Boosted classification trees result in minor to modest improvement in the accuracy in classifying cardiovascular outcomes compared to conventional classification trees

PubMed Central

Austin, Peter C; Lee, Douglas S

2011-01-01

Purpose: Classification trees are increasingly being used to classifying patients according to the presence or absence of a disease or health outcome. A limitation of classification trees is their limited predictive accuracy. In the data-mining and machine learning literature, boosting has been developed to improve classification. Boosting with classification trees iteratively grows classification trees in a sequence of reweighted datasets. In a given iteration, subjects that were misclassified in the previous iteration are weighted more highly than subjects that were correctly classified. Classifications from each of the classification trees in the sequence are combined through a weighted majority vote to produce a final classification. The authors' objective was to examine whether boosting improved the accuracy of classification trees for predicting outcomes in cardiovascular patients. Methods: We examined the utility of boosting classification trees for classifying 30-day mortality outcomes in patients hospitalized with either acute myocardial infarction or congestive heart failure. Results: Improvements in the misclassification rate using boosted classification trees were at best minor compared to when conventional classification trees were used. Minor to modest improvements to sensitivity were observed, with only a negligible reduction in specificity. For predicting cardiovascular mortality, boosted classification trees had high specificity, but low sensitivity. Conclusions: Gains in predictive accuracy for predicting cardiovascular outcomes were less impressive than gains in performance observed in the data mining literature. PMID:22254181

Research Support for the Laboratory for Lightwave Technology

DTIC Science & Technology

1992-12-31

34 .. . ."/ 12a. DISTRIBUTION AVAILABILITY STATEMENT 12b. DISTRIBUTION CODE UNLIMITED 13. ABSTRACT (Mawimum 200words) 4 SEE ATTACHED ABSTRACT DT I 14. SUBJECT...8217TERMS 15. NUMBER OF PAGES 16. PRICE CODE 17. SECURITY CLASSIFICATION 18. SECURITY CLASSIFICATION 19. SECURITY CLASSIFICATION 20. LIMITATION OF ABSTRACT...temperature ceramic nano- phase single crystal oxides that may be produced at a high rate . The synthesis of both glasses and ceramics using novel techniques

Gold-standard for computer-assisted morphological sperm analysis.

PubMed

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

2017-04-01

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

Characterization and delineation of caribou habitat on Unimak Island using remote sensing techniques

NASA Astrophysics Data System (ADS)

Atkinson, Brain M.

The assessment of herbivore habitat quality is traditionally based on quantifying the forages available to the animal across their home range through ground-based techniques. While these methods are highly accurate, they can be time-consuming and highly expensive, especially for herbivores that occupy vast spatial landscapes. The Unimak Island caribou herd has been decreasing in the last decade at rates that have prompted discussion of management intervention. Frequent inclement weather in this region of Alaska has provided for little opportunity to study the caribou forage habitat on Unimak Island. The overall objectives of this study were two-fold 1) to assess the feasibility of using high-resolution color and near-infrared aerial imagery to map the forage distribution of caribou habitat on Unimak Island and 2) to assess the use of a new high-resolution multispectral satellite imagery platform, RapidEye, and use of the "red-edge" spectral band on vegetation classification accuracy. Maximum likelihood classification algorithms were used to create land cover maps in aerial and satellite imagery. Accuracy assessments and transformed divergence values were produced to assess vegetative spectral information and classification accuracy. By using RapidEye and aerial digital imagery in a hierarchical supervised classification technique, we were able to produce a high resolution land cover map of Unimak Island. We obtained overall accuracy rates of 71.4 percent which are comparable to other land cover maps using RapidEye imagery. The "red-edge" spectral band included in the RapidEye imagery provides additional spectral information that allows for a more accurate overall classification, raising overall accuracy 5.2 percent.

Use of the Wound, Ischemia, foot Infection classification system in hemodialysis patients after endovascular treatment for critical limb ischemia.

PubMed

Tokuda, Takahiro; Hirano, Keisuke; Sakamoto, Yasunari; Mori, Shisuke; Kobayashi, Norihiro; Araki, Motoharu; Yamawaki, Masahiro; Ito, Yoshiaki

2017-12-07

The Wound, Ischemia, foot Infection (WIfI) classification system is used to predict the amputation risk in patients with critical limb ischemia (CLI). The validity of the WIfI classification system for hemodialysis (HD) patients with CLI is still unknown. This single-center study evaluated the prognostic value of WIfI stages in HD patients with CLI who had been treated with endovascular therapy (EVT). A retrospective analysis was performed of collected data on CLI patients treated with EVT between April 2007 and December 2015. All patients were classified according to their wound status, ischemia index, and extent of foot infection into the following four groups: very low risk, low risk, moderate risk, and high risk. Comorbidities and vascular lesions in each group were analyzed. The prognostic value of the WIfI classification was analyzed on the basis of the wound healing rate and amputation-free survival at 1 year. This study included 163 consecutive CLI patients who underwent HD and successful endovascular intervention. The rate of the high-risk group (36%) was the highest among the four groups, and the proportions of very-low-risk, low-risk, and moderate-risk patients were 10%, 18%, and 34%, respectively. The mean follow-up duration was 784 ± 650 days. The wound healing rates at 1 year were 92%, 70%, 75%, and 42% in the very-low-risk, low-risk, moderate-risk, and high-risk groups, respectively (P <.01). A similar trend was observed for the 1-year amputation-free survival among the groups (76%, 58%, 61%, and 46%, respectively; P = .02). The WIfI classification system predicted the wound healing and amputation risks in a highly selected group of HD patients with CLI treated with EVT, with a statistically significant difference between high-risk patients and other patients. Copyright © 2017 Society for Vascular Surgery. Published by Elsevier Inc. All rights reserved.

An Alternative Classification Scheme for Teaching Performance Incentives Using a Factor Analytic Approach.

ERIC Educational Resources Information Center

Mertler, Craig A.

This study attempted to (1) expand the dichotomous classification scheme typically used by educators and researchers to describe teaching incentives and (2) offer administrators and teachers an alternative framework within which to develop incentive systems. Elementary, middle, and high school teachers in Ohio rated 10 commonly instituted teaching…

Multi-label spacecraft electrical signal classification method based on DBN and random forest

PubMed Central

Li, Ke; Yu, Nan; Li, Pengfei; Song, Shimin; Wu, Yalei; Li, Yang; Liu, Meng

2017-01-01

In spacecraft electrical signal characteristic data, there exists a large amount of data with high-dimensional features, a high computational complexity degree, and a low rate of identification problems, which causes great difficulty in fault diagnosis of spacecraft electronic load systems. This paper proposes a feature extraction method that is based on deep belief networks (DBN) and a classification method that is based on the random forest (RF) algorithm; The proposed algorithm mainly employs a multi-layer neural network to reduce the dimension of the original data, and then, classification is applied. Firstly, we use the method of wavelet denoising, which was used to pre-process the data. Secondly, the deep belief network is used to reduce the feature dimension and improve the rate of classification for the electrical characteristics data. Finally, we used the random forest algorithm to classify the data and comparing it with other algorithms. The experimental results show that compared with other algorithms, the proposed method shows excellent performance in terms of accuracy, computational efficiency, and stability in addressing spacecraft electrical signal data. PMID:28486479

Multi-label spacecraft electrical signal classification method based on DBN and random forest.

PubMed

Li, Ke; Yu, Nan; Li, Pengfei; Song, Shimin; Wu, Yalei; Li, Yang; Liu, Meng

2017-01-01

In spacecraft electrical signal characteristic data, there exists a large amount of data with high-dimensional features, a high computational complexity degree, and a low rate of identification problems, which causes great difficulty in fault diagnosis of spacecraft electronic load systems. This paper proposes a feature extraction method that is based on deep belief networks (DBN) and a classification method that is based on the random forest (RF) algorithm; The proposed algorithm mainly employs a multi-layer neural network to reduce the dimension of the original data, and then, classification is applied. Firstly, we use the method of wavelet denoising, which was used to pre-process the data. Secondly, the deep belief network is used to reduce the feature dimension and improve the rate of classification for the electrical characteristics data. Finally, we used the random forest algorithm to classify the data and comparing it with other algorithms. The experimental results show that compared with other algorithms, the proposed method shows excellent performance in terms of accuracy, computational efficiency, and stability in addressing spacecraft electrical signal data.

Voice based gender classification using machine learning

NASA Astrophysics Data System (ADS)

Raahul, A.; Sapthagiri, R.; Pankaj, K.; Vijayarajan, V.

2017-11-01

Gender identification is one of the major problem speech analysis today. Tracing the gender from acoustic data i.e., pitch, median, frequency etc. Machine learning gives promising results for classification problem in all the research domains. There are several performance metrics to evaluate algorithms of an area. Our Comparative model algorithm for evaluating 5 different machine learning algorithms based on eight different metrics in gender classification from acoustic data. Agenda is to identify gender, with five different algorithms: Linear Discriminant Analysis (LDA), K-Nearest Neighbour (KNN), Classification and Regression Trees (CART), Random Forest (RF), and Support Vector Machine (SVM) on basis of eight different metrics. The main parameter in evaluating any algorithms is its performance. Misclassification rate must be less in classification problems, which says that the accuracy rate must be high. Location and gender of the person have become very crucial in economic markets in the form of AdSense. Here with this comparative model algorithm, we are trying to assess the different ML algorithms and find the best fit for gender classification of acoustic data.

Performance of the Delirium Rating Scale-Revised-98 Against Different Delirium Diagnostic Criteria in a Population With a High Prevalence of Dementia.

PubMed

Sepulveda, Esteban; Franco, José G; Trzepacz, Paula T; Gaviria, Ana M; Viñuelas, Eva; Palma, José; Ferré, Gisela; Grau, Imma; Vilella, Elisabet

2015-01-01

Delirium diagnosis in elderly is often complicated by underlying dementia. We evaluated performance of the Delirium Rating Scale-Revised-98 (DRS-R98) in patients with high dementia prevalence and also assessed concordance among past and current diagnostic criteria for delirium. Cross-sectional analysis of newly admitted patients to a skilled nursing facility over 6 months, who were rated within 24-48 hours after admission. Interview for Diagnostic and Statistical Manual of Mental Disorders, 3rd edition-R (DSM)-III-R, DSM-IV, DSM-5, and International Classification of Diseases 10th edition delirium ratings, administration of the DRS-R98, and assessment of dementia using the Informant Questionnaire on Cognitive Decline in the Elderly were independently performed by 3 researchers. Discriminant analyses (receiver operating characteristics curves) were used to study DRS-R98 accuracy against different diagnostic criteria. Hanley and McNeil test compared the area under the curve for DRS-R98's discriminant performance for all diagnostic criteria. Dementia was present in 85/125 (68.0%) subjects, and 36/125 (28.8%) met criteria for delirium by at least 1 classification system, whereas only 19/36 (52.8%) did by all. DSM-III-R diagnosed the most as delirious (27.2%), followed by DSM-5 (24.8%), DSM-IV-TR (22.4%), and International Classification of Diseases 10th edition (16%). DRS-R98 had the highest AUC when discriminating DSM-III-R delirium (92.9%), followed by DSM-IV (92.4%), DSM-5 (91%), and International Classification of Diseases 10th edition (90.5%), without statistical differences among them. The best DRS-R98 cutoff score was ≥14.5 for all diagnostic systems except International Classification of Diseases 10th edition (≥15.5). There is a low concordance across diagnostic systems for identification of delirium. The DRS-R98 performs well despite differences across classification systems perhaps because it broadly assesses phenomenology, even in this population with a high prevalence of dementia. Copyright © 2015 The Academy of Psychosomatic Medicine. Published by Elsevier Inc. All rights reserved.

High-Strain Rate Failure Modeling Incorporating Shear Banding and Fracture

DTIC Science & Technology

2017-11-22

High Strain Rate Failure Modeling Incorporating Shear Banding and Fracture The views, opinions and/or findings contained in this report are those of...SECURITY CLASSIFICATION OF: 1. REPORT DATE (DD-MM-YYYY) 4. TITLE AND SUBTITLE 13. SUPPLEMENTARY NOTES 12. DISTRIBUTION AVAILIBILITY STATEMENT 6. AUTHORS...Report as of 05-Dec-2017 Agreement Number: W911NF-13-1-0238 Organization: Columbia University Title: High Strain Rate Failure Modeling Incorporating

Correlation-based pattern recognition for implantable defibrillators.

PubMed Central

Wilkins, J.

1996-01-01

An estimated 300,000 Americans die each year from cardiac arrhythmias. Historically, drug therapy or surgery were the only treatment options available for patients suffering from arrhythmias. Recently, implantable arrhythmia management devices have been developed. These devices allow abnormal cardiac rhythms to be sensed and corrected in vivo. Proper arrhythmia classification is critical to selecting the appropriate therapeutic intervention. The classification problem is made more challenging by the power/computation constraints imposed by the short battery life of implantable devices. Current devices utilize heart rate-based classification algorithms. Although easy to implement, rate-based approaches have unacceptably high error rates in distinguishing supraventricular tachycardia (SVT) from ventricular tachycardia (VT). Conventional morphology assessment techniques used in ECG analysis often require too much computation to be practical for implantable devices. In this paper, a computationally-efficient, arrhythmia classification architecture using correlation-based morphology assessment is presented. The architecture classifies individuals heart beats by assessing similarity between an incoming cardiac signal vector and a series of prestored class templates. A series of these beat classifications are used to make an overall rhythm assessment. The system makes use of several new results in the field of pattern recognition. The resulting system achieved excellent accuracy in discriminating SVT and VT. PMID:8947674

Data Clustering and Evolving Fuzzy Decision Tree for Data Base Classification Problems

NASA Astrophysics Data System (ADS)

Chang, Pei-Chann; Fan, Chin-Yuan; Wang, Yen-Wen

Data base classification suffers from two well known difficulties, i.e., the high dimensionality and non-stationary variations within the large historic data. This paper presents a hybrid classification model by integrating a case based reasoning technique, a Fuzzy Decision Tree (FDT), and Genetic Algorithms (GA) to construct a decision-making system for data classification in various data base applications. The model is major based on the idea that the historic data base can be transformed into a smaller case-base together with a group of fuzzy decision rules. As a result, the model can be more accurately respond to the current data under classifying from the inductions by these smaller cases based fuzzy decision trees. Hit rate is applied as a performance measure and the effectiveness of our proposed model is demonstrated by experimentally compared with other approaches on different data base classification applications. The average hit rate of our proposed model is the highest among others.

Classification tree analysis to enhance targeting for follow-up exam of colorectal cancer screening

PubMed Central

2013-01-01

Background Follow-up rate after a fecal occult blood test (FOBT) is low worldwide. In order to increase the follow-up rate, segmentation of the target population has been proposed as a promising strategy, because an intervention can then be tailored toward specific subgroups of the population rather than using one type of intervention for all groups. The aim of this study is to identify subgroups that share the same patterns of characteristics related to follow-up exams after FOBT. Methods The study sample consisted of 143 patients aged 50–69 years who were requested to undergo follow-up exams after FOBT. A classification tree analysis was performed, using the follow-up rate as a dependent variable and sociodemographic variables, psychological variables, past FOBT and follow-up exam, family history of colorectal cancer (CRC), and history of bowel disease as predictive variables. Results The follow-up rate in 143 participants was 74.1% (n = 106). A classification tree analysis identified four subgroups as follows; (1) subgroup with a high degree of fear of CRC, unemployed and with a history of bowel disease (n = 24, 100.0% follow-up rate), (2) subgroup with a high degree of fear of CRC, unemployed and with no history of bowel disease (n = 17, 82.4% follow-up rate), (3) subgroup with a high degree of fear of CRC and employed (n = 24, 66.7% follow-up rate), and (4) subgroup with a low degree of fear of CRC (n = 78, 66.7% follow-up rate). Conclusion The identification of four subgroups with a diverse range of follow-up rates for CRC screening indicates the direction to take in future development of an effective tailored intervention strategy. PMID:24112563

Modified Cut-Off Value of the Urine Protein-To-Creatinine Ratio Is Helpful for Identifying Patients at High Risk for Chronic Kidney Disease: Validation of the Revised Japanese Guideline.

PubMed

Yamamoto, Hiroyuki; Yamamoto, Kyoko; Yoshida, Katsumi; Shindoh, Chiyohiko; Takeda, Kyoko; Monden, Masami; Izumo, Hiroko; Niinuma, Hiroyuki; Nishi, Yutaro; Niwa, Koichiro; Komatsu, Yasuhiro

2015-11-01

Chronic kidney disease (CKD) is a global public health issue, and strategies for its early detection and intervention are imperative. The latest Japanese CKD guideline recommends that patients without diabetes should be classified using the urine protein-to-creatinine ratio (PCR) instead of the urine albumin-to-creatinine ratio (ACR); however, no validation studies are available. This study aimed to validate the PCR-based CKD risk classification compared with the ACR-based classification and to explore more accurate classification methods. We analyzed two previously reported datasets that included diabetic and/or cardiovascular patients who were classified into early CKD stages. In total, 860 patients (131 diabetic patients and 729 cardiovascular patients, including 193 diabetic patients) were enrolled. We assessed the CKD risk classification of each patient according to the estimated glomerular filtration rate and the ACR-based or PCR-based classification. The use of the cut-off value recommended in the current guideline (PCR 0.15 g/g creatinine) resulted in risk misclassification rates of 26.0% and 16.6% for the two datasets. The misclassification was primarily caused by underestimation. Moderate to substantial agreement between each classification was achieved: Cohen's kappa, 0.56 (95% confidence interval, 0.45-0.69) and 0.72 (0.67-0.76) in each dataset, respectively. To improve the accuracy, we tested various candidate PCR cut-off values, showing that a PCR cut-off value of 0.08-0.10 g/g creatinine resulted in improvement in the misclassification rates and kappa values. Modification of the PCR cut-off value would improve its efficacy to identify high-risk populations who will benefit from early intervention.

Large-scale optimization-based classification models in medicine and biology.

PubMed

Lee, Eva K

2007-06-01

We present novel optimization-based classification models that are general purpose and suitable for developing predictive rules for large heterogeneous biological and medical data sets. Our predictive model simultaneously incorporates (1) the ability to classify any number of distinct groups; (2) the ability to incorporate heterogeneous types of attributes as input; (3) a high-dimensional data transformation that eliminates noise and errors in biological data; (4) the ability to incorporate constraints to limit the rate of misclassification, and a reserved-judgment region that provides a safeguard against over-training (which tends to lead to high misclassification rates from the resulting predictive rule); and (5) successive multi-stage classification capability to handle data points placed in the reserved-judgment region. To illustrate the power and flexibility of the classification model and solution engine, and its multi-group prediction capability, application of the predictive model to a broad class of biological and medical problems is described. Applications include: the differential diagnosis of the type of erythemato-squamous diseases; predicting presence/absence of heart disease; genomic analysis and prediction of aberrant CpG island meythlation in human cancer; discriminant analysis of motility and morphology data in human lung carcinoma; prediction of ultrasonic cell disruption for drug delivery; identification of tumor shape and volume in treatment of sarcoma; discriminant analysis of biomarkers for prediction of early atherosclerois; fingerprinting of native and angiogenic microvascular networks for early diagnosis of diabetes, aging, macular degeneracy and tumor metastasis; prediction of protein localization sites; and pattern recognition of satellite images in classification of soil types. In all these applications, the predictive model yields correct classification rates ranging from 80 to 100%. This provides motivation for pursuing its use as a medical diagnostic, monitoring and decision-making tool.

Classification and correlates of eating disorders among Blacks: findings from the National Survey of American Life.

PubMed

Taylor, Jacquelyn Y; Caldwell, Cleopatra Howard; Baser, Raymond E; Matusko, Niki; Faison, Nakesha; Jackson, James S

2013-02-01

To assess classification adjustments and examine correlates of eating disorders among Blacks. The National Survey of American Life (NSAL) was conducted from 2001-2003 and consisted of adults (n=5,191) and adolescents (n=1,170). The World Mental Health Composite International Diagnostic Interview (WMH-CIDI-World Health Organization 2004-modified) and DSM-IV-TR eating disorder criteria were used. Sixty-six percent of African American and 59% Caribbean Black adults were overweight or obese, while 30% and 29% of adolescents were overweight or obese. Although lifetime rates of anorexia nervosa and bulimia nervosa were low, binge eating disorder was high for both ethnic groups among adults and adolescents. Eliminating certain classification criteria resulted in higher rates of eating disorders for all groups. Culturally sensitive criteria should be incorporated into future versions of Diagnostic Statistical Manual (DSM) classifications for eating disorders that consider within-group ethnic variations.

Classification of time-of-flight secondary ion mass spectrometry spectra from complex Cu-Fe sulphides by principal component analysis and artificial neural networks.

PubMed

Kalegowda, Yogesh; Harmer, Sarah L

2013-01-08

Artificial neural network (ANN) and a hybrid principal component analysis-artificial neural network (PCA-ANN) classifiers have been successfully implemented for classification of static time-of-flight secondary ion mass spectrometry (ToF-SIMS) mass spectra collected from complex Cu-Fe sulphides (chalcopyrite, bornite, chalcocite and pyrite) at different flotation conditions. ANNs are very good pattern classifiers because of: their ability to learn and generalise patterns that are not linearly separable; their fault and noise tolerance capability; and high parallelism. In the first approach, fragments from the whole ToF-SIMS spectrum were used as input to the ANN, the model yielded high overall correct classification rates of 100% for feed samples, 88% for conditioned feed samples and 91% for Eh modified samples. In the second approach, the hybrid pattern classifier PCA-ANN was integrated. PCA is a very effective multivariate data analysis tool applied to enhance species features and reduce data dimensionality. Principal component (PC) scores which accounted for 95% of the raw spectral data variance, were used as input to the ANN, the model yielded high overall correct classification rates of 88% for conditioned feed samples and 95% for Eh modified samples. Copyright © 2012 Elsevier B.V. All rights reserved.

Clinical significance of erythropoietin receptor expression in oral squamous cell carcinoma

PubMed Central

2012-01-01

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

Classification and disease prediction via mathematical programming

NASA Astrophysics Data System (ADS)

Lee, Eva K.; Wu, Tsung-Lin

2007-11-01

In this chapter, we present classification models based on mathematical programming approaches. We first provide an overview on various mathematical programming approaches, including linear programming, mixed integer programming, nonlinear programming and support vector machines. Next, we present our effort of novel optimization-based classification models that are general purpose and suitable for developing predictive rules for large heterogeneous biological and medical data sets. Our predictive model simultaneously incorporates (1) the ability to classify any number of distinct groups; (2) the ability to incorporate heterogeneous types of attributes as input; (3) a high-dimensional data transformation that eliminates noise and errors in biological data; (4) the ability to incorporate constraints to limit the rate of misclassification, and a reserved-judgment region that provides a safeguard against over-training (which tends to lead to high misclassification rates from the resulting predictive rule) and (5) successive multi-stage classification capability to handle data points placed in the reserved judgment region. To illustrate the power and flexibility of the classification model and solution engine, and its multigroup prediction capability, application of the predictive model to a broad class of biological and medical problems is described. Applications include: the differential diagnosis of the type of erythemato-squamous diseases; predicting presence/absence of heart disease; genomic analysis and prediction of aberrant CpG island meythlation in human cancer; discriminant analysis of motility and morphology data in human lung carcinoma; prediction of ultrasonic cell disruption for drug delivery; identification of tumor shape and volume in treatment of sarcoma; multistage discriminant analysis of biomarkers for prediction of early atherosclerois; fingerprinting of native and angiogenic microvascular networks for early diagnosis of diabetes, aging, macular degeneracy and tumor metastasis; prediction of protein localization sites; and pattern recognition of satellite images in classification of soil types. In all these applications, the predictive model yields correct classification rates ranging from 80% to 100%. This provides motivation for pursuing its use as a medical diagnostic, monitoring and decision-making tool.

Classification by Using Multispectral Point Cloud Data

NASA Astrophysics Data System (ADS)

Liao, C. T.; Huang, H. H.

2012-07-01

Remote sensing images are generally recorded in two-dimensional format containing multispectral information. Also, the semantic information is clearly visualized, which ground features can be better recognized and classified via supervised or unsupervised classification methods easily. Nevertheless, the shortcomings of multispectral images are highly depending on light conditions, and classification results lack of three-dimensional semantic information. On the other hand, LiDAR has become a main technology for acquiring high accuracy point cloud data. The advantages of LiDAR are high data acquisition rate, independent of light conditions and can directly produce three-dimensional coordinates. However, comparing with multispectral images, the disadvantage is multispectral information shortage, which remains a challenge in ground feature classification through massive point cloud data. Consequently, by combining the advantages of both LiDAR and multispectral images, point cloud data with three-dimensional coordinates and multispectral information can produce a integrate solution for point cloud classification. Therefore, this research acquires visible light and near infrared images, via close range photogrammetry, by matching images automatically through free online service for multispectral point cloud generation. Then, one can use three-dimensional affine coordinate transformation to compare the data increment. At last, the given threshold of height and color information is set as threshold in classification.

Prognostic Performance and Reproducibility of the 1973 and 2004/2016 World Health Organization Grading Classification Systems in Non-muscle-invasive Bladder Cancer: A European Association of Urology Non-muscle Invasive Bladder Cancer Guidelines Panel Systematic Review.

PubMed

Soukup, Viktor; Čapoun, Otakar; Cohen, Daniel; Hernández, Virginia; Babjuk, Marek; Burger, Max; Compérat, Eva; Gontero, Paolo; Lam, Thomas; MacLennan, Steven; Mostafid, A Hugh; Palou, Joan; van Rhijn, Bas W G; Rouprêt, Morgan; Shariat, Shahrokh F; Sylvester, Richard; Yuan, Yuhong; Zigeuner, Richard

2017-11-01

Tumour grade is an important prognostic indicator in non-muscle-invasive bladder cancer (NMIBC). Histopathological classifications are limited by interobserver variability (reproducibility), which may have prognostic implications. European Association of Urology NMIBC guidelines suggest concurrent use of both 1973 and 2004/2016 World Health Organization (WHO) classifications. To compare the prognostic performance and reproducibility of the 1973 and 2004/2016 WHO grading systems for NMIBC. A systematic literature search was undertaken incorporating Medline, Embase, and the Cochrane Library. Studies were critically appraised for risk of bias (QUIPS). For prognosis, the primary outcome was progression to muscle-invasive or metastatic disease. Secondary outcomes were disease recurrence, and overall and cancer-specific survival. For reproducibility, the primary outcome was interobserver variability between pathologists. Secondary outcome was intraobserver variability (repeatability) by the same pathologist. Of 3593 articles identified, 20 were included in the prognostic review; three were eligible for the reproducibility review. Increasing tumour grade in both classifications was associated with higher disease progression and recurrence rates. Progression rates in grade 1 patients were similar to those in low-grade patients; progression rates in grade 3 patients were higher than those in high-grade patients. Survival data were limited. Reproducibility of the 2004/2016 system was marginally better than that of the 1973 system. Two studies on repeatability showed conflicting results. Most studies had a moderate to high risk of bias. Current grading classifications in NMIBC are suboptimal. The 1973 system identifies more aggressive tumours. Intra- and interobserver variability was slightly less in the 2004/2016 classification. We could not confirm that the 2004/2016 classification outperforms the 1973 classification in prediction of recurrence and progression. This article summarises the utility of two different grading systems for non-muscle-invasive bladder cancer. Both systems predict progression and recurrence, although pathologists vary in their reporting; suggestions for further improvements are made. Copyright © 2017 European Association of Urology. Published by Elsevier B.V. All rights reserved.

Sleep stage classification with low complexity and low bit rate.

PubMed

Virkkala, Jussi; Värri, Alpo; Hasan, Joel; Himanen, Sari-Leena; Müller, Kiti

2009-01-01

Standard sleep stage classification is based on visual analysis of central (usually also frontal and occipital) EEG, two-channel EOG, and submental EMG signals. The process is complex, using multiple electrodes, and is usually based on relatively high (200-500 Hz) sampling rates. Also at least 12 bit analog to digital conversion is recommended (with 16 bit storage) resulting in total bit rate of at least 12.8 kbit/s. This is not a problem for in-house laboratory sleep studies, but in the case of online wireless self-applicable ambulatory sleep studies, lower complexity and lower bit rates are preferred. In this study we further developed earlier single channel facial EMG/EOG/EEG-based automatic sleep stage classification. An algorithm with a simple decision tree separated 30 s epochs into wakefulness, SREM, S1/S2 and SWS using 18-45 Hz beta power and 0.5-6 Hz amplitude. Improvements included low complexity recursive digital filtering. We also evaluated the effects of a reduced sampling rate, reduced number of quantization steps and reduced dynamic range on the sleep data of 132 training and 131 testing subjects. With the studied algorithm, it was possible to reduce the sampling rate to 50 Hz (having a low pass filter at 90 Hz), and the dynamic range to 244 microV, with an 8 bit resolution resulting in a bit rate of 0.4 kbit/s. Facial electrodes and a low bit rate enables the use of smaller devices for sleep stage classification in home environments.

Efficacy measures associated to a plantar pressure based classification system in diabetic foot medicine.

PubMed

Deschamps, Kevin; Matricali, Giovanni Arnoldo; Desmet, Dirk; Roosen, Philip; Keijsers, Noel; Nobels, Frank; Bruyninckx, Herman; Staes, Filip

2016-09-01

The concept of 'classification' has, similar to many other diseases, been found to be fundamental in the field of diabetic medicine. In the current study, we aimed at determining efficacy measures of a recently published plantar pressure based classification system. Technical efficacy of the classification system was investigated by applying a high resolution, pixel-level analysis on the normalized plantar pressure pedobarographic fields of the original experimental dataset consisting of 97 patients with diabetes and 33 persons without diabetes. Clinical efficacy was assessed by considering the occurence of foot ulcers at the plantar aspect of the forefoot in this dataset. Classification efficacy was assessed by determining the classification recognition rate as well as its sensitivity and specificity using cross-validation subsets of the experimental dataset together with a novel cohort of 12 patients with diabetes. Pixel-level comparison of the four groups associated to the classification system highlighted distinct regional differences. Retrospective analysis showed the occurence of eleven foot ulcers in the experimental dataset since their gait analysis. Eight out of the eleven ulcers developed in a region of the foot which had the highest forces. Overall classification recognition rate exceeded 90% for all cross-validation subsets. Sensitivity and specificity of the four groups associated to the classification system exceeded respectively the 0.7 and 0.8 level in all cross-validation subsets. The results of the current study support the use of the novel plantar pressure based classification system in diabetic foot medicine. It may particularly serve in communication, diagnosis and clinical decision making. Copyright © 2016 Elsevier B.V. All rights reserved.

Reliability of classification for post-traumatic ankle osteoarthritis.

PubMed

Claessen, Femke M A P; Meijer, Diederik T; van den Bekerom, Michel P J; Gevers Deynoot, Barend D J; Mallee, Wouter H; Doornberg, Job N; van Dijk, C Niek

2016-04-01

The purpose of this study was to identify the most reliable classification system for clinical outcome studies to categorize post-traumatic-fracture-osteoarthritis. A total of 118 orthopaedic surgeons and residents-gathered in the Ankle Platform Study Collaborative Science of Variation Group-evaluated 128 anteroposterior and lateral radiographs of patients after a bi- or trimalleolar ankle fracture on a Web-based platform in order to rate post-traumatic osteoarthritis according to the classification systems coined by (1) van Dijk, (2) Kellgren, and (3) Takakura. Reliability was evaluated with the use of the Siegel and Castellan's multirater kappa measure. Differences between classification systems were compared using the two-sample Z-test. Interobserver agreement of surgeons who participated in the survey was fair for the van Dijk osteoarthritis scale (k = 0.24), and poor for the Takakura (k = 0.19) and the Kellgren systems (k = 0.18) according to the categorical rating of Landis and Koch. This difference in one categorical rating was found to be significant (p < 0.001, CI 0.046-0.053) with the high numbers of observers and cases available. This study documents fair interobserver agreement for the van Dijk osteoarthritis scale, and poor interobserver agreement for the Takakura and Kellgren osteoarthritis classification systems. Because of the low interobserver agreement for the van Dijk, Kellgren, and Takakura classification systems, those systems cannot be used for clinical decision-making. Development of diagnostic criteria on basis of consecutive patients, Level II.

Automatic classification for mammogram backgrounds based on bi-rads complexity definition and on a multi content analysis framework

NASA Astrophysics Data System (ADS)

Wu, Jie; Besnehard, Quentin; Marchessoux, Cédric

2011-03-01

Clinical studies for the validation of new medical imaging devices require hundreds of images. An important step in creating and tuning the study protocol is the classification of images into "difficult" and "easy" cases. This consists of classifying the image based on features like the complexity of the background, the visibility of the disease (lesions). Therefore, an automatic medical background classification tool for mammograms would help for such clinical studies. This classification tool is based on a multi-content analysis framework (MCA) which was firstly developed to recognize image content of computer screen shots. With the implementation of new texture features and a defined breast density scale, the MCA framework is able to automatically classify digital mammograms with a satisfying accuracy. BI-RADS (Breast Imaging Reporting Data System) density scale is used for grouping the mammograms, which standardizes the mammography reporting terminology and assessment and recommendation categories. Selected features are input into a decision tree classification scheme in MCA framework, which is the so called "weak classifier" (any classifier with a global error rate below 50%). With the AdaBoost iteration algorithm, these "weak classifiers" are combined into a "strong classifier" (a classifier with a low global error rate) for classifying one category. The results of classification for one "strong classifier" show the good accuracy with the high true positive rates. For the four categories the results are: TP=90.38%, TN=67.88%, FP=32.12% and FN =9.62%.

Muscle Injuries: A Brief Guide to Classification and Management

PubMed Central

Maffulli, Nicola; Del Buono, Angelo; Oliva, Francesco; Giai Via, Alessio; Frizziero, Antonio; Barazzuol, Michele; Brancaccio, Paola; Freschi, Marco; Galletti, Stefano; Lisitano, Gianfranco; Melegati, Gianluca; Nanni, Gianni; Pasta, Ghito; Ramponi, Carlo; Rizzo, Diego; Testa, Vittorino; Valent, Alessandro

2015-01-01

Muscle injuries are frequent in athletes. Despite their high incidence, advances in clinical diagnostic criteria and imaging, their optimal management and rehabilitation strategies are still debated in literature. Furthermore, reinjury rate is high after a muscle lesion, and an improper treatment or an early return to sports can increase the rate of reinjury and complications. Most muscle injuries are managed conservatively with excellent results, and surgery is normally advocated only for larger tears. This article reviews the current literature to provide physicians and rehabilitation specialists with the necessary basic tools to diagnose, classify and to treat muscle injuries. Based on anatomy, biomechanics, and imaging features of muscle injury, the use of a recently reported new classification system is also advocated. PMID:26535183

Method of and system for classifying emergency locating transmitters and emergency positions indicating radio beacons

NASA Technical Reports Server (NTRS)

Wren, Paul E. (Inventor)

1983-01-01

During a distress call, a distress location transmitter 10 generates a high frequency carrier signal 40 that is modulated by a predetermined distress waveform characteristic 29. The classification of user associated with the distress call is identified by periodically interrupting modulation 42; user classification is determined by the repetition rate of the interruptions, the interruption periods, or both.

Classifications for Cesarean Section: A Systematic Review

PubMed Central

Torloni, Maria Regina; Betran, Ana Pilar; Souza, Joao Paulo; Widmer, Mariana; Allen, Tomas; Gulmezoglu, Metin; Merialdi, Mario

2011-01-01

Background Rising cesarean section (CS) rates are a major public health concern and cause worldwide debates. To propose and implement effective measures to reduce or increase CS rates where necessary requires an appropriate classification. Despite several existing CS classifications, there has not yet been a systematic review of these. This study aimed to 1) identify the main CS classifications used worldwide, 2) analyze advantages and deficiencies of each system. Methods and Findings Three electronic databases were searched for classifications published 1968–2008. Two reviewers independently assessed classifications using a form created based on items rated as important by international experts. Seven domains (ease, clarity, mutually exclusive categories, totally inclusive classification, prospective identification of categories, reproducibility, implementability) were assessed and graded. Classifications were tested in 12 hypothetical clinical case-scenarios. From a total of 2948 citations, 60 were selected for full-text evaluation and 27 classifications identified. Indications classifications present important limitations and their overall score ranged from 2–9 (maximum grade = 14). Degree of urgency classifications also had several drawbacks (overall scores 6–9). Woman-based classifications performed best (scores 5–14). Other types of classifications require data not routinely collected and may not be relevant in all settings (scores 3–8). Conclusions This review and critical appraisal of CS classifications is a methodologically sound contribution to establish the basis for the appropriate monitoring and rational use of CS. Results suggest that women-based classifications in general, and Robson's classification, in particular, would be in the best position to fulfill current international and local needs and that efforts to develop an internationally applicable CS classification would be most appropriately placed in building upon this classification. The use of a single CS classification will facilitate auditing, analyzing and comparing CS rates across different settings and help to create and implement effective strategies specifically targeted to optimize CS rates where necessary. PMID:21283801

34 CFR 222.68 - What tax rates does the Secretary use if two or more different classifications of real property...

Code of Federal Regulations, 2011 CFR

2011-07-01

... different classifications of real property are taxed at different rates? 222.68 Section 222.68 Education... different classifications of real property are taxed at different rates? If the real property of an LEA and its generally comparable LEAs consists of two or more classifications of real property taxed at...

The SED Machine: A Robotic Spectrograph for Fast Transient Classification

NASA Astrophysics Data System (ADS)

Blagorodnova, Nadejda; Neill, James D.; Walters, Richard; Kulkarni, Shrinivas R.; Fremling, Christoffer; Ben-Ami, Sagi; Dekany, Richard G.; Fucik, Jason R.; Konidaris, Nick; Nash, Reston; Ngeow, Chow-Choong; Ofek, Eran O.; O’ Sullivan, Donal; Quimby, Robert; Ritter, Andreas; Vyhmeister, Karl E.

2018-03-01

Current time domain facilities are finding several hundreds of transient astronomical events a year. The discovery rate is expected to increase in the future as soon as new surveys such as the Zwicky Transient Facility (ZTF) and the Large Synoptic Sky Survey (LSST) come online. Presently, the rate at which transients are classified is approximately one order or magnitude lower than the discovery rate, leading to an increasing “follow-up drought”. Existing telescopes with moderate aperture can help address this deficit when equipped with spectrographs optimized for spectral classification. Here, we provide an overview of the design, operations and first results of the Spectral Energy Distribution Machine (SEDM), operating on the Palomar 60-inch telescope (P60). The instrument is optimized for classification and high observing efficiency. It combines a low-resolution (R ∼ 100) integral field unit (IFU) spectrograph with “Rainbow Camera” (RC), a multi-band field acquisition camera which also serves as multi-band (ugri) photometer. The SEDM was commissioned during the operation of the intermediate Palomar Transient Factory (iPTF) and has already lived up to its promise. The success of the SEDM demonstrates the value of spectrographs optimized for spectral classification.

Spatial patterns of brain atrophy in MCI patients, identified via high-dimensional pattern classification, predict subsequent cognitive decline

PubMed Central

Fan, Yong; Batmanghelich, Nematollah; Clark, Chris M.; Davatzikos, Christos

2010-01-01

Spatial patterns of brain atrophy in mild cognitive impairment (MCI) and Alzheimer’s disease (AD) were measured via methods of computational neuroanatomy. These patterns were spatially complex and involved many brain regions. In addition to the hippocampus and the medial temporal lobe gray matter, a number of other regions displayed significant atrophy, including orbitofrontal and medial-prefrontal grey matter, cingulate (mainly posterior), insula, uncus, and temporal lobe white matter. Approximately 2/3 of the MCI group presented patterns of atrophy that overlapped with AD, whereas the remaining 1/3 overlapped with cognitively normal individuals, thereby indicating that some, but not all, MCI patients have significant and extensive brain atrophy in this cohort of MCI patients. Importantly, the group with AD-like patterns presented much higher rate of MMSE decline in follow-up visits; conversely, pattern classification provided relatively high classification accuracy (87%) of the individuals that presented relatively higher MMSE decline within a year from baseline. High-dimensional pattern classification, a nonlinear multivariate analysis, provided measures of structural abnormality that can potentially be useful for individual patient classification, as well as for predicting progression and examining multivariate relationships in group analyses. PMID:18053747

Classification and Quality Evaluation of Tobacco Leaves Based on Image Processing and Fuzzy Comprehensive Evaluation

PubMed Central

Zhang, Fan; Zhang, Xinhong

2011-01-01

Most of classification, quality evaluation or grading of the flue-cured tobacco leaves are manually operated, which relies on the judgmental experience of experts, and inevitably limited by personal, physical and environmental factors. The classification and the quality evaluation are therefore subjective and experientially based. In this paper, an automatic classification method of tobacco leaves based on the digital image processing and the fuzzy sets theory is presented. A grading system based on image processing techniques was developed for automatically inspecting and grading flue-cured tobacco leaves. This system uses machine vision for the extraction and analysis of color, size, shape and surface texture. Fuzzy comprehensive evaluation provides a high level of confidence in decision making based on the fuzzy logic. The neural network is used to estimate and forecast the membership function of the features of tobacco leaves in the fuzzy sets. The experimental results of the two-level fuzzy comprehensive evaluation (FCE) show that the accuracy rate of classification is about 94% for the trained tobacco leaves, and the accuracy rate of the non-trained tobacco leaves is about 72%. We believe that the fuzzy comprehensive evaluation is a viable way for the automatic classification and quality evaluation of the tobacco leaves. PMID:22163744

Incidence of Radiologically Isolated Syndrome: A Population-Based Study.

PubMed

Forslin, Y; Granberg, T; Jumah, A Antwan; Shams, S; Aspelin, P; Kristoffersen-Wiberg, M; Martola, J; Fredrikson, S

2016-06-01

Incidental MR imaging findings resembling MS in asymptomatic individuals, fulfilling the Okuda criteria, are termed "radiologically isolated syndrome." Those with radiologically isolated syndrome are at high risk of their condition converting to MS. The epidemiology of radiologically isolated syndrome remains largely unknown, and there are no population-based studies, to our knowledge. Our aim was to study the population-based incidence of radiologically isolated syndrome in a high-incidence region for MS and to evaluate the effect on radiologically isolated syndrome incidence when revising the original radiologically isolated syndrome criteria by using the latest radiologic classification for dissemination in space. All 2272 brain MR imaging scans in 1907 persons obtained during 2013 in the Swedish county of Västmanland, with a population of 259,000 inhabitants, were blindly evaluated by a senior radiologist and a senior neuroradiologist. The Okuda criteria for radiologically isolated syndrome were applied by using both the Barkhof and Swanton classifications for dissemination in space. Assessments of clinical data were performed by a radiology resident and a senior neurologist. The cumulative incidence of radiologically isolated syndrome was 2 patients (0.1%), equaling an incidence rate of 0.8 cases per 100,000 person-years, in a region with an incidence rate of MS of 10.2 cases per 100,000 person-years. There was no difference in the radiologically isolated syndrome incidence rate when applying a modified version of the Okuda criteria by using the newer Swanton classification for dissemination in space. Radiologically isolated syndrome is uncommon in a high-incidence region for MS. Adapting the Okuda criteria to use the dissemination in space-Swanton classification may be feasible. Future studies on radiologically isolated syndrome may benefit from a collaborative approach to ensure adequate numbers of participants. © 2016 by American Journal of Neuroradiology.

A new classification scheme of plastic wastes based upon recycling labels

DOE Office of Scientific and Technical Information (OSTI.GOV)

Özkan, Kemal, E-mail: kozkan@ogu.edu.tr; Ergin, Semih, E-mail: sergin@ogu.edu.tr; Işık, Şahin, E-mail: sahini@ogu.edu.tr

Highlights: • PET, HPDE or PP types of plastics are considered. • An automated classification of plastic bottles based on the feature extraction and classification methods is performed. • The decision mechanism consists of PCA, Kernel PCA, FLDA, SVD and Laplacian Eigenmaps methods. • SVM is selected to achieve the classification task and majority voting technique is used. - Abstract: Since recycling of materials is widely assumed to be environmentally and economically beneficial, reliable sorting and processing of waste packaging materials such as plastics is very important for recycling with high efficiency. An automated system that can quickly categorize thesemore » materials is certainly needed for obtaining maximum classification while maintaining high throughput. In this paper, first of all, the photographs of the plastic bottles have been taken and several preprocessing steps were carried out. The first preprocessing step is to extract the plastic area of a bottle from the background. Then, the morphological image operations are implemented. These operations are edge detection, noise removal, hole removing, image enhancement, and image segmentation. These morphological operations can be generally defined in terms of the combinations of erosion and dilation. The effect of bottle color as well as label are eliminated using these operations. Secondly, the pixel-wise intensity values of the plastic bottle images have been used together with the most popular subspace and statistical feature extraction methods to construct the feature vectors in this study. Only three types of plastics are considered due to higher existence ratio of them than the other plastic types in the world. The decision mechanism consists of five different feature extraction methods including as Principal Component Analysis (PCA), Kernel PCA (KPCA), Fisher’s Linear Discriminant Analysis (FLDA), Singular Value Decomposition (SVD) and Laplacian Eigenmaps (LEMAP) and uses a simple experimental setup with a camera and homogenous backlighting. Due to the giving global solution for a classification problem, Support Vector Machine (SVM) is selected to achieve the classification task and majority voting technique is used as the decision mechanism. This technique equally weights each classification result and assigns the given plastic object to the class that the most classification results agree on. The proposed classification scheme provides high accuracy rate, and also it is able to run in real-time applications. It can automatically classify the plastic bottle types with approximately 90% recognition accuracy. Besides this, the proposed methodology yields approximately 96% classification rate for the separation of PET or non-PET plastic types. It also gives 92% accuracy for the categorization of non-PET plastic types into HPDE or PP.« less

48 CFR 47.305-9 - Commodity description and freight classification.

Code of Federal Regulations, 2010 CFR

2010-10-01

... freight classification. 47.305-9 Section 47.305-9 Federal Acquisition Regulations System FEDERAL... Commodity description and freight classification. (a) Generally, the freight rate for supplies is based on the rating applicable to the freight classification description published in the National Motor...

The Adam Walsh Act: An Examination of Sex Offender Risk Classification Systems.

PubMed

Zgoba, Kristen M; Miner, Michael; Levenson, Jill; Knight, Raymond; Letourneau, Elizabeth; Thornton, David

2016-12-01

This study was designed to compare the Adam Walsh Act (AWA) classification tiers with actuarial risk assessment instruments and existing state classification schemes in their respective abilities to identify sex offenders at high risk to re-offend. Data from 1,789 adult sex offenders released from prison in four states were collected (Minnesota, New Jersey, Florida, and South Carolina). On average, the sexual recidivism rate was approximately 5% at 5 years and 10% at 10 years. AWA Tier 2 offenders had higher Static-99R scores and higher recidivism rates than Tier 3 offenders, and in Florida, these inverse correlations were statistically significant. Actuarial measures and existing state tier systems, in contrast, did a better job of identifying high-risk offenders and recidivists. As well, we examined the distribution of risk assessment scores within and across tier categories, finding that a majority of sex offenders fall into AWA Tier 3, but more than half score low or moderately low on the Static-99R. The results indicate that the AWA sex offender classification scheme is a poor indicator of relative risk and is likely to result in a system that is less effective in protecting the public than those currently implemented in the states studied. © The Author(s) 2015.

Intelligent condition monitoring method for bearing faults from highly compressed measurements using sparse over-complete features

NASA Astrophysics Data System (ADS)

Ahmed, H. O. A.; Wong, M. L. D.; Nandi, A. K.

2018-01-01

Condition classification of rolling element bearings in rotating machines is important to prevent the breakdown of industrial machinery. A considerable amount of literature has been published on bearing faults classification. These studies aim to determine automatically the current status of a roller element bearing. Of these studies, methods based on compressed sensing (CS) have received some attention recently due to their ability to allow one to sample below the Nyquist sampling rate. This technology has many possible uses in machine condition monitoring and has been investigated as a possible approach for fault detection and classification in the compressed domain, i.e., without reconstructing the original signal. However, previous CS based methods have been found to be too weak for highly compressed data. The present paper explores computationally, for the first time, the effects of sparse autoencoder based over-complete sparse representations on the classification performance of highly compressed measurements of bearing vibration signals. For this study, the CS method was used to produce highly compressed measurements of the original bearing dataset. Then, an effective deep neural network (DNN) with unsupervised feature learning algorithm based on sparse autoencoder is used for learning over-complete sparse representations of these compressed datasets. Finally, the fault classification is achieved using two stages, namely, pre-training classification based on stacked autoencoder and softmax regression layer form the deep net stage (the first stage), and re-training classification based on backpropagation (BP) algorithm forms the fine-tuning stage (the second stage). The experimental results show that the proposed method is able to achieve high levels of accuracy even with extremely compressed measurements compared with the existing techniques.

Comparison of geometric morphometric outline methods in the discrimination of age-related differences in feather shape

PubMed Central

Sheets, H David; Covino, Kristen M; Panasiewicz, Joanna M; Morris, Sara R

2006-01-01

Background Geometric morphometric methods of capturing information about curves or outlines of organismal structures may be used in conjunction with canonical variates analysis (CVA) to assign specimens to groups or populations based on their shapes. This methodological paper examines approaches to optimizing the classification of specimens based on their outlines. This study examines the performance of four approaches to the mathematical representation of outlines and two different approaches to curve measurement as applied to a collection of feather outlines. A new approach to the dimension reduction necessary to carry out a CVA on this type of outline data with modest sample sizes is also presented, and its performance is compared to two other approaches to dimension reduction. Results Two semi-landmark-based methods, bending energy alignment and perpendicular projection, are shown to produce roughly equal rates of classification, as do elliptical Fourier methods and the extended eigenshape method of outline measurement. Rates of classification were not highly dependent on the number of points used to represent a curve or the manner in which those points were acquired. The new approach to dimensionality reduction, which utilizes a variable number of principal component (PC) axes, produced higher cross-validation assignment rates than either the standard approach of using a fixed number of PC axes or a partial least squares method. Conclusion Classification of specimens based on feather shape was not highly dependent of the details of the method used to capture shape information. The choice of dimensionality reduction approach was more of a factor, and the cross validation rate of assignment may be optimized using the variable number of PC axes method presented herein. PMID:16978414

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

PubMed

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

2016-01-01

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

[Land cover classification of Four Lakes Region in Hubei Province based on MODIS and ENVISAT data].

PubMed

Xue, Lian; Jin, Wei-Bin; Xiong, Qin-Xue; Liu, Zhang-Yong

2010-03-01

Based on the differences of back scattering coefficient in ENVISAT ASAR data, a classification was made on the towns, waters, and vegetation-covered areas in the Four Lakes Region of Hubei Province. According to the local cropping systems and phenological characteristics in the region, and by using the discrepancies of the MODIS-NDVI index from late April to early May, the vegetation-covered areas were classified into croplands and non-croplands. The classification results based on the above-mentioned procedure was verified by the classification results based on the ETM data with high spatial resolution. Based on the DEM data, the non-croplands were categorized into forest land and bottomland; and based on the discrepancies of mean NDVI index per month, the crops were identified as mid rice, late rice, and cotton, and the croplands were identified as paddy field and upland field. The land cover classification based on the MODIS data with low spatial resolution was basically consistent with that based on the ETM data with high spatial resolution, and the total error rate was about 13.15% when the classification results based on ETM data were taken as the standard. The utilization of the above-mentioned procedures for large scale land cover classification and mapping could make the fast tracking of regional land cover classification.

Influence of nuclei segmentation on breast cancer malignancy classification

NASA Astrophysics Data System (ADS)

Jelen, Lukasz; Fevens, Thomas; Krzyzak, Adam

2009-02-01

Breast Cancer is one of the most deadly cancers affecting middle-aged women. Accurate diagnosis and prognosis are crucial to reduce the high death rate. Nowadays there are numerous diagnostic tools for breast cancer diagnosis. In this paper we discuss a role of nuclear segmentation from fine needle aspiration biopsy (FNA) slides and its influence on malignancy classification. Classification of malignancy plays a very important role during the diagnosis process of breast cancer. Out of all cancer diagnostic tools, FNA slides provide the most valuable information about the cancer malignancy grade which helps to choose an appropriate treatment. This process involves assessing numerous nuclear features and therefore precise segmentation of nuclei is very important. In this work we compare three powerful segmentation approaches and test their impact on the classification of breast cancer malignancy. The studied approaches involve level set segmentation, fuzzy c-means segmentation and textural segmentation based on co-occurrence matrix. Segmented nuclei were used to extract nuclear features for malignancy classification. For classification purposes four different classifiers were trained and tested with previously extracted features. The compared classifiers are Multilayer Perceptron (MLP), Self-Organizing Maps (SOM), Principal Component-based Neural Network (PCA) and Support Vector Machines (SVM). The presented results show that level set segmentation yields the best results over the three compared approaches and leads to a good feature extraction with a lowest average error rate of 6.51% over four different classifiers. The best performance was recorded for multilayer perceptron with an error rate of 3.07% using fuzzy c-means segmentation.

A High Performance Computing Approach to Tree Cover Delineation in 1-m NAIP Imagery Using a Probabilistic Learning Framework

NASA Technical Reports Server (NTRS)

Basu, Saikat; Ganguly, Sangram; Michaelis, Andrew; Votava, Petr; Roy, Anshuman; Mukhopadhyay, Supratik; Nemani, Ramakrishna

2015-01-01

Tree cover delineation is a useful instrument in deriving Above Ground Biomass (AGB) density estimates from Very High Resolution (VHR) airborne imagery data. Numerous algorithms have been designed to address this problem, but most of them do not scale to these datasets, which are of the order of terabytes. In this paper, we present a semi-automated probabilistic framework for the segmentation and classification of 1-m National Agriculture Imagery Program (NAIP) for tree-cover delineation for the whole of Continental United States, using a High Performance Computing Architecture. Classification is performed using a multi-layer Feedforward Backpropagation Neural Network and segmentation is performed using a Statistical Region Merging algorithm. The results from the classification and segmentation algorithms are then consolidated into a structured prediction framework using a discriminative undirected probabilistic graphical model based on Conditional Random Field, which helps in capturing the higher order contextual dependencies between neighboring pixels. Once the final probability maps are generated, the framework is updated and re-trained by relabeling misclassified image patches. This leads to a significant improvement in the true positive rates and reduction in false positive rates. The tree cover maps were generated for the whole state of California, spanning a total of 11,095 NAIP tiles covering a total geographical area of 163,696 sq. miles. The framework produced true positive rates of around 88% for fragmented forests and 74% for urban tree cover areas, with false positive rates lower than 2% for both landscapes. Comparative studies with the National Land Cover Data (NLCD) algorithm and the LiDAR canopy height model (CHM) showed the effectiveness of our framework for generating accurate high-resolution tree-cover maps.

A High Performance Computing Approach to Tree Cover Delineation in 1-m NAIP Imagery using a Probabilistic Learning Framework

NASA Astrophysics Data System (ADS)

Basu, S.; Ganguly, S.; Michaelis, A.; Votava, P.; Roy, A.; Mukhopadhyay, S.; Nemani, R. R.

2015-12-01

Tree cover delineation is a useful instrument in deriving Above Ground Biomass (AGB) density estimates from Very High Resolution (VHR) airborne imagery data. Numerous algorithms have been designed to address this problem, but most of them do not scale to these datasets which are of the order of terabytes. In this paper, we present a semi-automated probabilistic framework for the segmentation and classification of 1-m National Agriculture Imagery Program (NAIP) for tree-cover delineation for the whole of Continental United States, using a High Performance Computing Architecture. Classification is performed using a multi-layer Feedforward Backpropagation Neural Network and segmentation is performed using a Statistical Region Merging algorithm. The results from the classification and segmentation algorithms are then consolidated into a structured prediction framework using a discriminative undirected probabilistic graphical model based on Conditional Random Field, which helps in capturing the higher order contextual dependencies between neighboring pixels. Once the final probability maps are generated, the framework is updated and re-trained by relabeling misclassified image patches. This leads to a significant improvement in the true positive rates and reduction in false positive rates. The tree cover maps were generated for the whole state of California, spanning a total of 11,095 NAIP tiles covering a total geographical area of 163,696 sq. miles. The framework produced true positive rates of around 88% for fragmented forests and 74% for urban tree cover areas, with false positive rates lower than 2% for both landscapes. Comparative studies with the National Land Cover Data (NLCD) algorithm and the LiDAR canopy height model (CHM) showed the effectiveness of our framework for generating accurate high-resolution tree-cover maps.

75 FR 68608 - Information Collection; Request for Authorization of Additional Classification and Rate, Standard...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-11-08

... Authorization of Additional Classification and Rate, Standard Form 1444 AGENCY: Department of Defense (DOD... of Additional Classification and Rate, Standard Form 1444. DATES: Comments may be submitted on or.../or business confidential information provided. FOR FURTHER INFORMATION CONTACT: Mr. Ernest Woodson...

Weight-elimination neural networks applied to coronary surgery mortality prediction.

PubMed

Ennett, Colleen M; Frize, Monique

2003-06-01

The objective was to assess the effectiveness of the weight-elimination cost function in improving classification performance of artificial neural networks (ANNs) and to observe how changing the a priori distribution of the training set affects network performance. Backpropagation feedforward ANNs with and without weight-elimination estimated mortality for coronary artery surgery patients. The ANNs were trained and tested on cases with 32 input variables describing the patient's medical history; the output variable was in-hospital mortality (mortality rates: training 3.7%, test 3.8%). Artificial training sets with mortality rates of 20%, 50%, and 80% were created to observe the impact of training with a higher-than-normal prevalence. When the results were averaged, weight-elimination networks achieved higher sensitivity rates than those without weight-elimination. Networks trained on higher-than-normal prevalence achieved higher sensitivity rates at the cost of lower specificity and correct classification. The weight-elimination cost function can improve the classification performance when the network is trained with a higher-than-normal prevalence. A network trained with a moderately high artificial mortality rate (artificial mortality rate of 20%) can improve the sensitivity of the model without significantly affecting other aspects of the model's performance. The ANN mortality model achieved comparable performance as additive and statistical models for coronary surgery mortality estimation in the literature.

46 CFR 565.9 - Commission review, suspension and prohibition of rates, charges, classifications, rules or...

Code of Federal Regulations, 2011 CFR

2011-10-01

..., charges, classifications, rules or regulations. 565.9 Section 565.9 Shipping FEDERAL MARITIME COMMISSION... Commission review, suspension and prohibition of rates, charges, classifications, rules or regulations. (a)(1..., charges, classifications, rules or regulations) from the Commission, each controlled carrier shall file a...

46 CFR 565.9 - Commission review, suspension and prohibition of rates, charges, classifications, rules or...

Code of Federal Regulations, 2010 CFR

2010-10-01

..., charges, classifications, rules or regulations. 565.9 Section 565.9 Shipping FEDERAL MARITIME COMMISSION... Commission review, suspension and prohibition of rates, charges, classifications, rules or regulations. (a)(1..., charges, classifications, rules or regulations) from the Commission, each controlled carrier shall file a...

78 FR 18252 - Prevailing Rate Systems; North American Industry Classification System Based Federal Wage System...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-03-26

...-AM78 Prevailing Rate Systems; North American Industry Classification System Based Federal Wage System... 2007 North American Industry Classification System (NAICS) codes currently used in Federal Wage System... (OPM) issued a final rule (73 FR 45853) to update the 2002 North American Industry Classification...

76 FR 53699 - Labor Surplus Area Classification Under Executive Orders 12073 and 10582

Federal Register 2010, 2011, 2012, 2013, 2014

2011-08-29

... DEPARTMENT OF LABOR Employment and Training Administration Labor Surplus Area Classification Under... estimates provided to ETA by the Bureau of Labor Statistics are used in making these classifications. The... classification criteria include a ``floor unemployment rate'' (6.0%) and a ``ceiling unemployment rate'' (10.0...

Detection of stress factors in crop and weed species using hyperspectral remote sensing reflectance

NASA Astrophysics Data System (ADS)

Henry, William Brien

The primary objective of this work was to determine if stress factors such as moisture stress or herbicide injury stress limit the ability to distinguish between weeds and crops using remotely sensed data. Additional objectives included using hyperspectral reflectance data to measure moisture content within a species, and to measure crop injury in response to drift rates of non-selective herbicides. Moisture stress did not reduce the ability to discriminate between species. Regardless of analysis technique, the trend was that as moisture stress increased, so too did the ability to distinguish between species. Signature amplitudes (SA) of the top 5 bands, discrete wavelet transforms (DWT), and multiple indices were promising analysis techniques. Discriminant models created from one year's data set and validated on additional data sets provided, on average, approximately 80% accurate classification among weeds and crop. This suggests that these models are relatively robust and could potentially be used across environmental conditions in field scenarios. Distinguishing between leaves grown at high-moisture stress and no-stress was met with limited success, primarily because there was substantial variation among samples within the treatments. Leaf water potential (LWP) was measured, and these were classified into three categories using indices. Classification accuracies were as high as 68%. The 10 bands most highly correlated to LWP were selected; however, there were no obvious trends or patterns in these top 10 bands with respect to time, species or moisture level, suggesting that LWP is an elusive parameter to quantify spectrally. In order to address herbicide injury stress and its impact on species discrimination, discriminant models were created from combinations of multiple indices. The model created from the second experimental run's data set and validated on the first experimental run's data provided an average of 97% correct classification of soybean and an overall average classification accuracy of 65% for all species. This suggests that these models are relatively robust and could potentially be used across a wide range of herbicide applications in field scenarios. From the pooled data set, a single discriminant model was created with multiple indices that discriminated soybean from weeds 88%, on average, regardless of herbicide, rate or species. Several analysis techniques including multiple indices, signature amplitude with spectral bands as features, and wavelet analysis were employed to distinguish between herbicide-treated and nontreated plants. Classification accuracy using signature amplitude (SA) analysis of paraquat injury on soybean was better than 75% for both 1/2 and 1/8X rates at 1, 4, and 7 DAA. Classification accuracy of paraquat injury on corn was better than 72% for the 1/2X rate at 1, 4, and 7 DAA. These data suggest that hyperspectral reflectance may be used to distinguish between healthy plants and injured plants to which herbicides have been applied; however, the classification accuracies remained at 75% or higher only when the higher rates of herbicide were applied. (Abstract shortened by UMI.)

Rational dissection of a high institutional cesarean section rate: an analysis using the Robson Ten Group Classification System.

PubMed

Tan, Jarrod K H; Tan, Eng Loy; Kanagalingan, Devendra; Tan, Lay Kok

2015-04-01

Cesarean section (CS) rates have risen far in excess of the optimal 15% recommended by the World Health Organization. The Robson Ten Group Classification System (TGCS) allows meaningful analysis of a CS rate. The aim of this study is to identify the leading patient categories contributing to our institution's CS rate. Prospective study of all women who delivered at the Singapore General Hospital from January 2008 to December 2011. The following data was recorded: parity, singleton/multiple pregnancy, previous CS, mode of labor onset and gestational age at delivery. CS rates were computed for each group, as well as their relative contribution to the overall CS rate. There were 6074 deliveries, in which 2011 (33.1%) women had CS delivery. Group 5 was the largest contributor to the overall CS rate (25.9%). Of the patients in this group, 18.8% had a successful vaginal birth after cesarean (VBAC). Group 2 was the second largest contributor to the overall CS rate at 18.0%. Group 10 had a high contribution of 16.1%. The TGCS allows easy identification of the leading contributing patient groups. The surprisingly high contribution of group 10 suggests that our institution, a tertiary multidisciplinary teaching hospital, manages a sizeable group of high-risk patients in its obstetric case mix accounting for the high CS rate. Almost one in five term pregnancies with one previous CS had a successful vaginal delivery, suggesting that the institutional attempted VBAC rate is higher than 20%. © 2014 The Authors. Journal of Obstetrics and Gynaecology Research © 2014 Japan Society of Obstetrics and Gynecology.

76 FR 5375 - Submission for OMB Review; Request for Authorization of Additional Classification and Rate...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-01-31

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

PubMed

Bailie, George R

2012-02-15

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

[Analysis of vegetation spatial and temporal variations in Qinghai Province based on remote sensing].

PubMed

Wang, Li-wen; Wei, Ya-xing; Niu, Zheng

2008-06-01

1 km MODIS NDVI time series data combining with decision tree classification, supervised classification and unsupervised classification was used to classify land cover type of Qinghai Province into 14 classes. In our classification system, sparse grassland and sparse shrub were emphasized, and their spatial distribution locations were labeled. From digital elevation model (DEM) of Qinghai Province, five elevation belts were achieved, and we utilized geographic information system (GIS) software to analyze vegetation cover variation on different elevation belts. Our research result shows that vegetation cover in Qinghai Province has been improved in recent five years. Vegetation cover area increases from 370047 km2 in 2001 to 374576 km2 in 2006, and vegetation cover rate increases by 0.63%. Among five grade elevation belts, vegetation cover ratio of high mountain belt is the highest (67.92%). The area of middle density grassland in high mountain belt is the largest, of which area is 94 003 km2. Increased area of dense grassland in high mountain belt is the greatest (1280 km2). During five years, the biggest variation is the conversion from sparse grassland to middle density grassland in high mountain belt, of which area is 15931 km2.

A Spacecraft Electrical Characteristics Multi-Label Classification Method Based on Off-Line FCM Clustering and On-Line WPSVM

PubMed Central

Li, Ke; Liu, Yi; Wang, Quanxin; Wu, Yalei; Song, Shimin; Sun, Yi; Liu, Tengchong; Wang, Jun; Li, Yang; Du, Shaoyi

2015-01-01

This paper proposes a novel multi-label classification method for resolving the spacecraft electrical characteristics problems which involve many unlabeled test data processing, high-dimensional features, long computing time and identification of slow rate. Firstly, both the fuzzy c-means (FCM) offline clustering and the principal component feature extraction algorithms are applied for the feature selection process. Secondly, the approximate weighted proximal support vector machine (WPSVM) online classification algorithms is used to reduce the feature dimension and further improve the rate of recognition for electrical characteristics spacecraft. Finally, the data capture contribution method by using thresholds is proposed to guarantee the validity and consistency of the data selection. The experimental results indicate that the method proposed can obtain better data features of the spacecraft electrical characteristics, improve the accuracy of identification and shorten the computing time effectively. PMID:26544549

Columbia Classification Algorithm of Suicide Assessment (C-CASA): classification of suicidal events in the FDA's pediatric suicidal risk analysis of antidepressants.

PubMed

Posner, Kelly; Oquendo, Maria A; Gould, Madelyn; Stanley, Barbara; Davies, Mark

2007-07-01

To evaluate the link between antidepressants and suicidal behavior and ideation (suicidality) in youth, adverse events from pediatric clinical trials were classified in order to identify suicidal events. The authors describe the Columbia Classification Algorithm for Suicide Assessment (C-CASA), a standardized suicidal rating system that provided data for the pediatric suicidal risk analysis of antidepressants conducted by the Food and Drug Administration (FDA). Adverse events (N=427) from 25 pediatric antidepressant clinical trials were systematically identified by pharmaceutical companies. Randomly assigned adverse events were evaluated by three of nine independent expert suicidologists using the Columbia classification algorithm. Reliability of the C-CASA ratings and agreement with pharmaceutical company classification were estimated. Twenty-six new, possibly suicidal events (behavior and ideation) that were not originally identified by pharmaceutical companies were identified in the C-CASA, and 12 events originally labeled as suicidal by pharmaceutical companies were eliminated, which resulted in a total of 38 discrepant ratings. For the specific label of "suicide attempt," a relatively low level of agreement was observed between the C-CASA and pharmaceutical company ratings, with the C-CASA reporting a 50% reduction in ratings. Thus, although the C-CASA resulted in the identification of more suicidal events overall, fewer events were classified as suicide attempts. Additionally, the C-CASA ratings were highly reliable (intraclass correlation coefficient [ICC]=0.89). Utilizing a methodical, anchored approach to categorizing suicidality provides an accurate and comprehensive identification of suicidal events. The FDA's audit of the C-CASA demonstrated excellent transportability of this approach. The Columbia algorithm was used to classify suicidal adverse events in the recent FDA adult antidepressant safety analyses and has also been mandated to be applied to all anticonvulsant trials and other centrally acting agents and nonpsychotropic drugs.

Columbia Classification Algorithm of Suicide Assessment (C-CASA): Classification of Suicidal Events in the FDA’s Pediatric Suicidal Risk Analysis of Antidepressants

PubMed Central

Posner, Kelly; Oquendo, Maria A.; Gould, Madelyn; Stanley, Barbara; Davies, Mark

2013-01-01

Objective To evaluate the link between antidepressants and suicidal behavior and ideation (suicidality) in youth, adverse events from pediatric clinical trials were classified in order to identify suicidal events. The authors describe the Columbia Classification Algorithm for Suicide Assessment (C-CASA), a standardized suicidal rating system that provided data for the pediatric suicidal risk analysis of antide-pressants conducted by the Food and Drug Administration (FDA). Method Adverse events (N=427) from 25 pediatric antidepressant clinical trials were systematically identified by pharmaceutical companies. Randomly assigned adverse events were evaluated by three of nine independent expert suicidologists using the Columbia classification algorithm. Reliability of the C-CASA ratings and agreement with pharmaceutical company classification were estimated. Results Twenty-six new, possibly suicidal events (behavior and ideation) that were not originally identified by pharmaceutical companies were identified in the C-CASA, and 12 events originally labeled as suicidal by pharmaceutical companies were eliminated, which resulted in a total of 38 discrepant ratings. For the specific label of “suicide attempt,” a relatively low level of agreement was observed between the C-CASA and pharmaceutical company ratings, with the C-CASA reporting a 50% reduction in ratings. Thus, although the C-CASA resulted in the identification of more suicidal events overall, fewer events were classified as suicide attempts. Additionally, the C-CASA ratings were highly reliable (intraclass correlation coefficient [ICC]=0.89). Conclusions Utilizing a methodical, anchored approach to categorizing suicidality provides an accurate and comprehensive identification of suicidal events. The FDA’s audit of the C-CASA demonstrated excellent transportability of this approach. The Columbia algorithm was used to classify suicidal adverse events in the recent FDA adult antidepressant safety analyses and has also been mandated to be applied to all anticonvulsant trials and other centrally acting agents and nonpsychotropic drugs. PMID:17606655

Feature selection and classification of multiparametric medical images using bagging and SVM

NASA Astrophysics Data System (ADS)

Fan, Yong; Resnick, Susan M.; Davatzikos, Christos

2008-03-01

This paper presents a framework for brain classification based on multi-parametric medical images. This method takes advantage of multi-parametric imaging to provide a set of discriminative features for classifier construction by using a regional feature extraction method which takes into account joint correlations among different image parameters; in the experiments herein, MRI and PET images of the brain are used. Support vector machine classifiers are then trained based on the most discriminative features selected from the feature set. To facilitate robust classification and optimal selection of parameters involved in classification, in view of the well-known "curse of dimensionality", base classifiers are constructed in a bagging (bootstrap aggregating) framework for building an ensemble classifier and the classification parameters of these base classifiers are optimized by means of maximizing the area under the ROC (receiver operating characteristic) curve estimated from their prediction performance on left-out samples of bootstrap sampling. This classification system is tested on a sex classification problem, where it yields over 90% classification rates for unseen subjects. The proposed classification method is also compared with other commonly used classification algorithms, with favorable results. These results illustrate that the methods built upon information jointly extracted from multi-parametric images have the potential to perform individual classification with high sensitivity and specificity.

C-fuzzy variable-branch decision tree with storage and classification error rate constraints

NASA Astrophysics Data System (ADS)

Yang, Shiueng-Bien

2009-10-01

The C-fuzzy decision tree (CFDT), which is based on the fuzzy C-means algorithm, has recently been proposed. The CFDT is grown by selecting the nodes to be split according to its classification error rate. However, the CFDT design does not consider the classification time taken to classify the input vector. Thus, the CFDT can be improved. We propose a new C-fuzzy variable-branch decision tree (CFVBDT) with storage and classification error rate constraints. The design of the CFVBDT consists of two phases-growing and pruning. The CFVBDT is grown by selecting the nodes to be split according to the classification error rate and the classification time in the decision tree. Additionally, the pruning method selects the nodes to prune based on the storage requirement and the classification time of the CFVBDT. Furthermore, the number of branches of each internal node is variable in the CFVBDT. Experimental results indicate that the proposed CFVBDT outperforms the CFDT and other methods.

Classification of Clouds in Satellite Imagery Using Adaptive Fuzzy Sparse Representation.

PubMed

Jin, Wei; Gong, Fei; Zeng, Xingbin; Fu, Randi

2016-12-16

Automatic cloud detection and classification using satellite cloud imagery have various meteorological applications such as weather forecasting and climate monitoring. Cloud pattern analysis is one of the research hotspots recently. Since satellites sense the clouds remotely from space, and different cloud types often overlap and convert into each other, there must be some fuzziness and uncertainty in satellite cloud imagery. Satellite observation is susceptible to noises, while traditional cloud classification methods are sensitive to noises and outliers; it is hard for traditional cloud classification methods to achieve reliable results. To deal with these problems, a satellite cloud classification method using adaptive fuzzy sparse representation-based classification (AFSRC) is proposed. Firstly, by defining adaptive parameters related to attenuation rate and critical membership, an improved fuzzy membership is introduced to accommodate the fuzziness and uncertainty of satellite cloud imagery; secondly, by effective combination of the improved fuzzy membership function and sparse representation-based classification (SRC), atoms in training dictionary are optimized; finally, an adaptive fuzzy sparse representation classifier for cloud classification is proposed. Experiment results on FY-2G satellite cloud image show that, the proposed method not only improves the accuracy of cloud classification, but also has strong stability and adaptability with high computational efficiency.

A novel end-to-end classifier using domain transferred deep convolutional neural networks for biomedical images.

PubMed

Pang, Shuchao; Yu, Zhezhou; Orgun, Mehmet A

2017-03-01

Highly accurate classification of biomedical images is an essential task in the clinical diagnosis of numerous medical diseases identified from those images. Traditional image classification methods combined with hand-crafted image feature descriptors and various classifiers are not able to effectively improve the accuracy rate and meet the high requirements of classification of biomedical images. The same also holds true for artificial neural network models directly trained with limited biomedical images used as training data or directly used as a black box to extract the deep features based on another distant dataset. In this study, we propose a highly reliable and accurate end-to-end classifier for all kinds of biomedical images via deep learning and transfer learning. We first apply domain transferred deep convolutional neural network for building a deep model; and then develop an overall deep learning architecture based on the raw pixels of original biomedical images using supervised training. In our model, we do not need the manual design of the feature space, seek an effective feature vector classifier or segment specific detection object and image patches, which are the main technological difficulties in the adoption of traditional image classification methods. Moreover, we do not need to be concerned with whether there are large training sets of annotated biomedical images, affordable parallel computing resources featuring GPUs or long times to wait for training a perfect deep model, which are the main problems to train deep neural networks for biomedical image classification as observed in recent works. With the utilization of a simple data augmentation method and fast convergence speed, our algorithm can achieve the best accuracy rate and outstanding classification ability for biomedical images. We have evaluated our classifier on several well-known public biomedical datasets and compared it with several state-of-the-art approaches. We propose a robust automated end-to-end classifier for biomedical images based on a domain transferred deep convolutional neural network model that shows a highly reliable and accurate performance which has been confirmed on several public biomedical image datasets. Copyright © 2017 Elsevier Ireland Ltd. All rights reserved.

[Stage-adjusted treatment for haemorrhoidal disease].

