Intelligent Sensing and Classification in DSR-Based Ad Hoc Networks

NASA Astrophysics Data System (ADS)

Dempsey, Tae; Sahin, Gokhan; Morton, Yu T. (Jade

Wireless ad hoc networks have fundamentally altered today's battlefield, with applications ranging from unmanned air vehicles to randomly deployed sensor networks. Security and vulnerabilities in wireless ad hoc networks have been considered at different layers, and many attack strategies have been proposed, including denial of service (DoS) through the intelligent jamming of the most critical packet types of flows in a network. This paper investigates the effectiveness of intelligent jamming in wireless ad hoc networks using the Dynamic Source Routing (DSR) and TCP protocols and introduces an intelligent classifier to facilitate the jamming of such networks. Assuming encrypted packet headers and contents, our classifier is based solely on the observable characteristics of size, inter-arrival timing, and direction and classifies packets with up to 99.4% accuracy in our experiments.

Classification and Vertical Structure of Radar Precipitation Echoes at Naqu in Central Tibetan Plateau during the TIPEX-III Field Campaign

NASA Astrophysics Data System (ADS)

Luo, Y.; Wang, H.; Ma, R.; Zipser, E. J.; Liu, C.

2017-12-01

This study examines the vertical structure of precipitation echoes in central Tibetan Plateau using observations collected at Naqu during the Third Tibetan Plateau Atmospheric Scientific Experiment in July-August 2014. Precipitation reaching the surface is classified into stratiform, convective, and other by analyzing the vertical profiles of reflectivity (Ze) at 30-m spacing and 3-s temporal resolution made with the vertical pointing C-band frequency-modulated continuous-wave (C-FMCW) radar. Radar echoes with non-zero surface rainfall rate are observed during 17.96% of the entire observing period. About 52.03% of the precipitation reaching the surface includes a bright band and lacks a thick layer (≥1 km) of large Ze (> 35 dBZ); these are classified as stratiform; non-stratiform echoes with Ze > 35 dBZ are classified as convective (4.99%); the remainder (42.98%) as other. Based on concurrent measurements made with a collocated disdrometer, the classified stratiform, convective, and other precipitation echoes contribute 53.84%, 23.08%, and 23.08%, respectively, to the surface rainfall amount. Distinct internal structural features of each echo type are revealed by collectively analyzing the vertical profiles of Ze, radial velocity (Vr), and spectral width (SW) observed by the C-FMCW radar. The stratiform precipitation contains a melting-layer centered at 0.97 km above ground with an average depth of 415 m. The median Ze at 0°C -15°C levels in convective regions at Naqu is weaker than those in some midlatitude continental convection and stronger than those in some tropical continents, suggesting that convective intensity measured by mixed-phase microphysical processes at Naqu is intermediate.

Attribute-driven transfer learning for detecting novel buried threats with ground-penetrating radar

NASA Astrophysics Data System (ADS)

Colwell, Kenneth A.; Collins, Leslie M.

2016-05-01

Ground-penetrating radar (GPR) technology is an effective method of detecting buried explosive threats. The system uses a binary classifier to distinguish "targets", or buried threats, from "nontargets" arising from system prescreener false alarms; this classifier is trained on a dataset of previously-observed buried threat types. However, the threat environment is not static, and new threat types that appear must be effectively detected even if they are not highly similar to every previously-observed type. Gathering a new dataset that includes a new threat type is expensive and time-consuming; minimizing the amount of new data required to effectively detect the new type is therefore valuable. This research aims to reduce the number of training examples needed to effectively detect new types using transfer learning, which leverages previous learning tasks to accelerate and improve new ones. Further, new types have attribute data, such as composition, components, construction, and size, which can be observed without GPR and typically are not explicitly included in the learning process. Since attribute tags for buried threats determine many aspects of their GPR representation, a new threat type's attributes can be highly relevant to the transfer-learning process. In this work, attribute data is used to drive transfer learning, both by using attributes to select relevant dataset examples for classifier fusion, and by extending a relevance vector machine (RVM) model to perform intelligent attribute clustering and selection. Classification performance results for both the attribute-only case and the low-data case are presented, using a dataset containing a variety of threat types.

Minibeasts and Butterflies. First Grade. Anchorage School District Elementary Science Program.

ERIC Educational Resources Information Center

Defendorf, Jean, Ed.

This publication provides information and activities for teaching about insects and process skills including observing, classifying, collecting and interpreting data, inferring, measuring, and predicting. There are 13 lessons. Lessons 1 through 3 deal with insects, in general, and with moths and butterflies. Lessons 4 through 7 consist of…

Towards SSVEP-based, portable, responsive Brain-Computer Interface.

PubMed

Kaczmarek, Piotr; Salomon, Pawel

2015-08-01

A Brain-Computer Interface in motion control application requires high system responsiveness and accuracy. SSVEP interface consisted of 2-8 stimuli and 2 channel EEG amplifier was presented in this paper. The observed stimulus is recognized based on a canonical correlation calculated in 1 second window, ensuring high interface responsiveness. A threshold classifier with hysteresis (T-H) was proposed for recognition purposes. Obtained results suggest that T-H classifier enables to significantly increase classifier performance (resulting in accuracy of 76%, while maintaining average false positive detection rate of stimulus different then observed one between 2-13%, depending on stimulus frequency). It was shown that the parameters of T-H classifier, maximizing true positive rate, can be estimated by gradient-based search since the single maximum was observed. Moreover the preliminary results, performed on a test group (N=4), suggest that for T-H classifier exists a certain set of parameters for which the system accuracy is similar to accuracy obtained for user-trained classifier.

