equipment condition. An anomaly will modify the signal mean. At the present time, the expert system DIVA, developed to automate diagnosis, requests the operator to identify the nature of the pattern change thus indicated. In order to minimize operator intervention, we have to automate on the one hand classification and on the other hand, detection and segmentation of the patterns. The purpose of this study is to develop a new automatic system for the segmentation and classification of signals. The segmentation is based on syntactic pattern recognition. For the classification, a decision tree is used. The signals to process are the rms values of the vibrations measured on rotating machines. These signals are randomly sampled. All processing is automatic and no a priori statistical knowledge on the signals is required. The segmentation performances are assessed by tests on vibratory signals. (author). 31 figs

GLOBAL LAND COVER CLASSIFICATION USING MODIS SURFACE REFLECTANCE PROSUCTS

Directory of Open Access Journals (Sweden)

K. Fukue

2016-06-01

Full Text Available The objective of this study is to develop high accuracy land cover classification algorithm for Global scale by using multi-temporal MODIS land reflectance products. In this study, time-domain co-occurrence matrix was introduced as a classification feature which provides time-series signature of land covers. Further, the non-parametric minimum distance classifier was introduced for timedomain co-occurrence matrix, which performs multi-dimensional pattern matching for time-domain co-occurrence matrices of a classification target pixel and each classification classes. The global land cover classification experiments have been conducted by applying the proposed classification method using 46 multi-temporal(in one year SR(Surface Reflectance and NBAR(Nadir BRDF-Adjusted Reflectance products, respectively. IGBP 17 land cover categories were used in our classification experiments. As the results, SR and NBAR products showed similar classification accuracy of 99%.

Conduction Delay Learning Model for Unsupervised and Supervised Classification of Spatio-Temporal Spike Patterns.

Science.gov (United States)

Matsubara, Takashi

2017-01-01

Precise spike timing is considered to play a fundamental role in communications and signal processing in biological neural networks. Understanding the mechanism of spike timing adjustment would deepen our understanding of biological systems and enable advanced engineering applications such as efficient computational architectures. However, the biological mechanisms that adjust and maintain spike timing remain unclear. Existing algorithms adopt a supervised approach, which adjusts the axonal conduction delay and synaptic efficacy until the spike timings approximate the desired timings. This study proposes a spike timing-dependent learning model that adjusts the axonal conduction delay and synaptic efficacy in both unsupervised and supervised manners. The proposed learning algorithm approximates the Expectation-Maximization algorithm, and classifies the input data encoded into spatio-temporal spike patterns. Even in the supervised classification, the algorithm requires no external spikes indicating the desired spike timings unlike existing algorithms. Furthermore, because the algorithm is consistent with biological models and hypotheses found in existing biological studies, it could capture the mechanism underlying biological delay learning.

Classification of crystal structure using a convolutional neural network.

Science.gov (United States)

Park, Woon Bae; Chung, Jiyong; Jung, Jaeyoung; Sohn, Keemin; Singh, Satendra Pal; Pyo, Myoungho; Shin, Namsoo; Sohn, Kee-Sun

2017-07-01

A deep machine-learning technique based on a convolutional neural network (CNN) is introduced. It has been used for the classification of powder X-ray diffraction (XRD) patterns in terms of crystal system, extinction group and space group. About 150 000 powder XRD patterns were collected and used as input for the CNN with no handcrafted engineering involved, and thereby an appropriate CNN architecture was obtained that allowed determination of the crystal system, extinction group and space group. In sharp contrast with the traditional use of powder XRD pattern analysis, the CNN never treats powder XRD patterns as a deconvoluted and discrete peak position or as intensity data, but instead the XRD patterns are regarded as nothing but a pattern similar to a picture. The CNN interprets features that humans cannot recognize in a powder XRD pattern. As a result, accuracy levels of 81.14, 83.83 and 94.99% were achieved for the space-group, extinction-group and crystal-system classifications, respectively. The well trained CNN was then used for symmetry identification of unknown novel inorganic compounds.

Using Fractal and Local Binary Pattern Features for Classification of ECOG Motor Imagery Tasks Obtained from the Right Brain Hemisphere.

Science.gov (United States)

Xu, Fangzhou; Zhou, Weidong; Zhen, Yilin; Yuan, Qi; Wu, Qi

2016-09-01

The feature extraction and classification of brain signal is very significant in brain-computer interface (BCI). In this study, we describe an algorithm for motor imagery (MI) classification of electrocorticogram (ECoG)-based BCI. The proposed approach employs multi-resolution fractal measures and local binary pattern (LBP) operators to form a combined feature for characterizing an ECoG epoch recording from the right hemisphere of the brain. A classifier is trained by using the gradient boosting in conjunction with ordinary least squares (OLS) method. The fractal intercept, lacunarity and LBP features are extracted to classify imagined movements of either the left small finger or the tongue. Experimental results on dataset I of BCI competition III demonstrate the superior performance of our method. The cross-validation accuracy and accuracy is 90.6% and 95%, respectively. Furthermore, the low computational burden of this method makes it a promising candidate for real-time BCI systems.

Impact of Age and Aerobic Exercise Training on Conduit Artery Wall Thickness: Role of the Shear Pattern.

Science.gov (United States)

Tanahashi, Koichiro; Kosaki, Keisei; Sawano, Yuriko; Yoshikawa, Toru; Tagawa, Kaname; Kumagai, Hiroshi; Akazawa, Nobuhiko; Maeda, Seiji

2017-01-01

Hemodynamic shear stress is the frictional force of blood on the arterial wall. The shear pattern in the conduit artery affects the endothelium and may participate in the development and progression of atherosclerosis. We investigated the role of the shear pattern in age- and aerobic exercise-induced changes in conduit artery wall thickness via cross-sectional and interventional studies. In a cross-sectional study, we found that brachial shear rate patterns and brachial artery intima-media thickness (IMT) correlated with age. Additionally, brachial artery shear rate patterns were associated with brachial artery IMT in 102 middle-aged and older individuals. In an interventional study, 39 middle-aged and older subjects were divided into 2 groups: control and exercise. The exercise group completed 12 weeks of aerobic exercise training. Aerobic exercise training significantly increased the antegrade shear rate and decreased the retrograde shear rate and brachial artery IMT. Moreover, changes in the brachial artery antegrade shear rate and the retrograde shear rate correlated with the change in brachial artery IMT. The results of the present study indicate that changes in brachial artery shear rate patterns may contribute to age- and aerobic exercise training-induced changes in brachial artery wall thickness. © 2017 S. Karger AG, Basel.

In-vivo determination of chewing patterns using FBG and artificial neural networks

Science.gov (United States)

Pegorini, Vinicius; Zen Karam, Leandro; Rocha Pitta, Christiano S.; Ribeiro, Richardson; Simioni Assmann, Tangriani; Cardozo da Silva, Jean Carlos; Bertotti, Fábio L.; Kalinowski, Hypolito J.; Cardoso, Rafael

2015-09-01

This paper reports the process of pattern classification of the chewing process of ruminants. We propose a simplified signal processing scheme for optical fiber Bragg grating (FBG) sensors based on machine learning techniques. The FBG sensors measure the biomechanical forces during jaw movements and an artificial neural network is responsible for the classification of the associated chewing pattern. In this study, three patterns associated to dietary supplement, hay and ryegrass were considered. Additionally, two other important events for ingestive behavior studies were monitored, rumination and idle period. Experimental results show that the proposed approach for pattern classification has been capable of differentiating the materials involved in the chewing process with a small classification error.

Acceleration of age-associated methylation patterns in HIV-1-infected adults.

Directory of Open Access Journals (Sweden)

Tammy M Rickabaugh

Full Text Available Patients with treated HIV-1-infection experience earlier occurrence of aging-associated diseases, raising speculation that HIV-1-infection, or antiretroviral treatment, may accelerate aging. We recently described an age-related co-methylation module comprised of hundreds of CpGs; however, it is unknown whether aging and HIV-1-infection exert negative health effects through similar, or disparate, mechanisms. We investigated whether HIV-1-infection would induce age-associated methylation changes. We evaluated DNA methylation levels at >450,000 CpG sites in peripheral blood mononuclear cells (PBMC of young (20-35 and older (36-56 adults in two separate groups of participants. Each age group for each data set consisted of 12 HIV-1-infected and 12 age-matched HIV-1-uninfected samples for a total of 96 samples. The effects of age and HIV-1 infection on methylation at each CpG revealed a strong correlation of 0.49, p<1 x 10(-200 and 0.47, p<1 x 10(-200. Weighted gene correlation network analysis (WGCNA identified 17 co-methylation modules; module 3 (ME3 was significantly correlated with age (cor=0.70 and HIV-1 status (cor=0.31. Older HIV-1+ individuals had a greater number of hypermethylated CpGs across ME3 (p=0.015. In a multivariate model, ME3 was significantly associated with age and HIV status (Data set 1: βage=0.007088, p=2.08 x 10(-9; βHIV=0.099574, p=0.0011; Data set 2: βage=0.008762, p=1.27 x 10(-5; βHIV=0.128649, p=0.0001. Using this model, we estimate that HIV-1 infection accelerates age-related methylation by approximately 13.7 years in data set 1 and 14.7 years in data set 2. The genes related to CpGs in ME3 are enriched for polycomb group target genes known to be involved in cell renewal and aging. The overlap between ME3 and an aging methylation module found in solid tissues is also highly significant (Fisher-exact p=5.6 x 10(-6, odds ratio=1.91. These data demonstrate that HIV-1 infection is associated with methylation patterns that

Automatic classification of liver scintigram patterns by computer

International Nuclear Information System (INIS)

Csernay, L.; Csirik, J.

1976-01-01

The pattern recognition of projection is one of the problems in the automatic evaluation of scintigrams. An algorythm and a computerized programme with the ability to classify the shapes of liver scintigrams has been elaborated by the authors. The programme differentiates not only normal and pathologic basic forms, but performs the identification of nine normal forms described by the literature. To pattern recognition structural and local parameters of the picture were defined. A detailed mechanism of the programme is given in their reports. The programme can classify 55 out of 60 actual liver scintigrams, a result different from subjective definition obtained in 5 cases. These were normal pattern of liver scans. No wrong definition was obtained when classifying normal and pathologic patterns

Masking Period Patterns & Forward Masking for Speech-Shaped Noise: Age-related effects

Science.gov (United States)

Grose, John H.; Menezes, Denise C.; Porter, Heather L.; Griz, Silvana

2015-01-01

Objective The purpose of this study was to assess age-related changes in temporal resolution in listeners with relatively normal audiograms. The hypothesis was that increased susceptibility to non-simultaneous masking contributes to the hearing difficulties experienced by older listeners in complex fluctuating backgrounds. Design Participants included younger (n = 11), middle-aged (n = 12), and older (n = 11) listeners with relatively normal audiograms. The first phase of the study measured masking period patterns for speech-shaped noise maskers and signals. From these data, temporal window shapes were derived. The second phase measured forward-masking functions, and assessed how well the temporal window fits accounted for these data. Results The masking period patterns demonstrated increased susceptibility to backward masking in the older listeners, compatible with a more symmetric temporal window in this group. The forward-masking functions exhibited an age-related decline in recovery to baseline thresholds, and there was also an increase in the variability of the temporal window fits to these data. Conclusions This study demonstrated an age-related increase in susceptibility to non-simultaneous masking, supporting the hypothesis that exacerbated non-simultaneous masking contributes to age-related difficulties understanding speech in fluctuating noise. Further support for this hypothesis comes from limited speech-in-noise data suggesting an association between susceptibility to forward masking and speech understanding in modulated noise. PMID:26230495

Early childhood growth patterns and school-age respiratory resistance, fractional exhaled nitric oxide and asthma.

Science.gov (United States)

Casas, Maribel; den Dekker, Herman T; Kruithof, Claudia J; Reiss, Irwin K; Vrijheid, Martine; de Jongste, Johan C; Jaddoe, Vincent W V; Duijts, Liesbeth

2016-12-01

Greater infant weight gain is associated with lower lung function and increased risk of childhood asthma. The role of early childhood peak growth patterns is unclear. We assessed the associations of individually derived early childhood peak growth patterns with respiratory resistance, fractional exhaled nitric oxide, wheezing patterns, and asthma until school-age. We performed a population-based prospective cohort study among 5364 children. Repeated growth measurements between 0 and 3 years of age were used to derive standard deviation scores (s.d.s) of peak height and weight velocities (PHV and PWV, respectively), and body mass index (BMI) and age at adiposity peak. Respiratory resistance and fractional exhaled nitric oxide were measured at 6 years of age. Wheezing patterns and asthma were prospectively assessed by annual questionnaires. We also assessed whether any association was explained by childhood weight status. Greater PHV was associated with lower respiratory resistance [Z-score (95% CI): -0.03 (-0.04, -0.01) per s.d.s increase] (n = 3382). Greater PWV and BMI at adiposity peak were associated with increased risks of early wheezing [relative risk ratio (95% CI): 1.11 (1.06, 1.16), 1.26 (1.11, 1.43), respectively] and persistent wheezing [relative risk ratio (95% CI): 1.09 (1.03, 1.16), 1.37 (1.17, 1.60), respectively] (n = 3189 and n = 3005, respectively). Childhood weight status partly explained these associations. No other associations were observed. PWV and BMI at adiposity peak are critical for lung developmental and risk of school-age wheezing. Follow-up studies at older ages are needed to elucidate whether these effects persist at later ages. © 2016 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Pattern Classification of Tropical Cyclone Tracks over the Western North Pacific using a Fuzzy Clustering Method

Science.gov (United States)

Kim, H.; Ho, C.; Kim, J.

2008-12-01

This study presents the pattern classification of tropical cyclone (TC) tracks over the western North Pacific (WNP) basin during the typhoon season (June through October) for 1965-2006 (total 42 years) using a fuzzy clustering method. After the fuzzy c-mean clustering algorithm to the TC trajectory interpolated into 20 segments of equivalent length, we divided the whole tracks into 7 patterns. The optimal number of the fuzzy cluster is determined by several validity measures. The classified TC track patterns represent quite different features in the recurving latitudes, genesis locations, and geographical pathways: TCs mainly forming in east-northern part of the WNP and striking Korean and Japan (C1); mainly forming in west-southern part of the WNP, traveling long pathway, and partly striking Japan (C2); mainly striking Taiwan and East China (C3); traveling near the east coast of Japan (C4); traveling the distant ocean east of Japan (C5); moving toward South China and Vietnam straightly (C6); and forming in the South China Sea (C7). Atmospheric environments related to each cluster show physically consistent with each TC track patterns. The straight track pattern is closely linked to a developed anticyclonic circulation to the north of the TC. It implies that this ridge acts as a steering flow forcing TCs to move to the northwest with a more west-oriented track. By contrast, recurving patterns occur commonly under the influence of the strong anomalous westerlies over the TC pathway but there definitely exist characteristic anomalous circulations over the mid- latitudes by pattern. Some clusters are closely related to the well-known large-scale phenomena. The C1 and C2 are highly related to the ENSO phase: The TCs in the C1 (C2) is more active during La Niña (El Niño). The TC activity in the C3 is associated with the WNP summer monsoon. The TCs in the C4 is more (less) vigorous during the easterly (westerly) phase of the stratospheric quasi-biennial oscillation

Analysis of the Carnegie Classification of Community Engagement: Patterns and Impact on Institutions

Science.gov (United States)

Driscoll, Amy

2014-01-01

This chapter describes the impact that participation in the Carnegie Classification for Community Engagement had on the institutions of higher learning that applied for the classification. This is described in terms of changes in direct community engagement, monitoring and reporting on community engagement, and levels of student and professor…

Maternal patterns of postpartum alcohol consumption by age: A longitudinal analysis of adult urban mothers

OpenAIRE

Liu, Weiwei; Mumford, Elizabeth A.; Petras, Hanno

2015-01-01

The purpose of this study is to investigate a) longitudinal patterns of maternal postpartum alcohol use as well as its variation by maternal age at child birth; b) within maternal age groups, the association between other maternal characteristics and alcohol use patterns for the purposes of informed prevention design. Study sample consists of 3,397 mothers from the Fragile Families and Child Wellbeing Study representing medium and large U.S. urban areas. Maternal drinking and binge drinking w...

Gender Difference on the Association between Dietary Patterns and Obesity in Chinese Middle-Aged and Elderly Populations.

Science.gov (United States)

Yuan, Ya-Qun; Li, Fan; Meng, Pai; You, Jie; Wu, Min; Li, Shu-Guang; Chen, Bo

2016-07-23

Dietary patterns are linked to obesity, but the gender difference in the association between dietary patterns and obesity remains unclear. We explored this gender difference in a middle-aged and elderly populations in Shanghai. Residents (n = 2046; aged ≥45 years; 968 men and 1078 women) who participated in the Shanghai Food Consumption Survey were studied. Factor analysis of data from four periods of 24-h dietary recalls (across 2012-2014) identified dietary patterns. Height, body weight, and waist circumference were measured to calculate the body mass index. A log binominal model examined the association between dietary patterns and obesity, stratified by gender. Four dietary patterns were identified for both genders: rice staple, wheat staple, snacks, and prudent patterns. The rice staple pattern was associated positively with abdominal obesity in men (prevalence ratio (PR) = 1.358; 95% confidence interval (CI) 1.132-1.639; p = 0.001), but was associated negatively with general obesity in women (PR = 0.745; 95% CI: 0.673-0.807; p = 0.031). Men in the highest quartile of the wheat staple pattern had significantly greater risk of central obesity (PR = 1.331; 95% CI: 1.094-1.627; p = 0.005). There may be gender differences in the association between dietary patterns and obesity in middle-aged and elderly populations in Shanghai, China.

Distinguishing Adolescents With Conduct Disorder From Typically Developing Youngsters Based on Pattern Classification of Brain Structural MRI

Directory of Open Access Journals (Sweden)

Jianing Zhang

2018-04-01

Full Text Available Background: Conduct disorder (CD is a mental disorder diagnosed in childhood or adolescence that presents antisocial behaviors, and is associated with structural alterations in brain. However, whether these structural alterations can distinguish CD from healthy controls (HCs remains unknown. Here, we quantified these structural differences and explored the classification ability of these quantitative features based on machine learning (ML.Materials and Methods: High-resolution 3D structural magnetic resonance imaging (sMRI was acquired from 60 CD subjects and 60 age-matched HCs. Voxel-based morphometry (VBM was used to assess the regional gray matter (GM volume difference. The significantly different regional GM volumes were then extracted as features, and input into three ML classifiers: logistic regression, random forest and support vector machine (SVM. We trained and tested these ML models for classifying CD from HCs by using fivefold cross-validation (CV.Results: Eight brain regions with abnormal GM volumes were detected, which mainly distributed in the frontal lobe, parietal lobe, anterior cingulate, cerebellum posterior lobe, lingual gyrus, and insula areas. We found that these ML models achieved comparable classification performance, with accuracy of 77.9 ∼ 80.4%, specificity of 73.3 ∼ 80.4%, sensitivity of 75.4 ∼ 87.5%, and area under the receiver operating characteristic curve (AUC of 0.76 ∼ 0.80.Conclusion: Based on sMRI and ML, the regional GM volumes may be used as potential imaging biomarkers for stable and accurate classification of CD.

The morphological /settlement pattern classification of South African settlements based on a settlement catchment approach, to inform facility allocation and service delivery

CSIR Research Space (South Africa)

Sogoni, Z

2016-07-01

Full Text Available / settlement pattern classification of South African settlements based on a settlement catchment approach, to inform facility allocation and service delivery Zukisa Sogoni Planning Africa Conference 2016 4 July 2Project Focus and Background • CSIR... services. • Purpose is to support application & planning for new investment & prevent “unsustainable” investments / White elephants. 3Outputs • National set of service delivery catchments • Profile information per individual catchment • Ranking...

FEEDING PATTERN TOWARD THE INCREASING OF NUTRITIONAL STATUS IN CHILDREN AGED 1–3 YEARS

OpenAIRE

Toni Subarkah; Nursalam Nursalam; Praba Diyan Rachmawati

2017-01-01

Introduction: The prevalence of nutritional status problems with underweight in Indonesia at the moments is (19,6%). Data showed that children with less nutritional status aged 1-3 years in Kalijudan, Surabaya are existed. Provide feeding pattern properly is one effort to improve the nutritional status by fulfilling the needs of the child nutrition. The purpose of this study was to explain the relationship of feeding pattern and nutritional status in children aged 1-3 years in the Kalijudan d...

Pattern recognition and modelling of earthquake registrations with interactive computer support

International Nuclear Information System (INIS)

Manova, Katarina S.

2004-01-01

The object of the thesis is Pattern Recognition. Pattern recognition i.e. classification, is applied in many fields: speech recognition, hand printed character recognition, medical analysis, satellite and aerial-photo interpretations, biology, computer vision, information retrieval and so on. In this thesis is studied its applicability in seismology. Signal classification is an area of great importance in a wide variety of applications. This thesis deals with the problem of (automatic) classification of earthquake signals, which are non-stationary signals. Non-stationary signal classification is an area of active research in the signal and image processing community. The goal of the thesis is recognition of earthquake signals according to their epicentral zone. Source classification i.e. recognition is based on transformation of seismograms (earthquake registrations) to images, via time-frequency transformations, and applying image processing and pattern recognition techniques for feature extraction, classification and recognition. The tested data include local earthquakes from seismic regions in Macedonia. By using actual seismic data it is shown that proposed methods provide satisfactory results for classification and recognition.(Author)

Prediction and classification of respiratory motion

CERN Document Server

Lee, Suk Jin

2014-01-01

This book describes recent radiotherapy technologies including tools for measuring target position during radiotherapy and tracking-based delivery systems. This book presents a customized prediction of respiratory motion with clustering from multiple patient interactions. The proposed method contributes to the improvement of patient treatments by considering breathing pattern for the accurate dose calculation in radiotherapy systems. Real-time tumor-tracking, where the prediction of irregularities becomes relevant, has yet to be clinically established. The statistical quantitative modeling for irregular breathing classification, in which commercial respiration traces are retrospectively categorized into several classes based on breathing pattern are discussed as well. The proposed statistical classification may provide clinical advantages to adjust the dose rate before and during the external beam radiotherapy for minimizing the safety margin. In the first chapter following the Introduction  to this book, we...