PubMed

Herold, A

2008-05-01

Haemorrhoidal disease is one of the most frequent disorders in western countries. The aim of individual therapy is eradication of symptoms achieved by normalisation of anatomy and physiology. Treatment is orientated to the stage of the disease: First-degree haemorrhoids are treated conservatively. In addition to high fibre diet, sclerotherapy is used. Haemorrhoids of the 2nd degree prolapse during defecation and return spontaneously. First-line treatment is rubber band ligation. Third-degree haemorrhoids that prolapse during defecation have to be digitally reduced. The majority of these patients need surgery. For segmental disorders haemorrhoidectomy according to Milligan-Morgan or Ferguson is recommended. In circular disease Stapler haemorrhoidopexy is now the procedure of choice. Using a classification orientated therapeutical regime orientated to the classification of haemorrhoidal disease offers high healing rates with a low rate of complications and recurrences.

A new classification scheme of plastic wastes based upon recycling labels.

PubMed

Özkan, Kemal; Ergin, Semih; Işık, Şahin; Işıklı, Idil

2015-01-01

Since recycling of materials is widely assumed to be environmentally and economically beneficial, reliable sorting and processing of waste packaging materials such as plastics is very important for recycling with high efficiency. An automated system that can quickly categorize these materials is certainly needed for obtaining maximum classification while maintaining high throughput. In this paper, first of all, the photographs of the plastic bottles have been taken and several preprocessing steps were carried out. The first preprocessing step is to extract the plastic area of a bottle from the background. Then, the morphological image operations are implemented. These operations are edge detection, noise removal, hole removing, image enhancement, and image segmentation. These morphological operations can be generally defined in terms of the combinations of erosion and dilation. The effect of bottle color as well as label are eliminated using these operations. Secondly, the pixel-wise intensity values of the plastic bottle images have been used together with the most popular subspace and statistical feature extraction methods to construct the feature vectors in this study. Only three types of plastics are considered due to higher existence ratio of them than the other plastic types in the world. The decision mechanism consists of five different feature extraction methods including as Principal Component Analysis (PCA), Kernel PCA (KPCA), Fisher's Linear Discriminant Analysis (FLDA), Singular Value Decomposition (SVD) and Laplacian Eigenmaps (LEMAP) and uses a simple experimental setup with a camera and homogenous backlighting. Due to the giving global solution for a classification problem, Support Vector Machine (SVM) is selected to achieve the classification task and majority voting technique is used as the decision mechanism. This technique equally weights each classification result and assigns the given plastic object to the class that the most classification results agree on. The proposed classification scheme provides high accuracy rate, and also it is able to run in real-time applications. It can automatically classify the plastic bottle types with approximately 90% recognition accuracy. Besides this, the proposed methodology yields approximately 96% classification rate for the separation of PET or non-PET plastic types. It also gives 92% accuracy for the categorization of non-PET plastic types into HPDE or PP. Copyright © 2014 Elsevier Ltd. All rights reserved.

High resolution esophageal manometry--the switch from "intuitive" visual interpretation to Chicago classification.

PubMed

Srinivas, M; Balakumaran, T A; Palaniappan, S; Srinivasan, Vijaya; Batcha, M; Venkataraman, Jayanthi

2014-03-01

High resolution esophageal manometry (HREM) has been interpreted all along by visual interpretation of color plots until the recent introduction of Chicago classification which categorises HREM using objective measurements. It compares HREM diagnosis of esophageal motor disorders by visual interpretation and Chicago classification. Using software Trace 1.2v, 77 consecutive tracings diagnosed by visual interpretation were re-analyzed by Chicago classification and findings compared for concordance between the two systems of interpretation. Kappa agreement rate between the two observations was determined. There were 57 males (74 %) and cohort median age was 41 years (range: 14-83 years). Majority of the referrals were for gastroesophageal reflux disease, dysphagia and achalasia. By "intuitive" visual interpretation, the tracing were reported as normal in 45 (58.4 %), achalasia 14 (18.2 %), ineffective esophageal motility 3 (3.9 %), nutcracker esophagus 11 (14.3 %) and nonspecific motility changes 4 (5.2 %). By Chicago classification, there was 100 % agreement (Kappa 1) for achalasia (type 1: 9; type 2: 5) and ineffective esophageal motility ("failed peristalsis" on visual interpretation). Normal esophageal motility, nutcracker esophagus and nonspecific motility disorder on visual interpretation were reclassified as rapid contraction and esophagogastric junction (EGJ) outflow obstruction by Chicago classification. Chicago classification identified distinct clinical phenotypes including EGJ outflow obstruction not identified by visual interpretation. A significant number of unclassified HREM by visual interpretation were also classified by it.

Universal Rate Model Selector: A Method to Quickly Find the Best-Fit Kinetic Rate Model for an Experimental Rate Profile

DTIC Science & Technology

2017-08-01

as an official Department of the Army position unless so designated by other authorizing documents. REPORT DOCUMENTATION PAGE Form Approved OMB...processes to find a kinetic rate model that provides a high degree of correlation with experimental data. Furthermore, the use of kinetic rate... correlation 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT 18. NUMBER OF PAGES 19a. NAME OF RESPONSIBLE PERSON Renu B

Multi-q pattern classification of polarization curves

NASA Astrophysics Data System (ADS)

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.

Identifying the optimal segmentors for mass classification in mammograms

NASA Astrophysics Data System (ADS)

Zhang, Yu; Tomuro, Noriko; Furst, Jacob; Raicu, Daniela S.

2015-03-01

In this paper, we present the results of our investigation on identifying the optimal segmentor(s) from an ensemble of weak segmentors, used in a Computer-Aided Diagnosis (CADx) system which classifies suspicious masses in mammograms as benign or malignant. This is an extension of our previous work, where we used various parameter settings of image enhancement techniques to each suspicious mass (region of interest (ROI)) to obtain several enhanced images, then applied segmentation to each image to obtain several contours of a given mass. Each segmentation in this ensemble is essentially a "weak segmentor" because no single segmentation can produce the optimal result for all images. Then after shape features are computed from the segmented contours, the final classification model was built using logistic regression. The work in this paper focuses on identifying the optimal segmentor(s) from an ensemble mix of weak segmentors. For our purpose, optimal segmentors are those in the ensemble mix which contribute the most to the overall classification rather than the ones that produced high precision segmentation. To measure the segmentors' contribution, we examined weights on the features in the derived logistic regression model and computed the average feature weight for each segmentor. The result showed that, while in general the segmentors with higher segmentation success rates had higher feature weights, some segmentors with lower segmentation rates had high classification feature weights as well.

EEG-based classification of imaginary left and right foot movements using beta rebound.

PubMed

Hashimoto, Yasunari; Ushiba, Junichi

2013-11-01

The purpose of this study was to investigate cortical lateralization of event-related (de)synchronization during left and right foot motor imagery tasks and to determine classification accuracy of the two imaginary movements in a brain-computer interface (BCI) paradigm. We recorded 31-channel scalp electroencephalograms (EEGs) from nine healthy subjects during brisk imagery tasks of left and right foot movements. EEG was analyzed with time-frequency maps and topographies, and the accuracy rate of classification between left and right foot movements was calculated. Beta rebound at the end of imagination (increase of EEG beta rhythm amplitude) was identified from the two EEGs derived from the right-shift and left-shift bipolar pairs at the vertex. This process enabled discrimination between right or left foot imagery at a high accuracy rate (maximum 81.6% in single trial analysis). These data suggest that foot motor imagery has potential to elicit left-right differences in EEG, while BCI using the unilateral foot imagery can achieve high classification accuracy, similar to ordinary BCI, based on hand motor imagery. By combining conventional discrimination techniques, the left-right discrimination of unilateral foot motor imagery provides a novel BCI system that could control a foot neuroprosthesis or a robotic foot. Copyright © 2013 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

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

PubMed

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

2018-03-26

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

Detection of Periodic Leg Movements by Machine Learning Methods Using Polysomnographic Parameters Other Than Leg Electromyography

PubMed Central

Umut, İlhan; Çentik, Güven

2016-01-01

The number of channels used for polysomnographic recording frequently causes difficulties for patients because of the many cables connected. Also, it increases the risk of having troubles during recording process and increases the storage volume. In this study, it is intended to detect periodic leg movement (PLM) in sleep with the use of the channels except leg electromyography (EMG) by analysing polysomnography (PSG) data with digital signal processing (DSP) and machine learning methods. PSG records of 153 patients of different ages and genders with PLM disorder diagnosis were examined retrospectively. A novel software was developed for the analysis of PSG records. The software utilizes the machine learning algorithms, statistical methods, and DSP methods. In order to classify PLM, popular machine learning methods (multilayer perceptron, K-nearest neighbour, and random forests) and logistic regression were used. Comparison of classified results showed that while K-nearest neighbour classification algorithm had higher average classification rate (91.87%) and lower average classification error value (RMSE = 0.2850), multilayer perceptron algorithm had the lowest average classification rate (83.29%) and the highest average classification error value (RMSE = 0.3705). Results showed that PLM can be classified with high accuracy (91.87%) without leg EMG record being present. PMID:27213008

Detection of Periodic Leg Movements by Machine Learning Methods Using Polysomnographic Parameters Other Than Leg Electromyography.

PubMed

Umut, İlhan; Çentik, Güven

2016-01-01

The number of channels used for polysomnographic recording frequently causes difficulties for patients because of the many cables connected. Also, it increases the risk of having troubles during recording process and increases the storage volume. In this study, it is intended to detect periodic leg movement (PLM) in sleep with the use of the channels except leg electromyography (EMG) by analysing polysomnography (PSG) data with digital signal processing (DSP) and machine learning methods. PSG records of 153 patients of different ages and genders with PLM disorder diagnosis were examined retrospectively. A novel software was developed for the analysis of PSG records. The software utilizes the machine learning algorithms, statistical methods, and DSP methods. In order to classify PLM, popular machine learning methods (multilayer perceptron, K-nearest neighbour, and random forests) and logistic regression were used. Comparison of classified results showed that while K-nearest neighbour classification algorithm had higher average classification rate (91.87%) and lower average classification error value (RMSE = 0.2850), multilayer perceptron algorithm had the lowest average classification rate (83.29%) and the highest average classification error value (RMSE = 0.3705). Results showed that PLM can be classified with high accuracy (91.87%) without leg EMG record being present.

Effects of stress typicality during speeded grammatical classification.

PubMed

Arciuli, Joanne; Cupples, Linda

2003-01-01

The experiments reported here were designed to investigate the influence of stress typicality during speeded grammatical classification of disyllabic English words by native and non-native speakers. Trochaic nouns and iambic gram verbs were considered to be typically stressed, whereas iambic nouns and trochaic verbs were considered to be atypically stressed. Experiments 1a and 2a showed that while native speakers classified typically stressed words individual more quickly and more accurately than atypically stressed words during differences reading, there were no overall effects during classification of spoken stimuli. However, a subgroup of native speakers with high error rates did show a significant effect during classification of spoken stimuli. Experiments 1b and 2b showed that non-native speakers classified typically stressed words more quickly and more accurately than atypically stressed words during reading. Typically stressed words were classified more accurately than atypically stressed words when the stimuli were spoken. Importantly, there was a significant relationship between error rates, vocabulary size and the size of the stress typicality effect in each experiment. We conclude that participants use information about lexical stress to help them distinguish between disyllabic nouns and verbs during speeded grammatical classification. This is especially so for individuals with a limited vocabulary who lack other knowledge (e.g., semantic knowledge) about the differences between these grammatical categories.

Application of Wavelet Transform for PDZ Domain Classification

PubMed Central

Daqrouq, Khaled; Alhmouz, Rami; Balamesh, Ahmed; Memic, Adnan

2015-01-01

PDZ domains have been identified as part of an array of signaling proteins that are often unrelated, except for the well-conserved structural PDZ domain they contain. These domains have been linked to many disease processes including common Avian influenza, as well as very rare conditions such as Fraser and Usher syndromes. Historically, based on the interactions and the nature of bonds they form, PDZ domains have most often been classified into one of three classes (class I, class II and others - class III), that is directly dependent on their binding partner. In this study, we report on three unique feature extraction approaches based on the bigram and trigram occurrence and existence rearrangements within the domain's primary amino acid sequences in assisting PDZ domain classification. Wavelet packet transform (WPT) and Shannon entropy denoted by wavelet entropy (WE) feature extraction methods were proposed. Using 115 unique human and mouse PDZ domains, the existence rearrangement approach yielded a high recognition rate (78.34%), which outperformed our occurrence rearrangements based method. The recognition rate was (81.41%) with validation technique. The method reported for PDZ domain classification from primary sequences proved to be an encouraging approach for obtaining consistent classification results. We anticipate that by increasing the database size, we can further improve feature extraction and correct classification. PMID:25860375

Benzodiazepine Use Among Low Back Pain Patients Concurrently Prescribed Opioids in the Military Health System

DTIC Science & Technology

2017-08-27

release. Distributibn is unlimited. 13. SUPPLEMENTARY NOTES 14. ABSTRACT 15. SUBJECT TERMS 16. SECURITY CLASSIFICATION OF~ 17. LIMITATION OF 18 ...chronic pain, there are high rates ( 18 -38%) of concurrent opioid and benzo prescribing. These high-risk prescribing patterns have contributed to the

39 CFR Appendix A to Subpart A of... - Mail Classification Schedule

Code of Federal Regulations, 2012 CFR

2012-07-01

... Density and Saturation Letters High Density and Saturation Flats/Parcels Carrier Route Letters Flats Not... Package Services Single-Piece Parcel Post Inbound Surface Parcel Post (at UPU rates) Bound Printed Matter... Single-Piece First-Class Mail International Standard Mail (Regular and Nonprofit) High Density and...

An experiment in multispectral, multitemporal crop classification using relaxation techniques

NASA Technical Reports Server (NTRS)

Davis, L. S.; Wang, C.-Y.; Xie, H.-C

1983-01-01

The paper describes the result of an experimental study concerning the use of probabilistic relaxation for improving pixel classification rates. Two LACIE sites were used in the study and in both cases, relaxation resulted in a marked improvement in classification rates.

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

PubMed Central

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. PMID:29182663

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

PubMed

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.

Development of a computer-based clinical decision support tool for selecting appropriate rehabilitation interventions for injured workers.

PubMed

Gross, Douglas P; Zhang, Jing; Steenstra, Ivan; Barnsley, Susan; Haws, Calvin; Amell, Tyler; McIntosh, Greg; Cooper, Juliette; Zaiane, Osmar

2013-12-01

To develop a classification algorithm and accompanying computer-based clinical decision support tool to help categorize injured workers toward optimal rehabilitation interventions based on unique worker characteristics. Population-based historical cohort design. Data were extracted from a Canadian provincial workers' compensation database on all claimants undergoing work assessment between December 2009 and January 2011. Data were available on: (1) numerous personal, clinical, occupational, and social variables; (2) type of rehabilitation undertaken; and (3) outcomes following rehabilitation (receiving time loss benefits or undergoing repeat programs). Machine learning, concerned with the design of algorithms to discriminate between classes based on empirical data, was the foundation of our approach to build a classification system with multiple independent and dependent variables. The population included 8,611 unique claimants. Subjects were predominantly employed (85 %) males (64 %) with diagnoses of sprain/strain (44 %). Baseline clinician classification accuracy was high (ROC = 0.86) for selecting programs that lead to successful return-to-work. Classification performance for machine learning techniques outperformed the clinician baseline classification (ROC = 0.94). The final classifiers were multifactorial and included the variables: injury duration, occupation, job attachment status, work status, modified work availability, pain intensity rating, self-rated occupational disability, and 9 items from the SF-36 Health Survey. The use of machine learning classification techniques appears to have resulted in classification performance better than clinician decision-making. The final algorithm has been integrated into a computer-based clinical decision support tool that requires additional validation in a clinical sample.

Machine learning algorithms for mode-of-action classification in toxicity assessment.

PubMed

Zhang, Yile; Wong, Yau Shu; Deng, Jian; Anton, Cristina; Gabos, Stephan; Zhang, Weiping; Huang, Dorothy Yu; Jin, Can

2016-01-01

Real Time Cell Analysis (RTCA) technology is used to monitor cellular changes continuously over the entire exposure period. Combining with different testing concentrations, the profiles have potential in probing the mode of action (MOA) of the testing substances. In this paper, we present machine learning approaches for MOA assessment. Computational tools based on artificial neural network (ANN) and support vector machine (SVM) are developed to analyze the time-concentration response curves (TCRCs) of human cell lines responding to tested chemicals. The techniques are capable of learning data from given TCRCs with known MOA information and then making MOA classification for the unknown toxicity. A novel data processing step based on wavelet transform is introduced to extract important features from the original TCRC data. From the dose response curves, time interval leading to higher classification success rate can be selected as input to enhance the performance of the machine learning algorithm. This is particularly helpful when handling cases with limited and imbalanced data. The validation of the proposed method is demonstrated by the supervised learning algorithm applied to the exposure data of HepG2 cell line to 63 chemicals with 11 concentrations in each test case. Classification success rate in the range of 85 to 95 % are obtained using SVM for MOA classification with two clusters to cases up to four clusters. Wavelet transform is capable of capturing important features of TCRCs for MOA classification. The proposed SVM scheme incorporated with wavelet transform has a great potential for large scale MOA classification and high-through output chemical screening.

Targeting an efficient target-to-target interval for P300 speller brain–computer interfaces

PubMed Central

Sellers, Eric W.; Wang, Xingyu

2013-01-01

Longer target-to-target intervals (TTI) produce greater P300 event-related potential amplitude, which can increase brain–computer interface (BCI) classification accuracy and decrease the number of flashes needed for accurate character classification. However, longer TTIs requires more time for each trial, which will decrease the information transfer rate of BCI. In this paper, a P300 BCI using a 7 × 12 matrix explored new flash patterns (16-, 18- and 21-flash pattern) with different TTIs to assess the effects of TTI on P300 BCI performance. The new flash patterns were designed to minimize TTI, decrease repetition blindness, and examine the temporal relationship between each flash of a given stimulus by placing a minimum of one (16-flash pattern), two (18-flash pattern), or three (21-flash pattern) non-target flashes between each target flashes. Online results showed that the 16-flash pattern yielded the lowest classification accuracy among the three patterns. The results also showed that the 18-flash pattern provides a significantly higher information transfer rate (ITR) than the 21-flash pattern; both patterns provide high ITR and high accuracy for all subjects. PMID:22350331

7 CFR 400.304 - Nonstandard Classification determinations.

Code of Federal Regulations, 2010 CFR

2010-01-01

... changes are necessary in assigned yields or premium rates under the conditions set forth in § 400.304(f... Classification determinations. (a) Nonstandard Classification determinations can affect a change in assigned yields, premium rates, or both from those otherwise prescribed by the insurance actuarial tables. (b...

Classification of Clouds in Satellite Imagery Using Adaptive Fuzzy Sparse Representation

PubMed Central

Jin, Wei; Gong, Fei; Zeng, Xingbin; Fu, Randi

2016-01-01

Automatic cloud detection and classification using satellite cloud imagery have various meteorological applications such as weather forecasting and climate monitoring. Cloud pattern analysis is one of the research hotspots recently. Since satellites sense the clouds remotely from space, and different cloud types often overlap and convert into each other, there must be some fuzziness and uncertainty in satellite cloud imagery. Satellite observation is susceptible to noises, while traditional cloud classification methods are sensitive to noises and outliers; it is hard for traditional cloud classification methods to achieve reliable results. To deal with these problems, a satellite cloud classification method using adaptive fuzzy sparse representation-based classification (AFSRC) is proposed. Firstly, by defining adaptive parameters related to attenuation rate and critical membership, an improved fuzzy membership is introduced to accommodate the fuzziness and uncertainty of satellite cloud imagery; secondly, by effective combination of the improved fuzzy membership function and sparse representation-based classification (SRC), atoms in training dictionary are optimized; finally, an adaptive fuzzy sparse representation classifier for cloud classification is proposed. Experiment results on FY-2G satellite cloud image show that, the proposed method not only improves the accuracy of cloud classification, but also has strong stability and adaptability with high computational efficiency. PMID:27999261

Correlation of the Rock Mass Rating (RMR) System with the Unified Soil Classification System (USCS): Introduction of the Weak Rock Mass Rating System (W-RMR)

NASA Astrophysics Data System (ADS)

Warren, Sean N.; Kallu, Raj R.; Barnard, Chase K.

2016-11-01

Underground gold mines in Nevada are exploiting increasingly deeper ore bodies comprised of weak to very weak rock masses. The Rock Mass Rating (RMR) classification system is widely used at underground gold mines in Nevada and is applicable in fair to good-quality rock masses, but is difficult to apply and loses reliability in very weak rock mass to soil-like material. Because very weak rock masses are transition materials that border engineering rock mass and soil classification systems, soil classification may sometimes be easier and more appropriate to provide insight into material behavior and properties. The Unified Soil Classification System (USCS) is the most likely choice for the classification of very weak rock mass to soil-like material because of its accepted use in tunnel engineering projects and its ability to predict soil-like material behavior underground. A correlation between the RMR and USCS systems was developed by comparing underground geotechnical RMR mapping to laboratory testing of bulk samples from the same locations, thereby assigning a numeric RMR value to the USCS classification that can be used in spreadsheet calculations and geostatistical analyses. The geotechnical classification system presented in this paper including a USCS-RMR correlation, RMR rating equations, and the Geo-Pick Strike Index is collectively introduced as the Weak Rock Mass Rating System (W-RMR). It is the authors' hope that this system will aid in the classification of weak rock masses and more usable design tools based on the RMR system. More broadly, the RMR-USCS correlation and the W-RMR system help define the transition between engineering soil and rock mass classification systems and may provide insight for geotechnical design in very weak rock masses.

Acoustic target detection and classification using neural networks

NASA Technical Reports Server (NTRS)

Robertson, James A.; Conlon, Mark

1993-01-01

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

Distinguishing between the Permeability Relationships with Absorption and Metabolism To Improve BCS and BDDCS Predictions in Early Drug Discovery

PubMed Central

2015-01-01

The biopharmaceutics classification system (BCS) and biopharmaceutics drug distribution classification system (BDDCS) are complementary classification systems that can improve, simplify, and accelerate drug discovery, development, and regulatory processes. Drug permeability has been widely accepted as a screening tool for determining intestinal absorption via the BCS during the drug development and regulatory approval processes. Currently, predicting clinically significant drug interactions during drug development is a known challenge for industry and regulatory agencies. The BDDCS, a modification of BCS that utilizes drug metabolism instead of intestinal permeability, predicts drug disposition and potential drug–drug interactions in the intestine, the liver, and most recently the brain. Although correlations between BCS and BDDCS have been observed with drug permeability rates, discrepancies have been noted in drug classifications between the two systems utilizing different permeability models, which are accepted as surrogate models for demonstrating human intestinal permeability by the FDA. Here, we recommend the most applicable permeability models for improving the prediction of BCS and BDDCS classifications. We demonstrate that the passive transcellular permeability rate, characterized by means of permeability models that are deficient in transporter expression and paracellular junctions (e.g., PAMPA and Caco-2), will most accurately predict BDDCS metabolism. These systems will inaccurately predict BCS classifications for drugs that particularly are substrates of highly expressed intestinal transporters. Moreover, in this latter case, a system more representative of complete human intestinal permeability is needed to accurately predict BCS absorption. PMID:24628254

Distinguishing between the permeability relationships with absorption and metabolism to improve BCS and BDDCS predictions in early drug discovery.

PubMed

Larregieu, Caroline A; Benet, Leslie Z

2014-04-07

The biopharmaceutics classification system (BCS) and biopharmaceutics drug distribution classification system (BDDCS) are complementary classification systems that can improve, simplify, and accelerate drug discovery, development, and regulatory processes. Drug permeability has been widely accepted as a screening tool for determining intestinal absorption via the BCS during the drug development and regulatory approval processes. Currently, predicting clinically significant drug interactions during drug development is a known challenge for industry and regulatory agencies. The BDDCS, a modification of BCS that utilizes drug metabolism instead of intestinal permeability, predicts drug disposition and potential drug-drug interactions in the intestine, the liver, and most recently the brain. Although correlations between BCS and BDDCS have been observed with drug permeability rates, discrepancies have been noted in drug classifications between the two systems utilizing different permeability models, which are accepted as surrogate models for demonstrating human intestinal permeability by the FDA. Here, we recommend the most applicable permeability models for improving the prediction of BCS and BDDCS classifications. We demonstrate that the passive transcellular permeability rate, characterized by means of permeability models that are deficient in transporter expression and paracellular junctions (e.g., PAMPA and Caco-2), will most accurately predict BDDCS metabolism. These systems will inaccurately predict BCS classifications for drugs that particularly are substrates of highly expressed intestinal transporters. Moreover, in this latter case, a system more representative of complete human intestinal permeability is needed to accurately predict BCS absorption.

77 FR 39747 - Changes in Postal Rates

Federal Register 2010, 2011, 2012, 2013, 2014

2012-07-05

... with the Commission of a proposal characterized as a minor classification change under 39 CFR parts 3090 and 3091, along with a conforming revision to the Mail Classification Schedule (MCS).\\1\\ The... Flat Rate Envelope options. \\1\\ Notice of United States Postal Service of Classification Changes, June...

Spectral feature design in high dimensional multispectral data

NASA Technical Reports Server (NTRS)

Chen, Chih-Chien Thomas; Landgrebe, David A.

1988-01-01

The High resolution Imaging Spectrometer (HIRIS) is designed to acquire images simultaneously in 192 spectral bands in the 0.4 to 2.5 micrometers wavelength region. It will make possible the collection of essentially continuous reflectance spectra at a spectral resolution sufficient to extract significantly enhanced amounts of information from return signals as compared to existing systems. The advantages of such high dimensional data come at a cost of increased system and data complexity. For example, since the finer the spectral resolution, the higher the data rate, it becomes impractical to design the sensor to be operated continuously. It is essential to find new ways to preprocess the data which reduce the data rate while at the same time maintaining the information content of the high dimensional signal produced. Four spectral feature design techniques are developed from the Weighted Karhunen-Loeve Transforms: (1) non-overlapping band feature selection algorithm; (2) overlapping band feature selection algorithm; (3) Walsh function approach; and (4) infinite clipped optimal function approach. The infinite clipped optimal function approach is chosen since the features are easiest to find and their classification performance is the best. After the preprocessed data has been received at the ground station, canonical analysis is further used to find the best set of features under the criterion that maximal class separability is achieved. Both 100 dimensional vegetation data and 200 dimensional soil data were used to test the spectral feature design system. It was shown that the infinite clipped versions of the first 16 optimal features had excellent classification performance. The overall probability of correct classification is over 90 percent while providing for a reduced downlink data rate by a factor of 10.

Poisoning by Herbs and Plants: Rapid Toxidromic Classification and Diagnosis.

PubMed

Diaz, James H

2016-03-01

The American Association of Poison Control Centers has continued to report approximately 50,000 telephone calls or 8% of incoming calls annually related to plant exposures, mostly in children. Although the frequency of plant ingestions in children is related to the presence of popular species in households, adolescents may experiment with hallucinogenic plants; and trekkers and foragers may misidentify poisonous plants as edible. Since plant exposures have continued at a constant rate, the objectives of this review were (1) to review the epidemiology of plant poisonings; and (2) to propose a rapid toxidromic classification system for highly toxic plant ingestions for field use by first responders in comparison to current classification systems. Internet search engines were queried to identify and select peer-reviewed articles on plant poisonings using the key words in order to classify plant poisonings into four specific toxidromes: cardiotoxic, neurotoxic, cytotoxic, and gastrointestinal-hepatotoxic. A simple toxidromic classification system of plant poisonings may permit rapid diagnoses of highly toxic versus less toxic and nontoxic plant ingestions both in households and outdoors; direct earlier management of potentially serious poisonings; and reduce costly inpatient evaluations for inconsequential plant ingestions. The current textbook classification schemes for plant poisonings were complex in comparison to the rapid classification system; and were based on chemical nomenclatures and pharmacological effects, and not on clearly presenting toxidromes. Validation of the rapid toxidromic classification system as compared to existing chemical classification systems for plant poisonings will require future adoption and implementation of the toxidromic system by its intended users. Copyright © 2016 Wilderness Medical Society. Published by Elsevier Inc. All rights reserved.

Does ASA classification impact success rates of endovascular aneurysm repairs?

PubMed

Conners, Michael S; Tonnessen, Britt H; Sternbergh, W Charles; Carter, Glen; Yoselevitz, Moises; Money, Samuel R

2002-09-01

The purpose of this study was to evaluate the technical success, clinical success, postoperative complication rate, need for a secondary procedure, and mortality rate with endovascular aneurysm repair (EAR), based on the physical status classification scheme advocated by the American Society of Anesthesiologists (ASA). At a single institution 167 patients underwent attempted EAR. Query of a prospectively maintained database supplemented with a retrospective review of medical records was used to gather statistics pertaining to patient demographics and outcome. In patients selected for EAR on the basis of acceptable anatomy, technical and clinical success rates were not significantly different among the different ASA classifications. Importantly, postoperative complication and 30-day mortality rates do not appear to significantly differ among the different ASA classifications in this patient population.

Optical signal processing using photonic reservoir computing

NASA Astrophysics Data System (ADS)

Salehi, Mohammad Reza; Dehyadegari, Louiza

2014-10-01

As a new approach to recognition and classification problems, photonic reservoir computing has such advantages as parallel information processing, power efficient and high speed. In this paper, a photonic structure has been proposed for reservoir computing which is investigated using a simple, yet, non-partial noisy time series prediction task. This study includes the application of a suitable topology with self-feedbacks in a network of SOA's - which lends the system a strong memory - and leads to adjusting adequate parameters resulting in perfect recognition accuracy (100%) for noise-free time series, which shows a 3% improvement over previous results. For the classification of noisy time series, the rate of accuracy showed a 4% increase and amounted to 96%. Furthermore, an analytical approach was suggested to solve rate equations which led to a substantial decrease in the simulation time, which is an important parameter in classification of large signals such as speech recognition, and better results came up compared with previous works.

Performance Evaluation of Frequency Transform Based Block Classification of Compound Image Segmentation Techniques

NASA Astrophysics Data System (ADS)

Selwyn, Ebenezer Juliet; Florinabel, D. Jemi

2018-04-01

Compound image segmentation plays a vital role in the compression of computer screen images. Computer screen images are images which are mixed with textual, graphical, or pictorial contents. In this paper, we present a comparison of two transform based block classification of compound images based on metrics like speed of classification, precision and recall rate. Block based classification approaches normally divide the compound images into fixed size blocks of non-overlapping in nature. Then frequency transform like Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT) are applied over each block. Mean and standard deviation are computed for each 8 × 8 block and are used as features set to classify the compound images into text/graphics and picture/background block. The classification accuracy of block classification based segmentation techniques are measured by evaluation metrics like precision and recall rate. Compound images of smooth background and complex background images containing text of varying size, colour and orientation are considered for testing. Experimental evidence shows that the DWT based segmentation provides significant improvement in recall rate and precision rate approximately 2.3% than DCT based segmentation with an increase in block classification time for both smooth and complex background images.

Recursive heuristic classification

NASA Technical Reports Server (NTRS)

Wilkins, David C.

1994-01-01

The author will describe a new problem-solving approach called recursive heuristic classification, whereby a subproblem of heuristic classification is itself formulated and solved by heuristic classification. This allows the construction of more knowledge-intensive classification programs in a way that yields a clean organization. Further, standard knowledge acquisition and learning techniques for heuristic classification can be used to create, refine, and maintain the knowledge base associated with the recursively called classification expert system. The method of recursive heuristic classification was used in the Minerva blackboard shell for heuristic classification. Minerva recursively calls itself every problem-solving cycle to solve the important blackboard scheduler task, which involves assigning a desirability rating to alternative problem-solving actions. Knowing these ratings is critical to the use of an expert system as a component of a critiquing or apprenticeship tutoring system. One innovation of this research is a method called dynamic heuristic classification, which allows selection among dynamically generated classification categories instead of requiring them to be prenumerated.

Measuring the involvement of people with HIV in treatment decision making using the control preferences scale.

PubMed

Kremer, Heidemarie; Ironson, Gail

2008-01-01

Since 1983, HIV patients have been advocating for participatory decision making. This study measures the involvement that HIV-positive people perceive in treatment decision making. A secondary objective is to assess the validity of the instrument used to examine decisional roles, the Control Preferences Scale (CPS). The authors interviewed 79 HIV-positive people, a sub-sample of a study on long survival with HIV, diverse with respect to ethnicity, age, gender, and sexual orientation. They compared the self- and researcher-rated decisional roles of participants on the CPS. They also assessed how well the CPS corresponds with Charles's paternalistic, shared- and informed-choice models of decision making about treatment based on decisional roles and information exchange. Most participants (75%) perceived collaborative/active involvement in decision making. Agreement (Kendall's tau-b) between self- and researcher-rated decisional roles on the CPS was 0.82, whereas agreement between self-ratings on the CPS and researcher ratings on Charles's classification was 0.60. Charles's classification was difficult if participants had chosen not to take their prescribed medication without being adequately informed about the risky consequences of this decision. In this study, HIV-positive people perceived a high level of involvement in decision making. Reliability and convergent validity of the CPS was high. Charles's classification was problematic because decisional roles and information exchange are distinct dimensions. Some people make risky treatment decisions on their own without being adequately informed. The CPS is a useful instrument to measure decisional role perceptions of HIV-positive people but needs to be complemented by an instrument measuring treatment knowledge.

Optimizing high performance computing workflow for protein functional annotation.

PubMed

Stanberry, Larissa; Rekepalli, Bhanu; Liu, Yuan; Giblock, Paul; Higdon, Roger; Montague, Elizabeth; Broomall, William; Kolker, Natali; Kolker, Eugene

2014-09-10

Functional annotation of newly sequenced genomes is one of the major challenges in modern biology. With modern sequencing technologies, the protein sequence universe is rapidly expanding. Newly sequenced bacterial genomes alone contain over 7.5 million proteins. The rate of data generation has far surpassed that of protein annotation. The volume of protein data makes manual curation infeasible, whereas a high compute cost limits the utility of existing automated approaches. In this work, we present an improved and optmized automated workflow to enable large-scale protein annotation. The workflow uses high performance computing architectures and a low complexity classification algorithm to assign proteins into existing clusters of orthologous groups of proteins. On the basis of the Position-Specific Iterative Basic Local Alignment Search Tool the algorithm ensures at least 80% specificity and sensitivity of the resulting classifications. The workflow utilizes highly scalable parallel applications for classification and sequence alignment. Using Extreme Science and Engineering Discovery Environment supercomputers, the workflow processed 1,200,000 newly sequenced bacterial proteins. With the rapid expansion of the protein sequence universe, the proposed workflow will enable scientists to annotate big genome data.

Optimizing high performance computing workflow for protein functional annotation

PubMed Central

Stanberry, Larissa; Rekepalli, Bhanu; Liu, Yuan; Giblock, Paul; Higdon, Roger; Montague, Elizabeth; Broomall, William; Kolker, Natali; Kolker, Eugene

2014-01-01

Functional annotation of newly sequenced genomes is one of the major challenges in modern biology. With modern sequencing technologies, the protein sequence universe is rapidly expanding. Newly sequenced bacterial genomes alone contain over 7.5 million proteins. The rate of data generation has far surpassed that of protein annotation. The volume of protein data makes manual curation infeasible, whereas a high compute cost limits the utility of existing automated approaches. In this work, we present an improved and optmized automated workflow to enable large-scale protein annotation. The workflow uses high performance computing architectures and a low complexity classification algorithm to assign proteins into existing clusters of orthologous groups of proteins. On the basis of the Position-Specific Iterative Basic Local Alignment Search Tool the algorithm ensures at least 80% specificity and sensitivity of the resulting classifications. The workflow utilizes highly scalable parallel applications for classification and sequence alignment. Using Extreme Science and Engineering Discovery Environment supercomputers, the workflow processed 1,200,000 newly sequenced bacterial proteins. With the rapid expansion of the protein sequence universe, the proposed workflow will enable scientists to annotate big genome data. PMID:25313296

Concordance of obesity classification between body mass index and percent body fat among school children in Saudi Arabia.

PubMed

Al-Mohaimeed, Abdulrahman; Ahmed, Saifuddin; Dandash, Khadiga; Ismail, Mohammed Saleh; Saquib, Nazmus

2015-03-05

In Saudi Arabia, where childhood obesity is a major public health issue, it is important to identify the best tool for obesity classification. Hence, we compared two field methods for their usefulness in epidemiological studies. The sample consisted of 874 primary school (grade I-IV) children, aged 6-10 years, and was obtained through a multi-stage random sampling procedure. Weight and height were measured, and BMI (kg/m(2)) was calculated. Percent body fat was determined with a Futrex analyzer that uses near infrared reactance (NIR) technology. Method specific cut-off values were used for obesity classification. Sensitivity, specificity, positive and negative predictive values were determined for BMI, and the agreement between BMI and percent body fat was calculated. Compared to boys, the mean BMI was higher in girls whereas the mean percent body fat was lower (p-values < 0.0001). According to BMI, the prevalence of overweight or obesity was significantly higher in girls (34.3% vs. 17.3%); as oppose to percent body fat, which was similar between the sexes (6.6% vs. 7.0%). The sensitivity of BMI to classify overweight or obesity was high (boys = 93%, girls = 100%); and its false-positive detection rate was also high (boys = 63%, girls = 81%). The agreement rate was low between these two methods (boys = 0.48, girls =0.24). There is poor agreement in obesity classification between BMI and percent body fat, using NIR method, among Saudi school children.

FT-MIR and NIR spectral data fusion: a synergetic strategy for the geographical traceability of Panax notoginseng.

PubMed

Li, Yun; Zhang, Jin-Yu; Wang, Yuan-Zhong

2018-01-01

Three data fusion strategies (low-llevel, mid-llevel, and high-llevel) combined with a multivariate classification algorithm (random forest, RF) were applied to authenticate the geographical origins of Panax notoginseng collected from five regions of Yunnan province in China. In low-level fusion, the original data from two spectra (Fourier transform mid-IR spectrum and near-IR spectrum) were directly concatenated into a new matrix, which then was applied for the classification. Mid-level fusion was the strategy that inputted variables extracted from the spectral data into an RF classification model. The extracted variables were processed by iterate variable selection of the RF model and principal component analysis. The use of high-level fusion combined the decision making of each spectroscopic technique and resulted in an ensemble decision. The results showed that the mid-level and high-level data fusion take advantage of the information synergy from two spectroscopic techniques and had better classification performance than that of independent decision making. High-level data fusion is the most effective strategy since the classification results are better than those of the other fusion strategies: accuracy rates ranged between 93% and 96% for the low-level data fusion, between 95% and 98% for the mid-level data fusion, and between 98% and 100% for the high-level data fusion. In conclusion, the high-level data fusion strategy for Fourier transform mid-IR and near-IR spectra can be used as a reliable tool for correct geographical identification of P. notoginseng. Graphical abstract The analytical steps of Fourier transform mid-IR and near-IR spectral data fusion for the geographical traceability of Panax notoginseng.

Climate Classification is an Important Factor in ­Assessing Hospital Performance Metrics

NASA Astrophysics Data System (ADS)

Boland, M. R.; Parhi, P.; Gentine, P.; Tatonetti, N. P.

2017-12-01

Context/Purpose: Climate is a known modulator of disease, but its impact on hospital performance metrics remains unstudied. Methods: We assess the relationship between Köppen-Geiger climate classification and hospital performance metrics, specifically 30-day mortality, as reported in Hospital Compare, and collected for the period July 2013 through June 2014 (7/1/2013 - 06/30/2014). A hospital-level multivariate linear regression analysis was performed while controlling for known socioeconomic factors to explore the relationship between all-cause mortality and climate. Hospital performance scores were obtained from 4,524 hospitals belonging to 15 distinct Köppen-Geiger climates and 2,373 unique counties. Results: Model results revealed that hospital performance metrics for mortality showed significant climate dependence (p<0.001) after adjusting for socioeconomic factors. Interpretation: Currently, hospitals are reimbursed by Governmental agencies using 30-day mortality rates along with 30-day readmission rates. These metrics allow Government agencies to rank hospitals according to their `performance' along these metrics. Various socioeconomic factors are taken into consideration when determining individual hospitals performance. However, no climate-based adjustment is made within the existing framework. Our results indicate that climate-based variability in 30-day mortality rates does exist even after socioeconomic confounder adjustment. Use of standardized high-level climate classification systems (such as Koppen-Geiger) would be useful to incorporate in future metrics. Conclusion: Climate is a significant factor in evaluating hospital 30-day mortality rates. These results demonstrate that climate classification is an important factor when comparing hospital performance across the United States.

Diagnosis of Chronic Kidney Disease Based on Support Vector Machine by Feature Selection Methods.

PubMed

Polat, Huseyin; Danaei Mehr, Homay; Cetin, Aydin

2017-04-01

As Chronic Kidney Disease progresses slowly, early detection and effective treatment are the only cure to reduce the mortality rate. Machine learning techniques are gaining significance in medical diagnosis because of their classification ability with high accuracy rates. The accuracy of classification algorithms depend on the use of correct feature selection algorithms to reduce the dimension of datasets. In this study, Support Vector Machine classification algorithm was used to diagnose Chronic Kidney Disease. To diagnose the Chronic Kidney Disease, two essential types of feature selection methods namely, wrapper and filter approaches were chosen to reduce the dimension of Chronic Kidney Disease dataset. In wrapper approach, classifier subset evaluator with greedy stepwise search engine and wrapper subset evaluator with the Best First search engine were used. In filter approach, correlation feature selection subset evaluator with greedy stepwise search engine and filtered subset evaluator with the Best First search engine were used. The results showed that the Support Vector Machine classifier by using filtered subset evaluator with the Best First search engine feature selection method has higher accuracy rate (98.5%) in the diagnosis of Chronic Kidney Disease compared to other selected methods.

All Rural Places Are Not Created Equal: Revisiting the Rural Mortality Penalty in the United States

PubMed Central

2014-01-01

Objectives. I investigated mortality disparities between urban and rural areas by measuring disparities in urban US areas compared with 6 rural classifications, ranging from suburban to remote locales. Methods. Data from the Compressed Mortality File, National Center for Health Statistics, from 1968 to 2007, was used to calculate age-adjusted mortality rates for all rural and urban regions by year. Criteria measuring disparity between regions included excess deaths, annual rate of change in mortality, and proportion of excess deaths by population size. I used multivariable analysis to test for differences in determinants across regions. Results. The rural mortality penalty existed in all rural classifications, but the degree of disparity varied considerably. Rural–urban continuum code 6 was highly disadvantaged, and rural–urban continuum code 9 displayed a favorable mortality profile. Population, socioeconomic, and health care determinants of mortality varied across regions. Conclusions. A 2-decade long trend in mortality disparities existed in all rural classifications, but the penalty was not distributed evenly. This constitutes an important public health problem. Research should target the slow rates of improvement in mortality in the rural United States as an area of concern. PMID:25211763

TeraSCREEN: multi-frequency multi-mode Terahertz screening for border checks

NASA Astrophysics Data System (ADS)

Alexander, Naomi E.; Alderman, Byron; Allona, Fernando; Frijlink, Peter; Gonzalo, Ramón; Hägelen, Manfred; Ibáñez, Asier; Krozer, Viktor; Langford, Marian L.; Limiti, Ernesto; Platt, Duncan; Schikora, Marek; Wang, Hui; Weber, Marc Andree

2014-06-01

The challenge for any security screening system is to identify potentially harmful objects such as weapons and explosives concealed under clothing. Classical border and security checkpoints are no longer capable of fulfilling the demands of today's ever growing security requirements, especially with respect to the high throughput generally required which entails a high detection rate of threat material and a low false alarm rate. TeraSCREEN proposes to develop an innovative concept of multi-frequency multi-mode Terahertz and millimeter-wave detection with new automatic detection and classification functionalities. The system developed will demonstrate, at a live control point, the safe automatic detection and classification of objects concealed under clothing, whilst respecting privacy and increasing current throughput rates. This innovative screening system will combine multi-frequency, multi-mode images taken by passive and active subsystems which will scan the subjects and obtain complementary spatial and spectral information, thus allowing for automatic threat recognition. The TeraSCREEN project, which will run from 2013 to 2016, has received funding from the European Union's Seventh Framework Programme under the Security Call. This paper will describe the project objectives and approach.

Submucosal invasion and risk of lymph node invasion in early Barrett’s cancer: potential impact of different classification systems on patient management

PubMed Central

Fotis, Dimitrios; Doukas, Michael; Wijnhoven, Bas PL; Didden, Paul; Biermann, Katharina; Bruno, Marco J

2015-01-01

Background Due to the high mortality and morbidity rates of esophagectomy, endoscopic mucosal resection (EMR) is increasingly used for the curative treatment of early low risk Barrett’s adenocarcinoma. Objective This retrospective cohort study aimed to assess the prevalence of lymph node metastases (LNM) in submucosal (T1b) esophageal adenocarcinomas (EAC) in relation to the absolute depth of submucosal tumor invasion and demonstrate the efficacy of EMR for low risk (well and moderately differentiated without lymphovascular invasion) EAC with sm1 invasion (submucosal invasion ≤500 µm) according to the Paris classification. Methods The pathology reports of patients undergoing endoscopic resection and surgery from January 1994 until December 2013 at one center were reviewed and 54 patients with submucosal invasion were included. LNM were evaluated in surgical specimens and by follow up examinations in case of EMR. Results No LNM were observed in 10 patients with sm1 adenocarcinomas that underwent endoscopic resection. Three of them underwent supplementary endoscopic eradication therapy with a median follow up of 27 months for patients with sm1 tumors. In the surgical series two patients (29%) with sm1 invasion according to the pragmatic classification (subdivision of the submucosa into three equal thirds), staged as sm2-3 in the Paris classification, had LNM. The rate of LNM for surgical patients with low risk sm1 tumors was 10% according to the pragmatic classification and 0% according to Paris classification. Conclusion Different classifications of the tumor invasion depth lead to different LNM risks and treatment strategies for sm1 adenocarcinomas. Patients with low risk sm1 adenocarcinomas appear to be suitable candidates for EMR. PMID:26668743

Impact of oesophagitis classification in evaluating healing of erosive oesophagitis after therapy with proton pump inhibitors: a pooled analysis.

PubMed

Yaghoobi, Mohammad; Padol, Sara; Yuan, Yuhong; Hunt, Richard H

2010-05-01

The results of clinical trials with proton pump inhibitors (PPIs) are usually based on the Hetzel-Dent (HD), Savary-Miller (SM), or Los Angeles (LA) classifications to describe the severity and assess the healing of erosive oesophagitis. However, it is not known whether these classifications are comparable. The aim of this study was to review systematically the literature to compare the healing rates of erosive oesophagitis with PPIs in clinical trials assessed by the HD, SM, or LA classifications. A recursive, English language literature search in PubMed and Cochrane databases to December 2006 was performed. Double-blind randomized control trials comparing a PPI with another PPI, an H2-RA or placebo using endoscopic assessment of the healing of oesophagitis by the HD, SM or LA, or their modified classifications at 4 or 8 weeks, were included in the study. The healing rates on treatment with the same PPI(s), and same endoscopic grade(s) were pooled and compared between different classifications using Fisher's exact test or chi2 test where appropriate. Forty-seven studies from 965 potential citations met inclusion criteria. Seventy-eight PPI arms were identified, with 27 using HD, 29 using SM, and 22 using LA for five marketed PPIs. There was insufficient data for rabeprazole and esomeprazole (week 4 only) to compare because they were evaluated by only one classification. When data from all PPIs were pooled, regardless of baseline oesophagitis grades, the LA healing rate was significantly higher than SM and HD at both 4 and 8 weeks (74, 71, and 68% at 4 weeks and 89, 84, and 83% at 8 weeks, respectively). The distribution of different grades in study population was available only for pantoprazole where it was not significantly different between LA and SM subgroups. When analyzing data for PPI and dose, the LA classification showed a higher healing rate for omeprazole 20 mg/day and pantoprazole 40 mg/day (significant at 8 weeks), whereas healing by SM classification was significantly higher for omeprazole 40 mg/day (no data for LA) and lansoprazole 30 mg/day at 4 and 8 weeks. The healing rate by individual oesophagitis grade was not always available or robust enough for meaningful analysis. However, a difference between classifications remained. There is a significant, but not always consistent, difference in oesophagitis healing rates with the same PPI(s) reported by the LA, SM, or HD classifications. The possible difference between grading classifications should be considered when interpreting or comparing healing rates for oesophagitis from different studies.

Culinary Occupations. Instructional Materials Committee Recommendations Report.

ERIC Educational Resources Information Center

Louisiana State Technical Resource Center, Natchitoches.

This resource listing contains those culinary occupations instructional materials given a rating of "highly recommended" or "recommended" by a committee of instructors. Titles are arranged alphabetically by title within each of the following Classification of Instructional Programs (CIP) categories: institutional management;…

Feature Selection and Parameters Optimization of SVM Using Particle Swarm Optimization for Fault Classification in Power Distribution Systems.

PubMed

Cho, Ming-Yuan; Hoang, Thi Thom

2017-01-01

Fast and accurate fault classification is essential to power system operations. In this paper, in order to classify electrical faults in radial distribution systems, a particle swarm optimization (PSO) based support vector machine (SVM) classifier has been proposed. The proposed PSO based SVM classifier is able to select appropriate input features and optimize SVM parameters to increase classification accuracy. Further, a time-domain reflectometry (TDR) method with a pseudorandom binary sequence (PRBS) stimulus has been used to generate a dataset for purposes of classification. The proposed technique has been tested on a typical radial distribution network to identify ten different types of faults considering 12 given input features generated by using Simulink software and MATLAB Toolbox. The success rate of the SVM classifier is over 97%, which demonstrates the effectiveness and high efficiency of the developed method.

Global dengue death before and after the new World Health Organization 2009 case classification: A systematic review and meta-regression analysis.

PubMed

Low, Gary Kim-Kuan; Ogston, Simon A; Yong, Mun-Hin; Gan, Seng-Chiew; Chee, Hui-Yee

2018-06-01

Since the introduction of 2009 WHO dengue case classification, no literature was found regarding its effect on dengue death. This study was to evaluate the effect of 2009 WHO dengue case classification towards dengue case fatality rate. Various databases were used to search relevant articles since 1995. Studies included were cohort and cross-sectional studies, all patients with dengue infection and must report the number of death or case fatality rate. The Joanna Briggs Institute appraisal checklist was used to evaluate the risk of bias of the full-texts. The studies were grouped according to the classification adopted: WHO 1997 and WHO 2009. Meta-regression was employed using a logistic transformation (log-odds) of the case fatality rate. The result of the meta-regression was the adjusted case fatality rate and odds ratio on the explanatory variables. A total of 77 studies were included in the meta-regression analysis. The case fatality rate for all studies combined was 1.14% with 95% confidence interval (CI) of 0.82-1.58%. The combined (unadjusted) case fatality rate for 69 studies which adopted WHO 1997 dengue case classification was 1.09% with 95% CI of 0.77-1.55%; and for eight studies with WHO 2009 was 1.62% with 95% CI of 0.64-4.02%. The unadjusted and adjusted odds ratio of case fatality using WHO 2009 dengue case classification was 1.49 (95% CI: 0.52, 4.24) and 0.83 (95% CI: 0.26, 2.63) respectively, compared to WHO 1997 dengue case classification. There was an apparent increase in trend of case fatality rate from the year 1992-2016. Neither was statistically significant. The WHO 2009 dengue case classification might have no effect towards the case fatality rate although the adjusted results indicated a lower case fatality rate. Future studies are required for an update in the meta-regression analysis to confirm the findings. Copyright © 2018 Elsevier B.V. All rights reserved.

Binning in Gaussian Kernel Regularization

DTIC Science & Technology

2005-04-01

OSU-SVM Matlab package, the SVM trained on 966 bins has a comparable test classification rate as the SVM trained on 27,179 samples, but reduces the...71.40%) on 966 randomly sampled data. Using the OSU-SVM Matlab package, the SVM trained on 966 bins has a comparable test classification rate as the...the OSU-SVM Matlab package, the SVM trained on 966 bins has a comparable test classification rate as the SVM trained on 27,179 samples, and reduces

[Endoprosthesis failure in the ankle joint : Histopathological diagnostics and classification].

PubMed

Müller, S; Walther, M; Röser, A; Krenn, V

2017-03-01

Endoprostheses of the ankle joint show higher revision rates of 3.29 revisions per 100 component years. The aims of this study were the application and modification of the consensus classification of the synovia-like interface membrane (SLIM) for periprosthetic failure of the ankle joint, the etiological clarification of periprosthetic pseudocysts and a detailed measurement of proliferative activity (Ki67) in the region of osteolysis. Tissue samples from 159 patients were examined according to the criteria of the standardized consensus classification. Of these, 117 cases were derived from periprosthetic membranes of the ankle. The control group included 42 tissue specimens from the hip and knee joints. Particle identification and characterization were carried out using the particle algorithm. An immunohistochemical examination with Ki67 proliferation was performed in all cases of ankle pseudocysts and 19 control cases. The consensus classification of SLIM is transferrable to endoprosthetic failure of the ankle joint. Periprosthetic pseudocysts with the histopathological characteristics of the appropriate SLIM subtype were detectable in 39 cases of ankle joint endoprostheses (33.3%). The mean value of the Ki67 index was 14% and showed an increased proliferation rate in periprosthetic pseudocysts of the ankle (p-value 0.02037). In periprosthetic pseudocysts an above average higher detection rate of type 1 SLIM induced by abrasion (51.3%) with an increased Ki67 proliferation fraction (p-value 0.02037) was found, which can be interpreted as local destructive intraosseus synovialitis. This can be the reason for formation of pseudocystic osteolysis caused by high mechanical stress in ankle endoprostheses. A simplified diagnostic classification scoring system of dysfunctional endoprostheses of the ankle is proposed for collation of periprosthetic pseudocysts, ossifications and the Ki67 proliferation fraction.

A Spiking Neural Network in sEMG Feature Extraction.

PubMed

Lobov, Sergey; Mironov, Vasiliy; Kastalskiy, Innokentiy; Kazantsev, Victor

2015-11-03

We have developed a novel algorithm for sEMG feature extraction and classification. It is based on a hybrid network composed of spiking and artificial neurons. The spiking neuron layer with mutual inhibition was assigned as feature extractor. We demonstrate that the classification accuracy of the proposed model could reach high values comparable with existing sEMG interface systems. Moreover, the algorithm sensibility for different sEMG collecting systems characteristics was estimated. Results showed rather equal accuracy, despite a significant sampling rate difference. The proposed algorithm was successfully tested for mobile robot control.

Use of Robson classification to assess cesarean section rate in Brazil: the role of source of payment for childbirth.

PubMed

Nakamura-Pereira, Marcos; do Carmo Leal, Maria; Esteves-Pereira, Ana Paula; Domingues, Rosa Maria Soares Madeira; Torres, Jacqueline Alves; Dias, Marcos Augusto Bastos; Moreira, Maria Elisabeth

2016-10-17

Cesarean section (CS) rates are increasing worldwide but there is some concern with this trend because of potential maternal and perinatal risks. The Robson classification is the standard method to monitor and compare CS rates. Our objective was to analyze CS rates in Brazil according to source of payment for childbirth (public or private) using the Robson classification. Data are from the 2011-2012 "Birth in Brazil" study, which used a national hospital-based sample of 23,940 women. We categorized all women into Robson groups and reported the relative size of each Robson group, the CS rate in each group and the absolute and relative contributions made by each to the overall CS rate. Differences were analyzed through chi-square and Z-test with a significance level of < 0.05. The overall CS rate in Brazil was 51.9 % (42.9 % in the public and 87.9 % in the private health sector). The Robson groups with the highest impact on Brazil's CS rate in both public and private sectors were group 2 (nulliparous, term, cephalic with induced or cesarean delivery before labor), group 5 (multiparous, term, cephalic presentation and previous cesarean section) and group 10 (cephalic preterm pregnancies), which accounted for more than 70 % of CS carried out in the country. High-risk women had significantly greater CS rates compared with low-risk women in almost all Robson groups in the public sector only. Public policies should be directed at reducing CS in nulliparous women, particularly by reducing the number of elective CS in these women, and encouraging vaginal birth after cesarean to reduce repeat CS in multiparous women.

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

PubMed

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

2006-03-01

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

A statewide investigation of geographic lung cancer incidence patterns and radon exposure in a low-smoking population.

PubMed

Ou, Judy Y; Fowler, Brynn; Ding, Qian; Kirchhoff, Anne C; Pappas, Lisa; Boucher, Kenneth; Akerley, Wallace; Wu, Yelena; Kaphingst, Kimberly; Harding, Garrett; Kepka, Deanna

2018-01-31

Lung cancer is the leading cause of cancer-related mortality in Utah despite having the nation's lowest smoking rate. Radon exposure and differences in lung cancer incidence between nonmetropolitan and metropolitan areas may explain this phenomenon. We compared smoking-adjusted lung cancer incidence rates between nonmetropolitan and metropolitan counties by predicted indoor radon level, sex, and cancer stage. We also compared lung cancer incidence by county classification between Utah and all SEER sites. SEER*Stat provided annual age-adjusted rates per 100,000 from 1991 to 2010 for each Utah county and all other SEER sites. County classification, stage, and sex were obtained from SEER*Stat. Smoking was obtained from Environmental Public Health Tracking estimates by Ortega et al. EPA provided low (< 2 pCi/L), moderate (2-4 pCi/L), and high (> 4 pCi/L) indoor radon levels for each county. Poisson models calculated overall, cancer stage, and sex-specific rates and p-values for smoking-adjusted and unadjusted models. LOESS smoothed trend lines compared incidence rates between Utah and all SEER sites by county classification. All metropolitan counties had moderate radon levels; 12 (63%) of the 19 nonmetropolitan counties had moderate predicted radon levels and 7 (37%) had high predicted radon levels. Lung cancer incidence rates were higher in nonmetropolitan counties than metropolitan counties (34.8 vs 29.7 per 100,000, respectively). Incidence of distant stage cancers was significantly higher in nonmetropolitan counties after controlling for smoking (16.7 vs 15.4, p = 0.02*). Incidence rates in metropolitan, moderate radon and nonmetropolitan, moderate radon counties were similar. Nonmetropolitan, high radon counties had a significantly higher incidence of lung cancer compared to nonmetropolitan, moderate radon counties after adjustment for smoking (41.7 vs 29.2, p < 0.0001*). Lung cancer incidence patterns in Utah were opposite of metropolitan/nonmetropolitan trends in other SEER sites. Lung cancer incidence and distant stage incidence rates were consistently higher in nonmetropolitan Utah counties than metropolitan counties, suggesting that limited access to preventative screenings may play a role in this disparity. Smoking-adjusted incidence rates in nonmetropolitan, high radon counties were significantly higher than moderate radon counties, suggesting that radon was also major contributor to lung cancer in these regions. National studies should account for geographic and environmental factors when examining nonmetropolitan/metropolitan differences in lung cancer.

Trifactorial classification system for osteotome sinus floor elevation based on an observational retrospective analysis of 926 implants followed up to 10 years.

PubMed

French, David; Nadji, Nabil; Liu, Shawn X; Larjava, Hannu

2015-06-01

A novel osteotome trifactorial classification system is proposed for transcrestal osteotome-mediated sinus floor elevation (OSFE) sites that includes residual bone height (RBH), sinus floor anatomy (contour), and multiple versus single sites OSFE (tenting). An analysis of RBH, contour, and tenting was retrospectively applied to a cohort of 926 implants placed using OSFE without added bone graft and followed up to 10 years. RBH was divided into three groups: high (RBH > 6 mm), mid (RBH = 4.1 to 6 mm), and low (RBH = 2 to 4 mm). The sinus "contour" was divided into four groups: flat, concave, angle, and septa. For "tenting", single versus multiple adjacent OSFE sites were compared. The prevalence of flat sinus floors increased as RBH decreased. RBH was a significant predictor of failure with rates as follows: low- RBH = 5.1%, mid-RBH = 1.5%, and high-RBH = 0.4%. Flat sinus floors and single sites as compared to multiple sites had higher observed failure rates but neither achieved statistical significance; however, the power of the study was limited by low numbers of failures. The osteotome trifactorial classification system as proposed can assist planning OSFE cases and may allow better comparison of future OSFE studies.

Staging of chronic myeloid leukemia in the imatinib era: an evaluation of the World Health Organization proposal.

PubMed

Cortes, Jorge E; Talpaz, Moshe; O'Brien, Susan; Faderl, Stefan; Garcia-Manero, Guillermo; Ferrajoli, Alessandra; Verstovsek, Srdan; Rios, Mary B; Shan, Jenny; Kantarjian, Hagop M

2006-03-15

Several staging classification systems, all of which were designed in the preimatinib era, are used for chronic myeloid leukemia (CML). The World Health Organization (WHO) recently proposed a new classification system that has not been validated clinically. The authors investigated the significance of the WHO classification system and compared it with the classification systems used to date in imatinib trials ("standard definition") to determine its impact in establishing the outcome of patients after therapy with imatinib. In total, 809 patients who received imatinib for CML were classified into chronic phase (CP), accelerated phase (AP), and blast phase (BP) based on standard definitions and then were reclassified according to the new WHO classification system. Their outcomes with imatinib therapy were compared, and the value of individual components of these classification systems was determined. With the WHO classification, 78 patients (10%) were reclassified: 45 patients (6%) were reclassified from CP to AP, 14 patients (2%) were reclassified from AP to CP, and 19 patients (2%) were reclassified from AP to BP. The rates of complete cytogenetic response for patients in CP, AP, and BP according to the standard definition were 72%, 45%, and 8%, respectively. After these patients were reclassified according to WHO criteria, the response rates were 77% (P = 0.07), 39% (P = 0.28), and 11% (P = 0.61), respectively. The 3-year survival rates were 91%, 65%, and 10%, respectively, according to the standard classification and 95% (P = 0.05), 63% (P = 0.76), and 16% (P = 0.18), respectively, according to the WHO classification. Patients who had a blast percentage of 20-29%, which is considered CML-BP according to the WHO classification, had a significantly better response rate (21% vs. 8%; P = 0.11) and 3-year survival rate (42% vs. 10%; P = 0.0001) compared with patients who had blasts > or = 30%. Different classification systems had an impact on the outcome of patients, and some prognostic features had different prognostic implications in the imatinib era. The authors believe that a new, uniform staging system for CML is warranted, and they propose such a system. (c) 2006 American Cancer Society.

Causes and temporal changes in nationally collected stillbirth audit data in high-resource settings.

PubMed

Norris, Tom; Manktelow, Bradley N; Smith, Lucy K; Draper, Elizabeth S

2017-06-01

Few high-income countries have an active national programme of stillbirth audit. From the three national programmes identified (UK, New Zealand, and the Netherlands) steady declines in annual stillbirth rates have been observed over the audit period between 1993 and 2014. Unexplained stillbirth remains the largest group in the classification of stillbirths, with a decline in intrapartum-related stillbirths, which could represent improvements in intrapartum care. All three national audits of stillbirths suggest that up to half of all reviewed stillbirths have elements of care that failed to follow standards and guidance. Variation in the classification of stillbirth, cause of death and frequency of risk factor groups limit our ability to draw meaningful conclusions as to the true scale of the burden and the changing epidemiology of stillbirths in high-income countries. International standardization of these would facilitate direct comparisons between countries. The observed declines in stillbirth rates over the period of perinatal audit, a possible consequence of recommendations for improved antenatal care, should serve to incentivise other countries to implement similar audit programmes. Crown Copyright © 2017. Published by Elsevier Ltd. All rights reserved.

Exploring the utility of narrative analysis in diagnostic decision making: picture-bound reference, elaboration, and fetal alcohol spectrum disorders.

PubMed

Thorne, John C; Coggins, Truman E; Carmichael Olson, Heather; Astley, Susan J

2007-04-01

To evaluate classification accuracy and clinical feasibility of a narrative analysis tool for identifying children with a fetal alcohol spectrum disorder (FASD). Picture-elicited narratives generated by 16 age-matched pairs of school-aged children (FASD vs. typical development [TD]) were coded for semantic elaboration and reference strategy by judges who were unaware of age, gender, and group membership of the participants. Receiver operating characteristic (ROC) curves were used to examine the classification accuracy of the resulting set of narrative measures for making 2 classifications: (a) for the 16 children diagnosed with FASD, low performance (n = 7) versus average performance (n = 9) on a standardized expressive language task and (b) FASD (n = 16) versus TD (n = 16). Combining the rates of semantic elaboration and pragmatically inappropriate reference perfectly matched a classification based on performance on the standardized language task. More importantly, the rate of ambiguous nominal reference was highly accurate in classifying children with an FASD regardless of their performance on the standardized language task (area under the ROC curve = .863, confidence interval = .736-.991). Results support further study of the diagnostic utility of narrative analysis using discourse level measures of elaboration and children's strategic use of reference.

Suicide Mortality Across Broad Occupational Groups in Greece: A Descriptive Study.

PubMed

Alexopoulos, Evangelos C; Kavalidou, Katerina; Messolora, Fani

2016-03-01

Several studies have investigated the relationship between specific occupations and suicide mortality, as suicide rates differ by profession. The aim of this study was to investigate suicide mortality ratios across broad occupational groups in Greece for both sexes in the period 2000-2009. Data of suicide deaths were retrieved from the Hellenic Statistical Authority and comparative mortality ratios were calculated. Occupational classification was based on the International Classification of Occupations (ISCO-88) and the coding for Intentional self-harm (X60-X84) was based on the international classification of diseases (ICD-10). Male dominant occupations, mainly armed forces, skilled farmers and elementary workers, and female high-skilled occupations were seen as high risk groups for suicide in a period of 10 years. The age-productive group of 30-39 years in Greek male elementary workers and the 50-59 age-productive group of Greek professional women proved to have the most elevated number of suicide deaths. Further research is needed into the work-related stressors of occupations with high suicide mortality risk and focused suicide prevention strategies should be applied within vulnerable working age populations.

The T-plasty: a modified YV-plasty for highly recurrent bladder neck contracture after transurethral surgery for benign hyperplasia of the prostate: clinical outcome and patient satisfaction.

PubMed

Reiss, C P; Rosenbaum, C M; Becker, A; Schriefer, P; Ludwig, T A; Engel, O; Riechardt, S; Fisch, M; Dahlem, R

2016-10-01

To describe a modified surgical technique for treatment of highly recurrent bladder neck contracture (BNC) after transurethral surgery for benign hyperplasia and to evaluate success rate and patient satisfaction of this novel technique. Ten patients with highly recurrent BNC and multiple prior attempts of endoscopic treatment underwent the T-plasty. Perioperative complications were recorded and classified according to the Clavien classification. Patient reported functional outcomes were retrospectively analysed using a standardized questionnaire assessing recurrence of stenosis, incontinence, satisfaction and changes in quality of life (QoL). The questionnaires included validated IPSS and SF-8-health survey items. Mean age at the time of surgery was 69.2 years (range 61-79), and the mean follow-up was 26 months (range 3-46). No complications grade 3 or higher according to the Clavien classification occurred. Success rate was 100 %. No de novo stress incontinence occurred. Urinary stream was described as very strong to moderate by 80 % of the patients, mean post-operative IPSS-score was 11.3 (range 4-29), and mean post-operative IPSS-QoL was 2.4 (range 1-5). Patients satisfaction was very high or high in 90 %, and QoL improved in 90 %. The SF-8-health survey showed values comparable to the reference population. The T-plasty represents a safe and valuable option in treating highly recurrent BNC after surgery for benign hyperplasia. It offers multiple advantages compared to other techniques such as a single-staged approach and the opportunity for reconstruction of a reliable wide bladder neck by usage of two well-vascularized flaps. Success rate, low rate of complications and preservation of continence are highly encouraging.

PCA based feature reduction to improve the accuracy of decision tree c4.5 classification

NASA Astrophysics Data System (ADS)

Nasution, M. Z. F.; Sitompul, O. S.; Ramli, M.

2018-03-01

Splitting attribute is a major process in Decision Tree C4.5 classification. However, this process does not give a significant impact on the establishment of the decision tree in terms of removing irrelevant features. It is a major problem in decision tree classification process called over-fitting resulting from noisy data and irrelevant features. In turns, over-fitting creates misclassification and data imbalance. Many algorithms have been proposed to overcome misclassification and overfitting on classifications Decision Tree C4.5. Feature reduction is one of important issues in classification model which is intended to remove irrelevant data in order to improve accuracy. The feature reduction framework is used to simplify high dimensional data to low dimensional data with non-correlated attributes. In this research, we proposed a framework for selecting relevant and non-correlated feature subsets. We consider principal component analysis (PCA) for feature reduction to perform non-correlated feature selection and Decision Tree C4.5 algorithm for the classification. From the experiments conducted using available data sets from UCI Cervical cancer data set repository with 858 instances and 36 attributes, we evaluated the performance of our framework based on accuracy, specificity and precision. Experimental results show that our proposed framework is robust to enhance classification accuracy with 90.70% accuracy rates.

Cascade classification of endocytoscopic images of colorectal lesions for automated pathological diagnosis

NASA Astrophysics Data System (ADS)

Itoh, Hayato; Mori, Yuichi; Misawa, Masashi; Oda, Masahiro; Kudo, Shin-ei; Mori, Kensaku

2018-02-01

This paper presents a new classification method for endocytoscopic images. Endocytoscopy is a new endoscope that enables us to perform conventional endoscopic observation and ultramagnified observation of cell level. This ultramagnified views (endocytoscopic images) make possible to perform pathological diagnosis only on endo-scopic views of polyps during colonoscopy. However, endocytoscopic image diagnosis requires higher experiences for physicians. An automated pathological diagnosis system is required to prevent the overlooking of neoplastic lesions in endocytoscopy. For this purpose, we propose a new automated endocytoscopic image classification method that classifies neoplastic and non-neoplastic endocytoscopic images. This method consists of two classification steps. At the first step, we classify an input image by support vector machine. We forward the image to the second step if the confidence of the first classification is low. At the second step, we classify the forwarded image by convolutional neural network. We reject the input image if the confidence of the second classification is also low. We experimentally evaluate the classification performance of the proposed method. In this experiment, we use about 16,000 and 4,000 colorectal endocytoscopic images as training and test data, respectively. The results show that the proposed method achieves high sensitivity 93.4% with small rejection rate 9.3% even for difficult test data.

Improving Classification of Protein Interaction Articles Using Context Similarity-Based Feature Selection.

PubMed

Chen, Yifei; Sun, Yuxing; Han, Bing-Qing

2015-01-01

Protein interaction article classification is a text classification task in the biological domain to determine which articles describe protein-protein interactions. Since the feature space in text classification is high-dimensional, feature selection is widely used for reducing the dimensionality of features to speed up computation without sacrificing classification performance. Many existing feature selection methods are based on the statistical measure of document frequency and term frequency. One potential drawback of these methods is that they treat features separately. Hence, first we design a similarity measure between the context information to take word cooccurrences and phrase chunks around the features into account. Then we introduce the similarity of context information to the importance measure of the features to substitute the document and term frequency. Hence we propose new context similarity-based feature selection methods. Their performance is evaluated on two protein interaction article collections and compared against the frequency-based methods. The experimental results reveal that the context similarity-based methods perform better in terms of the F1 measure and the dimension reduction rate. Benefiting from the context information surrounding the features, the proposed methods can select distinctive features effectively for protein interaction article classification.

TIM Barrel Protein Structure Classification Using Alignment Approach and Best Hit Strategy

NASA Astrophysics Data System (ADS)

Chu, Jia-Han; Lin, Chun Yuan; Chang, Cheng-Wen; Lee, Chihan; Yang, Yuh-Shyong; Tang, Chuan Yi

2007-11-01

The classification of protein structures is essential for their function determination in bioinformatics. It has been estimated that around 10% of all known enzymes have TIM barrel domains from the Structural Classification of Proteins (SCOP) database. With its high sequence variation and diverse functionalities, TIM barrel protein becomes to be an attractive target for protein engineering and for the evolution study. Hence, in this paper, an alignment approach with the best hit strategy is proposed to classify the TIM barrel protein structure in terms of superfamily and family levels in the SCOP. This work is also used to do the classification for class level in the Enzyme nomenclature (ENZYME) database. Two testing data sets, TIM40D and TIM95D, both are used to evaluate this approach. The resulting classification has an overall prediction accuracy rate of 90.3% for the superfamily level in the SCOP, 89.5% for the family level in the SCOP and 70.1% for the class level in the ENZYME. These results demonstrate that the alignment approach with the best hit strategy is a simple and viable method for the TIM barrel protein structure classification, even only has the amino acid sequences information.

Do pre-trained deep learning models improve computer-aided classification of digital mammograms?

NASA Astrophysics Data System (ADS)

Aboutalib, Sarah S.; Mohamed, Aly A.; Zuley, Margarita L.; Berg, Wendie A.; Luo, Yahong; Wu, Shandong

2018-02-01

Digital mammography screening is an important exam for the early detection of breast cancer and reduction in mortality. False positives leading to high recall rates, however, results in unnecessary negative consequences to patients and health care systems. In order to better aid radiologists, computer-aided tools can be utilized to improve distinction between image classifications and thus potentially reduce false recalls. The emergence of deep learning has shown promising results in the area of biomedical imaging data analysis. This study aimed to investigate deep learning and transfer learning methods that can improve digital mammography classification performance. In particular, we evaluated the effect of pre-training deep learning models with other imaging datasets in order to boost classification performance on a digital mammography dataset. Two types of datasets were used for pre-training: (1) a digitized film mammography dataset, and (2) a very large non-medical imaging dataset. By using either of these datasets to pre-train the network initially, and then fine-tuning with the digital mammography dataset, we found an increase in overall classification performance in comparison to a model without pre-training, with the very large non-medical dataset performing the best in improving the classification accuracy.

Muscle Injuries in Sports: A New Evidence-Informed and Expert Consensus-Based Classification with Clinical Application.

PubMed

Valle, Xavier; Alentorn-Geli, Eduard; Tol, Johannes L; Hamilton, Bruce; Garrett, William E; Pruna, Ricard; Til, Lluís; Gutierrez, Josep Antoni; Alomar, Xavier; Balius, Ramón; Malliaropoulos, Nikos; Monllau, Joan Carles; Whiteley, Rodney; Witvrouw, Erik; Samuelsson, Kristian; Rodas, Gil

2017-07-01

Muscle injuries are among the most common injuries in sport and continue to be a major concern because of training and competition time loss, challenging decision making regarding treatment and return to sport, and a relatively high recurrence rate. An adequate classification of muscle injury is essential for a full understanding of the injury and to optimize its management and return-to-play process. The ongoing failure to establish a classification system with broad acceptance has resulted from factors such as limited clinical applicability, and the inclusion of subjective findings and ambiguous terminology. The purpose of this article was to describe a classification system for muscle injuries with easy clinical application, adequate grouping of injuries with similar functional impairment, and potential prognostic value. This evidence-informed and expert consensus-based classification system for muscle injuries is based on a four-letter initialism system: MLG-R, respectively referring to the mechanism of injury (M), location of injury (L), grading of severity (G), and number of muscle re-injuries (R). The goal of the classification is to enhance communication between healthcare and sports-related professionals and facilitate rehabilitation and return-to-play decision making.

Better treatment strategies for patients with acute cholecystitis and American Society of Anesthesiologists classification 3 or greater.