Intelligent classifier for dynamic fault patterns based on hidden Markov model

NASA Astrophysics Data System (ADS)

Xu, Bo; Feng, Yuguang; Yu, Jinsong

2006-11-01

It's difficult to build precise mathematical models for complex engineering systems because of the complexity of the structure and dynamics characteristics. Intelligent fault diagnosis introduces artificial intelligence and works in a different way without building the analytical mathematical model of a diagnostic object, so it's a practical approach to solve diagnostic problems of complex systems. This paper presents an intelligent fault diagnosis method, an integrated fault-pattern classifier based on Hidden Markov Model (HMM). This classifier consists of dynamic time warping (DTW) algorithm, self-organizing feature mapping (SOFM) network and Hidden Markov Model. First, after dynamic observation vector in measuring space is processed by DTW, the error vector including the fault feature of being tested system is obtained. Then a SOFM network is used as a feature extractor and vector quantization processor. Finally, fault diagnosis is realized by fault patterns classifying with the Hidden Markov Model classifier. The importing of dynamic time warping solves the problem of feature extracting from dynamic process vectors of complex system such as aeroengine, and makes it come true to diagnose complex system by utilizing dynamic process information. Simulating experiments show that the diagnosis model is easy to extend, and the fault pattern classifier is efficient and is convenient to the detecting and diagnosing of new faults.

Neuropsychological Test Selection for Cognitive Impairment Classification: A Machine Learning Approach

PubMed Central

Williams, Jennifer A.; Schmitter-Edgecombe, Maureen; Cook, Diane J.

2016-01-01

Introduction Reducing the amount of testing required to accurately detect cognitive impairment is clinically relevant. The aim of this research was to determine the fewest number of clinical measures required to accurately classify participants as healthy older adult, mild cognitive impairment (MCI) or dementia using a suite of classification techniques. Methods Two variable selection machine learning models (i.e., naive Bayes, decision tree), a logistic regression, and two participant datasets (i.e., clinical diagnosis, clinical dementia rating; CDR) were explored. Participants classified using clinical diagnosis criteria included 52 individuals with dementia, 97 with MCI, and 161 cognitively healthy older adults. Participants classified using CDR included 154 individuals CDR = 0, 93 individuals with CDR = 0.5, and 25 individuals with CDR = 1.0+. Twenty-seven demographic, psychological, and neuropsychological variables were available for variable selection. Results No significant difference was observed between naive Bayes, decision tree, and logistic regression models for classification of both clinical diagnosis and CDR datasets. Participant classification (70.0 – 99.1%), geometric mean (60.9 – 98.1%), sensitivity (44.2 – 100%), and specificity (52.7 – 100%) were generally satisfactory. Unsurprisingly, the MCI/CDR = 0.5 participant group was the most challenging to classify. Through variable selection only 2 – 9 variables were required for classification and varied between datasets in a clinically meaningful way. Conclusions The current study results reveal that machine learning techniques can accurately classifying cognitive impairment and reduce the number of measures required for diagnosis. PMID:26332171

Low altitude temperature and humidity profile data for application to aircraft noise propagation

NASA Technical Reports Server (NTRS)

Connor, A. B.; Copeland, W. L.; Fulbright, D. C.

1975-01-01

A data search of the weather statistics from 11 widely dispersed geographical locations within the continental United States was conducted. The sites, located long both sea-coasts and in the interior, span the northern, southern, and middle latitudes. The weather statistics, retrieved from the records of these 11 sites, consist of two daily observations taken over a 10-year period. The data were sorted with respect to precipitation and surface winds and classified into temperature intervals of 5 C and relative humidity intervals of 10 percent for the lower 1400 meters of the atmosphere. These data were assembled in a statistical format and further classified into altitude increments of 200 meters. The data are presented as sets of tables for each site by season of the year and include both daily observations.

Distance Learning Materials for Elementary Astronomy with Lab

NASA Astrophysics Data System (ADS)

Castle, K. G.

2004-05-01

I have developed a distance learning astronomy course with an integral lab. The materials for this course are available from the site below. Test and quiz contents can be obtained upon request In this distance-learning format, students take quizzes online, tests in person and meet with the instructor for assistance. Student activities include homework, laboratory exercises and observing projects using household and community resources. This course (Astro 128) has been approved to fulfill general education requirements for University of California and the California State University system. Materials include instructions and reference materials for measuring parallax, analyzing radial velocity and light curves, finding ages of star clusters, tracking planets, recording sunrise or sunset time, simulating lunar phases, assessing lunar feature ages, classifying stellar spectra from tracings, and classifying galaxy morphology. Students analyze actual astronomical data from the literature in many cases. A comparatively large number of observational examples allows each student to work with a unique assignment. Course management includes a calendar where students schedule meetings with the instructor and WebCT test, quiz and grade maintenance. Course materials are supplied with links to data sets in PDF. This class was developed with technical assistance from the Instructional Technology Department at Diablo Valley College.

Combination of dynamic Bayesian network classifiers for the recognition of degraded characters

NASA Astrophysics Data System (ADS)

Likforman-Sulem, Laurence; Sigelle, Marc

2009-01-01

We investigate in this paper the combination of DBN (Dynamic Bayesian Network) classifiers, either independent or coupled, for the recognition of degraded characters. The independent classifiers are a vertical HMM and a horizontal HMM whose observable outputs are the image columns and the image rows respectively. The coupled classifiers, presented in a previous study, associate the vertical and horizontal observation streams into single DBNs. The scores of the independent and coupled classifiers are then combined linearly at the decision level. We compare the different classifiers -independent, coupled or linearly combined- on two tasks: the recognition of artificially degraded handwritten digits and the recognition of real degraded old printed characters. Our results show that coupled DBNs perform better on degraded characters than the linear combination of independent HMM scores. Our results also show that the best classifier is obtained by linearly combining the scores of the best coupled DBN and the best independent HMM.

An assessment of the effectiveness of a random forest classifier for land-cover classification

NASA Astrophysics Data System (ADS)

Rodriguez-Galiano, V. F.; Ghimire, B.; Rogan, J.; Chica-Olmo, M.; Rigol-Sanchez, J. P.