Age-related pattern of normal cranial bone marrow: MRI study

International Nuclear Information System (INIS)

Pan Shinong; Li Qi; Li Wei; Chen Zhian; Wu Zhenhua; Guo Qiyong; Liu Yunhui

2009-01-01

Objective: To investigate the age-related pattern of normal skull bone marrow with 3.0 T MR T 1 WI. Methods: Cranial MR T 1 WI images which were defined to be normal were retrospectively reviewed in 360 cases. Patients with known diffuse bone marrow disease, focal lesions, history of radiation treatment or steroid therapy were excluded, while patients whose cranial MRI and follow-up visits were all normal were included in this study. All the subjects were divided into 7 groups according to the age: 50 years group. Mid- and para- sagittal T 1 WI images were used to be analyzed and the type of cranial bone marrow was classified according to the thickness of diploe and the pattern of the signal characteristics. Statistical analysis was conducted to reveal the relationship between the age and the type. Results: The normal skull bone marrow could be divided into four types as follows: (1) Type-I: 115 cases, 47 of which appeared type- Ia and the mean thickness was (1.24±0.31) mm; 68 of which appeared type-Ib and the mean thickness was (1.76±0.37) mm. Type-II: 57 cases and the mean thickness was (2.78 ± 0.69) mm. Type-III: 148 cases, 18 of which appeared type-IIIa and the mean thickness was (2.33 ± 0.65) mm; 88 of which appeared type-IIIb and the mean thickness was (4.01± 0.86) mm; 42 of which appeared type-IIIc and the mean thickness was (4.31±0.73) mm. Type-IV: 40 cases, 25 of which appeared type-IVa and the mean thickness was (5.17±1.02) mm; 15 of which appeared type-IVb and the mean thickness was (5.85±1.45) mm. (2) 2 =266.36, P<0.01). Conclusion: There is characteristic in the distribution of normal skull bone marrow with age growing. And skull bone marrow transforms gradually from type-I to IV with aging. (authors)

Aggregation Operator Based Fuzzy Pattern Classifier Design

DEFF Research Database (Denmark)

Mönks, Uwe; Larsen, Henrik Legind; Lohweg, Volker

2009-01-01

This paper presents a novel modular fuzzy pattern classifier design framework for intelligent automation systems, developed on the base of the established Modified Fuzzy Pattern Classifier (MFPC) and allows designing novel classifier models which are hardware-efficiently implementable....... The performances of novel classifiers using substitutes of MFPC's geometric mean aggregator are benchmarked in the scope of an image processing application against the MFPC to reveal classification improvement potentials for obtaining higher classification rates....

Masking Period Patterns and Forward Masking for Speech-Shaped Noise: Age-Related Effects.

Science.gov (United States)

Grose, John H; Menezes, Denise C; Porter, Heather L; Griz, Silvana

2016-01-01

The purpose of this study was to assess age-related changes in temporal resolution in listeners with relatively normal audiograms. The hypothesis was that increased susceptibility to nonsimultaneous masking contributes to the hearing difficulties experienced by older listeners in complex fluctuating backgrounds. Participants included younger (n = 11), middle-age (n = 12), and older (n = 11) listeners with relatively normal audiograms. The first phase of the study measured masking period patterns for speech-shaped noise maskers and signals. From these data, temporal window shapes were derived. The second phase measured forward-masking functions and assessed how well the temporal window fits accounted for these data. The masking period patterns demonstrated increased susceptibility to backward masking in the older listeners, compatible with a more symmetric temporal window in this group. The forward-masking functions exhibited an age-related decline in recovery to baseline thresholds, and there was also an increase in the variability of the temporal window fits to these data. This study demonstrated an age-related increase in susceptibility to nonsimultaneous masking, supporting the hypothesis that exacerbated nonsimultaneous masking contributes to age-related difficulties understanding speech in fluctuating noise. Further support for this hypothesis comes from limited speech-in-noise data, suggesting an association between susceptibility to forward masking and speech understanding in modulated noise.

Automatic classification of blank substrate defects

Science.gov (United States)

Boettiger, Tom; Buck, Peter; Paninjath, Sankaranarayanan; Pereira, Mark; Ronald, Rob; Rost, Dan; Samir, Bhamidipati

2014-10-01

Mask preparation stages are crucial in mask manufacturing, since this mask is to later act as a template for considerable number of dies on wafer. Defects on the initial blank substrate, and subsequent cleaned and coated substrates, can have a profound impact on the usability of the finished mask. This emphasizes the need for early and accurate identification of blank substrate defects and the risk they pose to the patterned reticle. While Automatic Defect Classification (ADC) is a well-developed technology for inspection and analysis of defects on patterned wafers and masks in the semiconductors industry, ADC for mask blanks is still in the early stages of adoption and development. Calibre ADC is a powerful analysis tool for fast, accurate, consistent and automatic classification of defects on mask blanks. Accurate, automated classification of mask blanks leads to better usability of blanks by enabling defect avoidance technologies during mask writing. Detailed information on blank defects can help to select appropriate job-decks to be written on the mask by defect avoidance tools [1][4][5]. Smart algorithms separate critical defects from the potentially large number of non-critical defects or false defects detected at various stages during mask blank preparation. Mechanisms used by Calibre ADC to identify and characterize defects include defect location and size, signal polarity (dark, bright) in both transmitted and reflected review images, distinguishing defect signals from background noise in defect images. The Calibre ADC engine then uses a decision tree to translate this information into a defect classification code. Using this automated process improves classification accuracy, repeatability and speed, while avoiding the subjectivity of human judgment compared to the alternative of manual defect classification by trained personnel [2]. This paper focuses on the results from the evaluation of Automatic Defect Classification (ADC) product at MP Mask

Graduating the age-specific fertility pattern using Support Vector Machines

Directory of Open Access Journals (Sweden)

Anastasia Kostaki

2009-06-01

Full Text Available A topic of interest in demographic literature is the graduation of the age-specific fertility pattern. A standard graduation technique extensively used by demographers is to fit parametric models that accurately reproduce it. Non-parametric statistical methodology might be alternatively used for this graduation purpose. Support Vector Machines (SVM is a non-parametric methodology that could be utilized for fertility graduation purposes. This paper evaluates the SVM techniques as tools for graduating fertility rates In that we apply these techniques to empirical age specific fertility rates from a variety of populations, time period, and cohorts. Additionally, for comparison reasons we also fit known parametric models to the same empirical data sets.

Foot-strike pattern and performance in a marathon.

Science.gov (United States)

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 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.

APPLICATION OF SENSOR FUSION TO IMPROVE UAV IMAGE CLASSIFICATION

Directory of Open Access Journals (Sweden)

S. Jabari

2017-08-01

Full Text Available Image classification is one of the most important tasks of remote sensing projects including the ones that are based on using UAV images. Improving the quality of UAV images directly affects the classification results and can save a huge amount of time and effort in this area. In this study, we show that sensor fusion can improve image quality which results in increasing the accuracy of image classification. Here, we tested two sensor fusion configurations by using a Panchromatic (Pan camera along with either a colour camera or a four-band multi-spectral (MS camera. We use the Pan camera to benefit from its higher sensitivity and the colour or MS camera to benefit from its spectral properties. The resulting images are then compared to the ones acquired by a high resolution single Bayer-pattern colour camera (here referred to as HRC. We assessed the quality of the output images by performing image classification tests. The outputs prove that the proposed sensor fusion configurations can achieve higher accuracies compared to the images of the single Bayer-pattern colour camera. Therefore, incorporating a Pan camera on-board in the UAV missions and performing image fusion can help achieving higher quality images and accordingly higher accuracy classification results.

Application of Sensor Fusion to Improve Uav Image Classification

Science.gov (United States)

Jabari, S.; Fathollahi, F.; Zhang, Y.

2017-08-01

Image classification is one of the most important tasks of remote sensing projects including the ones that are based on using UAV images. Improving the quality of UAV images directly affects the classification results and can save a huge amount of time and effort in this area. In this study, we show that sensor fusion can improve image quality which results in increasing the accuracy of image classification. Here, we tested two sensor fusion configurations by using a Panchromatic (Pan) camera along with either a colour camera or a four-band multi-spectral (MS) camera. We use the Pan camera to benefit from its higher sensitivity and the colour or MS camera to benefit from its spectral properties. The resulting images are then compared to the ones acquired by a high resolution single Bayer-pattern colour camera (here referred to as HRC). We assessed the quality of the output images by performing image classification tests. The outputs prove that the proposed sensor fusion configurations can achieve higher accuracies compared to the images of the single Bayer-pattern colour camera. Therefore, incorporating a Pan camera on-board in the UAV missions and performing image fusion can help achieving higher quality images and accordingly higher accuracy classification results.

Auroral arc classification scheme based on the observed arc-associated electric field pattern

International Nuclear Information System (INIS)

Marklund, G.

1983-06-01

Radar and rocket electric field observations of auroral arcs have earlier been used to identify essentially four different arc types, namely anticorrelation and correlation arcs (with, respectively, decreased and increased arc-assocaited field) and asymmetric and reversal arcs. In this paper rocket double probe and supplementary observations from the literature, obtained under various geophysical conditions, are used to organize the different arc types on a physical rather than morphological basis. This classification is based on the relative influence on the arc electric field pattern from the two current continuity mechanisms, polarisation electric fields and Birkeland currents. In this context the tangential electric field plays an essential role and it is thus important that it can be obtained with both high accuracy and resolution. In situ observations by sounding rockets are shown to be better suited for this specific task than monostatic radar observations. Depending on the dominating mechanism, estimated quantitatively for a number of arc-crossings, the different arc types have been grouped into the following main categories: Polarisation arcs, Birkeland current arcs and combination arcs. Finally the high altitude potential distributions corresponding to some of the different arc types are presented. (author)

Evaluation of the histopathological classifications of American cutaneous and mucocutaneous leishmaniasis

Directory of Open Access Journals (Sweden)

A. L. Bittencourt

1991-03-01

Full Text Available In order to evaluate the reliability of histopathological classifications of cutaneous and mucocutaneous leishmaniasis the authors compared the histopathological patterns of two biopsies taken simultaneously from the same patient, and classified the material according to Ridley et al. (1980, to Magalhães et al. (1986a, and to a more simplified classification with only three patterns. District histopathological aspects, were observed in different lesions or even in the same lesion. The authors concluded that histopathological patterns do not represent a stage of tegumentary leishmaniasis, thus they can not be correlated with prognosis and therapeutical response as suggested in the literature.

An ontology-based intrusion patterns classification system | Shonubi ...

African Journals Online (AJOL)

Studies have shown that computer intrusions have been on the increase in recent times. Many techniques and patterns are being used by intruders to gain access to data on host computer networks. In this work, intrusion patterns were identified and classified and inherent knowledge were represented using an ontology of ...

Patterns - “A crime solver”

Science.gov (United States)

Nagasupriya, A; Dhanapal, Raghu; Reena, K; Saraswathi, TR; Ramachandran, CR

2011-01-01

Objective: This study is intended to analyze the predominant pattern of lip and finger prints in males and females and to correlate lip print and finger print for gender identity. Materials and Methods: The study sample comprised of 200 students of Vishnu Dental College, Bhimavaram, Andhra Pradesh, 100 males and 100 females aged between 18 to 27 years. Brown/pink colored lip stick was applied on the lips and the subject was asked to spread it uniformly over the lips. Lip prints were traced in the normal rest position of the lips with the help of cellophane tape. The imprint of the left thumb was taken on a white chart sheet and visualized using magnifying lens. While three main types of finger prints are identified, the classification of lip prints is simplified into branched, reticular, and vertical types. Association between lip prints and finger prints was statistically tested using Chi-square test. Results: This study showed that lip and finger patterns did not reveal statistically significant results within the gender. The correlation between lip and finger patterns for gender identification, was statistically significant. In males, branched type of lip pattern associated with arch, loop, and whorl type of finger pattern was most significant. In females, vertical lip pattern associated with arch finger pattern and reticular lip pattern associated with whorl finger patterns were most significant. Conclusion: We conclude that a correlative study between the lip print and finger print will be very useful in forensic science for gender identification. PMID:22022131

Ahmad's NPRT System: A Practical Innovation for Documenting Male Pattern Baldness

OpenAIRE

Ahmad, Muhammad

2016-01-01

Various classifications for male pattern baldness are mentioned in the literature. The 'Norwood's classification is the most commonly used but it has certain limitations. The new system has included 'three' extra features which were not mentioned in any other classification. It provides an opportunity to document the full and correct picture while documenting male pattern baldness. It also aids in assessing the treatment for various degrees of baldness.

Ahmad's NPRT system: A practical innovation for documenting male pattern baldness

Directory of Open Access Journals (Sweden)

Muhammad Ahmad

2016-01-01

Full Text Available Various classifications for male pattern baldness are mentioned in the literature. The 'Norwood's classification is the most commonly used but it has certain limitations. The new system has included 'three' extra features which were not mentioned in any other classification. It provides an opportunity to document the full and correct picture while documenting male pattern baldness. It also aids in assessing the treatment for various degrees of baldness.

What makes a pattern? Matching decoding methods to data in multivariate pattern analysis

Directory of Open Access Journals (Sweden)

Philip A Kragel

2012-11-01

Full Text Available Research in neuroscience faces the challenge of integrating information across different spatial scales of brain function. A promising technique for harnessing information at a range of spatial scales is multivariate pattern analysis (MVPA of functional magnetic resonance imaging (fMRI data. While the prevalence of MVPA has increased dramatically in recent years, its typical implementations for classification of mental states utilize only a subset of the information encoded in local fMRI signals. We review published studies employing multivariate pattern classification since the technique’s introduction, which reveal an extensive focus on the improved detection power that linear classifiers provide over traditional analysis techniques. We demonstrate using simulations and a searchlight approach, however, that nonlinear classifiers are capable of extracting distinct information about interactions within a local region. We conclude that for spatially localized analyses, such as searchlight and region of interest, multiple classification approaches should be compared in order to match fMRI analyses to the properties of local circuits.

Description and recognition of patterns in stochastic signals. [Electroencephalograms

Energy Technology Data Exchange (ETDEWEB)

Flik, T [Technische Univ. Berlin (F.R. Germany). Informatik-Forschungsgruppe Rechnerorganisation und Schaltwerke

1975-10-01

A method is shown for the description and recognition of patterns in stochastic signals such as electroencephalograms. For pattern extraction the signal is segmented at times of minimum amplitudes. The describing features consist of geometric values of the so defined patterns. The classification algorithm is based on the regression analysis, which is well known in the field of character recognition. For an economic classification a method is proposed which reduces the number of features. The quality of this pattern recognition method is demonstrated by the detection of spike wave complexes in electroencephalograms. The pattern description and recognition are provided for processing on a digital computer. (DE)

Pattern of skin diseases in paediatric age group and adolescents

Directory of Open Access Journals (Sweden)

Sayal S

1998-01-01

Full Text Available A total of 300 patients from first day of life to 17 years of age were analysed for pattern of skin disorders. School going children formed majority (41.3% of cases followed by preschool children (32%. Infections formed the commonest disorder (31 % followed by eczemas (24%, papulosquamous disorders (12%, infestation (8.6% and urticaria (5.3% while vitiligo, acne vulgaris, alopecia areata and genodermatoses were seen in 2.7% cases each.

Measuring patterns of disability using the International Classification of Functioning, Disability and Health in the post-acute stroke rehabilitation setting.

Science.gov (United States)

Goljar, Nika; Burger, Helena; Vidmar, Gaj; Leonardi, Matilde; Marincek, Crt

2011-06-01

To determine whether the International Classification of Functioning, Disability and Health (ICF) model is adequate for assessing disability patterns in stroke survivors in the sub-acute rehabilitation setting in terms of potential changes in functional profiles over time. Functional profiles of 197 stroke patients were assessed using the ICF Checklist and the Functional Independence Measure (FIMTM) at admission and discharge from rehabilitation hospital. The ICF Checklist was applied based on medical documentation and rehabilitation team meetings. Descriptive analyses were performed to identify changes in ICF categories and qualifiers from admission to discharge, and correlations between different improvement measures were calculated. Mean rehabilitation duration was 60 days; patients' mean age was 60 years, with mean FIM-score 75 at admission. Mean FIM-score improvement at discharge was 12.5. Within Body Functions, changes in at least 10% of patients were found regarding 13 categories; no categories within Body Structures, 24 within Activities and Participation, and 2 within Environmental Factors. Changes were mostly due to improvement in qualifiers, except for within Environmental Factors, where they were due to use of additional categories. Correlations between improvements in Body Functions and Activities and Participation (regarding capacity and performance), as well as between capacity and performance within Activities and Participation, were approximately 0.4. Rating ICF categories with qualifiers enables the detection of changes in functional profiles of stroke patients who underwent an inpatient rehabilitation programme. :

The assignment of occupation densities and the rural soil classification in the territorial classification plans

International Nuclear Information System (INIS)

Velez R, Luis Anibal; Largacha R, Antonio

2003-01-01

Regulations concerning land use density controls in non-urban areas are often ineffective in protecting the rural character of such areas, within the context of Colombia's territorial organization plans What they tend to do is indirectly promote urban expansion through the fixing of minimum plot sizes Rather than question the methods employed in land use classification, the present study uses the case of the rural area of Santa Elena on the outskirts of Medellin and the existing zoning controls as established in the territorial organization plan, and focuses on the regulations established for each land use category as they effect occupation densities and patterns of plot fragmentation. On the one hand a simulation is undertaken of the trends which would result from land occupation in accordance with existing regulations concerning land use classification and plot size This indicates that the overall effect would be to disperse settlement patterns and fragment the landscape Secondly, an alternative scenario is developed based on the modification of minimum plot sizes for each of the three land use classifications established in the existing plan (protection, rural and suburban) In this way, and through the perspective of landscape ecology, It Is shown that in certain cases less dispersion and greater concentration of settlements can be achieved, and in other cases dispersion is minimized The use of GIS is fundamental in the development of such simulation techniques

Functional classifications for cerebral palsy: correlations between the gross motor function classification system (GMFCS), the manual ability classification system (MACS) and the communication function classification system (CFCS).

Science.gov (United States)

Compagnone, Eliana; Maniglio, Jlenia; Camposeo, Serena; Vespino, Teresa; Losito, Luciana; De Rinaldis, Marta; Gennaro, Leonarda; Trabacca, Antonio

2014-11-01

This study aimed to investigate a possible correlation between the gross motor function classification system-expanded and revised (GMFCS-E&R), the manual abilities classification system (MACS) and the communication function classification system (CFCS) functional levels in children with cerebral palsy (CP) by CP subtype. It was also geared to verify whether there is a correlation between these classification systems and intellectual functioning (IF) and parental socio-economic status (SES). A total of 87 children (47 males and 40 females, age range 4-18 years, mean age 8.9±4.2) were included in the study. A strong correlation was found between the three classifications: Level V of the GMFCS-E&R corresponds to Level V of the MACS (rs=0.67, p=0.001); the same relationship was found for the CFCS and the MACS (rs=0.73, p<0.001) and for the GMFCS-E&R and the CFCS (rs=0.61, p=0.001). The correlations between the IQ and the global functional disability profile were strong or moderate (GMFCS and IQ: rs=0.66, p=0.001; MACS and IQ: rs=0.58, p=0.001; CFCS and MACS: rs=0.65, p=0.001). The Kruskal-Wallis test was used to determine if there were differences between the GMFCS-E&R, the CFCS and the MACS by CP type. CP types showed different scores for the IQ level (Chi-square=8.59, df=2, p=0.014), the GMFCS-E&R (Chi-square=36.46, df=2, p<0.001), the CFCS (Chi-square=12.87, df=2, p=0.002), and the MACS Level (Chi-square=13.96, df=2, p<0.001) but no significant differences emerged for the SES (Chi-square=1.19, df=2, p=0.554). This study shows how the three functional classifications (GMFCS-E&R, CFCS and MACS) complement each other to provide a better description of the functional profile of CP. The systematic evaluation of the IQ can provide useful information about a possible future outcome for every functional level. The SES does not appear to affect functional profiles. Copyright © 2014 Elsevier Ltd. All rights reserved.

A Novel Classification System for Injuries After Electronic Cigarette Explosions.

Science.gov (United States)

Patterson, Scott B; Beckett, Allison R; Lintner, Alicia; Leahey, Carly; Greer, Ashley; Brevard, Sidney B; Simmons, Jon D; Kahn, Steven A

Electronic cigarettes (e-cigarettes) contain lithium batteries that have been known to explode and/or cause fires that have resulted in burn injury. The purpose of this article is to present a case study, review injuries caused by e-cigarettes, and present a novel classification system from the newly emerging patterns of burns. A case study was presented and online media reports for e-cigarette burns were queried with search terms "e-cigarette burns" and "electronic cigarette burns." The reports and injury patterns were tabulated. Analysis was then performed to create a novel classification system based on the distinct injury patterns seen in the study. Two patients were seen at our regional burn center after e-cigarette burns. One had an injury to his thigh and penis that required operative intervention after ignition of this device in his pocket. The second had a facial burn and corneal abrasions when the device exploded while he was inhaling vapor. The Internet search and case studies resulted in 26 cases for evaluation. The burn patterns were divided in direct injury from the device igniting and indirect injury when the device caused a house or car fire. A numerical classification was created: direct injury: type 1 (hand injury) 7 cases, type 2 (face injury) 8 cases, type 3 (waist/groin injury) 11 cases, and type 5a (inhalation injury from using device) 2 cases; indirect injury: type 4 (house fire injury) 7 cases and type 5b (inhalation injury from fire started by the device) 4 cases. Multiple e-cigarette injuries are occurring in the United States and distinct patterns of burns are emerging. The classification system developed in this article will aid in further study and future regulation of these dangerous devices.