PubMed

Yun, Sung Su; Hwang, Dae Wook; Kim, Se Won; Park, Sang Hwan; Park, Sang Jin; Lee, Dong Shick; Kim, Hong Jin

2010-07-01

Laparoscopic cholecystectomy is the best treatment choice for acute cholecystitis. However, it still carries high conversion and mortality rates. The purpose of this study was to find out better treatment strategies for high surgical risk patients with acute cholecystitis. Between January 2002 and June 2008, we performed percutaneous cholecystostomy instead of emergency cholecystectomy in 44 patients with acute cholecystitis and American Society of Anesthesiologists (ASA) classification 3 or greater. This was performed in 31 patients as a bridge procedure before elective cholecystectomy (bridge group) and as a palliative procedure in 11 patients (palliation group). The mean age of patients was 71.6 years (range 52-86 years). The mean ASA classifications before and after percutaneous cholecystostomy were 3.3 +/- 0.5 and 2.5 +/- 0.6, respectively, in the bridge group, and 3.6 +/- 0.7 and 3.1 +/- 1.0, in the palliation group, respectively. Percutaneous cholecystostomy was technically successful in all patients. There were two deaths after percutaneous cholecystostomy in the palliation group due to underlying ischemic heart disease and multiple organ failure. Resumption of oral intake was possible 2.9 +/- 1.8 days in the bridge group and 3.9 +/- 3.5 days in the palliation group after percutaneous cholecystostomy. We attempted 17 laparoscopic cholecystectomies and experienced one failure due to bile duct injury (success rate: 94.1%). The postoperative course of all cholecystectomy patients was uneventful. Percutaneous cholecystostomy is an effective bridge procedure before cholecystectomy in patients with acute cholecystitis and ASA classification 3 or greater.

High area rate reconnaissance (HARR) and mine reconnaissance/hunter (MR/H) exploratory development programs

NASA Astrophysics Data System (ADS)

Lathrop, John D.

1995-06-01

This paper describes the sea mine countermeasures developmental context, technology goals, and progress to date of the two principal Office of Naval Research exploratory development programs addressing sea mine reconnaissance and minehunting technology development. The first of these programs, High Area Rate Reconnaissance, is developing toroidal volume search sonar technology, sidelooking sonar technology, and associated signal processing technologies (motion compensation, beamforming, and computer-aided detection and classification) for reconnaissance and hunting against volume mines and proud bottom mines from 21-inch diameter vehicles operating in deeper waters. The second of these programs, Amphibious Operation Area Mine Reconnaissance/Hunter, is developing a suite of sensor technologies (synthetic aperture sonar, ahead-looking sonar, superconducting magnetic field gradiometer, and electro-optic sensor) and associated signal processing technologies for reconnaissance and hunting against all mine types (including buried mines) in shallow water and very shallow water from 21-inch diameter vehicles. The technologies under development by these two programs must provide excellent capabilities for mine detection, mine classification, and discrimination against false targets.

Recommendations for routine reporting on indications for cesarean delivery in developing countries.

PubMed

Stanton, Cynthia; Ronsmans, Carine

2008-09-01

Cesarean delivery rates are increasing rapidly in many developing countries, particularly among wealthy women. Poor women have lower rates, often so low that they do not reach the minimum rate of 1 percent. Little data are available on clinical indications for cesarean section, information that could assist in understanding why cesarean delivery rates have changed. This paper presents recommendations for routine reporting on indications for cesarean delivery in developing countries. These recommendations resulted from an international consultation of researchers held in February 2006 to promote the collection of comparable data to understand change in, or composition of, the cesarean delivery rate in developing countries. Data are presented from selected countries, categorizing cesareans by three classification systems. A single classification system was recommended for use in both high and low cesarean delivery rate settings, given that underuse and overuse of cesarean section are evident within many populations. The group recommended a hierarchical categorization, prioritizing cesareans performed for absolute maternal indications. Categorization among the remaining nonabsolute indications is based on the primary indication for the procedure and include maternal and fetal indications and psychosocial indications, required for high cesarean delivery rate settings. Data on indications for cesarean sections are available everywhere the procedure is performed. All that is required is compilation and review at facility and at higher levels. Advocacy within ministries of health and medical professional organizations is required to advance these recommendations since researchers have inadequately communicated the health effects of both underuse and overuse of cesarean delivery.

Sensitivity and Specificity of Long Wave Infrared Imaging for Attention-Deficit/Hyperactivity Disorder

ERIC Educational Resources Information Center

Coben, Robert; Myers, Thomas E.

2009-01-01

Objective: This study was the first to investigate the efficacy of long wave infrared (LWIR) imaging as a diagnostic tool for ADHD. Method: with ADHD and a high level of specificity (94%) in discriminating those with ADHD from those with other diagnoses. The overall classification rate was 73.16%. This was indicative of a high level of…

Microstructurally Based Prediction of High Strain Failure Modes in Crystalline Solids

DTIC Science & Technology

2016-07-05

SECURITY CLASSIFICATION OF: New three-dimensional dislocation-density based crystalline plasticity formulations was used with grain-boundary (GB...Army Research Office P.O. Box 12211 Research Triangle Park, NC 27709-2211 High strain-rate; failure, crsytalline plasticity , dislocation-density...Solids Report Title New three-dimensional dislocation-density based crystalline plasticity formulations was used with grain-boundary (GB) kinematic

Ordered and Ultra-High Aspect Ratio Nanocapillary Arrays as a Model System

DTIC Science & Technology

2015-10-13

formation and deep pore growth of anodized aluminum oxide ( AAO )-based nanocapillary arrays as the basis for high density, safe and high rate gas... anodized aluminum oxide , nanocapillary arrays 16. SECURITY CLASSIFICATION OF: Unclassified 17. LIMITATION OF ABSTRACT 18. NUMBER OF PAGES 19a. NAME... Aluminum Page 7 Copyright © 2015 Mainstream Engineering Corporation CPE Mitigation Schemes  Control thermal and flow profile -> even anodization

A reliable Raman-spectroscopy-based approach for diagnosis, classification and follow-up of B-cell acute lymphoblastic leukemia

NASA Astrophysics Data System (ADS)

Managò, Stefano; Valente, Carmen; Mirabelli, Peppino; Circolo, Diego; Basile, Filomena; Corda, Daniela; de Luca, Anna Chiara

2016-04-01

Acute lymphoblastic leukemia type B (B-ALL) is a neoplastic disorder that shows high mortality rates due to immature lymphocyte B-cell proliferation. B-ALL diagnosis requires identification and classification of the leukemia cells. Here, we demonstrate the use of Raman spectroscopy to discriminate normal lymphocytic B-cells from three different B-leukemia transformed cell lines (i.e., RS4;11, REH, MN60 cells) based on their biochemical features. In combination with immunofluorescence and Western blotting, we show that these Raman markers reflect the relative changes in the potential biological markers from cell surface antigens, cytoplasmic proteins, and DNA content and correlate with the lymphoblastic B-cell maturation/differentiation stages. Our study demonstrates the potential of this technique for classification of B-leukemia cells into the different differentiation/maturation stages, as well as for the identification of key biochemical changes under chemotherapeutic treatments. Finally, preliminary results from clinical samples indicate high consistency of, and potential applications for, this Raman spectroscopy approach.

Structural brain changes versus self-report: machine-learning classification of chronic fatigue syndrome patients.

PubMed

Sevel, Landrew S; Boissoneault, Jeff; Letzen, Janelle E; Robinson, Michael E; Staud, Roland

2018-05-30

Chronic fatigue syndrome (CFS) is a disorder associated with fatigue, pain, and structural/functional abnormalities seen during magnetic resonance brain imaging (MRI). Therefore, we evaluated the performance of structural MRI (sMRI) abnormalities in the classification of CFS patients versus healthy controls and compared it to machine learning (ML) classification based upon self-report (SR). Participants included 18 CFS patients and 15 healthy controls (HC). All subjects underwent T1-weighted sMRI and provided visual analogue-scale ratings of fatigue, pain intensity, anxiety, depression, anger, and sleep quality. sMRI data were segmented using FreeSurfer and 61 regions based on functional and structural abnormalities previously reported in patients with CFS. Classification was performed in RapidMiner using a linear support vector machine and bootstrap optimism correction. We compared ML classifiers based on (1) 61 a priori sMRI regional estimates and (2) SR ratings. The sMRI model achieved 79.58% classification accuracy. The SR (accuracy = 95.95%) outperformed both sMRI models. Estimates from multiple brain areas related to cognition, emotion, and memory contributed strongly to group classification. This is the first ML-based group classification of CFS. Our findings suggest that sMRI abnormalities are useful for discriminating CFS patients from HC, but SR ratings remain most effective in classification tasks.

MicroRNA Expression Profile Selection for Cancer Staging Classification Using Backpropagation

NASA Astrophysics Data System (ADS)

Anjarwati; Wibowo, Adi; Adhy, Satriyo; Kusumaningrum, Retno

2018-05-01

Ovarian cancer, breast cancer, and lung cancer are deadly diseases and require serious treatment. The cancers are among the fifth most common causes of cancer-induced deaths especially for woman. The high mortality rate of cancer is caused by the lack of effective strategies for early detection of the cancer, whereas if its detected in the early stages, the life survival of cancer patients will be 90%, otherwise the survival rate only 30% when the cancers detected on metastasis stages or cancer cells have spread from a primary site of cancer. MicroRNAs can be used as potential biomarkers for cancer due to their profile expression on the cancers. In this paper, we proposed the feature selection of microRNA expression profiles for classification of the cancers stages using Backpropagation Neural Network. The Cancer stages are classified into before metastasis and after metastasis. Several combinations of the microRNA expression profiles from medical references are compared to find the best features for the classification. The accuracy and the mean square errors are used as basis testing the comparison.

Classification of ground glass opacity lesion characteristic based on texture feature using lung CT image

NASA Astrophysics Data System (ADS)

Sebatubun, M. M.; Haryawan, C.; Windarta, B.

2018-03-01

Lung cancer causes a high mortality rate in the world than any other cancers. That can be minimised if the symptoms and cancer cells have been detected early. One of the techniques used to detect lung cancer is by computed tomography (CT) scan. CT scan images have been used in this study to identify one of the lesion characteristics named ground glass opacity (GGO). It has been used to determine the level of malignancy of the lesion. There were three phases in identifying GGO: image cropping, feature extraction using grey level co-occurrence matrices (GLCM) and classification using Naïve Bayes Classifier. In order to improve the classification results, the most significant feature was sought by feature selection using gain ratio evaluation. Based on the results obtained, the most significant features could be identified by using feature selection method used in this research. The accuracy rate increased from 83.33% to 91.67%, the sensitivity from 82.35% to 94.11% and the specificity from 84.21% to 89.47%.

Molecular classifications of breast carcinoma with similar terminology and different definitions: are they the same?

PubMed

Tang, Ping; Wang, Jianmin; Bourne, Patria

2008-04-01

There are 4 major molecular classifications in the literature that divide breast carcinoma into basal and nonbasal subtypes, with basal subtypes associated with poor prognosis. Basal subtype is defined as positive for cytokeratin (CK) 5/6, CK14, and/or CK17 in CK classification; negative for ER, PR, and HER2 in triple negative (TN) classification; negative for ER and negative or positive for HER2 in ER/HER2 classification; and positive for CK5/6, CK14, CK17, and/or EGFR; and negative for ER, PR, and HER2 in CK/TN classification. These classifications use similar terminology but different definitions; it is critical to understand the precise relationship between them. We compared these 4 classifications in 195 breast carcinomas and found that (1) the rates of basal subtypes varied from 5% to 36% for ductal carcinoma in situ and 14% to 40% for invasive ductal carcinoma. (2) The rates of basal subtypes varied from 19% to 76% for HG carcinoma and 1% to 7% for NHG carcinoma. (3) The rates of basal subtypes were strongly associated with tumor grades (P < .001) in all classifications and associated with tumor types (in situ versus invasive ductal carcinomas) in TN (P < .001) and CK/TN classifications (P = .035). (4) These classifications were related but not interchangeable (kappa ranges from 0.140 to 0.658 for HG carcinoma and from 0.098 to 0.654 for NHG carcinoma). In conclusion, although these classifications all divide breast carcinoma into basal and nonbasal subtypes, they are not interchangeable. More studies are needed to evaluate to their values in predicting prognosis and guiding individualized therapy.

Detection and Classification of Whale Acoustic Signals

NASA Astrophysics Data System (ADS)

Xian, Yin

This dissertation focuses on two vital challenges in relation to whale acoustic signals: detection and classification. In detection, we evaluated the influence of the uncertain ocean environment on the spectrogram-based detector, and derived the likelihood ratio of the proposed Short Time Fourier Transform detector. Experimental results showed that the proposed detector outperforms detectors based on the spectrogram. The proposed detector is more sensitive to environmental changes because it includes phase information. In classification, our focus is on finding a robust and sparse representation of whale vocalizations. Because whale vocalizations can be modeled as polynomial phase signals, we can represent the whale calls by their polynomial phase coefficients. In this dissertation, we used the Weyl transform to capture chirp rate information, and used a two dimensional feature set to represent whale vocalizations globally. Experimental results showed that our Weyl feature set outperforms chirplet coefficients and MFCC (Mel Frequency Cepstral Coefficients) when applied to our collected data. Since whale vocalizations can be represented by polynomial phase coefficients, it is plausible that the signals lie on a manifold parameterized by these coefficients. We also studied the intrinsic structure of high dimensional whale data by exploiting its geometry. Experimental results showed that nonlinear mappings such as Laplacian Eigenmap and ISOMAP outperform linear mappings such as PCA and MDS, suggesting that the whale acoustic data is nonlinear. We also explored deep learning algorithms on whale acoustic data. We built each layer as convolutions with either a PCA filter bank (PCANet) or a DCT filter bank (DCTNet). With the DCT filter bank, each layer has different a time-frequency scale representation, and from this, one can extract different physical information. Experimental results showed that our PCANet and DCTNet achieve high classification rate on the whale vocalization data set. The word error rate of the DCTNet feature is similar to the MFSC in speech recognition tasks, suggesting that the convolutional network is able to reveal acoustic content of speech signals.

48 CFR 47.305-9 - Commodity description and freight classification.

Code of Federal Regulations, 2014 CFR

2014-10-01

... of previously shipped items, and different freight classifications may apply, the contracting officer... freight classification. 47.305-9 Section 47.305-9 Federal Acquisition Regulations System FEDERAL... Commodity description and freight classification. (a) Generally, the freight rate for supplies is based on...

48 CFR 47.305-9 - Commodity description and freight classification.

Code of Federal Regulations, 2013 CFR

2013-10-01

... of previously shipped items, and different freight classifications may apply, the contracting officer... freight classification. 47.305-9 Section 47.305-9 Federal Acquisition Regulations System FEDERAL... Commodity description and freight classification. (a) Generally, the freight rate for supplies is based on...

48 CFR 47.305-9 - Commodity description and freight classification.

Code of Federal Regulations, 2012 CFR

2012-10-01

... of previously shipped items, and different freight classifications may apply, the contracting officer... freight classification. 47.305-9 Section 47.305-9 Federal Acquisition Regulations System FEDERAL... Commodity description and freight classification. (a) Generally, the freight rate for supplies is based on...

48 CFR 47.305-9 - Commodity description and freight classification.

Code of Federal Regulations, 2011 CFR

2011-10-01

... of previously shipped items, and different freight classifications may apply, the contracting officer... freight classification. 47.305-9 Section 47.305-9 Federal Acquisition Regulations System FEDERAL... Commodity description and freight classification. (a) Generally, the freight rate for supplies is based on...

77 FR 59989 - Labor Surplus Area Classification Under Executive Orders

Federal Register 2010, 2011, 2012, 2013, 2014

2012-10-01

... DEPARTMENT OF LABOR Employment and Training Administration Labor Surplus Area Classification Under... Bureau of Labor Statistics are used in making these classifications. The average unemployment rate for all states includes data for the Commonwealth of Puerto Rico. The basic LSA classification criteria...

Multilingual vocal emotion recognition and classification using back propagation neural network

NASA Astrophysics Data System (ADS)

Kayal, Apoorva J.; Nirmal, Jagannath

2016-03-01

This work implements classification of different emotions in different languages using Artificial Neural Networks (ANN). Mel Frequency Cepstral Coefficients (MFCC) and Short Term Energy (STE) have been considered for creation of feature set. An emotional speech corpus consisting of 30 acted utterances per emotion has been developed. The emotions portrayed in this work are Anger, Joy and Neutral in each of English, Marathi and Hindi languages. Different configurations of Artificial Neural Networks have been employed for classification purposes. The performance of the classifiers has been evaluated by False Negative Rate (FNR), False Positive Rate (FPR), True Positive Rate (TPR) and True Negative Rate (TNR).

Classification of EEG Signals Based on Pattern Recognition Approach.

PubMed

Amin, Hafeez Ullah; Mumtaz, Wajid; Subhani, Ahmad Rauf; Saad, Mohamad Naufal Mohamad; Malik, Aamir Saeed

2017-01-01

Feature extraction is an important step in the process of electroencephalogram (EEG) signal classification. The authors propose a "pattern recognition" approach that discriminates EEG signals recorded during different cognitive conditions. Wavelet based feature extraction such as, multi-resolution decompositions into detailed and approximate coefficients as well as relative wavelet energy were computed. Extracted relative wavelet energy features were normalized to zero mean and unit variance and then optimized using Fisher's discriminant ratio (FDR) and principal component analysis (PCA). A high density EEG dataset validated the proposed method (128-channels) by identifying two classifications: (1) EEG signals recorded during complex cognitive tasks using Raven's Advance Progressive Metric (RAPM) test; (2) EEG signals recorded during a baseline task (eyes open). Classifiers such as, K-nearest neighbors (KNN), Support Vector Machine (SVM), Multi-layer Perceptron (MLP), and Naïve Bayes (NB) were then employed. Outcomes yielded 99.11% accuracy via SVM classifier for coefficient approximations (A5) of low frequencies ranging from 0 to 3.90 Hz. Accuracy rates for detailed coefficients were 98.57 and 98.39% for SVM and KNN, respectively; and for detailed coefficients (D5) deriving from the sub-band range (3.90-7.81 Hz). Accuracy rates for MLP and NB classifiers were comparable at 97.11-89.63% and 91.60-81.07% for A5 and D5 coefficients, respectively. In addition, the proposed approach was also applied on public dataset for classification of two cognitive tasks and achieved comparable classification results, i.e., 93.33% accuracy with KNN. The proposed scheme yielded significantly higher classification performances using machine learning classifiers compared to extant quantitative feature extraction. These results suggest the proposed feature extraction method reliably classifies EEG signals recorded during cognitive tasks with a higher degree of accuracy.

Classification of EEG Signals Based on Pattern Recognition Approach

PubMed Central

Amin, Hafeez Ullah; Mumtaz, Wajid; Subhani, Ahmad Rauf; Saad, Mohamad Naufal Mohamad; Malik, Aamir Saeed

2017-01-01

Feature extraction is an important step in the process of electroencephalogram (EEG) signal classification. The authors propose a “pattern recognition” approach that discriminates EEG signals recorded during different cognitive conditions. Wavelet based feature extraction such as, multi-resolution decompositions into detailed and approximate coefficients as well as relative wavelet energy were computed. Extracted relative wavelet energy features were normalized to zero mean and unit variance and then optimized using Fisher's discriminant ratio (FDR) and principal component analysis (PCA). A high density EEG dataset validated the proposed method (128-channels) by identifying two classifications: (1) EEG signals recorded during complex cognitive tasks using Raven's Advance Progressive Metric (RAPM) test; (2) EEG signals recorded during a baseline task (eyes open). Classifiers such as, K-nearest neighbors (KNN), Support Vector Machine (SVM), Multi-layer Perceptron (MLP), and Naïve Bayes (NB) were then employed. Outcomes yielded 99.11% accuracy via SVM classifier for coefficient approximations (A5) of low frequencies ranging from 0 to 3.90 Hz. Accuracy rates for detailed coefficients were 98.57 and 98.39% for SVM and KNN, respectively; and for detailed coefficients (D5) deriving from the sub-band range (3.90–7.81 Hz). Accuracy rates for MLP and NB classifiers were comparable at 97.11–89.63% and 91.60–81.07% for A5 and D5 coefficients, respectively. In addition, the proposed approach was also applied on public dataset for classification of two cognitive tasks and achieved comparable classification results, i.e., 93.33% accuracy with KNN. The proposed scheme yielded significantly higher classification performances using machine learning classifiers compared to extant quantitative feature extraction. These results suggest the proposed feature extraction method reliably classifies EEG signals recorded during cognitive tasks with a higher degree of accuracy. PMID:29209190

A Semi-Automated Machine Learning Algorithm for Tree Cover Delineation from 1-m Naip Imagery Using a High Performance Computing Architecture

NASA Astrophysics Data System (ADS)

Basu, S.; Ganguly, S.; Nemani, R. R.; Mukhopadhyay, S.; Milesi, C.; Votava, P.; Michaelis, A.; Zhang, G.; Cook, B. D.; Saatchi, S. S.; Boyda, E.

2014-12-01

Accurate tree cover delineation is a useful instrument in the derivation of Above Ground Biomass (AGB) density estimates from Very High Resolution (VHR) satellite imagery data. Numerous algorithms have been designed to perform tree cover delineation in high to coarse resolution satellite imagery, but most of them do not scale to terabytes of data, typical in these VHR datasets. In this paper, we present an automated probabilistic framework for the segmentation and classification of 1-m VHR data as obtained from the National Agriculture Imagery Program (NAIP) for deriving tree cover estimates for the whole of Continental United States, using a High Performance Computing Architecture. The results from the classification and segmentation algorithms are then consolidated into a structured prediction framework using a discriminative undirected probabilistic graphical model based on Conditional Random Field (CRF), which helps in capturing the higher order contextual dependencies between neighboring pixels. Once the final probability maps are generated, the framework is updated and re-trained by incorporating expert knowledge through the relabeling of misclassified image patches. This leads to a significant improvement in the true positive rates and reduction in false positive rates. The tree cover maps were generated for the state of California, which covers a total of 11,095 NAIP tiles and spans a total geographical area of 163,696 sq. miles. Our framework produced correct detection rates of around 85% for fragmented forests and 70% for urban tree cover areas, with false positive rates lower than 3% for both regions. Comparative studies with the National Land Cover Data (NLCD) algorithm and the LiDAR high-resolution canopy height model shows the effectiveness of our algorithm in generating accurate high-resolution tree cover maps.

The impact of ICD-9 revascularization procedure codes on estimates of racial disparities in ischemic stroke.

PubMed

Boan, Andrea D; Voeks, Jenifer H; Feng, Wuwei Wayne; Bachman, David L; Jauch, Edward C; Adams, Robert J; Ovbiagele, Bruce; Lackland, Daniel T

2014-01-01

The use of International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9) diagnostic codes can identify racial disparities in ischemic stroke hospitalizations; however, inclusion of revascularization procedure codes as acute stroke events may affect the magnitude of the risk difference. This study assesses the impact of excluding revascularization procedure codes in the ICD-9 definition of ischemic stroke, compared with the traditional inclusive definition, on racial disparity estimates for stroke incidence and recurrence. Patients discharged with a diagnosis of ischemic stroke (ICD-9 codes 433.00-434.91 and 436) were identified from a statewide inpatient discharge database from 2010 to 2012. Race-age specific disparity estimates of stroke incidence and recurrence and 1-year cumulative recurrent stroke rates were compared between the routinely used traditional classification and a modified classification of stroke that excluded primary ICD-9 cerebral revascularization procedures codes (38.12, 00.61, and 00.63). The traditional classification identified 7878 stroke hospitalizations, whereas the modified classification resulted in 18% fewer hospitalizations (n = 6444). The age-specific black to white rate ratios were significantly higher in the modified than in the traditional classification for stroke incidence (rate ratio, 1.50; 95% confidence interval [CI], 1.43-1.58 vs. rate ratio, 1.24; 95% CI, 1.18-1.30, respectively). In whites, the 1-year cumulative recurrence rate was significantly reduced by 46% (45-64 years) and 49% (≥ 65 years) in the modified classification, largely explained by a higher rate of cerebral revascularization procedures among whites. There were nonsignificant reductions of 14% (45-64 years) and 19% (≥ 65 years) among blacks. Including cerebral revascularization procedure codes overestimates hospitalization rates for ischemic stroke and significantly underestimates the racial disparity estimates in stroke incidence and recurrence. Copyright © 2014 National Stroke Association. Published by Elsevier Inc. All rights reserved.

Using Gaussian mixture models to detect and classify dolphin whistles and pulses.

PubMed

Peso Parada, Pablo; Cardenal-López, Antonio

2014-06-01

In recent years, a number of automatic detection systems for free-ranging cetaceans have been proposed that aim to detect not just surfaced, but also submerged, individuals. These systems are typically based on pattern-recognition techniques applied to underwater acoustic recordings. Using a Gaussian mixture model, a classification system was developed that detects sounds in recordings and classifies them as one of four types: background noise, whistles, pulses, and combined whistles and pulses. The classifier was tested using a database of underwater recordings made off the Spanish coast during 2011. Using cepstral-coefficient-based parameterization, a sound detection rate of 87.5% was achieved for a 23.6% classification error rate. To improve these results, two parameters computed using the multiple signal classification algorithm and an unpredictability measure were included in the classifier. These parameters, which helped to classify the segments containing whistles, increased the detection rate to 90.3% and reduced the classification error rate to 18.1%. Finally, the potential of the multiple signal classification algorithm and unpredictability measure for estimating whistle contours and classifying cetacean species was also explored, with promising results.

New technique for real-time distortion-invariant multiobject recognition and classification

NASA Astrophysics Data System (ADS)

Hong, Rutong; Li, Xiaoshun; Hong, En; Wang, Zuyi; Wei, Hongan

2001-04-01

A real-time hybrid distortion-invariant OPR system was established to make 3D multiobject distortion-invariant automatic pattern recognition. Wavelet transform technique was used to make digital preprocessing of the input scene, to depress the noisy background and enhance the recognized object. A three-layer backpropagation artificial neural network was used in correlation signal post-processing to perform multiobject distortion-invariant recognition and classification. The C-80 and NOA real-time processing ability and the multithread programming technology were used to perform high speed parallel multitask processing and speed up the post processing rate to ROIs. The reference filter library was constructed for the distortion version of 3D object model images based on the distortion parameter tolerance measuring as rotation, azimuth and scale. The real-time optical correlation recognition testing of this OPR system demonstrates that using the preprocessing, post- processing, the nonlinear algorithm os optimum filtering, RFL construction technique and the multithread programming technology, a high possibility of recognition and recognition rate ere obtained for the real-time multiobject distortion-invariant OPR system. The recognition reliability and rate was improved greatly. These techniques are very useful to automatic target recognition.

Odontogenic keratocyst: What is in the name?

PubMed Central

Nayak, Meghanand T.; Singh, Anjali; Singhvi, Abhishek; Sharma, Rohit

2013-01-01

The classification of odontogenic cysts is complicated and can create confusion for both clinicians and pathologists. The odontogenic keratocyst (OKC) is an enigmatic developmental cyst that deserves special attention. It has characteristic histopathological and clinical features; but, what makes this cyst special is its aggressive behavior and high recurrence rate. Despite of many classifications and nomenclature, unfortunately the clinicians still have to face difficulties in the management of this commonly found jaw lesion. This article is an effort to provide an overview of various aspects of OKC with emphasis on nomenclature, recurrence, molecular aspects, and management of OKC. PMID:24082717

Integrating image processing and classification technology into automated polarizing film defect inspection

NASA Astrophysics Data System (ADS)

Kuo, Chung-Feng Jeffrey; Lai, Chun-Yu; Kao, Chih-Hsiang; Chiu, Chin-Hsun

2018-05-01

In order to improve the current manual inspection and classification process for polarizing film on production lines, this study proposes a high precision automated inspection and classification system for polarizing film, which is used for recognition and classification of four common defects: dent, foreign material, bright spot, and scratch. First, the median filter is used to remove the impulse noise in the defect image of polarizing film. The random noise in the background is smoothed by the improved anisotropic diffusion, while the edge detail of the defect region is sharpened. Next, the defect image is transformed by Fourier transform to the frequency domain, combined with a Butterworth high pass filter to sharpen the edge detail of the defect region, and brought back by inverse Fourier transform to the spatial domain to complete the image enhancement process. For image segmentation, the edge of the defect region is found by Canny edge detector, and then the complete defect region is obtained by two-stage morphology processing. For defect classification, the feature values, including maximum gray level, eccentricity, the contrast, and homogeneity of gray level co-occurrence matrix (GLCM) extracted from the images, are used as the input of the radial basis function neural network (RBFNN) and back-propagation neural network (BPNN) classifier, 96 defect images are then used as training samples, and 84 defect images are used as testing samples to validate the classification effect. The result shows that the classification accuracy by using RBFNN is 98.9%. Thus, our proposed system can be used by manufacturing companies for a higher yield rate and lower cost. The processing time of one single image is 2.57 seconds, thus meeting the practical application requirement of an industrial production line.

Uncovering state-dependent relationships in shallow lakes using Bayesian latent variable regression.

PubMed

Vitense, Kelsey; Hanson, Mark A; Herwig, Brian R; Zimmer, Kyle D; Fieberg, John

2018-03-01

Ecosystems sometimes undergo dramatic shifts between contrasting regimes. Shallow lakes, for instance, can transition between two alternative stable states: a clear state dominated by submerged aquatic vegetation and a turbid state dominated by phytoplankton. Theoretical models suggest that critical nutrient thresholds differentiate three lake types: highly resilient clear lakes, lakes that may switch between clear and turbid states following perturbations, and highly resilient turbid lakes. For effective and efficient management of shallow lakes and other systems, managers need tools to identify critical thresholds and state-dependent relationships between driving variables and key system features. Using shallow lakes as a model system for which alternative stable states have been demonstrated, we developed an integrated framework using Bayesian latent variable regression (BLR) to classify lake states, identify critical total phosphorus (TP) thresholds, and estimate steady state relationships between TP and chlorophyll a (chl a) using cross-sectional data. We evaluated the method using data simulated from a stochastic differential equation model and compared its performance to k-means clustering with regression (KMR). We also applied the framework to data comprising 130 shallow lakes. For simulated data sets, BLR had high state classification rates (median/mean accuracy >97%) and accurately estimated TP thresholds and state-dependent TP-chl a relationships. Classification and estimation improved with increasing sample size and decreasing noise levels. Compared to KMR, BLR had higher classification rates and better approximated the TP-chl a steady state relationships and TP thresholds. We fit the BLR model to three different years of empirical shallow lake data, and managers can use the estimated bifurcation diagrams to prioritize lakes for management according to their proximity to thresholds and chance of successful rehabilitation. Our model improves upon previous methods for shallow lakes because it allows classification and regression to occur simultaneously and inform one another, directly estimates TP thresholds and the uncertainty associated with thresholds and state classifications, and enables meaningful constraints to be built into models. The BLR framework is broadly applicable to other ecosystems known to exhibit alternative stable states in which regression can be used to establish relationships between driving variables and state variables. © 2017 by the Ecological Society of America.

78 FR 63248 - Labor Surplus Area Classification under Executive Orders 12073 and 10582

Federal Register 2010, 2011, 2012, 2013, 2014

2013-10-23

... DEPARTMENT OF LABOR Employment and Training Administration Labor Surplus Area Classification under... Statistics unemployment estimates to make these classifications. The average unemployment rate for all states includes data for the Commonwealth of Puerto Rico. The basic LSA classification criteria include a ``floor...

42 CFR 412.60 - DRG classification and weighting factors.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 42 Public Health 2 2011-10-01 2011-10-01 false DRG classification and weighting factors. 412.60... Determining Prospective Payment Federal Rates for Inpatient Operating Costs § 412.60 DRG classification and weighting factors. (a) Diagnosis-related groups. CMS establishs a classification of inpatient hospital...

42 CFR 412.60 - DRG classification and weighting factors.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 42 Public Health 2 2010-10-01 2010-10-01 false DRG classification and weighting factors. 412.60... Determining Prospective Payment Federal Rates for Inpatient Operating Costs § 412.60 DRG classification and weighting factors. (a) Diagnosis-related groups. CMS establishs a classification of inpatient hospital...

Producing a satellite-derived map and modelling Spartina alterniflora expansion for Willapa Bay in Washington State

NASA Astrophysics Data System (ADS)

Berlin, Cynthia Jane

1998-12-01

This research addresses the identification of the areal extent of the intertidal wetlands of Willapa Bay, Washington, and the evaluation of the potential for exotic Spartina alterniflora (smooth cordgrass) expansion in the bay using a spatial geographic approach. It is hoped that the results will address not only the management needs of the study area but provide a research design that may be applied to studies of other coastal wetlands. Four satellite images, three Landsat Multi-Spectral (MSS) and one Thematic Mapper (TM), are used to derive a map showing areas of water, low, middle and high intertidal, and upland. Two multi-date remote sensing mapping techniques are assessed: a supervised classification using density-slicing and an unsupervised classification using an ISODATA algorithm. Statistical comparisons are made between the resultant derived maps and the U.S.G.S. topographic maps for the Willapa Bay area. The potential for Spartina expansion in the bay is assessed using a sigmoidal (logistic) growth model and a spatial modelling procedure for four possible growth scenarios: without management controls (Business-as-Usual), with moderate management controls (e.g. harvesting to eliminate seed setting), under a hypothetical increase in the growth rate that may reflect favorable environmental changes, and under a hypothetical decrease in the growth rate that may reflect aggressive management controls. Comparisons for the statistics of the two mapping techniques suggest that although the unsupervised classification method performed satisfactorily, the supervised classification (density-slicing) method provided more satisfactory results. Results from the modelling of potential Spartina expansion suggest that Spartina expansion will proceed rapidly for the Business-as-Usual and hypothetical increase in the growth rate scenario, and at a slower rate for the elimination of seed setting and hypothetical decrease in the growth rate scenarios, until all potential habitat is filled.

On-line analysis of algae in water by discrete three-dimensional fluorescence spectroscopy.

PubMed

Zhao, Nanjing; Zhang, Xiaoling; Yin, Gaofang; Yang, Ruifang; Hu, Li; Chen, Shuang; Liu, Jianguo; Liu, Wenqing

2018-03-19

In view of the problem of the on-line measurement of algae classification, a method of algae classification and concentration determination based on the discrete three-dimensional fluorescence spectra was studied in this work. The discrete three-dimensional fluorescence spectra of twelve common species of algae belonging to five categories were analyzed, the discrete three-dimensional standard spectra of five categories were built, and the recognition, classification and concentration prediction of algae categories were realized by the discrete three-dimensional fluorescence spectra coupled with non-negative weighted least squares linear regression analysis. The results show that similarities between discrete three-dimensional standard spectra of different categories were reduced and the accuracies of recognition, classification and concentration prediction of the algae categories were significantly improved. By comparing with that of the chlorophyll a fluorescence excitation spectra method, the recognition accuracy rate in pure samples by discrete three-dimensional fluorescence spectra is improved 1.38%, and the recovery rate and classification accuracy in pure diatom samples 34.1% and 46.8%, respectively; the recognition accuracy rate of mixed samples by discrete-three dimensional fluorescence spectra is enhanced by 26.1%, the recovery rate of mixed samples with Chlorophyta 37.8%, and the classification accuracy of mixed samples with diatoms 54.6%.

Presence of indicator plant species as a predictor of wetland vegetation integrity

USGS Publications Warehouse

Stapanian, Martin A.; Adams, Jean V.; Gara, Brian

2013-01-01

We fit regression and classification tree models to vegetation data collected from Ohio (USA) wetlands to determine (1) which species best predict Ohio vegetation index of biotic integrity (OVIBI) score and (2) which species best predict high-quality wetlands (OVIBI score >75). The simplest regression tree model predicted OVIBI score based on the occurrence of three plant species: skunk-cabbage (Symplocarpus foetidus), cinnamon fern (Osmunda cinnamomea), and swamp rose (Rosa palustris). The lowest OVIBI scores were best predicted by the absence of the selected plant species rather than by the presence of other species. The simplest classification tree model predicted high-quality wetlands based on the occurrence of two plant species: skunk-cabbage and marsh-fern (Thelypteris palustris). The overall misclassification rate from this tree was 13 %. Again, low-quality wetlands were better predicted than high-quality wetlands by the absence of selected species rather than the presence of other species using the classification tree model. Our results suggest that a species’ wetland status classification and coefficient of conservatism are of little use in predicting wetland quality. A simple, statistically derived species checklist such as the one created in this study could be used by field biologists to quickly and efficiently identify wetland sites likely to be regulated as high-quality, and requiring more intensive field assessments. Alternatively, it can be used for advanced determinations of low-quality wetlands. Agencies can save considerable money by screening wetlands for the presence/absence of such “indicator” species before issuing permits.

Levels of Evidence in Cosmetic Surgery: Analysis and Recommendations Using a New CLEAR Classification

PubMed Central

2013-01-01

Background: The Level of Evidence rating was introduced in 2011 to grade the quality of publications. This system evaluates study design but does not assess several other quality indicators. This study introduces a new “Cosmetic Level of Evidence And Recommendation” (CLEAR) classification that includes additional methodological criteria and compares this new classification with the existing system. Methods: All rated publications in the Cosmetic Section of Plastic and Reconstructive Surgery, July 2011 through June 2013, were evaluated. The published Level of Evidence rating (1–5) and criteria relevant to study design and methodology for each study were tabulated. A new CLEAR rating was assigned to each article, including a recommendation grade (A–D). The published Level of Evidence rating (1–5) was compared with the recommendation grade determined using the CLEAR classification. Results: Among the 87 cosmetic articles, 48 studies (55%) were designated as level 4. Three articles were assigned a level 1, but they contained deficiencies sufficient to undermine the conclusions. The correlation between the published Level of Evidence classification (1–5) and CLEAR Grade (A–D) was weak (ρ = 0.11, not significant). Only 41 studies (48%) evaluated consecutive patients or consecutive patients meeting inclusion criteria. Conclusions: The CLEAR classification considers methodological factors in evaluating study reliability. A prospective study among consecutive patients meeting eligibility criteria, with a reported inclusion rate, the use of contemporaneous controls when indicated, and consideration of confounders is a realistic goal. Such measures are likely to improve study quality. PMID:25289261

Shift-invariant discrete wavelet transform analysis for retinal image classification.

PubMed

Khademi, April; Krishnan, Sridhar

2007-12-01

This work involves retinal image classification and a novel analysis system was developed. From the compressed domain, the proposed scheme extracts textural features from wavelet coefficients, which describe the relative homogeneity of localized areas of the retinal images. Since the discrete wavelet transform (DWT) is shift-variant, a shift-invariant DWT was explored to ensure that a robust feature set was extracted. To combat the small database size, linear discriminant analysis classification was used with the leave one out method. 38 normal and 48 abnormal (exudates, large drusens, fine drusens, choroidal neovascularization, central vein and artery occlusion, histoplasmosis, arteriosclerotic retinopathy, hemi-central retinal vein occlusion and more) were used and a specificity of 79% and sensitivity of 85.4% were achieved (the average classification rate is 82.2%). The success of the system can be accounted to the highly robust feature set which included translation, scale and semi-rotational, features. Additionally, this technique is database independent since the features were specifically tuned to the pathologies of the human eye.

Formalized classification of moss litters in swampy spruce forests of intermontane depressions of Kuznetsk Alatau

NASA Astrophysics Data System (ADS)

Efremova, T. T.; Avrova, A. F.; Efremov, S. P.

2016-09-01

The approaches of multivariate statistics have been used for the numerical classification of morphogenetic types of moss litters in swampy spruce forests according to their physicochemical properties (the ash content, decomposition degree, bulk density, pH, mass, and thickness). Three clusters of moss litters— peat, peaty, and high-ash peaty—have been specified. The functions of classification for identification of new objects have been calculated and evaluated. The degree of decomposition and the ash content are the main classification parameters of litters, though all other characteristics are also statistically significant. The final prediction accuracy of the assignment of a litter to a particular cluster is 86%. Two leading factors participating in the clustering of litters have been determined. The first factor—the degree of transformation of plant remains (quality)—specifies 49% of the total variance, and the second factor—the accumulation rate (quantity)— specifies 26% of the total variance. The morphogenetic structure and physicochemical properties of the clusters of moss litters are characterized.

Gender classification from video under challenging operating conditions

NASA Astrophysics Data System (ADS)

Mendoza-Schrock, Olga; Dong, Guozhu

2014-06-01

The literature is abundant with papers on gender classification research. However the majority of such research is based on the assumption that there is enough resolution so that the subject's face can be resolved. Hence the majority of the research is actually in the face recognition and facial feature area. A gap exists for gender classification under challenging operating conditions—different seasonal conditions, different clothing, etc.—and when the subject's face cannot be resolved due to lack of resolution. The Seasonal Weather and Gender (SWAG) Database is a novel database that contains subjects walking through a scene under operating conditions that span a calendar year. This paper exploits a subset of that database—the SWAG One dataset—using data mining techniques, traditional classifiers (ex. Naïve Bayes, Support Vector Machine, etc.) and traditional (canny edge detection, etc.) and non-traditional (height/width ratios, etc.) feature extractors to achieve high correct gender classification rates (greater than 85%). Another novelty includes exploiting frame differentials.

An Efficient Hardware Circuit for Spike Sorting Based on Competitive Learning Networks.

PubMed

Chen, Huan-Yuan; Chen, Chih-Chang; Hwang, Wen-Jyi

2017-09-28

This study aims to present an effective VLSI circuit for multi-channel spike sorting. The circuit supports the spike detection, feature extraction and classification operations. The detection circuit is implemented in accordance with the nonlinear energy operator algorithm. Both the peak detection and area computation operations are adopted for the realization of the hardware architecture for feature extraction. The resulting feature vectors are classified by a circuit for competitive learning (CL) neural networks. The CL circuit supports both online training and classification. In the proposed architecture, all the channels share the same detection, feature extraction, learning and classification circuits for a low area cost hardware implementation. The clock-gating technique is also employed for reducing the power dissipation. To evaluate the performance of the architecture, an application-specific integrated circuit (ASIC) implementation is presented. Experimental results demonstrate that the proposed circuit exhibits the advantages of a low chip area, a low power dissipation and a high classification success rate for spike sorting.

An Efficient Hardware Circuit for Spike Sorting Based on Competitive Learning Networks

PubMed Central

Chen, Huan-Yuan; Chen, Chih-Chang

2017-01-01

This study aims to present an effective VLSI circuit for multi-channel spike sorting. The circuit supports the spike detection, feature extraction and classification operations. The detection circuit is implemented in accordance with the nonlinear energy operator algorithm. Both the peak detection and area computation operations are adopted for the realization of the hardware architecture for feature extraction. The resulting feature vectors are classified by a circuit for competitive learning (CL) neural networks. The CL circuit supports both online training and classification. In the proposed architecture, all the channels share the same detection, feature extraction, learning and classification circuits for a low area cost hardware implementation. The clock-gating technique is also employed for reducing the power dissipation. To evaluate the performance of the architecture, an application-specific integrated circuit (ASIC) implementation is presented. Experimental results demonstrate that the proposed circuit exhibits the advantages of a low chip area, a low power dissipation and a high classification success rate for spike sorting. PMID:28956859

Image classification using multiscale information fusion based on saliency driven nonlinear diffusion filtering.

PubMed

Hu, Weiming; Hu, Ruiguang; Xie, Nianhua; Ling, Haibin; Maybank, Stephen

2014-04-01

In this paper, we propose saliency driven image multiscale nonlinear diffusion filtering. The resulting scale space in general preserves or even enhances semantically important structures such as edges, lines, or flow-like structures in the foreground, and inhibits and smoothes clutter in the background. The image is classified using multiscale information fusion based on the original image, the image at the final scale at which the diffusion process converges, and the image at a midscale. Our algorithm emphasizes the foreground features, which are important for image classification. The background image regions, whether considered as contexts of the foreground or noise to the foreground, can be globally handled by fusing information from different scales. Experimental tests of the effectiveness of the multiscale space for the image classification are conducted on the following publicly available datasets: 1) the PASCAL 2005 dataset; 2) the Oxford 102 flowers dataset; and 3) the Oxford 17 flowers dataset, with high classification rates.

A real-time phenotyping framework using machine learning for plant stress severity rating in soybean.

PubMed

Naik, Hsiang Sing; Zhang, Jiaoping; Lofquist, Alec; Assefa, Teshale; Sarkar, Soumik; Ackerman, David; Singh, Arti; Singh, Asheesh K; Ganapathysubramanian, Baskar

2017-01-01

Phenotyping is a critical component of plant research. Accurate and precise trait collection, when integrated with genetic tools, can greatly accelerate the rate of genetic gain in crop improvement. However, efficient and automatic phenotyping of traits across large populations is a challenge; which is further exacerbated by the necessity of sampling multiple environments and growing replicated trials. A promising approach is to leverage current advances in imaging technology, data analytics and machine learning to enable automated and fast phenotyping and subsequent decision support. In this context, the workflow for phenotyping (image capture → data storage and curation → trait extraction → machine learning/classification → models/apps for decision support) has to be carefully designed and efficiently executed to minimize resource usage and maximize utility. We illustrate such an end-to-end phenotyping workflow for the case of plant stress severity phenotyping in soybean, with a specific focus on the rapid and automatic assessment of iron deficiency chlorosis (IDC) severity on thousands of field plots. We showcase this analytics framework by extracting IDC features from a set of ~4500 unique canopies representing a diverse germplasm base that have different levels of IDC, and subsequently training a variety of classification models to predict plant stress severity. The best classifier is then deployed as a smartphone app for rapid and real time severity rating in the field. We investigated 10 different classification approaches, with the best classifier being a hierarchical classifier with a mean per-class accuracy of ~96%. We construct a phenotypically meaningful 'population canopy graph', connecting the automatically extracted canopy trait features with plant stress severity rating. We incorporated this image capture → image processing → classification workflow into a smartphone app that enables automated real-time evaluation of IDC scores using digital images of the canopy. We expect this high-throughput framework to help increase the rate of genetic gain by providing a robust extendable framework for other abiotic and biotic stresses. We further envision this workflow embedded onto a high throughput phenotyping ground vehicle and unmanned aerial system that will allow real-time, automated stress trait detection and quantification for plant research, breeding and stress scouting applications.

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

PubMed

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.

Anxiety Disorders in Childhood: Casting a Nomological Net

ERIC Educational Resources Information Center

Weems, Carl F.; Stickle, Timothy R.

2005-01-01

Empirical research highlights the need for improving the childhood anxiety disorder diagnostic classification system. In particular, inconsistencies in the stability estimates of childhood anxiety disorders and high rates of comorbidity call into the question the utility of the current "DSM" criteria. This paper makes a case for utilizing a…

Industrial Landscapes: Perception and Classification as Learning Activities

ERIC Educational Resources Information Center

Peters, Gary; Larkin, Robert P.

1977-01-01

Suggests a high school or college level program of subjective perception and evaluation of industrial landscapes. Slides of local or national industrial sites can be rated and classified as pleasing or unpleasing in terms of variables such as architectural style of building, smokestacks, age, and visible pollution. (AV)

Heart Rate Variability Dynamics for the Prognosis of Cardiovascular Risk

PubMed Central

Ramirez-Villegas, Juan F.; Lam-Espinosa, Eric; Ramirez-Moreno, David F.; Calvo-Echeverry, Paulo C.; Agredo-Rodriguez, Wilfredo

2011-01-01

Statistical, spectral, multi-resolution and non-linear methods were applied to heart rate variability (HRV) series linked with classification schemes for the prognosis of cardiovascular risk. A total of 90 HRV records were analyzed: 45 from healthy subjects and 45 from cardiovascular risk patients. A total of 52 features from all the analysis methods were evaluated using standard two-sample Kolmogorov-Smirnov test (KS-test). The results of the statistical procedure provided input to multi-layer perceptron (MLP) neural networks, radial basis function (RBF) neural networks and support vector machines (SVM) for data classification. These schemes showed high performances with both training and test sets and many combinations of features (with a maximum accuracy of 96.67%). Additionally, there was a strong consideration for breathing frequency as a relevant feature in the HRV analysis. PMID:21386966

The magic of numbers: malignant melanoma between science and pseudoscience.

PubMed

Weyers, Wolfgang

2011-06-01

In 2009, a new system for staging and classification of malignant melanoma has been proposed by the American Joint Committee on Cancer (AJCC). The AJCC recommends that staging of primary melanoma be based on 3 criteria, namely, thickness, ulceration, and mitotic rate, the latter substituting Clark levels in the previous classification. In melanomas measuring ≤1 mm in thickness, ulceration or finding of single mitotic figure in the dermis defines stage T1b. According to the AJCC, sentinel lymph node dissection should be considered for those melanomas because of a significantly impaired prognosis. As with other prognostic parameters, however, assessment of mitotic rate, with one mitotic figure being the cutoff point, is highly unreliable, and statistics based on such data lack validity. Despite the large database being employed, they may be pseudoscience rather than science.

[Pay attention to the application of the international intraocular retinoblastoma classification and sequential multiple modality treatment].

PubMed

Fan, X Q

2017-08-11

Retinoblastoma (RB) is the most common intraocular malignancy in childhood. It may seriously affect vision, and even threaten the life. The early diagnosis rate of RB in China remains low, and the majority of patients are at late phase with high rates of enucleation and mortality. The International Intraocular Retinoblastoma Classification and TNM staging system are guidances for therapeutic choices and bases for prognosis evaluation. Based on the sequential multi-method treatment modality, chemotherapy combined with local therapy is the mainstream in dealing with RB, which may maximize the results of eye saving and even vision retaining. New therapeutic techniques including supra-selective ophthalmic artery interventional chemotherapy and intravitreal chemotherapy can further improve the efficacy of treatment, especially the eye salvage rate. The overall level of RB treatment should be improved by promoting the international staging, new therapeutic techniques, and the sequential multiple modality treatment. (Chin J Ophthalmol, 2017, 53: 561 - 565) .

Preoperative classification assessment reliability and influence on the length of intertrochanteric fracture operations.

PubMed

Shen, Jing; Hu, FangKe; Zhang, LiHai; Tang, PeiFu; Bi, ZhengGang

2013-04-01

The accuracy of intertrochanteric fracture classification is important; indeed, the patient outcomes are dependent on their classification. The aim of this study was to use the AO classification system to evaluate the variation in classification between X-ray and computed tomography (CT)/3D CT images. Then, differences in the length of surgery were evaluated based on two examinations. Intertrochanteric fractures were reviewed and surgeons were interviewed. The rates of correct discrimination and misclassification (overestimates and underestimates) probabilities were determined. The impact of misclassification on length of surgery was also evaluated. In total, 370 patents and four surgeons were included in the study. All patients had X-ray images and 210 patients had CT/3D CT images. Of them, 214 and 156 patients were treated by intramedullary and extramedullary fixation systems, respectively. The mean length of surgery was 62.1 ± 17.7 min. The overall rate of correct discrimination was 83.8 % and in the classification of A1, A2 and A3 were 80.0, 85.7 and 82.4 %, respectively. The rate of misclassification showed no significant difference between stable and unstable fractures (21.3 vs 13.1 %, P = 0.173). The overall rates of overestimates and underestimates were significantly different (5 vs 11.25 %, P = 0.041). Subtracting the rate of overestimates from underestimates had a positive correlation with prolonged surgery and showed a significant difference with intramedullary fixation (P < 0.001). Classification based on the AO system was good in terms of consistency. CT/3D CT examination was more reliable and more helpful for preoperative assessment, especially for performance of an intramedullary fixation.

Rate of caesarean sections according to the Robson classification: Analysis in a French perinatal network - Interest and limitations of the French medico-administrative data (PMSI).

PubMed

Lafitte, A-S; Dolley, P; Le Coutour, X; Benoist, G; Prime, L; Thibon, P; Dreyfus, M

2018-02-01

The objective of our study was to determine, in accordance with WHO recommendations, the rates of Caesarean sections in a French perinatal network according to the Robson classification and determine the benefit of the medico-administrative data (PMSI) to collect this indicator. This study aimed to identify the main groups contributing to local variations in the rates of Caesarean sections. A descriptive multicentric study was conducted in 13 maternity units of a French perinatal network. The rates of Caesarean sections and the contribution of each group of the Robson classification were calculated for all Caesarean sections performed in 2014. The agreement of the classification of Caesarean sections according to Robson using medico-administrative data and data collected in the patient records was measured by the Kappa index. We also analysed a 6 groups simplified Robson classification only using data from PMSI, which do not inform about parity and onset of labour. The rate of Caesarean sections was 19% (14.5-33.2) in 2014 (2924 out of 15413 deliveries). The most important contributors to the total rates were groups 1, 2 and 5, representing respectively 14.3%, 16.7% and 32.1% of the Caesarean sections. The rates were significantly different in level 1, 2b and 3 maternity units in groups 1 to 4, level 2a maternity units in group 5, and level 3 maternity units in groups 6 and 7. The agreement between the simplified Robson classification produced using the medical records and the medico-administrative data was excellent, with a Kappa index of 0.985 (0.980-0.990). To reduce the rates of Caesarean sections, audits should be conducted on groups 1, 2 and 5 and local protocols developed. Simply by collecting the parity data, the excellent metrological quality of the medico-administrative data would allow systematisation of the Robson classification for each hospital. Copyright © 2017. Published by Elsevier Masson SAS.

Micromachined cascade virtual impactor with a flow rate distributor for wide range airborne particle classification

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kim, Yong-Ho; Maeng, Jwa-Young; Park, Dongho

2007-07-23

This letter reports a module for airborne particle classification, which consists of a micromachined three-stage virtual impactor for classifying airborne particles according to their size and a flow rate distributor for supplying the required flow rate to the virtual impactor. Dioctyl sebacate particles, 100-600 nm in diameter, and carbon particles, 0.6-10 {mu}m in diameter, were used for particle classification. The collection efficiency and cutoff diameter were examined. The measured cutoff diameters of the first, second, and third stages were 135 nm, 1.9 {mu}m, and 4.8 {mu}m, respectively.

Review: correlations between oxygen affinity and sequence classifications of plant hemoglobins.

PubMed

Smagghe, Benoit J; Hoy, Julie A; Percifield, Ryan; Kundu, Suman; Hargrove, Mark S; Sarath, Gautam; Hilbert, Jean-Louis; Watts, Richard A; Dennis, Elizabeth S; Peacock, W James; Dewilde, Sylvia; Moens, Luc; Blouin, George C; Olson, John S; Appleby, Cyril A

2009-12-01

Plants express three phylogenetic classes of hemoglobins (Hb) based on sequence analyses. Class 1 and 2 Hbs are full-length globins with the classical eight helix Mb-like fold, whereas Class 3 plant Hbs resemble the truncated globins found in bacteria. With the exception of the specialized leghemoglobins, the physiological functions of these plant hemoglobins remain unknown. We have reviewed and, in some cases, measured new oxygen binding properties of a large number of Class 1 and 2 plant nonsymbiotic Hbs and leghemoglobins. We found that sequence classification correlates with distinct extents of hexacoordination with the distal histidine and markedly different overall oxygen affinities and association and dissociation rate constants. These results suggest strong selective pressure for the evolution of distinct physiological functions. The leghemoglobins evolved from the Class 2 globins and show no hexacoordination, very high rates of O(2) binding ( approximately 250 muM(-1) s(-1)), moderately high rates of O(2) dissociation ( approximately 5-15 s(-1)), and high oxygen affinity (K(d) or P(50) approximately 50 nM). These properties both facilitate O(2) diffusion to respiring N(2) fixing bacteria and reduce O(2) tension in the root nodules of legumes. The Class 1 plant Hbs show weak hexacoordination (K(HisE7) approximately 2), moderate rates of O(2) binding ( approximately 25 muM(-1) s(-1)), very small rates of O(2) dissociation ( approximately 0.16 s(-1)), and remarkably high O(2) affinities (P(50) approximately 2 nM), suggesting a function involving O(2) and nitric oxide (NO) scavenging. The Class 2 Hbs exhibit strong hexacoordination (K(HisE7) approximately 100), low rates of O(2) binding ( approximately 1 muM(-1) s(-1)), moderately low O(2) dissociation rate constants ( approximately 1 s(-1)), and moderate, Mb-like O(2) affinities (P(50) approximately 340 nM), perhaps suggesting a sensing role for sustained low, micromolar levels of oxygen.

Automated classification of mouse pup isolation syllables: from cluster analysis to an Excel-based "mouse pup syllable classification calculator".

PubMed

Grimsley, Jasmine M S; Gadziola, Marie A; Wenstrup, Jeffrey J

2012-01-01

Mouse pups vocalize at high rates when they are cold or isolated from the nest. The proportions of each syllable type produced carry information about disease state and are being used as behavioral markers for the internal state of animals. Manual classifications of these vocalizations identified 10 syllable types based on their spectro-temporal features. However, manual classification of mouse syllables is time consuming and vulnerable to experimenter bias. This study uses an automated cluster analysis to identify acoustically distinct syllable types produced by CBA/CaJ mouse pups, and then compares the results to prior manual classification methods. The cluster analysis identified two syllable types, based on their frequency bands, that have continuous frequency-time structure, and two syllable types featuring abrupt frequency transitions. Although cluster analysis computed fewer syllable types than manual classification, the clusters represented well the probability distributions of the acoustic features within syllables. These probability distributions indicate that some of the manually classified syllable types are not statistically distinct. The characteristics of the four classified clusters were used to generate a Microsoft Excel-based mouse syllable classifier that rapidly categorizes syllables, with over a 90% match, into the syllable types determined by cluster analysis.

Multiclass Classification for the Differential Diagnosis on the ADHD Subtypes Using Recursive Feature Elimination and Hierarchical Extreme Learning Machine: Structural MRI Study

PubMed Central

Qureshi, Muhammad Naveed Iqbal; Min, Beomjun; Jo, Hang Joon; Lee, Boreom

2016-01-01

The classification of neuroimaging data for the diagnosis of certain brain diseases is one of the main research goals of the neuroscience and clinical communities. In this study, we performed multiclass classification using a hierarchical extreme learning machine (H-ELM) classifier. We compared the performance of this classifier with that of a support vector machine (SVM) and basic extreme learning machine (ELM) for cortical MRI data from attention deficit/hyperactivity disorder (ADHD) patients. We used 159 structural MRI images of children from the publicly available ADHD-200 MRI dataset. The data consisted of three types, namely, typically developing (TDC), ADHD-inattentive (ADHD-I), and ADHD-combined (ADHD-C). We carried out feature selection by using standard SVM-based recursive feature elimination (RFE-SVM) that enabled us to achieve good classification accuracy (60.78%). In this study, we found the RFE-SVM feature selection approach in combination with H-ELM to effectively enable the acquisition of high multiclass classification accuracy rates for structural neuroimaging data. In addition, we found that the most important features for classification were the surface area of the superior frontal lobe, and the cortical thickness, volume, and mean surface area of the whole cortex. PMID:27500640

Multiclass Classification for the Differential Diagnosis on the ADHD Subtypes Using Recursive Feature Elimination and Hierarchical Extreme Learning Machine: Structural MRI Study.

PubMed

Qureshi, Muhammad Naveed Iqbal; Min, Beomjun; Jo, Hang Joon; Lee, Boreom

2016-01-01

The classification of neuroimaging data for the diagnosis of certain brain diseases is one of the main research goals of the neuroscience and clinical communities. In this study, we performed multiclass classification using a hierarchical extreme learning machine (H-ELM) classifier. We compared the performance of this classifier with that of a support vector machine (SVM) and basic extreme learning machine (ELM) for cortical MRI data from attention deficit/hyperactivity disorder (ADHD) patients. We used 159 structural MRI images of children from the publicly available ADHD-200 MRI dataset. The data consisted of three types, namely, typically developing (TDC), ADHD-inattentive (ADHD-I), and ADHD-combined (ADHD-C). We carried out feature selection by using standard SVM-based recursive feature elimination (RFE-SVM) that enabled us to achieve good classification accuracy (60.78%). In this study, we found the RFE-SVM feature selection approach in combination with H-ELM to effectively enable the acquisition of high multiclass classification accuracy rates for structural neuroimaging data. In addition, we found that the most important features for classification were the surface area of the superior frontal lobe, and the cortical thickness, volume, and mean surface area of the whole cortex.

Localized thin-section CT with radiomics feature extraction and machine learning to classify early-detected pulmonary nodules from lung cancer screening

NASA Astrophysics Data System (ADS)

Tu, Shu-Ju; Wang, Chih-Wei; Pan, Kuang-Tse; Wu, Yi-Cheng; Wu, Chen-Te

2018-03-01

Lung cancer screening aims to detect small pulmonary nodules and decrease the mortality rate of those affected. However, studies from large-scale clinical trials of lung cancer screening have shown that the false-positive rate is high and positive predictive value is low. To address these problems, a technical approach is greatly needed for accurate malignancy differentiation among these early-detected nodules. We studied the clinical feasibility of an additional protocol of localized thin-section CT for further assessment on recalled patients from lung cancer screening tests. Our approach of localized thin-section CT was integrated with radiomics features extraction and machine learning classification which was supervised by pathological diagnosis. Localized thin-section CT images of 122 nodules were retrospectively reviewed and 374 radiomics features were extracted. In this study, 48 nodules were benign and 74 malignant. There were nine patients with multiple nodules and four with synchronous multiple malignant nodules. Different machine learning classifiers with a stratified ten-fold cross-validation were used and repeated 100 times to evaluate classification accuracy. Of the image features extracted from the thin-section CT images, 238 (64%) were useful in differentiating between benign and malignant nodules. These useful features include CT density (p  =  0.002 518), sigma (p  =  0.002 781), uniformity (p  =  0.032 41), and entropy (p  =  0.006 685). The highest classification accuracy was 79% by the logistic classifier. The performance metrics of this logistic classification model was 0.80 for the positive predictive value, 0.36 for the false-positive rate, and 0.80 for the area under the receiver operating characteristic curve. Our approach of direct risk classification supervised by the pathological diagnosis with localized thin-section CT and radiomics feature extraction may support clinical physicians in determining truly malignant nodules and therefore reduce problems in lung cancer screening.

Localized thin-section CT with radiomics feature extraction and machine learning to classify early-detected pulmonary nodules from lung cancer screening.

PubMed

Tu, Shu-Ju; Wang, Chih-Wei; Pan, Kuang-Tse; Wu, Yi-Cheng; Wu, Chen-Te

2018-03-14

Lung cancer screening aims to detect small pulmonary nodules and decrease the mortality rate of those affected. However, studies from large-scale clinical trials of lung cancer screening have shown that the false-positive rate is high and positive predictive value is low. To address these problems, a technical approach is greatly needed for accurate malignancy differentiation among these early-detected nodules. We studied the clinical feasibility of an additional protocol of localized thin-section CT for further assessment on recalled patients from lung cancer screening tests. Our approach of localized thin-section CT was integrated with radiomics features extraction and machine learning classification which was supervised by pathological diagnosis. Localized thin-section CT images of 122 nodules were retrospectively reviewed and 374 radiomics features were extracted. In this study, 48 nodules were benign and 74 malignant. There were nine patients with multiple nodules and four with synchronous multiple malignant nodules. Different machine learning classifiers with a stratified ten-fold cross-validation were used and repeated 100 times to evaluate classification accuracy. Of the image features extracted from the thin-section CT images, 238 (64%) were useful in differentiating between benign and malignant nodules. These useful features include CT density (p  =  0.002 518), sigma (p  =  0.002 781), uniformity (p  =  0.032 41), and entropy (p  =  0.006 685). The highest classification accuracy was 79% by the logistic classifier. The performance metrics of this logistic classification model was 0.80 for the positive predictive value, 0.36 for the false-positive rate, and 0.80 for the area under the receiver operating characteristic curve. Our approach of direct risk classification supervised by the pathological diagnosis with localized thin-section CT and radiomics feature extraction may support clinical physicians in determining truly malignant nodules and therefore reduce problems in lung cancer screening.

Suicide Mortality Across Broad Occupational Groups in Greece: A Descriptive Study

PubMed Central

Alexopoulos, Evangelos C.; Kavalidou, Katerina; Messolora, Fani

2015-01-01

Background Several studies have investigated the relationship between specific occupations and suicide mortality, as suicide rates differ by profession. The aim of this study was to investigate suicide mortality ratios across broad occupational groups in Greece for both sexes in the period 2000–2009. Methods Data of suicide deaths were retrieved from the Hellenic Statistical Authority and comparative mortality ratios were calculated. Occupational classification was based on the International Classification of Occupations (ISCO-88) and the coding for Intentional self-harm (X60–X84) was based on the international classification of diseases (ICD-10). Results Male dominant occupations, mainly armed forces, skilled farmers and elementary workers, and female high-skilled occupations were seen as high risk groups for suicide in a period of 10 years. The age-productive group of 30–39 years in Greek male elementary workers and the 50–59 age-productive group of Greek professional women proved to have the most elevated number of suicide deaths. Conclusion Further research is needed into the work-related stressors of occupations with high suicide mortality risk and focused suicide prevention strategies should be applied within vulnerable working age populations. PMID:27014484

A novel risk classification system for 30-day mortality in children undergoing surgery

PubMed Central

Walter, Arianne I.; Jones, Tamekia L.; Huang, Eunice Y.; Davis, Robert L.

2018-01-01

A simple, objective and accurate way of grouping children undergoing surgery into clinically relevant risk groups is needed. The purpose of this study, is to develop and validate a preoperative risk classification system for postsurgical 30-day mortality for children undergoing a wide variety of operations. The National Surgical Quality Improvement Project-Pediatric participant use file data for calendar years 2012–2014 was analyzed to determine preoperative variables most associated with death within 30 days of operation (D30). Risk groups were created using classification tree analysis based on these preoperative variables. The resulting risk groups were validated using 2015 data, and applied to neonates and higher risk CPT codes to determine validity in high-risk subpopulations. A five-level risk classification was found to be most accurate. The preoperative need for ventilation, oxygen support, inotropic support, sepsis, the need for emergent surgery and a do not resuscitate order defined non-overlapping groups with observed rates of D30 that vary from 0.075% (Very Low Risk) to 38.6% (Very High Risk). When CPT codes where death was never observed are eliminated or when the system is applied to neonates, the groupings remained predictive of death in an ordinal manner. PMID:29351327

An improved strategy for skin lesion detection and classification using uniform segmentation and feature selection based approach.

PubMed

Nasir, Muhammad; Attique Khan, Muhammad; Sharif, Muhammad; Lali, Ikram Ullah; Saba, Tanzila; Iqbal, Tassawar

2018-02-21

Melanoma is the deadliest type of skin cancer with highest mortality rate. However, the annihilation in early stage implies a high survival rate therefore, it demands early diagnosis. The accustomed diagnosis methods are costly and cumbersome due to the involvement of experienced experts as well as the requirements for highly equipped environment. The recent advancements in computerized solutions for these diagnoses are highly promising with improved accuracy and efficiency. In this article, we proposed a method for the classification of melanoma and benign skin lesions. Our approach integrates preprocessing, lesion segmentation, features extraction, features selection, and classification. Preprocessing is executed in the context of hair removal by DullRazor, whereas lesion texture and color information are utilized to enhance the lesion contrast. In lesion segmentation, a hybrid technique has been implemented and results are fused using additive law of probability. Serial based method is applied subsequently that extracts and fuses the traits such as color, texture, and HOG (shape). The fused features are selected afterwards by implementing a novel Boltzman Entropy method. Finally, the selected features are classified by Support Vector Machine. The proposed method is evaluated on publically available data set PH2. Our approach has provided promising results of sensitivity 97.7%, specificity 96.7%, accuracy 97.5%, and F-score 97.5%, which are significantly better than the results of existing methods available on the same data set. The proposed method detects and classifies melanoma significantly good as compared to existing methods. © 2018 Wiley Periodicals, Inc.

Cascaded VLSI neural network architecture for on-line learning

NASA Technical Reports Server (NTRS)

Thakoor, Anilkumar P. (Inventor); Duong, Tuan A. (Inventor); Daud, Taher (Inventor)

1992-01-01

High-speed, analog, fully-parallel, and asynchronous building blocks are cascaded for larger sizes and enhanced resolution. A hardware compatible algorithm permits hardware-in-the-loop learning despite limited weight resolution. A computation intensive feature classification application was demonstrated with this flexible hardware and new algorithm at high speed. This result indicates that these building block chips can be embedded as an application specific coprocessor for solving real world problems at extremely high data rates.

Cascaded VLSI neural network architecture for on-line learning

NASA Technical Reports Server (NTRS)

Duong, Tuan A. (Inventor); Daud, Taher (Inventor); Thakoor, Anilkumar P. (Inventor)

1995-01-01

High-speed, analog, fully-parallel and asynchronous building blocks are cascaded for larger sizes and enhanced resolution. A hardware-compatible algorithm permits hardware-in-the-loop learning despite limited weight resolution. A comparison-intensive feature classification application has been demonstrated with this flexible hardware and new algorithm at high speed. This result indicates that these building block chips can be embedded as application-specific-coprocessors for solving real-world problems at extremely high data rates.

Sleep staging with movement-related signals.

PubMed

Jansen, B H; Shankar, K

1993-05-01

Body movement related signals (i.e., activity due to postural changes and the ballistocardiac effort) were recorded from six normal volunteers using the static-charge-sensitive bed (SCSB). Visual sleep staging was performed on the basis of simultaneously recorded EEG, EMG and EOG signals. A statistical classification technique was used to determine if reliable sleep staging could be performed using only the SCSB signal. A classification rate of between 52% and 75% was obtained for sleep staging in the five conventional sleep stages and the awake state. These rates improved from 78% to 89% for classification between awake, REM and non-REM sleep and from 86% to 98% for awake versus asleep classification.

Assessing the inherent uncertainty of one-dimensional diffusions

NASA Astrophysics Data System (ADS)

Eliazar, Iddo; Cohen, Morrel H.

2013-01-01

In this paper we assess the inherent uncertainty of one-dimensional diffusion processes via a stochasticity classification which provides an à la Mandelbrot categorization into five states of uncertainty: infra-mild, mild, borderline, wild, and ultra-wild. Two settings are considered. (i) Stopped diffusions: the diffusion initiates from a high level and is stopped once it first reaches a low level; in this setting we analyze the inherent uncertainty of the diffusion's maximal exceedance above its initial high level. (ii) Stationary diffusions: the diffusion is in dynamical statistical equilibrium; in this setting we analyze the inherent uncertainty of the diffusion's equilibrium level. In both settings general closed-form analytic results are established, and their application is exemplified by stock prices in the stopped-diffusions setting, and by interest rates in the stationary-diffusions setting. These results provide a highly implementable decision-making tool for the classification of uncertainty in the context of one-dimensional diffusions.

Improvement of an algorithm for recognition of liveness using perspiration in fingerprint devices

NASA Astrophysics Data System (ADS)

Parthasaradhi, Sujan T.; Derakhshani, Reza; Hornak, Lawrence A.; Schuckers, Stephanie C.

2004-08-01

Previous work in our laboratory and others have demonstrated that spoof fingers made of a variety of materials including silicon, Play-Doh, clay, and gelatin (gummy finger) can be scanned and verified when compared to a live enrolled finger. Liveness, i.e. to determine whether the introduced biometric is coming from a live source, has been suggested as a means to circumvent attacks using spoof fingers. We developed a new liveness method based on perspiration changes in the fingerprint image. Recent results showed approximately 90% classification rate using different classification methods for various technologies including optical, electro-optical, and capacitive DC, a shorter time window and a diverse dataset. This paper focuses on improvement of the live classification rate by using a weight decay method during the training phase in order to improve the generalization and reduce the variance of the neural network based classifier. The dataset included fingerprint images from 33 live subjects, 33 spoofs created with dental impression material and Play-Doh, and fourteen cadaver fingers. 100% live classification was achieved with 81.8 to 100% spoof classification, depending on the device technology. The weight-decay method improves upon past reports by increasing the live and spoof classification rate.

Activity classification based on inertial and barometric pressure sensors at different anatomical locations.

PubMed

Moncada-Torres, A; Leuenberger, K; Gonzenbach, R; Luft, A; Gassert, R

2014-07-01

Miniature, wearable sensor modules are a promising technology to monitor activities of daily living (ADL) over extended periods of time. To assure both user compliance and meaningful results, the selection and placement site of sensors requires careful consideration. We investigated these aspects for the classification of 16 ADL in 6 healthy subjects under laboratory conditions using ReSense, our custom-made inertial measurement unit enhanced with a barometric pressure sensor used to capture activity-related altitude changes. Subjects wore a module on each wrist and ankle, and one on the trunk. Activities comprised whole body movements as well as gross and dextrous upper-limb activities. Wrist-module data outperformed the other locations for the three activity groups. Specifically, overall classification accuracy rates of almost 93% and more than 95% were achieved for the repeated holdout and user-specific validation methods, respectively, for all 16 activities. Including the altitude profile resulted in a considerable improvement of up to 20% in the classification accuracy for stair ascent and descent. The gyroscopes provided no useful information for activity classification under this scheme. The proposed sensor setting could allow for robust long-term activity monitoring with high compliance in different patient populations.

Comparison of using single- or multi-polarimetric TerraSAR-X images for segmentation and classification of man-made maritime objects

NASA Astrophysics Data System (ADS)

Teutsch, Michael; Saur, Günter

2011-11-01

Spaceborne SAR imagery offers high capability for wide-ranging maritime surveillance especially in situations, where AIS (Automatic Identification System) data is not available. Therefore, maritime objects have to be detected and optional information such as size, orientation, or object/ship class is desired. In recent research work, we proposed a SAR processing chain consisting of pre-processing, detection, segmentation, and classification for single-polarimetric (HH) TerraSAR-X StripMap images to finally assign detection hypotheses to class "clutter", "non-ship", "unstructured ship", or "ship structure 1" (bulk carrier appearance) respectively "ship structure 2" (oil tanker appearance). In this work, we extend the existing processing chain and are now able to handle full-polarimetric (HH, HV, VH, VV) TerraSAR-X data. With the possibility of better noise suppression using the different polarizations, we slightly improve both the segmentation and the classification process. In several experiments we demonstrate the potential benefit for segmentation and classification. Precision of size and orientation estimation as well as correct classification rates are calculated individually for single- and quad-polarization and compared to each other.

Validation of a selective ensemble-based classification scheme for myoelectric control using a three-dimensional Fitts' Law test.

PubMed

Scheme, Erik J; Englehart, Kevin B

2013-07-01

When controlling a powered upper limb prosthesis it is important not only to know how to move the device, but also when not to move. A novel approach to pattern recognition control, using a selective multiclass one-versus-one classification scheme has been shown to be capable of rejecting unintended motions. This method was shown to outperform other popular classification schemes when presented with muscle contractions that did not correspond to desired actions. In this work, a 3-D Fitts' Law test is proposed as a suitable alternative to using virtual limb environments for evaluating real-time myoelectric control performance. The test is used to compare the selective approach to a state-of-the-art linear discriminant analysis classification based scheme. The framework is shown to obey Fitts' Law for both control schemes, producing linear regression fittings with high coefficients of determination (R(2) > 0.936). Additional performance metrics focused on quality of control are discussed and incorporated in the evaluation. Using this framework the selective classification based scheme is shown to produce significantly higher efficiency and completion rates, and significantly lower overshoot and stopping distances, with no significant difference in throughput.

Locally Weighted Score Estimation for Quantile Classification in Binary Regression Models

PubMed Central

Rice, John D.; Taylor, Jeremy M. G.

2016-01-01

One common use of binary response regression methods is classification based on an arbitrary probability threshold dictated by the particular application. Since this is given to us a priori, it is sensible to incorporate the threshold into our estimation procedure. Specifically, for the linear logistic model, we solve a set of locally weighted score equations, using a kernel-like weight function centered at the threshold. The bandwidth for the weight function is selected by cross validation of a novel hybrid loss function that combines classification error and a continuous measure of divergence between observed and fitted values; other possible cross-validation functions based on more common binary classification metrics are also examined. This work has much in common with robust estimation, but diers from previous approaches in this area in its focus on prediction, specifically classification into high- and low-risk groups. Simulation results are given showing the reduction in error rates that can be obtained with this method when compared with maximum likelihood estimation, especially under certain forms of model misspecification. Analysis of a melanoma data set is presented to illustrate the use of the method in practice. PMID:28018492

High-density force myography: A possible alternative for upper-limb prosthetic control.