2012-01-01

Land cover monitoring using remotely sensed data requires robust classification methods which allow for the accurate mapping of complex land cover and land use categories. Random forest (RF) is a powerful machine learning classifier that is relatively unknown in land remote sensing and has not been evaluated thoroughly by the remote sensing community compared to more conventional pattern recognition techniques. Key advantages of RF include: their non-parametric nature; high classification accuracy; and capability to determine variable importance. However, the split rules for classification are unknown, therefore RF can be considered to be black box type classifier. RF provides an algorithm for estimating missing values; and flexibility to perform several types of data analysis, including regression, classification, survival analysis, and unsupervised learning. In this paper, the performance of the RF classifier for land cover classification of a complex area is explored. Evaluation was based on several criteria: mapping accuracy, sensitivity to data set size and noise. Landsat-5 Thematic Mapper data captured in European spring and summer were used with auxiliary variables derived from a digital terrain model to classify 14 different land categories in the south of Spain. Results show that the RF algorithm yields accurate land cover classifications, with 92% overall accuracy and a Kappa index of 0.92. RF is robust to training data reduction and noise because significant differences in kappa values were only observed for data reduction and noise addition values greater than 50 and 20%, respectively. Additionally, variables that RF identified as most important for classifying land cover coincided with expectations. A McNemar test indicates an overall better performance of the random forest model over a single decision tree at the 0.00001 significance level.

Software platform for managing the classification of error- related potentials of observers

NASA Astrophysics Data System (ADS)

Asvestas, P.; Ventouras, E.-C.; Kostopoulos, S.; Sidiropoulos, K.; Korfiatis, V.; Korda, A.; Uzunolglu, A.; Karanasiou, I.; Kalatzis, I.; Matsopoulos, G.

2015-09-01

Human learning is partly based on observation. Electroencephalographic recordings of subjects who perform acts (actors) or observe actors (observers), contain a negative waveform in the Evoked Potentials (EPs) of the actors that commit errors and of observers who observe the error-committing actors. This waveform is called the Error-Related Negativity (ERN). Its detection has applications in the context of Brain-Computer Interfaces. The present work describes a software system developed for managing EPs of observers, with the aim of classifying them into observations of either correct or incorrect actions. It consists of an integrated platform for the storage, management, processing and classification of EPs recorded during error-observation experiments. The system was developed using C# and the following development tools and frameworks: MySQL, .NET Framework, Entity Framework and Emgu CV, for interfacing with the machine learning library of OpenCV. Up to six features can be computed per EP recording per electrode. The user can select among various feature selection algorithms and then proceed to train one of three types of classifiers: Artificial Neural Networks, Support Vector Machines, k-nearest neighbour. Next the classifier can be used for classifying any EP curve that has been inputted to the database.

Sink or Float. Modified Primary. Revised. Anchorage School District Elementary Science Program.

ERIC Educational Resources Information Center

Defendorf, Jean, Ed.

This publication provides information and activities for teaching about water, whether certain objects will sink or float, and process skills including observing, classifying, inferring, measuring, predicting, and collecting and interpreting data. There are 14 lessons in the unit. The first four lessons deal with the classification of objects and…

Mystery Powders. [Modified Primary]. Revised. Anchorage School District Elementary Science Program.

ERIC Educational Resources Information Center

Anchorage School District, AK.

This publication provides information and activities for identifying objects using the five senses and process skills including observing, classifying, collecting and interpreting data, inferring, and predicting. Lessons 1 through 3 deal with the identification of an unknown substance and the physical properties of powders. Lessons 4 through 6 are…

How well Can We Classify SWOT-derived Water Surface Profiles?

NASA Astrophysics Data System (ADS)

Frasson, R. P. M.; Wei, R.; Picamilh, C.; Durand, M. T.

2015-12-01

The upcoming Surface Water Ocean Topography (SWOT) mission will detect water bodies and measure water surface elevation throughout the globe. Within its continental high resolution mask, SWOT is expected to deliver measurements of river width, water elevation and slope of rivers wider than ~50 m. The definition of river reaches is an integral step of the computation of discharge based on SWOT's observables. As poorly defined reaches can negatively affect the accuracy of discharge estimations, we seek strategies to break up rivers into physically meaningful sections. In the present work, we investigate how accurately we can classify water surface profiles based on simulated SWOT observations. We assume that most river sections can be classified as either M1 (mild slope, with depth larger than the normal depth), or A1 (adverse slope with depth larger than the critical depth). This assumption allows the classification to be based solely on the second derivative of water surface profiles, with convex profiles being classified as A1 and concave profiles as M1. We consider a HEC-RAS model of the Sacramento River as a representation of the true state of the river. We employ the SWOT instrument simulator to generate a synthetic pass of the river, which includes our best estimates of height measurement noise and geolocation errors. We process the resulting point cloud of water surface heights with the RiverObs package, which delineates the river center line and draws the water surface profile. Next, we identify inflection points in the water surface profile and classify the sections between the inflection points. Finally, we compare our limited classification of simulated SWOT-derived water surface profile to the "exact" classification of the modeled Sacramento River. With this exercise, we expect to determine if SWOT observations can be used to find inflection points in water surface profiles, which would bring knowledge of flow regimes into the definition of river reaches.

Observing Galaxy Mergers in Simulations

NASA Astrophysics Data System (ADS)

Snyder, Gregory

2018-01-01

I will describe results on mergers and morphology of distant galaxies. By mock-observing 3D cosmological simulations, we aim to contrast theory with data, design better diagnostics of physical processes, and examine unexpected signatures of galaxy formation. Recently, we conducted mock surveys of the Illustris Simulations to learn how mergers would appear in deep HST and JWST surveys. With this approach, we reconciled merger rates estimated using observed close galaxy pairs with intrinsic merger rates predicted by theory. This implies that the merger-pair observability time is probably shorter in the early universe, and therefore that major mergers are more common than implied by the simplest arguments. Further, we show that disturbance-based diagnostics of late-stage mergers can be improved significantly by combining multi-dimensional image information with simulated merger identifications to train automated classifiers. We then apply these classifiers to real measurements from the CANDELS fields, recovering a merger fraction increasing with redshift in broad agreement with pair fractions and simulations, and with statistical errors smaller by a factor of two than classical morphology estimators. This emphasizes the importance of using robust training sets, including cosmological simulations and multidimensional data, for interpreting observed processes in galaxy evolution.