Dissimilarity Representations in Lung Parenchyma Classification

DEFF Research Database (Denmark)

Sørensen, Lauge Emil Borch Laurs; de Bruijne, Marleen

2009-01-01

parenchyma classification. This allows for the classifiers to work on dissimilarities between objects, which might be a more natural way of representing lung parenchyma. In this context, dissimilarity is defined between CT regions of interest (ROI)s. ROIs are represented by their CT attenuation histogram...... and ROI dissimilarity is defined as a histogram dissimilarity measure between the attenuation histograms. In this setting, the full histograms are utilized according to the chosen histogram dissimilarity measure. We apply this idea to classification of different emphysema patterns as well as normal...... are built in this representation. This is also the general trend in lung parenchyma classification in computed tomography (CT) images, where the features often are measures on feature histograms. Instead, we propose to build normal density based classifiers in dissimilarity representations for lung...

Pattern classification approach to characterizing solitary pulmonary nodules imaged on high-resolution computed tomography

Science.gov (United States)

McNitt-Gray, Michael F.; Hart, Eric M.; Goldin, Jonathan G.; Yao, Chih-Wei; Aberle, Denise R.

1996-04-01

The purpose of our study was to characterize solitary pulmonary nodules (SPN) as benign or malignant based on pattern classification techniques using size, shape, density and texture features extracted from HRCT images. HRCT images of patients with a SPN are acquired, routed through a PACS and displayed on a thoracic radiology workstation. Using the original data, the SPN is semiautomatically contoured using a nodule/background threshold. The contour is used to calculate size and several shape parameters, including compactness and bending energy. Pixels within the interior of the contour are used to calculate several features including: (1) nodule density-related features, such as representative Hounsfield number and moment of inertia, and (2) texture measures based on the spatial gray level dependence matrix and fractal dimension. The true diagnosis of the SPN is established by histology from biopsy or, in the case of some benign nodules, extended follow-up. Multi-dimensional analyses of the features are then performed to determine which features can discriminate between benign and malignant nodules. When a sufficient number of cases are obtained two pattern classifiers, a linear discriminator and a neural network, are trained and tested using a select subset of features. Preliminary data from nine (9) nodule cases have been obtained and several features extracted. While the representative CT number is a reasonably good indicator, it is an inconclusive predictor of SPN diagnosis when considered by itself. Separation between benign and malignant nodules improves when other features, such as the distribution of density as measured by moment of inertia, are included in the analysis. Software has been developed and preliminary results have been obtained which show that individual features may not be sufficient to discriminate between benign and malignant nodules. However, combinations of these features may be able to discriminate between these two classes. With

Ecstacy and cocaine : Patterns of use among prime age individuals in Amsterdam

NARCIS (Netherlands)

van Ours, J.C.

2005-01-01

This paper uses information about prime age individuals living in Amsterdam to study the patterns of use of ecstasy and cocaine.The information was collected in surveys in 1994, 1997 and 2001.The analysis shows that the use of ecstasy and cocaine is mainly influenced by calendar year, family

Visualization of dietary patterns and their associations with age-related macular degeneration

Science.gov (United States)

PURPOSE: We aimed to visualize the relationship of predominant dietary patterns and their associations with AMD. METHODS: A total of 8103 eyes from 4088 participants in the baseline Age-Related Eye Disease Study (AREDS) were classified into three groups: control (n=2739), early AMD (n=4599), and adv...

Biogeographic classification of the Caspian Sea

Science.gov (United States)

Fendereski, F.; Vogt, M.; Payne, M. R.; Lachkar, Z.; Gruber, N.; Salmanmahiny, A.; Hosseini, S. A.

2014-11-01

Like other inland seas, the Caspian Sea (CS) has been influenced by climate change and anthropogenic disturbance during recent decades, yet the scientific understanding of this water body remains poor. In this study, an eco-geographical classification of the CS based on physical information derived from space and in situ data is developed and tested against a set of biological observations. We used a two-step classification procedure, consisting of (i) a data reduction with self-organizing maps (SOMs) and (ii) a synthesis of the most relevant features into a reduced number of marine ecoregions using the hierarchical agglomerative clustering (HAC) method. From an initial set of 12 potential physical variables, 6 independent variables were selected for the classification algorithm, i.e., sea surface temperature (SST), bathymetry, sea ice, seasonal variation of sea surface salinity (DSSS), total suspended matter (TSM) and its seasonal variation (DTSM). The classification results reveal a robust separation between the northern and the middle/southern basins as well as a separation of the shallow nearshore waters from those offshore. The observed patterns in ecoregions can be attributed to differences in climate and geochemical factors such as distance from river, water depth and currents. A comparison of the annual and monthly mean Chl a concentrations between the different ecoregions shows significant differences (one-way ANOVA, P qualitative evaluation of differences in community composition based on recorded presence-absence patterns of 25 different species of plankton, fish and benthic invertebrate also confirms the relevance of the ecoregions as proxies for habitats with common biological characteristics.

Consensus Definition for Atrophy Associated with Age-Related Macular Degeneration on OCT: Classification of Atrophy Report 3.

Science.gov (United States)

Sadda, Srinivas R; Guymer, Robyn; Holz, Frank G; Schmitz-Valckenberg, Steffen; Curcio, Christine A; Bird, Alan C; Blodi, Barbara A; Bottoni, Ferdinando; Chakravarthy, Usha; Chew, Emily Y; Csaky, Karl; Danis, Ronald P; Fleckenstein, Monika; Freund, K Bailey; Grunwald, Juan; Hoyng, Carel B; Jaffe, Glenn J; Liakopoulos, Sandra; Monés, Jordi M; Pauleikhoff, Daniel; Rosenfeld, Philip J; Sarraf, David; Spaide, Richard F; Tadayoni, Ramin; Tufail, Adnan; Wolf, Sebastian; Staurenghi, Giovanni

2018-04-01

To develop consensus terminology and criteria for defining atrophy based on OCT findings in the setting of age-related macular degeneration (AMD). Consensus meeting. Panel of retina specialists, image reading center experts, retinal histologists, and optics engineers. As part of the Classification of Atrophy Meetings (CAM) program, an international group of experts surveyed the existing literature, performed a masked analysis of longitudinal multimodal imaging for a series of eyes with AMD, and reviewed the results of this analysis to define areas of agreement and disagreement. Through consensus discussions at 3 meetings over 12 months, a classification system based on OCT was proposed for atrophy secondary to AMD. Specific criteria were defined to establish the presence of atrophy. A consensus classification system for atrophy and OCT-based criteria to identify atrophy. OCT was proposed as the reference standard or base imaging method to diagnose and stage atrophy. Other methods, including fundus autofluorescence, near-infrared reflectance, and color imaging, provided complementary and confirmatory information. Recognizing that photoreceptor atrophy can occur without retinal pigment epithelium (RPE) atrophy and that atrophy can undergo an evolution of different stages, 4 terms and histologic candidates were proposed: complete RPE and outer retinal atrophy (cRORA), incomplete RPE and outer retinal atrophy, complete outer retinal atrophy, and incomplete outer retinal atrophy. Specific OCT criteria to diagnose cRORA were proposed: (1) a region of hypertransmission of at least 250 μm in diameter, (2) a zone of attenuation or disruption of the RPE of at least 250 μm in diameter, (3) evidence of overlying photoreceptor degeneration, and (4) absence of scrolled RPE or other signs of an RPE tear. A classification system and criteria for OCT-defined atrophy in the setting of AMD has been proposed based on an international consensus. This classification is a more complete

Interrater reliability of a Pilates movement-based classification system.

Science.gov (United States)

Yu, Kwan Kenny; Tulloch, Evelyn; Hendrick, Paul

2015-01-01

To determine the interrater reliability for identification of a specific movement pattern using a Pilates Classification system. Videos of 5 subjects performing specific movement tasks were sent to raters trained in the DMA-CP classification system. Ninety-six raters completed the survey. Interrater reliability for the detection of a directional bias was excellent (Pi = 0.92, and K(free) = 0.89). Interrater reliability for classifying an individual into a specific subgroup was moderate (Pi = 0.64, K(free) = 0.55) however raters who had completed levels 1-4 of the DMA-CP training and reported using the assessment daily demonstrated excellent reliability (Pi = 0.89 and K(free) = 0.87). The reliability of the classification system demonstrated almost perfect agreement in determining the existence of a specific movement pattern and classifying into a subgroup for experienced raters. There was a trend for greater reliability associated with increased levels of training and experience of the raters. Copyright © 2014 Elsevier Ltd. All rights reserved.

Differential patterns of implicit emotional processing in Alzheimer's disease and healthy aging.

Science.gov (United States)

García-Rodríguez, Beatriz; Fusari, Anna; Rodríguez, Beatriz; Hernández, José Martín Zurdo; Ellgring, Heiner

2009-01-01

Implicit memory for emotional facial expressions (EFEs) was investigated in young adults, healthy old adults, and mild Alzheimer's disease (AD) patients. Implicit memory is revealed by the effect of experience on performance by studying previously encoded versus novel stimuli, a phenomenon referred to as perceptual priming. The aim was to assess the changes in the patterns of priming as a function of aging and dementia. Participants identified EFEs taken from the Facial Action Coding System and the stimuli used represented the emotions of happiness, sadness, surprise, fear, anger, and disgust. In the study phase, participants rated the pleasantness of 36 faces using a Likert-type scale. Subsequently, the response to the 36 previously studied and 36 novel EFEs was tested when they were randomly presented in a cued naming task. The results showed that implicit memory for EFEs is preserved in AD and aging, and no specific age-related effects on implicit memory for EFEs were observed. However, different priming patterns were evident in AD patients that may reflect pathological brain damage and the effect of stimulus complexity. These findings provide evidence of how progressive neuropathological changes in the temporal and frontal areas may affect emotional processing in more advanced stages of the disease.

Discriminative Chemical Patterns: Automatic and Interactive Design.

Science.gov (United States)

Bietz, Stefan; Schomburg, Karen T; Hilbig, Matthias; Rarey, Matthias

2015-08-24

The classification of molecules with respect to their inhibiting, activating, or toxicological potential constitutes a central aspect in the field of cheminformatics. Often, a discriminative feature is needed to distinguish two different molecule sets. Besides physicochemical properties, substructures and chemical patterns belong to the descriptors most frequently applied for this purpose. As a commonly used example of this descriptor class, SMARTS strings represent a powerful concept for the representation and processing of abstract chemical patterns. While their usage facilitates a convenient way to apply previously derived classification rules on new molecule sets, the manual generation of useful SMARTS patterns remains a complex and time-consuming process. Here, we introduce SMARTSminer, a new algorithm for the automatic derivation of discriminative SMARTS patterns from preclassified molecule sets. Based on a specially adapted subgraph mining algorithm, SMARTSminer identifies structural features that are frequent in only one of the given molecule classes. In comparison to elemental substructures, it also supports the consideration of general and specific SMARTS features. Furthermore, SMARTSminer is integrated into an interactive pattern editor named SMARTSeditor. This allows for an intuitive visualization on the basis of the SMARTSviewer concept as well as interactive adaption and further improvement of the generated patterns. Additionally, a new molecular matching feature provides an immediate feedback on a pattern's matching behavior across the molecule sets. We demonstrate the utility of the SMARTSminer functionality and its integration into the SMARTSeditor software in several different classification scenarios.

Classification of pulsating flow patterns in curved pipes.

Science.gov (United States)

Tada, S; Oshima, S; Yamane, R

1996-08-01

The fully developed periodic laminar flow of incompressible Newtonian fluids through a pipe of circular cross section, which is coiled in a circle, was simulated numerically. The flow patterns are characterized by three parameters: the Womersley number Wo, the Dean number De, and the amplitude ratio beta. The effect of these parameters on the flow was studied in the range 2.19 secondary flow evolved with increasing Womersley number and Dean number is explained. The secondary flow patterns are classified into three main groups: the viscosity-dominated type, the inertia-dominated type, and the convection-dominated type. It was found that when the amplitude ratio of the volumetric flow rate is equal to 1.0, four to six vortices of the secondary flow appear at high Dean numbers, and the Lyne-type flow patterns disappear at beta > or = 0.50.

A Novel Vehicle Classification Using Embedded Strain Gauge Sensors

Directory of Open Access Journals (Sweden)

Qi Wang

2008-11-01

Full Text Available Abstract: This paper presents a new vehicle classification and develops a traffic monitoring detector to provide reliable vehicle classification to aid traffic management systems. The basic principle of this approach is based on measuring the dynamic strain caused by vehicles across pavement to obtain the corresponding vehicle parameters – wheelbase and number of axles – to then accurately classify the vehicle. A system prototype with five embedded strain sensors was developed to validate the accuracy and effectiveness of the classification method. According to the special arrangement of the sensors and the different time a vehicle arrived at the sensors one can estimate the vehicle’s speed accurately, corresponding to the estimated vehicle wheelbase and number of axles. Because of measurement errors and vehicle characteristics, there is a lot of overlap between vehicle wheelbase patterns. Therefore, directly setting up a fixed threshold for vehicle classification often leads to low-accuracy results. Using the machine learning pattern recognition method to deal with this problem is believed as one of the most effective tools. In this study, support vector machines (SVMs were used to integrate the classification features extracted from the strain sensors to automatically classify vehicles into five types, ranging from small vehicles to combination trucks, along the lines of the Federal Highway Administration vehicle classification guide. Test bench and field experiments will be introduced in this paper. Two support vector machines classification algorithms (one-against-all, one-against-one are used to classify single sensor data and multiple sensor combination data. Comparison of the two classification method results shows that the classification accuracy is very close using single data or multiple data. Our results indicate that using multiclass SVM-based fusion multiple sensor data significantly improves

Patterns of frontoparietal activation as a marker for unsuccessful visuospatial processing in healthy aging.

Science.gov (United States)

Drag, Lauren L; Light, Sharee N; Langenecker, Scott A; Hazlett, Kathleen E; Wilde, Elisabeth A; Welsh, Robert; Steinberg, Brett A; Bieliauskas, Linas A

2016-09-01

Visuospatial abilities are sensitive to age-related decline, although the neural basis for this decline (and its everyday behavioral correlates) is as yet poorly understood. fMRI was employed to examine age-related differences in patterns of functional activation that underlie changes in visuospatial processing. All participants completed a brief neuropsychological battery and also a figure ground task (FGT) assessing visuospatial processing while fMRI was recorded. Participants included 16 healthy older adults (OA; aged 69-82 years) and 16 healthy younger adults (YA; aged 20-35 years). We examined age-related differences in behavioral performance on the FGT in relation to patterns of fMRI activation. OA demonstrated reduced performance on the FGT task and showed increased activation of supramarginal parietal cortex as well as increased activation of frontal and temporal regions compared to their younger counterparts. Performance on the FGT related to increased supramarginal gyrus activity and increased medial prefrontal activity in OAs, but not YAs. Our results are consistent with an anterior-posterior compensation model. Successful FGT performance requires the perception and integration of multiple stimuli and thus it is plausible that healthy aging may be accompanied by changes in visuospatial processing that mimic a subtle form of dorsal simultanagnosia. Overall, decreased visuospatial processing in OA relates to an altered frontoparietal neurobiological signature that may contribute to the general phenomenon of increasingly fragmented execution of behavior associated with normal aging.

Artificial Neural Network approach to develop unique Classification and Raga identification tools for Pattern Recognition in Carnatic Music

Science.gov (United States)

Srimani, P. K.; Parimala, Y. G.

2011-12-01

A unique approach has been developed to study patterns in ragas of Carnatic Classical music based on artificial neural networks. Ragas in Carnatic music which have found their roots in the Vedic period, have grown on a Scientific foundation over thousands of years. However owing to its vastness and complexities it has always been a challenge for scientists and musicologists to give an all encompassing perspective both qualitatively and quantitatively. Cognition, comprehension and perception of ragas in Indian classical music have always been the subject of intensive research, highly intriguing and many facets of these are hitherto not unravelled. This paper is an attempt to view the melakartha ragas with a cognitive perspective using artificial neural network based approach which has given raise to very interesting results. The 72 ragas of the melakartha system were defined through the combination of frequencies occurring in each of them. The data sets were trained using several neural networks. 100% accurate pattern recognition and classification was obtained using linear regression, TLRN, MLP and RBF networks. Performance of the different network topologies, by varying various network parameters, were compared. Linear regression was found to be the best performing network.

Patterns of brain structural connectivity differentiate normal weight from overweight subjects.

Science.gov (United States)

Gupta, Arpana; Mayer, Emeran A; Sanmiguel, Claudia P; Van Horn, John D; Woodworth, Davis; Ellingson, Benjamin M; Fling, Connor; Love, Aubrey; Tillisch, Kirsten; Labus, Jennifer S

2015-01-01

Alterations in the hedonic component of ingestive behaviors have been implicated as a possible risk factor in the pathophysiology of overweight and obese individuals. Neuroimaging evidence from individuals with increasing body mass index suggests structural, functional, and neurochemical alterations in the extended reward network and associated networks. To apply a multivariate pattern analysis to distinguish normal weight and overweight subjects based on gray and white-matter measurements. Structural images (N = 120, overweight N = 63) and diffusion tensor images (DTI) (N = 60, overweight N = 30) were obtained from healthy control subjects. For the total sample the mean age for the overweight group (females = 32, males = 31) was 28.77 years (SD = 9.76) and for the normal weight group (females = 32, males = 25) was 27.13 years (SD = 9.62). Regional segmentation and parcellation of the brain images was performed using Freesurfer. Deterministic tractography was performed to measure the normalized fiber density between regions. A multivariate pattern analysis approach was used to examine whether brain measures can distinguish overweight from normal weight individuals. 1. White-matter classification: The classification algorithm, based on 2 signatures with 17 regional connections, achieved 97% accuracy in discriminating overweight individuals from normal weight individuals. For both brain signatures, greater connectivity as indexed by increased fiber density was observed in overweight compared to normal weight between the reward network regions and regions of the executive control, emotional arousal, and somatosensory networks. In contrast, the opposite pattern (decreased fiber density) was found between ventromedial prefrontal cortex and the anterior insula, and between thalamus and executive control network regions. 2. Gray-matter classification: The classification algorithm, based on 2 signatures with 42 morphological features, achieved 69

Patterns of brain structural connectivity differentiate normal weight from overweight subjects

Science.gov (United States)

Gupta, Arpana; Mayer, Emeran A.; Sanmiguel, Claudia P.; Van Horn, John D.; Woodworth, Davis; Ellingson, Benjamin M.; Fling, Connor; Love, Aubrey; Tillisch, Kirsten; Labus, Jennifer S.

2015-01-01

Background Alterations in the hedonic component of ingestive behaviors have been implicated as a possible risk factor in the pathophysiology of overweight and obese individuals. Neuroimaging evidence from individuals with increasing body mass index suggests structural, functional, and neurochemical alterations in the extended reward network and associated networks. Aim To apply a multivariate pattern analysis to distinguish normal weight and overweight subjects based on gray and white-matter measurements. Methods Structural images (N = 120, overweight N = 63) and diffusion tensor images (DTI) (N = 60, overweight N = 30) were obtained from healthy control subjects. For the total sample the mean age for the overweight group (females = 32, males = 31) was 28.77 years (SD = 9.76) and for the normal weight group (females = 32, males = 25) was 27.13 years (SD = 9.62). Regional segmentation and parcellation of the brain images was performed using Freesurfer. Deterministic tractography was performed to measure the normalized fiber density between regions. A multivariate pattern analysis approach was used to examine whether brain measures can distinguish overweight from normal weight individuals. Results 1. White-matter classification: The classification algorithm, based on 2 signatures with 17 regional connections, achieved 97% accuracy in discriminating overweight individuals from normal weight individuals. For both brain signatures, greater connectivity as indexed by increased fiber density was observed in overweight compared to normal weight between the reward network regions and regions of the executive control, emotional arousal, and somatosensory networks. In contrast, the opposite pattern (decreased fiber density) was found between ventromedial prefrontal cortex and the anterior insula, and between thalamus and executive control network regions. 2. Gray-matter classification: The classification algorithm, based on 2 signatures with 42

Model sparsity and brain pattern interpretation of classification models in neuroimaging

DEFF Research Database (Denmark)

Rasmussen, Peter Mondrup; Madsen, Kristoffer Hougaard; Churchill, Nathan W

2012-01-01

Interest is increasing in applying discriminative multivariate analysis techniques to the analysis of functional neuroimaging data. Model interpretation is of great importance in the neuroimaging context, and is conventionally based on a ‘brain map’ derived from the classification model. In this ...

Solar ultraviolet and the occupational radiant exposure of Queensland school teachers: A comparative study between teaching classifications and behavior patterns.