PubMed

Radmand, Ashkan; Scheme, Erik; Englehart, Kevin

2016-01-01

Several multiple degree-of-freedom upper-limb prostheses that have the promise of highly dexterous control have recently been developed. Inadequate controllability, however, has limited adoption of these devices. Introducing more robust control methods will likely result in higher acceptance rates. This work investigates the suitability of using high-density force myography (HD-FMG) for prosthetic control. HD-FMG uses a high-density array of pressure sensors to detect changes in the pressure patterns between the residual limb and socket caused by the contraction of the forearm muscles. In this work, HD-FMG outperforms the standard electromyography (EMG)-based system in detecting different wrist and hand gestures. With the arm in a fixed, static position, eight hand and wrist motions were classified with 0.33% error using the HD-FMG technique. Comparatively, classification errors in the range of 2.2%-11.3% have been reported in the literature for multichannel EMG-based approaches. As with EMG, position variation in HD-FMG can introduce classification error, but incorporating position variation into the training protocol reduces this effect. Channel reduction was also applied to the HD-FMG technique to decrease the dimensionality of the problem as well as the size of the sensorized area. We found that with informed, symmetric channel reduction, classification error could be decreased to 0.02%.

"Rate My Therapist": Automated Detection of Empathy in Drug and Alcohol Counseling via Speech and Language Processing.

PubMed

Xiao, Bo; Imel, Zac E; Georgiou, Panayiotis G; Atkins, David C; Narayanan, Shrikanth S

2015-01-01

The technology for evaluating patient-provider interactions in psychotherapy-observational coding-has not changed in 70 years. It is labor-intensive, error prone, and expensive, limiting its use in evaluating psychotherapy in the real world. Engineering solutions from speech and language processing provide new methods for the automatic evaluation of provider ratings from session recordings. The primary data are 200 Motivational Interviewing (MI) sessions from a study on MI training methods with observer ratings of counselor empathy. Automatic Speech Recognition (ASR) was used to transcribe sessions, and the resulting words were used in a text-based predictive model of empathy. Two supporting datasets trained the speech processing tasks including ASR (1200 transcripts from heterogeneous psychotherapy sessions and 153 transcripts and session recordings from 5 MI clinical trials). The accuracy of computationally-derived empathy ratings were evaluated against human ratings for each provider. Computationally-derived empathy scores and classifications (high vs. low) were highly accurate against human-based codes and classifications, with a correlation of 0.65 and F-score (a weighted average of sensitivity and specificity) of 0.86, respectively. Empathy prediction using human transcription as input (as opposed to ASR) resulted in a slight increase in prediction accuracies, suggesting that the fully automatic system with ASR is relatively robust. Using speech and language processing methods, it is possible to generate accurate predictions of provider performance in psychotherapy from audio recordings alone. This technology can support large-scale evaluation of psychotherapy for dissemination and process studies.

Kraken: ultrafast metagenomic sequence classification using exact alignments

PubMed Central

2014-01-01

Kraken is an ultrafast and highly accurate program for assigning taxonomic labels to metagenomic DNA sequences. Previous programs designed for this task have been relatively slow and computationally expensive, forcing researchers to use faster abundance estimation programs, which only classify small subsets of metagenomic data. Using exact alignment of k-mers, Kraken achieves classification accuracy comparable to the fastest BLAST program. In its fastest mode, Kraken classifies 100 base pair reads at a rate of over 4.1 million reads per minute, 909 times faster than Megablast and 11 times faster than the abundance estimation program MetaPhlAn. Kraken is available at http://ccb.jhu.edu/software/kraken/. PMID:24580807

An evaluation of computer assisted clinical classification algorithms.

PubMed

Chute, C G; Yang, Y; Buntrock, J

1994-01-01

The Mayo Clinic has a long tradition of indexing patient records in high resolution and volume. Several algorithms have been developed which promise to help human coders in the classification process. We evaluate variations on code browsers and free text indexing systems with respect to their speed and error rates in our production environment. The more sophisticated indexing systems save measurable time in the coding process, but suffer from incompleteness which requires a back-up system or human verification. Expert Network does the best job of rank ordering clinical text, potentially enabling the creation of thresholds for the pass through of computer coded data without human review.

A real-time heat strain risk classifier using heart rate and skin temperature.

PubMed

Buller, Mark J; Latzka, William A; Yokota, Miyo; Tharion, William J; Moran, Daniel S

2008-12-01

Heat injury is a real concern to workers engaged in physically demanding tasks in high heat strain environments. Several real-time physiological monitoring systems exist that can provide indices of heat strain, e.g. physiological strain index (PSI), and provide alerts to medical personnel. However, these systems depend on core temperature measurement using expensive, ingestible thermometer pills. Seeking a better solution, we suggest the use of a model which can identify the probability that individuals are 'at risk' from heat injury using non-invasive measures. The intent is for the system to identify individuals who need monitoring more closely or who should apply heat strain mitigation strategies. We generated a model that can identify 'at risk' (PSI 7.5) workers from measures of heart rate and chest skin temperature. The model was built using data from six previously published exercise studies in which some subjects wore chemical protective equipment. The model has an overall classification error rate of 10% with one false negative error (2.7%), and outperforms an earlier model and a least squares regression model with classification errors of 21% and 14%, respectively. Additionally, the model allows the classification criteria to be adjusted based on the task and acceptable level of risk. We conclude that the model could be a valuable part of a multi-faceted heat strain management system.

Selecting Feature Subsets Based on SVM-RFE and the Overlapping Ratio with Applications in Bioinformatics.

PubMed

Lin, Xiaohui; Li, Chao; Zhang, Yanhui; Su, Benzhe; Fan, Meng; Wei, Hai

2017-12-26

Feature selection is an important topic in bioinformatics. Defining informative features from complex high dimensional biological data is critical in disease study, drug development, etc. Support vector machine-recursive feature elimination (SVM-RFE) is an efficient feature selection technique that has shown its power in many applications. It ranks the features according to the recursive feature deletion sequence based on SVM. In this study, we propose a method, SVM-RFE-OA, which combines the classification accuracy rate and the average overlapping ratio of the samples to determine the number of features to be selected from the feature rank of SVM-RFE. Meanwhile, to measure the feature weights more accurately, we propose a modified SVM-RFE-OA (M-SVM-RFE-OA) algorithm that temporally screens out the samples lying in a heavy overlapping area in each iteration. The experiments on the eight public biological datasets show that the discriminative ability of the feature subset could be measured more accurately by combining the classification accuracy rate with the average overlapping degree of the samples compared with using the classification accuracy rate alone, and shielding the samples in the overlapping area made the calculation of the feature weights more stable and accurate. The methods proposed in this study can also be used with other RFE techniques to define potential biomarkers from big biological data.

19 CFR 141.90 - Notation of tariff classification and value on invoice.

Code of Federal Regulations, 2010 CFR

2010-04-01

... 19 Customs Duties 2 2010-04-01 2010-04-01 false Notation of tariff classification and value on... classification and value on invoice. (a) [Reserved] (b) Classification and rate of duty. The importer or customs... invoice value which have been made to arrive at the aggregate entered value. In addition, the entered unit...

Dental panoramic image analysis for enhancement biomarker of mandibular condyle for osteoporosis early detection

NASA Astrophysics Data System (ADS)

Suprijanto; Azhari; Juliastuti, E.; Septyvergy, A.; Setyagar, N. P. P.

2016-03-01

Osteoporosis is a degenerative disease characterized by low Bone Mineral Density (BMD). Currently, a BMD level is determined by Dual Energy X-ray Absorptiometry (DXA) at the lumbar vertebrae and femur. Previous studies reported that dental panoramic radiography image has potential information for early osteoporosis detection. This work reported alternative scheme, that consists of the determination of the Region of Interest (ROI) the condyle mandibular in the image as biomarker and feature extraction from ROI and classification of bone conditions. The minimum value of intensity in the cavity area is used to compensate an offset on the ROI. For feature extraction, the fraction of intensity values in the ROI that represent high bone density and the ROI total area is perfomed. The classification will be evaluated from the ability of each feature and its combinations for the BMD detection in 2 classes (normal and abnormal), with the artificial neural network method. The evaluation system used 105 panoramic image data from menopause women which consist of 36 training data and 69 test data that were divided into 2 classes. The 2 classes of classification obtained 88.0% accuracy rate and 88.0% sensitivity rate.

Joint modelling of serological and hospitalization data reveals that high levels of pre-existing immunity and school holidays shaped the influenza A pandemic of 2009 in The Netherlands

PubMed Central

te Beest, Dennis E.; Birrell, Paul J; Wallinga, Jacco; De Angelis, Daniela; van Boven, Michiel

2015-01-01

Obtaining a quantitative understanding of the transmission dynamics of influenza A is important for predicting healthcare demand and assessing the likely impact of intervention measures. The pandemic of 2009 provides an ideal platform for developing integrative analyses as it has been studied intensively, and a wealth of data sources is available. Here, we analyse two complementary datasets in a disease transmission framework: cross-sectional serological surveys providing data on infection attack rates, and hospitalization data that convey information on the timing and duration of the pandemic. We estimate key epidemic determinants such as infection and hospitalization rates, and the impact of a school holiday. In contrast to previous approaches, our novel modelling of serological data with mixture distributions provides a probabilistic classification of individual samples (susceptible, immune and infected), propagating classification uncertainties to the transmission model and enabling serological classifications to be informed by hospitalization data. The analyses show that high levels of immunity among persons 20 years and older provide a consistent explanation of the skewed attack rates observed during the pandemic and yield precise estimates of the probability of hospitalization per infection (1–4 years: 0.00096 (95%CrI: 0.00078–0.0012); 5–19 years: 0.00036 (0.00031–0.0044); 20–64 years: 0.0015 (0.00091–0.0020); 65+ years: 0.0084 (0.0028–0.016)). The analyses suggest that in The Netherlands, the school holiday period reduced the number of infectious contacts between 5- and 9-year-old children substantially (estimated reduction: 54%; 95%CrI: 29–82%), thereby delaying the unfolding of the pandemic in The Netherlands by approximately a week. PMID:25540241

Efficacy of the Kyoto Classification of Gastritis in Identifying Patients at High Risk for Gastric Cancer.

PubMed

Sugimoto, Mitsushige; Ban, Hiromitsu; Ichikawa, Hitomi; Sahara, Shu; Otsuka, Taketo; Inatomi, Osamu; Bamba, Shigeki; Furuta, Takahisa; Andoh, Akira

2017-01-01

Objective The Kyoto gastritis classification categorizes the endoscopic characteristics of Helicobacter pylori (H. pylori) infection-associated gastritis and identifies patterns associated with a high risk of gastric cancer. We investigated its efficacy, comparing scores in patients with H. pylori-associated gastritis and with gastric cancer. Methods A total of 1,200 patients with H. pylori-positive gastritis alone (n=932), early-stage H. pylori-positive gastric cancer (n=189), and successfully treated H. pylori-negative cancer (n=79) were endoscopically graded according to the Kyoto gastritis classification for atrophy, intestinal metaplasia, fold hypertrophy, nodularity, and diffuse redness. Results The prevalence of O-II/O-III-type atrophy according to the Kimura-Takemoto classification in early-stage H. pylori-positive gastric cancer and successfully treated H. pylori-negative cancer groups was 45.1%, which was significantly higher than in subjects with gastritis alone (12.7%, p<0.001). Kyoto gastritis scores of atrophy and intestinal metaplasia in the H. pylori-positive cancer group were significantly higher than in subjects with gastritis alone (all p<0.001). No significant differences were noted in the rates of gastric fold hypertrophy or diffuse redness between the two groups. In a multivariate analysis, the risks for H. pylori-positive gastric cancer increased with intestinal metaplasia (odds ratio: 4.453, 95% confidence interval: 3.332-5.950, <0.001) and male sex (1.737, 1.102-2.739, p=0.017). Conclusion Making an appropriate diagnosis and detecting patients at high risk is crucial for achieving total eradication of gastric cancer. The scores of intestinal metaplasia and atrophy of the scoring system in the Kyoto gastritis classification may thus be useful for detecting these patients.

Efficacy of the Kyoto Classification of Gastritis in Identifying Patients at High Risk for Gastric Cancer

PubMed Central

Sugimoto, Mitsushige; Ban, Hiromitsu; Ichikawa, Hitomi; Sahara, Shu; Otsuka, Taketo; Inatomi, Osamu; Bamba, Shigeki; Furuta, Takahisa; Andoh, Akira

2017-01-01

Objective The Kyoto gastritis classification categorizes the endoscopic characteristics of Helicobacter pylori (H. pylori) infection-associated gastritis and identifies patterns associated with a high risk of gastric cancer. We investigated its efficacy, comparing scores in patients with H. pylori-associated gastritis and with gastric cancer. Methods A total of 1,200 patients with H. pylori-positive gastritis alone (n=932), early-stage H. pylori-positive gastric cancer (n=189), and successfully treated H. pylori-negative cancer (n=79) were endoscopically graded according to the Kyoto gastritis classification for atrophy, intestinal metaplasia, fold hypertrophy, nodularity, and diffuse redness. Results The prevalence of O-II/O-III-type atrophy according to the Kimura-Takemoto classification in early-stage H. pylori-positive gastric cancer and successfully treated H. pylori-negative cancer groups was 45.1%, which was significantly higher than in subjects with gastritis alone (12.7%, p<0.001). Kyoto gastritis scores of atrophy and intestinal metaplasia in the H. pylori-positive cancer group were significantly higher than in subjects with gastritis alone (all p<0.001). No significant differences were noted in the rates of gastric fold hypertrophy or diffuse redness between the two groups. In a multivariate analysis, the risks for H. pylori-positive gastric cancer increased with intestinal metaplasia (odds ratio: 4.453, 95% confidence interval: 3.332-5.950, <0.001) and male sex (1.737, 1.102-2.739, p=0.017). Conclusion Making an appropriate diagnosis and detecting patients at high risk is crucial for achieving total eradication of gastric cancer. The scores of intestinal metaplasia and atrophy of the scoring system in the Kyoto gastritis classification may thus be useful for detecting these patients. PMID:28321054

A new interferential multispectral image compression algorithm based on adaptive classification and curve-fitting

NASA Astrophysics Data System (ADS)

Wang, Ke-Yan; Li, Yun-Song; Liu, Kai; Wu, Cheng-Ke

2008-08-01

A novel compression algorithm for interferential multispectral images based on adaptive classification and curve-fitting is proposed. The image is first partitioned adaptively into major-interference region and minor-interference region. Different approximating functions are then constructed for two kinds of regions respectively. For the major interference region, some typical interferential curves are selected to predict other curves. These typical curves are then processed by curve-fitting method. For the minor interference region, the data of each interferential curve are independently approximated. Finally the approximating errors of two regions are entropy coded. The experimental results show that, compared with JPEG2000, the proposed algorithm not only decreases the average output bit-rate by about 0.2 bit/pixel for lossless compression, but also improves the reconstructed images and reduces the spectral distortion greatly, especially at high bit-rate for lossy compression.

WHO Global Survey on Maternal and Perinatal Health in Latin America: classifying caesarean sections

PubMed Central

2009-01-01

Background Caesarean section rates continue to increase worldwide with uncertain medical consequences. Auditing and analysing caesarean section rates and other perinatal outcomes in a reliable and continuous manner is critical for understanding reasons caesarean section changes over time. Methods We analyzed data on 97,095 women delivering in 120 facilities in 8 countries, collected as part of the 2004-2005 Global Survey on Maternal and Perinatal Health in Latin America. The objective of this analysis was to test if the "10-group" or "Robson" classification could help identify which groups of women are contributing most to the high caesarean section rates in Latin America, and if it could provide information useful for health care providers in monitoring and planning effective actions to reduce these rates. Results The overall rate of caesarean section was 35.4%. Women with single cephalic pregnancy at term without previous caesarean section who entered into labour spontaneously (groups 1 and 3) represented 60% of the total obstetric population. Although women with a term singleton cephalic pregnancy with a previous caesarean section (group 5) represented only 11.4% of the obstetric population, this group was the largest contributor to the overall caesarean section rate (26.7% of all the caesarean sections). The second and third largest contributors to the overall caesarean section rate were nulliparous women with single cephalic pregnancy at term either in spontaneous labour (group 1) or induced or delivered by caesarean section before labour (group 2), which were responsible for 18.3% and 15.3% of all caesarean deliveries, respectively. Conclusion The 10-group classification could be easily applied to a multicountry dataset without problems of inconsistencies or misclassification. Specific groups of women were clearly identified as the main contributors to the overall caesarean section rate. This classification could help health care providers to plan practical and effective actions targeting specific groups of women to improve maternal and perinatal care. PMID:19874598

Classification method, spectral diversity, band combination and accuracy assessment evaluation for urban feature detection

NASA Astrophysics Data System (ADS)

Erener, A.

2013-04-01

Automatic extraction of urban features from high resolution satellite images is one of the main applications in remote sensing. It is useful for wide scale applications, namely: urban planning, urban mapping, disaster management, GIS (geographic information systems) updating, and military target detection. One common approach to detecting urban features from high resolution images is to use automatic classification methods. This paper has four main objectives with respect to detecting buildings. The first objective is to compare the performance of the most notable supervised classification algorithms, including the maximum likelihood classifier (MLC) and the support vector machine (SVM). In this experiment the primary consideration is the impact of kernel configuration on the performance of the SVM. The second objective of the study is to explore the suitability of integrating additional bands, namely first principal component (1st PC) and the intensity image, for original data for multi classification approaches. The performance evaluation of classification results is done using two different accuracy assessment methods: pixel based and object based approaches, which reflect the third aim of the study. The objective here is to demonstrate the differences in the evaluation of accuracies of classification methods. Considering consistency, the same set of ground truth data which is produced by labeling the building boundaries in the GIS environment is used for accuracy assessment. Lastly, the fourth aim is to experimentally evaluate variation in the accuracy of classifiers for six different real situations in order to identify the impact of spatial and spectral diversity on results. The method is applied to Quickbird images for various urban complexity levels, extending from simple to complex urban patterns. The simple surface type includes a regular urban area with low density and systematic buildings with brick rooftops. The complex surface type involves almost all kinds of challenges, such as high dense build up areas, regions with bare soil, and small and large buildings with different rooftops, such as concrete, brick, and metal. Using the pixel based accuracy assessment it was shown that the percent building detection (PBD) and quality percent (QP) of the MLC and SVM depend on the complexity and texture variation of the region. Generally, PBD values range between 70% and 90% for the MLC and SVM, respectively. No substantial improvements were observed when the SVM and MLC classifications were developed by the addition of more variables, instead of the use of only four bands. In the evaluation of object based accuracy assessment, it was demonstrated that while MLC and SVM provide higher rates of correct detection, they also provide higher rates of false alarms.

Classification of ictal and seizure-free HRV signals with focus on lateralization of epilepsy.

PubMed

Behbahani, Soroor; Dabanloo, Nader Jafarnia; Nasrabadi, Ali Motie; Dourado, Antonio

2016-01-01

Epileptic onsets often affect the autonomic function of the body during a seizure, whether it is in ictal, interictal or post-ictal periods. The different effects of localization and lateralization of seizures on heart rate variability (HRV) emphasize the importance of autonomic function changes in epileptic patients. On the other hand, the detection of seizures is of primary interests in evaluating the epileptic patients. In the current paper, we analyzed the HRV signal to develop a reliable offline seizure-detection algorithm to focus on the effects of lateralization on HRV. We assessed the HRV during 5-min segments of continuous electrocardiogram (ECG) recording with a total number of 170 seizures occurred in 16 patients, composed of 86 left-sided and 84 right-sided focus seizures. Relatively high and low-frequency components of the HRV were computed using spectral analysis. Poincaré parameters of each heart rate time series considered as non-linear features. We fed these features to the Support Vector Machines (SVMs) to find a robust classification method to classify epileptic and non-epileptic signals. Leave One Out Cross-Validation (LOOCV) approach was used to demonstrate the consistency of the classification results. Our obtained classification accuracy confirms that the proposed scheme has a potential in classifying HRV signals to epileptic and non-epileptic classes. The accuracy rates for right-sided and left-sided focus seizures were obtained as 86.74% and 79.41%, respectively. The main finding of our study is that the patients with right-sided focus epilepsy showed more reduction in parasympathetic activity and more increase in sympathetic activity. It can be a marker of impaired vagal activity associated with increased cardiovascular risk and arrhythmias. Our results suggest that lateralization of the seizure onset zone could exert different influences on heart rate changes. A right-sided seizure would cause an ictal tachycardia whereas a left-sided seizure would result in an ictal bradycardia.

34 CFR 222.68 - What tax rates does the Secretary use if two or more different classifications of real property...

Code of Federal Regulations, 2010 CFR

2010-07-01

... 34 Education 1 2010-07-01 2010-07-01 false What tax rates does the Secretary use if two or more different classifications of real property are taxed at different rates? 222.68 Section 222.68 Education Regulations of the Offices of the Department of Education OFFICE OF ELEMENTARY AND SECONDARY EDUCATION...

An evaluation of classification systems for stillbirth

PubMed Central

Flenady, Vicki; Frøen, J Frederik; Pinar, Halit; Torabi, Rozbeh; Saastad, Eli; Guyon, Grace; Russell, Laurie; Charles, Adrian; Harrison, Catherine; Chauke, Lawrence; Pattinson, Robert; Koshy, Rachel; Bahrin, Safiah; Gardener, Glenn; Day, Katie; Petersson, Karin; Gordon, Adrienne; Gilshenan, Kristen

2009-01-01

Background Audit and classification of stillbirths is an essential part of clinical practice and a crucial step towards stillbirth prevention. Due to the limitations of the ICD system and lack of an international approach to an acceptable solution, numerous disparate classification systems have emerged. We assessed the performance of six contemporary systems to inform the development of an internationally accepted approach. Methods We evaluated the following systems: Amended Aberdeen, Extended Wigglesworth; PSANZ-PDC, ReCoDe, Tulip and CODAC. Nine teams from 7 countries applied the classification systems to cohorts of stillbirths from their regions using 857 stillbirth cases. The main outcome measures were: the ability to retain the important information about the death using the InfoKeep rating; the ease of use according to the Ease rating (both measures used a five-point scale with a score <2 considered unsatisfactory); inter-observer agreement and the proportion of unexplained stillbirths. A randomly selected subset of 100 stillbirths was used to assess inter-observer agreement. Results InfoKeep scores were significantly different across the classifications (p ≤ 0.01) due to low scores for Wigglesworth and Aberdeen. CODAC received the highest mean (SD) score of 3.40 (0.73) followed by PSANZ-PDC, ReCoDe and Tulip [2.77 (1.00), 2.36 (1.21), 1.92 (1.24) respectively]. Wigglesworth and Aberdeen resulted in a high proportion of unexplained stillbirths and CODAC and Tulip the lowest. While Ease scores were different (p ≤ 0.01), all systems received satisfactory scores; CODAC received the highest score. Aberdeen and Wigglesworth showed poor agreement with kappas of 0.35 and 0.25 respectively. Tulip performed best with a kappa of 0.74. The remainder had good to fair agreement. Conclusion The Extended Wigglesworth and Amended Aberdeen systems cannot be recommended for classification of stillbirths. Overall, CODAC performed best with PSANZ-PDC and ReCoDe performing well. Tulip was shown to have the best agreement and a low proportion of unexplained stillbirths. The virtues of these systems need to be considered in the development of an international solution to classification of stillbirths. Further studies are required on the performance of classification systems in the context of developing countries. Suboptimal agreement highlights the importance of instituting measures to ensure consistency for any classification system. PMID:19538759

An evaluation of classification systems for stillbirth.

PubMed

Flenady, Vicki; Frøen, J Frederik; Pinar, Halit; Torabi, Rozbeh; Saastad, Eli; Guyon, Grace; Russell, Laurie; Charles, Adrian; Harrison, Catherine; Chauke, Lawrence; Pattinson, Robert; Koshy, Rachel; Bahrin, Safiah; Gardener, Glenn; Day, Katie; Petersson, Karin; Gordon, Adrienne; Gilshenan, Kristen

2009-06-19

Audit and classification of stillbirths is an essential part of clinical practice and a crucial step towards stillbirth prevention. Due to the limitations of the ICD system and lack of an international approach to an acceptable solution, numerous disparate classification systems have emerged. We assessed the performance of six contemporary systems to inform the development of an internationally accepted approach. We evaluated the following systems: Amended Aberdeen, Extended Wigglesworth; PSANZ-PDC, ReCoDe, Tulip and CODAC. Nine teams from 7 countries applied the classification systems to cohorts of stillbirths from their regions using 857 stillbirth cases. The main outcome measures were: the ability to retain the important information about the death using the InfoKeep rating; the ease of use according to the Ease rating (both measures used a five-point scale with a score <2 considered unsatisfactory); inter-observer agreement and the proportion of unexplained stillbirths. A randomly selected subset of 100 stillbirths was used to assess inter-observer agreement. InfoKeep scores were significantly different across the classifications (p < or = 0.01) due to low scores for Wigglesworth and Aberdeen. CODAC received the highest mean (SD) score of 3.40 (0.73) followed by PSANZ-PDC, ReCoDe and Tulip [2.77 (1.00), 2.36 (1.21), 1.92 (1.24) respectively]. Wigglesworth and Aberdeen resulted in a high proportion of unexplained stillbirths and CODAC and Tulip the lowest. While Ease scores were different (p < or = 0.01), all systems received satisfactory scores; CODAC received the highest score. Aberdeen and Wigglesworth showed poor agreement with kappas of 0.35 and 0.25 respectively. Tulip performed best with a kappa of 0.74. The remainder had good to fair agreement. The Extended Wigglesworth and Amended Aberdeen systems cannot be recommended for classification of stillbirths. Overall, CODAC performed best with PSANZ-PDC and ReCoDe performing well. Tulip was shown to have the best agreement and a low proportion of unexplained stillbirths. The virtues of these systems need to be considered in the development of an international solution to classification of stillbirths. Further studies are required on the performance of classification systems in the context of developing countries. Suboptimal agreement highlights the importance of instituting measures to ensure consistency for any classification system.

Sound Classification in Hearing Aids Inspired by Auditory Scene Analysis

NASA Astrophysics Data System (ADS)

Büchler, Michael; Allegro, Silvia; Launer, Stefan; Dillier, Norbert

2005-12-01

A sound classification system for the automatic recognition of the acoustic environment in a hearing aid is discussed. The system distinguishes the four sound classes "clean speech," "speech in noise," "noise," and "music." A number of features that are inspired by auditory scene analysis are extracted from the sound signal. These features describe amplitude modulations, spectral profile, harmonicity, amplitude onsets, and rhythm. They are evaluated together with different pattern classifiers. Simple classifiers, such as rule-based and minimum-distance classifiers, are compared with more complex approaches, such as Bayes classifier, neural network, and hidden Markov model. Sounds from a large database are employed for both training and testing of the system. The achieved recognition rates are very high except for the class "speech in noise." Problems arise in the classification of compressed pop music, strongly reverberated speech, and tonal or fluctuating noises.

Identifying Wrist Fracture Patients with High Accuracy by Automatic Categorization of X-ray Reports

PubMed Central

de Bruijn, Berry; Cranney, Ann; O’Donnell, Siobhan; Martin, Joel D.; Forster, Alan J.

2006-01-01

The authors performed this study to determine the accuracy of several text classification methods to categorize wrist x-ray reports. We randomly sampled 751 textual wrist x-ray reports. Two expert reviewers rated the presence (n = 301) or absence (n = 450) of an acute fracture of wrist. We developed two information retrieval (IR) text classification methods and a machine learning method using a support vector machine (TC-1). In cross-validation on the derivation set (n = 493), TC-1 outperformed the two IR based methods and six benchmark classifiers, including Naive Bayes and a Neural Network. In the validation set (n = 258), TC-1 demonstrated consistent performance with 93.8% accuracy; 95.5% sensitivity; 92.9% specificity; and 87.5% positive predictive value. TC-1 was easy to implement and superior in performance to the other classification methods. PMID:16929046

Cervical cancer survival prediction using hybrid of SMOTE, CART and smooth support vector machine

NASA Astrophysics Data System (ADS)

Purnami, S. W.; Khasanah, P. M.; Sumartini, S. H.; Chosuvivatwong, V.; Sriplung, H.

2016-04-01

According to the WHO, every two minutes there is one patient who died from cervical cancer. The high mortality rate is due to the lack of awareness of women for early detection. There are several factors that supposedly influence the survival of cervical cancer patients, including age, anemia status, stage, type of treatment, complications and secondary disease. This study wants to classify/predict cervical cancer survival based on those factors. Various classifications methods: classification and regression tree (CART), smooth support vector machine (SSVM), three order spline SSVM (TSSVM) were used. Since the data of cervical cancer are imbalanced, synthetic minority oversampling technique (SMOTE) is used for handling imbalanced dataset. Performances of these methods are evaluated using accuracy, sensitivity and specificity. Results of this study show that balancing data using SMOTE as preprocessing can improve performance of classification. The SMOTE-SSVM method provided better result than SMOTE-TSSVM and SMOTE-CART.

A Noise-Filtered Under-Sampling Scheme for Imbalanced Classification.

PubMed

Kang, Qi; Chen, XiaoShuang; Li, SiSi; Zhou, MengChu

2017-12-01

Under-sampling is a popular data preprocessing method in dealing with class imbalance problems, with the purposes of balancing datasets to achieve a high classification rate and avoiding the bias toward majority class examples. It always uses full minority data in a training dataset. However, some noisy minority examples may reduce the performance of classifiers. In this paper, a new under-sampling scheme is proposed by incorporating a noise filter before executing resampling. In order to verify the efficiency, this scheme is implemented based on four popular under-sampling methods, i.e., Undersampling + Adaboost, RUSBoost, UnderBagging, and EasyEnsemble through benchmarks and significance analysis. Furthermore, this paper also summarizes the relationship between algorithm performance and imbalanced ratio. Experimental results indicate that the proposed scheme can improve the original undersampling-based methods with significance in terms of three popular metrics for imbalanced classification, i.e., the area under the curve, -measure, and -mean.

Tuberculosis disease diagnosis using artificial immune recognition system.

PubMed

Shamshirband, Shahaboddin; Hessam, Somayeh; Javidnia, Hossein; Amiribesheli, Mohsen; Vahdat, Shaghayegh; Petković, Dalibor; Gani, Abdullah; Kiah, Miss Laiha Mat

2014-01-01

There is a high risk of tuberculosis (TB) disease diagnosis among conventional methods. This study is aimed at diagnosing TB using hybrid machine learning approaches. Patient epicrisis reports obtained from the Pasteur Laboratory in the north of Iran were used. All 175 samples have twenty features. The features are classified based on incorporating a fuzzy logic controller and artificial immune recognition system. The features are normalized through a fuzzy rule based on a labeling system. The labeled features are categorized into normal and tuberculosis classes using the Artificial Immune Recognition Algorithm. Overall, the highest classification accuracy reached was for the 0.8 learning rate (α) values. The artificial immune recognition system (AIRS) classification approaches using fuzzy logic also yielded better diagnosis results in terms of detection accuracy compared to other empirical methods. Classification accuracy was 99.14%, sensitivity 87.00%, and specificity 86.12%.

Evolution and classification of the CRISPR-Cas systems

PubMed Central

S. Makarova, Kira; H. Haft, Daniel; Barrangou, Rodolphe; J. J. Brouns, Stan; Charpentier, Emmanuelle; Horvath, Philippe; Moineau, Sylvain; J. M. Mojica, Francisco; I. Wolf, Yuri; Yakunin, Alexander F.; van der Oost, John; V. Koonin, Eugene

2012-01-01

The CRISPR–Cas (clustered regularly interspaced short palindromic repeats–CRISPR-associated proteins) modules are adaptive immunity systems that are present in many archaea and bacteria. These defence systems are encoded by operons that have an extraordinarily diverse architecture and a high rate of evolution for both the cas genes and the unique spacer content. Here, we provide an updated analysis of the evolutionary relationships between CRISPR–Cas systems and Cas proteins. Three major types of CRISPR–Cas system are delineated, with a further division into several subtypes and a few chimeric variants. Given the complexity of the genomic architectures and the extremely dynamic evolution of the CRISPR–Cas systems, a unified classification of these systems should be based on multiple criteria. Accordingly, we propose a `polythetic' classification that integrates the phylogenies of the most common cas genes, the sequence and organization of the CRISPR repeats and the architecture of the CRISPR–cas loci. PMID:21552286

Caesarean Section in Peru: Analysis of Trends Using the Robson Classification System

PubMed Central

2016-01-01

Introduction Cesarean section rates continue to increase worldwide while the reasons appear to be multiple, complex and, in many cases, country specific. Over the last decades, several classification systems for caesarean section have been created and proposed to monitor and compare caesarean section rates in a standardized, reliable, consistent and action-oriented manner with the aim to understand the drivers and contributors of this trend. The aims of the present study were to conduct an analysis in the three Peruvian geographical regions to assess levels and trends of delivery by caesarean section using the Robson classification for caesarean section, identify the groups of women with highest caesarean section rates and assess variation of maternal and perinatal outcomes according to caesarean section levels in each group over time. Material and Methods Data from 549,681 pregnant women included in the Peruvian Perinatal Information System database from 43 maternal facilities in three Peruvian geographical regions from 2000 and 2010 were studied. The data were analyzed using the Robson classification and women were studied in the ten groups in the classification. Cochran-Armitage test was used to evaluate time trends in the rates of caesarean section rates and; logistic regression was used to evaluate risk for each classification. Results The caesarean section rate was 27% and a yearly increase in the overall caesarean section rates from 2000 to 2010 from 23.5% to 30% (time trend p<0.001) was observed. Robson groups 1, 3 (nulliparous and multiparas, respectively, with a single cephalic term pregnancy in spontaneous labour), 5 (multiparas with a previous uterine scar with a single, cephalic, term pregnancy) and 7 (multiparas with a single breech pregnancy with or without previous scars) showed an increase in the caesarean section rates over time. Robson groups 1 and 3 were significantly associated with stillbirths (OR 1.43, CI95% 1.17–1.72; OR 3.53, CI95% 2.95–4.2) and maternal mortality (OR 3.39, CI95% 1.59–7.22; OR 8.05, CI95% 3.34–19.41). Discussion The caesarean section rates increased in the last years as result of increased CS in groups with spontaneous labor and in-group of multiparas with a scarred uterus. Women included in groups 1 y 3 were associated to maternal perinatal complications. Women with previous cesarean section constitute the most important determinant of overall cesarean section rates. The use of Robson classification becomes an useful tool for monitoring cesarean section in low human development index countries. PMID:26840693

Inter-observer variance with the diagnosis of myelodysplastic syndromes (MDS) following the 2008 WHO classification.

PubMed

Font, P; Loscertales, J; Benavente, C; Bermejo, A; Callejas, M; Garcia-Alonso, L; Garcia-Marcilla, A; Gil, S; Lopez-Rubio, M; Martin, E; Muñoz, C; Ricard, P; Soto, C; Balsalobre, P; Villegas, A

2013-01-01

Morphology is the basis of the diagnosis of myelodysplastic syndromes (MDS). The WHO classification offers prognostic information and helps with the treatment decisions. However, morphological changes are subject to potential inter-observer variance. The aim of our study was to explore the reliability of the 2008 WHO classification of MDS, reviewing 100 samples previously diagnosed with MDS using the 2001 WHO criteria. Specimens were collected from 10 hospitals and were evaluated by 10 morphologists, working in five pairs. Each observer evaluated 20 samples, and each sample was analyzed independently by two morphologists. The second observer was blinded to the clinical and laboratory data, except for the peripheral blood (PB) counts. Nineteen cases were considered as unclassified MDS (MDS-U) by the 2001 WHO classification, but only three remained as MDS-U by the 2008 WHO proposal. Discordance was observed in 26 of the 95 samples considered suitable (27 %). Although there were a high number of observers taking part, the rate of discordance was quite similar among the five pairs. The inter-observer concordance was very good regarding refractory anemia with excess blasts type 1 (RAEB-1) (10 of 12 cases, 84 %), RAEB-2 (nine of 10 cases, 90 %), and also good regarding refractory cytopenia with multilineage dysplasia (37 of 50 cases, 74 %). However, the categories with unilineage dysplasia were not reproducible in most of the cases. The rate of concordance with refractory cytopenia with unilineage dysplasia was 40 % (two of five cases) and 25 % with RA with ring sideroblasts (two of eight). Our results show that the 2008 WHO classification gives a more accurate stratification of MDS but also illustrates the difficulty in diagnosing MDS with unilineage dysplasia.

Clinicopathological analysis of biopsy-proven diabetic nephropathy based on the Japanese classification of diabetic nephropathy.

PubMed

Furuichi, Kengo; Shimizu, Miho; Yuzawa, Yukio; Hara, Akinori; Toyama, Tadashi; Kitamura, Hiroshi; Suzuki, Yoshiki; Sato, Hiroshi; Uesugi, Noriko; Ubara, Yoshifumi; Hohino, Junichi; Hisano, Satoshi; Ueda, Yoshihiko; Nishi, Shinichi; Yokoyama, Hitoshi; Nishino, Tomoya; Kohagura, Kentaro; Ogawa, Daisuke; Mise, Koki; Shibagaki, Yugo; Makino, Hirofumi; Matsuo, Seiichi; Wada, Takashi

2018-06-01

The Japanese classification of diabetic nephropathy reflects the risks of mortality, cardiovascular events and kidney prognosis and is clinically useful. Furthermore, pathological findings of diabetic nephropathy are useful for predicting prognoses. In this study, we evaluated the characteristics of pathological findings in relation to the Japanese classification of diabetic nephropathy and their ability to predict prognosis. The clinical data of 600 biopsy-confirmed diabetic nephropathy patients were collected retrospectively from 13 centers across Japan. Composite kidney events, kidney death, cardiovascular events, all-cause mortality, and decreasing rate of estimated GFR (eGFR) were evaluated based on the Japanese classification of diabetic nephropathy. The median observation period was 70.4 (IQR 20.9-101.0) months. Each stage had specific characteristic pathological findings. Diffuse lesions, interstitial fibrosis and/or tubular atrophy (IFTA), interstitial cell infiltration, arteriolar hyalinosis, and intimal thickening were detected in more than half the cases, even in Stage 1. An analysis of the impacts on outcomes in all data showed that hazard ratios of diffuse lesions, widening of the subendothelial space, exudative lesions, mesangiolysis, IFTA, and interstitial cell infiltration were 2.7, 2.8, 2.7, 2.6, 3.5, and 3.7, respectively. Median declining speed of eGFR in all cases was 5.61 mL/min/1.73 m 2 /year, and the median rate of declining kidney function within 2 years after kidney biopsy was 24.0%. This study indicated that pathological findings could categorize the high-risk group as well as the Japanese classification of diabetic nephropathy. Further study using biopsy specimens is required to clarify the pathogenesis of diabetic kidney disease.

Geographical classification of apple based on hyperspectral imaging

NASA Astrophysics Data System (ADS)

Guo, Zhiming; Huang, Wenqian; Chen, Liping; Zhao, Chunjiang; Peng, Yankun

2013-05-01

Attribute of apple according to geographical origin is often recognized and appreciated by the consumers. It is usually an important factor to determine the price of a commercial product. Hyperspectral imaging technology and supervised pattern recognition was attempted to discriminate apple according to geographical origins in this work. Hyperspectral images of 207 Fuji apple samples were collected by hyperspectral camera (400-1000nm). Principal component analysis (PCA) was performed on hyperspectral imaging data to determine main efficient wavelength images, and then characteristic variables were extracted by texture analysis based on gray level co-occurrence matrix (GLCM) from dominant waveband image. All characteristic variables were obtained by fusing the data of images in efficient spectra. Support vector machine (SVM) was used to construct the classification model, and showed excellent performance in classification results. The total classification rate had the high classify accuracy of 92.75% in the training set and 89.86% in the prediction sets, respectively. The overall results demonstrated that the hyperspectral imaging technique coupled with SVM classifier can be efficiently utilized to discriminate Fuji apple according to geographical origins.

A cDNA microarray gene expression data classifier for clinical diagnostics based on graph theory.

PubMed

Benso, Alfredo; Di Carlo, Stefano; Politano, Gianfranco

2011-01-01

Despite great advances in discovering cancer molecular profiles, the proper application of microarray technology to routine clinical diagnostics is still a challenge. Current practices in the classification of microarrays' data show two main limitations: the reliability of the training data sets used to build the classifiers, and the classifiers' performances, especially when the sample to be classified does not belong to any of the available classes. In this case, state-of-the-art algorithms usually produce a high rate of false positives that, in real diagnostic applications, are unacceptable. To address this problem, this paper presents a new cDNA microarray data classification algorithm based on graph theory and is able to overcome most of the limitations of known classification methodologies. The classifier works by analyzing gene expression data organized in an innovative data structure based on graphs, where vertices correspond to genes and edges to gene expression relationships. To demonstrate the novelty of the proposed approach, the authors present an experimental performance comparison between the proposed classifier and several state-of-the-art classification algorithms.

Attention Recognition in EEG-Based Affective Learning Research Using CFS+KNN Algorithm.

PubMed

Hu, Bin; Li, Xiaowei; Sun, Shuting; Ratcliffe, Martyn

2018-01-01

The research detailed in this paper focuses on the processing of Electroencephalography (EEG) data to identify attention during the learning process. The identification of affect using our procedures is integrated into a simulated distance learning system that provides feedback to the user with respect to attention and concentration. The authors propose a classification procedure that combines correlation-based feature selection (CFS) and a k-nearest-neighbor (KNN) data mining algorithm. To evaluate the CFS+KNN algorithm, it was test against CFS+C4.5 algorithm and other classification algorithms. The classification performance was measured 10 times with different 3-fold cross validation data. The data was derived from 10 subjects while they were attempting to learn material in a simulated distance learning environment. A self-assessment model of self-report was used with a single valence to evaluate attention on 3 levels (high, neutral, low). It was found that CFS+KNN had a much better performance, giving the highest correct classification rate (CCR) of % for the valence dimension divided into three classes.

Does semi-automatic bone-fragment segmentation improve the reproducibility of the Letournel acetabular fracture classification?

PubMed

Boudissa, M; Orfeuvre, B; Chabanas, M; Tonetti, J

2017-09-01

The Letournel classification of acetabular fracture shows poor reproducibility in inexperienced observers, despite the introduction of 3D imaging. We therefore developed a method of semi-automatic segmentation based on CT data. The present prospective study aimed to assess: (1) whether semi-automatic bone-fragment segmentation increased the rate of correct classification; (2) if so, in which fracture types; and (3) feasibility using the open-source itksnap 3.0 software package without incurring extra cost for users. Semi-automatic segmentation of acetabular fractures significantly increases the rate of correct classification by orthopedic surgery residents. Twelve orthopedic surgery residents classified 23 acetabular fractures. Six used conventional 3D reconstructions provided by the center's radiology department (conventional group) and 6 others used reconstructions obtained by semi-automatic segmentation using the open-source itksnap 3.0 software package (segmentation group). Bone fragments were identified by specific colors. Correct classification rates were compared between groups on Chi 2 test. Assessment was repeated 2 weeks later, to determine intra-observer reproducibility. Correct classification rates were significantly higher in the "segmentation" group: 114/138 (83%) versus 71/138 (52%); P<0.0001. The difference was greater for simple (36/36 (100%) versus 17/36 (47%); P<0.0001) than complex fractures (79/102 (77%) versus 54/102 (53%); P=0.0004). Mean segmentation time per fracture was 27±3min [range, 21-35min]. The segmentation group showed excellent intra-observer correlation coefficients, overall (ICC=0.88), and for simple (ICC=0.92) and complex fractures (ICC=0.84). Semi-automatic segmentation, identifying the various bone fragments, was effective in increasing the rate of correct acetabular fracture classification on the Letournel system by orthopedic surgery residents. It may be considered for routine use in education and training. III: prospective case-control study of a diagnostic procedure. Copyright © 2017 Elsevier Masson SAS. All rights reserved.

The new Epstein gleason score classification significantly reduces upgrading in prostate cancer patients.

PubMed

De Nunzio, Cosimo; Pastore, Antonio Luigi; Lombardo, Riccardo; Simone, Giuseppe; Leonardo, Costantino; Mastroianni, Riccardo; Collura, Devis; Muto, Giovanni; Gallucci, Michele; Carbone, Antonio; Fuschi, Andrea; Dutto, Lorenzo; Witt, Joern Heinrich; De Dominicis, Carlo; Tubaro, Andrea

2018-06-01

To evaluate the differences between the old and the new Gleason score classification systems in upgrading and downgrading rates. Between 2012 and 2015, we identified 9703 patients treated with retropubic radical prostatectomy (RP) in four tertiary centers. Biopsy specimens as well as radical prostatectomy specimens were graded according to both 2005 Gleason and 2014 ISUP five-tier Gleason grading system (five-tier GG system). Upgrading and downgrading rates on radical prostatectomy were first recorded for both classifications and then compared. The accuracy of the biopsy for each histological classification was determined by using the kappa coefficient of agreement and by assessing sensitivity, specificity, positive and negative predictive value. The five-tier GG system presented a lower clinically significant upgrading rate (1895/9703: 19,5% vs 2332/9703:24.0%; p = .001) and a similar clinically significant downgrading rate (756/9703: 7,7% vs 779/9703: 8%; p = .267) when compared to the 2005 ISUP classification. When evaluating their accuracy, the new five-tier GG system presented a better specificity (91% vs 83%) and a better negative predictive value (78% vs 60%). The kappa-statistics measures of agreement between needle biopsy and radical prostatectomy specimens were poor and good respectively for the five-tier GG system and for the 2005 Gleason score (k = 0.360 ± 0.007 vs k = 0.426 ± 0.007). The new Epstein classification significantly reduces upgrading events. The implementation of this new classification could better define prostate cancer aggressiveness with important clinical implications, particularly in prostate cancer management. Copyright © 2018 Elsevier Ltd, BASO ~ The Association for Cancer Surgery, and the European Society of Surgical Oncology. All rights reserved.

Semantic Labelling of Ultra Dense Mls Point Clouds in Urban Road Corridors Based on Fusing Crf with Shape Priors

NASA Astrophysics Data System (ADS)

Yao, W.; Polewski, P.; Krzystek, P.

2017-09-01

In this paper, a labelling method for the semantic analysis of ultra-high point density MLS data (up to 4000 points/m2) in urban road corridors is developed based on combining a conditional random field (CRF) for the context-based classification of 3D point clouds with shape priors. The CRF uses a Random Forest (RF) for generating the unary potentials of nodes and a variant of the contrastsensitive Potts model for the pair-wise potentials of node edges. The foundations of the classification are various geometric features derived by means of co-variance matrices and local accumulation map of spatial coordinates based on local neighbourhoods. Meanwhile, in order to cope with the ultra-high point density, a plane-based region growing method combined with a rule-based classifier is applied to first fix semantic labels for man-made objects. Once such kind of points that usually account for majority of entire data amount are pre-labeled; the CRF classifier can be solved by optimizing the discriminative probability for nodes within a subgraph structure excluded from pre-labeled nodes. The process can be viewed as an evidence fusion step inferring a degree of belief for point labelling from different sources. The MLS data used for this study were acquired by vehicle-borne Z+F phase-based laser scanner measurement, which permits the generation of a point cloud with an ultra-high sampling rate and accuracy. The test sites are parts of Munich City which is assumed to consist of seven object classes including impervious surfaces, tree, building roof/facade, low vegetation, vehicle and pole. The competitive classification performance can be explained by the diverse factors: e.g. the above ground height highlights the vertical dimension of houses, trees even cars, but also attributed to decision-level fusion of graph-based contextual classification approach with shape priors. The use of context-based classification methods mainly contributed to smoothing of labelling by removing outliers and the improvement in underrepresented object classes. In addition, the routine operation of a context-based classification for such high density MLS data becomes much more efficient being comparable to non-contextual classification schemes.

Applicability of the ICD-11 proposal for PTSD: a comparison of prevalence and comorbidity rates with the DSM-IV PTSD classification in two post-conflict samples

PubMed Central

Stammel, Nadine; Abbing, Eva M.; Heeke, Carina; Knaevelsrud, Christine

2015-01-01

Background The World Health Organization recently proposed significant changes to the posttraumatic stress disorder (PTSD) diagnostic criteria in the 11th edition of the International Classification of Diseases (ICD-11). Objective The present study investigated the impact of these changes in two different post-conflict samples. Method Prevalence and rates of concurrent depression and anxiety, socio-demographic characteristics, and indicators of clinical severity according to ICD-11 in 1,075 Cambodian and 453 Colombian civilians exposed to civil war and genocide were compared to those according to the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV). Results Results indicated significantly lower prevalence rates under the ICD-11 proposal (8.1% Cambodian sample and 44.4% Colombian sample) compared to the DSM-IV (11.2% Cambodian sample and 55.0% Colombian sample). Participants meeting a PTSD diagnosis only under the ICD-11 proposal had significantly lower rates of concurrent depression and a lower concurrent total score (depression and anxiety) compared to participants meeting only DSM-IV diagnostic criteria. There were no significant differences in socio-demographic characteristics and indicators of clinical severity between these two groups. Conclusions The lower prevalence of PTSD according to the ICD-11 proposal in our samples of persons exposed to a high number of traumatic events may counter criticism of previous PTSD classifications to overuse the PTSD diagnosis in populations exposed to extreme stressors. Also another goal, to better distinguish PTSD from comorbid disorders could be supported with our data. PMID:25989951

Applicability of the ICD-11 proposal for PTSD: a comparison of prevalence and comorbidity rates with the DSM-IV PTSD classification in two post-conflict samples.

PubMed

Stammel, Nadine; Abbing, Eva M; Heeke, Carina; Knaevelsrud, Christine

2015-01-01

The World Health Organization recently proposed significant changes to the posttraumatic stress disorder (PTSD) diagnostic criteria in the 11th edition of the International Classification of Diseases (ICD-11). The present study investigated the impact of these changes in two different post-conflict samples. Prevalence and rates of concurrent depression and anxiety, socio-demographic characteristics, and indicators of clinical severity according to ICD-11 in 1,075 Cambodian and 453 Colombian civilians exposed to civil war and genocide were compared to those according to the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV). Results indicated significantly lower prevalence rates under the ICD-11 proposal (8.1% Cambodian sample and 44.4% Colombian sample) compared to the DSM-IV (11.2% Cambodian sample and 55.0% Colombian sample). Participants meeting a PTSD diagnosis only under the ICD-11 proposal had significantly lower rates of concurrent depression and a lower concurrent total score (depression and anxiety) compared to participants meeting only DSM-IV diagnostic criteria. There were no significant differences in socio-demographic characteristics and indicators of clinical severity between these two groups. The lower prevalence of PTSD according to the ICD-11 proposal in our samples of persons exposed to a high number of traumatic events may counter criticism of previous PTSD classifications to overuse the PTSD diagnosis in populations exposed to extreme stressors. Also another goal, to better distinguish PTSD from comorbid disorders could be supported with our data.

Sleep versus wake classification from heart rate variability using computational intelligence: consideration of rejection in classification models.

PubMed

Lewicke, Aaron; Sazonov, Edward; Corwin, Michael J; Neuman, Michael; Schuckers, Stephanie

2008-01-01

Reliability of classification performance is important for many biomedical applications. A classification model which considers reliability in the development of the model such that unreliable segments are rejected would be useful, particularly, in large biomedical data sets. This approach is demonstrated in the development of a technique to reliably determine sleep and wake using only the electrocardiogram (ECG) of infants. Typically, sleep state scoring is a time consuming task in which sleep states are manually derived from many physiological signals. The method was tested with simultaneous 8-h ECG and polysomnogram (PSG) determined sleep scores from 190 infants enrolled in the collaborative home infant monitoring evaluation (CHIME) study. Learning vector quantization (LVQ) neural network, multilayer perceptron (MLP) neural network, and support vector machines (SVMs) are tested as the classifiers. After systematic rejection of difficult to classify segments, the models can achieve 85%-87% correct classification while rejecting only 30% of the data. This corresponds to a Kappa statistic of 0.65-0.68. With rejection, accuracy improves by about 8% over a model without rejection. Additionally, the impact of the PSG scored indeterminate state epochs is analyzed. The advantages of a reliable sleep/wake classifier based only on ECG include high accuracy, simplicity of use, and low intrusiveness. Reliability of the classification can be built directly in the model, such that unreliable segments are rejected.

Application of visible and near-infrared spectroscopy to classification of Miscanthus species

DOE PAGES

Jin, Xiaoli; Chen, Xiaoling; Xiao, Liang; ...

2017-04-03

Here, the feasibility of visible and near infrared (NIR) spectroscopy as tool to classify Miscanthus samples was explored in this study. Three types of Miscanthus plants, namely, M. sinensis, M. sacchariflorus and M. fIoridulus, were analyzed using a NIR spectrophotometer. Several classification models based on the NIR spectra data were developed using line discriminated analysis (LDA), partial least squares (PLS), least squares support vector machine regression (LSSVR), radial basis function (RBF) and neural network (NN). The principal component analysis (PCA) presented rough classification with overlapping samples, while the models of Line_LSSVR, RBF_LSSVR and RBF_NN presented almost same calibration and validationmore » results. Due to the higher speed of Line_LSSVR than RBF_LSSVR and RBF_NN, we selected the line_LSSVR model as a representative. In our study, the model based on line_LSSVR showed higher accuracy than LDA and PLS models. The total correct classification rates of 87.79 and 96.51% were observed based on LDA and PLS model in the testing set, respectively, while the line_LSSVR showed 99.42% of total correct classification rate. Meanwhile, the lin_LSSVR model in the testing set showed correct classification rate of 100, 100 and 96.77% for M. sinensis, M. sacchariflorus and M. fIoridulus, respectively. The lin_LSSVR model assigned 99.42% of samples to the right groups, except one M. fIoridulus sample. The results demonstrated that NIR spectra combined with a preliminary morphological classification could be an effective and reliable procedure for the classification of Miscanthus species.« less

Application of visible and near-infrared spectroscopy to classification of Miscanthus species

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jin, Xiaoli; Chen, Xiaoling; Xiao, Liang

Here, the feasibility of visible and near infrared (NIR) spectroscopy as tool to classify Miscanthus samples was explored in this study. Three types of Miscanthus plants, namely, M. sinensis, M. sacchariflorus and M. fIoridulus, were analyzed using a NIR spectrophotometer. Several classification models based on the NIR spectra data were developed using line discriminated analysis (LDA), partial least squares (PLS), least squares support vector machine regression (LSSVR), radial basis function (RBF) and neural network (NN). The principal component analysis (PCA) presented rough classification with overlapping samples, while the models of Line_LSSVR, RBF_LSSVR and RBF_NN presented almost same calibration and validationmore » results. Due to the higher speed of Line_LSSVR than RBF_LSSVR and RBF_NN, we selected the line_LSSVR model as a representative. In our study, the model based on line_LSSVR showed higher accuracy than LDA and PLS models. The total correct classification rates of 87.79 and 96.51% were observed based on LDA and PLS model in the testing set, respectively, while the line_LSSVR showed 99.42% of total correct classification rate. Meanwhile, the lin_LSSVR model in the testing set showed correct classification rate of 100, 100 and 96.77% for M. sinensis, M. sacchariflorus and M. fIoridulus, respectively. The lin_LSSVR model assigned 99.42% of samples to the right groups, except one M. fIoridulus sample. The results demonstrated that NIR spectra combined with a preliminary morphological classification could be an effective and reliable procedure for the classification of Miscanthus species.« less

Application of visible and near-infrared spectroscopy to classification of Miscanthus species.

PubMed

Jin, Xiaoli; Chen, Xiaoling; Xiao, Liang; Shi, Chunhai; Chen, Liang; Yu, Bin; Yi, Zili; Yoo, Ji Hye; Heo, Kweon; Yu, Chang Yeon; Yamada, Toshihiko; Sacks, Erik J; Peng, Junhua

2017-01-01

The feasibility of visible and near infrared (NIR) spectroscopy as tool to classify Miscanthus samples was explored in this study. Three types of Miscanthus plants, namely, M. sinensis, M. sacchariflorus and M. fIoridulus, were analyzed using a NIR spectrophotometer. Several classification models based on the NIR spectra data were developed using line discriminated analysis (LDA), partial least squares (PLS), least squares support vector machine regression (LSSVR), radial basis function (RBF) and neural network (NN). The principal component analysis (PCA) presented rough classification with overlapping samples, while the models of Line_LSSVR, RBF_LSSVR and RBF_NN presented almost same calibration and validation results. Due to the higher speed of Line_LSSVR than RBF_LSSVR and RBF_NN, we selected the line_LSSVR model as a representative. In our study, the model based on line_LSSVR showed higher accuracy than LDA and PLS models. The total correct classification rates of 87.79 and 96.51% were observed based on LDA and PLS model in the testing set, respectively, while the line_LSSVR showed 99.42% of total correct classification rate. Meanwhile, the lin_LSSVR model in the testing set showed correct classification rate of 100, 100 and 96.77% for M. sinensis, M. sacchariflorus and M. fIoridulus, respectively. The lin_LSSVR model assigned 99.42% of samples to the right groups, except one M. fIoridulus sample. The results demonstrated that NIR spectra combined with a preliminary morphological classification could be an effective and reliable procedure for the classification of Miscanthus species.

Application of visible and near-infrared spectroscopy to classification of Miscanthus species

PubMed Central

Shi, Chunhai; Chen, Liang; Yu, Bin; Yi, Zili; Yoo, Ji Hye; Heo, Kweon; Yu, Chang Yeon; Yamada, Toshihiko; Sacks, Erik J.; Peng, Junhua

2017-01-01

The feasibility of visible and near infrared (NIR) spectroscopy as tool to classify Miscanthus samples was explored in this study. Three types of Miscanthus plants, namely, M. sinensis, M. sacchariflorus and M. fIoridulus, were analyzed using a NIR spectrophotometer. Several classification models based on the NIR spectra data were developed using line discriminated analysis (LDA), partial least squares (PLS), least squares support vector machine regression (LSSVR), radial basis function (RBF) and neural network (NN). The principal component analysis (PCA) presented rough classification with overlapping samples, while the models of Line_LSSVR, RBF_LSSVR and RBF_NN presented almost same calibration and validation results. Due to the higher speed of Line_LSSVR than RBF_LSSVR and RBF_NN, we selected the line_LSSVR model as a representative. In our study, the model based on line_LSSVR showed higher accuracy than LDA and PLS models. The total correct classification rates of 87.79 and 96.51% were observed based on LDA and PLS model in the testing set, respectively, while the line_LSSVR showed 99.42% of total correct classification rate. Meanwhile, the lin_LSSVR model in the testing set showed correct classification rate of 100, 100 and 96.77% for M. sinensis, M. sacchariflorus and M. fIoridulus, respectively. The lin_LSSVR model assigned 99.42% of samples to the right groups, except one M. fIoridulus sample. The results demonstrated that NIR spectra combined with a preliminary morphological classification could be an effective and reliable procedure for the classification of Miscanthus species. PMID:28369059

How Well Do Molecular and Pedigree Relatedness Correspond, in Populations with Diverse Mating Systems, and Various Types and Quantities of Molecular and Demographic Data?

PubMed

Kopps, Anna M; Kang, Jungkoo; Sherwin, William B; Palsbøll, Per J

2015-06-30

Kinship analyses are important pillars of ecological and conservation genetic studies with potentially far-reaching implications. There is a need for power analyses that address a range of possible relationships. Nevertheless, such analyses are rarely applied, and studies that use genetic-data-based-kinship inference often ignore the influence of intrinsic population characteristics. We investigated 11 questions regarding the correct classification rate of dyads to relatedness categories (relatedness category assignments; RCA) using an individual-based model with realistic life history parameters. We investigated the effects of the number of genetic markers; marker type (microsatellite, single nucleotide polymorphism SNP, or both); minor allele frequency; typing error; mating system; and the number of overlapping generations under different demographic conditions. We found that (i) an increasing number of genetic markers increased the correct classification rate of the RCA so that up to >80% first cousins can be correctly assigned; (ii) the minimum number of genetic markers required for assignments with 80 and 95% correct classifications differed between relatedness categories, mating systems, and the number of overlapping generations; (iii) the correct classification rate was improved by adding additional relatedness categories and age and mitochondrial DNA data; and (iv) a combination of microsatellite and single-nucleotide polymorphism data increased the correct classification rate if <800 SNP loci were available. This study shows how intrinsic population characteristics, such as mating system and the number of overlapping generations, life history traits, and genetic marker characteristics, can influence the correct classification rate of an RCA study. Therefore, species-specific power analyses are essential for empirical studies. Copyright © 2015 Kopps et al.

Critical object recognition in millimeter-wave images with robustness to rotation and scale.

PubMed

Mohammadzade, Hoda; Ghojogh, Benyamin; Faezi, Sina; Shabany, Mahdi

2017-06-01

Locating critical objects is crucial in various security applications and industries. For example, in security applications, such as in airports, these objects might be hidden or covered under shields or secret sheaths. Millimeter-wave images can be utilized to discover and recognize the critical objects out of the hidden cases without any health risk due to their non-ionizing features. However, millimeter-wave images usually have waves in and around the detected objects, making object recognition difficult. Thus, regular image processing and classification methods cannot be used for these images and additional pre-processings and classification methods should be introduced. This paper proposes a novel pre-processing method for canceling rotation and scale using principal component analysis. In addition, a two-layer classification method is introduced and utilized for recognition. Moreover, a large dataset of millimeter-wave images is collected and created for experiments. Experimental results show that a typical classification method such as support vector machines can recognize 45.5% of a type of critical objects at 34.2% false alarm rate (FAR), which is a drastically poor recognition. The same method within the proposed recognition framework achieves 92.9% recognition rate at 0.43% FAR, which indicates a highly significant improvement. The significant contribution of this work is to introduce a new method for analyzing millimeter-wave images based on machine vision and learning approaches, which is not yet widely noted in the field of millimeter-wave image analysis.

Genetically Guided Statin Therapy

DTIC Science & Technology

2017-03-01

prevent cardiovascular disease . Long-term adherence is a challenge, due, in part, to statin intolerance due to musculoskeletal side effects. In objective...Statins, cholesterol, LDL, cardiovascular disease , genetic-informed strategy, statin prescription, statin adherence 16. SECURITY CLASSIFICATION OF: 17...28 Mar 2017. 1.0 SUMMARY Statins are well established for lowering cholesterol and preventing cardiovascular disease . High rates of statin

Diagnostic Efficiency of Several Methods of Identifying Socially Rejected Children and Effect of Participation Rate on Classification Accuracy

ERIC Educational Resources Information Center

McKown, Clark; Gumbiner, Laura M.; Johnson, Jason

2011-01-01

Social rejection is associated with a wide variety of negative outcomes. Early identification of social rejection and intervention to minimize its negative impact is thus important. However, sociometric methods, which are considered high in validity for identifying socially rejected children, are frequently not used because of (a) procedural…

Betel Nut Chewing Behavior among Adolescents in Eastern Taiwan: A Cluster Analysis

ERIC Educational Resources Information Center

Chen, Han-Ying; Waigandt, Alex C.

2009-01-01

The prevalence of betel nut chewing among junior high school students is highest in the eastern region of Taiwan (Lin, 1990). Although there is some research on the prevalence rate, little effort has been paid to developing a classification of betel nut chewing behavior applicable to adolescents. Eight-hundred and forty-three students, including…

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

PubMed

Fries, Christopher J

2008-11-01

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

Integrating Human and Machine Intelligence in Galaxy Morphology Classification Tasks

NASA Astrophysics Data System (ADS)

Beck, Melanie Renee

The large flood of data flowing from observatories presents significant challenges to astronomy and cosmology--challenges that will only be magnified by projects currently under development. Growth in both volume and velocity of astrophysics data is accelerating: whereas the Sloan Digital Sky Survey (SDSS) has produced 60 terabytes of data in the last decade, the upcoming Large Synoptic Survey Telescope (LSST) plans to register 30 terabytes per night starting in the year 2020. Additionally, the Euclid Mission will acquire imaging for 5 x 107 resolvable galaxies. The field of galaxy evolution faces a particularly challenging future as complete understanding often cannot be reached without analysis of detailed morphological galaxy features. Historically, morphological analysis has relied on visual classification by astronomers, accessing the human brains capacity for advanced pattern recognition. However, this accurate but inefficient method falters when confronted with many thousands (or millions) of images. In the SDSS era, efforts to automate morphological classifications of galaxies (e.g., Conselice et al., 2000; Lotz et al., 2004) are reasonably successful and can distinguish between elliptical and disk-dominated galaxies with accuracies of 80%. While this is statistically very useful, a key problem with these methods is that they often cannot say which 80% of their samples are accurate. Furthermore, when confronted with the more complex task of identifying key substructure within galaxies, automated classification algorithms begin to fail. The Galaxy Zoo project uses a highly innovative approach to solving the scalability problem of visual classification. Displaying images of SDSS galaxies to volunteers via a simple and engaging web interface, www.galaxyzoo.org asks people to classify images by eye. Within the first year hundreds of thousands of members of the general public had classified each of the 1 million SDSS galaxies an average of 40 times. Galaxy Zoo thus solved both the visual classification problem of time efficiency and improved accuracy by producing a distribution of independent classifications for each galaxy. While crowd-sourced galaxy classifications have proven their worth, challenges remain before establishing this method as a critical and standard component of the data processing pipelines for the next generation of surveys. In particular, though innovative, crowd-sourcing techniques do not have the capacity to handle the data volume and rates expected in the next generation of surveys. These algorithms will be delegated to handle the majority of the classification tasks, freeing citizen scientists to contribute their efforts on subtler and more complex assignments. This thesis presents a solution through an integration of visual and automated classifications, preserving the best features of both human and machine. We demonstrate the effectiveness of such a system through a re-analysis of visual galaxy morphology classifications collected during the Galaxy Zoo 2 (GZ2) project. We reprocess the top-level question of the GZ2 decision tree with a Bayesian classification aggregation algorithm dubbed SWAP, originally developed for the Space Warps gravitational lens project. Through a simple binary classification scheme we increase the classification rate nearly 5-fold classifying 226,124 galaxies in 92 days of GZ2 project time while reproducing labels derived from GZ2 classification data with 95.7% accuracy. We next combine this with a Random Forest machine learning algorithm that learns on a suite of non-parametric morphology indicators widely used for automated morphologies. We develop a decision engine that delegates tasks between human and machine and demonstrate that the combined system provides a factor of 11.4 increase in the classification rate, classifying 210,803 galaxies in just 32 days of GZ2 project time with 93.1% accuracy. As the Random Forest algorithm requires a minimal amount of computational cost, this result has important implications for galaxy morphology identification tasks in the era of Euclid and other large-scale surveys.

The Rural Inpatient Mortality Study: Does Urban-Rural County Classification Predict Hospital Mortality in California?

PubMed

Linnen, Daniel T; Kornak, John; Stephens, Caroline

2018-03-28

Evidence suggests an association between rurality and decreased life expectancy. To determine whether rural hospitals have higher hospital mortality, given that very sick patients may be transferred to regional hospitals. In this ecologic study, we combined Medicare hospital mortality ratings (N = 1267) with US census data, critical access hospital classification, and National Center for Health Statistics urban-rural county classifications. Ratings included mortality for coronary artery bypass grafting, stroke, chronic obstructive pulmonary disease, heart attack, heart failure, and pneumonia across 277 California hospitals between July 2011 and June 2014. We used generalized estimating equations to evaluate the association of urban-rural county classifications on mortality ratings. Unfavorable Medicare hospital mortality rating "worse than the national rate" compared with "better" or "same." Compared with large central "metro" (metropolitan) counties, hospitals in medium-sized metro counties had 6.4 times the odds of rating "worse than the national rate" for hospital mortality (95% confidence interval = 2.8-14.8, p < 0.001). For hospitals in small metro counties, the odds of having such a rating were 3.7 times greater (95% confidence interval = 0.7-23.4, p = 0.12), although not statistically significant. Few ratings were provided for rural counties, and analysis of rural counties was underpowered. Hospitals in medium-sized metro counties are associated with unfavorable Medicare mortality ratings, but current methods to assign mortality ratings may hinder fair comparisons. Patient transfers from rural locations to regional medical centers may contribute to these results, a potential factor that future research should examine.

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

NASA Astrophysics Data System (ADS)

Brolly, M.; Iro, S.

2016-12-01

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

Statistical sensor fusion of ECG data using automotive-grade sensors

NASA Astrophysics Data System (ADS)

Koenig, A.; Rehg, T.; Rasshofer, R.

2015-11-01

Driver states such as fatigue, stress, aggression, distraction or even medical emergencies continue to be yield to severe mistakes in driving and promote accidents. A pathway towards improving driver state assessment can be found in psycho-physiological measures to directly quantify the driver's state from physiological recordings. Although heart rate is a well-established physiological variable that reflects cognitive stress, obtaining heart rate contactless and reliably is a challenging task in an automotive environment. Our aim was to investigate, how sensory fusion of two automotive grade sensors would influence the accuracy of automatic classification of cognitive stress levels. We induced cognitive stress in subjects and estimated levels from their heart rate signals, acquired from automotive ready ECG sensors. Using signal quality indices and Kalman filters, we were able to decrease Root Mean Squared Error (RMSE) of heart rate recordings by 10 beats per minute. We then trained a neural network to classify the cognitive workload state of subjects from heart rate and compared classification performance for ground truth, the individual sensors and the fused heart rate signal. We obtained an increase of 5 % higher correct classification by fusing signals as compared to individual sensors, staying only 4 % below the maximally possible classification accuracy from ground truth. These results are a first step towards real world applications of psycho-physiological measurements in vehicle settings. Future implementations of driver state modeling will be able to draw from a larger pool of data sources, such as additional physiological values or vehicle related data, which can be expected to drive classification to significantly higher values.

29 CFR 697.2 - Industry wage rates and effective dates.

Code of Federal Regulations, 2011 CFR

2011-07-01

... of goods for commerce, as these terms are defined in section 3 of the Fair Labor Standards Act of... classifications in which such employee is engaged. Industry Minimum wage EffectiveOctober 3, 2005 EffectiveOctober...) Classification A 4.09 4.09 4.09 (2) Classification B 3.92 3.92 3.92 (3) Classification C 3.88 3.88 3.88 (e...

A Novel Energy-Efficient Approach for Human Activity Recognition.

PubMed

Zheng, Lingxiang; Wu, Dihong; Ruan, Xiaoyang; Weng, Shaolin; Peng, Ao; Tang, Biyu; Lu, Hai; Shi, Haibin; Zheng, Huiru

2017-09-08

In this paper, we propose a novel energy-efficient approach for mobile activity recognition system (ARS) to detect human activities. The proposed energy-efficient ARS, using low sampling rates, can achieve high recognition accuracy and low energy consumption. A novel classifier that integrates hierarchical support vector machine and context-based classification (HSVMCC) is presented to achieve a high accuracy of activity recognition when the sampling rate is less than the activity frequency, i.e., the Nyquist sampling theorem is not satisfied. We tested the proposed energy-efficient approach with the data collected from 20 volunteers (14 males and six females) and the average recognition accuracy of around 96.0% was achieved. Results show that using a low sampling rate of 1Hz can save 17.3% and 59.6% of energy compared with the sampling rates of 5 Hz and 50 Hz. The proposed low sampling rate approach can greatly reduce the power consumption while maintaining high activity recognition accuracy. The composition of power consumption in online ARS is also investigated in this paper.

Conjugate-Gradient Neural Networks in Classification of Multisource and Very-High-Dimensional Remote Sensing Data

NASA Technical Reports Server (NTRS)

Benediktsson, J. A.; Swain, P. H.; Ersoy, O. K.

1993-01-01

Application of neural networks to classification of remote sensing data is discussed. Conventional two-layer backpropagation is found to give good results in classification of remote sensing data but is not efficient in training. A more efficient variant, based on conjugate-gradient optimization, is used for classification of multisource remote sensing and geographic data and very-high-dimensional data. The conjugate-gradient neural networks give excellent performance in classification of multisource data, but do not compare as well with statistical methods in classification of very-high-dimentional data.

Classification Accuracy and Acceptability of the Integrated Screening and Intervention System Teacher Rating Form

ERIC Educational Resources Information Center

Daniels, Brian; Volpe, Robert J.; Fabiano, Gregory A.; Briesch, Amy M.

2017-01-01

This study examines the classification accuracy and teacher acceptability of a problem-focused screener for academic and disruptive behavior problems, which is directly linked to evidence-based intervention. Participants included 39 classroom teachers from 2 public school districts in the Northeastern United States. Teacher ratings were obtained…

An Examination of the Changing Rates of Autism in Special Education

ERIC Educational Resources Information Center

Brock, Stephen E.

2006-01-01

Using U.S. Department of Education data, the current study examined changes in the rates of special education eligibility classifications. This was done to determine if classification substitution might be an explanation for increases in the number of students being found eligible for special education using the Autism criteria. Results reveal…

Built-up Areas Extraction in High Resolution SAR Imagery based on the method of Multiple Feature Weighted Fusion

NASA Astrophysics Data System (ADS)

Liu, X.; Zhang, J. X.; Zhao, Z.; Ma, A. D.

2015-06-01

Synthetic aperture radar in the application of remote sensing technology is becoming more and more widely because of its all-time and all-weather operation, feature extraction research in high resolution SAR image has become a hot topic of concern. In particular, with the continuous improvement of airborne SAR image resolution, image texture information become more abundant. It's of great significance to classification and extraction. In this paper, a novel method for built-up areas extraction using both statistical and structural features is proposed according to the built-up texture features. First of all, statistical texture features and structural features are respectively extracted by classical method of gray level co-occurrence matrix and method of variogram function, and the direction information is considered in this process. Next, feature weights are calculated innovatively according to the Bhattacharyya distance. Then, all features are weighted fusion. At last, the fused image is classified with K-means classification method and the built-up areas are extracted after post classification process. The proposed method has been tested by domestic airborne P band polarization SAR images, at the same time, two groups of experiments based on the method of statistical texture and the method of structural texture were carried out respectively. On the basis of qualitative analysis, quantitative analysis based on the built-up area selected artificially is enforced, in the relatively simple experimentation area, detection rate is more than 90%, in the relatively complex experimentation area, detection rate is also higher than the other two methods. In the study-area, the results show that this method can effectively and accurately extract built-up areas in high resolution airborne SAR imagery.

The Classification of Romanian High-Schools

ERIC Educational Resources Information Center

Ivan, Ion; Milodin, Daniel; Naie, Lucian

2006-01-01

The article tries to tackle the issue of high-schools classification from one city, district or from Romania. The classification criteria are presented. The National Database of Education is also presented and the application of criteria is illustrated. An algorithm for high-school multi-rang classification is proposed in order to build classes of…

Survival rates and prognostic predictors of high grade brain stem gliomas in childhood: a systematic review and meta-analysis.

PubMed

Hassan, Hadeel; Pinches, Anne; Picton, Susan V; Phillips, Robert S

2017-10-01

Diagnosis of a pediatric high grade brain stem glioma is devastating with dismal outcomes. This systematic review and meta-analysis was undertaken to determine the survival rates and assess potential prognostic factors including selected interventions. Studies included involved pediatric participants with high grade brain stem gliomas diagnosed by magnetic resonance imaging or biopsy reporting overall survival rates. Meta-analysis was undertaken using a binomial random effects model. Sixty-five studies (2336 participants) were included. Meta-analysis showed 1 year overall survival (OS) of 41% (95% confidence interval (CI) 38-44%, I-sq 52%, 2083 participants), 2 year OS of 15.3% (95% confidence interval 12-20%, I-sq 73.1%, 1329 participants) and 3 year OS of 7.3% (95% confidence interval 5.2-10%, I-sq 26%, 584 participants). Meta-analyses of median overall survival results was not possible due to the lack of reported measures of variance. Subgroup analysis comparing date of study, classification of tumor, use of temozolomide, non-standard interventions or phase 1/2 versus other studies demonstrated no difference in survival outcomes. There was insufficient data to undertake subgroup meta-analysis of patient age, duration of symptoms, K27M histone mutations and AVCR1 mutations. Survival outcomes of high grade brain stem gliomas have remained very poor, and do not clearly vary according to classification, phase of study or use of different therapeutic interventions. Future studies should harmonize outcome and prognostic variable reporting to enable accurate meta-analysis and better exploration of prognosis.