Primary mass discrimination of high energy cosmic rays using PNN and k-NN methods

NASA Astrophysics Data System (ADS)

Rastegarzadeh, G.; Nemati, M.

2018-02-01

Probabilistic neural network (PNN) and k-Nearest Neighbors (k-NN) methods are widely used data classification techniques. In this paper, these two methods have been used to classify the Extensive Air Shower (EAS) data sets which were simulated using the CORSIKA code for three primary cosmic rays. The primaries are proton, oxygen and iron nuclei at energies of 100 TeV-10 PeV. This study is performed in the following of the investigations into the primary cosmic ray mass sensitive observables. We propose a new approach for measuring the mass sensitive observables of EAS in order to improve the primary mass separation. In this work, the EAS observables measurement has performed locally instead of total measurements. Also the relationships between the included number of observables in the classification methods and the prediction accuracy have been investigated. We have shown that the local measurements and inclusion of more mass sensitive observables in the classification processes can improve the classifying quality and also we have shown that muons and electrons energy density can be considered as primary mass sensitive observables in primary mass classification. Also it must be noted that this study is performed for Tehran observation level without considering the details of any certain EAS detection array.

Snoring classified: The Munich-Passau Snore Sound Corpus.

PubMed

Janott, Christoph; Schmitt, Maximilian; Zhang, Yue; Qian, Kun; Pandit, Vedhas; Zhang, Zixing; Heiser, Clemens; Hohenhorst, Winfried; Herzog, Michael; Hemmert, Werner; Schuller, Björn

2018-03-01

Snoring can be excited in different locations within the upper airways during sleep. It was hypothesised that the excitation locations are correlated with distinct acoustic characteristics of the snoring noise. To verify this hypothesis, a database of snore sounds is developed, labelled with the location of sound excitation. Video and audio recordings taken during drug induced sleep endoscopy (DISE) examinations from three medical centres have been semi-automatically screened for snore events, which subsequently have been classified by ENT experts into four classes based on the VOTE classification. The resulting dataset containing 828 snore events from 219 subjects has been split into Train, Development, and Test sets. An SVM classifier has been trained using low level descriptors (LLDs) related to energy, spectral features, mel frequency cepstral coefficients (MFCC), formants, voicing, harmonic-to-noise ratio (HNR), spectral harmonicity, pitch, and microprosodic features. An unweighted average recall (UAR) of 55.8% could be achieved using the full set of LLDs including formants. Best performing subset is the MFCC-related set of LLDs. A strong difference in performance could be observed between the permutations of train, development, and test partition, which may be caused by the relatively low number of subjects included in the smaller classes of the strongly unbalanced data set. A database of snoring sounds is presented which are classified according to their sound excitation location based on objective criteria and verifiable video material. With the database, it could be demonstrated that machine classifiers can distinguish different excitation location of snoring sounds in the upper airway based on acoustic parameters. Copyright © 2018 Elsevier Ltd. All rights reserved.

The Education and Training of Marine Technicians.

ERIC Educational Resources Information Center

Chan, Gordon L.

This report includes a study of the need for marine technicians in California, implications for the national scene, and observations made at a national conference held in Florida in 1968. Problems treated are: (1) definition of a marine technician, (2) how marine technicians should be classified, (3) how great is the demand for them, (4) the type…

Optimal aggregation of binary classifiers for multiclass cancer diagnosis using gene expression profiles.

PubMed

Yukinawa, Naoto; Oba, Shigeyuki; Kato, Kikuya; Ishii, Shin

2009-01-01

Multiclass classification is one of the fundamental tasks in bioinformatics and typically arises in cancer diagnosis studies by gene expression profiling. There have been many studies of aggregating binary classifiers to construct a multiclass classifier based on one-versus-the-rest (1R), one-versus-one (11), or other coding strategies, as well as some comparison studies between them. However, the studies found that the best coding depends on each situation. Therefore, a new problem, which we call the "optimal coding problem," has arisen: how can we determine which coding is the optimal one in each situation? To approach this optimal coding problem, we propose a novel framework for constructing a multiclass classifier, in which each binary classifier to be aggregated has a weight value to be optimally tuned based on the observed data. Although there is no a priori answer to the optimal coding problem, our weight tuning method can be a consistent answer to the problem. We apply this method to various classification problems including a synthesized data set and some cancer diagnosis data sets from gene expression profiling. The results demonstrate that, in most situations, our method can improve classification accuracy over simple voting heuristics and is better than or comparable to state-of-the-art multiclass predictors.

On the design of classifiers for crop inventories

NASA Technical Reports Server (NTRS)

Heydorn, R. P.; Takacs, H. C.

1986-01-01

Crop proportion estimators that use classifications of satellite data to correct, in an additive way, a given estimate acquired from ground observations are discussed. A linear version of these estimators is optimal, in terms of minimum variance, when the regression of the ground observations onto the satellite observations in linear. When this regression is not linear, but the reverse regression (satellite observations onto ground observations) is linear, the estimator is suboptimal but still has certain appealing variance properties. In this paper expressions are derived for those regressions which relate the intercepts and slopes to conditional classification probabilities. These expressions are then used to discuss the question of classifier designs that can lead to low-variance crop proportion estimates. Variance expressions for these estimates in terms of classifier omission and commission errors are also derived.

The ASAS-SN Catalog of Variable Stars I: The Serendipitous Survey

NASA Astrophysics Data System (ADS)

Jayasinghe, T.; Kochanek, C. S.; Stanek, K. Z.; Shappee, B. J.; Holoien, T. W.-S.; Thompson, Todd A.; Prieto, J. L.; Dong, Subo; Pawlak, M.; Shields, J. V.; Pojmanski, G.; Otero, S.; Britt, C. A.; Will, D.