Science.gov (United States)

Downs, Nathan J; Harrison, Simone L; Chavez, Daniel R Garzon; Parisi, Alfio V

2016-05-01

Classroom teachers located in Queensland, Australia are exposed to high levels of ambient solar ultraviolet as part of the occupational requirement to provide supervision of children during lunch and break times. We investigated the relationship between periods of outdoor occupational radiant exposure and available ambient solar radiation across different teaching classifications and schools relative to the daily occupational solar ultraviolet radiation (HICNIRP) protection standard of 30J/m(2). Self-reported daily sun exposure habits (n=480) and personal radiant exposures were monitored using calibrated polysulphone dosimeters (n=474) in 57 teaching staff from 6 different schools located in tropical north and southern Queensland. Daily radiant exposure patterns among teaching groups were compared to the ambient UV-Index. Personal sun exposures were stratified among teaching classifications, school location, school ownership (government vs non-government), and type (primary vs secondary). Median daily radiant exposures were 15J/m(2) and 5J/m(2)HICNIRP for schools located in northern and southern Queensland respectively. Of the 474 analyzed dosimeter-days, 23.0% were found to exceed the solar radiation protection standard, with the highest prevalence found among physical education teachers (57.4% dosimeter-days), followed by teacher aides (22.6% dosimeter-days) and classroom teachers (18.1% dosimeter-days). In Queensland, peak outdoor exposure times of teaching staff correspond with periods of extreme UV-Index. The daily occupational HICNIRP radiant exposure standard was exceeded in all schools and in all teaching classifications. Copyright © 2016 Elsevier B.V. All rights reserved.

Ethnicity and socioeconomic status are related to dietary patterns at age 5 in the Amsterdam born children and their development (ABCD) cohort.

Science.gov (United States)

Rashid, Viyan; Engberink, Marielle F; van Eijsden, Manon; Nicolaou, Mary; Dekker, Louise H; Verhoeff, Arnoud P; Weijs, Peter J M

2018-01-08

Health inequalities are already present at young age and tend to vary with ethnicity and socioeconomic status (SES). Diet is a major determinant of overweight, and studying dietary patterns as a whole in relation to overweight rather than single nutrients or foods has been suggested. We derived dietary patterns at age 5 and determined whether ethnicity and SES were both related to these dietary patterns. We analysed 2769 validated Food Frequency Questionnaires filled in by mothers of children (5.7 ± 0.5y) in the Amsterdam Born Children and their Development (ABCD) cohort. Food items were reduced to 41 food groups. Energy adjusted intake per food group (g/d) was used to derive dietary patterns using Principal Component Analysis and children were given a pattern score for each dietary pattern. We defined 5 ethnic groups (Dutch, Surinamese, Turkish, Moroccan, other ethnicities) and 3 SES groups (low, middle, high, based on maternal education). Multivariate ANOVA, with adjustment for age, gender and maternal age, was used to test potential associations between ethnicity or SES and dietary pattern scores. Post-hoc analyses with Bonferroni adjustment were used to examine differences between groups. Principal Component Analysis identified 4 dietary patterns: a snacking, full-fat, meat and healthy dietary pattern, explaining 21% of the variation in dietary intake. Ethnicity was related to the dietary pattern scores (p pattern, whereas Turkish children scored high on full-fat and Surinamese children on the meat pattern. SES was related to the snacking, full-fat and meat patterns (p pattern and low on the full-fat pattern. This study indicates that both ethnicity and SES are relevant for dietary patterns at age 5 and may enable more specific nutrition education to specific ethnic and low socioeconomic status target groups.

Physical activity patterns in patients with early and late age-related macular degeneration

DEFF Research Database (Denmark)

Subhi, Yousif; Sørensen, Torben Lykke

2016-01-01

INTRODUCTION: Age-related macular degeneration (AMD) leads to visual impairment that affects visual functioning and thereby the ability to be physically active. We investigated physical activity patterns in patients with AMD. METHODS: Patients with early and late AMD and elderly controls were...

Eruptive pattern classification on Mount Etna (Sicily) and Piton de la Fournaise (La Réunion)

Science.gov (United States)

Falsaperla, Susanna; Langer, Horst; Ferrazzini, Valérie

2016-04-01

In the framework of the European MEDiterrranean Supersite Volcanoes (MED­SUV) project, Mt. Etna (Italy) and Piton de la Fournaise (La Réunion) were chosen as "European Supersite Demonstrator" and test site, respectively, to promote the transfer and implementation of efficient tools for the identification of impending volcanic activity. Both are "open-conduit volcanoes", forming ideal sites for the test and validation of innovative concepts, which can contribute to minimize volcanic hazard. One of the aims of the MED-SUV project was the development of software for machine learning applicable to data processing for early-warning purposes. Near-real time classification of continuous seismic data stream has been carried out in the control room of INGV Osservatorio Etneo since 2010. Subsequently, automatic alert procedures were activated. In the light of the excellent results for the 24/7 surveillance of Etna, we examine the portability of tools developed in the framework of the project when applied to seismic data recorded at Piton de la Fournaise. In the present application to data recorded at Piton de la Fournaise, the classifier aims at highlighting changes in the frequency content of the background seismic signal heralding the activation of the volcanic source and the imminent eruption. We describe the preliminary results of this test on a set of data of nearly two years starting on January 2014. This period follows three years of inactivity and deflation of the volcano and marks a renewal of the volcano activity with inflation, deep seismicity (-7km bsl) and five eruptions with fountains and lava flows that lasted from a few hours to more than two months. We discuss here the necessary tuning for the implementation of the software to the new dataset analyzed. We also propose a comparison with the results of pattern classification regarding recent eruptive activity at Etna.

Use of Pattern Classification Algorithms to Interpret Passive and Active Data Streams from a Walking-Speed Robotic Sensor Platform

Science.gov (United States)

Dieckman, Eric Allen

In order to perform useful tasks for us, robots must have the ability to notice, recognize, and respond to objects and events in their environment. This requires the acquisition and synthesis of information from a variety of sensors. Here we investigate the performance of a number of sensor modalities in an unstructured outdoor environment, including the Microsoft Kinect, thermal infrared camera, and coffee can radar. Special attention is given to acoustic echolocation measurements of approaching vehicles, where an acoustic parametric array propagates an audible signal to the oncoming target and the Kinect microphone array records the reflected backscattered signal. Although useful information about the target is hidden inside the noisy time domain measurements, the Dynamic Wavelet Fingerprint process (DWFP) is used to create a time-frequency representation of the data. A small-dimensional feature vector is created for each measurement using an intelligent feature selection process for use in statistical pattern classification routines. Using our experimentally measured data from real vehicles at 50 m, this process is able to correctly classify vehicles into one of five classes with 94% accuracy. Fully three-dimensional simulations allow us to study the nonlinear beam propagation and interaction with real-world targets to improve classification results.

Age-related individual variability in memory performance is associated with amygdala-hippocampal circuit function and emotional pattern separation

Science.gov (United States)

Leal, Stephanie L.; Noche, Jessica A.; Murray, Elizabeth A.; Yassa, Michael A.

2018-01-01

While aging is generally associated with episodic memory decline, not all older adults exhibit memory loss. Furthermore, emotional memories are not subject to the same extent of forgetting and appear preserved in aging. We conducted high-resolution fMRI during a task involving pattern separation of emotional information in older adults with and without age-related memory impairment (characterized by performance on a word-list learning task: low performers: LP vs. high performers: HP). We found signals consistent with emotional pattern separation in hippocampal dentate (DG)/CA3 in HP but not in LP individuals, suggesting a deficit in emotional pattern separation. During false recognition, we found increased DG/CA3 activity in LP individuals, suggesting that hyperactivity may be associated with overgeneralization. We additionally observed a selective deficit in basolateral amygdala—lateral entorhinal cortex—DG/CA3 functional connectivity in LP individuals during pattern separation of negative information. During negative false recognition, LP individuals showed increased medial temporal lobe functional connectivity, consistent with overgeneralization. Overall, these results suggest a novel mechanistic account of individual differences in emotional memory alterations exhibited in aging. PMID:27723500

Age-related individual variability in memory performance is associated with amygdala-hippocampal circuit function and emotional pattern separation.

Science.gov (United States)

Leal, Stephanie L; Noche, Jessica A; Murray, Elizabeth A; Yassa, Michael A

2017-01-01

While aging is generally associated with episodic memory decline, not all older adults exhibit memory loss. Furthermore, emotional memories are not subject to the same extent of forgetting and appear preserved in aging. We conducted high-resolution fMRI during a task involving pattern separation of emotional information in older adults with and without age-related memory impairment (characterized by performance on a word-list learning task: low performers: LP vs. high performers: HP). We found signals consistent with emotional pattern separation in hippocampal dentate (DG)/CA3 in HP but not in LP individuals, suggesting a deficit in emotional pattern separation. During false recognition, we found increased DG/CA3 activity in LP individuals, suggesting that hyperactivity may be associated with overgeneralization. We additionally observed a selective deficit in basolateral amygdala-lateral entorhinal cortex-DG/CA3 functional connectivity in LP individuals during pattern separation of negative information. During negative false recognition, LP individuals showed increased medial temporal lobe functional connectivity, consistent with overgeneralization. Overall, these results suggest a novel mechanistic account of individual differences in emotional memory alterations exhibited in aging. Copyright © 2016 Elsevier Inc. All rights reserved.

Differential Aging Signals in Abdominal CT Scans.

Science.gov (United States)

Orlov, Nikita V; Makrogiannis, Sokratis; Ferrucci, Luigi; Goldberg, Ilya G

2017-12-01

Changes in the composition of body tissues are major aging phenotypes, but they have been difficult to study in depth. Here we describe age-related change in abdominal tissues observable in computed tomography (CT) scans. We used pattern recognition and machine learning to detect and quantify these changes in a model-agnostic fashion. CT scans of abdominal L4 sections were obtained from Baltimore Longitudinal Study of Aging (BLSA) participants. Age-related change in the constituent tissues were determined by training machine classifiers to differentiate age groups within male and female strata ("Younger" at 50-70 years old vs "Older" at 80-99 years old). The accuracy achieved by the classifiers in differentiating the age cohorts was used as a surrogate measure of the aging signal in the different tissues. The highest accuracy for discriminating age differences was 0.76 and 0.72 for males and females, respectively. The classification accuracy was 0.79 and 0.71 for adipose tissue, 0.70 and 0.68 for soft tissue, and 0.65 and 0.64 for bone. Using image data from a large sample of well-characterized pool of participants dispersed over a wide age range, we explored age-related differences in gross morphology and texture of abdominal tissues. This technology is advantageous for tracking effects of biological aging and predicting adverse outcomes when compared to the traditional use of specific molecular biomarkers. Application of pattern recognition and machine learning as a tool for analyzing medical images may provide much needed insight into tissue changes occurring with aging and, further, connect these changes with their metabolic and functional consequences. Published by Elsevier Inc.

AGE AT MARRIAGE AND FERTILITY PATTERN OF ADOLESCENT MARRIED WOMEN IN RURAL BANGLADESH

Directory of Open Access Journals (Sweden)

Shaila Ahmed

2007-07-01

Full Text Available This cross sectional descriptive study was conducted in two purposively selected rural areas of Faridpur district - Alfadanga and Boalmari. The objectives were to find out the age at marriage and fertility pattern amongst the adolescent married women residing in the study areas. A total of 426 women were selected purposively and interviewed using a pre-tested structured questionnaire. Most (97.2% were in the age group of 15-19 years, being married by 15.5 ± 1.5 years. Although 57.5% had a secondary level education, almost all (97% were found to be housewives. Monthly income was between Taka 2001-4000 in 41.3% of the households. Regarding fertility pattern, 19% of the adolescent women were found to be pregnant at the time of survey. The total fertility rate (TFR among this age group was estimated to be 2.6 per woman. To help improve the situation, awareness on the negative consequences of early marriage and consequent childbearing needs to be created not only among the young adolescent girls but should be targeted towards their parents too. Ibrahim Med. Coll. J. 2007; 1(2: 9-12

The ITE Land classification: Providing an environmental stratification of Great Britain.

Science.gov (United States)

Bunce, R G; Barr, C J; Gillespie, M K; Howard, D C

1996-01-01

The surface of Great Britain (GB) varies continuously in land cover from one area to another. The objective of any environmentally based land classification is to produce classes that match the patterns that are present by helping to define clear boundaries. The more appropriate the analysis and data used, the better the classes will fit the natural patterns. The observation of inter-correlations between ecological factors is the basis for interpreting ecological patterns in the field, and the Institute of Terrestrial Ecology (ITE) Land Classification formalises such subjective ideas. The data inevitably comprise a large number of factors in order to describe the environment adequately. Single factors, such as altitude, would only be useful on a national basis if they were the only dominant causative agent of ecological variation.The ITE Land Classification has defined 32 environmental categories called 'land classes', initially based on a sample of 1-km squares in Great Britain but subsequently extended to all 240 000 1-km squares. The original classification was produced using multivariate analysis of 75 environmental variables. The extension to all squares in GB was performed using a combination of logistic discrimination and discriminant functions. The classes have provided a stratification for successive ecological surveys, the results of which have characterised the classes in terms of botanical, zoological and landscape features.The classification has also been applied to integrate diverse datasets including satellite imagery, soils and socio-economic information. A variety of models have used the structure of the classification, for example to show potential land use change under different economic conditions. The principal data sets relevant for planning purposes have been incorporated into a user-friendly computer package, called the 'Countryside Information System'.

Detection of tuberculosis patterns in digital photographs of chest X-ray images using Deep Learning: feasibility study.

Science.gov (United States)

Becker, A S; Blüthgen, C; Phi van, V D; Sekaggya-Wiltshire, C; Castelnuovo, B; Kambugu, A; Fehr, J; Frauenfelder, T

2018-03-01

To evaluate the feasibility of Deep Learning-based detection and classification of pathological patterns in a set of digital photographs of chest X-ray (CXR) images of tuberculosis (TB) patients. In this prospective, observational study, patients with previously diagnosed TB were enrolled. Photographs of their CXRs were taken using a consumer-grade digital still camera. The images were stratified by pathological patterns into classes: cavity, consolidation, effusion, interstitial changes, miliary pattern or normal examination. Image analysis was performed with commercially available Deep Learning software in two steps. Pathological areas were first localised; detected areas were then classified. Detection was assessed using receiver operating characteristics (ROC) analysis, and classification using a confusion matrix. The study cohort was 138 patients with human immunodeficiency virus (HIV) and TB co-infection (median age 34 years, IQR 28-40); 54 patients were female. Localisation of pathological areas was excellent (area under the ROC curve 0.82). The software could perfectly distinguish pleural effusions from intraparenchymal changes. The most frequent misclassifications were consolidations as cavitations, and miliary patterns as interstitial patterns (and vice versa). Deep Learning analysis of CXR photographs is a promising tool. Further efforts are needed to build larger, high-quality data sets to achieve better diagnostic performance.

Classification for Inconsistent Decision Tables

KAUST Repository

Azad, Mohammad

2016-09-28

Decision trees have been used widely to discover patterns from consistent data set. But if the data set is inconsistent, where there are groups of examples with equal values of conditional attributes but different labels, then to discover the essential patterns or knowledge from the data set is challenging. Three approaches (generalized, most common and many-valued decision) have been considered to handle such inconsistency. The decision tree model has been used to compare the classification results among three approaches. Many-valued decision approach outperforms other approaches, and M_ws_entM greedy algorithm gives faster and better prediction accuracy.

Classification for Inconsistent Decision Tables

KAUST Repository

Azad, Mohammad; Moshkov, Mikhail

2016-01-01

Decision trees have been used widely to discover patterns from consistent data set. But if the data set is inconsistent, where there are groups of examples with equal values of conditional attributes but different labels, then to discover the essential patterns or knowledge from the data set is challenging. Three approaches (generalized, most common and many-valued decision) have been considered to handle such inconsistency. The decision tree model has been used to compare the classification results among three approaches. Many-valued decision approach outperforms other approaches, and M_ws_entM greedy algorithm gives faster and better prediction accuracy.

Classification of brain MRI with big data and deep 3D convolutional neural networks

Science.gov (United States)

Wegmayr, Viktor; Aitharaju, Sai; Buhmann, Joachim

2018-02-01

Our ever-aging society faces the growing problem of neurodegenerative diseases, in particular dementia. Magnetic Resonance Imaging provides a unique tool for non-invasive investigation of these brain diseases. However, it is extremely difficult for neurologists to identify complex disease patterns from large amounts of three-dimensional images. In contrast, machine learning excels at automatic pattern recognition from large amounts of data. In particular, deep learning has achieved impressive results in image classification. Unfortunately, its application to medical image classification remains difficult. We consider two reasons for this difficulty: First, volumetric medical image data is considerably scarcer than natural images. Second, the complexity of 3D medical images is much higher compared to common 2D images. To address the problem of small data set size, we assemble the largest dataset ever used for training a deep 3D convolutional neural network to classify brain images as healthy (HC), mild cognitive impairment (MCI) or Alzheimers disease (AD). We use more than 20.000 images from subjects of these three classes, which is almost 9x the size of the previously largest data set. The problem of high dimensionality is addressed by using a deep 3D convolutional neural network, which is state-of-the-art in large-scale image classification. We exploit its ability to process the images directly, only with standard preprocessing, but without the need for elaborate feature engineering. Compared to other work, our workflow is considerably simpler, which increases clinical applicability. Accuracy is measured on the ADNI+AIBL data sets, and the independent CADDementia benchmark.

Fingerprint pattern classification approach based on the coordinate geometry of singularities

CSIR Research Space (South Africa)

Msiza, IS

2009-10-01

Full Text Available of fingerprint matching, it serves to reduce the duration of the query. The fingerprint classes discussed in this document are the Central Twins (CT), Tented Arch (TA), Left Loop (LL), Right Loop (RL) and the Plain Arch (PA). The classification rules employed...

Real-time detection and classification of anomalous events in streaming data

Science.gov (United States)

Ferragut, Erik M.; Goodall, John R.; Iannacone, Michael D.; Laska, Jason A.; Harrison, Lane T.

2016-04-19

A system is described for receiving a stream of events and scoring the events based on anomalousness and maliciousness (or other classification). The events can be displayed to a user in user-defined groupings in an animated fashion. The system can include a plurality of anomaly detectors that together implement an algorithm to identify low probability events and detect atypical traffic patterns. The atypical traffic patterns can then be classified as being of interest or not. In one particular example, in a network environment, the classification can be whether the network traffic is malicious or not.

Evaluation of Current Approaches to Stream Classification and a Heuristic Guide to Developing Classifications of Integrated Aquatic Networks

Science.gov (United States)

Melles, S. J.; Jones, N. E.; Schmidt, B. J.

2014-03-01

Conservation and management of fresh flowing waters involves evaluating and managing effects of cumulative impacts on the aquatic environment from disturbances such as: land use change, point and nonpoint source pollution, the creation of dams and reservoirs, mining, and fishing. To assess effects of these changes on associated biotic communities it is necessary to monitor and report on the status of lotic ecosystems. A variety of stream classification methods are available to assist with these tasks, and such methods attempt to provide a systematic approach to modeling and understanding complex aquatic systems at various spatial and temporal scales. Of the vast number of approaches that exist, it is useful to group them into three main types. The first involves modeling longitudinal species turnover patterns within large drainage basins and relating these patterns to environmental predictors collected at reach and upstream catchment scales; the second uses regionalized hierarchical classification to create multi-scale, spatially homogenous aquatic ecoregions by grouping adjacent catchments together based on environmental similarities; and the third approach groups sites together on the basis of similarities in their environmental conditions both within and between catchments, independent of their geographic location. We review the literature with a focus on more recent classifications to examine the strengths and weaknesses of the different approaches. We identify gaps or problems with the current approaches, and we propose an eight-step heuristic process that may assist with development of more flexible and integrated aquatic classifications based on the current understanding, network thinking, and theoretical underpinnings.

Development and comparison of circulation type classifications using the COST 733 dataset and software

Czech Academy of Sciences Publication Activity Database

Philipp, A.; Beck, Ch.; Huth, Radan; Jacobeit, J.

2016-01-01

Roč. 36, č. 7 (2016), s. 2673-2691 ISSN 0899-8418 Institutional support: RVO:68378289 Keywords : circulation type classification * weather types * Rand index * pattern correlation * manual classification * threshold-based classification * principal component analysis * cluster analysis Subject RIV: DG - Athmosphere Sciences, Meteorology Impact factor: 3.760, year: 2016 http://onlinelibrary.wiley.com/doi/10.1002/joc.3920/abstract

Estimation of Diverse Porang (Amorphophallus Muelleri Blume Age in Forest Are Based on Brancing Pattern of Leaf Petiolule

Directory of Open Access Journals (Sweden)

Nunung Harijati

2014-11-01

Full Text Available Porang is higher plant which has unique morphology. Tuber, petiole and lamina are the main part of its body. Even Porang doesn’t have true stem, only petiole, its lamina not simple leaves but compound leaves with special pattern. Branching patterns of petiolule are not same in different age. Therefore the aim of research was to observe branching pattern of petiolule from Porang age 1-4 that lived in their native habitat i.e. forest. The research was conducted in secondary forest in Sumberbendo village, Madiun. Determination porang age was helped by expert farmer that worked with porang for long time. The result showed that Porang age 1 had petiolule with braching type 1-0. Porang age 2 was 1-3, Porang age 3 was 1-3-2, and Porang age 4 was 1-3-2-2. The petiolule which acted as a base of new branch had one or few single simple leaf. The leave could be both sinus and un-sinus leaves located in between two initial or base branching. The position of the leaves were opposite or alternate along with petiolule. If location single leaf just in point branching, the new petiolule morphology was not be considered as petiolule but midrib.