The application of compressed sensing to long-term acoustic emission-based structural health monitoring

NASA Astrophysics Data System (ADS)

Cattaneo, Alessandro; Park, Gyuhae; Farrar, Charles; Mascareñas, David

2012-04-01

The acoustic emission (AE) phenomena generated by a rapid release in the internal stress of a material represent a promising technique for structural health monitoring (SHM) applications. AE events typically result in a discrete number of short-time, transient signals. The challenge associated with capturing these events using classical techniques is that very high sampling rates must be used over extended periods of time. The result is that a very large amount of data is collected to capture a phenomenon that rarely occurs. Furthermore, the high energy consumption associated with the required high sampling rates makes the implementation of high-endurance, low-power, embedded AE sensor nodes difficult to achieve. The relatively rare occurrence of AE events over long time scales implies that these measurements are inherently sparse in the spike domain. The sparse nature of AE measurements makes them an attractive candidate for the application of compressed sampling techniques. Collecting compressed measurements of sparse AE signals will relax the requirements on the sampling rate and memory demands. The focus of this work is to investigate the suitability of compressed sensing techniques for AE-based SHM. The work explores estimating AE signal statistics in the compressed domain for low-power classification applications. In the event compressed classification finds an event of interest, ι1 norm minimization will be used to reconstruct the measurement for further analysis. The impact of structured noise on compressive measurements is specifically addressed. The suitability of a particular algorithm, called Justice Pursuit, to increase robustness to a small amount of arbitrary measurement corruption is investigated.

OPTIMAL TIME-SERIES SELECTION OF QUASARS

DOE Office of Scientific and Technical Information (OSTI.GOV)

Butler, Nathaniel R.; Bloom, Joshua S.

2011-03-15

We present a novel method for the optimal selection of quasars using time-series observations in a single photometric bandpass. Utilizing the damped random walk model of Kelly et al., we parameterize the ensemble quasar structure function in Sloan Stripe 82 as a function of observed brightness. The ensemble model fit can then be evaluated rigorously for and calibrated with individual light curves with no parameter fitting. This yields a classification in two statistics-one describing the fit confidence and the other describing the probability of a false alarm-which can be tuned, a priori, to achieve high quasar detection fractions (99% completenessmore » with default cuts), given an acceptable rate of false alarms. We establish the typical rate of false alarms due to known variable stars as {approx}<3% (high purity). Applying the classification, we increase the sample of potential quasars relative to those known in Stripe 82 by as much as 29%, and by nearly a factor of two in the redshift range 2.5 < z < 3, where selection by color is extremely inefficient. This represents 1875 new quasars in a 290 deg{sup 2} field. The observed rates of both quasars and stars agree well with the model predictions, with >99% of quasars exhibiting the expected variability profile. We discuss the utility of the method at high redshift and in the regime of noisy and sparse data. Our time-series selection complements well-independent selection based on quasar colors and has strong potential for identifying high-redshift quasars for Baryon Acoustic Oscillations and other cosmology studies in the LSST era.« less

[New molecular classification of colorectal cancer, pancreatic cancer and stomach cancer: Towards "à la carte" treatment?].

PubMed

Dreyer, Chantal; Afchain, Pauline; Trouilloud, Isabelle; André, Thierry

2016-01-01

This review reports 3 of recently published molecular classifications of the 3 main gastro-intestinal cancers: gastric, pancreatic and colorectal adenocarcinoma. In colorectal adenocarcinoma, 6 independent classifications were combined to finally hold 4 molecular sub-groups, Consensus Molecular Subtypes (CMS 1-4), linked to various clinical, molecular and survival data. CMS1 (14% MSI with immune activation); CMS2 (37%: canonical with epithelial differentiation and activation of the WNT/MYC pathway); CMS3 (13% metabolic with epithelial differentiation and RAS mutation); CMS4 (23%: mesenchymal with activation of TGFβ pathway and angiogenesis with stromal invasion). In gastric adenocarcinoma, 4 groups were established: subtype "EBV" (9%, high frequency of PIK3CA mutations, hypermetylation and amplification of JAK2, PD-L1 and PD-L2), subtype "MSI" (22%, high rate of mutation), subtype "genomically stable tumor" (20%, diffuse histology type and mutations of RAS and genes encoding integrins and adhesion proteins including CDH1) and subtype "tumors with chromosomal instability" (50%, intestinal type, aneuploidy and receptor tyrosine kinase amplification). In pancreatic adenocarcinomas, a classification in four sub-groups has been proposed, stable subtype (20%, aneuploidy), locally rearranged subtype (30%, focal event on one or two chromosoms), scattered subtype (36%,<200 structural variation events), and unstable subtype (14%,>200 structural variation events, defects in DNA maintenance). Although currently away from the care of patients, these classifications open the way to "à la carte" treatment depending on molecular biology. Copyright © 2016 Société Française du Cancer. Published by Elsevier Masson SAS. All rights reserved.

CNN for breaking text-based CAPTCHA with noise

NASA Astrophysics Data System (ADS)

Liu, Kaixuan; Zhang, Rong; Qing, Ke

2017-07-01

A CAPTCHA ("Completely Automated Public Turing test to tell Computers and Human Apart") system is a program that most humans can pass but current computer programs could hardly pass. As the most common type of CAPTCHAs , text-based CAPTCHA has been widely used in different websites to defense network bots. In order to breaking textbased CAPTCHA, in this paper, two trained CNN models are connected for the segmentation and classification of CAPTCHA images. Then base on these two models, we apply sliding window segmentation and voting classification methods realize an end-to-end CAPTCHA breaking system with high success rate. The experiment results show that our method is robust and effective in breaking text-based CAPTCHA with noise.

A hazard and risk classification system for catastrophic rock slope failures in Norway

NASA Astrophysics Data System (ADS)

Hermanns, R.; Oppikofer, T.; Anda, E.; Blikra, L. H.; Böhme, M.; Bunkholt, H.; Dahle, H.; Devoli, G.; Eikenæs, O.; Fischer, L.; Harbitz, C. B.; Jaboyedoff, M.; Loew, S.; Yugsi Molina, F. X.

2012-04-01

The Geological Survey of Norway carries out systematic geologic mapping of potentially unstable rock slopes in Norway that can cause a catastrophic failure. As catastrophic failure we describe failures that involve substantial fragmentation of the rock mass during run-out and that impact an area larger than that of a rock fall (shadow angle of ca. 28-32° for rock falls). This includes therefore rock slope failures that lead to secondary effects, such as a displacement wave when impacting a water body or damming of a narrow valley. Our systematic mapping revealed more than 280 rock slopes with significant postglacial deformation, which might represent localities of large future rock slope failures. This large number necessitates prioritization of follow-up activities, such as more detailed investigations, periodic monitoring and permanent monitoring and early-warning. In the past hazard and risk were assessed qualitatively for some sites, however, in order to compare sites so that political and financial decisions can be taken, it was necessary to develop a quantitative hazard and risk classification system. A preliminary classification system was presented and discussed with an expert group of Norwegian and international experts and afterwards adapted following their recommendations. This contribution presents the concept of this final hazard and risk classification that should be used in Norway in the upcoming years. Historical experience and possible future rockslide scenarios in Norway indicate that hazard assessment of large rock slope failures must be scenario-based, because intensity of deformation and present displacement rates, as well as the geological structures activated by the sliding rock mass can vary significantly on a given slope. In addition, for each scenario the run-out of the rock mass has to be evaluated. This includes the secondary effects such as generation of displacement waves or landslide damming of valleys with the potential of later outburst floods. It became obvious that large rock slope failures cannot be evaluated on a slope scale with frequency analyses of historical and prehistorical events only, as multiple rockslides have occurred within one century on a single slope that prior to the recent failures had been inactive for several thousand years. In addition, a systematic analysis on temporal distribution indicates that rockslide activity following deglaciation after the Last Glacial Maximum has been much higher than throughout the Holocene. Therefore the classification system has to be based primarily on the geological conditions on the deforming slope and on the deformation rates and only to a lesser weight on a frequency analyses. Our hazard classification therefore is primarily based on several criteria: 1) Development of the back-scarp, 2) development of the lateral release surfaces, 3) development of the potential basal sliding surface, 4) morphologic expression of the basal sliding surface, 5) kinematic feasibility tests for different displacement mechanisms, 6) landslide displacement rates, 7) change of displacement rates (acceleration), 8) increase of rockfall activity on the unstable rock slope, 9) Presence post-glacial events of similar size along the affected slope and its vicinity. For each of these criteria several conditions are possible to choose from (e.g. different velocity classes for the displacement rate criterion). A score is assigned to each condition and the sum of all scores gives the total susceptibility score. Since many of these observations are somewhat uncertain, the classification system is organized in a decision tree where probabilities can be assigned to each condition. All possibilities in the decision tree are computed and the individual probabilities giving the same total score are summed. Basic statistics show the minimum and maximum total scores of a scenario, as well as the mean and modal value. The final output is a cumulative frequency distribution of the susceptibility scores that can be divided into several classes, which are interpreted as susceptibility classes (very high, high, medium, low, and very low). Today the Norwegian Planning and Building Act uses hazard classes with annual probabilities of impact on buildings producing damages (<1/100, <1/1000, <1/5000 and zero for critical buildings). However, up to now there is not enough scientific knowledge to predict large rock slope failures in these strict classes. Therefore, the susceptibility classes will be matched with the hazard classes from the Norwegian Building Act (e.g. very high susceptibility represents the hazard class with annual probability >1/100). The risk analysis focuses on the potential fatalities of a worst case rock slide scenario and its secondary effects only and is done in consequence classes with a decimal logarithmic scale. However we recommend for all high risk objects that municipalities carry out detailed risk analyses. Finally, the hazard and risk classification system will give recommendations where surveillance in form of continuous 24/7 monitoring systems coupled with early-warning systems (high risk class) or periodic monitoring (medium risk class) should be carried out. These measures are understood as to reduce the risk of life loss due to a rock slope failure close to 0 as population can be evacuated on time if a change of stability situation occurs. The final hazard and risk classification for all potentially unstable rock slopes in Norway, including all data used for its classification will be published within the national landslide database (available on www.skrednett.no).

Automated Classification of Selected Data Elements from Free-text Diagnostic Reports for Clinical Research.

PubMed

Löpprich, Martin; Krauss, Felix; Ganzinger, Matthias; Senghas, Karsten; Riezler, Stefan; Knaup, Petra

2016-08-05

In the Multiple Myeloma clinical registry at Heidelberg University Hospital, most data are extracted from discharge letters. Our aim was to analyze if it is possible to make the manual documentation process more efficient by using methods of natural language processing for multiclass classification of free-text diagnostic reports to automatically document the diagnosis and state of disease of myeloma patients. The first objective was to create a corpus consisting of free-text diagnosis paragraphs of patients with multiple myeloma from German diagnostic reports, and its manual annotation of relevant data elements by documentation specialists. The second objective was to construct and evaluate a framework using different NLP methods to enable automatic multiclass classification of relevant data elements from free-text diagnostic reports. The main diagnoses paragraph was extracted from the clinical report of one third randomly selected patients of the multiple myeloma research database from Heidelberg University Hospital (in total 737 selected patients). An EDC system was setup and two data entry specialists performed independently a manual documentation of at least nine specific data elements for multiple myeloma characterization. Both data entries were compared and assessed by a third specialist and an annotated text corpus was created. A framework was constructed, consisting of a self-developed package to split multiple diagnosis sequences into several subsequences, four different preprocessing steps to normalize the input data and two classifiers: a maximum entropy classifier (MEC) and a support vector machine (SVM). In total 15 different pipelines were examined and assessed by a ten-fold cross-validation, reiterated 100 times. For quality indication the average error rate and the average F1-score were conducted. For significance testing the approximate randomization test was used. The created annotated corpus consists of 737 different diagnoses paragraphs with a total number of 865 coded diagnosis. The dataset is publicly available in the supplementary online files for training and testing of further NLP methods. Both classifiers showed low average error rates (MEC: 1.05; SVM: 0.84) and high F1-scores (MEC: 0.89; SVM: 0.92). However the results varied widely depending on the classified data element. Preprocessing methods increased this effect and had significant impact on the classification, both positive and negative. The automatic diagnosis splitter increased the average error rate significantly, even if the F1-score decreased only slightly. The low average error rates and high average F1-scores of each pipeline demonstrate the suitability of the investigated NPL methods. However, it was also shown that there is no best practice for an automatic classification of data elements from free-text diagnostic reports.

40 CFR 86.085-20 - Incomplete vehicles, classification.

Code of Federal Regulations, 2010 CFR

2010-07-01

..., classification. (a) An incomplete truck less than 8,500 pounds gross vehicle weight rating shall be classified by... 40 Protection of Environment 18 2010-07-01 2010-07-01 false Incomplete vehicles, classification... PROGRAMS (CONTINUED) CONTROL OF EMISSIONS FROM NEW AND IN-USE HIGHWAY VEHICLES AND ENGINES General...

Improving Pattern Recognition and Neural Network Algorithms with Applications to Solar Panel Energy Optimization

NASA Astrophysics Data System (ADS)

Zamora Ramos, Ernesto

Artificial Intelligence is a big part of automation and with today's technological advances, artificial intelligence has taken great strides towards positioning itself as the technology of the future to control, enhance and perfect automation. Computer vision includes pattern recognition and classification and machine learning. Computer vision is at the core of decision making and it is a vast and fruitful branch of artificial intelligence. In this work, we expose novel algorithms and techniques built upon existing technologies to improve pattern recognition and neural network training, initially motivated by a multidisciplinary effort to build a robot that helps maintain and optimize solar panel energy production. Our contributions detail an improved non-linear pre-processing technique to enhance poorly illuminated images based on modifications to the standard histogram equalization for an image. While the original motivation was to improve nocturnal navigation, the results have applications in surveillance, search and rescue, medical imaging enhancing, and many others. We created a vision system for precise camera distance positioning motivated to correctly locate the robot for capture of solar panel images for classification. The classification algorithm marks solar panels as clean or dirty for later processing. Our algorithm extends past image classification and, based on historical and experimental data, it identifies the optimal moment in which to perform maintenance on marked solar panels as to minimize the energy and profit loss. In order to improve upon the classification algorithm, we delved into feedforward neural networks because of their recent advancements, proven universal approximation and classification capabilities, and excellent recognition rates. We explore state-of-the-art neural network training techniques offering pointers and insights, culminating on the implementation of a complete library with support for modern deep learning architectures, multilayer percepterons and convolutional neural networks. Our research with neural networks has encountered a great deal of difficulties regarding hyperparameter estimation for good training convergence rate and accuracy. Most hyperparameters, including architecture, learning rate, regularization, trainable parameters (or weights) initialization, and so on, are chosen via a trial and error process with some educated guesses. However, we developed the first quantitative method to compare weight initialization strategies, a critical hyperparameter choice during training, to estimate among a group of candidate strategies which would make the network converge to the highest classification accuracy faster with high probability. Our method provides a quick, objective measure to compare initialization strategies to select the best possible among them beforehand without having to complete multiple training sessions for each candidate strategy to compare final results.

"Rate My Therapist": Automated Detection of Empathy in Drug and Alcohol Counseling via Speech and Language Processing

PubMed Central

Xiao, Bo; Imel, Zac E.; Georgiou, Panayiotis G.; Atkins, David C.; Narayanan, Shrikanth S.

2015-01-01

The technology for evaluating patient-provider interactions in psychotherapy–observational coding–has not changed in 70 years. It is labor-intensive, error prone, and expensive, limiting its use in evaluating psychotherapy in the real world. Engineering solutions from speech and language processing provide new methods for the automatic evaluation of provider ratings from session recordings. The primary data are 200 Motivational Interviewing (MI) sessions from a study on MI training methods with observer ratings of counselor empathy. Automatic Speech Recognition (ASR) was used to transcribe sessions, and the resulting words were used in a text-based predictive model of empathy. Two supporting datasets trained the speech processing tasks including ASR (1200 transcripts from heterogeneous psychotherapy sessions and 153 transcripts and session recordings from 5 MI clinical trials). The accuracy of computationally-derived empathy ratings were evaluated against human ratings for each provider. Computationally-derived empathy scores and classifications (high vs. low) were highly accurate against human-based codes and classifications, with a correlation of 0.65 and F-score (a weighted average of sensitivity and specificity) of 0.86, respectively. Empathy prediction using human transcription as input (as opposed to ASR) resulted in a slight increase in prediction accuracies, suggesting that the fully automatic system with ASR is relatively robust. Using speech and language processing methods, it is possible to generate accurate predictions of provider performance in psychotherapy from audio recordings alone. This technology can support large-scale evaluation of psychotherapy for dissemination and process studies. PMID:26630392

Diagnosis of major depressive disorder by combining multimodal information from heart rate dynamics and serum proteomics using machine-learning algorithm.

PubMed

Kim, Eun Young; Lee, Min Young; Kim, Se Hyun; Ha, Kyooseob; Kim, Kwang Pyo; Ahn, Yong Min

2017-06-02

Major depressive disorder (MDD) is a systemic and multifactorial disorder that involves abnormalities in multiple biochemical pathways and the autonomic nervous system. This study applied a machine-learning method to classify MDD and control groups by incorporating data from serum proteomic analysis and heart rate variability (HRV) analysis for the identification of novel peripheral biomarkers. The study subjects consisted of 25 drug-free female MDD patients and 25 age- and sex-matched healthy controls. First, quantitative serum proteome profiles were analyzed by liquid chromatography-tandem mass spectrometry using pooled serum samples from 10 patients and 10 controls. Next, candidate proteins were quantified with multiple reaction monitoring (MRM) in 50 subjects. We also analyzed 22 linear and nonlinear HRV parameters in 50 subjects. Finally, we identified a combined biomarker panel consisting of proteins and HRV indexes using a support vector machine with recursive feature elimination. A separation between MDD and control groups was achieved using five parameters (apolipoprotein B, group-specific component, ceruloplasmin, RMSSD, and SampEn) at 80.1% classification accuracy. A combination of HRV and proteomic data achieved better classification accuracy. A high classification accuracy can be achieved by combining multimodal information from heart rate dynamics and serum proteomics in MDD. Our approach can be helpful for accurate clinical diagnosis of MDD. Further studies using larger, independent cohorts are needed to verify the role of these candidate biomarkers for MDD diagnosis. Copyright © 2017 Elsevier Inc. All rights reserved.

Evaluation of a Web-Based App Demonstrating an Exclusionary Algorithmic Approach to TNM Cancer Staging

PubMed Central

2015-01-01

Background TNM staging plays a critical role in the evaluation and management of a range of different types of cancers. The conventional combinatorial approach to the determination of an anatomic stage relies on the identification of distinct tumor (T), node (N), and metastasis (M) classifications to generate a TNM grouping. This process is inherently inefficient due to the need for scrupulous review of the criteria specified for each classification to ensure accurate assignment. An exclusionary approach to TNM staging based on sequential constraint of options may serve to minimize the number of classifications that need to be reviewed to accurately determine an anatomic stage. Objective Our aim was to evaluate the usability and utility of a Web-based app configured to demonstrate an exclusionary approach to TNM staging. Methods Internal medicine residents, surgery residents, and oncology fellows engaged in clinical training were asked to evaluate a Web-based app developed as an instructional aid incorporating (1) an exclusionary algorithm that polls tabulated classifications and sorts them into ranked order based on frequency counts, (2) reconfiguration of classification criteria to generate disambiguated yes/no questions that function as selection and exclusion prompts, and (3) a selectable grid of TNM groupings that provides dynamic graphic demonstration of the effects of sequentially selecting or excluding specific classifications. Subjects were asked to evaluate the performance of this app after completing exercises simulating the staging of different types of cancers encountered during training. Results Survey responses indicated high levels of agreement with statements supporting the usability and utility of this app. Subjects reported that its user interface provided a clear display with intuitive controls and that the exclusionary approach to TNM staging it demonstrated represented an efficient process of assignment that helped to clarify distinctions between tumor, node, and metastasis classifications. High overall usefulness ratings were bolstered by supplementary comments suggesting that this app might be readily adopted for use in clinical practice. Conclusions A Web-based app that utilizes an exclusionary algorithm to prompt the assignment of tumor, node, and metastasis classifications may serve as an effective instructional aid demonstrating an efficient and informative approach to TNM staging. PMID:28410163

Risk factors and classification of stillbirth in a Middle Eastern population: a retrospective study.

PubMed

Kunjachen Maducolil, Mariam; Abid, Hafsa; Lobo, Rachael Marian; Chughtai, Ambreen Qayyum; Afzal, Arjumand Muhammad; Saleh, Huda Abdullah Hussain; Lindow, Stephen W

2017-12-21

To estimate the incidence of stillbirth, explore the associated maternal and fetal factors and to evaluate the most appropriate classification of stillbirth for a multiethnic population. This is a retrospective population-based study of stillbirth in a large tertiary unit. Data of each stillbirth with a gestational age >/=24 weeks in the year 2015 were collected from electronic medical records and analyzed. The stillbirth rate for our multiethnic population is 7.81 per 1000 births. Maternal medical factors comprised 52.4% in which the rates of hypertensive disorders, diabetes and other medical disorders were 22.5%, 20.8% and 8.3%, respectively. The most common fetal factor was intrauterine growth restriction (IUGR) (22.5%) followed by congenital anomalies (21.6%). All cases were categorized using the Wigglesworth, Aberdeen, Tulip, ReCoDe and International Classification of Diseases-perinatal mortality (ICD-PM) classifications and the rates of unclassified stillbirths were 59.2%, 46.6%, 16.6%, 11.6% and 7.5%, respectively. An autopsy was performed in 9.1% of cases reflecting local religious and cultural sensitivities. This study highlighted the modifiable risk factors among the Middle Eastern population. The most appropriate classification was the ICD-PM. The low rates of autopsy prevented a detailed evaluation of stillbirths, therefore it is suggested that a minimally invasive autopsy [postmortem magnetic resonance imaging (MRI)] may improve the quality of care.

Study on Biopharmaceutics Classification and Oral Bioavailability of a Novel Multikinase Inhibitor NCE for Cancer Therapy

PubMed Central

Yang, Yang; Fan, Chun-Mei; He, Xuan; Ren, Ke; Zhang, Jin-Kun; He, Ying-Ju; Yu, Luo-Ting; Zhao, Ying-Lan; Gong, Chang-Yang; Zheng, Yu; Song, Xiang-Rong; Zeng, Jun

2014-01-01

Specific biopharmaceutics classification investigation and study on phamacokinetic profile of a novel drug candidate (2-methylcarbamoyl-4-{4-[3- (trifluoromethyl) benzamido] phenoxy} pyridinium 4-methylbenzenesulfonate monohydrate, NCE) were carried out. Equilibrium solubility and intrinsic dissolution rate (IDR) of NCE were estimated in different phosphate buffers. Effective intestinal permeability (Peff) of NCE was determined using single-pass intestinal perfusion technique in rat duodenum, jejunum and ileum at three concentrations. Theophylline (high permeability) and ranitidine (low permeability) were also applied to access the permeability of NCE as reference compounds. The bioavailability after intragastrical and intravenous administration was measured in beagle dogs. The solubility of NCE in tested phosphate buffers was quite low with the maximum solubility of 81.73 μg/mL at pH 1.0. The intrinsic dissolution ratio of NCE was 1 × 10−4 mg·min−1·cm−2. The Peff value of NCE in all intestinal segments was more proximate to the high-permeability reference theophylline. Therefore, NCE was classified as class II drug according to Biopharmaceutics Classification System due to its low solubility and high intestinal permeability. In addition, concentration-dependent permeability was not observed in all the segments, indicating that there might be passive transportation for NCE. The absolute oral bioavailability of NCE in beagle dogs was 26.75%. Therefore, dissolution promotion will be crucial for oral formulation development and intravenous administration route will also be suggested for further NCE formulation development. All the data would provide a reference for biopharmaceutics classification research of other novel drug candidates. PMID:24776763

Study on biopharmaceutics classification and oral bioavailability of a novel multikinase inhibitor NCE for cancer therapy.

PubMed

Yang, Yang; Fan, Chun-Mei; He, Xuan; Ren, Ke; Zhang, Jin-Kun; He, Ying-Ju; Yu, Luo-Ting; Zhao, Ying-Lan; Gong, Chang-Yang; Zheng, Yu; Song, Xiang-Rong; Zeng, Jun

2014-04-25

Specific biopharmaceutics classification investigation and study on phamacokinetic profile of a novel drug candidate (2-methylcarbamoyl-4-{4-[3- (trifluoromethyl) benzamido] phenoxy} pyridinium 4-methylbenzenesulfonate monohydrate, NCE) were carried out. Equilibrium solubility and intrinsic dissolution rate (IDR) of NCE were estimated in different phosphate buffers. Effective intestinal permeability (P(eff)) of NCE was determined using single-pass intestinal perfusion technique in rat duodenum, jejunum and ileum at three concentrations. Theophylline (high permeability) and ranitidine (low permeability) were also applied to access the permeability of NCE as reference compounds. The bioavailability after intragastrical and intravenous administration was measured in beagle dogs. The solubility of NCE in tested phosphate buffers was quite low with the maximum solubility of 81.73 μg/mL at pH 1.0. The intrinsic dissolution ratio of NCE was 1 × 10⁻⁴ mg·min⁻¹·cm⁻². The P(eff) value of NCE in all intestinal segments was more proximate to the high-permeability reference theophylline. Therefore, NCE was classified as class II drug according to Biopharmaceutics Classification System due to its low solubility and high intestinal permeability. In addition, concentration-dependent permeability was not observed in all the segments, indicating that there might be passive transportation for NCE. The absolute oral bioavailability of NCE in beagle dogs was 26.75%. Therefore, dissolution promotion will be crucial for oral formulation development and intravenous administration route will also be suggested for further NCE formulation development. All the data would provide a reference for biopharmaceutics classification research of other novel drug candidates.

Oral cancer screening: serum Raman spectroscopic approach

NASA Astrophysics Data System (ADS)

Sahu, Aditi K.; Dhoot, Suyash; Singh, Amandeep; Sawant, Sharada S.; Nandakumar, Nikhila; Talathi-Desai, Sneha; Garud, Mandavi; Pagare, Sandeep; Srivastava, Sanjeeva; Nair, Sudhir; Chaturvedi, Pankaj; Murali Krishna, C.

2015-11-01

Serum Raman spectroscopy (RS) has previously shown potential in oral cancer diagnosis and recurrence prediction. To evaluate the potential of serum RS in oral cancer screening, premalignant and cancer-specific detection was explored in the present study using 328 subjects belonging to healthy controls, premalignant, disease controls, and oral cancer groups. Spectra were acquired using a Raman microprobe. Spectral findings suggest changes in amino acids, lipids, protein, DNA, and β-carotene across the groups. A patient-wise approach was employed for data analysis using principal component linear discriminant analysis. In the first step, the classification among premalignant, disease control (nonoral cancer), oral cancer, and normal samples was evaluated in binary classification models. Thereafter, two screening-friendly classification approaches were explored to further evaluate the clinical utility of serum RS: a single four-group model and normal versus abnormal followed by determining the type of abnormality model. Results demonstrate the feasibility of premalignant and specific cancer detection. The normal versus abnormal model yields better sensitivity and specificity rates of 64 and 80% these rates are comparable to standard screening approaches. Prospectively, as the current screening procedure of visual inspection is useful mainly for high-risk populations, serum RS may serve as a useful adjunct for early and specific detection of oral precancers and cancer.

Noninvasive forward-scattering system for rapid detection, characterization, and identification of Listeria colonies: image processing and data analysis

NASA Astrophysics Data System (ADS)

Rajwa, Bartek; Bayraktar, Bulent; Banada, Padmapriya P.; Huff, Karleigh; Bae, Euiwon; Hirleman, E. Daniel; Bhunia, Arun K.; Robinson, J. Paul

2006-10-01

Bacterial contamination by Listeria monocytogenes puts the public at risk and is also costly for the food-processing industry. Traditional methods for pathogen identification require complicated sample preparation for reliable results. Previously, we have reported development of a noninvasive optical forward-scattering system for rapid identification of Listeria colonies grown on solid surfaces. The presented system included application of computer-vision and patternrecognition techniques to classify scatter pattern formed by bacterial colonies irradiated with laser light. This report shows an extension of the proposed method. A new scatterometer equipped with a high-resolution CCD chip and application of two additional sets of image features for classification allow for higher accuracy and lower error rates. Features based on Zernike moments are supplemented by Tchebichef moments, and Haralick texture descriptors in the new version of the algorithm. Fisher's criterion has been used for feature selection to decrease the training time of machine learning systems. An algorithm based on support vector machines was used for classification of patterns. Low error rates determined by cross-validation, reproducibility of the measurements, and robustness of the system prove that the proposed technology can be implemented in automated devices for detection and classification of pathogenic bacteria.

Inline inspection of textured plastics surfaces

NASA Astrophysics Data System (ADS)

Michaeli, Walter; Berdel, Klaus

2011-02-01

This article focuses on the inspection of plastics web materials exhibiting irregular textures such as imitation wood or leather. They are produced in a continuous process at high speed. In this process, various defects occur sporadically. However, current inspection systems for plastics surfaces are able to inspect unstructured products or products with regular, i.e., highly periodic, textures, only. The proposed inspection algorithm uses the local binary pattern operator for texture feature extraction. For classification, semisupervised as well as supervised approaches are used. A simple concept for semisupervised classification is presented and applied for defect detection. The resulting defect-maps are presented to the operator. He assigns class labels that are used to train the supervised classifier in order to distinguish between different defect types. A concept for parallelization is presented allowing the efficient use of standard multicore processor PC hardware. Experiments with images of a typical product acquired in an industrial setting show a detection rate of 97% while achieving a false alarm rate below 1%. Real-time tests show that defects can be reliably detected even at haul-off speeds of 30 m/min. Further applications of the presented concept can be found in the inspection of other materials.

Obesity classification in military personnel: a comparison of body fat, waist circumference, and body mass index measurements.

PubMed

Heinrich, Katie M; Jitnarin, Nattinee; Suminski, Richard R; Berkel, LaVerne; Hunter, Christine M; Alvarez, Lisa; Brundige, Antionette R; Peterson, Alan L; Foreyt, John P; Haddock, C Keith; Poston, Walker S C

2008-01-01

The purpose of this study was to evaluate obesity classifications from body fat percentage (BF%), body mass index (BMI), and waist circumference (WC). A total of 451 overweight/obese active duty military personnel completed all three assessments. Most were obese (men, 81%; women, 98%) using National Institutes of Health (NIH) BF% standards (men, >25%; women, >30%). Using the higher World Health Organization (WHO) BF >35% standard, 86% of women were obese. BMI (55.5% and 51.4%) and WC (21.4% and 31.9%) obesity rates were substantially lower for men and women, respectively (p < 0.05). BMI/WC were accurate discriminators for BF% obesity (theta for all comparisons >0.75, p < 0.001). Optimal cutoff points were lower than NIH/WHO standards; WC = 100 cm and BMI = 29 maximized sensitivity and specificity for men, and WC = 79 cm and BMI = 25.5 (NIH) or WC = 83 cm and BMI = 26 (WHO) maximized sensitivity and specificity for women. Both WC and BMI measures had high rates of false negatives compared to BF%. However, at a population level, WC/BMI are useful obesity measures, demonstrating fair-to-high discriminatory power.

Combat ocular trauma and systemic injury.

PubMed

Weichel, Eric D; Colyer, Marcus H

2008-11-01

To review the recent literature regarding combat ocular trauma during hostilities in Operations Iraqi Freedom and Enduring Freedom, describe the classification of combat ocular trauma, and offer strategies that may assist in the management of eye injuries. Several recent publications have highlighted features of combat ocular trauma from Operation Iraqi Freedom. The most common cause of today's combat ocular injuries is unconventional fragmentary munitions causing significant blast injuries. These explosive munitions cause high rates of concomitant nonocular injuries such as traumatic brain injury, amputation, and other organ injuries. The most frequent ocular injuries include open-globe and adnexal lacerations. The extreme severity of combat-related open-globe injuries leads to high rates of primary enucleation and retained intraocular foreign bodies. Visual outcomes of intraocular foreign body injuries are similar to other series despite delayed removal, and no cases of endophthalmitis have occurred. Despite these advances, however, significant vision loss persists in cases of perforating globe injuries as well as open and closed-globe trauma involving the posterior segment. This review summarizes the recent literature describing ocular and systemic injuries sustained during Operations Iraqi and Enduring Freedom. An emphasis on classification of ocular injuries as well as a discussion of main outcome measures and complications is discussed.

Predicting adherence of patients with HF through machine learning techniques.

PubMed

Karanasiou, Georgia Spiridon; Tripoliti, Evanthia Eleftherios; Papadopoulos, Theofilos Grigorios; Kalatzis, Fanis Georgios; Goletsis, Yorgos; Naka, Katerina Kyriakos; Bechlioulis, Aris; Errachid, Abdelhamid; Fotiadis, Dimitrios Ioannis

2016-09-01

Heart failure (HF) is a chronic disease characterised by poor quality of life, recurrent hospitalisation and high mortality. Adherence of patient to treatment suggested by the experts has been proven a significant deterrent of the above-mentioned serious consequences. However, the non-adherence rates are significantly high; a fact that highlights the importance of predicting the adherence of the patient and enabling experts to adjust accordingly patient monitoring and management. The aim of this work is to predict the adherence of patients with HF, through the application of machine learning techniques. Specifically, it aims to classify a patient not only as medication adherent or not, but also as adherent or not in terms of medication, nutrition and physical activity (global adherent). Two classification problems are addressed: (i) if the patient is global adherent or not and (ii) if the patient is medication adherent or not. About 11 classification algorithms are employed and combined with feature selection and resampling techniques. The classifiers are evaluated on a dataset of 90 patients. The patients are characterised as medication and global adherent, based on clinician estimation. The highest detection accuracy is 82 and 91% for the first and the second classification problem, respectively.

Application of Fourier transform midinfrared spectroscopy to the discrimination between Irish artisanal honey and such honey adulterated with various sugar syrups.

PubMed

Kelly, J Daniel; Petisco, Cristina; Downey, Gerard

2006-08-23

A collection of authentic artisanal Irish honeys (n = 580) and certain of these honeys adulterated by fully inverted beet syrup (n = 280), high-fructose corn syrup (n = 160), partial invert cane syrup (n = 120), dextrose syrup (n = 160), and beet sucrose (n = 120) was assembled. All samples were adjusted to 70 degrees Bx and scanned in the midinfrared region (800-4000 cm(-1)) by attenuated total reflectance sample accessory. By use of soft independent modeling of class analogy (SIMCA) and partial least-squares (PLS) classification, authentic honey and honey adulterated by beet sucrose, dextrose syrups, and partial invert corn syrup could be identified with correct classification rates of 96.2%, 97.5%, 95.8%, and 91.7%, respectively. This combination of spectroscopic technique and chemometric methods was not able to unambiguously detect adulteration by high-fructose corn syrup or fully inverted beet syrup.

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

DTIC Science & Technology

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

78 FR 15377 - Agency Information Collection Activities; Submission for OMB Review; Comment Request; Requests To...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-03-11

... for OMB Review; Comment Request; Requests To Approve Conformed Wage Classifications and Unconventional... Classifications and Unconventional Fringe Benefit Plans Under the Davis-Bacon and Related Acts and Contract Work... collection consist of: (A) Reports of conformed classifications and wage rates and (B) requests for approval...

48 CFR 52.222-6 - Davis-Bacon Act.

Code of Federal Regulations, 2011 CFR

2011-10-01

... defined in paragraph (a)(1)(i), or the “secondary site of the work” as defined in paragraph (a)(1)(ii) of... the classification of work actually performed, without regard to skill, except as provided in the... classification may be compensated at the rate specified for each classification for the time actually worked...

48 CFR 52.222-6 - Davis-Bacon Act.

Code of Federal Regulations, 2010 CFR

2010-10-01

... defined in paragraph (a)(1)(i), or the “secondary site of the work” as defined in paragraph (a)(1)(ii) of... the classification of work actually performed, without regard to skill, except as provided in the... classification may be compensated at the rate specified for each classification for the time actually worked...

Cesarean Section Rate Analysis in University Hospital Tuzla - According to Robson's Classification.

PubMed

Fatusic, Jasenko; Hudic, Igor; Fatusic, Zlatan; Zildzic-Moralic, Aida; Zivkovic, Milorad

2016-06-01

For last decades, there has public concern about increasing Cesarean Section (CS) rates, and it is an issue of international public health concern. According to World Health Organisation (WHO) there is no justification to have more than 10-15% CS births. WHO proposes the Robson ten-group classification, as a global standard for assessing, monitoring and comparing cesarean section rates. The aim of this study was to investigate Cesarean section rate at University Hospital Tuzla, Bosnia and Herzegovina. Cross sectional study was conducted for one-year period, 2015. Statistical analysis and graph-table presentation was performed using Excel 2010 and Microsoft Office programs. Out of 3,672 births, a total of 936 births were performed by CS. Percentage of the total number of CS to the total birth number was 25,47%. According to Robson classification, the largest was group 5 with relative contribution of 29,80%. On second and third place were group 1 and 2 with relative contribution of 26,06% and 15,78% respectively. Groups 1, 2, 5 made account of realtive contribution of 71,65%. All other groups had entirely relative contribution of 28,35%. Robson 10-group classification provides easy way in collecting information about CS rate. It is important that efforts to reduce the overall CS rate should focus on reducing the primary CS. Data from our study confirm this attitude.

Mechanisms of starch digestion by α-amylase-Structural basis for kinetic properties.

PubMed

Dhital, Sushil; Warren, Frederick J; Butterworth, Peter J; Ellis, Peter R; Gidley, Michael J

2017-03-24

Recent studies of the mechanisms determining the rate and extent of starch digestion by α-amylase are reviewed in the light of current widely-used classifications for (a) the proportions of rapidly-digestible (RDS), slowly-digestible (SDS), and resistant starch (RS) based on in vitro digestibility, and (b) the types of resistant starch (RS 1,2,3,4…) based on physical and/or chemical form. Based on methodological advances and new mechanistic insights, it is proposed that both classification systems should be modified. Kinetic analysis of digestion profiles provides a robust set of parameters that should replace the classification of starch as a combination of RDS, SDS, and RS from a single enzyme digestion experiment. This should involve determination of the minimum number of kinetic processes needed to describe the full digestion profile, together with the proportion of starch involved in each process, and the kinetic properties of each process. The current classification of resistant starch types as RS1,2,3,4 should be replaced by one which recognizes the essential kinetic nature of RS (enzyme digestion rate vs. small intestinal passage rate), and that there are two fundamental origins for resistance based on (i) rate-determining access/binding of enzyme to substrate and (ii) rate-determining conversion of substrate to product once bound.

Wing Shape as an Indicator of Larval Rearing Conditions for Aedes albopictus and Ae. aegypti (Diptera: Culicidae)

PubMed Central

Stephens, C. R.; Juliano, S. A.

2012-01-01

Estimating a mosquito’s vector competence, or likelihood of transmitting disease, if it takes an infectious blood meal, is an important aspect of predicting when and where outbreaks of infectious diseases will occur. Vector competence can be affected by rearing temperature and inter- and intraspecific competition experienced by the individual mosquito during its larval development. This research investigates whether a new morphological indicator of larval rearing conditions, wing shape, can be used to distinguish reliably temperature and competitive conditions experienced during larval stages. Aedes albopictus and Aedes aegypti larvae were reared in low intra-specific, high intra-specific, or high inter-specific competition treatments at either 22°C or 32°C. The right wing of each dried female was removed and photographed. Nineteen landmarks and twenty semilandmarks were digitized on each wing. Shape variables were calculated using geometric morphometric software. Canonical variate analysis, randomization multivariate analysis of variance, and visualization of landmark movement using deformation grids provided evidence that although semilandmark position was significantly affected by larval competition and temperature for both species, the differences in position did not translate into differences in wing shape, as shown in deformation grids. Two classification procedures yielded success rates of 26–49%. Accounting for wing size produced no increase in classification success. There appeared to be a significant relationship between shape and size. These results, particularly the low success rate of classification based on wing shape, show that shape is unlikely to be a reliable indicator of larval rearing competition and temperature conditions for Aedes albopictus and Aedes aegypti. PMID:22897054

Crop classification using temporal stacks of multispectral satellite imagery

NASA Astrophysics Data System (ADS)

Moody, Daniela I.; Brumby, Steven P.; Chartrand, Rick; Keisler, Ryan; Longbotham, Nathan; Mertes, Carly; Skillman, Samuel W.; Warren, Michael S.

2017-05-01

The increase in performance, availability, and coverage of multispectral satellite sensor constellations has led to a drastic increase in data volume and data rate. Multi-decadal remote sensing datasets at the petabyte scale are now available in commercial clouds, with new satellite constellations generating petabytes/year of daily high-resolution global coverage imagery. The data analysis capability, however, has lagged behind storage and compute developments, and has traditionally focused on individual scene processing. We present results from an ongoing effort to develop satellite imagery analysis tools that aggregate temporal, spatial, and spectral information and can scale with the high-rate and dimensionality of imagery being collected. We investigate and compare the performance of pixel-level crop identification using tree-based classifiers and its dependence on both temporal and spectral features. Classification performance is assessed using as ground-truth Cropland Data Layer (CDL) crop masks generated by the US Department of Agriculture (USDA). The CDL maps contain 30m spatial resolution, pixel-level labels for around 200 categories of land cover, but are however only available post-growing season. The analysis focuses on McCook county in South Dakota and shows crop classification using a temporal stack of Landsat 8 (L8) imagery over the growing season, from April through October. Specifically, we consider the temporal L8 stack depth, as well as different normalized band difference indices, and evaluate their contribution to crop identification. We also show an extension of our algorithm to map corn and soy crops in the state of Mato Grosso, Brazil.

Emotional Valence, Arousal, and Threat Ratings of 160 Chinese Words among Adolescents

PubMed Central

Ho, Samuel M. Y.; Mak, Christine W. Y.; Yeung, Dannii; Duan, Wenjie; Tang, Sandy; Yeung, June C.; Ching, Rita

2015-01-01

This study was conducted to provide ratings of valence/pleasantness, arousal/excitement, and threat/potential harm for 160 Chinese words. The emotional valence classification (positive, negative, or neutral) of all of the words corresponded to that of the equivalent English language words. More than 90% of the participants, junior high school students aged between 12 and 17 years, understood the words. The participants were from both mainland China and Hong Kong, thus the words can be applied to adolescents familiar with either simplified (e.g. in mainland China) or traditional Chinese (e.g. in Hong Kong) with a junior secondary school education or higher. We also established eight words with negative valence, high threat, and high arousal ratings to facilitate future research, especially on attentional and memory biases among individuals prone to anxiety. Thus, the new emotional word list provides a useful source of information for affective research in the Chinese language. PMID:26226604

An embedded implementation based on adaptive filter bank for brain-computer interface systems.

PubMed

Belwafi, Kais; Romain, Olivier; Gannouni, Sofien; Ghaffari, Fakhreddine; Djemal, Ridha; Ouni, Bouraoui

2018-07-15

Brain-computer interface (BCI) is a new communication pathway for users with neurological deficiencies. The implementation of a BCI system requires complex electroencephalography (EEG) signal processing including filtering, feature extraction and classification algorithms. Most of current BCI systems are implemented on personal computers. Therefore, there is a great interest in implementing BCI on embedded platforms to meet system specifications in terms of time response, cost effectiveness, power consumption, and accuracy. This article presents an embedded-BCI (EBCI) system based on a Stratix-IV field programmable gate array. The proposed system relays on the weighted overlap-add (WOLA) algorithm to perform dynamic filtering of EEG-signals by analyzing the event-related desynchronization/synchronization (ERD/ERS). The EEG-signals are classified, using the linear discriminant analysis algorithm, based on their spatial features. The proposed system performs fast classification within a time delay of 0.430 s/trial, achieving an average accuracy of 76.80% according to an offline approach and 80.25% using our own recording. The estimated power consumption of the prototype is approximately 0.7 W. Results show that the proposed EBCI system reduces the overall classification error rate for the three datasets of the BCI-competition by 5% compared to other similar implementations. Moreover, experiment shows that the proposed system maintains a high accuracy rate with a short processing time, a low power consumption, and a low cost. Performing dynamic filtering of EEG-signals using WOLA increases the recognition rate of ERD/ERS patterns of motor imagery brain activity. This approach allows to develop a complete prototype of a EBCI system that achieves excellent accuracy rates. Copyright © 2018 Elsevier B.V. All rights reserved.

Pulsar Search Using Supervised Machine Learning

NASA Astrophysics Data System (ADS)

Ford, John M.

2017-05-01

Pulsars are rapidly rotating neutron stars which emit a strong beam of energy through mechanisms that are not entirely clear to physicists. These very dense stars are used by astrophysicists to study many basic physical phenomena, such as the behavior of plasmas in extremely dense environments, behavior of pulsar-black hole pairs, and tests of general relativity. Many of these tasks require a large ensemble of pulsars to provide enough statistical information to answer the scientific questions posed by physicists. In order to provide more pulsars to study, there are several large-scale pulsar surveys underway, which are generating a huge backlog of unprocessed data. Searching for pulsars is a very labor-intensive process, currently requiring skilled people to examine and interpret plots of data output by analysis programs. An automated system for screening the plots will speed up the search for pulsars by a very large factor. Research to date on using machine learning and pattern recognition has not yielded a completely satisfactory system, as systems with the desired near 100% recall have false positive rates that are higher than desired, causing more manual labor in the classification of pulsars. This work proposed to research, identify, propose and develop methods to overcome the barriers to building an improved classification system with a false positive rate of less than 1% and a recall of near 100% that will be useful for the current and next generation of large pulsar surveys. The results show that it is possible to generate classifiers that perform as needed from the available training data. While a false positive rate of 1% was not reached, recall of over 99% was achieved with a false positive rate of less than 2%. Methods of mitigating the imbalanced training and test data were explored and found to be highly effective in enhancing classification accuracy.

Characterization of groups using composite kernels and multi-source fMRI analysis data: application to schizophrenia

PubMed Central

Castro, Eduardo; Martínez-Ramón, Manel; Pearlson, Godfrey; Sui, Jing; Calhoun, Vince D.

2011-01-01

Pattern classification of brain imaging data can enable the automatic detection of differences in cognitive processes of specific groups of interest. Furthermore, it can also give neuroanatomical information related to the regions of the brain that are most relevant to detect these differences by means of feature selection procedures, which are also well-suited to deal with the high dimensionality of brain imaging data. This work proposes the application of recursive feature elimination using a machine learning algorithm based on composite kernels to the classification of healthy controls and patients with schizophrenia. This framework, which evaluates nonlinear relationships between voxels, analyzes whole-brain fMRI data from an auditory task experiment that is segmented into anatomical regions and recursively eliminates the uninformative ones based on their relevance estimates, thus yielding the set of most discriminative brain areas for group classification. The collected data was processed using two analysis methods: the general linear model (GLM) and independent component analysis (ICA). GLM spatial maps as well as ICA temporal lobe and default mode component maps were then input to the classifier. A mean classification accuracy of up to 95% estimated with a leave-two-out cross-validation procedure was achieved by doing multi-source data classification. In addition, it is shown that the classification accuracy rate obtained by using multi-source data surpasses that reached by using single-source data, hence showing that this algorithm takes advantage of the complimentary nature of GLM and ICA. PMID:21723948

The joint use of the tangential electric field and surface Laplacian in EEG classification.

PubMed

Carvalhaes, C G; de Barros, J Acacio; Perreau-Guimaraes, M; Suppes, P

2014-01-01

We investigate the joint use of the tangential electric field (EF) and the surface Laplacian (SL) derivation as a method to improve the classification of EEG signals. We considered five classification tasks to test the validity of such approach. In all five tasks, the joint use of the components of the EF and the SL outperformed the scalar potential. The smallest effect occurred in the classification of a mental task, wherein the average classification rate was improved by 0.5 standard deviations. The largest effect was obtained in the classification of visual stimuli and corresponded to an improvement of 2.1 standard deviations.

Two Approaches to Estimation of Classification Accuracy Rate under Item Response Theory

ERIC Educational Resources Information Center

Lathrop, Quinn N.; Cheng, Ying

2013-01-01

Within the framework of item response theory (IRT), there are two recent lines of work on the estimation of classification accuracy (CA) rate. One approach estimates CA when decisions are made based on total sum scores, the other based on latent trait estimates. The former is referred to as the Lee approach, and the latter, the Rudner approach,…

Flare rates and the McIntosh active-region classifications

NASA Technical Reports Server (NTRS)

Bornmann, P. L.; Shaw, D.

1994-01-01

Multiple linear regression analysis was used to derive the effective solar flare contributions of each of the McIntosh classification parameters. The best fits to the combined average number of M- and X-class X-ray flares per day were found when the flare contributions were assumed to be multiplicative rather than additive. This suggests that nonlinear processes may amplify the effects of the following different active-region properties encoded in the McIntosh classifications: the length of the sunspot group, the size and shape of the largest spot, and the distribution of spots within the group. Since many of these active-region properties are correlated with magnetic field strengths and fluxes, we suggest that the derived correlations reflect a more fundamental relationship between flare production and the magnetic properties of the region. The derived flare contributions for the individual McIntosh parameters can be used to derive a flare rate for each of the three-parameter McIntosh classes. These derived flare rates can be interpreted as smoothed values that may provide better estimates of an active region's expected flare rate when rare classes are reported or when the multiple observing sites report slightly different classifications.

Identification of Anisomerous Motor Imagery EEG Signals Based on Complex Algorithms

PubMed Central

Zhang, Zhiwen; Duan, Feng; Zhou, Xin; Meng, Zixuan

2017-01-01

Motor imagery (MI) electroencephalograph (EEG) signals are widely applied in brain-computer interface (BCI). However, classified MI states are limited, and their classification accuracy rates are low because of the characteristics of nonlinearity and nonstationarity. This study proposes a novel MI pattern recognition system that is based on complex algorithms for classifying MI EEG signals. In electrooculogram (EOG) artifact preprocessing, band-pass filtering is performed to obtain the frequency band of MI-related signals, and then, canonical correlation analysis (CCA) combined with wavelet threshold denoising (WTD) is used for EOG artifact preprocessing. We propose a regularized common spatial pattern (R-CSP) algorithm for EEG feature extraction by incorporating the principle of generic learning. A new classifier combining the K-nearest neighbor (KNN) and support vector machine (SVM) approaches is used to classify four anisomerous states, namely, imaginary movements with the left hand, right foot, and right shoulder and the resting state. The highest classification accuracy rate is 92.5%, and the average classification accuracy rate is 87%. The proposed complex algorithm identification method can significantly improve the identification rate of the minority samples and the overall classification performance. PMID:28874909

Multimethod latent class analysis

PubMed Central

Nussbeck, Fridtjof W.; Eid, Michael

2015-01-01

Correct and, hence, valid classifications of individuals are of high importance in the social sciences as these classifications are the basis for diagnoses and/or the assignment to a treatment. The via regia to inspect the validity of psychological ratings is the multitrait-multimethod (MTMM) approach. First, a latent variable model for the analysis of rater agreement (latent rater agreement model) will be presented that allows for the analysis of convergent validity between different measurement approaches (e.g., raters). Models of rater agreement are transferred to the level of latent variables. Second, the latent rater agreement model will be extended to a more informative MTMM latent class model. This model allows for estimating (i) the convergence of ratings, (ii) method biases in terms of differential latent distributions of raters and differential associations of categorizations within raters (specific rater bias), and (iii) the distinguishability of categories indicating if categories are satisfyingly distinct from each other. Finally, an empirical application is presented to exemplify the interpretation of the MTMM latent class model. PMID:26441714

Development of a neural-based forecasting tool to classify recreational water quality using fecal indicator organisms.

PubMed

Motamarri, Srinivas; Boccelli, Dominic L

2012-09-15

Users of recreational waters may be exposed to elevated pathogen levels through various point/non-point sources. Typical daily notifications rely on microbial analysis of indicator organisms (e.g., Escherichia coli) that require 18, or more, hours to provide an adequate response. Modeling approaches, such as multivariate linear regression (MLR) and artificial neural networks (ANN), have been utilized to provide quick predictions of microbial concentrations for classification purposes, but generally suffer from high false negative rates. This study introduces the use of learning vector quantization (LVQ)--a direct classification approach--for comparison with MLR and ANN approaches and integrates input selection for model development with respect to primary and secondary water quality standards within the Charles River Basin (Massachusetts, USA) using meteorologic, hydrologic, and microbial explanatory variables. Integrating input selection into model development showed that discharge variables were the most important explanatory variables while antecedent rainfall and time since previous events were also important. With respect to classification, all three models adequately represented the non-violated samples (>90%). The MLR approach had the highest false negative rates associated with classifying violated samples (41-62% vs 13-43% (ANN) and <16% (LVQ)) when using five or more explanatory variables. The ANN performance was more similar to LVQ when a larger number of explanatory variables were utilized, but the ANN performance degraded toward MLR performance as explanatory variables were removed. Overall, the use of LVQ as a direct classifier provided the best overall classification ability with respect to violated/non-violated samples for both standards. Copyright © 2012 Elsevier Ltd. All rights reserved.

Spiking Neural Classifier with Lumped Dendritic Nonlinearity and Binary Synapses: A Current Mode VLSI Implementation and Analysis.

PubMed

Bhaduri, Aritra; Banerjee, Amitava; Roy, Subhrajit; Kar, Sougata; Basu, Arindam

2018-03-01

We present a neuromorphic current mode implementation of a spiking neural classifier with lumped square law dendritic nonlinearity. It has been shown previously in software simulations that such a system with binary synapses can be trained with structural plasticity algorithms to achieve comparable classification accuracy with fewer synaptic resources than conventional algorithms. We show that even in real analog systems with manufacturing imperfections (CV of 23.5% and 14.4% for dendritic branch gains and leaks respectively), this network is able to produce comparable results with fewer synaptic resources. The chip fabricated in [Formula: see text]m complementary metal oxide semiconductor has eight dendrites per cell and uses two opposing cells per class to cancel common-mode inputs. The chip can operate down to a [Formula: see text] V and dissipates 19 nW of static power per neuronal cell and [Formula: see text] 125 pJ/spike. For two-class classification problems of high-dimensional rate encoded binary patterns, the hardware achieves comparable performance as software implementation of the same with only about a 0.5% reduction in accuracy. On two UCI data sets, the IC integrated circuit has classification accuracy comparable to standard machine learners like support vector machines and extreme learning machines while using two to five times binary synapses. We also show that the system can operate on mean rate encoded spike patterns, as well as short bursts of spikes. To the best of our knowledge, this is the first attempt in hardware to perform classification exploiting dendritic properties and binary synapses.

High Dimensional Classification Using Features Annealed Independence Rules.

PubMed

Fan, Jianqing; Fan, Yingying

2008-01-01

Classification using high-dimensional features arises frequently in many contemporary statistical studies such as tumor classification using microarray or other high-throughput data. The impact of dimensionality on classifications is largely poorly understood. In a seminal paper, Bickel and Levina (2004) show that the Fisher discriminant performs poorly due to diverging spectra and they propose to use the independence rule to overcome the problem. We first demonstrate that even for the independence classification rule, classification using all the features can be as bad as the random guessing due to noise accumulation in estimating population centroids in high-dimensional feature space. In fact, we demonstrate further that almost all linear discriminants can perform as bad as the random guessing. Thus, it is paramountly important to select a subset of important features for high-dimensional classification, resulting in Features Annealed Independence Rules (FAIR). The conditions under which all the important features can be selected by the two-sample t-statistic are established. The choice of the optimal number of features, or equivalently, the threshold value of the test statistics are proposed based on an upper bound of the classification error. Simulation studies and real data analysis support our theoretical results and demonstrate convincingly the advantage of our new classification procedure.

THE BRIEF PSYCHIATRIC RATING SCALE IN POSITIVE AND NEGATIVE SUBTYPES OF SCHIZOPHRENIA

PubMed Central

Kulhara, P.; Mattoo, S.K.; Avasthi, A.; Malhotra, A.

1987-01-01

SUMMARY Usefulness of the Brief Psychiatric Rating Scale (BPRS) in distinguishing positive and negative subtypes of schizophrenia is presented. Ninety five schizophrenic patients were assessed on BPRS. Significant differences emerged between positive and negative subtypes of schizophrenia on items like emotional withdrawal, guilt feelings, tension, hallucinatory behaviour, motor retardation, blunted affect and excitement. Discriminant function equation generated by these items had a high rate of prediction of group membership either to positive or negative schizophrenia group. Principal components analysis of BPRS scores yielded factors which favour categorization of patients in positive, negative subtypes. The study provides support for classification of schizophrenia into these subtypes. PMID:21927241

An improvement of vehicle detection under shadow regions in satellite imagery

NASA Astrophysics Data System (ADS)

Karim, Shahid; Zhang, Ye; Ali, Saad; Asif, Muhammad Rizwan

2018-04-01

The processing of satellite imagery is dependent upon the quality of imagery. Due to low resolution, it is difficult to extract accurate information according to the requirements of applications. For the purpose of vehicle detection under shadow regions, we have used HOG for feature extraction, SVM is used for classification and HOG is discerned worthwhile tool for complex environments. Shadow images have been scrutinized and found very complex for detection as observed very low detection rates therefore our dedication is towards enhancement of detection rate under shadow regions by implementing appropriate preprocessing. Vehicles are precisely detected under non-shadow regions with high detection rate than shadow regions.

Conifer health classification for Colorado, 2008

USGS Publications Warehouse

Cole, Christopher J.; Noble, Suzanne M.; Blauer, Steven L.; Friesen, Beverly A.; Curry, Stacy E.; Bauer, Mark A.

2010-01-01

Colorado has undergone substantial changes in forests due to urbanization, wildfires, insect-caused tree mortality, and other human and environmental factors. The U.S. Geological Survey Rocky Mountain Geographic Science Center evaluated and developed a methodology for applying remotely-sensed imagery for assessing conifer health in Colorado. Two classes were identified for the purposes of this study: healthy and unhealthy (for example, an area the size of a 30- x 30-m pixel with 20 percent or greater visibly dead trees was defined as ?unhealthy?). Medium-resolution Landsat 5 Thematic Mapper imagery were collected. The normalized, reflectance-converted, cloud-filled Landsat scenes were merged to form a statewide image mosaic, and a Normalized Difference Vegetation Index (NDVI) and Renormalized Difference Infrared Index (RDII) were derived. A supervised maximum likelihood classification was done using the Landsat multispectral bands, the NDVI, the RDII, and 30-m U.S. Geological Survey National Elevation Dataset (NED). The classification was constrained to pixels identified in the updated landcover dataset as coniferous or mixed coniferous/deciduous vegetation. The statewide results were merged with a separate health assessment of Grand County, Colo., produced in late 2008. Sampling and validation was done by collecting field data and high-resolution imagery. The 86 percent overall classification accuracy attained in this study suggests that the data and methods used successfully characterized conifer conditions within Colorado. Although forest conditions for Lodgepole Pine (Pinus contorta) are easily characterized, classification uncertainty exists between healthy/unhealthy Ponderosa Pine (Pinus ponderosa), Pi?on (Pinus edulis), and Juniper (Juniperus sp.) vegetation. Some underestimation of conifer mortality in Summit County is likely, where recent (2008) cloud-free imagery was unavailable. These classification uncertainties are primarily due to the spatial and temporal resolution of Landsat, and of the NLCD derived from this sensor. It is believed that high- to moderate-resolution multispectral imagery, coupled with field data, could significantly reduce the uncertainty rates. The USGS produced a four-county follow-up conifer health assessment using high-resolution RapidEye remotely sensed imagery and field data collected in 2009.

Localized contourlet features in vehicle make and model recognition

NASA Astrophysics Data System (ADS)

Zafar, I.; Edirisinghe, E. A.; Acar, B. S.

2009-02-01

Automatic vehicle Make and Model Recognition (MMR) systems provide useful performance enhancements to vehicle recognitions systems that are solely based on Automatic Number Plate Recognition (ANPR) systems. Several vehicle MMR systems have been proposed in literature. In parallel to this, the usefulness of multi-resolution based feature analysis techniques leading to efficient object classification algorithms have received close attention from the research community. To this effect, Contourlet transforms that can provide an efficient directional multi-resolution image representation has recently been introduced. Already an attempt has been made in literature to use Curvelet/Contourlet transforms in vehicle MMR. In this paper we propose a novel localized feature detection method in Contourlet transform domain that is capable of increasing the classification rates up to 4%, as compared to the previously proposed Contourlet based vehicle MMR approach in which the features are non-localized and thus results in sub-optimal classification. Further we show that the proposed algorithm can achieve the increased classification accuracy of 96% at significantly lower computational complexity due to the use of Two Dimensional Linear Discriminant Analysis (2DLDA) for dimensionality reduction by preserving the features with high between-class variance and low inter-class variance.

A hybrid computational approach for efficient Alzheimer's disease classification based on heterogeneous data.

PubMed

Ding, Xuemei; Bucholc, Magda; Wang, Haiying; Glass, David H; Wang, Hui; Clarke, Dave H; Bjourson, Anthony John; Dowey, Le Roy C; O'Kane, Maurice; Prasad, Girijesh; Maguire, Liam; Wong-Lin, KongFatt

2018-06-27

There is currently a lack of an efficient, objective and systemic approach towards the classification of Alzheimer's disease (AD), due to its complex etiology and pathogenesis. As AD is inherently dynamic, it is also not clear how the relationships among AD indicators vary over time. To address these issues, we propose a hybrid computational approach for AD classification and evaluate it on the heterogeneous longitudinal AIBL dataset. Specifically, using clinical dementia rating as an index of AD severity, the most important indicators (mini-mental state examination, logical memory recall, grey matter and cerebrospinal volumes from MRI and active voxels from PiB-PET brain scans, ApoE, and age) can be automatically identified from parallel data mining algorithms. In this work, Bayesian network modelling across different time points is used to identify and visualize time-varying relationships among the significant features, and importantly, in an efficient way using only coarse-grained data. Crucially, our approach suggests key data features and their appropriate combinations that are relevant for AD severity classification with high accuracy. Overall, our study provides insights into AD developments and demonstrates the potential of our approach in supporting efficient AD diagnosis.

Tiny videos: a large data set for nonparametric video retrieval and frame classification.

PubMed

Karpenko, Alexandre; Aarabi, Parham

2011-03-01

In this paper, we present a large database of over 50,000 user-labeled videos collected from YouTube. We develop a compact representation called "tiny videos" that achieves high video compression rates while retaining the overall visual appearance of the video as it varies over time. We show that frame sampling using affinity propagation-an exemplar-based clustering algorithm-achieves the best trade-off between compression and video recall. We use this large collection of user-labeled videos in conjunction with simple data mining techniques to perform related video retrieval, as well as classification of images and video frames. The classification results achieved by tiny videos are compared with the tiny images framework [24] for a variety of recognition tasks. The tiny images data set consists of 80 million images collected from the Internet. These are the largest labeled research data sets of videos and images available to date. We show that tiny videos are better suited for classifying scenery and sports activities, while tiny images perform better at recognizing objects. Furthermore, we demonstrate that combining the tiny images and tiny videos data sets improves classification precision in a wider range of categories.

The prognostic value of natural killer cell infiltration in resected pulmonary adenocarcinoma.

PubMed

Takanami, I; Takeuchi, K; Giga, M

2001-06-01

Natural cytotoxicity caused by mediated natural killer cells is believed to play an important role in host-cancer defense mechanisms. Immunohistochemically, we have detected natural killer cells in tissue specimens from patients with pulmonary adenocarcinoma and have assessed their clinical characteristics. Using the monoclonal antibody for CD57 specific marker for natural killer cells, we quantified natural killer cell infiltration in 150 patients with pulmonary adenocarcinoma who underwent curative tumor resection to investigate the relationship between natural killer cell counts and clinicopathologic factors and prognosis. The natural killer cell count was significantly related to the regulation of tumor progression, involving T classification, N classification, and stage (P =.01 for T classification or stage; P =.02 for N classification). A significant difference in the rate of patient survival was detected between those patients whose tumors had either high or low natural killer cell counts in both the overall and stage I groups (P =.0002 for the overall group; P =.049 for the stage I group). These data indicate that natural killer infiltration may contribute to the regulation of tumor progression and that the natural killer cell count can serve as a useful prognostic marker in overall and stage I pulmonary adenocarcinoma.

A systematic review of the Robson classification for caesarean section: what works, doesn't work and how to improve it.

PubMed

Betrán, Ana Pilar; Vindevoghel, Nadia; Souza, Joao Paulo; Gülmezoglu, A Metin; Torloni, Maria Regina

2014-01-01

Caesarean sections (CS) rates continue to increase worldwide without a clear understanding of the main drivers and consequences. The lack of a standardized internationally-accepted classification system to monitor and compare CS rates is one of the barriers to a better understanding of this trend. The Robson's 10-group classification is based on simple obstetrical parameters (parity, previous CS, gestational age, onset of labour, fetal presentation and number of fetuses) and does not involve the indication for CS. This classification has become very popular over the last years in many countries. We conducted a systematic review to synthesize the experience of users on the implementation of this classification and proposed adaptations. Four electronic databases were searched. A three-step thematic synthesis approach and a qualitative metasummary method were used. 232 unique reports were identified, 97 were selected for full-text evaluation and 73 were included. These publications reported on the use of Robson's classification in over 33 million women from 31 countries. According to users, the main strengths of the classification are its simplicity, robustness, reliability and flexibility. However, missing data, misclassification of women and lack of definition or consensus on core variables of the classification are challenges. To improve the classification for local use and to decrease heterogeneity within groups, several subdivisions in each of the 10 groups have been proposed. Group 5 (women with previous CS) received the largest number of suggestions. The use of the Robson classification is increasing rapidly and spontaneously worldwide. Despite some limitations, this classification is easy to implement and interpret. Several suggested modifications could be useful to help facilities and countries as they work towards its implementation.

A Systematic Review of the Robson Classification for Caesarean Section: What Works, Doesn't Work and How to Improve It

PubMed Central

Betrán, Ana Pilar; Vindevoghel, Nadia; Souza, Joao Paulo; Gülmezoglu, A. Metin; Torloni, Maria Regina

2014-01-01

Background Caesarean sections (CS) rates continue to increase worldwide without a clear understanding of the main drivers and consequences. The lack of a standardized internationally-accepted classification system to monitor and compare CS rates is one of the barriers to a better understanding of this trend. The Robson's 10-group classification is based on simple obstetrical parameters (parity, previous CS, gestational age, onset of labour, fetal presentation and number of fetuses) and does not involve the indication for CS. This classification has become very popular over the last years in many countries. We conducted a systematic review to synthesize the experience of users on the implementation of this classification and proposed adaptations. Methods Four electronic databases were searched. A three-step thematic synthesis approach and a qualitative metasummary method were used. Results 232 unique reports were identified, 97 were selected for full-text evaluation and 73 were included. These publications reported on the use of Robson's classification in over 33 million women from 31 countries. According to users, the main strengths of the classification are its simplicity, robustness, reliability and flexibility. However, missing data, misclassification of women and lack of definition or consensus on core variables of the classification are challenges. To improve the classification for local use and to decrease heterogeneity within groups, several subdivisions in each of the 10 groups have been proposed. Group 5 (women with previous CS) received the largest number of suggestions. Conclusions The use of the Robson classification is increasing rapidly and spontaneously worldwide. Despite some limitations, this classification is easy to implement and interpret. Several suggested modifications could be useful to help facilities and countries as they work towards its implementation. PMID:24892928

Extended census transform histogram for land-use scene classification

NASA Astrophysics Data System (ADS)

Yuan, Baohua; Li, Shijin

2017-04-01

With the popular use of high-resolution satellite images, more and more research efforts have been focused on land-use scene classification. In scene classification, effective visual features can significantly boost the final performance. As a typical texture descriptor, the census transform histogram (CENTRIST) has emerged as a very powerful tool due to its effective representation ability. However, the most prominent limitation of CENTRIST is its small spatial support area, which may not necessarily be adept at capturing the key texture characteristics. We propose an extended CENTRIST (eCENTRIST), which is made up of three subschemes in a greater neighborhood scale. The proposed eCENTRIST not only inherits the advantages of CENTRIST but also encodes the more useful information of local structures. Meanwhile, multichannel eCENTRIST, which can capture the interactions from multichannel images, is developed to obtain higher categorization accuracy rates. Experimental results demonstrate that the proposed method can achieve competitive performance when compared to state-of-the-art methods.

[Joint endoprosthesis pathology. Histopathological diagnostics and classification].

PubMed

Krenn, V; Morawietz, L; Jakobs, M; Kienapfel, H; Ascherl, R; Bause, L; Kuhn, H; Matziolis, G; Skutek, M; Gehrke, T

2011-05-01

Prosthesis durability has steadily increased with high 10-year rates of 88-95%. However, four pathogenetic groups of diseases can decrease prosthesis durability: (1) periprosthetic wear particle disease (aseptic loosening) (2) bacterial infection (septic loosening) (3) periprosthetic ossification, and (4) arthrofibrosis. The histopathological "extended consensus classification of periprosthetic membranes" includes four types of membranes, arthrofibrosis, and osseous diseases of endoprosthetics: The four types of neosynovia are: wear particle-induced type (type I), mean prosthesis durability (MPD) in years 12.0; infectious type (type II), MPD 2.5; combined type (type III) MPD 4.2; and indeterminate type (type IV), MPD 5.5. Arthrofibrosis can be determined in three grades: grade 1 needs clinical information to be differentiated from a type IV membrane, and grades 2 & 3 can be diagnosed histopathologically. Periprosthetic ossification, osteopenia-induced fractures, and aseptic osteonecrosis can be histopathologically diagnosed safely with clinical information. The extended consensus classification of periprosthetic membranes may be a diagnostic groundwork for a future national endoprosthesis register.

Analysis of failure in patients with adenoid cystic carcinoma of the head and neck. An international collaborative study.

PubMed

Amit, Moran; Binenbaum, Yoav; Sharma, Kanika; Ramer, Naomi; Ramer, Ilana; Agbetoba, Abib; Miles, Brett; Yang, Xinjie; Lei, Delin; Bjøerndal, Kristine; Godballe, Christian; Mücke, Thomas; Wolff, Klaus-Dietrich; Fliss, Dan; Eckardt, André M; Copelli, Chiara; Sesenna, Enrico; Palmer, Frank; Patel, Snehal; Gil, Ziv

2014-07-01

Adenoid cystic carcinoma (ACC) is a locally aggressive tumor with a high prevalence of distant metastases. The purpose of this study was to identify independent predictors of outcome and to characterize the patterns of failure. An international retrospective review was conducted of 489 patients with ACC treated between 1985 and 2011 in 9 cancer centers worldwide. Five-year overall-survival (OS), disease-specific survival (DSS), and disease-free survival (DFS) were 76%, 80%, and 68%, respectively. Independent predictors of OS and DSS were: age, site, N classification, and presence of distant metastases. N classification, age, and bone invasion were associated with DFS on multivariate analysis. Age, tumor site, orbital invasion, and N classification were independent predictors of distant metastases. The clinical course of ACC is slow but persistent. Paranasal sinus origin is associated with the lowest distant metastases rate but with the poorest outcome. These prognostic estimates should be considered when tailoring treatment for patients with ACC. Copyright © 2013 Wiley Periodicals, Inc.

A new adaptive L1-norm for optimal descriptor selection of high-dimensional QSAR classification model for anti-hepatitis C virus activity of thiourea derivatives.

PubMed

Algamal, Z Y; Lee, M H

2017-01-01

A high-dimensional quantitative structure-activity relationship (QSAR) classification model typically contains a large number of irrelevant and redundant descriptors. In this paper, a new design of descriptor selection for the QSAR classification model estimation method is proposed by adding a new weight inside L1-norm. The experimental results of classifying the anti-hepatitis C virus activity of thiourea derivatives demonstrate that the proposed descriptor selection method in the QSAR classification model performs effectively and competitively compared with other existing penalized methods in terms of classification performance on both the training and the testing datasets. Moreover, it is noteworthy that the results obtained in terms of stability test and applicability domain provide a robust QSAR classification model. It is evident from the results that the developed QSAR classification model could conceivably be employed for further high-dimensional QSAR classification studies.

Application of Sal classification to parotid gland fine-needle aspiration cytology: 10-year retrospective analysis of 312 patients.

PubMed

Kilavuz, Ahmet Erdem; Songu, Murat; İmre, Abdulkadir; Arslanoğlu, Secil; Özkul, Yilmaz; Pinar, Ercan; Ateş, Düzgün

2018-05-01

The accuracy of fine-needle aspiration biopsy (FNAB) is controversial in parotid tumors. We aimed to compare FNAB results with the final histopathological diagnosis and to apply the "Sal classification" to our data and discuss its results and its place in parotid gland cytology. The FNAB cytological findings and final histological diagnosis were assessed retrospectively in 2 different scenarios based on the distribution of nondefinitive cytology, and we applied the Sal classification and determined malignancy rate, sensitivity, and specificity for each category. In 2 different scenarios FNAB sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were found to be 81%, 87%, 54.7%, and 96.1%; and 65.3%, 100%, 100%, and 96.1%, respectively. The malignancy rates and sensitivity and specificity were also calculated and discussed for each Sal category. We believe that the Sal classification has a great potential to be a useful tool in classification of parotid gland cytology. © 2018 Wiley Periodicals, Inc.

Infant homicide and accidental death in the United States, 1940-2005: ethics and epidemiological classification.

PubMed

Riggs, Jack E; Hobbs, Gerald R

2011-07-01

Potential ethical issues can arise during the process of epidemiological classification. For example, unnatural infant deaths are classified as accidental deaths or homicides. Societal sensitivity to the physical abuse and neglect of children has increased over recent decades. This enhanced sensitivity could impact reported infant homicide rates. Infant homicide and accident mortality rates in boys and girls in the USA from 1940 to 2005 were analysed. In 1940, infant accident mortality rates were over 20 times greater than infant homicide rates in both boys and girls. After about 1980, when the ratio of infant accident mortality rates to infant homicide rates decreased to less than five, and the sum of infant accident and homicide rates became relatively constant, further decreases in infant accident mortality rates were associated with increases in reported infant homicide rates. These findings suggest that the dramatic decline of accidental infant mortality and recent increased societal sensitivity to child abuse may be related to the increased infant homicide rates observed in the USA since 1980 rather than an actual increase in societal violence directed against infants. Ethical consequences of epidemiological classification, involving the principles of beneficence, non-maleficence and justice, are suggested by observed patterns in infant accidental deaths and homicides in the USA from 1940 to 2005.

[An analysis of caesarean sections in Uruguay by type of hospital].

PubMed

Aguirre, Rafael; Antón, José-Ignacio; Triunfo, Patricia

2018-04-20

To analyse on a comparative basis the incidence of caesarean sections among the different health care systems in Uruguay and with respect to the World Health Organization's (WHO) standards, taking into account the medical-obstetric characteristics of the births, particularly, the Robson classification. We examine 190,847 births registered by the Perinatal Information System in Uruguay between 2009 and 2014 by type of health care system. Using logit models, we analyse the probability of caesarean section taking into account the Robson classification, other risk factors and the mothers' characteristics. We compared the caesarean rates predicted by the different subsystems for a common population. Furthermore, we contrast the caesarean rates observed in each subsystem with the rates that resulted if the Uruguayan hospitals followed the guidelines of the sample of WHO reference hospitals. Private health systems in Uruguay exhibit a much higher incidence of caesarean sections than public ones, even after considering the medical-obstetric characteristics of the births. Caesarean rates are more than 75% higher than those observed if the WHO standards are applied. Uruguay has a very high incidence of caesarean sections with respect to WHO standards, particularly, in the private sector. This fact is unrelated to the clinical characteristics of the births. Copyright © 2018 SESPAS. Publicado por Elsevier España, S.L.U. All rights reserved.