2018-04-01

The All-Sky Automated Survey for Supernovae (ASAS-SN) is the first optical survey to routinely monitor the whole sky with a cadence of ˜2 - 3 days down to V≲ 17 mag. ASAS-SN has monitored the whole sky since 2014, collecting ˜100 - 500 epochs of observations per field. The V-band light curves for candidate variables identified during the search for supernovae are classified using a random forest classifier and visually verified. We present a catalog of 66,533 bright, new variable stars discovered during our search for supernovae, including 27,753 periodic variables and 38,780 irregular variables. V-band light curves for the ASAS-SN variables are available through the ASAS-SN variable stars database (https://asas-sn.osu.edu/variables). The database will begin to include the light curves of known variable stars in the near future along with the results for a systematic, all-sky variability survey.

Estimation from incomplete multinomial data. Ph.D. Thesis - Harvard Univ.

NASA Technical Reports Server (NTRS)

Credeur, K. R.

1978-01-01

The vector of multinomial cell probabilities was estimated from incomplete data, incomplete in that it contains partially classified observations. Each such partially classified observation was observed to fall in one of two or more selected categories but was not classified further into a single category. The data were assumed to be incomplete at random. The estimation criterion was minimization of risk for quadratic loss. The estimators were the classical maximum likelihood estimate, the Bayesian posterior mode, and the posterior mean. An approximation was developed for the posterior mean. The Dirichlet, the conjugate prior for the multinomial distribution, was assumed for the prior distribution.

Empirical Assessment of the Impact of Low-Cost Generic Programs on Adherence-Based Quality Measures

PubMed Central

Pauly, Nathan J.; Talbert, Jeffery C.; Brown, Joshua D.

2017-01-01

In the United States, federally-funded health plans are mandated to measure the quality of care. Adherence-based medication quality metrics depend on completeness of administrative claims data for accurate measurement. Low-cost generic programs (LCGPs) cause medications fills to be missing from claims data as medications are not adjudicated through a patient’s insurance. This study sought to assess the magnitude of the impact of LCGPs on these quality measures. Data from the 2012–2013 Medical Expenditure Panel Survey (MEPS) were used. Medication fills for select medication classes were classified as LCGP fills and individuals were classified as never, sometimes, and always users of LCGPs. Individuals were classified based on insurance type (private, Medicare, Medicaid, dual-eligible). The proportion of days covered (PDC) was calculated for each medication class and the proportion of users with PDC ≥ 0.80 was reported as an observed metric for what would be calculated based on claims data and a true metric which included missing medication fills due to LCGPs. True measures of adherence were higher than the observed measures. The effect’s magnitude was highest for private insurance and for medication classes utilized more often through LCGPs. Thus, medication-based quality measures may be underestimated due to LCGPs. PMID:28970427

A newly developed tool for classifying study designs in systematic reviews of interventions and exposures showed substantial reliability and validity.

PubMed

Seo, Hyun-Ju; Kim, Soo Young; Lee, Yoon Jae; Jang, Bo-Hyoung; Park, Ji-Eun; Sheen, Seung-Soo; Hahn, Seo Kyung

2016-02-01

To develop a study Design Algorithm for Medical Literature on Intervention (DAMI) and test its interrater reliability, construct validity, and ease of use. We developed and then revised the DAMI to include detailed instructions. To test the DAMI's reliability, we used a purposive sample of 134 primary, mainly nonrandomized studies. We then compared the study designs as classified by the original authors and through the DAMI. Unweighted kappa statistics were computed to test interrater reliability and construct validity based on the level of agreement between the original and DAMI classifications. Assessment time was also recorded to evaluate ease of use. The DAMI includes 13 study designs, including experimental and observational studies of interventions and exposure. Both the interrater reliability (unweighted kappa = 0.67; 95% CI [0.64-0.75]) and construct validity (unweighted kappa = 0.63, 95% CI [0.52-0.67]) were substantial. Mean classification time using the DAMI was 4.08 ± 2.44 minutes (range, 0.51-10.92). The DAMI showed substantial interrater reliability and construct validity. Furthermore, given its ease of use, it could be used to accurately classify medical literature for systematic reviews of interventions although minimizing disagreement between authors of such reviews. Copyright © 2016 Elsevier Inc. All rights reserved.

The Potential of AutoClass as an Asteroidal Data Mining Tool

NASA Astrophysics Data System (ADS)

Walker, Matthew; Ziffer, J.; Harvell, T.; Fernandez, Y. R.; Campins, H.

2011-05-01

AutoClass-C, an artificial intelligence program designed to classify large data sets, was developed by NASA to classify stars based upon their infrared colors. Wanting to investigate its ability to classify asteroidal data, we conducted a preliminary test to determine if it could accurately reproduce the Tholen taxonomy using the data from the Eight Color Asteroid Survey (ECAS). For our initial test, we limited ourselves to those asteroids belonging to S, C, or X classes, and to asteroids with a color difference error of less than +/- 0.05 magnitudes. Of those 406 asteroids, AutoClass was able to confidently classify 85%: identifying the remaining asteroids as belonging to more than one class. Of the 346 asteroids that AutoClass classified, all but 3 (<1%) were classified as they had been in the Tholen classification scheme. Inspired by our initial success, we reran AutoClass, this time including IRAS albedos and limiting the asteroids to those that had also been observed and classified in the Bus taxonomy. Of those 258 objects, AutoClass was able to classify 248 with greater than 75% certainty, and ranked albedo, not color, as the most influential factor. Interestingly, AutoClass consistently put P type objects in with the C class (there were 19 P types and 7 X types mixed in with the other 154 C types), and omitted P types from the group associated with the other X types (which had only one rogue B type in with its other 49 X-types). Autoclass classified the remaining classes with a high accuracy: placing one A and one CU type in with an otherwise perfect S group; placing three P type and one T type in an otherwise perfect D group; and placing the four remaining asteroids (V, A, R, and Q) into a class together.