Vocational Rehabilitation Service Patterns and Outcomes for Individuals with Autism of Different Ages

Science.gov (United States)

Chen, June L.; Sung, Connie; Pi, Sukyeong

2015-01-01

Young adults with autism spectrum disorders (ASD) often experience employment difficulties. Using Rehabilitation Service Administration data (RSA-911), this study investigated the service patterns and factors related to the employment outcomes of individuals with ASD in different age groups. Hierarchical logistic regression analyses were conducted…

Classification for longevity potential: the use of novel biomarkers

Directory of Open Access Journals (Sweden)

Marian Beekman

2016-10-01

Full Text Available Background: In older people chronological age may not be the best predictor of residual lifespan and mortality, because with age the heterogeneity in health is increasing. Biomarkers for biological age and residual lifespan are being developed to predict disease and mortality better at an individual level than chronological age. In the current paper we aim to classify a group of older people into those with longevity potential or controls.Methods: In the Leiden Longevity Study participated 1671 offspring of nonagenarian siblings, as the group with longevity potential, and 744 similarly aged controls. Using known risk factors for cardiovascular disease, previously reported markers for human longevity and other physiological measures as predictors, classification models for longevity potential were constructed with multiple logistic regression of the offspring-control status.Results: The Framingham Risk Score is predictive for longevity potential (AUC = 64.7. Physiological parameters involved in immune responses and glucose, lipid and energy metabolism further improve the prediction performance for longevity potential (AUCmale = 71.4, AUCfemale = 68.7.Conclusion: Using the Framingham Risk Score, the classification of older people in groups with longevity potential and controls is moderate, but can be improved to a reasonably good classification in combination with markers of immune response, glucose, lipid and energy metabolism. We show that individual classification of older people for longevity potential may be feasible using biomarkers from a wide variety of different biological processes.

A chart review of morbidity patterns among adult patients attending primary care setting in urban Odisha, India: An International Classification of Primary Care experience

Directory of Open Access Journals (Sweden)

Subhashisa Swain

2017-01-01

Full Text Available Introduction: Disease burden estimations based on sound epidemiological research provide the foundation for designing health services. Patients visiting a primary care often present with symptoms and signs. Understanding the burden is crucial for developing countries including India. The project aimed to record the reasons for encounter (RFE at primary care settings for estimating the burden at the health-care facility. Methodology: This cross-sectional study was undertaken at four urban health dispensaries of Bhubaneswar, Odisha, with the aim to explore the prevailing patterns of diseases among patients attending these facilities. Data collection spanned from May to October 2012. At each center, patients' information on age, sex, religion, and presenting illness was extracted from the outpatient records over these time period. Data were entered and analyzed in SPSS version 20, and the International Classification of Primary Care-2 was used for coding the illnesses. Results: In total, 2249 patient's records were extracted over 12 weeks. Out of them, 1241 (55.2% were male with mean age of 41.8 (±15.8 years vis-à -vis 38.2 (±14.1 years for females. Around 151 (6.7% had 2 or more symptoms or conditions. Overall, the most common categories were general and unspecified followed by digestive-related symptoms in both sexes. The most common symptoms among males were fever (11.4%, heart burn (8.1%, and vertigo or dizziness (3.6%. Similar pattern was seen among females. Respiratory (17.0% and cardiovascular (10.2% problems were the most common RFEs among males and females. The most common RFEs for acute care among males and females were fever, allergic rhinitis, upper respiratory tract infection, and acute bronchitis. Leading RFEs for chronic care among males were hypertension uncomplicated, heart burn, low back pain, whereas among females, hypertension and heartburn were mostly seen. Conclusion: Primary care settings are experiencing both communicable

Pattern of congenital brain malformations at a referral hospital in Saudi Arabia: An MRI study

International Nuclear Information System (INIS)

Alorainy, Ibrahim A.

2006-01-01

More than 2000 different congenital cerebral malformations have been described in the literature, for which several classification systems have been proposed. With the help of these classification systems, it is now possible, with neuroimaging, to time neuroembtyologic events. Magnetic resonance imaging (MRI), in particular, is useful in studying these malformations. This study evaluated the pattern of congenital brain malformations in a university referral hospital setting. The records of all MRI brain examinations at our hospital over a period of 3 years for children younger than 15 years of age were reviewed. Cases of congenital cerebral malformations were analyzed by sex, age at presentation, type of congenital cerebral malformation and other associated congenital cerebral malformations. Of the 808 MR examinations of different parts of the body for children in the study period, 719 (89%), on 581 patients, were of the brain. Eighty-six children (14.8%) were found to have single or multiple congenital brain malformations. In these children, 114 congenital brain malformations were identified, the commonest being cortical migrational defects (25 patients, 22%), neural tube closure defects (22 patients, 19%), and corpus callosum dysgenesis (22 patients 19%). The least common was vascular malformation. Sixteen patients (18.6%) had more than one congenital brain malformations. Neural tube closer defects, cortical migrational abnormalities, and corpus callosum anomalies were the commonest congenital brain malformations, while vascular malformations were the least common. Most of the identified malformations demonstrated the usual pattern, but a few showed unusual patterns and associations. (author)

A Western Diet Pattern Is Associated with Higher Concentrations of Blood and Bone Lead among Middle-Aged and Elderly Men.

Science.gov (United States)

Wang, Xin; Ding, Ning; Tucker, Katherine L; Weisskopf, Marc G; Sparrow, David; Hu, Howard; Park, Sung Kyun

2017-07-01

Background: Little is known about the effects of overall dietary pattern on lead concentration. Objective: We examined the association of overall dietary patterns, derived from a semiquantitative food frequency questionnaire, with bone and blood lead concentrations. Methods: These longitudinal analyses included mostly non-Hispanic white, middle-aged-to-elderly men from the Veterans Affairs Normative Aging Study. Long-term lead exposures were measured as tibia and patella lead concentrations by using K-shell-X-ray fluorescence. Short-term lead exposures were measured as blood lead concentrations by using graphite furnace atomic absorption spectroscopy. Dietary pattern scores were derived by using factor analysis. Linear mixed-effects models were utilized to predict blood lead concentrations among 983 men, aged 44-92 y at baseline, with a total of 3273 observations (during 1987-2008). We constructed linear regression models to determine the relations between dietary patterns and bone lead concentrations among 649 participants with an age range of 49-93 y. Results: Two major dietary patterns were identified: a prudent dietary pattern, characterized by high intakes of fruit, legumes, vegetables, whole grains, poultry, and seafood; and a Western dietary pattern, characterized by high intakes of processed meat, red meat, refined grains, high-fat dairy products, French fries, butter, and eggs. After adjusting for age, smoking status, body mass index, total energy intake, education, occupation, neighborhood-based education and income level, men in the highest tertile of the Western pattern score (compared with the lowest) had 0.91 μg/dL (95% CI: 0.41, 1.42 μg/dL) higher blood lead, 5.96 μg/g (95% CI: 1.76, 10.16 μg/g) higher patella lead, and 3.83 μg/g (95% CI: 0.97, 6.70 μg/g) higher tibia lead. No significant association was detected with the prudent dietary pattern in the adjusted model. Conclusions: These findings suggest that the Western diet is associated with

Automatic Classification of Station Quality by Image Based Pattern Recognition of Ppsd Plots

Science.gov (United States)

Weber, B.; Herrnkind, S.

2017-12-01

The number of seismic stations is growing and it became common practice to share station waveform data in real-time with the main data centers as IRIS, GEOFON, ORFEUS and RESIF. This made analyzing station performance of increasing importance for automatic real-time processing and station selection. The value of a station depends on different factors as quality and quantity of the data, location of the site and general station density in the surrounding area and finally the type of application it can be used for. The approach described by McNamara and Boaz (2006) became standard in the last decade. It incorporates a probability density function (PDF) to display the distribution of seismic power spectral density (PSD). The low noise model (LNM) and high noise model (HNM) introduced by Peterson (1993) are also displayed in the PPSD plots introduced by McNamara and Boaz allowing an estimation of the station quality. Here we describe how we established an automatic station quality classification module using image based pattern recognition on PPSD plots. The plots were split into 4 bands: short-period characteristics (0.1-0.8 s), body wave characteristics (0.8-5 s), microseismic characteristics (5-12 s) and long-period characteristics (12-100 s). The module sqeval connects to a SeedLink server, checks available stations, requests PPSD plots through the Mustang service from IRIS or PQLX/SQLX or from GIS (gempa Image Server), a module to generate different kind of images as trace plots, map plots, helicorder plots or PPSD plots. It compares the image based quality patterns for the different period bands with the retrieved PPSD plot. The quality of a station is divided into 5 classes for each of the 4 bands. Classes A, B, C, D define regular quality between LNM and HNM while the fifth class represents out of order stations with gain problems, missing data etc. Over all period bands about 100 different patterns are required to classify most of the stations available on the

[Patterns of brain ageing].

Science.gov (United States)

Fernández Viadero, Carlos; Verduga Vélez, Rosario; Crespo Santiago, Dámaso

2017-06-01

Neuroplasticity lends the brain a strong ability to adapt to changes in the environment that occur during ageing. Animal models have shown alterations in neurotransmission and imbalances in the expression of neural growth factor. Changes at the morphometric level are not constant. Volume loss is related to alterations in neuroplasticity and involvement of the cerebral neuropil. Although there are no conclusive data, physical exercise improves the molecular, biological, functional and behavioural-cognitive changes associated with brain ageing. The aged human brain has been described as showing weight and volume loss and increased ventricular size. However, neuroimaging shows significant variation and many healthy elderly individuals show no significant macroscopic changes. In most brain regions, the number of neurons remains stable throughout life. Neuroplasticity does not disappear with ageing, and changes in dendritic arborization and the density of spines and synapses are more closely related to brain activity than to age. At the molecular level, although the presence of altered Tau and β-amyloid proteins is used as a biomarker of neurodegenerative disease, postmortem studies show that these abnormal proteins are common in the brains of elderly people without dementia. Finally, due to the relationship between neurodegenerative diseases and metabolic alterations, this article analyses the influence of insulin-like growth factor and ageing, both in animal models and in humans, and the possible neuroprotective effect of insulin. Copyright © 2017 Sociedad Española de Geriatría y Gerontología. Publicado por Elsevier España, S.L.U. All rights reserved.

Categorizing Children: Automated Text Classification of CHILDES files

NARCIS (Netherlands)

Opsomer, Rob; Knoth, Peter; Wiering, Marco; van Polen, Freek; Trapman, Jantine

2008-01-01

In this paper we present the application of machine learning text classification methods to two tasks: categorization of children’s speech in the CHILDES Database according to gender and age. Both tasks are binary. For age, we distinguish two age groups between the age of 1.9 and 3.0 years old. The

Air Traffic Security: Aircraft Classification Using ADS-B Message’s Phase-Pattern

Directory of Open Access Journals (Sweden)

Mauro Leonardi

2017-10-01

Full Text Available Automatic Dependent Surveillance-Broadcast (ADS-B is a surveillance system used in Air Traffic Control. With this system, the aircraft transmits their own information (identity, position, velocity, etc. to any equipped listener for surveillance scope. The ADS-B is based on a very simple protocol and does not provide any kind of authentication and encryption, making it vulnerable to many types of cyber-attacks. In the paper, the use of the airplane/transmitter carrier phase is proposed as a feature to perform a classification of the aircraft and, therefore, distinguish legitimate messages from fake ones. The feature extraction process is described and a classification method is selected. Finally, a complete intruder detection algorithm is proposed and evaluated with real data.

Structured Literature Review of Electricity Consumption Classification Using Smart Meter Data

Directory of Open Access Journals (Sweden)

Alexander Martin Tureczek

2017-04-01

Full Text Available Smart meters for measuring electricity consumption are fast becoming prevalent in households. The meters measure consumption on a very fine scale, usually on a 15 min basis, and the data give unprecedented granularity of consumption patterns at household level. A multitude of papers have emerged utilizing smart meter data for deepening our knowledge of consumption patterns. This paper applies a modification of Okoli’s method for conducting structured literature reviews to generate an overview of research in electricity customer classification using smart meter data. The process assessed 2099 papers before identifying 34 significant papers, and highlights three key points: prominent methods, datasets and application. Three important findings are outlined. First, only a few papers contemplate future applications of the classification, rendering papers relevant only in a classification setting. Second; the encountered classification methods do not consider correlation or time series analysis when classifying. The identified papers fail to thoroughly analyze the statistical properties of the data, investigations that could potentially improve classification performance. Third, the description of the data utilized is of varying quality, with only 50% acknowledging missing values impact on the final sample size. A data description score for assessing the quality in data description has been developed and applied to all papers reviewed.

Drinking pattern and mortality in middle-aged men and women

DEFF Research Database (Denmark)

Tolstrup, Janne S; Jensen, Majken K; Tjønneland, Anne

2004-01-01

AIMS: To address the prospective association between alcohol drinking pattern and all-cause mortality. DESIGN: Population-based cohort study conducted between 1993 and 2003. SETTING: Denmark. PARTICIPANTS: A total of 26 909 men and 29 626 women aged 55-65 years. MEASUREMENTS: We obtained risk...... estimates for all-cause mortality for different levels of quantity and frequency of alcohol intake adjusted for life-style factors, including diet. FINDINGS: During follow-up, 1528 men and 915 women died. For the same average consumption of alcohol, a non-frequent intake implied a higher risk of death than...

Iris Image Classification Based on Hierarchical Visual Codebook.

Science.gov (United States)

Zhenan Sun; Hui Zhang; Tieniu Tan; Jianyu Wang

2014-06-01

Iris recognition as a reliable method for personal identification has been well-studied with the objective to assign the class label of each iris image to a unique subject. In contrast, iris image classification aims to classify an iris image to an application specific category, e.g., iris liveness detection (classification of genuine and fake iris images), race classification (e.g., classification of iris images of Asian and non-Asian subjects), coarse-to-fine iris identification (classification of all iris images in the central database into multiple categories). This paper proposes a general framework for iris image classification based on texture analysis. A novel texture pattern representation method called Hierarchical Visual Codebook (HVC) is proposed to encode the texture primitives of iris images. The proposed HVC method is an integration of two existing Bag-of-Words models, namely Vocabulary Tree (VT), and Locality-constrained Linear Coding (LLC). The HVC adopts a coarse-to-fine visual coding strategy and takes advantages of both VT and LLC for accurate and sparse representation of iris texture. Extensive experimental results demonstrate that the proposed iris image classification method achieves state-of-the-art performance for iris liveness detection, race classification, and coarse-to-fine iris identification. A comprehensive fake iris image database simulating four types of iris spoof attacks is developed as the benchmark for research of iris liveness detection.

Bio-geographic classification of the Caspian Sea

Science.gov (United States)

Fendereski, F.; Vogt, M.; Payne, M. R.; Lachkar, Z.; Gruber, N.; Salmanmahiny, A.; Hosseini, S. A.

2014-03-01

Like other inland seas, the Caspian Sea (CS) has been influenced by climate change and anthropogenic disturbance during recent decades, yet the scientific understanding of this water body remains poor. In this study, an eco-geographical classification of the CS based on physical information derived from space and in-situ data is developed and tested against a set of biological observations. We used a two-step classification procedure, consisting of (i) a data reduction with self-organizing maps (SOMs) and (ii) a synthesis of the most relevant features into a reduced number of marine ecoregions using the Hierarchical Agglomerative Clustering (HAC) method. From an initial set of 12 potential physical variables, 6 independent variables were selected for the classification algorithm, i.e., sea surface temperature (SST), bathymetry, sea ice, seasonal variation of sea surface salinity (DSSS), total suspended matter (TSM) and its seasonal variation (DTSM). The classification results reveal a robust separation between the northern and the middle/southern basins as well as a separation of the shallow near-shore waters from those off-shore. The observed patterns in ecoregions can be attributed to differences in climate and geochemical factors such as distance from river, water depth and currents. A comparison of the annual and monthly mean Chl a concentrations between the different ecoregions shows significant differences (Kruskal-Wallis rank test, P qualitative evaluation of differences in community composition based on recorded presence-absence patterns of 27 different species of plankton, fish and benthic invertebrate also confirms the relevance of the ecoregions as proxies for habitats with common biological characteristics.

Distinct brain metabolic patterns separately associated with cognition, motor function, and aging in Parkinson's disease dementia.

Science.gov (United States)

Ko, Ji Hyun; Katako, Audrey; Aljuaid, Maram; Goertzen, Andrew L; Borys, Andrew; Hobson, Douglas E; Kim, Seok Min; Lee, Chong Sik

2017-12-01

We explored whether patients with Parkinson's disease dementia (PDD) show a distinct spatial metabolic pattern that characterizes cognitive deficits in addition to motor dysfunction. Eighteen patients with PDD underwent 3 separate positron emission tomography sessions with [ 18 F]fluorodeoxyglucose (for glucose metabolism), fluorinated N-3-fluoropropyl-2-beta-carboxymethoxy-3-beta-(4-iodophenyl) nortropane (for dopamine transporter density) and Pittsburgh compound-B (for beta-amyloid load). We confirmed in PDD versus normal controls, overall hypometabolism in the posterior and prefrontal brain regions accompanied with hypermetabolism in subcortical structures and the cerebellar vermis. A multivariate network analysis then revealed 3 metabolic patterns that are separately associated with cognitive performance (p = 0.042), age (p = 0.042), and motor symptom severity (p = 0.039). The age-related pattern's association with aging was replicated in healthy controls (p = 0.047) and patients with Alzheimer's disease (p = 0.002). The cognition-related pattern's association with cognitive performance was observed, with a trend-level of correlation, in patients with dementia with Lewy bodies (p = 0.084) but not in patients with Alzheimer's disease (p = 0.974). We found no association with fluorinated N-3-fluoropropyl-2-beta-carboxymethoxy-3-beta-(4-iodophenyl) nortropane and Pittsburgh compound-B positron emission tomography with patients' cognitive performance. Copyright © 2017 Elsevier Inc. All rights reserved.

Acoustic transient classification with a template correlation processor.

Science.gov (United States)

Edwards, R T

1999-10-01

I present an architecture for acoustic pattern classification using trinary-trinary template correlation. In spite of its computational simplicity, the algorithm and architecture represent a method which greatly reduces bandwidth of the input, storage requirements of the classifier memory, and power consumption of the system without compromising classification accuracy. The linear system should be amenable to training using recently-developed methods such as Independent Component Analysis (ICA), and we predict that behavior will be qualitatively similar to that of structures in the auditory cortex.

Patterns and sources of personality development in old age.

Science.gov (United States)

Kandler, Christian; Kornadt, Anna E; Hagemeyer, Birk; Neyer, Franz J

2015-07-01

Despite abundant evidence that personality development continues in adulthood, little is known about the patterns and sources of personality development in old age. We thus investigated mean-level trends and individual differences in change as well as the genetic and environmental sources of rank-order continuity and change in several personality traits (neuroticism, extraversion, openness, agreeableness, conscientiousness, perceived control, and affect intensity) and well-being. In addition, we analyzed the interrelation between perceived control and change in other personality traits as well as between change in personality traits and change in well-being. We analyzed data from older adult twins, aged 64-85 years at Time 1 (N = 410; 135 males and 275 females; 134 monozygotic and 63 dizygotic twin pairs), collected at 2 different time points about 5 years apart. On average, neuroticism increased, whereas extraversion, conscientiousness, and perceived control significantly decreased over time. Change in perceived control was associated with change in neuroticism and conscientiousness, pointing to particular adaptation mechanisms specific to old age. Whereas individual differences in personality traits were fairly stable due to both genetic and environmental sources, individual differences in change were primarily due to environmental sources (beyond random error) indicating plasticity in old age. Even though the average level of well-being did not significantly change over time, individual well-being tended to decrease with strongly increasing levels of neuroticism as well as decreasing extraversion, conscientiousness, and perceived control, indicating that personality traits predict well-being but not vice versa. We discuss implications for theory on personality development across the lifespan. (c) 2015 APA, all rights reserved).

Expression Patterns of Odorant Receptors and Response Properties of Olfactory Sensory Neurons in Aged Mice

OpenAIRE

Lee, Anderson C.; Tian, Huikai; Grosmaitre, Xavier; Ma, Minghong

2009-01-01

The sense of smell deteriorates in normal aging, but the underling mechanisms are still elusive. Here we investigated age-related alterations in expression patterns of odorant receptor (OR) genes and functional properties of olfactory sensory neurons (OSNs)—2 critical factors that define the odor detection threshold in the olfactory epithelium. Using in situ hybridization for 9 representative OR genes, we compared the cell densities of each OR in coronal nose sections at different ages (3–27 ...

Temporal patterns of charcoal burning suicides among the working age population in Hong Kong SAR: the influence of economic activity status and sex

Directory of Open Access Journals (Sweden)

Law Chi-kin

2012-07-01

Full Text Available Abstract Background Charcoal burning in a sealed room has recently emerged as the second most common suicide means in Hong Kong, causing approximately 200 deaths each year. As charcoal burning suicide victims have a unique sociodemographic profile (i.e., predominantly economically active men, they may commit suicide at specific times. However, little is known about the temporal patterns of charcoal burning suicides. Methods Suicide data from 2001 to 2008 on victims of usual working age (20–59 were obtained from the registered death files of the Census and Statistics Department of Hong Kong. A total of 1649 cases of charcoal burning suicide were analyzed using a two-step procedure, which first examined the temporal asymmetries in the incidence of suicide, and second investigated whether these asymmetries were influenced by sex and/or economic activity status. Poisson regression analyses were employed to model the monthly and daily patterns of suicide by economic activity status and sex. Results Our findings revealed pronounced monthly and daily temporal variations in the pattern of charcoal burning suicides in Hong Kong. Consistent with previous findings on overall suicide deaths, there was an overall spring peak in April, and Monday was the common high risk day for all groups. Although sex determined the pattern of variation in charcoal burning suicides, the magnitude of the variation was influenced by the economic activity status of the victims. Conclusion The traditional classification of suicide methods as either violent or nonviolent tends to elide the temporal variations of specific methods. The interaction between sex and economic activity status observed in the present study indicates that sex should be taken into consideration when investigating the influence of economic activity status on temporal variations of suicide. This finding also suggests that suicide prevention efforts should be both time- and subgroup-specific.

Temporal patterns of charcoal burning suicides among the working age population in Hong Kong SAR: the influence of economic activity status and sex.