Prostate Specific Antigen (PSA) as Predicting Marker for Clinical Outcome and Evaluation of Early Toxicity Rate after High-Dose Rate Brachytherapy (HDR-BT) in Combination with Additional External Beam Radiation Therapy (EBRT) for High Risk Prostate Cancer.

PubMed

Ecke, Thorsten H; Huang-Tiel, Hui-Juan; Golka, Klaus; Selinski, Silvia; Geis, Berit Christine; Koswig, Stephan; Bathe, Katrin; Hallmann, Steffen; Gerullis, Holger

2016-11-10

High-dose-rate brachytherapy (HDR-BT) with external beam radiation therapy (EBRT) is a common treatment option for locally advanced prostate cancer (PCa). Seventy-nine male patients (median age 71 years, range 50 to 79) with high-risk PCa underwent HDR-BT following EBRT between December 2009 and January 2016 with a median follow-up of 21 months. HDR-BT was administered in two treatment sessions (one week interval) with 9 Gy per fraction using a planning system and the Ir192 treatment unit GammaMed Plus iX. EBRT was performed with CT-based 3D-conformal treatment planning with a total dose administration of 50.4 Gy with 1.8 Gy per fraction and five fractions per week. Follow-up for all patients was organized one, three, and five years after radiation therapy to evaluate early and late toxicity side effects, metastases, local recurrence, and prostate-specific antigen (PSA) value measured in ng/mL. The evaluated data included age, PSA at time of diagnosis, PSA density, BMI (body mass index), Gleason score, D'Amico risk classification for PCa, digital rectal examination (DRE), PSA value after one/three/five year(s) follow-up (FU), time of follow-up, TNM classification, prostate volume, and early toxicity rates. Early toxicity rates were 8.86% for gastrointestinal, and 6.33% for genitourinary side effects. Of all treated patients, 84.81% had no side effects. All reported complications in early toxicity were grade 1. PSA density at time of diagnosis ( p = 0.009), PSA on date of first HDR-BT ( p = 0.033), and PSA on date of first follow-up after one year ( p = 0.025) have statistical significance on a higher risk to get a local recurrence during follow-up. HDR-BT in combination with additional EBRT in the presented design for high-risk PCa results in high biochemical control rates with minimal side-effects. PSA is a negative predictive biomarker for local recurrence during follow-up. A longer follow-up is needed to assess long-term outcome and toxicities.

Etiological classifications of transient ischemic attacks: subtype classification by TOAST, CCS and ASCO--a pilot study.

PubMed

Amort, Margareth; Fluri, Felix; Weisskopf, Florian; Gensicke, Henrik; Bonati, Leo H; Lyrer, Philippe A; Engelter, Stefan T

2012-01-01

In patients with transient ischemic attacks (TIA), etiological classification systems are not well studied. The Trial of ORG 10172 in Acute Stroke Treatment (TOAST), the Causative Classification System (CCS), and the Atherosclerosis Small Vessel Disease Cardiac Source Other Cause (ASCO) classification may be useful to determine the underlying etiology. We aimed at testing the feasibility of each of the 3 systems. Furthermore, we studied and compared their prognostic usefulness. In a single-center TIA registry prospectively ascertained over 2 years, we applied 3 etiological classification systems. We compared the distribution of underlying etiologies, the rates of patients with determined versus undetermined etiology, and studied whether etiological subtyping distinguished TIA patients with versus without subsequent stroke or TIA within 3 months. The 3 systems were applicable in all 248 patients. A determined etiology with the highest level of causality was assigned similarly often with TOAST (35.9%), CCS (34.3%), and ASCO (38.7%). However, the frequency of undetermined causes differed significantly between the classification systems and was lowest for ASCO (TOAST: 46.4%; CCS: 37.5%; ASCO: 18.5%; p < 0.001). In TOAST, CCS, and ASCO, cardioembolism (19.4/14.5/18.5%) was the most common etiology, followed by atherosclerosis (11.7/12.9/14.5%). At 3 months, 33 patients (13.3%, 95% confidence interval 9.3-18.2%) had recurrent cerebral ischemic events. These were strokes in 13 patients (5.2%; 95% confidence interval 2.8-8.8%) and TIAs in 20 patients (8.1%, 95% confidence interval 5.0-12.2%). Patients with a determined etiology (high level of causality) had higher rates of subsequent strokes than those without a determined etiology [TOAST: 6.7% (95% confidence interval 2.5-14.1%) vs. 4.4% (95% confidence interval 1.8-8.9%); CSS: 9.3% (95% confidence interval 4.1-17.5%) vs. 3.1% (95% confidence interval 1.0-7.1%); ASCO: 9.4% (95% confidence interval 4.4-17.1%) vs. 2.6% (95% confidence interval 0.7-6.6%)]. However, this difference was only significant in the ASCO classification (p = 0.036). Using ASCO, there was neither an increase in risk of subsequent stroke among patients with incomplete diagnostic workup (at least one subtype scored 9) compared with patients with adequate workup (no subtype scored 9), nor among patients with multiple causes compared with patients with a single cause. In TIA patients, all etiological classification systems provided a similar distribution of underlying etiologies. The increase in stroke risk in TIA patients with determined versus undetermined etiology was most evident using the ASCO classification. Copyright © 2012 S. Karger AG, Basel.

Vertebral Body Growth After Craniospinal Irradiation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hartley, Katherine A.; Li Chenghong; Laningham, Fred H.

2008-04-01

Purpose: To estimate the effects of radiotherapy and clinical factors on vertebral growth in patients with medulloblastoma and supratentorial primitive neuroectodermal tumors treated with craniospinal irradiation (CSI) and chemotherapy. Methods and Materials: The height of eight individual or grouped vertebral bodies (C3, C3-C4, T4, T4-T5, C6-T3, T4-T7, L3, L1-L5) was measured before and after CSI (23.4 or 36-39.6 Gy) in 61 patients. Of the 61 patients, 40 were boys and 21 were girls (median age, 7 years; range, 3-13 years), treated between October 1996 and October 2003. Sagittal T{sub 1}-weighted magnetic resonance images were used for the craniocaudal measurements. Themore » measurements numbered 275 (median, 5/patient; range, 3-7). The median follow-up after CSI was 44.1 months (range, 13.8-74.9 months). Results: Significant growth was observed in all measured vertebrae. Excluding C3-C4, the growth rate of the grouped vertebrae was affected by age, gender, and CSI dose (risk classification). The risk classification alone affected the growth rates of C3 (p = 0.002) and L3 (p = 0.02). Before CSI, the length of all vertebral bodies was an increasing function of age (p <0.0001). The C3 length before CSI was affected by gender and risk classification: C3 was longer for female (p = 0.07) and high-risk (p = 0.07) patients. Conclusion: All vertebrae grew significantly after CSI, with the vertebrae of the boys and younger patients growing at a rate greater than that of their counterparts. The effect of age was similar across all vertebrae, and gender had the greatest effect on the growth of the lower cervical and upper thoracic vertebrae. The effect of the risk classification was greatest in the lumbar spine by a factor of {<=}10.« less

Classification problems of Mount Kenya soils

NASA Astrophysics Data System (ADS)

Mutuma, Evans; Csorba, Ádám; Wawire, Amos; Dobos, Endre; Michéli, Erika

2017-04-01

Soil sampling on the agricultural lands covering 1200 square kilometers in the Eastern part of Mount Kenya was carried out to assess the status of soil organic carbon (SOC) as a soil fertility indicator, and to create an up-to-date soil classification map. The geology of the area consists of volcanic rocks and recent superficial deposits. The volcanic rocks are related to the Pliocene time; mainly: lahars, phonolites, tuffs, basalt and ashes. A total of 28 open profiles and 49 augered profiles with 269 samples were collected. The samples were analyzed for total carbon, organic carbon, particle size distribution, percent bases, cation exchange capacity and pH among other parameters. The objective of the study was to evaluate the variability of SOC in different Reference Soil Groups (RGS) and to compare the determined classification units with the KENSOTER database. Soil classification was performed based on the World Reference Base (WRB) for Soil Resources 2014. Based on the earlier surveys, geological and environmental setting, Nitisols were expected to be the dominant soils of the sampled area. However, this was not the case. The major differences to earlier survey data (KENSOTER database) are the presence of high activity clays (CEC value range 27.6 cmol/kg - 70 cmol/kg), high silt content (range 32.6 % - 52.4 %) and silt/clay ratio (range of 0.6 - 1.4) keeping these soils out of the Nitisols RSG. There was good accordance in the morphological features with the earlier survey but failed the silt/clay ratio criteria for Nitisols. This observation calls attention to set new classification criteria for Nitisols and other soils of warm, humid regions with variable rate of weathering to avoid difficulties in interpretation. To address the classification problem, this paper further discusses the taxonomic relationships between the studied soils. On the contrary most of the diagnostic elements (like the presence Umbric horizon, Vitric and Andic properties) and the some qualifiers (Humic, Dystric, Clayic, Skeletic, Leptic, etc) represent useful information for land use and management in the area.

Assessment and classification of fistula-in-ano in patients with Crohn's disease by hydrogen peroxide enhanced transanal ultrasound.

PubMed

Sloots, C E; Felt-Bersma, R J; Poen, A C; Cuesta, M A; Meuwissen, S G

2001-09-01

Crohn's disease is well known for its perianal complications, among which fistulas-in-ano are the most common abnormalities. Fistulas-in-ano in Crohn's disease tend to be complex and have a high recurrence rate. Therefore the role of surgery is generally more conservative. Hydrogen peroxide enhanced transanal ultrasound has proven superior to physical examination, fistulography, computed tomography, and conventional ultrasound in demonstrating the fistula tract. This study examined the fistula tracks in patients with Crohn's disease. Forty-one patients with Crohn's disease and fistula-in-ano were investigated using physical examination, sondage of the fistula, proctoscopy and transanal ultrasound. Hydrogen peroxide was infused via a small catheter into the fistula. The main track and the ramification of the fistula were classified according to the anatomical Parks' classification. Only 9 (22%) patients had a single inter- or transsphincteric fistula. In 5 (12%) patients a single supra- or extrasphincteric fistula (high fistula) was found, in 14 (34%) more than one fistula track (ramified), and in 13 (32%) an anovaginal fistula. Thus 78% of patients had a surgically difficult to treat fistula. In the ramified fistula the main track follows the Parks' classification, but ramifications can have a bizarre pattern which is not in agreement with this classification. Optimal documentation by means of hydrogen peroxide enhanced transanal ultrasound is therefore mandatory before surgery or before other therapies such as anti-tumor necrosis factor treatment.

Real-Time Fault Classification for Plasma Processes

PubMed Central

Yang, Ryan; Chen, Rongshun

2011-01-01

Plasma process tools, which usually cost several millions of US dollars, are often used in the semiconductor fabrication etching process. If the plasma process is halted due to some process fault, the productivity will be reduced and the cost will increase. In order to maximize the product/wafer yield and tool productivity, a timely and effective fault process detection is required in a plasma reactor. The classification of fault events can help the users to quickly identify fault processes, and thus can save downtime of the plasma tool. In this work, optical emission spectroscopy (OES) is employed as the metrology sensor for in-situ process monitoring. Splitting into twelve different match rates by spectrum bands, the matching rate indicator in our previous work (Yang, R.; Chen, R.S. Sensors 2010, 10, 5703–5723) is used to detect the fault process. Based on the match data, a real-time classification of plasma faults is achieved by a novel method, developed in this study. Experiments were conducted to validate the novel fault classification. From the experimental results, we may conclude that the proposed method is feasible inasmuch that the overall accuracy rate of the classification for fault event shifts is 27 out of 28 or about 96.4% in success. PMID:22164001

Intelligibility of Target Signals in Sequential and Simultaneous Segregation Tasks

DTIC Science & Technology

2009-03-01

SUBJECT TERMS Informational masking; energetic masking, multimasker penalty, speech perception 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF...alter- nation rates were high enough to directly interfere with the perception of the F0 values of the speech signals and that they thus disrupted the...segregation effects seen in this experiment and those in which stream segregation with tones was examined. Experiments examining the perception of

[Death rate by malnutrition in children under the age of five, Colombia].

PubMed

Quiroga, Edwin Fernando

2012-01-01

Much higher mortalities occur in children under five in developing countries with high poverty rates compared with developed countries. Causes of death are related to perinatal conditions, measles, HIV/AIDS, diarrhea, respiratory diseases and others. Throughout the world, malnutrition has been identified as the underlying cause of approximately half of these deaths. Death rate due to malnutrition was described using an adjusted method that takes into account the difficulties of identifying malnutrition as a direct cause of death. A descriptive study included analysis of the International Classification of Diseases (ICD-10) vital statistics from 2003-2007. Death rates were estimated, a method of analysis of multiple causes was applied for infectious diseases, along with calculations of death probabilities. Malnutrition was associated with infectious diseases. The frequency of infectious disease as a direct cause of death was almost seven times higher in cases with the antecedent of malnutrition. When adjusted death rate values were used, the initial value increased nearly five times. The probability of death after the adjustment for inadequate classification increased approximately four times. The Analysis of Multiple Causes Method was established as an effective method in analyzing malnutrition and infectious diesease mortality in Colombia. Malnutrition may be a direct underlying cause of death in one of eight deaths in children <1 year old and one of three deaths in 1-4-year-olds.

Postoperative complications of contemporary open and robot-assisted laparoscopic radical prostatectomy using standardized reporting systems.

PubMed

Pompe, Raisa S; Beyer, Burkhard; Haese, Alexander; Preisser, Felix; Michl, Uwe; Steuber, Thomas; Graefen, Markus; Huland, Hartwig; Karakiewicz, Pierre I; Tilki, Derya

2018-05-04

To analyze time trends and contemporary rates of postoperative complications after RP and to compare the complication profile of ORP and RALP using standardized reporting systems. Retrospective analysis of 13,924 RP patients in a single institution (2005 to 2015). Complications were collected during hospital stay and via standardized questionnaire 3 months after and grouped into eight schemes. Since 2013, the revised Clavien-Dindo classification was used (n = 4,379). Annual incidence rates of different complications were graphically displayed. Multivariable logistic regression analyses compared complications between ORP and RALP after inverse probability of treatment weighting (IPTW). After introduction of standardized classification systems, complication rates have increased with a contemporary rate of 20.6% (2013 - 2015). While minor Clavien-Dindo grades represented the majority (I: 10.6%; II: 7.9%), severe complications (grades IV-V) were rare (<1%). In logistic regression analyses after IPTW, RALP was associated with less blood loss, shorter catheterization time and lower risk for Clavien-Dindo grade II and III complications. Our results emphasize the importance of standardized reporting systems for quality control and comparison across approaches or institutions. Contemporary complication rates in a high volume center remain low and are most frequently minor Clavien-Dindo grades. RALP had a slightly better complication profile compared to ORP. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

SU-E-I-59: Investigation of the Usefulness of a Standard Deviation and Mammary Gland Density as Indexes for Mammogram Classification.

PubMed

Takarabe, S; Yabuuchi, H; Morishita, J

2012-06-01

To investigate the usefulness of the standard deviation of pixel values in a whole mammary glands region and the percentage of a high- density mammary glands region to a whole mammary glands region as features for classification of mammograms into four categories based on the ACR BI-RADS breast composition. We used 36 digital mediolateral oblique view mammograms (18 patients) approved by our IRB. These images were classified into the four categories of breast compositions by an experienced breast radiologist and the results of the classification were regarded as a gold standard. First, a whole mammary region in a breast was divided into two regions such as a high-density mammary glands region and a low/iso-density mammary glands region by using a threshold value that was obtained from the pixel values corresponding to a pectoral muscle region. Then the percentage of a high-density mammary glands region to a whole mammary glands region was calculated. In addition, as a new method, the standard deviation of pixel values in a whole mammary glands region was calculated as an index based on the intermingling of mammary glands and fats. Finally, all mammograms were classified by using the combination of the percentage of a high-density mammary glands region and the standard deviation of each image. The agreement rates of the classification between our proposed method and gold standard was 86% (31/36). This result signified that our method has the potential to classify mammograms. The combination of the standard deviation of pixel values in a whole mammary glands region and the percentage of a high-density mammary glands region to a whole mammary glands region was available as features to classify mammograms based on the ACR BI- RADS breast composition. © 2012 American Association of Physicists in Medicine.

Three-dimensional obstacle classification in laser range data

NASA Astrophysics Data System (ADS)

Armbruster, Walter; Bers, Karl-Heinz

1998-10-01

The threat of hostile surveillance and weapon systems require military aircraft to fly under extreme conditions such as low altitude, high speed, poor visibility and incomplete terrain information. The probability of collision with natural and man-made obstacles during such contour missions is high if detection capability is restricted to conventional vision aids. Forward-looking scanning laser rangefinders which are presently being flight tested and evaluated at German proving grounds, provide a possible solution, having a large field of view, high angular and range resolution, a high pulse repetition rate, and sufficient pulse energy to register returns from wires at over 500 m range (depends on the system) with a high hit-and-detect probability. Despite the efficiency of the sensor, acceptance of current obstacle warning systems by test pilots is not very high, mainly due to the systems' inadequacies in obstacle recognition and visualization. This has motivated the development and the testing of more advanced 3d-scene analysis algorithm at FGAN-FIM to replace the obstacle recognition component of current warning systems. The basic ideas are to increase the recognition probability and to reduce the false alarm rate for hard-to-extract obstacles such as wires, by using more readily recognizable objects such as terrain, poles, pylons, trees, etc. by implementing a hierarchical classification procedure to generate a parametric description of the terrain surface as well as the class, position, orientation, size and shape of all objects in the scene. The algorithms can be used for other applications such as terrain following, autonomous obstacle avoidance, and automatic target recognition.

A Novel Energy-Efficient Approach for Human Activity Recognition

PubMed Central

Zheng, Lingxiang; Wu, Dihong; Ruan, Xiaoyang; Weng, Shaolin; Tang, Biyu; Lu, Hai; Shi, Haibin

2017-01-01

In this paper, we propose a novel energy-efficient approach for mobile activity recognition system (ARS) to detect human activities. The proposed energy-efficient ARS, using low sampling rates, can achieve high recognition accuracy and low energy consumption. A novel classifier that integrates hierarchical support vector machine and context-based classification (HSVMCC) is presented to achieve a high accuracy of activity recognition when the sampling rate is less than the activity frequency, i.e., the Nyquist sampling theorem is not satisfied. We tested the proposed energy-efficient approach with the data collected from 20 volunteers (14 males and six females) and the average recognition accuracy of around 96.0% was achieved. Results show that using a low sampling rate of 1Hz can save 17.3% and 59.6% of energy compared with the sampling rates of 5 Hz and 50 Hz. The proposed low sampling rate approach can greatly reduce the power consumption while maintaining high activity recognition accuracy. The composition of power consumption in online ARS is also investigated in this paper. PMID:28885560

Fuzzy Classification of High Resolution Remote Sensing Scenes Using Visual Attention Features.

PubMed

Li, Linyi; Xu, Tingbao; Chen, Yun

2017-01-01

In recent years the spatial resolutions of remote sensing images have been improved greatly. However, a higher spatial resolution image does not always lead to a better result of automatic scene classification. Visual attention is an important characteristic of the human visual system, which can effectively help to classify remote sensing scenes. In this study, a novel visual attention feature extraction algorithm was proposed, which extracted visual attention features through a multiscale process. And a fuzzy classification method using visual attention features (FC-VAF) was developed to perform high resolution remote sensing scene classification. FC-VAF was evaluated by using remote sensing scenes from widely used high resolution remote sensing images, including IKONOS, QuickBird, and ZY-3 images. FC-VAF achieved more accurate classification results than the others according to the quantitative accuracy evaluation indices. We also discussed the role and impacts of different decomposition levels and different wavelets on the classification accuracy. FC-VAF improves the accuracy of high resolution scene classification and therefore advances the research of digital image analysis and the applications of high resolution remote sensing images.

Fuzzy Classification of High Resolution Remote Sensing Scenes Using Visual Attention Features

PubMed Central

Xu, Tingbao; Chen, Yun

2017-01-01

In recent years the spatial resolutions of remote sensing images have been improved greatly. However, a higher spatial resolution image does not always lead to a better result of automatic scene classification. Visual attention is an important characteristic of the human visual system, which can effectively help to classify remote sensing scenes. In this study, a novel visual attention feature extraction algorithm was proposed, which extracted visual attention features through a multiscale process. And a fuzzy classification method using visual attention features (FC-VAF) was developed to perform high resolution remote sensing scene classification. FC-VAF was evaluated by using remote sensing scenes from widely used high resolution remote sensing images, including IKONOS, QuickBird, and ZY-3 images. FC-VAF achieved more accurate classification results than the others according to the quantitative accuracy evaluation indices. We also discussed the role and impacts of different decomposition levels and different wavelets on the classification accuracy. FC-VAF improves the accuracy of high resolution scene classification and therefore advances the research of digital image analysis and the applications of high resolution remote sensing images. PMID:28761440

An analysis of USSPACECOM's space surveillance network sensor tasking methodology

NASA Astrophysics Data System (ADS)

Berger, Jeff M.; Moles, Joseph B.; Wilsey, David G.

1992-12-01

This study provides the basis for the development of a cost/benefit assessment model to determine the effects of alterations to the Space Surveillance Network (SSN) on orbital element (OE) set accuracy. It provides a review of current methods used by NORAD and the SSN to gather and process observations, an alternative to the current Gabbard classification method, and the development of a model to determine the effects of observation rate and correction interval on OE set accuracy. The proposed classification scheme is based on satellite J2 perturbations. Specifically, classes were established based on mean motion, eccentricity, and inclination since J2 perturbation effects are functions of only these elements. Model development began by creating representative sensor observations using a highly accurate orbital propagation model. These observations were compared to predicted observations generated using the NORAD Simplified General Perturbation (SGP4) model and differentially corrected using a Bayes, sequential estimation, algorithm. A 10-run Monte Carlo analysis was performed using this model on 12 satellites using 16 different observation rate/correction interval combinations. An ANOVA and confidence interval analysis of the results show that this model does demonstrate the differences in steady state position error based on varying observation rate and correction interval.

[Relation between location of elements in periodic table and affinity for the malignant tumor (author's transl)].

PubMed

Ando, A; Hisada, K; Ando, I

1977-10-01

Affinity of many inorganic compounds for the malignant tumor was examined, using the rats which were subcutaneously transplanted with Yoshida sarcoma. And the relations between the uptake rate into the malignant tumor and in vitro binding power to the protein were investigated in these compounds. In these experiments, the bipositive ions and anions had not affinity for the tumor tissue with a few exceptions. On the other hand, Hg, Au and Bi, which have strong binding power to the protein, showed high uptake rate into the malignant tumor. As Hg++, Au+ and Bi+++ are soft acids according to classification of Lewis acids, it was thought that these elements would bind strongly to soft base (R-SH, R-S-) present in the tumor tissue. In many hard acids (according to classification of Lewis acids), the uptake rate into the tumor was shown as a function of ionic potentials (valency/ionic radii) of the metal ions. It is presumed that the chemical bond of these hard acids in the tumor tissue is ionic bond to hard base (R-COO-, R-PO3(2-), R-SO3-, R-NH2).

Discrimination of Aspergillus isolates at the species and strain level by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry fingerprinting.

PubMed

Hettick, Justin M; Green, Brett J; Buskirk, Amanda D; Kashon, Michael L; Slaven, James E; Janotka, Erika; Blachere, Francoise M; Schmechel, Detlef; Beezhold, Donald H

2008-09-15

Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) was used to generate highly reproducible mass spectral fingerprints for 12 species of fungi of the genus Aspergillus and 5 different strains of Aspergillus flavus. Prior to MALDI-TOF MS analysis, the fungi were subjected to three 1-min bead beating cycles in an acetonitrile/trifluoroacetic acid solvent. The mass spectra contain abundant peaks in the range of 5 to 20kDa and may be used to discriminate between species unambiguously. A discriminant analysis using all peaks from the MALDI-TOF MS data yielded error rates for classification of 0 and 18.75% for resubstitution and cross-validation methods, respectively. If a subset of 28 significant peaks is chosen, resubstitution and cross-validation error rates are 0%. Discriminant analysis of the MALDI-TOF MS data for 5 strains of A. flavus using all peaks yielded error rates for classification of 0 and 5% for resubstitution and cross-validation methods, respectively. These data indicate that MALDI-TOF MS data may be used for unambiguous identification of members of the genus Aspergillus at both the species and strain levels.

Comparison of EEG-Features and Classification Methods for Motor Imagery in Patients with Disorders of Consciousness

PubMed Central

Höller, Yvonne; Bergmann, Jürgen; Thomschewski, Aljoscha; Kronbichler, Martin; Höller, Peter; Crone, Julia S.; Schmid, Elisabeth V.; Butz, Kevin; Nardone, Raffaele; Trinka, Eugen

2013-01-01

Current research aims at identifying voluntary brain activation in patients who are behaviorally diagnosed as being unconscious, but are able to perform commands by modulating their brain activity patterns. This involves machine learning techniques and feature extraction methods such as applied in brain computer interfaces. In this study, we try to answer the question if features/classification methods which show advantages in healthy participants are also accurate when applied to data of patients with disorders of consciousness. A sample of healthy participants (N = 22), patients in a minimally conscious state (MCS; N = 5), and with unresponsive wakefulness syndrome (UWS; N = 9) was examined with a motor imagery task which involved imagery of moving both hands and an instruction to hold both hands firm. We extracted a set of 20 features from the electroencephalogram and used linear discriminant analysis, k-nearest neighbor classification, and support vector machines (SVM) as classification methods. In healthy participants, the best classification accuracies were seen with coherences (mean = .79; range = .53−.94) and power spectra (mean = .69; range = .40−.85). The coherence patterns in healthy participants did not match the expectation of central modulated -rhythm. Instead, coherence involved mainly frontal regions. In healthy participants, the best classification tool was SVM. Five patients had at least one feature-classifier outcome with p0.05 (none of which were coherence or power spectra), though none remained significant after false-discovery rate correction for multiple comparisons. The present work suggests the use of coherences in patients with disorders of consciousness because they show high reliability among healthy subjects and patient groups. However, feature extraction and classification is a challenging task in unresponsive patients because there is no ground truth to validate the results. PMID:24282545

Performance of Activity Classification Algorithms in Free-living Older Adults

PubMed Central

Sasaki, Jeffer Eidi; Hickey, Amanda; Staudenmayer, John; John, Dinesh; Kent, Jane A.; Freedson, Patty S.

2015-01-01

Purpose To compare activity type classification rates of machine learning algorithms trained on laboratory versus free-living accelerometer data in older adults. Methods Thirty-five older adults (21F and 14M ; 70.8 ± 4.9 y) performed selected activities in the laboratory while wearing three ActiGraph GT3X+ activity monitors (dominant hip, wrist, and ankle). Monitors were initialized to collect raw acceleration data at a sampling rate of 80 Hz. Fifteen of the participants also wore the GT3X+ in free-living settings and were directly observed for 2-3 hours. Time- and frequency- domain features from acceleration signals of each monitor were used to train Random Forest (RF) and Support Vector Machine (SVM) models to classify five activity types: sedentary, standing, household, locomotion, and recreational activities. All algorithms were trained on lab data (RFLab and SVMLab) and free-living data (RFFL and SVMFL) using 20 s signal sampling windows. Classification accuracy rates of both types of algorithms were tested on free-living data using a leave-one-out technique. Results Overall classification accuracy rates for the algorithms developed from lab data were between 49% (wrist) to 55% (ankle) for the SVMLab algorithms, and 49% (wrist) to 54% (ankle) for RFLab algorithms. The classification accuracy rates for SVMFL and RFFL algorithms ranged from 58% (wrist) to 69% (ankle) and from 61% (wrist) to 67% (ankle), respectively. Conclusion Our algorithms developed on free-living accelerometer data were more accurate in classifying activity type in free-living older adults than our algorithms developed on laboratory accelerometer data. Future studies should consider using free-living accelerometer data to train machine-learning algorithms in older adults. PMID:26673129

Performance of Activity Classification Algorithms in Free-Living Older Adults.

PubMed

Sasaki, Jeffer Eidi; Hickey, Amanda M; Staudenmayer, John W; John, Dinesh; Kent, Jane A; Freedson, Patty S

2016-05-01

The objective of this study is to compare activity type classification rates of machine learning algorithms trained on laboratory versus free-living accelerometer data in older adults. Thirty-five older adults (21 females and 14 males, 70.8 ± 4.9 yr) performed selected activities in the laboratory while wearing three ActiGraph GT3X+ activity monitors (in the dominant hip, wrist, and ankle; ActiGraph, LLC, Pensacola, FL). Monitors were initialized to collect raw acceleration data at a sampling rate of 80 Hz. Fifteen of the participants also wore GT3X+ in free-living settings and were directly observed for 2-3 h. Time- and frequency-domain features from acceleration signals of each monitor were used to train random forest (RF) and support vector machine (SVM) models to classify five activity types: sedentary, standing, household, locomotion, and recreational activities. All algorithms were trained on laboratory data (RFLab and SVMLab) and free-living data (RFFL and SVMFL) using 20-s signal sampling windows. Classification accuracy rates of both types of algorithms were tested on free-living data using a leave-one-out technique. Overall classification accuracy rates for the algorithms developed from laboratory data were between 49% (wrist) and 55% (ankle) for the SVMLab algorithms and 49% (wrist) to 54% (ankle) for the RFLab algorithms. The classification accuracy rates for SVMFL and RFFL algorithms ranged from 58% (wrist) to 69% (ankle) and from 61% (wrist) to 67% (ankle), respectively. Our algorithms developed on free-living accelerometer data were more accurate in classifying the activity type in free-living older adults than those on our algorithms developed on laboratory accelerometer data. Future studies should consider using free-living accelerometer data to train machine learning algorithms in older adults.

Lower limb injuries caused by improvised explosive devices: proposed 'Bastion classification' and prospective validation.

PubMed

Jacobs, N; Rourke, K; Rutherford, J; Hicks, A; Smith, S R C; Templeton, P; Adams, S A; Jansen, J O

2014-09-01

Complex lower limb injury caused by improvised explosive devices (IEDs) has become the signature wounding pattern of the conflict in Afghanistan. Current classifications neither describe this injury pattern well, nor correlate with management. There is need for a new classification, to aid communication between clinicians, and help evaluate interventions and outcomes. We propose such a classification, and present the results of an initial prospective evaluation. The classification was developed by a panel of military surgeons whilst deployed to Camp Bastion, Afghanistan. Injuries were divided into five classes, by anatomic level. Segmental injuries were recognised as a distinct entity. Associated injuries to the intraperitoneal abdomen, genitalia and perineum, pelvic ring, and upper limbs, which impact on clinical management and resources, were also accounted for. Between 1 November 2010 and 20 February 2011, 179 IED-related lower limb injuries in 103 consecutive casualties were classified, and their subsequent vascular and musculoskeletal treatment recorded. 69% of the injuries were traumatic amputations, and the remainder segmental injuries. 49% of casualties suffered bilateral lower limb amputation. The most common injury was class 3 (involving proximal lower leg or thigh, permitting effective above-knee tourniquet application, 49%), but more proximal patterns (class 4 or 5, preventing effective tourniquet application) accounted for 18% of injuries. Eleven casualties had associated intraperitoneal abdominal injuries, 41 suffered genital or perineal injuries, 9 had pelvic ring fractures, and 66 had upper limb injuries. The classification was easy to apply and correlated with management. The 'Bastion classification' is a pragmatic yet clinically relevant injury categorisation, which describes current injury patterns well, and should facilitate communication between clinicians, and the evaluation of interventions and outcomes. The validation cohort confirms that the injury burden from IEDs in the Helmand Province of Afghanistan remains high, with most casualties sustaining amputation through or above the knee. The rates of associated injury to the abdomen, perineum, pelvis and upper limbs are high. These findings have important implications for the training of military surgeons, staffing and resourcing of medical treatment facilities, to ensure an adequate skill mix to manage these complex and challenging injuries. Crown Copyright © 2014. Published by Elsevier Ltd. All rights reserved.

iACP-GAEnsC: Evolutionary genetic algorithm based ensemble classification of anticancer peptides by utilizing hybrid feature space.

PubMed

Akbar, Shahid; Hayat, Maqsood; Iqbal, Muhammad; Jan, Mian Ahmad

2017-06-01

Cancer is a fatal disease, responsible for one-quarter of all deaths in developed countries. Traditional anticancer therapies such as, chemotherapy and radiation, are highly expensive, susceptible to errors and ineffective techniques. These conventional techniques induce severe side-effects on human cells. Due to perilous impact of cancer, the development of an accurate and highly efficient intelligent computational model is desirable for identification of anticancer peptides. In this paper, evolutionary intelligent genetic algorithm-based ensemble model, 'iACP-GAEnsC', is proposed for the identification of anticancer peptides. In this model, the protein sequences are formulated, using three different discrete feature representation methods, i.e., amphiphilic Pseudo amino acid composition, g-Gap dipeptide composition, and Reduce amino acid alphabet composition. The performance of the extracted feature spaces are investigated separately and then merged to exhibit the significance of hybridization. In addition, the predicted results of individual classifiers are combined together, using optimized genetic algorithm and simple majority technique in order to enhance the true classification rate. It is observed that genetic algorithm-based ensemble classification outperforms than individual classifiers as well as simple majority voting base ensemble. The performance of genetic algorithm-based ensemble classification is highly reported on hybrid feature space, with an accuracy of 96.45%. In comparison to the existing techniques, 'iACP-GAEnsC' model has achieved remarkable improvement in terms of various performance metrics. Based on the simulation results, it is observed that 'iACP-GAEnsC' model might be a leading tool in the field of drug design and proteomics for researchers. Copyright © 2017 Elsevier B.V. All rights reserved.

Attachment-based classifications of children's family drawings: psychometric properties and relations with children's adjustment in kindergarten.

PubMed

Pianta, R C; Longmaid, K; Ferguson, J E

1999-06-01

Investigated an attachment-based theoretical framework and classification system, introduced by Kaplan and Main (1986), for interpreting children's family drawings. This study concentrated on the psychometric properties of the system and the relation between drawings classified using this system and teacher ratings of classroom social-emotional and behavioral functioning, controlling for child age, ethnic status, intelligence, and fine motor skills. This nonclinical sample consisted of 200 kindergarten children of diverse racial and socioeconomic status (SES). Limited support for reliability of this classification system was obtained. Kappas for overall classifications of drawings (e.g., secure) exceeded .80 and mean kappa for discrete drawing features (e.g., figures with smiles) was .82. Coders' endorsement of the presence of certain discrete drawing features predicted their overall classification at 82.5% accuracy. Drawing classification was related to teacher ratings of classroom functioning independent of child age, sex, race, SES, intelligence, and fine motor skills (with p values for the multivariate effects ranging from .043-.001). Results are discussed in terms of the psychometric properties of this system for classifying children's representations of family and the limitations of family drawing techniques for young children.

Unified framework for triaxial accelerometer-based fall event detection and classification using cumulants and hierarchical decision tree classifier.

PubMed

Kambhampati, Satya Samyukta; Singh, Vishal; Manikandan, M Sabarimalai; Ramkumar, Barathram

2015-08-01

In this Letter, the authors present a unified framework for fall event detection and classification using the cumulants extracted from the acceleration (ACC) signals acquired using a single waist-mounted triaxial accelerometer. The main objective of this Letter is to find suitable representative cumulants and classifiers in effectively detecting and classifying different types of fall and non-fall events. It was discovered that the first level of the proposed hierarchical decision tree algorithm implements fall detection using fifth-order cumulants and support vector machine (SVM) classifier. In the second level, the fall event classification algorithm uses the fifth-order cumulants and SVM. Finally, human activity classification is performed using the second-order cumulants and SVM. The detection and classification results are compared with those of the decision tree, naive Bayes, multilayer perceptron and SVM classifiers with different types of time-domain features including the second-, third-, fourth- and fifth-order cumulants and the signal magnitude vector and signal magnitude area. The experimental results demonstrate that the second- and fifth-order cumulant features and SVM classifier can achieve optimal detection and classification rates of above 95%, as well as the lowest false alarm rate of 1.03%.

A clinical decision-making mechanism for context-aware and patient-specific remote monitoring systems using the correlations of multiple vital signs.

PubMed

Forkan, Abdur Rahim Mohammad; Khalil, Ibrahim

2017-02-01

In home-based context-aware monitoring patient's real-time data of multiple vital signs (e.g. heart rate, blood pressure) are continuously generated from wearable sensors. The changes in such vital parameters are highly correlated. They are also patient-centric and can be either recurrent or can fluctuate. The objective of this study is to develop an intelligent method for personalized monitoring and clinical decision support through early estimation of patient-specific vital sign values, and prediction of anomalies using the interrelation among multiple vital signs. In this paper, multi-label classification algorithms are applied in classifier design to forecast these values and related abnormalities. We proposed a completely new approach of patient-specific vital sign prediction system using their correlations. The developed technique can guide healthcare professionals to make accurate clinical decisions. Moreover, our model can support many patients with various clinical conditions concurrently by utilizing the power of cloud computing technology. The developed method also reduces the rate of false predictions in remote monitoring centres. In the experimental settings, the statistical features and correlations of six vital signs are formulated as multi-label classification problem. Eight multi-label classification algorithms along with three fundamental machine learning algorithms are used and tested on a public dataset of 85 patients. Different multi-label classification evaluation measures such as Hamming score, F1-micro average, and accuracy are used for interpreting the prediction performance of patient-specific situation classifications. We achieved 90-95% Hamming score values across 24 classifier combinations for 85 different patients used in our experiment. The results are compared with single-label classifiers and without considering the correlations among the vitals. The comparisons show that multi-label method is the best technique for this problem domain. The evaluation results reveal that multi-label classification techniques using the correlations among multiple vitals are effective ways for early estimation of future values of those vitals. In context-aware remote monitoring this process can greatly help the doctors in quick diagnostic decision making. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Brain fingerprinting classification concealed information test detects US Navy military medical information with P300

PubMed Central

Farwell, Lawrence A.; Richardson, Drew C.; Richardson, Graham M.; Furedy, John J.

2014-01-01

A classification concealed information test (CIT) used the “brain fingerprinting” method of applying P300 event-related potential (ERP) in detecting information that is (1) acquired in real life and (2) unique to US Navy experts in military medicine. Military medicine experts and non-experts were asked to push buttons in response to three types of text stimuli. Targets contain known information relevant to military medicine, are identified to subjects as relevant, and require pushing one button. Subjects are told to push another button to all other stimuli. Probes contain concealed information relevant to military medicine, and are not identified to subjects. Irrelevants contain equally plausible, but incorrect/irrelevant information. Error rate was 0%. Median and mean statistical confidences for individual determinations were 99.9% with no indeterminates (results lacking sufficiently high statistical confidence to be classified). We compared error rate and statistical confidence for determinations of both information present and information absent produced by classification CIT (Is a probe ERP more similar to a target or to an irrelevant ERP?) vs. comparison CIT (Does a probe produce a larger ERP than an irrelevant?) using P300 plus the late negative component (LNP; together, P300-MERMER). Comparison CIT produced a significantly higher error rate (20%) and lower statistical confidences: mean 67%; information-absent mean was 28.9%, less than chance (50%). We compared analysis using P300 alone with the P300 + LNP. P300 alone produced the same 0% error rate but significantly lower statistical confidences. These findings add to the evidence that the brain fingerprinting methods as described here provide sufficient conditions to produce less than 1% error rate and greater than 95% median statistical confidence in a CIT on information obtained in the course of real life that is characteristic of individuals with specific training, expertise, or organizational affiliation. PMID:25565941

Application of wavelet transformation and adaptive neighborhood based modified backpropagation (ANMBP) for classification of brain cancer

NASA Astrophysics Data System (ADS)

Werdiningsih, Indah; Zaman, Badrus; Nuqoba, Barry

2017-08-01

This paper presents classification of brain cancer using wavelet transformation and Adaptive Neighborhood Based Modified Backpropagation (ANMBP). Three stages of the processes, namely features extraction, features reduction, and classification process. Wavelet transformation is used for feature extraction and ANMBP is used for classification process. The result of features extraction is feature vectors. Features reduction used 100 energy values per feature and 10 energy values per feature. Classifications of brain cancer are normal, alzheimer, glioma, and carcinoma. Based on simulation results, 10 energy values per feature can be used to classify brain cancer correctly. The correct classification rate of proposed system is 95 %. This research demonstrated that wavelet transformation can be used for features extraction and ANMBP can be used for classification of brain cancer.

The generalization ability of online SVM classification based on Markov sampling.

PubMed

Xu, Jie; Yan Tang, Yuan; Zou, Bin; Xu, Zongben; Li, Luoqing; Lu, Yang

2015-03-01

In this paper, we consider online support vector machine (SVM) classification learning algorithms with uniformly ergodic Markov chain (u.e.M.c.) samples. We establish the bound on the misclassification error of an online SVM classification algorithm with u.e.M.c. samples based on reproducing kernel Hilbert spaces and obtain a satisfactory convergence rate. We also introduce a novel online SVM classification algorithm based on Markov sampling, and present the numerical studies on the learning ability of online SVM classification based on Markov sampling for benchmark repository. The numerical studies show that the learning performance of the online SVM classification algorithm based on Markov sampling is better than that of classical online SVM classification based on random sampling as the size of training samples is larger.

Land Cover Classification in a Complex Urban-Rural Landscape with Quickbird Imagery

PubMed Central

Moran, Emilio Federico.

2010-01-01

High spatial resolution images have been increasingly used for urban land use/cover classification, but the high spectral variation within the same land cover, the spectral confusion among different land covers, and the shadow problem often lead to poor classification performance based on the traditional per-pixel spectral-based classification methods. This paper explores approaches to improve urban land cover classification with Quickbird imagery. Traditional per-pixel spectral-based supervised classification, incorporation of textural images and multispectral images, spectral-spatial classifier, and segmentation-based classification are examined in a relatively new developing urban landscape, Lucas do Rio Verde in Mato Grosso State, Brazil. This research shows that use of spatial information during the image classification procedure, either through the integrated use of textural and spectral images or through the use of segmentation-based classification method, can significantly improve land cover classification performance. PMID:21643433

Validation of statistical predictive models meant to select melanoma patients for sentinel lymph node biopsy.

PubMed

Sabel, Michael S; Rice, John D; Griffith, Kent A; Lowe, Lori; Wong, Sandra L; Chang, Alfred E; Johnson, Timothy M; Taylor, Jeremy M G

2012-01-01

To identify melanoma patients at sufficiently low risk of nodal metastases who could avoid sentinel lymph node biopsy (SLNB), several statistical models have been proposed based upon patient/tumor characteristics, including logistic regression, classification trees, random forests, and support vector machines. We sought to validate recently published models meant to predict sentinel node status. We queried our comprehensive, prospectively collected melanoma database for consecutive melanoma patients undergoing SLNB. Prediction values were estimated based upon four published models, calculating the same reported metrics: negative predictive value (NPV), rate of negative predictions (RNP), and false-negative rate (FNR). Logistic regression performed comparably with our data when considering NPV (89.4 versus 93.6%); however, the model's specificity was not high enough to significantly reduce the rate of biopsies (SLN reduction rate of 2.9%). When applied to our data, the classification tree produced NPV and reduction in biopsy rates that were lower (87.7 versus 94.1 and 29.8 versus 14.3, respectively). Two published models could not be applied to our data due to model complexity and the use of proprietary software. Published models meant to reduce the SLNB rate among patients with melanoma either underperformed when applied to our larger dataset, or could not be validated. Differences in selection criteria and histopathologic interpretation likely resulted in underperformance. Statistical predictive models must be developed in a clinically applicable manner to allow for both validation and ultimately clinical utility.

Validation of Statistical Predictive Models Meant to Select Melanoma Patients for Sentinel Lymph Node Biopsy

PubMed Central

Sabel, Michael S.; Rice, John D.; Griffith, Kent A.; Lowe, Lori; Wong, Sandra L.; Chang, Alfred E.; Johnson, Timothy M.; Taylor, Jeremy M.G.

2013-01-01

Introduction To identify melanoma patients at sufficiently low risk of nodal metastases who could avoid SLN biopsy (SLNB). Several statistical models have been proposed based upon patient/tumor characteristics, including logistic regression, classification trees, random forests and support vector machines. We sought to validate recently published models meant to predict sentinel node status. Methods We queried our comprehensive, prospectively-collected melanoma database for consecutive melanoma patients undergoing SLNB. Prediction values were estimated based upon 4 published models, calculating the same reported metrics: negative predictive value (NPV), rate of negative predictions (RNP), and false negative rate (FNR). Results Logistic regression performed comparably with our data when considering NPV (89.4% vs. 93.6%); however the model’s specificity was not high enough to significantly reduce the rate of biopsies (SLN reduction rate of 2.9%). When applied to our data, the classification tree produced NPV and reduction in biopsies rates that were lower 87.7% vs. 94.1% and 29.8% vs. 14.3%, respectively. Two published models could not be applied to our data due to model complexity and the use of proprietary software. Conclusions Published models meant to reduce the SLNB rate among patients with melanoma either underperformed when applied to our larger dataset, or could not be validated. Differences in selection criteria and histopathologic interpretation likely resulted in underperformance. Development of statistical predictive models must be created in a clinically applicable manner to allow for both validation and ultimately clinical utility. PMID:21822550

Integrating human and machine intelligence in galaxy morphology classification tasks

NASA Astrophysics Data System (ADS)

Beck, Melanie R.; Scarlata, Claudia; Fortson, Lucy F.; Lintott, Chris J.; Simmons, B. D.; Galloway, Melanie A.; Willett, Kyle W.; Dickinson, Hugh; Masters, Karen L.; Marshall, Philip J.; Wright, Darryl

2018-06-01

Quantifying galaxy morphology is a challenging yet scientifically rewarding task. As the scale of data continues to increase with upcoming surveys, traditional classification methods will struggle to handle the load. We present a solution through an integration of visual and automated classifications, preserving the best features of both human and machine. We demonstrate the effectiveness of such a system through a re-analysis of visual galaxy morphology classifications collected during the Galaxy Zoo 2 (GZ2) project. We reprocess the top-level question of the GZ2 decision tree with a Bayesian classification aggregation algorithm dubbed SWAP, originally developed for the Space Warps gravitational lens project. Through a simple binary classification scheme, we increase the classification rate nearly 5-fold classifying 226 124 galaxies in 92 d of GZ2 project time while reproducing labels derived from GZ2 classification data with 95.7 per cent accuracy. We next combine this with a Random Forest machine learning algorithm that learns on a suite of non-parametric morphology indicators widely used for automated morphologies. We develop a decision engine that delegates tasks between human and machine and demonstrate that the combined system provides at least a factor of 8 increase in the classification rate, classifying 210 803 galaxies in just 32 d of GZ2 project time with 93.1 per cent accuracy. As the Random Forest algorithm requires a minimal amount of computational cost, this result has important implications for galaxy morphology identification tasks in the era of Euclid and other large-scale surveys.

Automated detection of tuberculosis on sputum smeared slides using stepwise classification

NASA Astrophysics Data System (ADS)

Divekar, Ajay; Pangilinan, Corina; Coetzee, Gerrit; Sondh, Tarlochan; Lure, Fleming Y. M.; Kennedy, Sean

2012-03-01

Routine visual slide screening for identification of tuberculosis (TB) bacilli in stained sputum slides under microscope system is a tedious labor-intensive task and can miss up to 50% of TB. Based on the Shannon cofactor expansion on Boolean function for classification, a stepwise classification (SWC) algorithm is developed to remove different types of false positives, one type at a time, and to increase the detection of TB bacilli at different concentrations. Both bacilli and non-bacilli objects are first analyzed and classified into several different categories including scanty positive, high concentration positive, and several non-bacilli categories: small bright objects, beaded, dim elongated objects, etc. The morphological and contrast features are extracted based on aprior clinical knowledge. The SWC is composed of several individual classifiers. Individual classifier to increase the bacilli counts utilizes an adaptive algorithm based on a microbiologist's statistical heuristic decision process. Individual classifier to reduce false positive is developed through minimization from a binary decision tree to classify different types of true and false positive based on feature vectors. Finally, the detection algorithm is was tested on 102 independent confirmed negative and 74 positive cases. A multi-class task analysis shows high accordance rate for negative, scanty, and high-concentration as 88.24%, 56.00%, and 97.96%, respectively. A binary-class task analysis using a receiver operating characteristics method with the area under the curve (Az) is also utilized to analyze the performance of this detection algorithm, showing the superior detection performance on the high-concentration cases (Az=0.913) and cases mixed with high-concentration and scanty cases (Az=0.878).

Ensemble of sparse classifiers for high-dimensional biological data.

PubMed

Kim, Sunghan; Scalzo, Fabien; Telesca, Donatello; Hu, Xiao

2015-01-01

Biological data are often high in dimension while the number of samples is small. In such cases, the performance of classification can be improved by reducing the dimension of data, which is referred to as feature selection. Recently, a novel feature selection method has been proposed utilising the sparsity of high-dimensional biological data where a small subset of features accounts for most variance of the dataset. In this study we propose a new classification method for high-dimensional biological data, which performs both feature selection and classification within a single framework. Our proposed method utilises a sparse linear solution technique and the bootstrap aggregating algorithm. We tested its performance on four public mass spectrometry cancer datasets along with two other conventional classification techniques such as Support Vector Machines and Adaptive Boosting. The results demonstrate that our proposed method performs more accurate classification across various cancer datasets than those conventional classification techniques.

A space-based classification system for RF transients

NASA Astrophysics Data System (ADS)

Moore, K. R.; Call, D.; Johnson, S.; Payne, T.; Ford, W.; Spencer, K.; Wilkerson, J. F.; Baumgart, C.

The FORTE (Fast On-Orbit Recording of Transient Events) small satellite is scheduled for launch in mid 1995. The mission is to measure and classify VHF (30-300 MHz) electromagnetic pulses, primarily due to lightning, within a high noise environment dominated by continuous wave carriers such as TV and FM stations. The FORTE Event Classifier will use specialized hardware to implement signal processing and neural network algorithms that perform onboard classification of RF transients and carriers. Lightning events will also be characterized with optical data telemetered to the ground. A primary mission science goal is to develop a comprehensive understanding of the correlation between the optical flash and the VHF emissions from lightning. By combining FORTE measurements with ground measurements and/or active transmitters, other science issues can be addressed. Examples include the correlation of global precipitation rates with lightning flash rates and location, the effects of large scale structures within the ionosphere (such as traveling ionospheric disturbances and horizontal gradients in the total electron content) on the propagation of broad bandwidth RF signals, and various areas of lightning physics. Event classification is a key feature of the FORTE mission. Neural networks are promising candidates for this application. The authors describe the proposed FORTE Event Classifier flight system, which consists of a commercially available digital signal processing board and a custom board, and discuss work on signal processing and neural network algorithms.

The Molecular Classification of Medulloblastoma: Driving the next generation clinical trials

PubMed Central

Leary, Sarah E. S.; Olson, James M.

2012-01-01

Purpose of Review Most children diagnosed with cancer today are expected to be cured. Medulloblastoma, the most common pediatric malignant brain tumor, is an example of a disease that has benefitted from advances in diagnostic imaging, surgical techniques, radiation therapy and combination chemotherapy over the past decades. An incurable disease 50 years ago, approximately 70% of children with medulloblastoma are now cured of their disease. However, the pace of increasing the cure rate has slowed over the past two decades, and we have likely reached the maximal benefit that can be achieved with cytotoxic therapy and clinical risk stratification. Long-term toxicity of therapy also remains significant. To increase cure rates and decrease long-term toxicity, there is great interest in incorporating biologic “targeted” therapy into treatment of medulloblastoma, but this will require a paradigm shift in how we classify and study disease. Recent Findings Using genome-based high-throughput analytic techniques, several groups have independently reported methods of molecular classification of medulloblastoma within the past year. This has resulted in a working consensus to view medulloblastoma as four molecular subtypes including WNT pathway subtype, SHH pathway subtype, and two less well-defined subtypes, Group C and Group D. Summary Novel classification and risk stratification based on biologic subtypes of disease will form the basis of further study in medulloblastoma, and identify specific subtypes which warrant greater research focus. PMID:22189395

A comparison of basic deinterlacing approaches for a computer assisted diagnosis approach of videoscope images

NASA Astrophysics Data System (ADS)

Kage, Andreas; Canto, Marcia; Gorospe, Emmanuel; Almario, Antonio; Münzenmayer, Christian

2010-03-01

In the near future, Computer Assisted Diagnosis (CAD) which is well known in the area of mammography might be used to support clinical experts in the diagnosis of images derived from imaging modalities such as endoscopy. In the recent past, a few first approaches for computer assisted endoscopy have been presented already. These systems use a video signal as an input that is provided by the endoscopes video processor. Despite the advent of high-definition systems most standard endoscopy systems today still provide only analog video signals. These signals consist of interlaced images that can not be used in a CAD approach without deinterlacing. Of course, there are many different deinterlacing approaches known today. But most of them are specializations of some basic approaches. In this paper we present four basic deinterlacing approaches. We have used a database of non-interlaced images which have been degraded by artificial interlacing and afterwards processed by these approaches. The database contains regions of interest (ROI) of clinical relevance for the diagnosis of abnormalities in the esophagus. We compared the classification rates on these ROIs on the original images and after the deinterlacing. The results show that the deinterlacing has an impact on the classification rates. The Bobbing approach and the Motion Compensation approach achieved the best classification results in most cases.

Development of bacterial colony phenotyping instrument using reflected scatter light

NASA Astrophysics Data System (ADS)

Doh, Iyll-Joon

Bacterial rapid detection using optical scattering technology (BARDOT) involves in differentiating elastic scattering pattern of bacterial colony. This elastic light scatter technology has shown promising label-free classification rate. However, there is limited success in certain circumstances where either a growth media or a colony has higher opacity. This situation is due to the physical principles of the current BARDOT which mainly relies on optical patterns generated by transmitted signals. Incoming light is obstructed and cannot be transmitted through the dense bacterial colonies, such as Lactobacillus, Yeast, mold and soil bacteria. Moreover, a blood agar, widely used in clinical field, is an example of an opaque media that does not allow light to be transmitted through. Therefore, in this research, a newly designed reflection type scatterometer is presented. The reflection type scatterometer measures the elastic scattering pattern generated by reflected signal. A theoretical model to study the optical pattern characteristic with respect to bacterial colony morphology is presented. Both theoretical and experiment results show good agreement that the size of backward scattering pattern has positive correlation to colony aspect ratio, a colony elevation to diameter ratio. Four pathogenic bacteria on blood agar, Escherichia coli K12, Listeria innocua, Salmonella Typhimurium, and Staphylococcus aureus, are tested and measured with proposed instrument. The measured patterns are analyzed with a classification software, and high classification rate can be achieved.

Improved wavelet packet classification algorithm for vibrational intrusions in distributed fiber-optic monitoring systems

NASA Astrophysics Data System (ADS)

Wang, Bingjie; Pi, Shaohua; Sun, Qi; Jia, Bo

2015-05-01

An improved classification algorithm that considers multiscale wavelet packet Shannon entropy is proposed. Decomposition coefficients at all levels are obtained to build the initial Shannon entropy feature vector. After subtracting the Shannon entropy map of the background signal, components of the strongest discriminating power in the initial feature vector are picked out to rebuild the Shannon entropy feature vector, which is transferred to radial basis function (RBF) neural network for classification. Four types of man-made vibrational intrusion signals are recorded based on a modified Sagnac interferometer. The performance of the improved classification algorithm has been evaluated by the classification experiments via RBF neural network under different diffusion coefficients. An 85% classification accuracy rate is achieved, which is higher than the other common algorithms. The classification results show that this improved classification algorithm can be used to classify vibrational intrusion signals in an automatic real-time monitoring system.

Significance and Application of Digital Breast Tomosynthesis for the BI-RADS Classification of Breast Cancer.

PubMed

Cai, Si-Qing; Yan, Jian-Xiang; Chen, Qing-Shi; Huang, Mei-Ling; Cai, Dong-Lu

2015-01-01

Full-field digital mammography (FFDM) with dense breasts has a high rate of missed diagnosis, and digital breast tomosynthesis (DBT) could reduce organization overlapping and provide more reliable images for BI-RADS classification. This study aims to explore application of COMBO (FFDM+DBT) for effect and significance of BI-RADS classification of breast cancer. In this study, we selected 832 patients who had been treated from May 2013 to November 2013. Classify FFDM and COMBO examination according to BI-RADS separately and compare the differences for glands in the image of the same patient in judgment, mass characteristics display and indirect signs. Employ Paired Wilcoxon rank sum test was used in 79 breast cancer patients to find differences between two examine methods. The results indicated that COMBO pattern is able to observe more details in distribution of glands when estimating content. Paired Wilcoxon rank sum test showed that overall classification level of COMBO is higher significantly compared to FFDM to BI-RADS diagnosis and classification of breast (P<0.05). The area under FFDM ROC curve is 0.805, while that is 0.941 in COMBO pattern. COMBO shows relation of mass with the surrounding tissues, the calcification in the mass, and multiple foci clearly in breast cancer tissues. The optimal sensitivity of cut-off value in COMBO pattern is 82.9%, which is higher than that in FFDM (60%). They share the same specificity which is both 93.2%. Digital Breast Tomosynthesis (DBT) could be used for the BI-RADS classification in breast cancer in clinical.

An automatic device for detection and classification of malaria parasite species in thick blood film.

PubMed

Kaewkamnerd, Saowaluck; Uthaipibull, Chairat; Intarapanich, Apichart; Pannarut, Montri; Chaotheing, Sastra; Tongsima, Sissades

2012-01-01

Current malaria diagnosis relies primarily on microscopic examination of Giemsa-stained thick and thin blood films. This method requires vigorously trained technicians to efficiently detect and classify the malaria parasite species such as Plasmodium falciparum (Pf) and Plasmodium vivax (Pv) for an appropriate drug administration. However, accurate classification of parasite species is difficult to achieve because of inherent technical limitations and human inconsistency. To improve performance of malaria parasite classification, many researchers have proposed automated malaria detection devices using digital image analysis. These image processing tools, however, focus on detection of parasites on thin blood films, which may not detect the existence of parasites due to the parasite scarcity on the thin blood film. The problem is aggravated with low parasitemia condition. Automated detection and classification of parasites on thick blood films, which contain more numbers of parasite per detection area, would address the previous limitation. The prototype of an automatic malaria parasite identification system is equipped with mountable motorized units for controlling the movements of objective lens and microscope stage. This unit was tested for its precision to move objective lens (vertical movement, z-axis) and microscope stage (in x- and y-horizontal movements). The average precision of x-, y- and z-axes movements were 71.481 ± 7.266 μm, 40.009 ± 0.000 μm, and 7.540 ± 0.889 nm, respectively. Classification of parasites on 60 Giemsa-stained thick blood films (40 blood films containing infected red blood cells and 20 control blood films of normal red blood cells) was tested using the image analysis module. By comparing our results with the ones verified by trained malaria microscopists, the prototype detected parasite-positive and parasite-negative blood films at the rate of 95% and 68.5% accuracy, respectively. For classification performance, the thick blood films with Pv parasite was correctly classified with the success rate of 75% while the accuracy of Pf classification was 90%. This work presents an automatic device for both detection and classification of malaria parasite species on thick blood film. The system is based on digital image analysis and featured with motorized stage units, designed to easily be mounted on most conventional light microscopes used in the endemic areas. The constructed motorized module could control the movements of objective lens and microscope stage at high precision for effective acquisition of quality images for analysis. The analysis program could accurately classify parasite species, into Pf or Pv, based on distribution of chromatin size.

Cannabis Mobile Apps: A Content Analysis.

PubMed

Ramo, Danielle E; Popova, Lucy; Grana, Rachel; Zhao, Shirley; Chavez, Kathryn

2015-08-12

Mobile technology is pervasive and widely used to obtain information about drugs such as cannabis, especially in a climate of rapidly changing cannabis policy; yet the content of available cannabis apps is largely unknown. Understanding the resources available to those searching for cannabis apps will clarify how this technology is being used to reflect and influence cannabis use behavior. We investigated the content of 59 cannabis-related mobile apps for Apple and Android devices as of November 26, 2014. The Apple and Google Play app stores were searched using the terms "cannabis" and "marijuana." Three trained coders classified the top 20 apps for each term and each store, using a coding guide. Apps were examined for the presence of 20 content codes derived by the researchers. Total apps available for each search term were 124 for cannabis and 218 for marijuana in the Apple App Store, and 250 each for cannabis and marijuana on Google Play. The top 20 apps in each category in each store were coded for 59 independent apps (30 Apple, 29 Google Play). The three most common content areas were cannabis strain classification (33.9%), facts about cannabis (20.3%), and games (20.3%). In the Apple App Store, most apps were free (77%), all were rated "17+" years, and the average user rating was 3.9/5 stars. The most popular apps provided cannabis strain classifications (50%), dispensary information (27%), or general facts about cannabis (27%). Only one app (3%) provided information or resources related to cannabis abuse, addiction, or treatment. On Google Play, most apps were free (93%), rated "high maturity" (79%), and the average user rating was 4.1/5. The most popular app types offered games (28%), phone utilities (eg, wallpaper, clock; 21%) and cannabis food recipes (21%); no apps addressed abuse, addiction, or treatment. Cannabis apps are generally free and highly rated. Apps were most often informational (facts, strain classification), or recreational (games), likely reflecting and influencing the growing acceptance of cannabis for medical and recreational purposes. Apps addressing addiction or cessation were underrepresented in the most popular cannabis mobile apps. Differences among apps for Apple and Android platforms likely reflect differences in the population of users, developer choice, and platform regulations.

Cannabis Mobile Apps: A Content Analysis

PubMed Central

Popova, Lucy; Grana, Rachel; Zhao, Shirley; Chavez, Kathryn

2015-01-01

Background Mobile technology is pervasive and widely used to obtain information about drugs such as cannabis, especially in a climate of rapidly changing cannabis policy; yet the content of available cannabis apps is largely unknown. Understanding the resources available to those searching for cannabis apps will clarify how this technology is being used to reflect and influence cannabis use behavior. Objective We investigated the content of 59 cannabis-related mobile apps for Apple and Android devices as of November 26, 2014. Methods The Apple and Google Play app stores were searched using the terms “cannabis” and “marijuana.” Three trained coders classified the top 20 apps for each term and each store, using a coding guide. Apps were examined for the presence of 20 content codes derived by the researchers. Results Total apps available for each search term were 124 for cannabis and 218 for marijuana in the Apple App Store, and 250 each for cannabis and marijuana on Google Play. The top 20 apps in each category in each store were coded for 59 independent apps (30 Apple, 29 Google Play). The three most common content areas were cannabis strain classification (33.9%), facts about cannabis (20.3%), and games (20.3%). In the Apple App Store, most apps were free (77%), all were rated “17+” years, and the average user rating was 3.9/5 stars. The most popular apps provided cannabis strain classifications (50%), dispensary information (27%), or general facts about cannabis (27%). Only one app (3%) provided information or resources related to cannabis abuse, addiction, or treatment. On Google Play, most apps were free (93%), rated “high maturity” (79%), and the average user rating was 4.1/5. The most popular app types offered games (28%), phone utilities (eg, wallpaper, clock; 21%) and cannabis food recipes (21%); no apps addressed abuse, addiction, or treatment. Conclusions Cannabis apps are generally free and highly rated. Apps were most often informational (facts, strain classification), or recreational (games), likely reflecting and influencing the growing acceptance of cannabis for medical and recreational purposes. Apps addressing addiction or cessation were underrepresented in the most popular cannabis mobile apps. Differences among apps for Apple and Android platforms likely reflect differences in the population of users, developer choice, and platform regulations. PMID:26268634

Dynamic classification of fetal heart rates by hierarchical Dirichlet process mixture models.

PubMed

Yu, Kezi; Quirk, J Gerald; Djurić, Petar M

2017-01-01

In this paper, we propose an application of non-parametric Bayesian (NPB) models for classification of fetal heart rate (FHR) recordings. More specifically, we propose models that are used to differentiate between FHR recordings that are from fetuses with or without adverse outcomes. In our work, we rely on models based on hierarchical Dirichlet processes (HDP) and the Chinese restaurant process with finite capacity (CRFC). Two mixture models were inferred from real recordings, one that represents healthy and another, non-healthy fetuses. The models were then used to classify new recordings and provide the probability of the fetus being healthy. First, we compared the classification performance of the HDP models with that of support vector machines on real data and concluded that the HDP models achieved better performance. Then we demonstrated the use of mixture models based on CRFC for dynamic classification of the performance of (FHR) recordings in a real-time setting.

Classification of urine sediment based on convolution neural network

NASA Astrophysics Data System (ADS)

Pan, Jingjing; Jiang, Cunbo; Zhu, Tiantian

2018-04-01

By designing a new convolution neural network framework, this paper breaks the constraints of the original convolution neural network framework requiring large training samples and samples of the same size. Move and cropping the input images, generate the same size of the sub-graph. And then, the generated sub-graph uses the method of dropout, increasing the diversity of samples and preventing the fitting generation. Randomly select some proper subset in the sub-graphic set and ensure that the number of elements in the proper subset is same and the proper subset is not the same. The proper subsets are used as input layers for the convolution neural network. Through the convolution layer, the pooling, the full connection layer and output layer, we can obtained the classification loss rate of test set and training set. In the red blood cells, white blood cells, calcium oxalate crystallization classification experiment, the classification accuracy rate of 97% or more.

Dynamic classification of fetal heart rates by hierarchical Dirichlet process mixture models

PubMed Central

Yu, Kezi; Quirk, J. Gerald

2017-01-01

In this paper, we propose an application of non-parametric Bayesian (NPB) models for classification of fetal heart rate (FHR) recordings. More specifically, we propose models that are used to differentiate between FHR recordings that are from fetuses with or without adverse outcomes. In our work, we rely on models based on hierarchical Dirichlet processes (HDP) and the Chinese restaurant process with finite capacity (CRFC). Two mixture models were inferred from real recordings, one that represents healthy and another, non-healthy fetuses. The models were then used to classify new recordings and provide the probability of the fetus being healthy. First, we compared the classification performance of the HDP models with that of support vector machines on real data and concluded that the HDP models achieved better performance. Then we demonstrated the use of mixture models based on CRFC for dynamic classification of the performance of (FHR) recordings in a real-time setting. PMID:28953927

Spatial Classification of Orchards and Vineyards with High Spatial Resolution Panchromatic Imagery

DOE Office of Scientific and Technical Information (OSTI.GOV)

Warner, Timothy; Steinmaus, Karen L.

2005-02-01

New high resolution single spectral band imagery offers the capability to conduct image classifications based on spatial patterns in imagery. A classification algorithm based on autocorrelation patterns was developed to automatically extract orchards and vineyards from satellite imagery. The algorithm was tested on IKONOS imagery over Granger, WA, which resulted in a classification accuracy of 95%.

Acute promyelocytic leukaemia is highly frequent among acute myeloid leukaemias in Brazil: a hospital-based cancer registry study from 2001 to 2012.

PubMed

Thuler, Luiz Claudio Santos; Pombo-de-Oliveira, Maria S

2017-03-01

The WHO classification that defines subtypes of acute myeloid leukaemias (AMLs) is relatively unexplored at the population-based level. This study aimed to examine the frequency of acute promyelocytic leukaemia (APL or AML-M3) in Brazil. Data were extracted from 239 cancer centres (2001-2012) and categorized according to the International Classification of Diseases for Oncology (CID-O 3.0) and WHO classification (n = 9116). CID-O3 code 9866 identified 614 APL patients. AML not otherwise specified (NOS) was frequent, and the APL group represented the main subtype specified. The mean age of APL was lower than that of other AMLs (31.5, standard deviation (SD) 18.6 versus 40.9, SD 24.6; p < 0.001); there was a high frequency of APL in the 13-21-year-old (11.8 %) and ≤12.9-year-old (6.4 %) age groups. Time taken to begin treatment (as ≤14 days versus >14 days) and induction death rate were lower in APL than in other AML subtypes (p < 0.001). This report provides additional evidence on the distribution of APL among cases of AML in Brazil.

Automated target classification in high resolution dual frequency sonar imagery

NASA Astrophysics Data System (ADS)

Aridgides, Tom; Fernández, Manuel

2007-04-01

An improved computer-aided-detection / computer-aided-classification (CAD/CAC) processing string has been developed. The classified objects of 2 distinct strings are fused using the classification confidence values and their expansions as features, and using "summing" or log-likelihood-ratio-test (LLRT) based fusion rules. The utility of the overall processing strings and their fusion was demonstrated with new high-resolution dual frequency sonar imagery. Three significant fusion algorithm improvements were made. First, a nonlinear 2nd order (Volterra) feature LLRT fusion algorithm was developed. Second, a Box-Cox nonlinear feature LLRT fusion algorithm was developed. The Box-Cox transformation consists of raising the features to a to-be-determined power. Third, a repeated application of a subset feature selection / feature orthogonalization / Volterra feature LLRT fusion block was utilized. It was shown that cascaded Volterra feature LLRT fusion of the CAD/CAC processing strings outperforms summing, baseline single-stage Volterra and Box-Cox feature LLRT algorithms, yielding significant improvements over the best single CAD/CAC processing string results, and providing the capability to correctly call the majority of targets while maintaining a very low false alarm rate. Additionally, the robustness of cascaded Volterra feature fusion was demonstrated, by showing that the algorithm yields similar performance with the training and test sets.

Identification of the Geographic Origin of Parmigiano Reggiano (P.D.O.) Cheeses Deploying Non-Targeted Mass Spectrometry and Chemometrics.

PubMed

Popping, Bert; De Dominicis, Emiliano; Dante, Mario; Nocetti, Marco

2017-02-16

Parmigiano Reggiano is an Italian product with a protected designation of origin (P.D.O.). It is an aged hard cheese made from raw milk. P.D.O. products are protected by European regulations. Approximately 3 million wheels are produced each year, and the product attracts a relevant premium price due to its quality and all around the world well known typicity. Due to the high demand that exceeds the production, several fraudulent products can be found on the market. The rate of fraud is estimated between 20% and 40%, the latter predominantly in the grated form. We have developed a non-target method based on Liquid Chomatography-High Resolution Mass Spectrometry (LC-HRMS) that allows the discrimination of Parmigiano Reggiano from non-authentic products with milk from different geographical origins or products, where other aspects of the production process do not comply with the rules laid down in the production specifications for Parmeggiano Reggiano. Based on a database created with authentic samples provided by the Consortium of Parmigiano Reggiano Cheese, a reliable classification model was built. The overall classification capabilities of this non-targeted method was verified on 32 grated cheese samples. The classification was 87.5% accurate.

Glioma CpG island methylator phenotype (G-CIMP): biological and clinical implications.

PubMed

Malta, Tathiane M; de Souza, Camila F; Sabedot, Thais S; Silva, Tiago C; Mosella, Maritza S; Kalkanis, Steven N; Snyder, James; Castro, Ana Valeria B; Noushmehr, Houtan

2018-04-09

Gliomas are a heterogeneous group of brain tumors with distinct biological and clinical properties. Despite advances in surgical techniques and clinical regimens, treatment of high-grade glioma remains challenging and carries dismal rates of therapeutic success and overall survival. Challenges include the molecular complexity of gliomas, as well as inconsistencies in histopathological grading, resulting in an inaccurate prediction of disease progression and failure in the use of standard therapy. The updated 2016 World Health Organization (WHO) classification of tumors of the central nervous system reflects a refinement of tumor diagnostics by integrating the genotypic and phenotypic features, thereby narrowing the defined subgroups. The new classification recommends molecular diagnosis of isocitrate dehydrogenase (IDH) mutational status in gliomas. IDH-mutant gliomas manifest the cytosine-phosphate-guanine (CpG) island methylator phenotype (G-CIMP). Notably, the recent identification of clinically relevant subsets of G-CIMP tumors (G-CIMP-high and G-CIMP-low) provides a further refinement in glioma classification that is independent of grade and histology. This scheme may be useful for predicting patient outcome and may be translated into effective therapeutic strategies tailored to each patient. In this review, we highlight the evolution of our understanding of the G-CIMP subsets and how recent advances in characterizing the genome and epigenome of gliomas may influence future basic and translational research.

Constrained binary classification using ensemble learning: an application to cost-efficient targeted PrEP strategies.

PubMed

Zheng, Wenjing; Balzer, Laura; van der Laan, Mark; Petersen, Maya

2018-01-30

Binary classification problems are ubiquitous in health and social sciences. In many cases, one wishes to balance two competing optimality considerations for a binary classifier. For instance, in resource-limited settings, an human immunodeficiency virus prevention program based on offering pre-exposure prophylaxis (PrEP) to select high-risk individuals must balance the sensitivity of the binary classifier in detecting future seroconverters (and hence offering them PrEP regimens) with the total number of PrEP regimens that is financially and logistically feasible for the program. In this article, we consider a general class of constrained binary classification problems wherein the objective function and the constraint are both monotonic with respect to a threshold. These include the minimization of the rate of positive predictions subject to a minimum sensitivity, the maximization of sensitivity subject to a maximum rate of positive predictions, and the Neyman-Pearson paradigm, which minimizes the type II error subject to an upper bound on the type I error. We propose an ensemble approach to these binary classification problems based on the Super Learner methodology. This approach linearly combines a user-supplied library of scoring algorithms, with combination weights and a discriminating threshold chosen to minimize the constrained optimality criterion. We then illustrate the application of the proposed classifier to develop an individualized PrEP targeting strategy in a resource-limited setting, with the goal of minimizing the number of PrEP offerings while achieving a minimum required sensitivity. This proof of concept data analysis uses baseline data from the ongoing Sustainable East Africa Research in Community Health study. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

Identification of extremely premature infants at high risk of rehospitalization.

PubMed

Ambalavanan, Namasivayam; Carlo, Waldemar A; McDonald, Scott A; Yao, Qing; Das, Abhik; Higgins, Rosemary D

2011-11-01

Extremely low birth weight infants often require rehospitalization during infancy. Our objective was to identify at the time of discharge which extremely low birth weight infants are at higher risk for rehospitalization. Data from extremely low birth weight infants in Eunice Kennedy Shriver National Institute of Child Health and Human Development Neonatal Research Network centers from 2002-2005 were analyzed. The primary outcome was rehospitalization by the 18- to 22-month follow-up, and secondary outcome was rehospitalization for respiratory causes in the first year. Using variables and odds ratios identified by stepwise logistic regression, scoring systems were developed with scores proportional to odds ratios. Classification and regression-tree analysis was performed by recursive partitioning and automatic selection of optimal cutoff points of variables. A total of 3787 infants were evaluated (mean ± SD birth weight: 787 ± 136 g; gestational age: 26 ± 2 weeks; 48% male, 42% black). Forty-five percent of the infants were rehospitalized by 18 to 22 months; 14.7% were rehospitalized for respiratory causes in the first year. Both regression models (area under the curve: 0.63) and classification and regression-tree models (mean misclassification rate: 40%-42%) were moderately accurate. Predictors for the primary outcome by regression were shunt surgery for hydrocephalus, hospital stay of >120 days for pulmonary reasons, necrotizing enterocolitis stage II or higher or spontaneous gastrointestinal perforation, higher fraction of inspired oxygen at 36 weeks, and male gender. By classification and regression-tree analysis, infants with hospital stays of >120 days for pulmonary reasons had a 66% rehospitalization rate compared with 42% without such a stay. The scoring systems and classification and regression-tree analysis models identified infants at higher risk of rehospitalization and might assist planning for care after discharge.

Identification of Extremely Premature Infants at High Risk of Rehospitalization

PubMed Central

Carlo, Waldemar A.; McDonald, Scott A.; Yao, Qing; Das, Abhik; Higgins, Rosemary D.