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

Ye, Q.-Z., E-mail: tom6740@gmail.com

We present the results of BVRI photometry and classification of 53 unusual asteroids, including 35 near-Earth asteroids (NEAs), 6 high eccentricity/inclination asteroids, and 12 recently identified asteroid-pair candidates. Most of these asteroids were not classified prior to this work. For the few asteroids that have been previously studied, the results are generally in agreement. In addition to observing and classifying these objects, we merge the results from severalphotometric/spectroscopic surveys to create the largest-ever sample with 449 spectrally classified NEAs for statistical analysis. We identify a 'transition point' of the relative number of C/X-like and S-like NEAs at H {approx} 18more » {r_reversible} D {approx} 1 km with confidence level at {approx}95% or higher. We find that the C/X-like:S-like ratio for 18 {<=} H < 22 is about twice as high as that of H < 18 (0.33 {+-} 0.04 versus 0.17 {+-} 0.02), virtually supporting the hypothesis that smaller NEAs generally have less weathered surfaces (therefore less reddish appearance) due to younger collision ages.« less

A procedure for classifying textural facies in gravel-bed rivers

Treesearch

John M. Buffington; David R. Montgomery

1999-01-01

Textural patches (i.e., grain-size facies) are commonly observed in gravel-bed channels and are of significance for both physical and biological processes at subreach scales. We present a general framework for classifying textural patches that allows modification for particular study goals, while maintaining a basic degree of standardization. Textures are classified...

Mild Malformation of Cortical Development with Oligodendroglial Hyperplasia in Frontal Lobe Epilepsy: A New Clinico-Pathological Entity.

PubMed

Schurr, Johannes; Coras, Roland; Rössler, Karl; Pieper, Tom; Kudernatsch, Manfred; Holthausen, Hans; Winkler, Peter; Woermann, Friedrich; Bien, Christian G; Polster, Tilman; Schulz, Reinhard; Kalbhenn, Thilo; Urbach, Horst; Becker, Albert; Grunwald, Thomas; Huppertz, Hans-Juergen; Gil-Nagel, Antonio; Toledano, Rafael; Feucht, Martha; Mühlebner, Angelika; Czech, Thomas; Blümcke, Ingmar

2017-01-01

The histopathological spectrum of human epileptogenic brain lesions is widespread including common and rare variants of cortical malformations. However, 2-26% of epilepsy surgery specimens are histopathologically classified as nonlesional. We hypothesized that these specimens include also new diagnostic entities, in particular when presurgical magnetic resonance imaging (MRI) can identify abnormal signal intensities within the anatomical region of seizure onset. In our series of 1381 en bloc resected epilepsy surgery brain specimens, 52 cases could not be histopathologically classified and were considered nonlesional (3.7%). An increase of Olig2-, and PDGFR-alpha-immunoreactive oligodendroglia was observed in white matter and deep cortical layers in 22 of these patients (42%). Increased proliferation activity as well as heterotopic neurons in white matter were additional histopathological hallmarks. All patients suffered from frontal lobe epilepsy (FLE) with a median age of epilepsy onset at 4 years and 16 years at epilepsy surgery. Presurgical MRI suggested focal cortical dysplasia (FCD) in all patients. We suggest to classify this characteristic histopathology pattern as "mild malformation of cortical development with oligodendroglial hyperplasia (MOGHE)." Further insights into pathomechanisms of MOGHE may help to bridge the diagnostic gap in children and young adults with difficult-to-treat FLE. © 2016 International Society of Neuropathology.

Bayes classification of interferometric TOPSAR data

NASA Technical Reports Server (NTRS)

Michel, T. R.; Rodriguez, E.; Houshmand, B.; Carande, R.

1995-01-01

We report the Bayes classification of terrain types at different sites using airborne interferometric synthetic aperture radar (INSAR) data. A Gaussian maximum likelihood classifier was applied on multidimensional observations derived from the SAR intensity, the terrain elevation model, and the magnitude of the interferometric correlation. Training sets for forested, urban, agricultural, or bare areas were obtained either by selecting samples with known ground truth, or by k-means clustering of random sets of samples uniformly distributed across all sites, and subsequent assignments of these clusters using ground truth. The accuracy of the classifier was used to optimize the discriminating efficiency of the set of features that was chosen. The most important features include the SAR intensity, a canopy penetration depth model, and the terrain slope. We demonstrate the classifier's performance across sites using a unique set of training classes for the four main terrain categories. The scenes examined include San Francisco (CA) (predominantly urban and water), Mount Adams (WA) (forested with clear cuts), Pasadena (CA) (urban with mountains), and Antioch Hills (CA) (water, swamps, fields). Issues related to the effects of image calibration and the robustness of the classification to calibration errors are explored. The relative performance of single polarization Interferometric data classification is contrasted against classification schemes based on polarimetric SAR data.

An improved method of early diagnosis of smoking-induced respiratory changes using machine learning algorithms.