Science.gov (United States)

Law, Chi-kin; Leung, Candi M C

2012-07-06

Charcoal burning in a sealed room has recently emerged as the second most common suicide means in Hong Kong, causing approximately 200 deaths each year. As charcoal burning suicide victims have a unique sociodemographic profile (i.e., predominantly economically active men), they may commit suicide at specific times. However, little is known about the temporal patterns of charcoal burning suicides. Suicide data from 2001 to 2008 on victims of usual working age (20-59) were obtained from the registered death files of the Census and Statistics Department of Hong Kong. A total of 1649 cases of charcoal burning suicide were analyzed using a two-step procedure, which first examined the temporal asymmetries in the incidence of suicide, and second investigated whether these asymmetries were influenced by sex and/or economic activity status. Poisson regression analyses were employed to model the monthly and daily patterns of suicide by economic activity status and sex. Our findings revealed pronounced monthly and daily temporal variations in the pattern of charcoal burning suicides in Hong Kong. Consistent with previous findings on overall suicide deaths, there was an overall spring peak in April, and Monday was the common high risk day for all groups. Although sex determined the pattern of variation in charcoal burning suicides, the magnitude of the variation was influenced by the economic activity status of the victims. The traditional classification of suicide methods as either violent or nonviolent tends to elide the temporal variations of specific methods. The interaction between sex and economic activity status observed in the present study indicates that sex should be taken into consideration when investigating the influence of economic activity status on temporal variations of suicide. This finding also suggests that suicide prevention efforts should be both time- and subgroup-specific.

Temporal patterns of charcoal burning suicides among the working age population in Hong Kong SAR: the influence of economic activity status and sex

Science.gov (United States)

2012-01-01

Background Charcoal burning in a sealed room has recently emerged as the second most common suicide means in Hong Kong, causing approximately 200 deaths each year. As charcoal burning suicide victims have a unique sociodemographic profile (i.e., predominantly economically active men), they may commit suicide at specific times. However, little is known about the temporal patterns of charcoal burning suicides. Methods Suicide data from 2001 to 2008 on victims of usual working age (20–59) were obtained from the registered death files of the Census and Statistics Department of Hong Kong. A total of 1649 cases of charcoal burning suicide were analyzed using a two-step procedure, which first examined the temporal asymmetries in the incidence of suicide, and second investigated whether these asymmetries were influenced by sex and/or economic activity status. Poisson regression analyses were employed to model the monthly and daily patterns of suicide by economic activity status and sex. Results Our findings revealed pronounced monthly and daily temporal variations in the pattern of charcoal burning suicides in Hong Kong. Consistent with previous findings on overall suicide deaths, there was an overall spring peak in April, and Monday was the common high risk day for all groups. Although sex determined the pattern of variation in charcoal burning suicides, the magnitude of the variation was influenced by the economic activity status of the victims. Conclusion The traditional classification of suicide methods as either violent or nonviolent tends to elide the temporal variations of specific methods. The interaction between sex and economic activity status observed in the present study indicates that sex should be taken into consideration when investigating the influence of economic activity status on temporal variations of suicide. This finding also suggests that suicide prevention efforts should be both time- and subgroup-specific. PMID:22770504

Classifications, definitions and concepts of locality in Africa.

Science.gov (United States)

1983-01-01

Sub-Saharan Africa, one of the world's least urbanized regions, has within the last 30 years or so been experiencing a very high rate of urban population growth. The prevalence of small localities, invariably spread out over large land surfaces, complicates the classifications and definitions of localities as well as making the identification of settlement patterns in a mapping exercise and during a census or survey field operation difficult. The distinguishing features of a locality are: 1) a distinct (separate) population cluster, 2) inhabitants live in neighboring quarters, and 3) it has a name or a locally recognized status. Even in African countries, where the definition of locality as a distinct population cluster has been employed, problems have cropped up with respect to classifications and identifications. Another problem with the classification of localities is related to the rapid changes that occur in rural settlement patterns. Despite the rapid growth of urban localities within the past few years in sub-Saharan Africa, the proportion of the population living in urban localities is still low. Whereas a locality is a distinct population cluster, with definable boundaries, the village as employed in some countries does not have clear cut boundaries. The definition of a locality as a distinct population cluster in which the inhabitants live in neighboring living quarters and which has a name or a locally recognized status is highly recommended. The UN proposal for the adoption of a classification scheme by size of locality needs to be examined by African countries. The urban/rural dichotomy is recommended for the classification of some tabulations from censuses and surveys--especially on the total population and population of major and minor civil divisions.

Cognitive approaches for patterns analysis and security applications

Science.gov (United States)

Ogiela, Marek R.; Ogiela, Lidia

2017-08-01

In this paper will be presented new opportunities for developing innovative solutions for semantic pattern classification and visual cryptography, which will base on cognitive and bio-inspired approaches. Such techniques can be used for evaluation of the meaning of analyzed patterns or encrypted information, and allow to involve such meaning into the classification task or encryption process. It also allows using some crypto-biometric solutions to extend personalized cryptography methodologies based on visual pattern analysis. In particular application of cognitive information systems for semantic analysis of different patterns will be presented, and also a novel application of such systems for visual secret sharing will be described. Visual shares for divided information can be created based on threshold procedure, which may be dependent on personal abilities to recognize some image details visible on divided images.

Patterns of hand preference for pairs of actions and the classification of handedness.

Science.gov (United States)

Annett, Marian

2009-08-01

Pairs of actions such as write x throw and throw x racquet were examined for items of the Annett hand preference questionnaire (AHPQ). Right (R) and left (L) responses were described for frequencies of RR, RL, LR, and LL pairings (write x throw etc.) in a large representative combined sample with the aim of discovering the distribution over the population as a whole. The frequencies of RL pairings varied significantly over the different item pairs but the frequencies of LR pairings were fairly constant. An important difference was found between primary actions (originally write, throw, racquet, match, toothbrush, hammer with the later addition of scissors for right-handers) and non-primary actions (needle and thread, broom, spade, dealing playing cards, and unscrewing the lid of a jar). For primary actions, there were similar numbers of right and left writers using the 'other' hand. For non-primary actions more right-handers used the left hand than for primary actions but more left-handers did not use the right hand. That is, different frequencies of response to primary versus non-primary actions were found for right-handers but not for left-handers. The pattern of findings was repeated for a corresponding analysis of left-handed throwing x AHPQ actions. The findings have implications for the classification of hand preferences and for analyses of the nature of hand skill.

Compensatory neurofuzzy model for discrete data classification in biomedical

Science.gov (United States)

Ceylan, Rahime

2015-03-01

Biomedical data is separated to two main sections: signals and discrete data. So, studies in this area are about biomedical signal classification or biomedical discrete data classification. There are artificial intelligence models which are relevant to classification of ECG, EMG or EEG signals. In same way, in literature, many models exist for classification of discrete data taken as value of samples which can be results of blood analysis or biopsy in medical process. Each algorithm could not achieve high accuracy rate on classification of signal and discrete data. In this study, compensatory neurofuzzy network model is presented for classification of discrete data in biomedical pattern recognition area. The compensatory neurofuzzy network has a hybrid and binary classifier. In this system, the parameters of fuzzy systems are updated by backpropagation algorithm. The realized classifier model is conducted to two benchmark datasets (Wisconsin Breast Cancer dataset and Pima Indian Diabetes dataset). Experimental studies show that compensatory neurofuzzy network model achieved 96.11% accuracy rate in classification of breast cancer dataset and 69.08% accuracy rate was obtained in experiments made on diabetes dataset with only 10 iterations.

Segmentation and classification of cell cycle phases in fluorescence imaging.

Science.gov (United States)

Ersoy, Ilker; Bunyak, Filiz; Chagin, Vadim; Cardoso, M Christina; Palaniappan, Kannappan

2009-01-01

Current chemical biology methods for studying spatiotemporal correlation between biochemical networks and cell cycle phase progression in live-cells typically use fluorescence-based imaging of fusion proteins. Stable cell lines expressing fluorescently tagged protein GFP-PCNA produce rich, dynamically varying sub-cellular foci patterns characterizing the cell cycle phases, including the progress during the S-phase. Variable fluorescence patterns, drastic changes in SNR, shape and position changes and abundance of touching cells require sophisticated algorithms for reliable automatic segmentation and cell cycle classification. We extend the recently proposed graph partitioning active contours (GPAC) for fluorescence-based nucleus segmentation using regional density functions and dramatically improve its efficiency, making it scalable for high content microscopy imaging. We utilize surface shape properties of GFP-PCNA intensity field to obtain descriptors of foci patterns and perform automated cell cycle phase classification, and give quantitative performance by comparing our results to manually labeled data.

A systematic review and comprehensive classification of pectoralis major tears.

Science.gov (United States)

ElMaraghy, Amr W; Devereaux, Moira W

2012-03-01

Reported descriptions of pectoralis major (PM) injury are often inconsistent with the actual musculotendinous morphology. The literature lacks an injury classification system that is consistently applied and accurately reflects surgically relevant anatomic injury patterns, making meaningful comparison of treatment techniques and outcomes difficult. Published cases of PM injury between 1822 and 2010 were analyzed to identify incidence and injury patterns and the extent to which these injuries fit into a classification category. Recent work outlining the 3-dimensional anatomy of the PM muscle and tendon, as well as biomechanical studies of PM muscle segments, were reviewed to identify the aspects of musculotendinous anatomy that are clinically and surgically relevant to injury classification. We identified 365 cases of PM injury, with 75% occurring in the last 20 years; of these, 83% were a result of indirect trauma, with 48% occurring during weight-training activities. Injury patterns were not classified in any consistent way in timing, location, or tear extent, particularly with regard to affected muscle segments contributing to the PM's bilaminar tendon. A contemporary injury classification system is proposed that includes (1) injury timing (acute vs chronic), (2) injury location (at the muscle origin or muscle belly, at or between the musculotendinous junction and the tendinous insertion, or bony avulsion), and (3) standardized terminology addressing tear extent (anterior-to-posterior thickness and complete vs incomplete width) to more accurately reflect the musculotendinous morphology of PM injuries and better inform surgical management, rehabilitation, and research. Copyright © 2012 Journal of Shoulder and Elbow Surgery Board of Trustees. Published by Mosby, Inc. All rights reserved.

Compute raided classification of ventilation patterns inpatients with chronic obstructive pulmonary diseases at two-phase xenon-enhanced CT

Energy Technology Data Exchange (ETDEWEB)

Yoon, Son Ho; Goo, Jin Mo; Lee, Chang Hyun; Lee, You Kyung; Jin, Kwang Nam; Choo, Ji Yung; Lee, Nyoung Keun [Seoul National University College of Medicine, Seoul (Korea, Republic of); Jung, Julip; Hong, Helen [Dept. of Multimedia Engineering, Seoul Women' s University, Seoul (Korea, Republic of)

2014-06-15

To evaluate the technical feasibility, performance, and interobserver agreement of a computer-aided classification (CAC) system for regional ventilation at two-phase xenon-enhanced CT in patients with chronic obstructive pulmonary disease (COPD). Thirty-eight patients with COPD underwent two-phase xenon ventilation CT with resulting wash-in (WI) and wash-out (WO) xenon images. The regional ventilation in structural abnormalities was visually categorized into four patterns by consensus of two experienced radiologists who compared the xenon attenuation of structural abnormalities with that of adjacent normal parenchyma in the WI and WO images, and it served as the reference. Two series of image datasets of structural abnormalities were randomly extracted for optimization and validation. The proportion of agreement on a per-lesion basis and receiver operating characteristics on a per-pixel basis between CAC and reference were analyzed for optimization. Thereafter, six readers independently categorized the regional ventilation in structural abnormalities in the validation set without and with a CAC map. Interobserver agreement was also compared between assessments without and with CAC maps using multirater κ statistics. Computer-aided classification maps were successfully generated in 31 patients (81.5%). The proportion of agreement and the average area under the curve of optimized CAC maps were 94% (75/80) and 0.994, respectively. Multirater k value was improved from moderate (k=0.59: 95% confidence interval [CI], 0.56-0.62) at the initial assessment to excellent with the CAC map.

A proposed radiological classification of childhood intra-thoracic tuberculosis

International Nuclear Information System (INIS)

Marais, Ben J.; Gie, Robert P.; Schaaf, H. Simon; Hesseling, Anneke C.; Donald, Peter R.; Beyers, Nulda; Starke, Jeff R.

2004-01-01

One of the obstacles in discussing childhood tuberculosis (TB) is the lack of standard descriptive terminology to classify the diverse spectrum of disease. Accurate disease classification is important, because the correct identification of the specific disease entity has definite prognostic significance. Accurate classification will also improve study outcome definitions and facilitate scientific communication. The aim of this paper is to provide practical guidelines for the accurate radiological classification of intra-thoracic TB in children less than 15 years of age. The proposed radiological classification is based on the underlying disease and the principles of pathological disease progression. The hope is that the proposed classification will clarify concepts and stimulate discussion that may lead to future consensus. (orig.)

Self-reported optometric practise patterns in age-related macular degeneration.

Science.gov (United States)

Ly, Angelica; Nivison-Smith, Lisa; Zangerl, Barbara; Assaad, Nagi; Kalloniatis, Michael

2017-11-01

The use of advanced imaging in clinical practice is emerging and the use of this technology by optometrists in assessing patients with age-related macular degeneration is of interest. Therefore, this study explored contemporary, self-reported patterns of practice regarding age-related macular degeneration diagnosis and management using a cross-sectional survey of optometrists in Australia and New Zealand. Practising optometrists were surveyed on four key areas, namely, demographics, clinical skills and experience, assessment and management of age-related macular degeneration. Questions pertaining to self-rated competency, knowledge and attitudes used a five-point Likert scale. Completed responses were received from 127 and 87 practising optometrists in Australia and New Zealand, respectively. Advanced imaging showed greater variation in service delivery than traditional techniques (such as slitlamp funduscopy) and trended toward optical coherence tomography, which was routinely performed in age-related macular degeneration by 49 per cent of respondents. Optical coherence tomography was also associated with higher self-rated competency, knowledge and perceived relevance to practice than other modalities. Most respondents (93 per cent) indicated that they regularly applied patient symptoms, case history, visual function results and signs from traditional testing, when queried about their management of patients with age-related macular degeneration. Over half (63 per cent) also considered advanced imaging, while 31 per cent additionally considered all of these as well as the disease stage and clinical guidelines. Contrary to the evidence base, 68 and 34 per cent rated nutritional supplements as highly relevant or relevant in early age-related macular degeneration and normal aging changes, respectively. These results highlight the emergence of multimodal and advanced imaging (especially optical coherence tomography) in the assessment of age-related macular degeneration

Improving the analysis of near-spectroscopy data with multivariate classification of hemodynamic patterns: a theoretical formulation and validation.

Science.gov (United States)

Gemignani, Jessica; Middell, Eike; Barbour, Randall L; Graber, Harry L; Blankertz, Benjamin

2018-04-04

The statistical analysis of functional near infrared spectroscopy (fNIRS) data based on the general linear model (GLM) is often made difficult by serial correlations, high inter-subject variability of the hemodynamic response, and the presence of motion artifacts. In this work we propose to extract information on the pattern of hemodynamic activations without using any a priori model for the data, by classifying the channels as 'active' or 'not active' with a multivariate classifier based on linear discriminant analysis (LDA). This work is developed in two steps. First we compared the performance of the two analyses, using a synthetic approach in which simulated hemodynamic activations were combined with either simulated or real resting-state fNIRS data. This procedure allowed for exact quantification of the classification accuracies of GLM and LDA. In the case of real resting-state data, the correlations between classification accuracy and demographic characteristics were investigated by means of a Linear Mixed Model. In the second step, to further characterize the reliability of the newly proposed analysis method, we conducted an experiment in which participants had to perform a simple motor task and data were analyzed with the LDA-based classifier as well as with the standard GLM analysis. The results of the simulation study show that the LDA-based method achieves higher classification accuracies than the GLM analysis, and that the LDA results are more uniform across different subjects and, in contrast to the accuracies achieved by the GLM analysis, have no significant correlations with any of the demographic characteristics. Findings from the real-data experiment are consistent with the results of the real-plus-simulation study, in that the GLM-analysis results show greater inter-subject variability than do the corresponding LDA results. The results obtained suggest that the outcome of GLM analysis is highly vulnerable to violations of theoretical assumptions

Transforming landscape ecological evaluations using sub-pixel remote sensing classifications: A study of invasive saltcedar (Tamarix spp.)

Science.gov (United States)

Frazier, Amy E.

Invasive species disrupt landscape patterns and compromise the functionality of ecosystem processes. Non-native saltcedar (Tamarix spp.) poses significant threats to native vegetation and groundwater resources in the southwestern U.S. and Mexico, and quantifying spatial and temporal distribution patterns is essential for monitoring its spread. Advanced remote sensing classification techniques such as sub-pixel classifications are able to detect and discriminate saltcedar from native vegetation with high accuracy, but these types of classifications are not compatible with landscape metrics, which are the primary tool available for statistically assessing distribution patterns, because they do not have discrete class boundaries. The objective of this research is to develop new methods that allow sub-pixel classifications to be analyzed using landscape metrics. The research will be carried out through three specific aims: (1) develop and test a method to transform continuous sub-pixel classifications into categorical representations that are compatible with widely used landscape metric tools, (2) establish a gradient-based concept of landscape using sub-pixel classifications and the technique developed in the first objective to explore the relationships between pattern and process, and (3) generate a new super-resolution mapping technique method to predict the spatial locations of fractional land covers within a pixel. Results show that the threshold gradient method is appropriate for discretizing sub-pixel data, and can be used to generate increased information about the landscape compared to traditional single-value metrics. Additionally, the super-resolution classification technique was also able to provide detailed sub-pixel mapping information, but additional work will be needed to develop rigorous validation and accuracy assessment techniques.

Wavelet transform and ANNs for detection and classification of power signal disturbances

International Nuclear Information System (INIS)

Memon, A.P.; Uqaili, M.A.; Memon, Z.A.

2012-01-01

This article proposes WT (Wavelet Transform) and an ANN (Artificial Neural Network) based approach for detection and classification of EPQDs (Electrical Power Quality Disturbances). A modified WT known as ST (Stockwell Transform) is suggested for feature extraction and PNN (probabilistic Neural Network) for pattern classification. The ST possesses outstanding time-frequency resolution characteristics and its phase correction techniques determine the phase of the WT to the zero time point The feature vectors for the input of PNN are extracted using ST technique and these obtained features are discrete, logical, and unaffected to noisy data of distorted signals. The data of the models required to develop the distorted EPQ (Electrical Power Quality) signals, is obtained within the ranges specified by IEEE 1159-1995 in its literatures. The features vectors including noisy time varying data during steady state or transient condition and extracted using the ST, are trained through PNN for pattern classification. Their simulation results demonstrate that the proposed methodology is successful and can classify EPQDs even under a noisy environment very efficiently with an average classification accuracy of 96%. (author)

Comments on classification of uranium resources

Science.gov (United States)

Masters, Charles D.

1978-01-01

National resource assessments are intended to give some insight into future possibilities for the recovery of a desired resource. The resource numbers themselves only useful when related to economically controlled factors, such as industry capability as reflected in rated of production, rates of discovery, and technology development. To that end, it is useful to divide the resource base into component parts to which appropriate econometrics can be applied. A system of resource reporting adhering to these principles has been agreed to by the two major resource agencies in Government, the U>S. Geological Survey and the U.S. Bureau of Mines (USGS Bulletin 1450-A). Conceptually, then, a plan for resource reporting has been devised, and all resource reporting by these two agencies follows the agreed-upon pattern. Though conceptual agreement has been reached, each commodity has its own peculiar data problems; hence an operational definition to fit the conceptual pattern must be evolved for each mineral. Coal is the only commodity to date for which an operational agreement has been reached (USGS Bulletin 1450-B), but the basic essentials of an operational classification within the guideline of Bulletin 1450-A have been reported for oil and gas in USGS circular 725. The basic classification system is now well established and received general endorsement by Resources for the Future in a study of mineral resource classification systems prepared for the the Electric Power Research Institute (Schanz, 1976), and with respect to coal by the International Energy Agency.

A systematic framework to discover pattern for web spam classification

OpenAIRE

Jelodar, Hamed; Wang, Yongli; Yuan, Chi; Jiang, Xiaohui

2017-01-01

Web spam is a big problem for search engine users in World Wide Web. They use deceptive techniques to achieve high rankings. Although many researchers have presented the different approach for classification and web spam detection still it is an open issue in computer science. Analyzing and evaluating these websites can be an effective step for discovering and categorizing the features of these websites. There are several methods and algorithms for detecting those websites, such as decision t...