2011-01-01

OBJECTIVE: Extremely low birth weight infants often require rehospitalization during infancy. Our objective was to identify at the time of discharge which extremely low birth weight infants are at higher risk for rehospitalization. METHODS: Data from extremely low birth weight infants in Eunice Kennedy Shriver National Institute of Child Health and Human Development Neonatal Research Network centers from 2002–2005 were analyzed. The primary outcome was rehospitalization by the 18- to 22-month follow-up, and secondary outcome was rehospitalization for respiratory causes in the first year. Using variables and odds ratios identified by stepwise logistic regression, scoring systems were developed with scores proportional to odds ratios. Classification and regression-tree analysis was performed by recursive partitioning and automatic selection of optimal cutoff points of variables. RESULTS: A total of 3787 infants were evaluated (mean ± SD birth weight: 787 ± 136 g; gestational age: 26 ± 2 weeks; 48% male, 42% black). Forty-five percent of the infants were rehospitalized by 18 to 22 months; 14.7% were rehospitalized for respiratory causes in the first year. Both regression models (area under the curve: 0.63) and classification and regression-tree models (mean misclassification rate: 40%–42%) were moderately accurate. Predictors for the primary outcome by regression were shunt surgery for hydrocephalus, hospital stay of >120 days for pulmonary reasons, necrotizing enterocolitis stage II or higher or spontaneous gastrointestinal perforation, higher fraction of inspired oxygen at 36 weeks, and male gender. By classification and regression-tree analysis, infants with hospital stays of >120 days for pulmonary reasons had a 66% rehospitalization rate compared with 42% without such a stay. CONCLUSIONS: The scoring systems and classification and regression-tree analysis models identified infants at higher risk of rehospitalization and might assist planning for care after discharge. PMID:22007016

Can Statistical Machine Learning Algorithms Help for Classification of Obstructive Sleep Apnea Severity to Optimal Utilization of Polysomnography Resources?

PubMed

Bozkurt, Selen; Bostanci, Asli; Turhan, Murat

2017-08-11

The goal of this study is to evaluate the results of machine learning methods for the classification of OSA severity of patients with suspected sleep disorder breathing as normal, mild, moderate and severe based on non-polysomnographic variables: 1) clinical data, 2) symptoms and 3) physical examination. In order to produce classification models for OSA severity, five different machine learning methods (Bayesian network, Decision Tree, Random Forest, Neural Networks and Logistic Regression) were trained while relevant variables and their relationships were derived empirically from observed data. Each model was trained and evaluated using 10-fold cross-validation and to evaluate classification performances of all methods, true positive rate (TPR), false positive rate (FPR), Positive Predictive Value (PPV), F measure and Area Under Receiver Operating Characteristics curve (ROC-AUC) were used. Results of 10-fold cross validated tests with different variable settings promisingly indicated that the OSA severity of suspected OSA patients can be classified, using non-polysomnographic features, with 0.71 true positive rate as the highest and, 0.15 false positive rate as the lowest, respectively. Moreover, the test results of different variables settings revealed that the accuracy of the classification models was significantly improved when physical examination variables were added to the model. Study results showed that machine learning methods can be used to estimate the probabilities of no, mild, moderate, and severe obstructive sleep apnea and such approaches may improve accurate initial OSA screening and help referring only the suspected moderate or severe OSA patients to sleep laboratories for the expensive tests.

Classification of stillbirths is an ongoing dilemma.

PubMed

Nappi, Luigi; Trezza, Federica; Bufo, Pantaleo; Riezzo, Irene; Turillazzi, Emanuela; Borghi, Chiara; Bonaccorsi, Gloria; Scutiero, Gennaro; Fineschi, Vittorio; Greco, Pantaleo

2016-10-01

To compare different classification systems in a cohort of stillbirths undergoing a comprehensive workup; to establish whether a particular classification system is most suitable and useful in determining cause of death, purporting the lowest percentage of unexplained death. Cases of stillbirth at gestational age 22-41 weeks occurring at the Department of Gynecology and Obstetrics of Foggia University during a 4 year period were collected. The World Health Organization (WHO) diagnosis of stillbirth was used. All the data collection was based on the recommendations of an Italian diagnostic workup for stillbirth. Two expert obstetricians reviewed all cases and classified causes according to five classification systems. Relevant Condition at Death (ReCoDe) and Causes Of Death and Associated Conditions (CODAC) classification systems performed best in retaining information. The ReCoDe system provided the lowest rate of unexplained stillbirth (14%) compared to de Galan-Roosen (16%), CODAC (16%), Tulip (18%), Wigglesworth (62%). Classification of stillbirth is influenced by the multiplicity of possible causes and factors related to fetal death. Fetal autopsy, placental histology and cytogenetic analysis are strongly recommended to have a complete diagnostic evaluation. Commonly employed classification systems performed differently in our experience, the most satisfactory being the ReCoDe. Given the rate of "unexplained" cases, none can be considered optimal and further efforts are necessary to work out a clinically useful system.

The addition of entropy-based regularity parameters improves sleep stage classification based on heart rate variability.

PubMed

Aktaruzzaman, M; Migliorini, M; Tenhunen, M; Himanen, S L; Bianchi, A M; Sassi, R

2015-05-01

The work considers automatic sleep stage classification, based on heart rate variability (HRV) analysis, with a focus on the distinction of wakefulness (WAKE) from sleep and rapid eye movement (REM) from non-REM (NREM) sleep. A set of 20 automatically annotated one-night polysomnographic recordings was considered, and artificial neural networks were selected for classification. For each inter-heartbeat (RR) series, beside features previously presented in literature, we introduced a set of four parameters related to signal regularity. RR series of three different lengths were considered (corresponding to 2, 6, and 10 successive epochs, 30 s each, in the same sleep stage). Two sets of only four features captured 99 % of the data variance in each classification problem, and both of them contained one of the new regularity features proposed. The accuracy of classification for REM versus NREM (68.4 %, 2 epochs; 83.8 %, 10 epochs) was higher than when distinguishing WAKE versus SLEEP (67.6 %, 2 epochs; 71.3 %, 10 epochs). Also, the reliability parameter (Cohens's Kappa) was higher (0.68 and 0.45, respectively). Sleep staging classification based on HRV was still less precise than other staging methods, employing a larger variety of signals collected during polysomnographic studies. However, cheap and unobtrusive HRV-only sleep classification proved sufficiently precise for a wide range of applications.

Adhesives: Test Method, Group Assignment, and Categorization Guide for High-Loading-Rate Applications Preparation and Testing of Single Lap Joints (Ver. 2.2, Unlimited)

DTIC Science & Technology

2016-04-01

Gerard Chaney, and Charles Pergantis Weapons and Materials Research Directorate, ARL Coatings, Corrosion, and Engineered Polymers Branch (CCEPB...SUBJECT TERMS single lap joint, adhesive, sample preparation, testing, database, metadata, material pedigree, ISO 16. SECURITY CLASSIFICATION OF: 17...temperature/water immersion conditioning test for lap-joint test specimens using the test tubes and convection oven method

A Classification and Analysis of Contracting Literature

DTIC Science & Technology

1989-12-01

Pricing Model ( CAPM . This is a model designed by investment analysts to determine required rates of return given the systematic risk of a company. The...For the amount of risk they take, these profit margins were not excessively high. The author examined profitability in terms of the Capital Asset ...taxonomy was applied was limited , the results were necessarily qualified. However, at the least this application provided areas for further research

Computer-aided detection and diagnosis of masses and clustered microcalcifications from digital mammograms

NASA Astrophysics Data System (ADS)

Nishikawa, Robert M.; Giger, Maryellen L.; Doi, Kunio; Vyborny, Carl J.; Schmidt, Robert A.; Metz, Charles E.; Wu, Chris Y.; Yin, Fang-Fang; Jiang, Yulei; Huo, Zhimin; Lu, Ping; Zhang, Wei; Ema, Takahiro; Bick, Ulrich; Papaioannou, John; Nagel, Rufus H.

1993-07-01

We are developing an 'intelligent' workstation to assist radiologists in diagnosing breast cancer from mammograms. The hardware for the workstation will consist of a film digitizer, a high speed computer, a large volume storage device, a film printer, and 4 high resolution CRT monitors. The software for the workstation is a comprehensive package of automated detection and classification schemes. Two rule-based detection schemes have been developed, one for breast masses and the other for clustered microcalcifications. The sensitivity of both schemes is 85% with a false-positive rate of approximately 3.0 and 1.5 false detections per image, for the mass and cluster detection schemes, respectively. Computerized classification is performed by an artificial neural network (ANN). The ANN has a sensitivity of 100% with a specificity of 60%. Currently, the ANN, which is a three-layer, feed-forward network, requires as input ratings of 14 different radiographic features of the mammogram that were determined subjectively by a radiologist. We are in the process of developing automated techniques to objectively determine these 14 features. The workstation will be placed in the clinical reading area of the radiology department in the near future, where controlled clinical tests will be performed to measure its efficacy.

[Therapy of hemorrhoidal disease].

PubMed

Herold, A

2006-08-01

Hemorrhoidal disease is one of the most frequent disorders in western countries. The aim of individual therapy is freedom from symptoms achieved by normalisation of anatomy and physiology. Treatment is orientated to the stage of disease: haemorrhoids 1 are treated conservatively. In addition to high-fibre diet, sclerotherapy is used. Haemorrhoids 2 prolapse during defecation and return spontaneously. First-line treatment is rubber band ligation. Haemorrhoids 3 that prolapse during defecation have to be digitally reduced, and the majority need surgery. For segmental disorders, haemorrhoidectomy according to Milligan-Morgan or Ferguson is recommended. In circular disease, Stapler hemorrhoidopexy is now the procedure of choice. Using a therapeutic regime according to the hemorrhoidal disease classification offers high healing rates and low rates of complications and recurrence.

[A systematic review of worldwide natural history models of colorectal cancer: classification, transition rate and a recommendation for developing Chinese population-specific model].

PubMed

Li, Z F; Huang, H Y; Shi, J F; Guo, C G; Zou, S M; Liu, C C; Wang, Y; Wang, L; Zhu, S L; Wu, S L; Dai, M

2017-02-10

Objective: To review the worldwide studies on natural history models among colorectal cancer (CRC), and to inform building a Chinese population-specific CRC model and developing a platform for further evaluation of CRC screening and other interventions in population in China. Methods: A structured literature search process was conducted in PubMed and the target publication dates were from January 1995 to December 2014. Information about classification systems on both colorectal cancer and precancer on corresponding transition rate, were extracted and summarized. Indicators were mainly expressed by the medians and ranges of annual progression or regression rate. Results: A total of 24 studies were extracted from 1 022 studies, most were from America ( n =9), but 2 from China including 1 from the mainland area, mainly based on Markov model ( n =22). Classification systems for adenomas included progression risk ( n =9) and the sizes of adenoma ( n =13, divided into two ways) as follows: 1) Based on studies where adenoma was risk-dependent, the median annual transition rates, from ' normal status' to ' non-advanced adenoma', 'non-advanced' to ' advanced' and ' advanced adenoma' to CRC were 0.016 0 (range: 0.002 2-0.020 0), 0.020 (range: 0.002-0.177) and 0.044 (range: 0.005-0.063), respectively. 2) Median annual transition rates, based on studies where adenoma were classified by sizes, into <10 mm and ≥10 mm ( n =7), from ' normal' to adenoma <10 mm, from adenoma <10 mm to adenoma ≥10 mm and adenoma ≥ 10 mm to CRC, were 0.016 7 (range: 0.015 0-0.037 0), 0.020 (range: 0.015-0.035) and 0.040 0 (range: 0.008 5-0.050 0), respectively. 3) Median annual transition rates, based on studies where adenoma, were classified by sizes into diminutive (≤5 mm), small (6-9 mm) and large adenoma (≥10 mm) ( n =6), from ' normal' to diminutive adenoma,'diminutive' to ' small','small' to ' large', and large adenoma to CRC were 0.013 (range: 0.009-0.019), 0.043 (range: 0.020-0.085), 0.044 (range: 0.020-0.125) and 0.033 5 (range: 0.030-0.040), respectively. Staging system of CRC mainly included LRD (localized/regional/distant, n =10), Dukes' ( n =7) and TNM ( n =3). When using the LRD classification, the median annual transition rates from ' localized' to ' regional' and ' regional' to 'distant' were 0.28 (range: 0.20-0.33) and 0.40 (range: 0.24-0.63), respectively. Under the Dukes' classification, the median annual transition rates appeared as 0.583 (range: 0.050-0.910), 0.656 (range: 0.280-0.720) and 0.830 (range: 0.630-0.865) from Dukes' A to B, B to C and C to Dukes' D, respectively. Again, when using the TNM classification, very limited transition rate was reported. Serrated pathway was only described in one study. Conclusions: Studies on the natural history model of colorectal cancer was still limited worldwide. Adenoma seemed the most common status setting for precancer model, and the risk-dependent classification for adenoma was consistent with the most commonly used system in clinical practice as well as major cancer screening programs in China. Since the staging systems of cancers varied, and shortage of transition rates based on TNM classification (commonly used in China), there will be a challenge for building Chinese population-specific natural history model of colorectal cancer, information from other classification systems could be conditionally applied.

Drug safety: Pregnancy rating classifications and controversies.

PubMed

Wilmer, Erin; Chai, Sandy; Kroumpouzos, George

2016-01-01

This contribution consolidates data on international pregnancy rating classifications, including the former US Food and Drug Administration (FDA), Swedish, and Australian classification systems, as well as the evidence-based medicine system, and discusses discrepancies among them. It reviews the new Pregnancy and Lactation Labeling Rule (PLLR) that replaced the former FDA labeling system with narrative-based labeling requirements. PLLR emphasizes on human data and highlights pregnancy exposure registry information. In this context, the review discusses important data on the safety of most medications used in the management of skin disease in pregnancy. There are also discussions of controversies relevant to the safety of certain dermatologic medications during gestation. Copyright © 2016 Elsevier Inc. All rights reserved.

[High complication rate after surgical treatment of ankle fractures].

PubMed

Bjørslev, Naja; Ebskov, Lars; Lind, Marianne; Mersø, Camilla

2014-08-04

The purpose of this study was to determine the quality and re-operation rate of the surgical treatment of ankle fractures at a large university hospital. X-rays and patient records of 137 patients surgically treated for ankle fractures were analyzed for: 1) correct classification according to Lauge-Hansen, 2) if congruity of the ankle joint was achieved, 3) selection and placement of the hardware, and 4) the surgeon's level of education. Totally 32 of 137 did not receive an optimal treatment, 11 were re-operated. There was no clear correlation between incorrect operation and the surgeon's level of education.

Classification Influence of Features on Given Emotions and Its Application in Feature Selection

NASA Astrophysics Data System (ADS)

Xing, Yin; Chen, Chuang; Liu, Li-Long

2018-04-01

In order to solve the problem that there is a large amount of redundant data in high-dimensional speech emotion features, we analyze deeply the extracted speech emotion features and select better features. Firstly, a given emotion is classified by each feature. Secondly, the recognition rate is ranked in descending order. Then, the optimal threshold of features is determined by rate criterion. Finally, the better features are obtained. When applied in Berlin and Chinese emotional data set, the experimental results show that the feature selection method outperforms the other traditional methods.

Improved Online Support Vector Machines Spam Filtering Using String Kernels

NASA Astrophysics Data System (ADS)

Amayri, Ola; Bouguila, Nizar

A major bottleneck in electronic communications is the enormous dissemination of spam emails. Developing of suitable filters that can adequately capture those emails and achieve high performance rate become a main concern. Support vector machines (SVMs) have made a large contribution to the development of spam email filtering. Based on SVMs, the crucial problems in email classification are feature mapping of input emails and the choice of the kernels. In this paper, we present thorough investigation of several distance-based kernels and propose the use of string kernels and prove its efficiency in blocking spam emails. We detail a feature mapping variants in text classification (TC) that yield improved performance for the standard SVMs in filtering task. Furthermore, to cope for realtime scenarios we propose an online active framework for spam filtering.

Kruskal-Wallis-based computationally efficient feature selection for face recognition.

PubMed

Ali Khan, Sajid; Hussain, Ayyaz; Basit, Abdul; Akram, Sheeraz

2014-01-01

Face recognition in today's technological world, and face recognition applications attain much more importance. Most of the existing work used frontal face images to classify face image. However these techniques fail when applied on real world face images. The proposed technique effectively extracts the prominent facial features. Most of the features are redundant and do not contribute to representing face. In order to eliminate those redundant features, computationally efficient algorithm is used to select the more discriminative face features. Extracted features are then passed to classification step. In the classification step, different classifiers are ensemble to enhance the recognition accuracy rate as single classifier is unable to achieve the high accuracy. Experiments are performed on standard face database images and results are compared with existing techniques.

Advanced eddy current test signal analysis for steam generator tube defect classification and characterization

NASA Astrophysics Data System (ADS)

McClanahan, James Patrick

Eddy Current Testing (ECT) is a Non-Destructive Examination (NDE) technique that is widely used in power generating plants (both nuclear and fossil) to test the integrity of heat exchanger (HX) and steam generator (SG) tubing. Specifically for this research, laboratory-generated, flawed tubing data were examined. The purpose of this dissertation is to develop and implement an automated method for the classification and an advanced characterization of defects in HX and SG tubing. These two improvements enhanced the robustness of characterization as compared to traditional bobbin-coil ECT data analysis methods. A more robust classification and characterization of the tube flaw in-situ (while the SG is on-line but not when the plant is operating), should provide valuable information to the power industry. The following are the conclusions reached from this research. A feature extraction program acquiring relevant information from both the mixed, absolute and differential data was successfully implemented. The CWT was utilized to extract more information from the mixed, complex differential data. Image Processing techniques used to extract the information contained in the generated CWT, classified the data with a high success rate. The data were accurately classified, utilizing the compressed feature vector and using a Bayes classification system. An estimation of the upper bound for the probability of error, using the Bhattacharyya distance, was successfully applied to the Bayesian classification. The classified data were separated according to flaw-type (classification) to enhance characterization. The characterization routine used dedicated, flaw-type specific ANNs that made the characterization of the tube flaw more robust. The inclusion of outliers may help complete the feature space so that classification accuracy is increased. Given that the eddy current test signals appear very similar, there may not be sufficient information to make an extremely accurate (>95%) classification or an advanced characterization using this system. It is necessary to have a larger database fore more accurate system learning.

High-resolution 3-T MRI of the triangular fibrocartilage complex in the wrist: injury pattern and MR features.

PubMed

Zhan, Huili; Zhang, Huibo; Bai, Rongjie; Qian, Zhanhua; Liu, Yue; Zhang, Heng; Yin, Yuming

2017-12-01

To investigate if using high-resolution 3-T MRI can identify additional injuries of the triangular fibrocartilage complex (TFCC) beyond the Palmer classification. Eighty-six patients with surgically proven TFCC injury were included in this study. All patients underwent high-resolution 3-T MRI of the injured wrist. The MR imaging features of TFCC were analyzed according to the Palmer classification. According to the Palmer classification, 69 patients could be classified as having Palmer injuries (52 had traumatic tears and 17 had degenerative tears). There were 17 patients whose injuries could not be classified according to the Palmer classification: 13 had volar or dorsal capsular TFC detachment and 4 had a horizontal tear of the articular disk. Using high-resolution 3-T MRI, we have not only found all the TFCC injuries described in the Palmer classification, additional injury types were found in this study, including horizontal tear of the TFC and capsular TFC detachment. We propose the modified Palmer classification and add the injury types that were not included in the original Palmer classification.

48 CFR 22.406-4 - Apprentices and trainees.

Code of Federal Regulations, 2011 CFR

2011-10-01

... contractor's employment and payment records of apprentices and trainees made available pursuant to the clause... reject the classification and require the contractor to pay the affected employees at the rates applicable to the classification of the work actually performed. ...

48 CFR 22.406-4 - Apprentices and trainees.

Code of Federal Regulations, 2010 CFR

2010-10-01

... contractor's employment and payment records of apprentices and trainees made available pursuant to the clause... reject the classification and require the contractor to pay the affected employees at the rates applicable to the classification of the work actually performed. ...

The effects of high-frequency oscillations in hippocampal electrical activities on the classification of epileptiform events using artificial neural networks

NASA Astrophysics Data System (ADS)

Chiu, Alan W. L.; Jahromi, Shokrollah S.; Khosravani, Houman; Carlen, Peter L.; Bardakjian, Berj L.

2006-03-01

The existence of hippocampal high-frequency electrical activities (greater than 100 Hz) during the progression of seizure episodes in both human and animal experimental models of epilepsy has been well documented (Bragin A, Engel J, Wilson C L, Fried I and Buzsáki G 1999 Hippocampus 9 137-42 Khosravani H, Pinnegar C R, Mitchell J R, Bardakjian B L, Federico P and Carlen P L 2005 Epilepsia 46 1-10). However, this information has not been studied between successive seizure episodes or utilized in the application of seizure classification. In this study, we examine the dynamical changes of an in vitro low Mg2+ rat hippocampal slice model of epilepsy at different frequency bands using wavelet transforms and artificial neural networks. By dividing the time-frequency spectrum of each seizure-like event (SLE) into frequency bins, we can analyze their burst-to-burst variations within individual SLEs as well as between successive SLE episodes. Wavelet energy and wavelet entropy are estimated for intracellular and extracellular electrical recordings using sufficiently high sampling rates (10 kHz). We demonstrate that the activities of high-frequency oscillations in the 100-400 Hz range increase as the slice approaches SLE onsets and in later episodes of SLEs. Utilizing the time-dependent relationship between different frequency bands, we can achieve frequency-dependent state classification. We demonstrate that activities in the frequency range 100-400 Hz are critical for the accurate classification of the different states of electrographic seizure-like episodes (containing interictal, preictal and ictal states) in brain slices undergoing recurrent spontaneous SLEs. While preictal activities can be classified with an average accuracy of 77.4 ± 6.7% utilizing the frequency spectrum in the range 0-400 Hz, we can also achieve a similar level of accuracy by using a nonlinear relationship between 100-400 Hz and <4 Hz frequency bands only.

Affective Computing and the Impact of Gender and Age

PubMed Central

Rukavina, Stefanie; Gruss, Sascha; Hoffmann, Holger; Tan, Jun-Wen; Walter, Steffen; Traue, Harald C.

2016-01-01

Affective computing aims at the detection of users’ mental states, in particular, emotions and dispositions during human-computer interactions. Detection can be achieved by measuring multimodal signals, namely, speech, facial expressions and/or psychobiology. Over the past years, one major approach was to identify the best features for each signal using different classification methods. Although this is of high priority, other subject-specific variables should not be neglected. In our study, we analyzed the effect of gender, age, personality and gender roles on the extracted psychobiological features (derived from skin conductance level, facial electromyography and heart rate variability) as well as the influence on the classification results. In an experimental human-computer interaction, five different affective states with picture material from the International Affective Picture System and ULM pictures were induced. A total of 127 subjects participated in the study. Among all potentially influencing variables (gender has been reported to be influential), age was the only variable that correlated significantly with psychobiological responses. In summary, the conducted classification processes resulted in 20% classification accuracy differences according to age and gender, especially when comparing the neutral condition with four other affective states. We suggest taking age and gender specifically into account for future studies in affective computing, as these may lead to an improvement of emotion recognition accuracy. PMID:26939129

Pollen Bearing Honey Bee Detection in Hive Entrance Video Recorded by Remote Embedded System for Pollination Monitoring

NASA Astrophysics Data System (ADS)

Babic, Z.; Pilipovic, R.; Risojevic, V.; Mirjanic, G.

2016-06-01

Honey bees have crucial role in pollination across the world. This paper presents a simple, non-invasive, system for pollen bearing honey bee detection in surveillance video obtained at the entrance of a hive. The proposed system can be used as a part of a more complex system for tracking and counting of honey bees with remote pollination monitoring as a final goal. The proposed method is executed in real time on embedded systems co-located with a hive. Background subtraction, color segmentation and morphology methods are used for segmentation of honey bees. Classification in two classes, pollen bearing honey bees and honey bees that do not have pollen load, is performed using nearest mean classifier, with a simple descriptor consisting of color variance and eccentricity features. On in-house data set we achieved correct classification rate of 88.7% with 50 training images per class. We show that the obtained classification results are not far behind from the results of state-of-the-art image classification methods. That favors the proposed method, particularly having in mind that real time video transmission to remote high performance computing workstation is still an issue, and transfer of obtained parameters of pollination process is much easier.

Classification of CT examinations for COPD visual severity analysis

NASA Astrophysics Data System (ADS)

Tan, Jun; Zheng, Bin; Wang, Xingwei; Pu, Jiantao; Gur, David; Sciurba, Frank C.; Leader, J. Ken

2012-03-01

In this study we present a computational method of CT examination classification into visual assessed emphysema severity. The visual severity categories ranged from 0 to 5 and were rated by an experienced radiologist. The six categories were none, trace, mild, moderate, severe and very severe. Lung segmentation was performed for every input image and all image features are extracted from the segmented lung only. We adopted a two-level feature representation method for the classification. Five gray level distribution statistics, six gray level co-occurrence matrix (GLCM), and eleven gray level run-length (GLRL) features were computed for each CT image depicted segment lung. Then we used wavelets decomposition to obtain the low- and high-frequency components of the input image, and again extract from the lung region six GLCM features and eleven GLRL features. Therefore our feature vector length is 56. The CT examinations were classified using the support vector machine (SVM) and k-nearest neighbors (KNN) and the traditional threshold (density mask) approach. The SVM classifier had the highest classification performance of all the methods with an overall sensitivity of 54.4% and a 69.6% sensitivity to discriminate "no" and "trace visually assessed emphysema. We believe this work may lead to an automated, objective method to categorically classify emphysema severity on CT exam.

Use of the color trails test as an embedded measure of performance validity.

PubMed

Henry, George K; Algina, James

2013-01-01

One hundred personal injury litigants and disability claimants referred for a forensic neuropsychological evaluation were administered both portions of the Color Trails Test (CTT) as part of a more comprehensive battery of standardized tests. Subjects who failed two or more free-standing tests of cognitive performance validity formed the Failed Performance Validity (FPV) group, while subjects who passed all free-standing performance validity measures were assigned to the Passed Performance Validity (PPV) group. A cutscore of ≥45 seconds to complete Color Trails 1 (CT1) was associated with a classification accuracy of 78%, good sensitivity (66%) and high specificity (90%), while a cutscore of ≥84 seconds to complete Color Trails 2 (CT2) was associated with a classification accuracy of 82%, good sensitivity (74%) and high specificity (90%). A CT1 cutscore of ≥58 seconds, and a CT2 cutscore ≥100 seconds was associated with 100% positive predictive power at base rates from 20 to 50%.

Differentiation of arterioles from venules in mouse histology images using machine learning

NASA Astrophysics Data System (ADS)

Elkerton, J. S.; Xu, Yiwen; Pickering, J. G.; Ward, Aaron D.

2016-03-01

Analysis and morphological comparison of arteriolar and venular networks are essential to our understanding of multiple diseases affecting every organ system. We have developed and evaluated the first fully automatic software system for differentiation of arterioles from venules on high-resolution digital histology images of the mouse hind limb immunostained for smooth muscle α-actin. Classifiers trained on texture and morphologic features by supervised machine learning provided excellent classification accuracy for differentiation of arterioles and venules, achieving an area under the receiver operating characteristic curve of 0.90 and balanced false-positive and false-negative rates. Feature selection was consistent across cross-validation iterations, and a small set of three features was required to achieve the reported performance, suggesting potential generalizability of the system. This system eliminates the need for laborious manual classification of the hundreds of microvessels occurring in a typical sample, and paves the way for high-throughput analysis the arteriolar and venular networks in the mouse.

The PLATINO study: description of the distribution, stability, and mortality according to the Global Initiative for Chronic Obstructive Lung Disease classification from 2007 to 2017.

PubMed

Menezes, Ana M; Wehrmeister, Fernando C; Perez-Padilla, Rogelio; Viana, Karynna P; Soares, Claudia; Müllerova, Hana; Valdivia, Gonzalo; Jardim, José R; Montes de Oca, Maria

2017-01-01

The Global Initiative for Chronic Obstructive Lung Disease (GOLD) report provides a framework for classifying COPD reflecting the impacts of disease on patients and for targeting treatment recommendations. The GOLD 2017 introduced a new classification with 16 subgroups based on a composite of spirometry and symptoms/exacerbations. Data from the population-based PLATINO study, collected at baseline and at follow-up, in three sites in Latin America were analyzed to compare the following: 1) the distribution of COPD patients according to GOLD 2007, 2013, and 2017; 2) the stability of the 2007 and 2013 classifications; and 3) the mortality rate over time stratified by GOLD 2007, 2013, and 2017. Of the 524 COPD patients evaluated, most of them were classified as Grade I or II (GOLD 2007) and Group A or B (GOLD 2013), with ≈70% of those classified as Group A in GOLD 2013 also classified as Grade I in GOLD 2007 and the highest percentage (41%) in Group D (2013) classified as Grade III (2007). According to GOLD 2017, among patients with Grade I airflow limitation, 69% of them were categorized into Group A, whereas Grade IV patients were more evenly distributed among Groups A-D. Most of the patients classified by GOLD 2007 remained in the same airflow limitation group at the follow-up; a greater temporal variability was observed with GOLD 2013 classification. Incidence-mortality rate in patients classified by GOLD 2007 was positively associated with increasing severity of airflow obstruction; for GOLD 2013 and GOLD 2017 (Groups A-D), highest mortality rates were observed in Groups C and D. No clear pattern was observed for mortality across the GOLD 2017 subgroups. The PLATINO study data suggest that GOLD 2007 classification shows more stability over time compared with GOLD 2013. No clear patterns with respect to the distribution of patients or incidence-mortality rates were observed according to GOLD 2013/2017 classification.

Learning to Classify with Possible Sensor Failures

DTIC Science & Technology

2014-05-04

SVMs), have demonstrated good classification performance when the training data is representative of the test data [1, 2, 3]. However, in many real...Detection of people and animals using non- imaging sensors,” Information Fusion (FUSION), 2011 Proceedings of the 14th International Conference on, pp...classification methods in terms of both classification accuracy and anomaly detection rate using 1. REPORT DATE (DD-MM-YYYY) 4. TITLE AND SUBTITLE 13

Reliability of a novel, semi-quantitative scale for classification of structural brain magnetic resonance imaging in children with cerebral palsy.

PubMed

Fiori, Simona; Cioni, Giovanni; Klingels, Katrjin; Ortibus, Els; Van Gestel, Leen; Rose, Stephen; Boyd, Roslyn N; Feys, Hilde; Guzzetta, Andrea

2014-09-01

To describe the development of a novel rating scale for classification of brain structural magnetic resonance imaging (MRI) in children with cerebral palsy (CP) and to assess its interrater and intrarater reliability. The scale consists of three sections. Section 1 contains descriptive information about the patient and MRI. Section 2 contains the graphical template of brain hemispheres onto which the lesion is transposed. Section 3 contains the scoring system for the quantitative analysis of the lesion characteristics, grouped into different global scores and subscores that assess separately side, regions, and depth. A larger interrater and intrarater reliability study was performed in 34 children with CP (22 males, 12 females; mean age at scan of 9 y 5 mo [SD 3 y 3 mo], range 4 y-16 y 11 mo; Gross Motor Function Classification System level I, [n=22], II [n=10], and level III [n=2]). Very high interrater and intrarater reliability of the total score was found with indices above 0.87. Reliability coefficients of the lobar and hemispheric subscores ranged between 0.53 and 0.95. Global scores for hemispheres, basal ganglia, brain stem, and corpus callosum showed reliability coefficients above 0.65. This study presents the first visual, semi-quantitative scale for classification of brain structural MRI in children with CP. The high degree of reliability of the scale supports its potential application for investigating the relationship between brain structure and function and examining treatment response according to brain lesion severity in children with CP. © 2014 Mac Keith Press.

Classifying seismic waveforms from scratch: a case study in the alpine environment

NASA Astrophysics Data System (ADS)

Hammer, C.; Ohrnberger, M.; Fäh, D.

2013-01-01

Nowadays, an increasing amount of seismic data is collected by daily observatory routines. The basic step for successfully analyzing those data is the correct detection of various event types. However, the visually scanning process is a time-consuming task. Applying standard techniques for detection like the STA/LTA trigger still requires the manual control for classification. Here, we present a useful alternative. The incoming data stream is scanned automatically for events of interest. A stochastic classifier, called hidden Markov model, is learned for each class of interest enabling the recognition of highly variable waveforms. In contrast to other automatic techniques as neural networks or support vector machines the algorithm allows to start the classification from scratch as soon as interesting events are identified. Neither the tedious process of collecting training samples nor a time-consuming configuration of the classifier is required. An approach originally introduced for the volcanic task force action allows to learn classifier properties from a single waveform example and some hours of background recording. Besides a reduction of required workload this also enables to detect very rare events. Especially the latter feature provides a milestone point for the use of seismic devices in alpine warning systems. Furthermore, the system offers the opportunity to flag new signal classes that have not been defined before. We demonstrate the application of the classification system using a data set from the Swiss Seismological Survey achieving very high recognition rates. In detail we document all refinements of the classifier providing a step-by-step guide for the fast set up of a well-working classification system.

An AdaBoost Based Approach to Automatic Classification and Detection of Buildings Footprints, Vegetation Areas and Roads from Satellite Images

NASA Astrophysics Data System (ADS)

Gonulalan, Cansu

In recent years, there has been an increasing demand for applications to monitor the targets related to land-use, using remote sensing images. Advances in remote sensing satellites give rise to the research in this area. Many applications ranging from urban growth planning to homeland security have already used the algorithms for automated object recognition from remote sensing imagery. However, they have still problems such as low accuracy on detection of targets, specific algorithms for a specific area etc. In this thesis, we focus on an automatic approach to classify and detect building foot-prints, road networks and vegetation areas. The automatic interpretation of visual data is a comprehensive task in computer vision field. The machine learning approaches improve the capability of classification in an intelligent way. We propose a method, which has high accuracy on detection and classification. The multi class classification is developed for detecting multiple objects. We present an AdaBoost-based approach along with the supervised learning algorithm. The combi- nation of AdaBoost with "Attentional Cascade" is adopted from Viola and Jones [1]. This combination decreases the computation time and gives opportunity to real time applications. For the feature extraction step, our contribution is to combine Haar-like features that include corner, rectangle and Gabor. Among all features, AdaBoost selects only critical features and generates in extremely efficient cascade structured classifier. Finally, we present and evaluate our experimental results. The overall system is tested and high performance of detection is achieved. The precision rate of the final multi-class classifier is over 98%.

Study for urbanization corresponding to socio-economic activities in Savannaket, Laos using satellite remote sensing

NASA Astrophysics Data System (ADS)

Kimijiama, S.; Nagai, M.

2014-06-01

In Greater Mekong Sub-region (GMS), economic liberalization and deregulation facilitated by GMS Regional Economic Corporation Program (GMS-ECP) has triggered urbanization in the region. However, the urbanization rate and its linkage to socio-economic activities are ambiguous. The objectives of this paper are to: (a) determine the changes in urban area from 1972 to 2013 using remote sensing data, and (b) analyse the relationships between urbanization with respect to socio-economic activities in central Laos. The study employed supervised classification and human visible interpretation to determine changes in urbanization rate. Regression analysis was used to analyze the correlation between the urbanization rate and socio-economic variables. The result shows that the urban area increased significantly from 1972 to 2013. The socio-economic variables such as school enrollment, labour force, mortality rate, water source and sanitation highly correlated with the rate of urbanization during the period. The study concluded that identifying the highly correlated socio-economic variables with urbanization rate could enable us to conduct a further urbanization simulation. The simulation helps in designing policies for sustainable development.

Classification of speech and language profiles in 4-year old children with cerebral palsy: A prospective preliminary study

PubMed Central

Hustad, Katherine C.; Gorton, Kristin; Lee, Jimin

2010-01-01

Purpose Little is known about the speech and language abilities of children with cerebral palsy (CP) and there is currently no system for classifying speech and language profiles. Such a system would have epidemiological value and would have the potential to advance the development of interventions that improve outcomes. In this study, we propose and test a preliminary speech and language classification system by quantifying how well speech and language data differentiate among children classified into different hypothesized profile groups. Method Speech and language assessment data were collected in a laboratory setting from 34 children with CP (18 males; 16 females) who were a mean age of 54 months (SD 1.8 months). Measures of interest were vowel area, speech rate, language comprehension scores, and speech intelligibility ratings. Results Canonical discriminant function analysis showed that three functions accounted for 100% of the variance among profile groups, with speech variables accounting for 93% of the variance. Classification agreement varied from 74% to 97% using four different classification paradigms. Conclusions Results provide preliminary support for the classification of speech and language abilities of children with CP into four initial profile groups. Further research is necessary to validate the full classification system. PMID:20643795

Gene masking - a technique to improve accuracy for cancer classification with high dimensionality in microarray data.

PubMed

Saini, Harsh; Lal, Sunil Pranit; Naidu, Vimal Vikash; Pickering, Vincel Wince; Singh, Gurmeet; Tsunoda, Tatsuhiko; Sharma, Alok

2016-12-05

High dimensional feature space generally degrades classification in several applications. In this paper, we propose a strategy called gene masking, in which non-contributing dimensions are heuristically removed from the data to improve classification accuracy. Gene masking is implemented via a binary encoded genetic algorithm that can be integrated seamlessly with classifiers during the training phase of classification to perform feature selection. It can also be used to discriminate between features that contribute most to the classification, thereby, allowing researchers to isolate features that may have special significance. This technique was applied on publicly available datasets whereby it substantially reduced the number of features used for classification while maintaining high accuracies. The proposed technique can be extremely useful in feature selection as it heuristically removes non-contributing features to improve the performance of classifiers.

Time-frequency feature representation using multi-resolution texture analysis and acoustic activity detector for real-life speech emotion recognition.

PubMed

Wang, Kun-Ching

2015-01-14

The classification of emotional speech is mostly considered in speech-related research on human-computer interaction (HCI). In this paper, the purpose is to present a novel feature extraction based on multi-resolutions texture image information (MRTII). The MRTII feature set is derived from multi-resolution texture analysis for characterization and classification of different emotions in a speech signal. The motivation is that we have to consider emotions have different intensity values in different frequency bands. In terms of human visual perceptual, the texture property on multi-resolution of emotional speech spectrogram should be a good feature set for emotion classification in speech. Furthermore, the multi-resolution analysis on texture can give a clearer discrimination between each emotion than uniform-resolution analysis on texture. In order to provide high accuracy of emotional discrimination especially in real-life, an acoustic activity detection (AAD) algorithm must be applied into the MRTII-based feature extraction. Considering the presence of many blended emotions in real life, in this paper make use of two corpora of naturally-occurring dialogs recorded in real-life call centers. Compared with the traditional Mel-scale Frequency Cepstral Coefficients (MFCC) and the state-of-the-art features, the MRTII features also can improve the correct classification rates of proposed systems among different language databases. Experimental results show that the proposed MRTII-based feature information inspired by human visual perception of the spectrogram image can provide significant classification for real-life emotional recognition in speech.

Superiority of artificial neural networks for a genetic classification procedure.

PubMed

Sant'Anna, I C; Tomaz, R S; Silva, G N; Nascimento, M; Bhering, L L; Cruz, C D

2015-08-19

The correct classification of individuals is extremely important for the preservation of genetic variability and for maximization of yield in breeding programs using phenotypic traits and genetic markers. The Fisher and Anderson discriminant functions are commonly used multivariate statistical techniques for these situations, which allow for the allocation of an initially unknown individual to predefined groups. However, for higher levels of similarity, such as those found in backcrossed populations, these methods have proven to be inefficient. Recently, much research has been devoted to developing a new paradigm of computing known as artificial neural networks (ANNs), which can be used to solve many statistical problems, including classification problems. The aim of this study was to evaluate the feasibility of ANNs as an evaluation technique of genetic diversity by comparing their performance with that of traditional methods. The discriminant functions were equally ineffective in discriminating the populations, with error rates of 23-82%, thereby preventing the correct discrimination of individuals between populations. The ANN was effective in classifying populations with low and high differentiation, such as those derived from a genetic design established from backcrosses, even in cases of low differentiation of the data sets. The ANN appears to be a promising technique to solve classification problems, since the number of individuals classified incorrectly by the ANN was always lower than that of the discriminant functions. We envisage the potential relevant application of this improved procedure in the genomic classification of markers to distinguish between breeds and accessions.

An evaluation of unsupervised and supervised learning algorithms for clustering landscape types in the United States

USGS Publications Warehouse

Wendel, Jochen; Buttenfield, Barbara P.; Stanislawski, Larry V.

2016-01-01

Knowledge of landscape type can inform cartographic generalization of hydrographic features, because landscape characteristics provide an important geographic context that affects variation in channel geometry, flow pattern, and network configuration. Landscape types are characterized by expansive spatial gradients, lacking abrupt changes between adjacent classes; and as having a limited number of outliers that might confound classification. The US Geological Survey (USGS) is exploring methods to automate generalization of features in the National Hydrography Data set (NHD), to associate specific sequences of processing operations and parameters with specific landscape characteristics, thus obviating manual selection of a unique processing strategy for every NHD watershed unit. A chronology of methods to delineate physiographic regions for the United States is described, including a recent maximum likelihood classification based on seven input variables. This research compares unsupervised and supervised algorithms applied to these seven input variables, to evaluate and possibly refine the recent classification. Evaluation metrics for unsupervised methods include the Davies–Bouldin index, the Silhouette index, and the Dunn index as well as quantization and topographic error metrics. Cross validation and misclassification rate analysis are used to evaluate supervised classification methods. The paper reports the comparative analysis and its impact on the selection of landscape regions. The compared solutions show problems in areas of high landscape diversity. There is some indication that additional input variables, additional classes, or more sophisticated methods can refine the existing classification.

Benefits of Red-Edge Spectral Band and Texture Features for the Object-based Classification using RapidEye sSatellite Image data

NASA Astrophysics Data System (ADS)

Kim, H. O.; Yeom, J. M.

2014-12-01

Space-based remote sensing in agriculture is particularly relevant to issues such as global climate change, food security, and precision agriculture. Recent satellite missions have opened up new perspectives by offering high spatial resolution, various spectral properties, and fast revisit rates to the same regions. Here, we examine the utility of broadband red-edge spectral information in multispectral satellite image data for classifying paddy rice crops in South Korea. Additionally, we examine how object-based spectral features affect the classification of paddy rice growth stages. For the analysis, two seasons of RapidEye satellite image data were used. The results showed that the broadband red-edge information slightly improved the classification accuracy of the crop condition in heterogeneous paddy rice crop environments, particularly when single-season image data were used. This positive effect appeared to be offset by the multi-temporal image data. Additional texture information brought only a minor improvement or a slight decline, although it is well known to be advantageous for object-based classification in general. We conclude that broadband red-edge information derived from conventional multispectral satellite data has the potential to improve space-based crop monitoring. Because the positive or negative effects of texture features for object-based crop classification could barely be interpreted, the relationships between the textual properties and paddy rice crop parameters at the field scale should be further examined in depth.

Multi-class biological tissue classification based on a multi-classifier: Preliminary study of an automatic output power control for ultrasonic surgical units.

PubMed

Youn, Su Hyun; Sim, Taeyong; Choi, Ahnryul; Song, Jinsung; Shin, Ki Young; Lee, Il Kwon; Heo, Hyun Mu; Lee, Daeweon; Mun, Joung Hwan

2015-06-01

Ultrasonic surgical units (USUs) have the advantage of minimizing tissue damage during surgeries that require tissue dissection by reducing problems such as coagulation and unwanted carbonization, but the disadvantage of requiring manual adjustment of power output according to the target tissue. In order to overcome this limitation, it is necessary to determine the properties of in vivo tissues automatically. We propose a multi-classifier that can accurately classify tissues based on the unique impedance of each tissue. For this purpose, a multi-classifier was built based on single classifiers with high classification rates, and the classification accuracy of the proposed model was compared with that of single classifiers for various electrode types (Type-I: 6 mm invasive; Type-II: 3 mm invasive; Type-III: surface). The sensitivity and positive predictive value (PPV) of the multi-classifier by cross checks were determined. According to the 10-fold cross validation results, the classification accuracy of the proposed model was significantly higher (p<0.05 or <0.01) than that of existing single classifiers for all electrode types. In particular, the classification accuracy of the proposed model was highest when the 3mm invasive electrode (Type-II) was used (sensitivity=97.33-100.00%; PPV=96.71-100.00%). The results of this study are an important contribution to achieving automatic optimal output power adjustment of USUs according to the properties of individual tissues. Copyright © 2015 Elsevier Ltd. All rights reserved.

Compensation for Asbestos-Related Diseases in Japan: Utilization of Standard Classifications of Industry and Occupations

PubMed

Sawanyawisuth, Kittisak; Furuya, Sugio; Park, Eun-Kee; Myong, Jun-Pyo; Ramos-Bonilla, Juan Pablo; Chimed Ochir, Odgerel; Takahashi, Ken

2017-07-27

Background: Asbestos-related diseases (ARD) are occupational hazards with high mortality rates. To identify asbestos exposure by previous occupation is the main issue for ARD compensation for workers. This study aimed to identify risk groups by applying standard classifications of industries and occupations to a national database of compensated ARD victims in Japan. Methods: We identified occupations that carry a risk of asbestos exposure according to the International Standard Industrial Classification of All Economic Activities (ISIC). ARD compensation data from Japan between 2006 and 2013 were retrieved. Each compensated worker was classified by job section and group according to the ISIC code. Risk ratios for compensation were calculated according to the percentage of workers compensated because of ARD in each ISIC category. Results: In total, there were 6,916 workers with ARD who received compensation in Japan between 2008 and 2013. ISIC classification section F (construction) had the highest compensated risk ratio of 6.3. Section C (manufacturing) and section F (construction) had the largest number of compensated workers (2,868 and 3,463, respectively). In the manufacturing section C, 9 out of 13 divisions had a risk ratio of more than 1. For ISIC divisions in the construction section, construction of buildings (division 41) had the highest number of workers registering claims (2,504). Conclusion: ISIC classification of occupations that are at risk of developing ARD can be used to identify the actual risk of workers’ compensation at the national level. Creative Commons Attribution License

Patterns of recurrence and outcomes in surgically treated women with endometrial cancer according to ESMO-ESGO-ESTRO Consensus Conference risk groups: Results from the FRANCOGYN study Group.

PubMed

Bendifallah, Sofiane; Ouldamer, Lobna; Lavoue, Vincent; Canlorbe, Geoffroy; Raimond, Emilie; Coutant, Charles; Graesslin, Olivier; Touboul, Cyril; Collinet, Pierre; Daraï, Emile; Ballester, Marcos

2017-01-01

The purpose of this study was to analyse the endometrial cancer (EC) patterns of recurrence based on a large French multicentre database according to ESMO-ESGO-ESTRO classification. Data of women with histologically proven EC who received primary surgical treatment between January 2001 and December 2012 were retrospectively abstracted from seven institutions with prospectively maintained databases. The endpoints were recurrence, recurrence free survival (RFS) and overall survival (OS). Time to the first EC recurrence in a specific site was evaluated by using cumulative incidence analysis (Gray's test). Data from 829 women were analysed in whom recurrences were observed in 176 (21%) with a median and mean time to recurrence of 13 and 19.5months, respectively. High (35%) and high-intermediate risk groups (16%) were associated with higher recurrence rates compared with low (9%) and intermediate (9%) risk patients (p<0.0001). Women with high risk EC had a higher 5-year cumulative incidence of distant recurrence (20.7%) than women with high-intermediate, intermediate and low risk EC (5.6%, 3.5%, 3.3%), (p<0.001), respectively. Women with high risk and high-intermediate risk EC had a higher 5-year cumulative incidence of loco-regional recurrence (24.3% and 16.6%, respectively) than women with intermediate and low risk EC (6.6% and 6.5%, respectively), (p<0.001). We report specific time and site patterns of first recurrence according to the ESMO/ESGO/ESTRO classification. Sites and hazard rates for recurrence differ widely between subgroups over time. Defining patterns of EC recurrence may provide useful information for developing follow-up recommendations and designing therapeutic approaches. Copyright © 2016 Elsevier Inc. All rights reserved.

Noninvasive prenatal detection of sex chromosomal aneuploidies by sequencing circulating cell-free DNA from maternal plasma.

PubMed

Mazloom, Amin R; Džakula, Željko; Oeth, Paul; Wang, Huiquan; Jensen, Taylor; Tynan, John; McCullough, Ron; Saldivar, Juan-Sebastian; Ehrich, Mathias; van den Boom, Dirk; Bombard, Allan T; Maeder, Margo; McLennan, Graham; Meschino, Wendy; Palomaki, Glenn E; Canick, Jacob A; Deciu, Cosmin

2013-06-01

Whole-genome sequencing of circulating cell free (ccf) DNA from maternal plasma has enabled noninvasive prenatal testing for common autosomal aneuploidies. The purpose of this study was to extend the detection to include common sex chromosome aneuploidies (SCAs): [47,XXX], [45,X], [47,XXY], and [47,XYY] syndromes. Massively parallel sequencing was performed on ccf DNA isolated from the plasma of 1564 pregnant women with known fetal karyotype. A classification algorithm for SCA detection was constructed and trained on this cohort. Another study of 411 maternal samples from women with blinded-to-laboratory fetal karyotypes was then performed to determine the accuracy of the classification algorithm. In the training cohort, the new algorithm had a detection rate (DR) of 100% (95%CI: 82.3%, 100%), a false positive rate (FPR) of 0.1% (95%CI: 0%, 0.3%), and nonreportable rate of 6% (95%CI: 4.9%, 7.4%) for SCA determination. The blinded validation yielded similar results: DR of 96.2% (95%CI: 78.4%, 99.8%), FPR of 0.3% (95%CI: 0%, 1.8%), and nonreportable rate of 5% (95%CI: 3.2%, 7.7%) for SCA determination Noninvasive prenatal identification of the most common sex chromosome aneuploidies is possible using ccf DNA and massively parallel sequencing with a high DR and a low FPR. © 2013 John Wiley & Sons, Ltd.

A Generic Deep-Learning-Based Approach for Automated Surface Inspection.

PubMed

Ren, Ruoxu; Hung, Terence; Tan, Kay Chen

2018-03-01

Automated surface inspection (ASI) is a challenging task in industry, as collecting training dataset is usually costly and related methods are highly dataset-dependent. In this paper, a generic approach that requires small training data for ASI is proposed. First, this approach builds classifier on the features of image patches, where the features are transferred from a pretrained deep learning network. Next, pixel-wise prediction is obtained by convolving the trained classifier over input image. An experiment on three public and one industrial data set is carried out. The experiment involves two tasks: 1) image classification and 2) defect segmentation. The results of proposed algorithm are compared against several best benchmarks in literature. In the classification tasks, the proposed method improves accuracy by 0.66%-25.50%. In the segmentation tasks, the proposed method reduces error escape rates by 6.00%-19.00% in three defect types and improves accuracies by 2.29%-9.86% in all seven defect types. In addition, the proposed method achieves 0.0% error escape rate in the segmentation task of industrial data.

Reliability of the Walker Cranial Nonmetric Method and Implications for Sex Estimation.

PubMed

Lewis, Cheyenne J; Garvin, Heather M

2016-05-01

The cranial trait scoring method presented in Buikstra and Ubelaker (Standards for data collection from human skeletal remains. Fayetteville, AR: Arkansas Archeological Survey Research Series No. 44, 1994) and Walker (Am J Phys Anthropol, 136, 2008 and 39) is the most common nonmetric cranial sex estimation method utilized by physical and forensic anthropologists. As such, the reliability and accuracy of the method is vital to ensure its validity in forensic applications. In this study, inter- and intra-observer error rates for the Walker scoring method were calculated using a sample of U.S. White and Black individuals (n = 135). Cohen's weighted kappas, intraclass correlation coefficients, and percentage agreements indicate good agreement between trials and observers for all traits except the mental eminence. Slight disagreement in scoring, however, was found to impact sex classifications, leading to lower accuracy rates than those published by Walker. Furthermore, experience does appear to impact trait scoring and sex classification. The use of revised population-specific equations that avoid the mental eminence is highly recommended to minimize the potential for misclassifications. © 2016 American Academy of Forensic Sciences.

[Relations between location of elements in periodic table and affinity for the kidneys (author's transl)].

PubMed

Ando, A; Hisada, K; Ando, I

1977-10-01

The distribution of many inorganic compounds in rats was investigated in order to evaluate kidney affinity of inorganic compounds. In these experiments, 30%, 10-20% and 4-10% of administered dose was localized in the kidneys in 203Hg-acetate and 203 Bi-acetate, in H198AuCl4, 103PdCl2, 201TlCl, 210Pd(NO3)2 and H2(127M)TeO3, and in Na2(51)CrO4, 54MnCl2, (114m)InCl3 and 7BeCl2, respectively. Some bipositive ions and anions was hardly taken up into the kidneys. And in many hard acids according to classification of Lewis acids, the uptake rate into the kidneys was usually small. On the other hand, Hg, Au and Bi, which have strong binding power to the protein, showed high uptake rate in the kidneys. As Hg++, Au+ and Bi+++ was soft acids according to classification of Lewis acids, it was thought that these elements would bind strongly to soft base (RSH, RS-) present in the kidney.

Assessment of various supervised learning algorithms using different performance metrics

NASA Astrophysics Data System (ADS)

Susheel Kumar, S. M.; Laxkar, Deepak; Adhikari, Sourav; Vijayarajan, V.

2017-11-01

Our work brings out comparison based on the performance of supervised machine learning algorithms on a binary classification task. The supervised machine learning algorithms which are taken into consideration in the following work are namely Support Vector Machine(SVM), Decision Tree(DT), K Nearest Neighbour (KNN), Naïve Bayes(NB) and Random Forest(RF). This paper mostly focuses on comparing the performance of above mentioned algorithms on one binary classification task by analysing the Metrics such as Accuracy, F-Measure, G-Measure, Precision, Misclassification Rate, False Positive Rate, True Positive Rate, Specificity, Prevalence.

Cardiovascular, electrodermal, and respiratory response patterns to fear- and sadness-inducing films.

PubMed

Kreibig, Sylvia D; Wilhelm, Frank H; Roth, Walton T; Gross, James J

2007-09-01

Responses to fear- and sadness-inducing films were assessed using a broad range of cardiovascular (heart rate, T-wave amplitude, low- and high-frequency heart rate variability, stroke volume, preejection period, left-ventricular ejection time, Heather index, blood pressure, pulse amplitude and transit time, and finger temperature), electrodermal (level, response rate, and response amplitude), and respiratory (rate, tidal volume and its variability, inspiratory flow rate, duty cycle, and end-tidal pCO(2)) measures. Subjective emotional experience and facial behavior (Corrugator Supercilii and Zygomaticus Major EMG) served as control measures. Results indicated robust differential physiological response patterns for fear, sadness, and neutral (mean classification accuracy 85%). Findings are discussed in terms of the fight-flight and conservation-withdrawal responses and possible limitations of a valence-arousal categorization of emotion in affective space.

Perinatal mortality classification: an analysis of 112 cases of stillbirth.

PubMed

Reis, Ana Paula; Rocha, Ana; Lebre, Andrea; Ramos, Umbelina; Cunha, Ana

2017-10-01

This was a retrospective cohort analysis of stillbirths that occurred from January 2004 to December 2013 in our institution. We compared Tulip and Wigglesworth classification systems on a cohort of stillbirths and analysed the main differences between these two classifications. In this period, there were 112 stillbirths of a total of 31,758 births (stillbirth rate of 3.5 per 1000 births). There were 99 antepartum deaths and 13 intrapartum deaths. Foetal autopsy was performed in 99 cases and placental histopathological examination in all of the cases. The Wigglesworth found 'unknown' causes in 47 cases and the Tulip classification allocated 33 of these. Fourteen cases remained in the group of 'unknown' causes. Therefore, the Wigglesworth classification of stillbirths results in a higher proportion of unexplained stillbirths. We suggest that the traditional Wigglesworth classification should be substituted by a classification that manages the available information.

Model-Based Building Detection from Low-Cost Optical Sensors Onboard Unmanned Aerial Vehicles

NASA Astrophysics Data System (ADS)

Karantzalos, K.; Koutsourakis, P.; Kalisperakis, I.; Grammatikopoulos, L.

2015-08-01

The automated and cost-effective building detection in ultra high spatial resolution is of major importance for various engineering and smart city applications. To this end, in this paper, a model-based building detection technique has been developed able to extract and reconstruct buildings from UAV aerial imagery and low-cost imaging sensors. In particular, the developed approach through advanced structure from motion, bundle adjustment and dense image matching computes a DSM and a true orthomosaic from the numerous GoPro images which are characterised by important geometric distortions and fish-eye effect. An unsupervised multi-region, graphcut segmentation and a rule-based classification is responsible for delivering the initial multi-class classification map. The DTM is then calculated based on inpaininting and mathematical morphology process. A data fusion process between the detected building from the DSM/DTM and the classification map feeds a grammar-based building reconstruction and scene building are extracted and reconstructed. Preliminary experimental results appear quite promising with the quantitative evaluation indicating detection rates at object level of 88% regarding the correctness and above 75% regarding the detection completeness.

Selecting a Classification Ensemble and Detecting Process Drift in an Evolving Data Stream

DOE Office of Scientific and Technical Information (OSTI.GOV)

Heredia-Langner, Alejandro; Rodriguez, Luke R.; Lin, Andy

2015-09-30

We characterize the commercial behavior of a group of companies in a common line of business using a small ensemble of classifiers on a stream of records containing commercial activity information. This approach is able to effectively find a subset of classifiers that can be used to predict company labels with reasonable accuracy. Performance of the ensemble, its error rate under stable conditions, can be characterized using an exponentially weighted moving average (EWMA) statistic. The behavior of the EWMA statistic can be used to monitor a record stream from the commercial network and determine when significant changes have occurred. Resultsmore » indicate that larger classification ensembles may not necessarily be optimal, pointing to the need to search the combinatorial classifier space in a systematic way. Results also show that current and past performance of an ensemble can be used to detect when statistically significant changes in the activity of the network have occurred. The dataset used in this work contains tens of thousands of high level commercial activity records with continuous and categorical variables and hundreds of labels, making classification challenging.« less

Recognition of hand movements in a trans-radial amputated subject by sEMG.

PubMed

Atzori, Manfredo; Muller, Henning; Baechler, Micheal

2013-06-01

Trans-radially amputated persons who own a myoelectric prosthesis have currently some control via surface electromyography (sEMG). However, the control systems are still limited (as they include very few movements) and not always natural (as the subject has to learn to associate movements of the muscles with the movements of the prosthesis). The Ninapro project tries helping the scientific community to overcome these limits through the creation of electromyography data sources to test machine learning algorithms. In this paper the results gained from first tests made on an amputated subject with the Ninapro acquisition protocol are detailed. In agreement with neurological studies on cortical plasticity and on the anatomy of the forearm, the amputee produced stable signals for each movement in the test. Using a k-NN classification algorithm, we obtain an average classification rate of 61.5% on all 53 movements. Successively, we simplify the task reducing the number of movements to 13, resulting in no misclassified movements. This shows that for fewer movements a very high classification accuracy is possible without the subject having to learn the movements specifically.

Application of a Novel S3 Nanowire Gas Sensor Device in Parallel with GC-MS for the Identification of Rind Percentage of Grated Parmigiano Reggiano.

PubMed

Abbatangelo, Marco; Núñez-Carmona, Estefanía; Sberveglieri, Veronica; Zappa, Dario; Comini, Elisabetta; Sberveglieri, Giorgio

2018-05-18

Parmigiano Reggiano cheese is one of the most appreciated and consumed foods worldwide, especially in Italy, for its high content of nutrients and taste. However, these characteristics make this product subject to counterfeiting in different forms. In this study, a novel method based on an electronic nose has been developed to investigate the potentiality of this tool to distinguish rind percentages in grated Parmigiano Reggiano packages that should be lower than 18%. Different samples, in terms of percentage, seasoning and rind working process, were considered to tackle the problem at 360°. In parallel, GC-MS technique was used to give a name to the compounds that characterize Parmigiano and to relate them to sensors responses. Data analysis consisted of two stages: Multivariate analysis (PLS) and classification made in a hierarchical way with PLS-DA ad ANNs. Results were promising, in terms of correct classification of the samples. The correct classification rate (%) was higher for ANNs than PLS-DA, with correct identification approaching 100 percent.

Creating high-resolution time series land-cover classifications in rapidly changing forested areas with BULC-U in Google Earth Engine

NASA Astrophysics Data System (ADS)

Cardille, J. A.; Lee, J.

2017-12-01

With the opening of the Landsat archive, there is a dramatically increased potential for creating high-quality time series of land use/land-cover (LULC) classifications derived from remote sensing. Although LULC time series are appealing, their creation is typically challenging in two fundamental ways. First, there is a need to create maximally correct LULC maps for consideration at each time step; and second, there is a need to have the elements of the time series be consistent with each other, without pixels that flip improbably between covers due only to unavoidable, stray classification errors. We have developed the Bayesian Updating of Land Cover - Unsupervised (BULC-U) algorithm to address these challenges simultaneously, and introduce and apply it here for two related but distinct purposes. First, with minimal human intervention, we produced an internally consistent, high-accuracy LULC time series in rapidly changing Mato Grosso, Brazil for a time interval (1986-2000) in which cropland area more than doubled. The spatial and temporal resolution of the 59 LULC snapshots allows users to witness the establishment of towns and farms at the expense of forest. The new time series could be used by policy-makers and analysts to unravel important considerations for conservation and management, including the timing and location of past development, the rate and nature of changes in forest connectivity, the connection with road infrastructure, and more. The second application of BULC-U is to sharpen the well-known GlobCover 2009 classification from 300m to 30m, while improving accuracy measures for every class. The greatly improved resolution and accuracy permits a better representation of the true LULC proportions, the use of this map in models, and quantification of the potential impacts of changes. Given that there may easily be thousands and potentially millions of images available to harvest for an LULC time series, it is imperative to build useful algorithms requiring minimal human intervention. Through image segmentation and classification, BULC-U allows us to use both the spectral and spatial characteristics of imagery to sharpen classifications and create time series. It is hoped that this study may allow us and other users of this new method to consider time series across ever larger areas.

Morphological classification of odontogenic keratocysts using Bouligand-Minkowski fractal descriptors.

PubMed

Florindo, Joao B; Bruno, Odemir M; Landini, Gabriel

2017-02-01

The Odontogenic keratocyst (OKC) is a cystic lesion of the jaws, which has high growth and recurrence rates compared to other cysts of the jaws (for instance, radicular cyst, which is the most common jaw cyst type). For this reason OKCs are considered by some to be benign neoplasms. There exist two sub-types of OKCs (sporadic and syndromic) and the ability to discriminate between these sub-types, as well as other jaw cysts, is an important task in terms of disease diagnosis and prognosis. With the development of digital pathology, computational algorithms have become central to addressing this type of problem. Considering that only basic feature-based methods have been investigated in this problem before, we propose to use a different approach (the Bouligand-Minkowski descriptors) to assess the success rates achieved on the classification of a database of histological images of the epithelial lining of these cysts. This does not require the level of abstraction necessary to extract histologically-relevant features and therefore has the potential of being more robust than previous approaches. The descriptors were obtained by mapping pixel intensities into a three dimensional cloud of points in discrete space and applying morphological dilations with spheres of increasing radii. The descriptors were computed from the volume of the dilated set and submitted to a machine learning algorithm to classify the samples into diagnostic groups. This approach was capable of discriminating between OKCs and radicular cysts in 98% of images (100% of cases) and between the two sub-types of OKCs in 68% of images (71% of cases). These results improve over previously reported classification rates reported elsewhere and suggest that Bouligand-Minkowski descriptors are useful features to be used in histopathological images of these cysts. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

The definition of chronic lung disease in patients undergoing cardiac surgery: a comparison between the Society of Thoracic Surgeons and the American Thoracic Society/European Respiratory Society Classifications.

PubMed

Henry, L; Holmes, S D; Lamberti, J; Halpin, L; Hunt, S; Ad, N

2012-12-01

Early and late outcomes following cardiac surgery may be adversely affected in patients with chronic lung disease (CLD) and the presence of CLD is definition dependent. The purpose of this study was to compare the Society of Thoracic Surgeons (STS) definitions for CLD to the modified American Thoracic Society (ATS)/European Respiratory Society (ERS) definitions in diagnosing and classifying CLD among a cohort of cardiac surgery patients. A prospectively-designed study whereby high risk patients for CLD presenting for non-emergent cardiac surgery and had a history of asthma, a 10 or more pack year history of smoking or a persistent cough were included. All patients underwent spirometry testing within two weeks of surgery. The presence and severity of CLD was coded two times: 1) STS definitions with spirometry; 2) ATS/ERS guidelines. The rate of misclassification was determined using concordance and discordance rates. Sensitivity analysis of the STS spirometry definitions was calculated against the ATS/ERS definitions and respective classifications. The discordant rate for the STS spirometry driven definitions versus the ATS/ERS definitions was 21%. Forty patients (21%) classified as no CLD by the STS spirometry definition were found to have CLD by the ATS/ERS definition. The STS classification had 68% sensitivity (84/124) when identifying any CLD and only 26% sensitivity (14/54) when identifying moderate CLD. The current STS spirometry driven definitions for CLD did not perform as well as the ATS/ERS definitions in diagnosing and classifying the degree of CLD. Consideration should be given to using the ATS/ERS definitions.

A manual and an automatic TERS based virus discrimination

NASA Astrophysics Data System (ADS)

Olschewski, Konstanze; Kämmer, Evelyn; Stöckel, Stephan; Bocklitz, Thomas; Deckert-Gaudig, Tanja; Zell, Roland; Cialla-May, Dana; Weber, Karina; Deckert, Volker; Popp, Jürgen

2015-02-01

Rapid techniques for virus identification are more relevant today than ever. Conventional virus detection and identification strategies generally rest upon various microbiological methods and genomic approaches, which are not suited for the analysis of single virus particles. In contrast, the highly sensitive spectroscopic technique tip-enhanced Raman spectroscopy (TERS) allows the characterisation of biological nano-structures like virions on a single-particle level. In this study, the feasibility of TERS in combination with chemometrics to discriminate two pathogenic viruses, Varicella-zoster virus (VZV) and Porcine teschovirus (PTV), was investigated. In a first step, chemometric methods transformed the spectral data in such a way that a rapid visual discrimination of the two examined viruses was enabled. In a further step, these methods were utilised to perform an automatic quality rating of the measured spectra. Spectra that passed this test were eventually used to calculate a classification model, through which a successful discrimination of the two viral species based on TERS spectra of single virus particles was also realised with a classification accuracy of 91%.Rapid techniques for virus identification are more relevant today than ever. Conventional virus detection and identification strategies generally rest upon various microbiological methods and genomic approaches, which are not suited for the analysis of single virus particles. In contrast, the highly sensitive spectroscopic technique tip-enhanced Raman spectroscopy (TERS) allows the characterisation of biological nano-structures like virions on a single-particle level. In this study, the feasibility of TERS in combination with chemometrics to discriminate two pathogenic viruses, Varicella-zoster virus (VZV) and Porcine teschovirus (PTV), was investigated. In a first step, chemometric methods transformed the spectral data in such a way that a rapid visual discrimination of the two examined viruses was enabled. In a further step, these methods were utilised to perform an automatic quality rating of the measured spectra. Spectra that passed this test were eventually used to calculate a classification model, through which a successful discrimination of the two viral species based on TERS spectra of single virus particles was also realised with a classification accuracy of 91%. Electronic supplementary information (ESI) available. See DOI: 10.1039/c4nr07033j

Grade classification of neuroepithelial tumors using high-resolution magic-angle spinning proton nuclear magnetic resonance spectroscopy and pattern recognition.

PubMed

Chen, WenXue; Lou, HaiYan; Zhang, HongPing; Nie, Xiu; Lan, WenXian; Yang, YongXia; Xiang, Yun; Qi, JianPin; Lei, Hao; Tang, HuiRu; Chen, FenEr; Deng, Feng

2011-07-01

Clinical data have shown that survival rates vary considerably among brain tumor patients, according to the type and grade of the tumor. Metabolite profiles of intact tumor tissues measured with high-resolution magic-angle spinning proton nuclear magnetic resonance spectroscopy (HRMAS (1)H NMRS) can provide important information on tumor biology and metabolism. These metabolic fingerprints can then be used for tumor classification and grading, with great potential value for tumor diagnosis. We studied the metabolic characteristics of 30 neuroepithelial tumor biopsies, including two astrocytomas (grade I), 12 astrocytomas (grade II), eight anaplastic astrocytomas (grade III), three glioblastomas (grade IV) and five medulloblastomas (grade IV) from 30 patients using HRMAS (1)H NMRS. The results were correlated with pathological features using multivariate data analysis, including principal component analysis (PCA). There were significant differences in the levels of N-acetyl-aspartate (NAA), creatine, myo-inositol, glycine and lactate between tumors of different grades (P<0.05). There were also significant differences in the ratios of NAA/creatine, lactate/creatine, myo-inositol/creatine, glycine/creatine, scyllo-inositol/creatine and alanine/creatine (P<0.05). A soft independent modeling of class analogy model produced a predictive accuracy of 87% for high-grade (grade III-IV) brain tumors with a sensitivity of 87% and a specificity of 93%. HRMAS (1)H NMR spectroscopy in conjunction with pattern recognition thus provides a potentially useful tool for the rapid and accurate classification of human brain tumor grades.

Obesity, hypertension and diabetes mellitus affect complication rate of different nephrectomy techniques.

PubMed

Hua, X; Ying-Ying, C; Zu-Jun, F; Gang, X; Zu-Quan, X; Qiang, D; Hao-Wen, J

2014-12-01

To investigate whether obesity, hypertension, and diabetes mellitus (DM) would increase post-nephrectomy complication rates using standardized classification method. We retrospectively included 843 patients from March 2006 to November 2012, of whom 613 underwent radical nephrectomy (RN) and 229 had partial nephrectomy (PN). Modified Clavien classification system was applied to quantify complication severity of nephrectomy. Fisher's exact or chi-square test was used to assess the relationship between complication rates and obesity, hypertension, as well as DM. The prevalence of obesity, hypertension, and DM was 11.51%, 30.84%, 8.78%, respectively. The overall complication rate was 19.31%, 30.04%, 35.71% and 36.36% for laparoscopic radical nephrectomy (LRN), open-RN, LPN and open-PN respectively. An increasing trend of low grade complication rate as BMI increased was observed in LRN (P=.027) and open-RN (P<.001). Obese patients had greater chance to have low grade complications in LRN (OR=4.471; 95% CI: 1.290-17.422; P=0.031) and open-RN (OR=2.448; 95% CI: 1.703-3.518; P<.001). Patients with hypertension were more likely to have low grade complications, especially grade ii complications in open-RN (OR=1.526; 95% CI: 1.055-2.206; P=.026) and open PN (OR=2.032; 95% CI: 1.199-3.443; P=.009). DM was also associated with higher grade i complication rate in open-RN (OR=2.490; 95% CI: 331-4.657; P=.016) and open-PN (OR=4.425; 95% CI: 1.815-10.791; P=.013). High grade complication rates were similar in comparison. Obesity, hypertension, and DM were closely associated with increased post-nephrectomy complication rates, mainly low grade complications. Copyright © 2013 AEU. Published by Elsevier Espana. All rights reserved.

Spatial Trends and Variability of Vertical Accretion Rates in the Barataria Basin, Louisiana, U.S.A. using Pb-210 and Cs-137 radiochemistry

NASA Astrophysics Data System (ADS)

Shrull, S.; Wilson, C.; Snedden, G.; Bentley, S. J.

2017-12-01

Barataria Basin on the south Louisiana coast is experiencing some of the greatest amounts of coastal land loss in the United States with rates as high as 23.1 km2 lost per year. In an attempt to help slow or reverse land loss, millions of dollars are being spent to create sediment diversions to increase the amount of available inorganic sediments to these vulnerable coastal marsh areas. A better understanding of the spatial trends and patterns of background accretion rates needs to be established in order to effectively implement such structures. Core samples from 25 Coastwide Reference Monitoring System (CRMS) sites spanning inland freshwater to coastal saline areas within the basin were extracted, and using vertical accretion rates from Cs-137 & Pb-210 radionuclide detection, mineral versus organic sediment composition, grain size distribution, and spatial trends of bulk densities, the controls on the accretion rates of the marsh soils will be constrained. Initial rates show a range from 0.31 cm/year to 1.02 cm/year with the average being 0.79 cm/year. Preliminary results suggest that location and proximity to an inorganic sediment source (i.e. river/tributary or open water) have a stronger influence on vertical accretion rates than marsh classification and salinity, with no clear relationship between vertical accretion and salinity. Down-core sediment composition and bulk density analyses observed at a number of the sites likely suggest episodic sedimentation and show different vertical accretion rates through time. Frequency and length of inundation (i.e. hydroperiod), and land/marsh classification from the CRMS data set will be further investigated to constrain the spatial variability in vertical accretion for the basin.

Excess mortality in women of reproductive age from low-income countries: a Swedish national register study.

PubMed

Esscher, Annika; Haglund, Bengt; Högberg, Ulf; Essén, Birgitta

2013-04-01

Cause-of-death statistics is widely used to monitor the health of a population. African immigrants have, in several European studies, shown to be at an increased risk of maternal death, but few studies have investigated cause-specific mortality rates in female immigrants. In this national study, based on the Swedish Cause of Death Register, we studied 27,957 women of reproductive age (aged 15-49 years) who died between 1988 and 2007. Age-standardized mortality rates per 100,000 person years and relative risks for death and underlying causes of death, grouped according to the International Statistical Classification of Diseases and Related Health Problems, 10th Revision, were calculated and compared between women born in Sweden and in low-, middle- and high-income countries. The total age-standardized mortality rate per 100,000 person years was significantly higher for women born in low-income (84.4) and high-income countries (83.7), but lower for women born in middle-income countries (57.5), as compared with Swedish-born women (68.1). The relative risk of dying from infectious disease was 15.0 (95% confidence interval 10.8-20.7) and diseases related to pregnancy was 6.6 (95% confidence interval 2.6-16.5) for women born in low-income countries, as compared to Swedish-born women. Women born in low-income countries are at the highest risk of dying during reproductive age in Sweden, with the largest discrepancy in mortality rates seen for infectious diseases and diseases related to pregnancy, a cause of death pattern similar to the one in their countries of birth. The World Bank classification of economies may be a useful tool in migration research.

Excess mortality in women of reproductive age from low-income countries: a Swedish national register study

PubMed Central

Haglund, Bengt; Högberg, Ulf; Essén, Birgitta

2013-01-01

Background: Cause-of-death statistics is widely used to monitor the health of a population. African immigrants have, in several European studies, shown to be at an increased risk of maternal death, but few studies have investigated cause-specific mortality rates in female immigrants. Methods: In this national study, based on the Swedish Cause of Death Register, we studied 27 957 women of reproductive age (aged 15–49 years) who died between 1988 and 2007. Age-standardized mortality rates per 100 000 person years and relative risks for death and underlying causes of death, grouped according to the International Statistical Classification of Diseases and Related Health Problems, 10th Revision, were calculated and compared between women born in Sweden and in low-, middle- and high-income countries. Results: The total age-standardized mortality rate per 100 000 person years was significantly higher for women born in low-income (84.4) and high-income countries (83.7), but lower for women born in middle-income countries (57.5), as compared with Swedish-born women (68.1). The relative risk of dying from infectious disease was 15.0 (95% confidence interval 10.8–20.7) and diseases related to pregnancy was 6.6 (95% confidence interval 2.6–16.5) for women born in low-income countries, as compared to Swedish-born women. Conclusions: Women born in low-income countries are at the highest risk of dying during reproductive age in Sweden, with the largest discrepancy in mortality rates seen for infectious diseases and diseases related to pregnancy, a cause of death pattern similar to the one in their countries of birth. The World Bank classification of economies may be a useful tool in migration research. PMID:22850186

Measuring severe maternal morbidity: validation of potential measures.