PubMed

Amaral, Jorge L M; Lopes, Agnaldo J; Jansen, José M; Faria, Alvaro C D; Melo, Pedro L

2013-12-01

The purpose of this study was to develop an automatic classifier to increase the accuracy of the forced oscillation technique (FOT) for diagnosing early respiratory abnormalities in smoking patients. The data consisted of FOT parameters obtained from 56 volunteers, 28 healthy and 28 smokers with low tobacco consumption. Many supervised learning techniques were investigated, including logistic linear classifiers, k nearest neighbor (KNN), neural networks and support vector machines (SVM). To evaluate performance, the ROC curve of the most accurate parameter was established as baseline. To determine the best input features and classifier parameters, we used genetic algorithms and a 10-fold cross-validation using the average area under the ROC curve (AUC). In the first experiment, the original FOT parameters were used as input. We observed a significant improvement in accuracy (KNN=0.89 and SVM=0.87) compared with the baseline (0.77). The second experiment performed a feature selection on the original FOT parameters. This selection did not cause any significant improvement in accuracy, but it was useful in identifying more adequate FOT parameters. In the third experiment, we performed a feature selection on the cross products of the FOT parameters. This selection resulted in a further increase in AUC (KNN=SVM=0.91), which allows for high diagnostic accuracy. In conclusion, machine learning classifiers can help identify early smoking-induced respiratory alterations. The use of FOT cross products and the search for the best features and classifier parameters can markedly improve the performance of machine learning classifiers. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

SPECKLE INTERFEROMETRY AT THE U.S. NAVAL OBSERVATORY. XVI

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

Mason, Brian D.; Hartkopf, William I.; Wycoff, Gary L., E-mail: bdm@usno.navy.mil, E-mail: wih@usno.navy.mil

2011-05-15

The results of 1031 speckle-interferometric observations of double stars, made with the 26 inch refractor of the U.S. Naval Observatory, are presented. Each speckle-interferometric observation of a system represents a combination of over two thousand short-exposure images. These observations are averaged into 457 mean relative positions and range in separation from 0.''15 to 16.''94, with a median separation of 3.''03. The range in V-band magnitudes for the primary (secondary) of observed targets is 3.1-12.9 (3.2-13.3). This is the sixteenth in a series of papers presenting measurements obtained with this system and covers the period 2009 January 12 through 2009 Decembermore » 17. Included in these data are 12 older measurements whose positions were previously deemed possibly aberrant, but are no longer classified this way following a confirming observation. Also, 10 pairs with a single observation are herein confirmed. This paper also includes the first data obtained using a new ICCD with fiber optic cables.« less

New observations, new theoretical results and controversies regarding PC 3-5 waves

NASA Astrophysics Data System (ADS)

Takahashi, K.

Observations and theories of medium- to long-period (Pc 3-5) magnetic pulsations excited by magnetospheric particles are described. Satellite observations indicate that most pulsations can be classified into two groups according to their magnetic field polarization. One group has a transverse magnetic perturbation and the other strongly compressional perturbation. Despite this difference in polarization they share common characteristics, including large azimuthal wave number, westward propagation, and antisymmetric field-aligned structure. Recent theories describe these observations in a unified framework. It has been pointed out that trapped energetic ions play an important role in determining the instability threshold and the mode structure of the pulsations. Observations and theories of energetic particle response to the excited pulsations are also described.

VizieR Online Data Catalog: 280 one-opposition near Earth asteroids (Vaduvescu+, 2018)

NASA Astrophysics Data System (ADS)

Vaduvescu, O.; Hudin, L.; Mocnik, T.; Char, F.; Sonka, A.; Tudor, V.; Ordonez-Etxeberria, I.; Diaz Alfaro, M.; Ashley, R.; Errmann, R.; Short, P.; Moloceniuc, A.; Cornea, R.; Inceu, V.; Zavoianu, D.; Popescu, M.; Curelaru, L.; Mihalea, S.; Stoian, A.-M.; Boldea, A.; Toma, R.; Fields, L.; Grigore, V.; Stoev, H.; Lopez-Martinez, F.; Humphries, N.; Sowicka, P.; Ramanjooloo, Y.; Manilla-Robles, A.; Riddick, F. C.; Jimenez-Lujan, F.; Mendez, J.; Aceituno, F.; Sota, A.; Jones, D.; Hidalgo, S.; Murabito, S.; Oteo, I.; Bongiovanni, A.; Zamora, O.; Pyrzas, S.; Tanausu, R.; Font, J.; Bereciartua, A.; Perez-Fournon, I.; Martinez-Vazquez, C. E.; Monelli, M.; Cicuendez, L.; Monteagudo, L.; Agulli, I.; Bouy, H.; Huelamo, N.; Monguio, M.; Gaensicke, B. T.; Steeghs, D.; Gentile-Fusillo, N. P.; Hollands, M. A.; Toloza, O.; Manser, C. J.; Dhillon, V.; Sahman, D.; Fitzsimmons, A.; McNeill, A.; Thompson, A.; Tabor, M.; Murphy, D. N. A.; Davies, J.; Snodgrass, C.; Triaud, A. H. M. J.; Groot, P. J.; Macfarlane, S.; Peletier, R.; Sen, S.; Ikiz, T.; Hoekstra, H.; Herbonnet, R.; Koehlinger, F.; Greimel, R.; Afonso, A.; Parker, Q. A.; Kong, A. K. H.; Bassa, C.; Pleunis, Z.

2017-10-01

Table 2 lists the observing log of the EURONEAR 2013-2016 one-opposition near Earth asteroids (NEAs) recovery project. The Tables includes 457 observed fields (437 using the INT, 12 using the WHT and 4 using the OGS). We ordered the table based on the asteroid designation (first column) then the observing date (start night), listing the apparent magnitude V (according to MPC ephemerides), the proper motion miu and the positional uncertainty of the targets (as shown on the observing date by MPC at 3σ level), the number of acquired images (including nearby fields), and the exposure time (in seconds). In the last three columns we list the current status of the targets (as classified in the paper by Aug 2017), the MPS publication that includes our recovery, and some comments that can include the PHA classification, other used telescopes (WHT or OGS), the track-and-stack technique (TS, whenever used), other possible external stations (MPC observatory code) and the date of later recovery (given only for later recoveries when we were unable to find the targets or for joined simultaneous recoveries). (1 data file).