Longitudinal variability of time-location/activity patterns of population at different ages: a longitudinal study in California

Directory of Open Access Journals (Sweden)

Cassady Diana L

2011-09-01

Full Text Available Abstract Background Longitudinal time-activity data are important for exposure modeling, since the extent to which short-term time-activity data represent long-term activity patterns is not well understood. This study was designed to evaluate longitudinal variations in human time-activity patterns. Method We report on 24-hour recall diaries and questionnaires collected via the internet from 151 parents of young children (mostly under age 55, and from 55 older adults of ages 55 and older, for both a weekday and a weekend day every three months over an 18-month period. Parents also provided data for their children. The self-administrated diary and questionnaire distinguished ~30 frequently visited microenvironments and ~20 activities which we selected to represent opportunities for exposure to toxic environmental compounds. Due to the non-normal distribution of time-location/activity data, we employed generalized linear mixed-distribution mixed-effect models to examine intra- and inter-individual variations. Here we describe variation in the likelihood of and time spent engaging in an activity or being in a microenvironment by age group, day-type (weekday/weekend, season (warm/cool, sex, employment status, and over the follow-up period. Results As expected, day-type and season influence time spent in many location and activity categories. Longitudinal changes were also observed, e.g., young children slept less with increasing follow-up, transit time increased, and time spent on working and shopping decreased during the study, possibly related to human physiological changes with age and changes in macro-economic factors such as gas prices and the economic recession. Conclusions This study provides valuable new information about time-activity assessed longitudinally in three major age groups and greatly expands our knowledge about intra- and inter-individual variations in time-location/activity patterns. Longitudinal variations beyond weekly and

Role of Dietary Pattern Analysis in Determining Cognitive Status in Elderly Australian Adults

Directory of Open Access Journals (Sweden)

Kimberly Ashby-Mitchell

2015-02-01

Full Text Available Principal Component Analysis (PCA was used to determine the association between dietary patterns and cognitive function and to examine how classification systems based on food groups and food items affect levels of association between diet and cognitive function. The present study focuses on the older segment of the Australian Diabetes, Obesity and Lifestyle Study (AusDiab sample (age 60+ that completed the food frequency questionnaire at Wave 1 (1999/2000 and the mini-mental state examination and tests of memory, verbal ability and processing speed at Wave 3 (2012. Three methods were used in order to classify these foods before applying PCA. In the first instance, the 101 individual food items asked about in the questionnaire were used (no categorisation. In the second and third instances, foods were combined and reduced to 32 and 20 food groups, respectively, based on nutrient content and culinary usage—a method employed in several other published studies for PCA. Logistic regression analysis and generalized linear modelling was used to analyse the relationship between PCA-derived dietary patterns and cognitive outcome. Broader food group classifications resulted in a greater proportion of food use variance in the sample being explained (use of 101 individual foods explained 23.22% of total food use, while use of 32 and 20 food groups explained 29.74% and 30.74% of total variance in food use in the sample, respectively. Three dietary patterns were found to be associated with decreased odds of cognitive impairment (CI. Dietary patterns derived from 101 individual food items showed that for every one unit increase in ((Fruit and Vegetable Pattern: p = 0.030, OR 1.061, confidence interval: 1.006–1.118; (Fish, Legumes and Vegetable Pattern: p = 0.040, OR 1.032, confidence interval: 1.001–1.064; (Dairy, Cereal and Eggs Pattern: p = 0.003, OR 1.020, confidence interval: 1.007–1.033, the odds of cognitive impairment decreased. Different

Modified Angle's Classification for Primary Dentition.

Science.gov (United States)

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.

Fibrin network pattern changes of platelet-rich fibrin in young versus old age group of individuals: A cell block cytology study

Directory of Open Access Journals (Sweden)

Shravanthi Raghav Yajamanya

2016-01-01

Full Text Available Background: To evaluate variations in fibrin network patterns of the platelet-rich fibrin (PRF in different age groups. Materials and Methods: Ninety-five patients were divided into three age groups: Group 1: (20–39 years; Group 2: (40–59 years; and Group 3: (60 years and above. PRF was prepared from blood samples of all patients and were subjected to cell block cytology method of histological analysis and slides were prepared to histologically assess the age-related changes in (i fibrin network patterns in terms of density and (ii entrapment of platelets and white blood cells (WBCs within fibrin meshwork. Results: Two types of fibrin network pattern arrangements noticed: Dense and loose types in three age groups. However, there was a noticeable decrease in the dense type of fibrin network with progressing age and increase in the loose type of fibrin arrangement. Furthermore, variation in a number of platelets and WBCs entrapped within fibrin network in relation to age was noticed. Conclusion: From the current study it can be concluded that age can be considered as one of the influencing factors on quality of PRF in terms of fibrin network patterns and hence, platelet and WBCs entrapment within these fibrin networks.

An evaluation of classification algorithms for intrusion detection ...

African Journals Online (AJOL)

An evaluation of classification algorithms for intrusion detection. ... Log in or Register to get access to full text downloads. ... Most of the available IDSs use all the 41 features in the network to evaluate and search for intrusive pattern in which ...

Proteomic classification of breast cancer.

LENUS (Irish Health Repository)

Kamel, Dalia

2012-11-01

Being a significant health problem that affects patients in various age groups, breast cancer has been extensively studied to date. Recently, molecular breast cancer classification has advanced significantly with the availability of genomic profiling technologies. Proteomic technologies have also advanced from traditional protein assays including enzyme-linked immunosorbent assay, immunoblotting and immunohistochemistry to more comprehensive approaches including mass spectrometry and reverse phase protein lysate arrays (RPPA). The purpose of this manuscript is to review the current protein markers that influence breast cancer prediction and prognosis and to focus on novel advances in proteomic classification of breast cancer.

A Novel Texture Classification Procedure by using Association Rules

Directory of Open Access Journals (Sweden)

L. Jaba Sheela

2008-11-01

Full Text Available Texture can be defined as a local statistical pattern of texture primitives in observer’s domain of interest. Texture classification aims to assign texture labels to unknown textures, according to training samples and classification rules. Association rules have been used in various applications during the past decades. Association rules capture both structural and statistical information, and automatically identify the structures that occur most frequently and relationships that have significant discriminative power. So, association rules can be adapted to capture frequently occurring local structures in textures. This paper describes the usage of association rules for texture classification problem. The performed experimental studies show the effectiveness of the association rules. The overall success rate is about 98%.

Learning-induced pattern classification in a chaotic neural network

International Nuclear Information System (INIS)

Li, Yang; Zhu, Ping; Xie, Xiaoping; He, Guoguang; Aihara, Kazuyuki

2012-01-01

In this Letter, we propose a Hebbian learning rule with passive forgetting (HLRPF) for use in a chaotic neural network (CNN). We then define the indices based on the Euclidean distance to investigate the evolution of the weights in a simplified way. Numerical simulations demonstrate that, under suitable external stimulations, the CNN with the proposed HLRPF acts as a fuzzy-like pattern classifier that performs much better than an ordinary CNN. The results imply relationship between learning and recognition. -- Highlights: ► Proposing a Hebbian learning rule with passive forgetting (HLRPF). ► Defining indices to investigate the evolution of the weights simply. ► The chaotic neural network with HLRPF acts as a fuzzy-like pattern classifier. ► The pattern classifier ability of the network is improved much.

EEG classification of emotions using emotion-specific brain functional network.

Science.gov (United States)

Gonuguntla, V; Shafiq, G; Wang, Y; Veluvolu, K C

2015-08-01

The brain functional network perspective forms the basis to relate mechanisms of brain functions. This work analyzes the network mechanisms related to human emotion based on synchronization measure - phase-locking value in EEG to formulate the emotion specific brain functional network. Based on network dissimilarities between emotion and rest tasks, most reactive channel pairs and the reactive band corresponding to emotions are identified. With the identified most reactive pairs, the subject-specific functional network is formed. The identified subject-specific and emotion-specific dynamic network pattern show significant synchrony variation in line with the experiment protocol. The same network pattern are then employed for classification of emotions. With the study conducted on the 4 subjects, an average classification accuracy of 62 % was obtained with the proposed technique.

Comparing Features for Classification of MEG Responses to Motor Imagery.

Directory of Open Access Journals (Sweden)

Hanna-Leena Halme

Full Text Available Motor imagery (MI with real-time neurofeedback could be a viable approach, e.g., in rehabilitation of cerebral stroke. Magnetoencephalography (MEG noninvasively measures electric brain activity at high temporal resolution and is well-suited for recording oscillatory brain signals. MI is known to modulate 10- and 20-Hz oscillations in the somatomotor system. In order to provide accurate feedback to the subject, the most relevant MI-related features should be extracted from MEG data. In this study, we evaluated several MEG signal features for discriminating between left- and right-hand MI and between MI and rest.MEG was measured from nine healthy participants imagining either left- or right-hand finger tapping according to visual cues. Data preprocessing, feature extraction and classification were performed offline. The evaluated MI-related features were power spectral density (PSD, Morlet wavelets, short-time Fourier transform (STFT, common spatial patterns (CSP, filter-bank common spatial patterns (FBCSP, spatio-spectral decomposition (SSD, and combined SSD+CSP, CSP+PSD, CSP+Morlet, and CSP+STFT. We also compared four classifiers applied to single trials using 5-fold cross-validation for evaluating the classification accuracy and its possible dependence on the classification algorithm. In addition, we estimated the inter-session left-vs-right accuracy for each subject.The SSD+CSP combination yielded the best accuracy in both left-vs-right (mean 73.7% and MI-vs-rest (mean 81.3% classification. CSP+Morlet yielded the best mean accuracy in inter-session left-vs-right classification (mean 69.1%. There were large inter-subject differences in classification accuracy, and the level of the 20-Hz suppression correlated significantly with the subjective MI-vs-rest accuracy. Selection of the classification algorithm had only a minor effect on the results.We obtained good accuracy in sensor-level decoding of MI from single-trial MEG data. Feature extraction

Age-related patterns in work-related injury claims from older New Zealanders, 2009-2013: Implications of injury for an aging workforce.

Science.gov (United States)

Lilley, Rebbecca; Jaye, Chrystal; Davie, Gabrielle; Keeling, Sally; Waters, Debra; Egan, Richard

2018-01-01

This study describes the incidence, nature and cause of work-related injuries in older New Zealand workers to understand the risks of work-related injury in this rapidly aging population. Data for the period 2009-2013 from 25,455 injured workers aged 55-79 years, extracted from national work-related injury entitlement claims, were stratified by age group and analysed by sex, industry, injury type and cause. Age-specific claims rates were calculated by year, sex and ethnicity. Patterns of injury differed by age: 70-79 year olds had the highest injury rates and proportion of claims due to falls (45%), for the self-employed (32%), for the agriculture sector (24%), and for fatal injuries (5%). The burden of work-related injuries in older workers, particularly in those aged over 70, will increase with their increasing participation in work. Workplace injury prevention strategies and interventions need to consider the specific characteristics and vulnerabilities of older workers. Copyright © 2017 Elsevier Ltd. All rights reserved.

Patterns in chaos

International Nuclear Information System (INIS)

Chirikov, B.V.

1990-01-01

Classification of chaotic patterns in classical Hamiltonian systems is given as a series of levels with increasing disorder. Hamiltonian dynamics is presented, including the renormalization chaos, based upon the fairly simple resonant theory. First estimates for the critical structure and related statistical anomalies in arbitrary dimensions are discussed. 49 refs

A comparative evaluation of sequence classification programs

Directory of Open Access Journals (Sweden)

Bazinet Adam L

2012-05-01

Full Text Available Abstract Background A fundamental problem in modern genomics is to taxonomically or functionally classify DNA sequence fragments derived from environmental sampling (i.e., metagenomics. Several different methods have been proposed for doing this effectively and efficiently, and many have been implemented in software. In addition to varying their basic algorithmic approach to classification, some methods screen sequence reads for ’barcoding genes’ like 16S rRNA, or various types of protein-coding genes. Due to the sheer number and complexity of methods, it can be difficult for a researcher to choose one that is well-suited for a particular analysis. Results We divided the very large number of programs that have been released in recent years for solving the sequence classification problem into three main categories based on the general algorithm they use to compare a query sequence against a database of sequences. We also evaluated the performance of the leading programs in each category on data sets whose taxonomic and functional composition is known. Conclusions We found significant variability in classification accuracy, precision, and resource consumption of sequence classification programs when used to analyze various metagenomics data sets. However, we observe some general trends and patterns that will be useful to researchers who use sequence classification programs.

Classification of Farmland Landscape Structure in Multiple Scales

Science.gov (United States)

Jiang, P.; Cheng, Q.; Li, M.

2017-12-01

Farmland is one of the basic terrestrial resources that support the development and survival of human beings and thus plays a crucial role in the national security of every country. Pattern change is the intuitively spatial representation of the scale and quality variation of farmland. Through the characteristic development of spatial shapes as well as through changes in system structures, functions and so on, farmland landscape patterns may indicate the landscape health level. Currently, it is still difficult to perform positioning analyses of landscape pattern changes that reflect the landscape structure variations of farmland with an index model. Depending on a number of spatial properties such as locations and adjacency relations, distance decay, fringe effect, and on the model of patch-corridor-matrix that is applied, this study defines a type system of farmland landscape structure on the national, provincial, and city levels. According to such a definition, the classification model of farmland landscape-structure type at the pixel scale is developed and validated based on mathematical-morphology concepts and on spatial-analysis methods. Then, the laws that govern farmland landscape-pattern change in multiple scales are analyzed from the perspectives of spatial heterogeneity, spatio-temporal evolution, and function transformation. The result shows that the classification model of farmland landscape-structure type can reflect farmland landscape-pattern change and its effects on farmland production function. Moreover, farmland landscape change in different scales displayed significant disparity in zonality, both within specific regions and in urban-rural areas.

Hoarseness in School-Aged Children and Effectiveness of Voice Therapy in International Classification of Functioning Framework.

Science.gov (United States)

Akın Şenkal, Özgül; Özer, Cem

2015-09-01

The hoarseness in school-aged children disrupts the educational process because it affects the social progress, communication skills, and self-esteem of children. Besides otorhinolaryngological examination, the first treatment option is voice therapy when hoarseness occurs. The aim of the study was to determine the factors increasing the hoarseness in school-aged children by parental interview and to know preferable voice therapy on school-aged children within the frame of International Classification of Functioning (ICF). Retrospective analysis of data gathered from patient files. A total of 75 children (56 boys and 19 girls) were examined retrospectively. The age range of school-aged children is 7-14 years and average is 10.86 ± 2.51. A detailed history was taken from parents of children involved in this study. Information about vocal habits of children was gathered within the frame of ICF and then the voice therapies of children were started by scheduling appointments by an experienced speech-language pathologist. The differences between before and after voice therapy according to applied voice therapy methods, statistically significant differences were determined between maximum phonation time values and s/z rate. The relationship between voice therapy sessions and s/z rate with middle degree significance was found with physiological voice therapy sessions. According to ICF labels, most of voice complaints are matching with "body functions" and "activity and limitations." The appropriate voice therapy methods for hoarseness in school-aged children must be chosen and applied by speech-language therapists. The detailed history, which is received from family during the examination, within the frame of ICF affects the processes of choosing the voice therapy method and application of them positively. Child's family is very important for a successful management. Copyright © 2015 The Voice Foundation. Published by Elsevier Inc. All rights reserved.

Mortality and morbidity pattern in small-for gestational age and appropriate-for-gestational age very preterm babies: a hospital based study

International Nuclear Information System (INIS)

Muhammad, T.; Khattak, A.A.; Rehman, S.U.

2009-01-01

Very preterm babies are important group of paediatric babies who require special attention. These babies are known to have increased risk of morbidity and mortality. Studying the morbidity and mortality pattern for this important paediatric group can help in better understanding of their care in the hospital settings. Objective of the study was to compare the mortality and morbidity pattern in Small-for-gestational age and appropriate-for-gestational age very preterm babies. This hospital based prospective (cohort) study was conducted at the department of Paediatrics, Postgraduate Medical Institute, Lady Reading Hospital, Peshawar from March 2008 to April 2009. One hundred Small-for-gestational age (SGA) live born very preterm babies were compared with 100 appropriate-for-gestational age (AGA) very preterm babies having similar gestational ages. Information regarding gestational age, birth weight, mortality, and morbidity (in terms of various biochemical and clinical markers) were recorded on a pre-designed questionnaire. Data analysis was done using SPSS version 15. Results were interpreted in terms of descriptive (mean, proportions, standard deviation) and inferential statistical tests (with p-values). There was no difference between the two groups (SGA Vs AGA) with regards to gestational age and gender of the babies The mean weight of SGA babies was significantly lower as compared to AGA babies (1.1+-0.16 Kg Vs 1.5+-0.2 Kg; p=0.001). As compared to AGA babies, the SGA babies had a higher mortality (40% Vs 22%, p=0.006), and higher morbidity in terms of hyperbilirubinaemia (67% Vs 51%, p=0.02) and hypocalcaemia (24% Vs 10%, p=0.02). The difference in the mortality between the two groups was more prominent in babies with gestational age < 31 weeks (71.4% for SGA as compared to 39.3 % for AGA very preterm babies with gestational age < 31 weeks). Very preterm SGA infants have significantly higher mortality and morbidity in comparison to the AGA babies. In deciding

Neuromuscular disease classification system

Science.gov (United States)

Sáez, Aurora; Acha, Begoña; Montero-Sánchez, Adoración; Rivas, Eloy; Escudero, Luis M.; Serrano, Carmen

2013-06-01

Diagnosis of neuromuscular diseases is based on subjective visual assessment of biopsies from patients by the pathologist specialist. A system for objective analysis and classification of muscular dystrophies and neurogenic atrophies through muscle biopsy images of fluorescence microscopy is presented. The procedure starts with an accurate segmentation of the muscle fibers using mathematical morphology and a watershed transform. A feature extraction step is carried out in two parts: 24 features that pathologists take into account to diagnose the diseases and 58 structural features that the human eye cannot see, based on the assumption that the biopsy is considered as a graph, where the nodes are represented by each fiber, and two nodes are connected if two fibers are adjacent. A feature selection using sequential forward selection and sequential backward selection methods, a classification using a Fuzzy ARTMAP neural network, and a study of grading the severity are performed on these two sets of features. A database consisting of 91 images was used: 71 images for the training step and 20 as the test. A classification error of 0% was obtained. It is concluded that the addition of features undetectable by the human visual inspection improves the categorization of atrophic patterns.

Atmospheric circulation classification comparison based on wildfires in Portugal

Science.gov (United States)

Pereira, M. G.; Trigo, R. M.

2009-04-01

variables. To achieve these objectives we consider the main classifications for Iberia developed within the framework of COST action 733 (Radan Huth et al., 2008). This European project aims to provide a wide range of atmospheric circulation classifications for Europe and sub-regions (http://www.cost733.org/) with an ambitious objective of assessing, comparing and classifying all relevant weather situations in Europe. Pereira et al. (2005) "Synoptic patterns associated with large summer forest fires in Portugal". Agricultural and Forest Meteorology,129, 11-25. Radan Huth et al. (2008) "Classifications of Atmospheric circulation patterns. Recent advances and applications". Trends and Directions in Climate Research: Ann. N.Y. Acad. Sci. 1146: 105-152. doi: 10.1196/annals.1446.019. Trigo R.M., DaCamara C. (2000) "Circulation Weather Types and their impact on the precipitation regime in Portugal". Int J of Climatology, 20, 1559-1581.

Clonal Selection Based Artificial Immune System for Generalized Pattern Recognition

Science.gov (United States)

Huntsberger, Terry

2011-01-01

The last two decades has seen a rapid increase in the application of AIS (Artificial Immune Systems) modeled after the human immune system to a wide range of areas including network intrusion detection, job shop scheduling, classification, pattern recognition, and robot control. JPL (Jet Propulsion Laboratory) has developed an integrated pattern recognition/classification system called AISLE (Artificial Immune System for Learning and Exploration) based on biologically inspired models of B-cell dynamics in the immune system. When used for unsupervised or supervised classification, the method scales linearly with the number of dimensions, has performance that is relatively independent of the total size of the dataset, and has been shown to perform as well as traditional clustering methods. When used for pattern recognition, the method efficiently isolates the appropriate matches in the data set. The paper presents the underlying structure of AISLE and the results from a number of experimental studies.

Age related patterns of immunoglobulin serum levels in the Quechua Indians of Andean Mountains

Science.gov (United States)

Memeo, S. A.; Piantanelli, L.; Mazzufferi, G.; Guerra, L.; Nikolitz, M.; Fabris, N.

1982-03-01

Age-dependent changes of IgA, IgG, IgM, and IgD serum levels in a population of Quechua Indians of Peruvian Andes at 4 300 m were investigated. A first increase and a subsequent decrease in IgA and IgM levels were observed with advancing age. IgG and IgD only display an increase during development. More or less pronounced sex-related changes were also found in all Ig classes, the sex dependent pattern of IgA being the more evident one. It has been suggested that sexual, genetic and environmental influences strongly superimpose to high altitude related changes in Ig profile during ageing.

Early differential processing of material images: Evidence from ERP classification.

Science.gov (United States)

Wiebel, Christiane B; Valsecchi, Matteo; Gegenfurtner, Karl R

2014-06-24

Investigating the temporal dynamics of natural image processing using event-related potentials (ERPs) has a long tradition in object recognition research. In a classical Go-NoGo task two characteristic effects have been emphasized: an early task independent category effect and a later task-dependent target effect. Here, we set out to use this well-established Go-NoGo paradigm to study the time course of material categorization. Material perception has gained more and more interest over the years as its importance in natural viewing conditions has been ignored for a long time. In addition to analyzing standard ERPs, we conducted a single trial ERP pattern analysis. To validate this procedure, we also measured ERPs in two object categories (people and animals). Our linear classification procedure was able to largely capture the overall pattern of results from the canonical analysis of the ERPs and even extend it. We replicate the known target effect (differential Go-NoGo potential at frontal sites) for the material images. Furthermore, we observe task-independent differential activity between the two material categories as early as 140 ms after stimulus onset. Using our linear classification approach, we show that material categories can be differentiated consistently based on the ERP pattern in single trials around 100 ms after stimulus onset, independent of the target-related status. This strengthens the idea of early differential visual processing of material categories independent of the task, probably due to differences in low-level image properties and suggests pattern classification of ERP topographies as a strong instrument for investigating electrophysiological brain activity. © 2014 ARVO.

Classification and risk assessment of individuals with familial polyposis, Gardner's syndrome, and familial non-polyposis colon cancer from [3H]thymidine labeling patterns in colonic epithelial cells

International Nuclear Information System (INIS)

Lipkin, M.; Blattner, W.A.; Gardner, E.J.; Burt, R.W.; Lynch, H.; Deschner, E.; Winawer, S.; Fraumeni, J.F. Jr.