PubMed

Main, Elliott K; Abreo, Anisha; McNulty, Jennifer; Gilbert, William; McNally, Colleen; Poeltler, Debra; Lanner-Cusin, Katarina; Fenton, Douglas; Gipps, Theresa; Melsop, Kathryn; Greene, Naomi; Gould, Jeffrey B; Kilpatrick, Sarah

2016-05-01

Both maternal mortality rate and severe maternal morbidity rate have risen significantly in the United Sates. Recently, the Centers for Disease Control and Prevention introduced International Classification of Diseases, 9th revision, criteria for defining severe maternal morbidity with the use of administrative data sources; however, those criteria have not been validated with the use of chart reviews. The primary aim of the current study was to validate the Centers for Disease Control and Prevention International Classification of Diseases, 9th revision, criteria for the identification of severe maternal morbidity. This analysis initially required the development of a reproducible set of clinical conditions that were judged to be consistent with severe maternal morbidity to be used as the clinical gold standard for validation. Alternative criteria for severe maternal morbidity were also examined. The 67,468 deliveries that occurred during a 12-month period from 16 participating California hospitals were screened initially for severe maternal morbidity with the presence of any of 4 criteria: (1) Centers for Disease Control and Prevention International Classification of Diseases, 9th revision, diagnosis and procedure codes; (2) prolonged postpartum length of stay (>3 standard deviations beyond the mean length of stay for the California population); (3) any maternal intensive care unit admissions (with the use of hospital billing sources); and (4) the administration of any blood product (with the use of transfusion service data). Complete medical records for all screen-positive cases were examined to determine whether they satisfied the criteria for the clinical gold standard (determined by 4 rounds of a modified Delphi technique). Descriptive and statistical analyses that included area under the receiver operating characteristic curve and C-statistic were performed. The Centers for Disease Control and Prevention International Classification of Diseases, 9th revision, criteria had a reasonably high sensitivity of 0.77 and a positive predictive value of 0.44 with a C-statistic of 0.87. The most important source of false-positive cases were mothers whose only criterion was 1-2 units of blood products. The Centers for Disease Control and Prevention International Classification of Diseases, 9th revision, criteria screen rate ranged from 0.51-2.45% among hospitals. True positive severe maternal morbidity ranged from 0.05-1.13%. When hospitals were grouped by their neonatal intensive care unit level of care, severe maternal morbidity rates were statistically lower at facilities with lower level neonatal intensive care units (P < .0001). The Centers for Disease Control and Prevention International Classification of Diseases, 9th revision, criteria can serve as a reasonable administrative metric for measuring severe maternal morbidity at population levels. Caution should be used with the use of these criteria for individual hospitals, because case-mix effects appear to be strong. Copyright © 2016 Elsevier Inc. All rights reserved.

Epidemiology of Hospitalizations Associated with Invasive Candidiasis, United States, 2002–20121

PubMed Central

Strollo, Sara; Lionakis, Michail S.; Adjemian, Jennifer; Steiner, Claudia A.

2017-01-01

Invasive candidiasis is a major nosocomial fungal disease in the United States associated with high rates of illness and death. We analyzed inpatient hospitalization records from the Healthcare Cost and Utilization Project to estimate incidence of invasive candidiasis–associated hospitalizations in the United States. We extracted data for 33 states for 2002–2012 by using codes from the International Classification of Diseases, 9th Revision, Clinical Modification, for invasive candidiasis; we excluded neonatal cases. The overall age-adjusted average annual rate was 5.3 hospitalizations/100,000 population. Highest risk was for adults >65 years of age, particularly men. Median length of hospitalization was 21 days; 22% of patients died during hospitalization. Median unadjusted associated cost for inpatient care was $46,684. Age-adjusted annual rates decreased during 2005–2012 for men (annual change –3.9%) and women (annual change –4.5%) and across nearly all age groups. We report a high mortality rate and decreasing incidence of hospitalizations for this disease. PMID:27983497

Epidemiology of Hospitalizations Associated with Invasive Candidiasis, United States, 2002-20121.

PubMed

Strollo, Sara; Lionakis, Michail S; Adjemian, Jennifer; Steiner, Claudia A; Prevots, D Rebecca

2016-01-01

Invasive candidiasis is a major nosocomial fungal disease in the United States associated with high rates of illness and death. We analyzed inpatient hospitalization records from the Healthcare Cost and Utilization Project to estimate incidence of invasive candidiasis-associated hospitalizations in the United States. We extracted data for 33 states for 2002-2012 by using codes from the International Classification of Diseases, 9th Revision, Clinical Modification, for invasive candidiasis; we excluded neonatal cases. The overall age-adjusted average annual rate was 5.3 hospitalizations/100,000 population. Highest risk was for adults >65 years of age, particularly men. Median length of hospitalization was 21 days; 22% of patients died during hospitalization. Median unadjusted associated cost for inpatient care was $46,684. Age-adjusted annual rates decreased during 2005-2012 for men (annual change -3.9%) and women (annual change -4.5%) and across nearly all age groups. We report a high mortality rate and decreasing incidence of hospitalizations for this disease.

SeqRate: sequence-based protein folding type classification and rates prediction

PubMed Central

2010-01-01

Background Protein folding rate is an important property of a protein. Predicting protein folding rate is useful for understanding protein folding process and guiding protein design. Most previous methods of predicting protein folding rate require the tertiary structure of a protein as an input. And most methods do not distinguish the different kinetic nature (two-state folding or multi-state folding) of the proteins. Here we developed a method, SeqRate, to predict both protein folding kinetic type (two-state versus multi-state) and real-value folding rate using sequence length, amino acid composition, contact order, contact number, and secondary structure information predicted from only protein sequence with support vector machines. Results We systematically studied the contributions of individual features to folding rate prediction. On a standard benchmark dataset, the accuracy of folding kinetic type classification is 80%. The Pearson correlation coefficient and the mean absolute difference between predicted and experimental folding rates (sec-1) in the base-10 logarithmic scale are 0.81 and 0.79 for two-state protein folders, and 0.80 and 0.68 for three-state protein folders. SeqRate is the first sequence-based method for protein folding type classification and its accuracy of fold rate prediction is improved over previous sequence-based methods. Its performance can be further enhanced with additional information, such as structure-based geometric contacts, as inputs. Conclusions Both the web server and software of predicting folding rate are publicly available at http://casp.rnet.missouri.edu/fold_rate/index.html. PMID:20438647

Cognitive Precursors of Receptive vs. Expressive Language.

ERIC Educational Resources Information Center

Smolak, Linda

1982-01-01

The relationship of object permanence and classification skills to receptive and expressive language development was investigated in infants. Object permanence, classification, and parent-child verbal interaction ratings were about equally related to language comprehension functioning, while permanence was more strongly related to language…

Using Relative Improvement over Chance (RIOC) to Examine Agreement between Tests: Three Case Examples Using Studies of Developmental Coordination Disorder (DCD) in Children

ERIC Educational Resources Information Center

Cairney, John; Streiner, David L.

2011-01-01

Although statistics such as kappa and phi are commonly used to assess agreement between tests, in situations where the base rate of a disorder in a population is low or high, these statistics tend to underestimate actual agreement. This can occur even if the tests are good and the classification of subjects is adequate. Relative improvement over…

The identification of high potential archers based on fitness and motor ability variables: A Support Vector Machine approach.

PubMed

Taha, Zahari; Musa, Rabiu Muazu; P P Abdul Majeed, Anwar; Alim, Muhammad Muaz; Abdullah, Mohamad Razali

2018-02-01

Support Vector Machine (SVM) has been shown to be an effective learning algorithm for classification and prediction. However, the application of SVM for prediction and classification in specific sport has rarely been used to quantify/discriminate low and high-performance athletes. The present study classified and predicted high and low-potential archers from a set of fitness and motor ability variables trained on different SVMs kernel algorithms. 50 youth archers with the mean age and standard deviation of 17.0 ± 0.6 years drawn from various archery programmes completed a six arrows shooting score test. Standard fitness and ability measurements namely hand grip, vertical jump, standing broad jump, static balance, upper muscle strength and the core muscle strength were also recorded. Hierarchical agglomerative cluster analysis (HACA) was used to cluster the archers based on the performance variables tested. SVM models with linear, quadratic, cubic, fine RBF, medium RBF, as well as the coarse RBF kernel functions, were trained based on the measured performance variables. The HACA clustered the archers into high-potential archers (HPA) and low-potential archers (LPA), respectively. The linear, quadratic, cubic, as well as the medium RBF kernel functions models, demonstrated reasonably excellent classification accuracy of 97.5% and 2.5% error rate for the prediction of the HPA and the LPA. The findings of this investigation can be valuable to coaches and sports managers to recognise high potential athletes from a combination of the selected few measured fitness and motor ability performance variables examined which would consequently save cost, time and effort during talent identification programme. Copyright © 2017 Elsevier B.V. All rights reserved.

ATLS Hypovolemic Shock Classification by Prediction of Blood Loss in Rats Using Regression Models.

PubMed

Choi, Soo Beom; Choi, Joon Yul; Park, Jee Soo; Kim, Deok Won

2016-07-01

In our previous study, our input data set consisted of 78 rats, the blood loss in percent as a dependent variable, and 11 independent variables (heart rate, systolic blood pressure, diastolic blood pressure, mean arterial pressure, pulse pressure, respiration rate, temperature, perfusion index, lactate concentration, shock index, and new index (lactate concentration/perfusion)). The machine learning methods for multicategory classification were applied to a rat model in acute hemorrhage to predict the four Advanced Trauma Life Support (ATLS) hypovolemic shock classes for triage in our previous study. However, multicategory classification is much more difficult and complicated than binary classification. We introduce a simple approach for classifying ATLS hypovolaemic shock class by predicting blood loss in percent using support vector regression and multivariate linear regression (MLR). We also compared the performance of the classification models using absolute and relative vital signs. The accuracies of support vector regression and MLR models with relative values by predicting blood loss in percent were 88.5% and 84.6%, respectively. These were better than the best accuracy of 80.8% of the direct multicategory classification using the support vector machine one-versus-one model in our previous study for the same validation data set. Moreover, the simple MLR models with both absolute and relative values could provide possibility of the future clinical decision support system for ATLS classification. The perfusion index and new index were more appropriate with relative changes than absolute values.

Synoptic typing: interdisciplinary application methods with three practical hydroclimatological examples

NASA Astrophysics Data System (ADS)

Siegert, C. M.; Leathers, D. J.; Levia, D. F.

2017-05-01

Synoptic classification is a methodology that represents diverse atmospheric variables and allows researchers to relate large-scale atmospheric circulation patterns to regional- and small-scale terrestrial processes. Synoptic classification has often been applied to questions concerning the surface environment. However, full applicability has been under-utilized to date, especially in disciplines such as hydroclimatology, which are intimately linked to atmospheric inputs. This paper aims to (1) outline the development of a daily synoptic calendar for the Mid-Atlantic (USA), (2) define seasonal synoptic patterns occurring in the region, and (3) provide hydroclimatological examples whereby the cascading response of precipitation characteristics, soil moisture, and streamflow are explained by synoptic classification. Together, achievement of these objectives serves as a guide for development and use of a synoptic calendar for hydroclimatological studies. In total 22 unique synoptic types were identified, derived from a combination of 12 types occurring in the winter (DJF), 13 in spring (MAM), 9 in summer (JJA), and 11 in autumn (SON). This includes six low pressure systems, four high pressure systems, one cold front, three north/northwest flow regimes, three south/southwest flow regimes, and five weakly defined regimes. Pairwise comparisons indicated that 84.3 % had significantly different rainfall magnitudes, 86.4 % had different rainfall durations, and 84.7 % had different rainfall intensities. The largest precipitation-producing classifications were not restricted to low pressure systems, but rather to patterns with access to moisture sources from the Atlantic Ocean and easterly (on-shore) winds, which transport moisture inland. These same classifications resulted in comparable rates of soil moisture recharge and streamflow discharge, illustrating the applicability of synoptic classification for a range of hydroclimatological research objectives.

Improved wetland remote sensing in Yellowstone National Park using classification trees to combine TM imagery and ancillary environmental data

USGS Publications Warehouse

Wright, C.; Gallant, Alisa L.

2007-01-01

The U.S. Fish and Wildlife Service uses the term palustrine wetland to describe vegetated wetlands traditionally identified as marsh, bog, fen, swamp, or wet meadow. Landsat TM imagery was combined with image texture and ancillary environmental data to model probabilities of palustrine wetland occurrence in Yellowstone National Park using classification trees. Model training and test locations were identified from National Wetlands Inventory maps, and classification trees were built for seven years spanning a range of annual precipitation. At a coarse level, palustrine wetland was separated from upland. At a finer level, five palustrine wetland types were discriminated: aquatic bed (PAB), emergent (PEM), forested (PFO), scrub–shrub (PSS), and unconsolidated shore (PUS). TM-derived variables alone were relatively accurate at separating wetland from upland, but model error rates dropped incrementally as image texture, DEM-derived terrain variables, and other ancillary GIS layers were added. For classification trees making use of all available predictors, average overall test error rates were 7.8% for palustrine wetland/upland models and 17.0% for palustrine wetland type models, with consistent accuracies across years. However, models were prone to wetland over-prediction. While the predominant PEM class was classified with omission and commission error rates less than 14%, we had difficulty identifying the PAB and PSS classes. Ancillary vegetation information greatly improved PSS classification and moderately improved PFO discrimination. Association with geothermal areas distinguished PUS wetlands. Wetland over-prediction was exacerbated by class imbalance in likely combination with spatial and spectral limitations of the TM sensor. Wetland probability surfaces may be more informative than hard classification, and appear to respond to climate-driven wetland variability. The developed method is portable, relatively easy to implement, and should be applicable in other settings and over larger extents.

Problems of stock definition in estimating relative contributions of Atlantic striped bass to the coastal fishery

USGS Publications Warehouse

Waldman, John R.; Fabrizio, Mary C.

1994-01-01

Stock contribution studies of mixed-stock fisheries rely on the application of classification algorithms to samples of unknown origin. Although the performance of these algorithms can be assessed, there are no guidelines regarding decisions about including minor stocks, pooling stocks into regional groups, or sampling discrete substocks to adequately characterize a stock. We examined these questions for striped bass Morone saxatilis of the U.S. Atlantic coast by applying linear discriminant functions to meristic and morphometric data from fish collected from spawning areas. Some of our samples were from the Hudson and Roanoke rivers and four tributaries of the Chesapeake Bay. We also collected fish of mixed-stock origin from the Atlantic Ocean near Montauk, New York. Inclusion of the minor stock from the Roanoke River in the classification algorithm decreased the correct-classification rate, whereas grouping of the Roanoke River and Chesapeake Bay stock into a regional (''southern'') group increased the overall resolution. The increased resolution was offset by our inability to obtain separate contribution estimates of the groups that were pooled. Although multivariate analysis of variance indicated significant differences among Chesapeake Bay substocks, increasing the number of substocks in the discriminant analysis decreased the overall correct-classification rate. Although the inclusion of one, two, three, or four substocks in the classification algorithm did not greatly affect the overall correct-classification rates, the specific combination of substocks significantly affected the relative contribution estimates derived from the mixed-stock sample. Future studies of this kind must balance the costs and benefits of including minor stocks and would profit from examination of the variation in discriminant characters among all Chesapeake Bay substocks.

Impact of heterogeneity on groundwater salinization due to coastal pumping

NASA Astrophysics Data System (ADS)

Yu, X.; Michael, H. A.

2017-12-01

Groundwater abstraction causes and accelerates seawater intrusion in many coastal areas. In heterogeneous aquifers, preferential flow paths can lead to fast intrusion, while low permeability layers can serve as barriers. The extent to which different types of heterogeneous aquifers are vulnerable to pumping-induced seawater intrusion has not been well studied. Here we show that the connectedness of pumping location and local boundary condition drive salinization patterns. Salinization patterns in homogeneous aquifers were relatively simple and only related to the hydraulic properties and pumping rate. The salinization rates and patterns in heterogeneous aquifers were much more complicated and related to pumping location, rate and depth, preferential flow path locations, and local boundary conditions. An intrusion classification approach was developed with three types in homogeneous aquifers and four types in heterogeneous aquifers. After classification the main factors of salinized areas, intrusion rates and salinization time could be identified. The ranges of these salinization assessment criteria suggested different aspect of groundwater vulnerability in each class. We anticipate the classification approach to be a starting point for more comprehensive groundwater abstraction vulnerability assessment (including consideration of pumping rates, locations and depths, connectivity, preferential flow paths, etc.), which is critical for coastal water resources management.

Clinical presentation and outcome prediction of clinical, serological, and histopathological classification schemes in ANCA-associated vasculitis with renal involvement.

PubMed

Córdova-Sánchez, Bertha M; Mejía-Vilet, Juan M; Morales-Buenrostro, Luis E; Loyola-Rodríguez, Georgina; Uribe-Uribe, Norma O; Correa-Rotter, Ricardo

2016-07-01

Several classification schemes have been developed for anti-neutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV), with actual debate focusing on their clinical and prognostic performance. Sixty-two patients with renal biopsy-proven AAV from a single center in Mexico City diagnosed between 2004 and 2013 were analyzed and classified under clinical (granulomatosis with polyangiitis [GPA], microscopic polyangiitis [MPA], renal limited vasculitis [RLV]), serological (proteinase 3 anti-neutrophil cytoplasmic antibodies [PR3-ANCA], myeloperoxidase anti-neutrophil cytoplasmic antibodies [MPO-ANCA], ANCA negative), and histopathological (focal, crescenteric, mixed-type, sclerosing) categories. Clinical presentation parameters were compared at baseline between classification groups, and the predictive value of different classification categories for disease and renal remission, relapse, renal, and patient survival was analyzed. Serological classification predicted relapse rate (PR3-ANCA hazard ratio for relapse 2.93, 1.20-7.17, p = 0.019). There were no differences in disease or renal remission, renal, or patient survival between clinical and serological categories. Histopathological classification predicted response to therapy, with a poorer renal remission rate for sclerosing group and those with less than 25 % normal glomeruli; in addition, it adequately delimited 24-month glomerular filtration rate (eGFR) evolution, but it did not predict renal nor patient survival. On multivariate models, renal replacement therapy (RRT) requirement (HR 8.07, CI 1.75-37.4, p = 0.008) and proteinuria (HR 1.49, CI 1.03-2.14, p = 0.034) at presentation predicted renal survival, while age (HR 1.10, CI 1.01-1.21, p = 0.041) and infective events during the induction phase (HR 4.72, 1.01-22.1, p = 0.049) negatively influenced patient survival. At present, ANCA-based serological classification may predict AAV relapses, but neither clinical nor serological categories predict renal or patient survival. Age, renal function and proteinuria at presentation, histopathology, and infectious complications constitute the main outcome predictors and should be considered for individualized management.

Clinical efficacy of transcatheter aortic valve replacement for severe aortic stenosis in high-risk patients: the PREVAIL JAPAN trial.

PubMed

Sawa, Yoshiki; Takayama, Morimasa; Mitsudo, Kazuaki; Nanto, Shinsuke; Takanashi, Shuichiro; Komiya, Tatsuhiko; Kuratani, Toru; Tobaru, Tetsuya; Goto, Tsuyoshi

2015-01-01

Transcatheter aortic valve replacement (TAVR) is suggested to be less invasive and/or equally effective in comparison to conventional aortic valve replacement for high-risk symptomatic aortic stenosis patients. We herein report the initial results of a pivotal clinical trial of TAVR in Japan (the PREVAIL JAPAN). Sixty-four aortic stenosis patients (mean age 84.3 ± 6.1 years) not suitable for surgery were enrolled at three centers in Japan, with a primary composite endpoint of the 6-month post-procedure improvements in the aortic valve area and New York Heart Association (NYHA) functional classification. A transfemoral approach was used in 37 patients and a transapical approach was used in 27. The device success rate was 91.9 %. After 30 days and 6 months, the rates of mortality from any cause were 8.1 and 11.3 %, respectively. At 6 months, symptomatic stroke was found in 3.1 % of the patients, and silent infarction in 7.8 %. The aortic valve area and mean pressure gradient were significantly improved over time with both approaches (p < 0.001). At 6 months, the NYHA functional classification based on a conventional physician's assessment was improved in 87.9 % of the patients. We found results that were equivalent to those in other major TAVR trials, such as an acceptable 30-day survival (91.9 %), suggesting that balloon-expandable TAVR is effective for small Japanese AS patients classified as high-risk or inoperable.

The nutritional status of hospitalized children: Has this subject been overlooked?

PubMed

Kapçı, Nermin; Akçam, Mustafa; Koca, Tuğba; Dereci, Selim; Kapcı, Mücahit

2015-07-01

To determine the nutritional status of hospitalized children at the time of admission and to investigate the relationship between diagnosis and nutritional status. Body weight, height, triceps skinfold thickness, and mid-arm circumference were measured on admission and percentages of weight-for-age, weight-for-height, body mass index, mid-arm circumference, and triceps skinfold thickness were calculated. The nutritional status was evaluated using the Waterlow, Gomez, and other anthropometric assessments. A total of 511 patients were included in the study with a mean age of 5.8±4.9 years. Malnutrition was determined in 52.7% of patients according to the Waterlow classification. Mild malnutrition was determined in 39%, moderate in 12%, and severe in 1.7%, with the characteristics of acute malnutrition in 23.9%, acute-chronic in 7.3%, and chronic in 21.5%. The highest rate of malnutrition was in the 0-2 years age group (62.3%). According to the Gomez classification, malnutrition rate was determined as 46.8%. The rates of malnutrition in malignant, gastrointestinal, and infectious diseases were 60%, 59.8%, and 54.5%, respectively. The prevalence of malnutrition in hospitalized children was noticeably high. The nutritional evaluation of all patients and an early start to nutritional support could provide a significant positive contribution.

School Socioeconomic Classification, Funding, and the New Jersey High School Proficiency Assessment (HSPA)

ERIC Educational Resources Information Center

Bao, D. H.; Romeo, George C.; Harvey, Roberta

2010-01-01

This study examines the relationship between educational effectiveness, as measured by the New Jersey High School Proficiency Assessment (HSPA), and funding of school districts based on socioeconomic classification. Results indicate there is a strong relationship between performance in HSPA, socioeconomic classification, and the different sources…

Developing classification criteria for polymyalgia rheumatica: comparison of views from an expert panel and wider survey.

PubMed

Dasgupta, Bhaskar; Salvarani, Carlo; Schirmer, Michael; Crowson, Cynthia S; Maradit-Kremers, Hilal; Hutchings, Andrew; Matteson, Eric L

2008-02-01

This report summarizes the findings from a consensus process to identify potential classification criteria for polymyalgia rheumatica (PMR). A 3-stage hybrid consensus approach was used to develop potential PMR classification criteria. The first stage consisted of a facilitated meeting of 27 international experts who anonymously rated the importance of 68 potential criteria. The second stage involved a meeting of the experts, who were provided with the results of the first round of ratings and were then asked to re-rate the criteria. In the third stage, the wider acceptance of the 43 criteria that received > 50% support at round 2 was evaluated using an extended mailed survey of 111 rheumatologists and 53 nonrheumatologists in the United States, Canada, and Northern and Western Europe. A total of 68 and 50 criteria were identified and rated in round 1 and round 2, respectively. In round 2, 43 of the 50 items achieved at least 50% support, including 10 core criteria achieving 100% support. In round 3, over 70% of survey respondents agreed on the importance of 7 core criteria. These were age >or=50 years, duration >or=2 weeks, bilateral shoulder and/or pelvic girdle aching, duration of morning stiffness > 45 min, elevated erythrocyte sedimentation rate, elevated C-reactive protein, and rapid steroid response (> 75% global response within 1 wk to prednisolone/prednisone 15 20 mg daily). Among physical signs, more than 70% of survey respondents agreed on the importance of assessing pain and limitation of shoulder (84%) and/or hip (76%) on motion, but agreement was low for peripheral signs like carpal tunnel, tenosynovitis, and peripheral arthritis. There are differences in opinion as to what PMR is and how it should be treated. These findings make it important to develop classification criteria for PMR. The next step is to perform an international prospective study to evaluate the utility of candidate classification criteria for PMR in patients presenting with the polymyalgic syndrome.

Di-codon Usage for Gene Classification

NASA Astrophysics Data System (ADS)

Nguyen, Minh N.; Ma, Jianmin; Fogel, Gary B.; Rajapakse, Jagath C.

Classification of genes into biologically related groups facilitates inference of their functions. Codon usage bias has been described previously as a potential feature for gene classification. In this paper, we demonstrate that di-codon usage can further improve classification of genes. By using both codon and di-codon features, we achieve near perfect accuracies for the classification of HLA molecules into major classes and sub-classes. The method is illustrated on 1,841 HLA sequences which are classified into two major classes, HLA-I and HLA-II. Major classes are further classified into sub-groups. A binary SVM using di-codon usage patterns achieved 99.95% accuracy in the classification of HLA genes into major HLA classes; and multi-class SVM achieved accuracy rates of 99.82% and 99.03% for sub-class classification of HLA-I and HLA-II genes, respectively. Furthermore, by combining codon and di-codon usages, the prediction accuracies reached 100%, 99.82%, and 99.84% for HLA major class classification, and for sub-class classification of HLA-I and HLA-II genes, respectively.

Assessment of differences between repeated pulse wave velocity measurements in terms of 'bias' in the extrapolated cardiovascular risk and the classification of aortic stiffness: is a single PWV measurement enough?

PubMed

Papaioannou, T G; Protogerou, A D; Nasothimiou, E G; Tzamouranis, D; Skliros, N; Achimastos, A; Papadogiannis, D; Stefanadis, C I

2012-10-01

Currently, there is no recommendation regarding the minimum number of pulse wave velocity (PWV) measurements to optimize individual's cardiovascular risk (CVR) stratification. The aim of this study was to examine differences between three single consecutive and averaged PWV measurements in terms of the extrapolated CVR and the classification of aortic stiffness as normal. In 60 subjects who referred for CVR assessment, three repeated measurements of blood pressure (BP), heart rate and PWV were performed. The reproducibility was evaluated by the intraclass correlation coefficient (ICC) and mean±s.d. of differences. The absolute differences between single and averaged PWV measurements were classified as: ≤0.25, 0.26-0.49, 0.50-0.99 and ≥1 m s(-1). A difference ≥0.5 m s(-1) (corresponding to 7.5% change in CVR, meta-analysis data from >12 000 subjects) was considered as clinically meaningful; PWV values (single or averaged) were classified as normal according to respective age-corrected normal values (European Network data). Kappa statistic was used to evaluate the agreement between classifications. PWV for the first, second and third measurement was 7.0±1.9, 6.9±1.9, 6.9±2.0 m s(-1), respectively (P=0.319); BP and heart rate did not vary significantly. A good reproducibility between single measurements was observed (ICC>0.94, s.d. ranged between 0.43 and 0.64 m s(-1)). A high percent with difference ≥0.5 m s(-1) was observed between: any pair of the three single PWV measurements (26.6-38.3%); the first or second single measurement and the average of the first and second (18.3%); any single measurement and the average of three measurements (10-20%). In only up to 5% a difference ≥0.5 m s(-1) was observed between the average of three and the average of any two PWV measurements. There was no significant agreement regarding PWV classification as normal between: the first or second measurement and the averaged PWV values. There was significant agreement in classification made by the average of the first two and the average of three PWV measurements (κ=0.85, P<0.001). Even when high reproducibility in PWV measurement is succeeded single measurements provide quite variable results in terms of the extrapolated CVR and the classification of aortic stiffness as normal. The average of two PWV measurements provides similar results with the average of three.

Pattern recognition of satellite cloud imagery for improved weather prediction

NASA Technical Reports Server (NTRS)

Gautier, Catherine; Somerville, Richard C. J.; Volfson, Leonid B.

1986-01-01

The major accomplishment was the successful development of a method for extracting time derivative information from geostationary meteorological satellite imagery. This research is a proof-of-concept study which demonstrates the feasibility of using pattern recognition techniques and a statistical cloud classification method to estimate time rate of change of large-scale meteorological fields from remote sensing data. The cloud classification methodology is based on typical shape function analysis of parameter sets characterizing the cloud fields. The three specific technical objectives, all of which were successfully achieved, are as follows: develop and test a cloud classification technique based on pattern recognition methods, suitable for the analysis of visible and infrared geostationary satellite VISSR imagery; develop and test a methodology for intercomparing successive images using the cloud classification technique, so as to obtain estimates of the time rate of change of meteorological fields; and implement this technique in a testbed system incorporating an interactive graphics terminal to determine the feasibility of extracting time derivative information suitable for comparison with numerical weather prediction products.

Driver Fatigue Classification With Independent Component by Entropy Rate Bound Minimization Analysis in an EEG-Based System.

PubMed

Chai, Rifai; Naik, Ganesh R; Nguyen, Tuan Nghia; Ling, Sai Ho; Tran, Yvonne; Craig, Ashley; Nguyen, Hung T

2017-05-01

This paper presents a two-class electroencephal-ography-based classification for classifying of driver fatigue (fatigue state versus alert state) from 43 healthy participants. The system uses independent component by entropy rate bound minimization analysis (ERBM-ICA) for the source separation, autoregressive (AR) modeling for the features extraction, and Bayesian neural network for the classification algorithm. The classification results demonstrate a sensitivity of 89.7%, a specificity of 86.8%, and an accuracy of 88.2%. The combination of ERBM-ICA (source separator), AR (feature extractor), and Bayesian neural network (classifier) provides the best outcome with a p-value < 0.05 with the highest value of area under the receiver operating curve (AUC-ROC = 0.93) against other methods such as power spectral density as feature extractor (AUC-ROC = 0.81). The results of this study suggest the method could be utilized effectively for a countermeasure device for driver fatigue identification and other adverse event applications.

The effect of mental ill health on absence from work in different occupational classifications: analysis of routine data in the British Household Panel Survey.

PubMed

Whittaker, Will; Sutton, Matt; Macdonald, Sara; Maxwell, Margaret; Smith, Michael; Wilson, Philip; Morrison, Jill

2012-12-01

To investigate relationship of mental ill health to absence from work in different occupational classifications. Examined sickness absence, mental health (GHQ-12), physical health, job characteristics, and personal characteristics in 18 waves of the British Household Panel Survey. Overall sickness absence rate was 1.68%. Increased absence was associated with age greater than 45 years, female gender, lower occupational classification, and public-sector employers. Decreased absence was associated with part-time working. Scoring 4 or more on the General Health Questionnaire 12-item version (GHQ-12 caseness) was strongly associated with sickness absence. Public-sector employers had highest rates of sickness absence. GHQ-12 caseness had largest impact on absence in the public and nonprofit sectors, whereas physical health problems impacted more in the private sector. GHQ-12 caseness is strongly associated with increased absence in all classifications of occupations. Differences between sectors require further investigation.

Pattern classification using an olfactory model with PCA feature selection in electronic noses: study and application.

PubMed

Fu, Jun; Huang, Canqin; Xing, Jianguo; Zheng, Junbao

2012-01-01

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.

Improved classification accuracy by feature extraction using genetic algorithms

NASA Astrophysics Data System (ADS)

Patriarche, Julia; Manduca, Armando; Erickson, Bradley J.

2003-05-01

A feature extraction algorithm has been developed for the purposes of improving classification accuracy. The algorithm uses a genetic algorithm / hill-climber hybrid to generate a set of linearly recombined features, which may be of reduced dimensionality compared with the original set. The genetic algorithm performs the global exploration, and a hill climber explores local neighborhoods. Hybridizing the genetic algorithm with a hill climber improves both the rate of convergence, and the final overall cost function value; it also reduces the sensitivity of the genetic algorithm to parameter selection. The genetic algorithm includes the operators: crossover, mutation, and deletion / reactivation - the last of these effects dimensionality reduction. The feature extractor is supervised, and is capable of deriving a separate feature space for each tissue (which are reintegrated during classification). A non-anatomical digital phantom was developed as a gold standard for testing purposes. In tests with the phantom, and with images of multiple sclerosis patients, classification with feature extractor derived features yielded lower error rates than using standard pulse sequences, and with features derived using principal components analysis. Using the multiple sclerosis patient data, the algorithm resulted in a mean 31% reduction in classification error of pure tissues.

Electronic Nose: A Promising Tool For Early Detection Of Alicyclobacillus spp In Soft Drinks

NASA Astrophysics Data System (ADS)

Concina, I.; Bornšek, M.; Baccelliere, S.; Falasconi, M.; Sberveglieri, G.

2009-05-01

In the present work we investigate the potential use of the Electronic Nose EOS835 (SACMI scarl, Italy) to early detect Alicyclobacillus spp in two flavoured soft drinks. These bacteria have been acknowledged by producer companies as a major quality control target microorganisms because of their ability to survive commercial pasteurization processes and produce taint compounds in final product. Electronic Nose was able to distinguish between uncontaminated and contaminated products before the taint metabolites were identifiable by an untrained panel. Classification tests showed an excellent rate of correct classification for both drinks (from 86% uo to 100%). High performance liquid chromatography analyses showed no presence of the main metabolite at a level of 200 ppb, thus confirming the skill of the Electronic Nose technology in performing an actual early diagnosis of contamination.

Log-ratio transformed major element based multidimensional classification for altered High-Mg igneous rocks

NASA Astrophysics Data System (ADS)

Verma, Surendra P.; Rivera-Gómez, M. Abdelaly; Díaz-González, Lorena; Quiroz-Ruiz, Alfredo

2016-12-01

A new multidimensional classification scheme consistent with the chemical classification of the International Union of Geological Sciences (IUGS) is proposed for the nomenclature of High-Mg altered rocks. Our procedure is based on an extensive database of major element (SiO2, TiO2, Al2O3, Fe2O3t, MnO, MgO, CaO, Na2O, K2O, and P2O5) compositions of a total of 33,868 (920 High-Mg and 32,948 "Common") relatively fresh igneous rock samples. The database consisting of these multinormally distributed samples in terms of their isometric log-ratios was used to propose a set of 11 discriminant functions and 6 diagrams to facilitate High-Mg rock classification. The multinormality required by linear discriminant and canonical analysis was ascertained by a new computer program DOMuDaF. One multidimensional function can distinguish the High-Mg and Common igneous rocks with high percent success values of about 86.4% and 98.9%, respectively. Similarly, from 10 discriminant functions the High-Mg rocks can also be classified as one of the four rock types (komatiite, meimechite, picrite, and boninite), with high success values of about 88%-100%. Satisfactory functioning of this new classification scheme was confirmed by seven independent tests. Five further case studies involving application to highly altered rocks illustrate the usefulness of our proposal. A computer program HMgClaMSys was written to efficiently apply the proposed classification scheme, which will be available for online processing of igneous rock compositional data. Monte Carlo simulation modeling and mass-balance computations confirmed the robustness of our classification with respect to analytical errors and postemplacement compositional changes.

Combining Decision Rules from Classification Tree Models and Expert Assessment to Estimate Occupational Exposure to Diesel Exhaust for a Case-Control Study

PubMed Central

Friesen, Melissa C.; Wheeler, David C.; Vermeulen, Roel; Locke, Sarah J.; Zaebst, Dennis D.; Koutros, Stella; Pronk, Anjoeka; Colt, Joanne S.; Baris, Dalsu; Karagas, Margaret R.; Malats, Nuria; Schwenn, Molly; Johnson, Alison; Armenti, Karla R.; Rothman, Nathanial; Stewart, Patricia A.; Kogevinas, Manolis; Silverman, Debra T.

2016-01-01

Objectives: To efficiently and reproducibly assess occupational diesel exhaust exposure in a Spanish case-control study, we examined the utility of applying decision rules that had been extracted from expert estimates and questionnaire response patterns using classification tree (CT) models from a similar US study. Methods: First, previously extracted CT decision rules were used to obtain initial ordinal (0–3) estimates of the probability, intensity, and frequency of occupational exposure to diesel exhaust for the 10 182 jobs reported in a Spanish case-control study of bladder cancer. Second, two experts reviewed the CT estimates for 350 jobs randomly selected from strata based on each CT rule’s agreement with the expert ratings in the original study [agreement rate, from 0 (no agreement) to 1 (perfect agreement)]. Their agreement with each other and with the CT estimates was calculated using weighted kappa (κ w) and guided our choice of jobs for subsequent expert review. Third, an expert review comprised all jobs with lower confidence (low-to-moderate agreement rates or discordant assignments, n = 931) and a subset of jobs with a moderate to high CT probability rating and with moderately high agreement rates (n = 511). Logistic regression was used to examine the likelihood that an expert provided a different estimate than the CT estimate based on the CT rule agreement rates, the CT ordinal rating, and the availability of a module with diesel-related questions. Results: Agreement between estimates made by two experts and between estimates made by each of the experts and the CT estimates was very high for jobs with estimates that were determined by rules with high CT agreement rates (κ w: 0.81–0.90). For jobs with estimates based on rules with lower agreement rates, moderate agreement was observed between the two experts (κ w: 0.42–0.67) and poor-to-moderate agreement was observed between the experts and the CT estimates (κ w: 0.09–0.57). In total, the expert review of 1442 jobs changed 156 probability estimates, 128 intensity estimates, and 614 frequency estimates. The expert was more likely to provide a different estimate when the CT rule agreement rate was <0.8, when the CT ordinal ratings were low to moderate, or when a module with diesel questions was available. Conclusions: Our reliability assessment provided important insight into where to prioritize additional expert review; as a result, only 14% of the jobs underwent expert review, substantially reducing the exposure assessment burden. Overall, we found that we could efficiently, reproducibly, and reliably apply CT decision rules from one study to assess exposure in another study. PMID:26732820

Development of the Connecticut Airway Risk Evaluation (CARE) system to improve handoff communication in pediatric patients with tracheotomy.

PubMed

Lawrason Hughes, Amy; Murray, Nicole; Valdez, Tulio A; Kelly, Raeanne; Kavanagh, Katherine

2014-01-01

National attention has focused on the importance of handoffs in medicine. Our practice during airway patient handoffs is to communicate a patient-specific emergency plan for airway reestablishment; patients who are not intubatable by standard means are at higher risk for failure. There is currently no standard classification system describing airway risk in tracheotomized patients. To introduce and assess the interrater reliability of a simple airway risk classification system, the Connecticut Airway Risk Evaluation (CARE) system. We created a novel classification system, the CARE system, based on ease of intubation and the need for ventilation: group 1, easily intubatable; group 2, intubatable with special equipment and/or maneuvers; group 3, not intubatable. A "v" was appended to any group number to indicate the need for mechanical ventilation. We performed a retrospective medical chart review of patients aged 0 to 18 years who were undergoing tracheotomy at our tertiary care pediatric hospital between January 2000 and April 2011. INTERVENTIONS Each patient's medical history, including airway disease and means of intubation, was reviewed by 4 raters. Patient airways were separately rated as CARE groups 1, 2, or 3, each group with or without a v appended, as appropriate, based on the available information. After the patients were assigned to an airway group by each of the 4 raters, the interrater reliability was calculated to determine the ease of use of the rating system. We identified complete data for 155 of 169 patients (92%), resulting in a total of 620 ratings. Based on the patient's ease of intubation, raters categorized tracheotomized patients into group 1 (70%, 432 of 620); group 2 (25%, 157 of 620); or group 3 (5%, 29 of 620), each with a v appended if appropriate. The interrater reliability was κ = 0.95. We propose an airway risk classification system for tracheotomized patients, CARE, that has high interrater reliability and is easy to use and interpret. As medical providers and national organizations place more focus on improvements in interprovider communication, the creation of an airway handoff tool is integral to improving patient safety and airway management strategies following tracheotomy complications.

Extension of mixture-of-experts networks for binary classification of hierarchical data.

PubMed

Ng, Shu-Kay; McLachlan, Geoffrey J

2007-09-01

For many applied problems in the context of medically relevant artificial intelligence, the data collected exhibit a hierarchical or clustered structure. Ignoring the interdependence between hierarchical data can result in misleading classification. In this paper, we extend the mechanism for mixture-of-experts (ME) networks for binary classification of hierarchical data. Another extension is to quantify cluster-specific information on data hierarchy by random effects via the generalized linear mixed-effects model (GLMM). The extension of ME networks is implemented by allowing for correlation in the hierarchical data in both the gating and expert networks via the GLMM. The proposed model is illustrated using a real thyroid disease data set. In our study, we consider 7652 thyroid diagnosis records from 1984 to early 1987 with complete information on 20 attribute values. We obtain 10 independent random splits of the data into a training set and a test set in the proportions 85% and 15%. The test sets are used to assess the generalization performance of the proposed model, based on the percentage of misclassifications. For comparison, the results obtained from the ME network with independence assumption are also included. With the thyroid disease data, the misclassification rate on test sets for the extended ME network is 8.9%, compared to 13.9% for the ME network. In addition, based on model selection methods described in Section 2, a network with two experts is selected. These two expert networks can be considered as modeling two groups of patients with high and low incidence rates. Significant variation among the predicted cluster-specific random effects is detected in the patient group with low incidence rate. It is shown that the extended ME network outperforms the ME network for binary classification of hierarchical data. With the thyroid disease data, useful information on the relative log odds of patients with diagnosed conditions at different periods can be evaluated. This information can be taken into consideration for the assessment of treatment planning of the disease. The proposed extended ME network thus facilitates a more general approach to incorporate data hierarchy mechanism in network modeling.

Validity of the prenatal risk overview for detecting drug use disorders in pregnancy.

PubMed

Harrison, Patricia A; Godecker, Amy; Sidebottom, Abbey

2012-11-01

To validate the Prenatal Risk Overview (PRO) drug use questions against a structured diagnostic interview among pregnant women. Prenatal care patients were administered the PRO at intake and then asked to consent to a research diagnostic interview. Of 1,367 women asked to participate, 1,274 consented and 745 completed the study. Three drug use items comprised one of 13 PRO psychosocial risk domains. The Structured Clinical Interview for DSM-IV (SCID) was used as the validation instrument. To assess criterion validity, the Moderate/High and High Risk classifications were cross-tabulated with SCID Drug Use Disorder diagnoses. In response to the PRO, almost one third of participants (29.4%) reported drug use during the 12 months pre-pregnancy awareness and 11.0% reported use post-pregnancy awareness; 7.0% met SCID diagnostic criteria for Drug Abuse, Drug Dependence, or both, primarily for marijuana use. Drug Use Disorder sensitivity and specificity rates for the PRO Moderate/High Risk classifications were 88.5% and 74.3%, respectively, and for High Risk only, 78.8% and 87.3%. The PRO yielded substantial self-reporting of drug use before and after pregnancy awareness with high sensitivity and specificity for detecting Drug Use Disorders. PRO results can inform decisions about appropriate clinical responses. © 2012 Wiley Periodicals, Inc.

Outcomes of Critical Limb Ischemia in an Urban, Safety Net Hospital Population with High WIfI Amputation Scores

PubMed Central

Ward, Robert; Dunn, Joie; Clavijo, Leonardo; Shavelle, David; Rowe, Vincent; Woo, Karen

2017-01-01

Background Patients presenting to a public hospital with critical limb ischemia (CLI) typically have advanced disease with significant comorbidities. The purpose of this study was to assess the influence of revascularization on 1-year amputation rate of CLI patients presenting to Los Angeles County USC Medical Center, classified according to the Society for Vascular Surgery Wound, Ischemia and foot Infection (WIfI). Methods A retrospective review of patients who presented to a public hospital with CLI from February 2010 to July 2014 was performed. Patients were classified according to the WIfI system. Only patients with complete data who survived at least 12 months after presentation were included. Results Ninety-three patients with 98 affected limbs were included. The mean age was 62.8 years. Eighty-two patients (84%) had hypertension and 71 (72%) had diabetes. Fifty (57.5%) limbs had Trans-Atlantic Inter-Society Consensus (TASC) C or D femoral–popliteal lesions and 82 (98%) had significant infrapopliteal disease. The majority had moderate or high WIfI amputation and revascularization scores. Eighty-four (86%) limbs underwent open, endovascular, or hybrid revascularization. Overall, one year major amputation (OYMA) rate was 26.5%. In limbs with high WIfI amputation score, the OYMA was 34.5%: 21.4% in those who were revascularized and 57% in those who were not. On univariable analysis, factors associated with increased risk of OYMA were nonrevascularization (P = 0.005), hyperlipidemia (P = 0.06), hemodialysis (P = 0.005), gangrene (P = 0.02), ulcer classification (P = 0.05), WIfI amputation score (P = 0.026), and WIfI wound grade (P = 0.04). On multivariable analysis, increasing WIfI amputation score (odds ratio [OR] 1.84, 95% confidence interval [CI] 1.0–3.39) was associated with increased risk of OYMA while revascularization (OR 0.24, 95% CI 0.07–0.80) was associated with decreased risk of OYMA. Conclusions The OYMA rates in this population were consistent with those predicted by the WIfI classification system. In this population, revascularization significantly reduced the risk of amputation. Comorbidities including diabetes mellitus and TASC classification did not moderate the association of WIfI amputation score with risk of 1-year major amputation. PMID:27546850

Outcomes of Critical Limb Ischemia in an Urban, Safety Net Hospital Population with High WIfI Amputation Scores.

PubMed

Ward, Robert; Dunn, Joie; Clavijo, Leonardo; Shavelle, David; Rowe, Vincent; Woo, Karen

2017-01-01

Patients presenting to a public hospital with critical limb ischemia (CLI) typically have advanced disease with significant comorbidities. The purpose of this study was to assess the influence of revascularization on 1-year amputation rate of CLI patients presenting to Los Angeles County USC Medical Center, classified according to the Society for Vascular Surgery Wound, Ischemia and foot Infection (WIfI). A retrospective review of patients who presented to a public hospital with CLI from February 2010 to July 2014 was performed. Patients were classified according to the WIfI system. Only patients with complete data who survived at least 12 months after presentation were included. Ninety-three patients with 98 affected limbs were included. The mean age was 62.8 years. Eighty-two patients (84%) had hypertension and 71 (72%) had diabetes. Fifty (57.5%) limbs had Trans-Atlantic Inter-Society Consensus (TASC) C or D femoral-popliteal lesions and 82 (98%) had significant infrapopliteal disease. The majority had moderate or high WIfI amputation and revascularization scores. Eighty-four (86%) limbs underwent open, endovascular, or hybrid revascularization. Overall, one year major amputation (OYMA) rate was 26.5%. In limbs with high WIfI amputation score, the OYMA was 34.5%: 21.4% in those who were revascularized and 57% in those who were not. On univariable analysis, factors associated with increased risk of OYMA were nonrevascularization (P = 0.005), hyperlipidemia (P = 0.06), hemodialysis (P = 0.005), gangrene (P = 0.02), ulcer classification (P = 0.05), WIfI amputation score (P = 0.026), and WIfI wound grade (P = 0.04). On multivariable analysis, increasing WIfI amputation score (odds ratio [OR] 1.84, 95% confidence interval [CI] 1.0-3.39) was associated with increased risk of OYMA while revascularization (OR 0.24, 95% CI 0.07-0.80) was associated with decreased risk of OYMA. The OYMA rates in this population were consistent with those predicted by the WIfI classification system. In this population, revascularization significantly reduced the risk of amputation. Comorbidities including diabetes mellitus and TASC classification did not moderate the association of WIfI amputation score with risk of 1-year major amputation. Published by Elsevier Inc.

Combining Passive Microwave Rain Rate Retrieval with Visible and Infrared Cloud Classification.

NASA Astrophysics Data System (ADS)

Miller, Shawn William

The relation between cloud type and rain rate has been investigated here from different approaches. Previous studies and intercomparisons have indicated that no single passive microwave rain rate algorithm is an optimal choice for all types of precipitating systems. Motivated by the upcoming Tropical Rainfall Measuring Mission (TRMM), an algorithm which combines visible and infrared cloud classification with passive microwave rain rate estimation was developed and analyzed in a preliminary manner using data from the Tropical Ocean Global Atmosphere-Coupled Ocean Atmosphere Response Experiment (TOGA-COARE). Overall correlation with radar rain rate measurements across five case studies showed substantial improvement in the combined algorithm approach when compared to the use of any single microwave algorithm. An automated neural network cloud classifier for use over both land and ocean was independently developed and tested on Advanced Very High Resolution Radiometer (AVHRR) data. The global classifier achieved strict accuracy for 82% of the test samples, while a more localized version achieved strict accuracy for 89% of its own test set. These numbers provide hope for the eventual development of a global automated cloud classifier for use throughout the tropics and the temperate zones. The localized classifier was used in conjunction with gridded 15-minute averaged radar rain rates at 8km resolution produced from the current operational network of National Weather Service (NWS) radars, to investigate the relation between cloud type and rain rate over three regions of the continental United States and adjacent waters. The results indicate a substantially lower amount of available moisture in the Front Range of the Rocky Mountains than in the Midwest or in the eastern Gulf of Mexico.

Cesarean section trends in the Nordic Countries - a comparative analysis with the Robson classification.

PubMed

Pyykönen, Aura; Gissler, Mika; Løkkegaard, Ellen; Bergholt, Thomas; Rasmussen, Steen C; Smárason, Alexander; Bjarnadóttir, Ragnheiður I; Másdóttir, Birna B; Källén, Karin; Klungsoyr, Kari; Albrechtsen, Susanne; Skjeldestad, Finn E; Tapper, Anna-Maija

2017-05-01

The cesarean rates are low but increasing in most Nordic countries. Using the Robson classification, we analyzed which obstetric groups have contributed to the changes in the cesarean rates. Retrospective population-based registry study including all deliveries (3 398 586) between 2000 and 2011 in Denmark, Finland, Iceland, Norway and Sweden. The Robson group distribution, cesarean rate and contribution of each Robson group were analyzed nationally for four 3-year time periods. For each country, we analyzed which groups contributed to the change in the total cesarean rate. Between the first and the last time period studied, the total cesarean rates increased in Denmark (16.4 to 20.7%), Norway (14.4 to 16.5%) and Sweden (15.5 to 17.1%), but towards the end of our study, the cesarean rates stabilized or even decreased. The increase was explained mainly by increases in the absolute contribution from R5 (women with previous cesarean) and R2a (induced labor on nulliparous). In Finland, the cesarean rate decreased slightly (16.5 to 16.2%) mainly due to decrease among R5 and R6-R7 (breech presentation, nulliparous/multiparous). In Iceland, the cesarean rate decreased in all parturient groups (17.6 to 15.3%), most essentially among nulliparous women despite the increased induction rates. The increased total cesarean rates in the Nordic countries are explained by increased cesarean rates among nulliparous women, and by an increased percentage of women with previous cesarean. Meanwhile, induction rates on nulliparous increased significantly, but the impact on the total cesarean rate was unclear. The Robson classification facilitates benchmarking and targeting efforts for lowering the cesarean rates. © 2017 Nordic Federation of Societies of Obstetrics and Gynecology.

Validation of a new classification system for interprosthetic femoral fractures.

PubMed

Pires, Robinson Esteves Santos; Silveira, Marcelo Peixoto Sena; Resende, Alessandra Regina da Silva; Junior, Egidio Oliveira Santana; Campos, Tulio Vinicius Oliveira; Santos, Leandro Emilio Nascimento; Balbachevsky, Daniel; Andrade, Marco Antônio Percope de

2017-07-01

Interprosthetic femoral fracture (IFF) incidence is gradually increasing as the population is progressively ageing. However, treatment remains challenging due to several contributing factors, such as poor bone quality, patient comorbidities, small interprosthetic fragment, and prostheses instability. An effective and specific classification system is essential to optimize treatment management, therefore diminishing complication rates. This study aims to validate a previously described classification system for interprosthetic femoral fractures. Copyright © 2017 Elsevier Ltd. All rights reserved.

A Method of Spatial Mapping and Reclassification for High-Spatial-Resolution Remote Sensing Image Classification

PubMed Central

Wang, Guizhou; Liu, Jianbo; He, Guojin

2013-01-01

This paper presents a new classification method for high-spatial-resolution remote sensing images based on a strategic mechanism of spatial mapping and reclassification. The proposed method includes four steps. First, the multispectral image is classified by a traditional pixel-based classification method (support vector machine). Second, the panchromatic image is subdivided by watershed segmentation. Third, the pixel-based multispectral image classification result is mapped to the panchromatic segmentation result based on a spatial mapping mechanism and the area dominant principle. During the mapping process, an area proportion threshold is set, and the regional property is defined as unclassified if the maximum area proportion does not surpass the threshold. Finally, unclassified regions are reclassified based on spectral information using the minimum distance to mean algorithm. Experimental results show that the classification method for high-spatial-resolution remote sensing images based on the spatial mapping mechanism and reclassification strategy can make use of both panchromatic and multispectral information, integrate the pixel- and object-based classification methods, and improve classification accuracy. PMID:24453808

Combination of laser-induced breakdown spectroscopy and Raman spectroscopy for multivariate classification of bacteria

NASA Astrophysics Data System (ADS)

Prochazka, D.; Mazura, M.; Samek, O.; Rebrošová, K.; Pořízka, P.; Klus, J.; Prochazková, P.; Novotný, J.; Novotný, K.; Kaiser, J.

2018-01-01

In this work, we investigate the impact of data provided by complementary laser-based spectroscopic methods on multivariate classification accuracy. Discrimination and classification of five Staphylococcus bacterial strains and one strain of Escherichia coli is presented. The technique that we used for measurements is a combination of Raman spectroscopy and Laser-Induced Breakdown Spectroscopy (LIBS). Obtained spectroscopic data were then processed using Multivariate Data Analysis algorithms. Principal Components Analysis (PCA) was selected as the most suitable technique for visualization of bacterial strains data. To classify the bacterial strains, we used Neural Networks, namely a supervised version of Kohonen's self-organizing maps (SOM). We were processing results in three different ways - separately from LIBS measurements, from Raman measurements, and we also merged data from both mentioned methods. The three types of results were then compared. By applying the PCA to Raman spectroscopy data, we observed that two bacterial strains were fully distinguished from the rest of the data set. In the case of LIBS data, three bacterial strains were fully discriminated. Using a combination of data from both methods, we achieved the complete discrimination of all bacterial strains. All the data were classified with a high success rate using SOM algorithm. The most accurate classification was obtained using a combination of data from both techniques. The classification accuracy varied, depending on specific samples and techniques. As for LIBS, the classification accuracy ranged from 45% to 100%, as for Raman Spectroscopy from 50% to 100% and in case of merged data, all samples were classified correctly. Based on the results of the experiments presented in this work, we can assume that the combination of Raman spectroscopy and LIBS significantly enhances discrimination and classification accuracy of bacterial species and strains. The reason is the complementarity in obtained chemical information while using these two methods.

75 FR 69145 - Postal Rate Changes

Federal Register 2010, 2011, 2012, 2013, 2014

2010-11-10

.... Attachment 2--a redacted copy of Governors' Decision No. 09-15 which establishes prices and classifications... Notice states that Governors' Decision No. 09-15 established prices and classifications not of general... annual inflation information from the Consumer Price Index for All Urban Consumers. Id. Based on this and...

29 CFR 4.163 - Section 4(c) of the Act.

Code of Federal Regulations, 2013 CFR

2013-07-01

... their support contracts. Thus, specific contract requirements from one contract may be broken out and... substantially the same job classifications, the predecessor contract which covers the greater portion of the... bargaining agreement. However, failure to include in the wage determination any job classification, wage rate...

29 CFR 4.163 - Section 4(c) of the Act.

Code of Federal Regulations, 2012 CFR

2012-07-01

... their support contracts. Thus, specific contract requirements from one contract may be broken out and... substantially the same job classifications, the predecessor contract which covers the greater portion of the... bargaining agreement. However, failure to include in the wage determination any job classification, wage rate...

29 CFR 4.163 - Section 4(c) of the Act.

Code of Federal Regulations, 2014 CFR

2014-07-01

... their support contracts. Thus, specific contract requirements from one contract may be broken out and... substantially the same job classifications, the predecessor contract which covers the greater portion of the... bargaining agreement. However, failure to include in the wage determination any job classification, wage rate...

29 CFR 4.163 - Section 4(c) of the Act.

Code of Federal Regulations, 2011 CFR

2011-07-01

... their support contracts. Thus, specific contract requirements from one contract may be broken out and... substantially the same job classifications, the predecessor contract which covers the greater portion of the... bargaining agreement. However, failure to include in the wage determination any job classification, wage rate...

76 FR 16460 - Parcel Select Price and Classification Changes

Federal Register 2010, 2011, 2012, 2013, 2014

2011-03-23

... POSTAL REGULATORY COMMISSION [Docket No. CP2011-64; Order No. 698] Parcel Select Price and... a recently-filed Postal Service notice of rate and classification changes affecting Parcel Select. The Postal Service seeks to implement new prices for Parcel Select for forwarding and return to sender...

Semi-Automated Classification of Seafloor Data Collected on the Delmarva Inner Shelf

NASA Astrophysics Data System (ADS)

Sweeney, E. M.; Pendleton, E. A.; Brothers, L. L.; Mahmud, A.; Thieler, E. R.

2017-12-01

We tested automated classification methods on acoustic bathymetry and backscatter data collected by the U.S. Geological Survey (USGS) and National Oceanic and Atmospheric Administration (NOAA) on the Delmarva inner continental shelf to efficiently and objectively identify sediment texture and geomorphology. Automated classification techniques are generally less subjective and take significantly less time than manual classification methods. We used a semi-automated process combining unsupervised and supervised classification techniques to characterize seafloor based on bathymetric slope and relative backscatter intensity. Statistical comparison of our automated classification results with those of a manual classification conducted on a subset of the acoustic imagery indicates that our automated method was highly accurate (95% total accuracy and 93% Kappa). Our methods resolve sediment ridges, zones of flat seafloor and areas of high and low backscatter. We compared our classification scheme with mean grain size statistics of samples collected in the study area and found that strong correlations between backscatter intensity and sediment texture exist. High backscatter zones are associated with the presence of gravel and shells mixed with sand, and low backscatter areas are primarily clean sand or sand mixed with mud. Slope classes further elucidate textural and geomorphologic differences in the seafloor, such that steep slopes (>0.35°) with high backscatter are most often associated with the updrift side of sand ridges and bedforms, whereas low slope with high backscatter correspond to coarse lag or shell deposits. Low backscatter and high slopes are most often found on the downdrift side of ridges and bedforms, and low backscatter and low slopes identify swale areas and sand sheets. We found that poor acoustic data quality was the most significant cause of inaccurate classification results, which required additional user input to mitigate. Our method worked well along the primarily sandy Delmarva inner continental shelf, and outlines a method that can be used to efficiently and consistently produce surficial geologic interpretations of the seafloor from ground-truthed geophysical or hydrographic data.

EOG and EMG: two important switches in automatic sleep stage classification.

PubMed

Estrada, E; Nazeran, H; Barragan, J; Burk, J R; Lucas, E A; Behbehani, K

2006-01-01

Sleep is a natural periodic state of rest for the body, in which the eyes are usually closed and consciousness is completely or partially lost. In this investigation we used the EOG and EMG signals acquired from 10 patients undergoing overnight polysomnography with their sleep stages determined by expert sleep specialists based on RK rules. Differentiation between Stage 1, Awake and REM stages challenged a well trained neural network classifier to distinguish between classes when only EEG-derived signal features were used. To meet this challenge and improve the classification rate, extra features extracted from EOG and EMG signals were fed to the classifier. In this study, two simple feature extraction algorithms were applied to EOG and EMG signals. The statistics of the results were calculated and displayed in an easy to visualize fashion to observe tendencies for each sleep stage. Inclusion of these features show a great promise to improve the classification rate towards the target rate of 100%

Progress toward the determination of correct classification rates in fire debris analysis.

PubMed

Waddell, Erin E; Song, Emma T; Rinke, Caitlin N; Williams, Mary R; Sigman, Michael E

2013-07-01

Principal components analysis (PCA), linear discriminant analysis (LDA), and quadratic discriminant analysis (QDA) were used to develop a multistep classification procedure for determining the presence of ignitable liquid residue in fire debris and assigning any ignitable liquid residue present into the classes defined under the American Society for Testing and Materials (ASTM) E 1618-10 standard method. A multistep classification procedure was tested by cross-validation based on model data sets comprised of the time-averaged mass spectra (also referred to as total ion spectra) of commercial ignitable liquids and pyrolysis products from common building materials and household furnishings (referred to simply as substrates). Fire debris samples from laboratory-scale and field test burns were also used to test the model. The optimal model's true-positive rate was 81.3% for cross-validation samples and 70.9% for fire debris samples. The false-positive rate was 9.9% for cross-validation samples and 8.9% for fire debris samples. © 2013 American Academy of Forensic Sciences.

Artificial neural network for normal, hypertensive, and preeclamptic pregnancy classification using maternal heart rate variability indexes.

PubMed

Tejera, Eduardo; Jose Areias, Maria; Rodrigues, Ana; Ramõa, Ana; Manuel Nieto-Villar, Jose; Rebelo, Irene

2011-09-01

A model construction for classification of women with normal, hypertensive and preeclamptic pregnancy in different gestational ages using maternal heart rate variability (HRV) indexes. In the present work, we applied the artificial neural network for the classification problem, using the signal composed by the time intervals between consecutive RR peaks (RR) (n = 568) obtained from ECG records. Beside the HRV indexes, we also considered other factors like maternal history and blood pressure measurements. The obtained result reveals sensitivity for preeclampsia around 80% that increases for hypertensive and normal pregnancy groups. On the other hand, specificity is around 85-90%. These results indicate that the combination of HRV indexes with artificial neural networks (ANN) could be helpful for pregnancy study and characterization.

APOL1 Oligomerization as the Key Mediator of Kidney Disease in African Americans

DTIC Science & Technology

2015-10-01

kidney disease that accounts for the high rate of kidney disease in African Americans. This work is based on the hypothesize that APOL1 kidney disease...microscopy- based approaches. 15. SUBJECT TERMS Kidney, ESRD, APOL1, African American 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT 18. NUMBER...is based on the hypothesize that APOL1 kidney disease in African Americans results from abnormal aggregation of the APOL1 risk variant protein in an

Targeting MRS-Defined Dominant Intraprostatic Lesions with Inverse-Planned High Dose Rate Brachytherapy

DTIC Science & Technology

2010-06-01

known frequency positions of each of these peaks [30]. Spectroscopic voxels were classified using the standardized scoring system proposed by Jung et...indicative of malignancy (red voxels). The center image shows the voxel classifications described by Jung et al, and the right image shows the suspicious...J. Star-Lack, D. B. Vigneron, J. Pauly , J. Kurhanewicz, S. J. Nelson, Journal of Magnetic Resonance Imaging 7(4), 745 (1997). [26] J. Star-Lack, S

Relationship between risk classifications used to organize the demand for oral health in a small city of São Paulo, Brazil.

PubMed

Peres, João; Mendes, Karine Laura Cortellazzi; Wada, Ronaldo Seichi; Sousa, Maria da Luz Rosario de

2017-06-01

Oral health teams can work with both information of the people related to the family context as individual epidemiological through risk ratings, considering equity and service organization. The purpose of our study was to evaluate the association between tools that classify individual and family risk. The study group consisted of students from the age group of 5-6 years and 11-12 years who were classified regarding risk of caries and whether their parents had periodontal disease, in addition to the family risk. There was an association between the risk rating for decay in children (n = 128) and family risk classification with Coef C = 0.338 and p = 0.01, indicating that the higher the family's risk, the higher the risk of caries. Similarly, the association between the risk classification for periodontal disease in parents and family risk classification with Coef C = 0.5503 and p = 0.03 indicated that the higher the family risk, the higher the risk of periodontal disease. It can be concluded that the use of family risk rating tool is indicated as a possibility of ordering actions of the dental service, organizing their demand with greater equity, in this access door.

Estimating the exceedance probability of rain rate by logistic regression

NASA Technical Reports Server (NTRS)

Chiu, Long S.; Kedem, Benjamin

1990-01-01

Recent studies have shown that the fraction of an area with rain intensity above a fixed threshold is highly correlated with the area-averaged rain rate. To estimate the fractional rainy area, a logistic regression model, which estimates the conditional probability that rain rate over an area exceeds a fixed threshold given the values of related covariates, is developed. The problem of dependency in the data in the estimation procedure is bypassed by the method of partial likelihood. Analyses of simulated scanning multichannel microwave radiometer and observed electrically scanning microwave radiometer data during the Global Atlantic Tropical Experiment period show that the use of logistic regression in pixel classification is superior to multiple regression in predicting whether rain rate at each pixel exceeds a given threshold, even in the presence of noisy data. The potential of the logistic regression technique in satellite rain rate estimation is discussed.

Examining the relation between rock mass cuttability index and rock drilling properties

NASA Astrophysics Data System (ADS)

Yetkin, Mustafa E.; Özfırat, M. Kemal; Yenice, Hayati; Şimşir, Ferhan; Kahraman, Bayram

2016-12-01

Drilling rate is a substantial index value in drilling and excavation operations at mining. It is not only a help in determining physical and mechanical features of rocks, but also delivers strong estimations about instantaneous cutting rates. By this way, work durations to be finished on time, proper machine/equipment selection and efficient excavation works can be achieved. In this study, physical and mechanical properties of surrounding rocks and ore zones are determined by investigations carried out on specimens taken from an underground ore mine. Later, relationships among rock mass classifications, drillability rates, cuttability, and abrasivity have been investigated using multi regression analysis. As a result, equations having high regression rates have been found out among instantaneous cutting rates and geomechanical properties of rocks. Moreover, excavation machine selection for the study area has been made at the best possible interval.

Who Must We Target Now to Minimize Future Cardiovascular Events and Total Mortality?: Lessons From the Surveillance, Prevention and Management of Diabetes Mellitus (SUPREME-DM) Cohort Study.

PubMed

Desai, Jay R; Vazquez-Benitez, Gabriela; Xu, Zhiyuan; Schroeder, Emily B; Karter, Andrew J; Steiner, John F; Nichols, Gregory A; Reynolds, Kristi; Xu, Stanley; Newton, Katherine; Pathak, Ram D; Waitzfelder, Beth; Lafata, Jennifer Elston; Butler, Melissa G; Kirchner, H Lester; Thomas, Abraham; O'Connor, Patrick J

2015-09-01

Examining trends in cardiovascular events and mortality in US health systems can guide the design of targeted clinical and public health strategies to reduce cardiovascular events and mortality rates. We conducted an observational cohort study from 2005 to 2011 among 1.25 million diabetic subjects and 1.25 million nondiabetic subjects from 11 health systems that participate in the Surveillance, Prevention and Management of Diabetes Mellitus (SUPREME-DM) DataLink. Annual rates (per 1000 person-years) of myocardial infarction/acute coronary syndrome (International Classification of Diseases-Ninth Revision, 410.0–410.91, 411.1–411.8), stroke (International Classification of Diseases-Ninth Revision, 430–432.9, 433–434.9), heart failure (International Classification of Diseases-Ninth Revision, 428–428.9), and all-cause mortality were monitored by diabetes mellitus (DM) status, age, sex, race/ethnicity, and a prior cardiovascular history. We observed significant declines in cardiovascular events and mortality rates in subjects with and without DM. However, there was substantial variation by age, sex, race/ethnicity, and prior cardiovascular history. Mortality declined from 44.7 to 27.1 (P<0.0001) for those with DM and cardiovascular disease (CVD), from 11.2 to 10.9 (P=0.03) for those with DM only, and from 18.9 to 13.0 (P<0.0001) for those with CVD only. Yet, in the [almost equal to]85% of subjects with neither DM nor CVD, overall mortality (7.0 to 6.8; P=0.10) and stroke rates (1.6–1.6; P=0.77) did not decline and heart failure rates increased (0.9–1.15; P=0.0005). To sustain improvements in myocardial infarction, stroke, heart failure, and mortality, health systems that have successfully focused on care improvement in high-risk adults with DM or CVD must broaden their improvement strategies to target lower risk adults who have not yet developed DM or CVD.

Thermographic image analysis as a pre-screening tool for the detection of canine bone cancer

NASA Astrophysics Data System (ADS)

Subedi, Samrat; Umbaugh, Scott E.; Fu, Jiyuan; Marino, Dominic J.; Loughin, Catherine A.; Sackman, Joseph

2014-09-01

Canine bone cancer is a common type of cancer that grows fast and may be fatal. It usually appears in the limbs which is called "appendicular bone cancer." Diagnostic imaging methods such as X-rays, computed tomography (CT scan), and magnetic resonance imaging (MRI) are more common methods in bone cancer detection than invasive physical examination such as biopsy. These imaging methods have some disadvantages; including high expense, high dose of radiation, and keeping the patient (canine) motionless during the imaging procedures. This project study identifies the possibility of using thermographic images as a pre-screening tool for diagnosis of bone cancer in dogs. Experiments were performed with thermographic images from 40 dogs exhibiting the disease bone cancer. Experiments were performed with color normalization using temperature data provided by the Long Island Veterinary Specialists. The images were first divided into four groups according to body parts (Elbow/Knee, Full Limb, Shoulder/Hip and Wrist). Each of the groups was then further divided into three sub-groups according to views (Anterior, Lateral and Posterior). Thermographic pattern of normal and abnormal dogs were analyzed using feature extraction and pattern classification tools. Texture features, spectral feature and histogram features were extracted from the thermograms and were used for pattern classification. The best classification success rate in canine bone cancer detection is 90% with sensitivity of 100% and specificity of 80% produced by anterior view of full-limb region with nearest neighbor classification method and normRGB-lum color normalization method. Our results show that it is possible to use thermographic imaging as a pre-screening tool for detection of canine bone cancer.

Automated Segmentation and Classification of Coral using Fluid Lensing from Unmanned Airborne Platforms

NASA Technical Reports Server (NTRS)

Instrella, Ron; Chirayath, Ved

2016-01-01

In recent years, there has been a growing interest among biologists in monitoring the short and long term health of the world's coral reefs. The environmental impact of climate change poses a growing threat to these biologically diverse and fragile ecosystems, prompting scientists to use remote sensing platforms and computer vision algorithms to analyze shallow marine systems. In this study, we present a novel method for performing coral segmentation and classification from aerial data collected from small unmanned aerial vehicles (sUAV). Our method uses Fluid Lensing algorithms to remove and exploit strong optical distortions created along the air-fluid boundary to produce cm-scale resolution imagery of the ocean floor at depths up to 5 meters. A 3D model of the reef is reconstructed using structure from motion (SFM) algorithms, and the associated depth information is combined with multidimensional maximum a posteriori (MAP) estimation to separate organic from inorganic material and classify coral morphologies in the Fluid-Lensed transects. In this study, MAP estimation is performed using a set of manually classified 100 x 100 pixel training images to determine the most probable coral classification within an interrogated region of interest. Aerial footage of a coral reef was captured off the coast of American Samoa and used to test our proposed method. 90 x 20 meter transects of the Samoan coastline undergo automated classification and are manually segmented by a marine biologist for comparison, leading to success rates as high as 85%. This method has broad applications for coastal remote sensing, and will provide marine biologists access to large swaths of high resolution, segmented coral imagery.

Automated Segmentation and Classification of Coral using Fluid Lensing from Unmanned Airborne Platforms

NASA Astrophysics Data System (ADS)

Instrella, R.; Chirayath, V.

2015-12-01

In recent years, there has been a growing interest among biologists in monitoring the short and long term health of the world's coral reefs. The environmental impact of climate change poses a growing threat to these biologically diverse and fragile ecosystems, prompting scientists to use remote sensing platforms and computer vision algorithms to analyze shallow marine systems. In this study, we present a novel method for performing coral segmentation and classification from aerial data collected from small unmanned aerial vehicles (sUAV). Our method uses Fluid Lensing algorithms to remove and exploit strong optical distortions created along the air-fluid boundary to produce cm-scale resolution imagery of the ocean floor at depths up to 5 meters. A 3D model of the reef is reconstructed using structure from motion (SFM) algorithms, and the associated depth information is combined with multidimensional maximum a posteriori (MAP) estimation to separate organic from inorganic material and classify coral morphologies in the Fluid-Lensed transects. In this study, MAP estimation is performed using a set of manually classified 100 x 100 pixel training images to determine the most probable coral classification within an interrogated region of interest. Aerial footage of a coral reef was captured off the coast of American Samoa and used to test our proposed method. 90 x 20 meter transects of the Samoan coastline undergo automated classification and are manually segmented by a marine biologist for comparison, leading to success rates as high as 85%. This method has broad applications for coastal remote sensing, and will provide marine biologists access to large swaths of high resolution, segmented coral imagery.

Classifying smoking urges via machine learning

PubMed Central

Dumortier, Antoine; Beckjord, Ellen; Shiffman, Saul; Sejdić, Ervin

2016-01-01

Background and objective Smoking is the largest preventable cause of death and diseases in the developed world, and advances in modern electronics and machine learning can help us deliver real-time intervention to smokers in novel ways. In this paper, we examine different machine learning approaches to use situational features associated with having or not having urges to smoke during a quit attempt in order to accurately classify high-urge states. Methods To test our machine learning approaches, specifically, Bayes, discriminant analysis and decision tree learning methods, we used a dataset collected from over 300 participants who had initiated a quit attempt. The three classification approaches are evaluated observing sensitivity, specificity, accuracy and precision. Results The outcome of the analysis showed that algorithms based on feature selection make it possible to obtain high classification rates with only a few features selected from the entire dataset. The classification tree method outperformed the naive Bayes and discriminant analysis methods, with an accuracy of the classifications up to 86%. These numbers suggest that machine learning may be a suitable approach to deal with smoking cessation matters, and to predict smoking urges, outlining a potential use for mobile health applications. Conclusions In conclusion, machine learning classifiers can help identify smoking situations, and the search for the best features and classifier parameters significantly improves the algorithms’ performance. In addition, this study also supports the usefulness of new technologies in improving the effect of smoking cessation interventions, the management of time and patients by therapists, and thus the optimization of available health care resources. Future studies should focus on providing more adaptive and personalized support to people who really need it, in a minimum amount of time by developing novel expert systems capable of delivering real-time interventions. PMID:28110725

Use of routinely available clinical, nutritional, and functional criteria to classify cachexia in advanced cancer patients.

PubMed

Vigano, Antonio A L; Morais, José A; Ciutto, Lorella; Rosenthall, Leonard; di Tomasso, Jonathan; Khan, Sarah; Olders, Henry; Borod, Manuel; Kilgour, Robert D

2017-10-01

Cachexia is a highly prevalent syndrome in cancer and chronic diseases. However, due to the heterogeneous features of cancer cachexia, its identification and classification challenge clinical practitioners. To determine the clinical relevance of a cancer cachexia classification system in advanced cancer patients. Beginning with the four-stage classification system proposed for cachexia [non-cachexia (NCa), pre-cachexia (PCa), cachexia (Ca) and refractory cachexia (RCa)], we assigned patients to these cachexia stages according to five classification criteria available in clinical practice: 1) biochemistry (high C-reactive protein or leukocytes, or hypoalbuminemia, or anemia), 2) food intake (normal/decreased), weight loss: 3) moderate (≤5%) or 4) significant (>5%/past six months) and 5) performance status (Eastern Cooperative Oncology Group Performance Status ≥ 3). We then determined if symptom severity, body composition changes, functional levels, hospitalizations and survival rates varied significantly across cachexia stages. Two-hundred and ninety-seven advanced cancer patients with primary gastrointestinal and lung tumors were included. Patients were classified into Ca (36%), PCa and RCa (21%, respectively) and NCa (15%). Significant (p < 0.05) differences were observed among cachexia stages for most of the outcome measures (symptoms, body composition, handgrip strength, emergency room visits and length of hospital stays) according to cachexia severity. Survival also differed between cachexia stages (except between PCa and Ca). Five clinical criteria can be used to stage cancer cachexia patients and predict important clinical, nutritional and functional outcomes. The lack of statistical difference between PCa and Ca in almost all clinical outcomes examined suggests either that the PCa group includes patients already affected by early cachexia or that more precise criteria are needed to differentiate PCa from Ca patients. More studies are required to validate these findings. Copyright © 2016. Published by Elsevier Ltd.