1984-01-01

A probabilistic analysis has been developed to assist the binary classification and risk assessment of members of familial colon cancer kindreds. The analysis is based on the microautoradiographic observation of [ 3 H]thymidine-labeled epithelial cells in colonic mucosa of the kindred members. From biopsies of colonic mucosa which are labeled with [ 3 H]thymidine in vitro, the degree of similarity of each subject's cell-labeling pattern measured over entire crypts was automatically compared to the labeling patterns of high-risk and low-risk reference populations. Each individual was then presumptively classified and assigned to one of the reference populations, and a degree of risk for the classification was provided. In carrying out the analysis, a linear score was calculated for each individual relative to each of the reference populations, and the classification was based on the polarity of the score difference; the degree of risk was then quantitated from the magnitude of the score difference. When the method was applied to kindreds having either familial polyposis or familial non-polyposis colon cancer, it effectively segregated individuals affected with disease from others at low risk, with sensitivity and specificity ranging from 71 to 92%. Further application of the method to asymptomatic family members believed to be at 50% risk on the basis of pedigree evaluation revealed a biomodal distribution to nearly zero or full risk. The accuracy and simplicity of this approach and its capability of revealing early stages of abnormal colonic epithelial cell development indicate potential for preclinical screening of subjects at risk in cancer-prone kindreds and for assisting the analysis of modes of inheritance

Demograficheskie izmenenija v Germanii i novaja territorial'naja struktura starenija [Demographic change in Germany and reversal of spatial ageing patterns

Directory of Open Access Journals (Sweden)

Swiaczny Frank

2010-01-01

Full Text Available The paper presents the result of a spatial analysis considering the effect of demographic ageing and ageing-in-place processes in Germany according to spatially differentiated ageing patterns among urban, sub-urban and rural counties up to 2025. As to the latest official population forecast counties of urban core regions will undergo a slower ageing process than other types of counties, resulting in a reversal of ageing patterns. Urban core areas in this analysis will gain demographically from their net migration surplus while suburban housing locations of the past will be no longer able to attract enough young migrants to compensate for their now rapidly ageing baby boomer generation. The process presented is typical for the fate of (suburban housing areas with homogenous populations under conditions of ageing and shrinking if spatial mobility in ageing population groups is declining.

Associations of dietary patterns with bone mass, muscle strength and balance in a cohort of Australian middle-aged women.

Science.gov (United States)

Wu, Feitong; Wills, Karen; Laslett, Laura L; Oldenburg, Brian; Jones, Graeme; Winzenberg, Tania

2017-10-01

Influences of dietary patterns on musculoskeletal health are poorly understood in middle-aged women. This cross-sectional analysis from a cohort of 347 women (aged 36-57 years) aimed to examine associations between dietary patterns and musculoskeletal health outcomes in middle-aged women. Diet was measured by the Cancer Council of Victoria FFQ. Total body bone mineral content (TB BMC), femoral neck and lumbar spine bone density (dual-energy X-ray absorptiometry), lower limbs muscle strength (LMS) and balance tests (timed up and go test, step test, functional reach test (FRT) and lateral reach test) were also measured. Exploratory factor analysis was used to identify dietary patterns and scores for each pattern generated using factor loadings with absolute values ≥0·20. Associations between food pattern scores and musculoskeletal outcomes were assessed using multivariable linear regression. Three dietary patterns were identified: 'Healthy' (high consumption of a plant-based diet - vegetables, legumes, fruit, tomatoes, nuts, snacks, garlic, whole grains and low intake of high-fat dairy products), 'high protein, high fat' (red meats, poultry, processed meats, potatoes, cruciferous and dark-yellow vegetables, fish, chips, spirits and high-fat dairy products) and 'Processed foods' (high intakes of meat pies, hamburgers, beer, sweets, fruit juice, processed meats, snacks, spirits, pizza and low intake of cruciferous vegetables). After adjustment for confounders, Healthy pattern was positively associated with LMS, whereas Processed foods pattern was inversely associated with TB BMC and FRT. The associations were not significant after accounting for multiple comparisons. There were no associations with any other outcomes. These results suggest that maintaining a healthy diet could contribute to bone acquisition, muscle strength and balance in adult life. However, while they provide some support for further investigating dietary strategies for prevention of age

The infant disorganised attachment classification: "Patterning within the disturbance of coherence".

Science.gov (United States)

Reijman, Sophie; Foster, Sarah; Duschinsky, Robbie

2018-03-01

Since its introduction by Main and Solomon in 1990, the infant disorganised attachment classification has functioned as a predictor of mental health in developmental psychology research. It has also been used by practitioners as an indicator of inadequate parenting and developmental risk, at times with greater confidence than research would support. Although attachment disorganisation takes many forms, it is generally understood to reflect a child's experience of being repeatedly alarmed by their parent's behaviour. In this paper we analyse how the infant disorganised attachment classification has been stabilised and interpreted, reporting results from archival study, ethnographic observations at four training institutes for coding disorganised attachment, interviews with researchers, certified coders and clinicians, and focus groups with child welfare practitioners. Our analysis points to the role of power/knowledge disjunctures in hindering communication between key groups: Main and Solomon and their readers; the oral culture of coders and the written culture of published papers; the research community and practitioners. We highlight how understandings of disorganised attachment have been magnetised by a simplified image of a child fearful of his or her own parent. Copyright © 2018 The Author(s). Published by Elsevier Ltd.. All rights reserved.

Classifying Classifications

DEFF Research Database (Denmark)

Debus, Michael S.

2017-01-01

This paper critically analyzes seventeen game classifications. The classifications were chosen on the basis of diversity, ranging from pre-digital classification (e.g. Murray 1952), over game studies classifications (e.g. Elverdam & Aarseth 2007) to classifications of drinking games (e.g. LaBrie et...... al. 2013). The analysis aims at three goals: The classifications’ internal consistency, the abstraction of classification criteria and the identification of differences in classification across fields and/or time. Especially the abstraction of classification criteria can be used in future endeavors...... into the topic of game classifications....

A study for Unsafe Act classification under crew interaction during procedure-driven operation

International Nuclear Information System (INIS)

Choi, Sun Yeong; Park, Jinkyun; Kim, Yochan; Kim, Seunghwan; Jung, Wondea

2016-01-01

Highlights: • The procedure driven operation was divided into four stages by considering the crew relations such as instructions and responses. • Ten patterns of UA occurrence paths and the related operators per path were identified. • The UA type classification scheme was proposed based on the ten patterns of UA occurrence paths. • A case study to implement the UA type classification and to define the related operators per UA was performed. • The UA type classification scheme can be practical in that it prevents bias by subjective judgment. - Abstract: In this study, a method for UA (Unsafe Act) classification under a simulated procedure driven operation was proposed. To this end, a procedure driven operation was divided into four stages by considering the crew relations such as instructions and responses. Based on the four stages of a procedure driven operation, ten patterns of UA occurrence paths and the related operators per path were identified. From the ten types of UA occurrence paths including related operators, it is practicable to trace when and by whom a UA is initiated during a procedure driven operation, and the interaction or causality among the crew after the UA is initiated. Therefore, the types of UAs were classified into ‘Instruction UA’, ‘Reporting UA’, and ‘Execution UA’ by considering the initiation time and initiator of UA. A case study to implement the UA type classification and to define the related operators per UA was performed with the ISLOCA scenario simulator training data. The UA classification scheme proposed in this paper can be practical in that it does not require expertise relatively in a human performance analysis and it prevents bias by subjective judgment because it is based on an observation-based approach to exclude subjective judgment.

Pattern recognition approach to nondestructive evaluation of materials

International Nuclear Information System (INIS)

Chen, C.H.

1987-01-01

In this paper, a pattern recognition approach to the ultrasonic nondestructive evaluation of materials is examined. Emphasis is placed on identifying effective features from time and frequency domains, correlation functions and impulse responses to classify aluminum plate specimens into three major defect geometry categories: flat, angular cut and circular hole defects. A multi-stage classification procedure is developed which can further determine the angles and sizes for defect characterization and classification. The research clearly demonstrates that the pattern recognition approach can significantly improve the nondestructive material evaluation capability of the ultrasonic methods without resorting to the solution of highly complex mathematical inverse problems

Estimated accuracy of classification of defects detected in welded joints by radiographic tests

International Nuclear Information System (INIS)

Siqueira, M.H.S.; De Silva, R.R.; De Souza, M.P.V.; Rebello, J.M.A.; Caloba, L.P.; Mery, D.

2004-01-01

This work is a study to estimate the accuracy of classification of the main classes of weld defects detected by radiography test, such as: undercut, lack of penetration, porosity, slag inclusion, crack or lack of fusion. To carry out this work non-linear pattern classifiers were developed, using neural networks, and the largest number of radiographic patterns as possible was used as well as statistical inference techniques of random selection of samples with and without repositioning (bootstrap) in order to estimate the accuracy of the classification. The results pointed to an estimated accuracy of around 80% for the classes of defects analyzed. (author)

Estimated accuracy of classification of defects detected in welded joints by radiographic tests

Energy Technology Data Exchange (ETDEWEB)

Siqueira, M.H.S.; De Silva, R.R.; De Souza, M.P.V.; Rebello, J.M.A. [Federal Univ. of Rio de Janeiro, Dept., of Metallurgical and Materials Engineering, Rio de Janeiro (Brazil); Caloba, L.P. [Federal Univ. of Rio de Janeiro, Dept., of Electrical Engineering, Rio de Janeiro (Brazil); Mery, D. [Pontificia Unversidad Catolica de Chile, Escuela de Ingenieria - DCC, Dept. de Ciencia de la Computacion, Casilla, Santiago (Chile)

2004-07-01

This work is a study to estimate the accuracy of classification of the main classes of weld defects detected by radiography test, such as: undercut, lack of penetration, porosity, slag inclusion, crack or lack of fusion. To carry out this work non-linear pattern classifiers were developed, using neural networks, and the largest number of radiographic patterns as possible was used as well as statistical inference techniques of random selection of samples with and without repositioning (bootstrap) in order to estimate the accuracy of the classification. The results pointed to an estimated accuracy of around 80% for the classes of defects analyzed. (author)

Classifications of Acute Scaphoid Fractures: A Systematic Literature Review.

Science.gov (United States)

Ten Berg, Paul W; Drijkoningen, Tessa; Strackee, Simon D; Buijze, Geert A

2016-05-01

Background In the lack of consensus, surgeon-based preference determines how acute scaphoid fractures are classified. There is a great variety of classification systems with considerable controversies. Purposes The purpose of this study was to provide an overview of the different classification systems, clarifying their subgroups and analyzing their popularity by comparing citation indexes. The intention was to improve data comparison between studies using heterogeneous fracture descriptions. Methods We performed a systematic review of the literature based on a search of medical literature from 1950 to 2015, and a manual search using the reference lists in relevant book chapters. Only original descriptions of classifications of acute scaphoid fractures in adults were included. Popularity was based on citation index as reported in the databases of Web of Science (WoS) and Google Scholar. Articles that were cited <10 times in WoS were excluded. Results Our literature search resulted in 308 potentially eligible descriptive reports of which 12 reports met the inclusion criteria. We distinguished 13 different (sub) classification systems based on (1) fracture location, (2) fracture plane orientation, and (3) fracture stability/displacement. Based on citations numbers, the Herbert classification was most popular, followed by the Russe and Mayo classifications. All classification systems were based on plain radiography. Conclusions Most classification systems were based on fracture location, displacement, or stability. Based on the controversy and limited reliability of current classification systems, suggested research areas for an updated classification include three-dimensional fracture pattern etiology and fracture fragment mobility assessed by dynamic imaging.

A simplified immunohistochemical classification of skeletal muscle fibres in mouse

Directory of Open Access Journals (Sweden)

M. Kammoun

2014-06-01

Full Text Available The classification of muscle fibres is of particular interest for the study of the skeletal muscle properties in a wide range of scientific fields, especially animal phenotyping. It is therefore important to define a reliable method for classifying fibre types. The aim of this study was to establish a simplified method for the immunohistochemical classification of fibres in mouse. To carry it out, we first tested a combination of several anti myosin heavy chain (MyHC antibodies in order to choose a minimum number of antibodies to implement a semi-automatic classification. Then, we compared the classification of fibres to the MyHC electrophoretic pattern on the same samples. Only two anti MyHC antibodies on serial sections with the fluorescent labeling of the Laminin were necessary to classify properly fibre types in Tibialis Anterior and Soleus mouse muscles in normal physiological conditions. This classification was virtually identical to the classification realized by the electrophoretic separation of MyHC. This immunohistochemical classification can be applied to the total area of Tibialis Anterior and Soleus mouse muscles. Thus, we provide here a useful, simple and time-efficient method for immunohistochemical classification of fibres, applicable for research in mouse

Modified angle's classification for primary dentition

Directory of Open Access Journals (Sweden)

Kaushik Narendra Chandranee

2017-01-01

Full Text Available Aim: 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. Methods: 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. Results: 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. Conclusions: 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.

Prediction of customer behaviour analysis using classification algorithms

Science.gov (United States)

Raju, Siva Subramanian; Dhandayudam, Prabha

2018-04-01

Customer Relationship management plays a crucial role in analyzing of customer behavior patterns and their values with an enterprise. Analyzing of customer data can be efficient performed using various data mining techniques, with the goal of developing business strategies and to enhance the business. In this paper, three classification models (NB, J48, and MLPNN) are studied and evaluated for our experimental purpose. The performance measures of the three classifications are compared using three different parameters (accuracy, sensitivity, specificity) and experimental results expose J48 algorithm has better accuracy with compare to NB and MLPNN algorithm.

Associations of Maternal Dietary Patterns during Pregnancy with Offspring Adiposity from Birth Until 54 Months of Age

Directory of Open Access Journals (Sweden)

Ling-Wei Chen

2016-12-01

Full Text Available Most studies linking maternal diet with offspring adiposity have focused on single nutrients or foods, but a dietary pattern approach is more representative of the overall diet. We thus aimed to investigate the relations between maternal dietary patterns and offspring adiposity in a multi-ethnic Asian mother–offspring cohort in Singapore. We derived maternal dietary patterns using maternal dietary intake information at 26–28 weeks of gestation, of which associations with offspring body mass index (BMI, abdominal circumference (AC, subscapular skinfold (SS, and triceps skinfold (TS were assessed using longitudinal data analysis (linear mixed effects (LME and multiple linear regression at ages 0, 3, 6, 9, 12, 15, 18, 24, 36, 48, and 54 months. Three dietary patterns were derived: (1 vegetables-fruit-and-white rice (VFR; (2 seafood-and-noodles (SfN; and (3 pasta-cheese-and-bread (PCB. In the LME model adjusting for potential confounders, each standard deviation (SD increase in maternal VFR pattern score was associated with 0.09 mm lower offspring TS. Individual time-point analysis additionally revealed that higher VFR score was generally associated with lower postnatal offspring BMI z-score, TS, SS, and sum of skinfolds (SS + TS at ages 18 months and older. Maternal adherence to a dietary pattern characterized by higher intakes of fruit and vegetables and lower intakes of fast food was associated with lower offspring adiposity.

Applying deep neural networks to HEP job classification

International Nuclear Information System (INIS)

Wang, L; Shi, J; Yan, X

2015-01-01

The cluster of IHEP computing center is a middle-sized computing system which provides 10 thousands CPU cores, 5 PB disk storage, and 40 GB/s IO throughput. Its 1000+ users come from a variety of HEP experiments. In such a system, job classification is an indispensable task. Although experienced administrator can classify a HEP job by its IO pattern, it is unpractical to classify millions of jobs manually. We present how to solve this problem with deep neural networks in a supervised learning way. Firstly, we built a training data set of 320K samples by an IO pattern collection agent and a semi-automatic process of sample labelling. Then we implemented and trained DNNs models with Torch. During the process of model training, several meta-parameters was tuned with cross-validations. Test results show that a 5- hidden-layer DNNs model achieves 96% precision on the classification task. By comparison, it outperforms a linear model by 8% precision. (paper)

Deep learning application: rubbish classification with aid of an android device

Science.gov (United States)

Liu, Sijiang; Jiang, Bo; Zhan, Jie

2017-06-01

Deep learning is a very hot topic currently in pattern recognition and artificial intelligence researches. Aiming at the practical problem that people usually don't know correct classifications some rubbish should belong to, based on the powerful image classification ability of the deep learning method, we have designed a prototype system to help users to classify kinds of rubbish. Firstly the CaffeNet Model was adopted for our classification network training on the ImageNet dataset, and the trained network was deployed on a web server. Secondly an android app was developed for users to capture images of unclassified rubbish, upload images to the web server for analyzing backstage and retrieve the feedback, so that users can obtain the classification guide by an android device conveniently. Tests on our prototype system of rubbish classification show that: an image of one single type of rubbish with origin shape can be better used to judge its classification, while an image containing kinds of rubbish or rubbish with changed shape may fail to help users to decide rubbish's classification. However, the system still shows promising auxiliary function for rubbish classification if the network training strategy can be optimized further.

Dietary Pattern Trajectories from 6 to 12 Months of Age in a Multi-Ethnic Asian Cohort.

Science.gov (United States)

Lim, Geraldine Huini; Toh, Jia Ying; Aris, Izzuddin M; Chia, Ai-Ru; Han, Wee Meng; Saw, Seang Mei; Godfrey, Keith M; Gluckman, Peter D; Chong, Yap-Seng; Yap, Fabian; Lee, Yung Seng; Kramer, Michael S; Chong, Mary Foong-Fong

2016-06-15

Little is known about the dietary patterns of Asian infants in the first year of life, nor of their associations with maternal socio-demographic factors. Based on the Growing Up in Singapore towards healthy Outcomes (GUSTO) mother-offspring cohort, cross-sectional dietary patterns were derived by factor analysis using 24-h recalls and food diaries of infants at 6-, 9- and 12-months of age. Dietary pattern trajectories were modeled by mapping similar dietary patterns across each age using multilevel mixed models. Associations with maternal socio-demographic variables, collected through questionnaires during pregnancy, were assessed using general linear models. In n = 486 infants, four dietary pattern trajectories were established from 6- to 12-months. Predominantly breastmilk: mainly breastmilk and less formula milk, rice porridge, vegetables, fruits and low-fat fish and meat, Easy-to-prepare foods: infant cereals, juices, cakes and biscuits and Noodles (in soup) and seafood: noodle and common accompaniments. In adjusted models, higher maternal education attainment was correlated with higher start scores on Predominantly breastmilk, but lowest education attainment increased its adherence over time. Older mothers had higher start scores on Easy-to-prepare foods, but younger mothers had increased adherence over time. Chinese mothers had higher start scores on Predominantly breastmilk but greater adherence to GUIDELINES over time, while Indian mothers had higher start scores on Easy-to-prepare foods but greater adherence to Predominantly breastmilk with time (p dietary patterns established during weaning are strongly influenced by maternal socio-demographic factors and remain stable over the first year of life.

Classification of diffuse lung diseases: why and how.

Science.gov (United States)

Hansell, David M

2013-09-01

The understanding of complex lung diseases, notably the idiopathic interstitial pneumonias and small airways diseases, owes as much to repeated attempts over the years to classify them as to any single conceptual breakthrough. One of the many benefits of a successful classification scheme is that it allows workers, within and between disciplines, to be clear that they are discussing the same disease. This may be of particular importance in the recruitment of individuals for a clinical trial that requires a standardized and homogeneous study population. Different specialties require fundamentally different things from a classification: for epidemiologic studies, a classification that requires categorization of individuals according to histopathologic pattern is not usually practicable. Conversely, a scheme that simply divides diffuse parenchymal disease into inflammatory and noninflammatory categories is unlikely to further the understanding about the pathogenesis of disease. Thus, for some disease groupings, for example, pulmonary vasculopathies, there may be several appropriate classifications, each with its merits and demerits. There has been an interesting shift in the past few years, from the accepted primacy of histopathology as the sole basis on which the classification of parenchymal lung disease has rested, to new ways of considering how these entities relate to each other. Some inventive thinking has resulted in new classifications that undoubtedly benefit patients and clinicians in their endeavor to improve management and outcome. The challenge of understanding the logic behind current classifications and their shortcomings are explored in various examples of lung diseases.

Woven fabric defects detection based on texture classification algorithm

International Nuclear Information System (INIS)

Ben Salem, Y.; Nasri, S.

2011-01-01

In this paper we have compared two famous methods in texture classification to solve the problem of recognition and classification of defects occurring in a textile manufacture. We have compared local binary patterns method with co-occurrence matrix. The classifier used is the support vector machines (SVM). The system has been tested using TILDA database. The results obtained are interesting and show that LBP is a good method for the problems of recognition and classifcation defects, it gives a good running time especially for the real time applications.

Co-occurrence of Local Anisotropic Gradient Orientations (CoLlAGe): A new radiomics descriptor.

Science.gov (United States)

Prasanna, Prateek; Tiwari, Pallavi; Madabhushi, Anant

2016-11-22

In this paper, we introduce a new radiomic descriptor, Co-occurrence of Local Anisotropic Gradient Orientations (CoLlAGe) for capturing subtle differences between benign and pathologic phenotypes which may be visually indistinguishable on routine anatomic imaging. CoLlAGe seeks to capture and exploit local anisotropic differences in voxel-level gradient orientations to distinguish similar appearing phenotypes. CoLlAGe involves assigning every image voxel an entropy value associated with the co-occurrence matrix of gradient orientations computed around every voxel. The hypothesis behind CoLlAGe is that benign and pathologic phenotypes even though they may appear similar on anatomic imaging, will differ in their local entropy patterns, in turn reflecting subtle local differences in tissue microarchitecture. We demonstrate CoLlAGe's utility in three clinically challenging classification problems: distinguishing (1) radiation necrosis, a benign yet confounding effect of radiation treatment, from recurrent tumors on T1-w MRI in 42 brain tumor patients, (2) different molecular sub-types of breast cancer on DCE-MRI in 65 studies and (3) non-small cell lung cancer (adenocarcinomas) from benign fungal infection (granulomas) on 120 non-contrast CT studies. For each of these classification problems, CoLlAGE in conjunction with a random forest classifier outperformed state of the art radiomic descriptors (Haralick, Gabor, Histogram of Gradient Orientations).