Oral care and nosocomial pneumonia: a systematic review

PubMed Central

Vilela, Maria Carolina Nunes; Ferreira, Gustavo Zanna; Santos, Paulo Sérgio da Silva; de Rezende, Nathalie Pepe Medeiros

2015-01-01

To perform a systematic review of the literature on the control of oral biofilms and the incidence of nosocomial pneumonia, in addition to assessing and classifying studies as to the grade of recommendation and level of evidence. The review was based on PubMed, LILACS, and Scopus databases, from January 1st, 2000 until December 31st, 2012. Studies evaluating oral hygiene care related to nosocomial infections in patients hospitalized in intensive care units were selected according to the inclusion criteria. Full published articles available in English, Spanish, or Portuguese, which approached chemical or mechanical oral hygiene techniques in preventing pneumonia, interventions performed, and their results were included. After analysis, the articles were classified according to level of evidence and grade of recommendation according to the criteria of the Oxford Centre for Evidence-Based Medicine. A total of 297 abstracts were found, 14 of which were full articles that met our criteria. Most articles included a study group with chlorhexidine users and a control group with placebo users for oral hygiene in the prevention of pneumonia. All articles were classified as B in the level of evidence, and 12 articles were classified as 2B and two articles as 2C in grade of recommendation. It was observed that the control of oral biofilm reduces the incidence of nosocomial pneumonia, but the fact that most articles had an intermediate grade of recommendation makes clear the need to conduct randomized controlled trials with minimal bias to establish future guidelines for oral hygiene in intensive care units. PMID:25946053

Driven to distraction: The nature and apparent purpose of interruptions in critical care and implications for HIT.

PubMed

Mamykina, Lena; Carter, Eileen J; Sheehan, Barbara; Stanley Hum, R; Twohig, Bridget C; Kaufman, David R

2017-05-01

To examine the apparent purpose of interruptions in a Pediatric Intensive Care Unit and opportunities to reduce their burden with informatics solutions. In this prospective observational study, researchers shadowed clinicians in the unit for one hour at a time, recording all interruptions participating clinicians experienced or initiated, their starting time, duration, and a short description that could help to infer their apparent purpose. All captured interruptions were classified inductively on their source and apparent purpose and on the optimal representational media for fulfilling their apparent purpose. The researchers observed thirty-four one-hour sessions with clinicians in the unit, including 21 nurses and 13 residents and house physicians. The physicians were interrupted on average 11.9 times per hour and interrupted others 8.8 times per hour. Nurses were interrupted 8.6 times per hour and interrupted others 5.1 times per hour. The apparent purpose of interruptions included Information Seeking and Sharing (n=259, 46.3%), Directives and Requests (n=70, 12%), Shared Decision-Making (n=49, 8.8%), Direct Patient Care (n=36, 6.4%), Social (n=71, 12.7%), Device Alarms (n=28, 5%), and Non-Clinical (n=10, 1.8%); 6.6% were not classified due to insufficient description. Of all captured interruptions, 29.5% were classified as being better served with informational displays or computer-mediated communication. Deeper understanding of the purpose of interruptions in critical care can help to distinguish between interruptions that require face-to-face conversation and those that can be eliminated with informatics solutions. The proposed taxonomy of interruptions and representational analysis can be used to further advance the science of interruptions in clinical care. Copyright © 2017 Elsevier Inc. All rights reserved.

Eligibility for Renal Denervation: Anatomical Classification and Results in Essential Resistant Hypertension

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

Okada, Takuya, E-mail: okabone@gmail.com; Pellerin, Olivier; Savard, Sébastien

PurposeTo classify the renal artery (RA) anatomy based on specific requirements for endovascular renal artery denervation (RDN) in patients with drug-resistant hypertension (RH).Materials and MethodsThe RA anatomy of 122 consecutive RH patients was evaluated by computed tomography angiography and classified as two types: A (main RA ≥20 mm in length and ≥4.0 mm in diameter) or B (main RA <20 mm in length or main RA <4.0 mm in diameter). The A type included three subtypes: A1 (without accessory RAs), A2 (with accessory RAs <3.0 mm in diameter), and A3 (with accessory RAs ≥3.0 mm in diameter]. A1 and A2 types were eligible for RDN withmore » the Simplicity Flex catheter. Type B included twi subtypes based on the main RA length and diameter. Patients were accordingly classified into three eligibility categories: complete (CE; both RAs were eligible), partial (PE; one eligible RA), and noneligibility (NE; no eligible RA).ResultsBilateral A1 type was the most prevalent and was observed in 48.4 % of the patients followed by the A1/A2 type (18 %). CE, PE, and NE were observed in 69.7, 22.9, and 7.4 % of patients, respectively. The prevalence of accessory RAs was 41 %.ConclusionsOf RH patients, 30.3 % were not eligible for bilateral RDN with the current Simplicity Flex catheter. This classification provides the basis for standardized reporting to allow for pooling of results of larger patient cohorts in the future.« less

The ASAS-SN catalogue of variable stars I: The Serendipitous Survey

NASA Astrophysics Data System (ADS)

Jayasinghe, T.; Kochanek, C. S.; Stanek, K. Z.; Shappee, B. J.; Holoien, T. W.-S.; Thompson, Toda A.; Prieto, J. L.; Dong, Subo; Pawlak, M.; Shields, J. V.; Pojmanski, G.; Otero, S.; Britt, C. A.; Will, D.

2018-07-01

The All-Sky Automated Survey for Supernovae (ASAS-SN) is the first optical survey to routinely monitor the whole sky with a cadence of ˜2-3 d down to V ≲ 17 mag. ASAS-SN has monitored the whole sky since 2014, collecting ˜100-500 epochs of observations per field. The V-band light curves for candidate variables identified during the search for supernovae are classified using a random forest classifier and visually verified. We present a catalogue of 66 179 bright, new variable stars discovered during our search for supernovae, including 27 479 periodic variables and 38 700 irregular variables. V-band light curves for the ASAS-SN variables are available through the ASAS-SN variable stars data base (https://asas-sn.osu.edu/variables). The data base will begin to include the light curves of known variable stars in the near future along with the results for a systematic, all-sky variability survey.

Ensembles of novelty detection classifiers for structural health monitoring using guided waves

NASA Astrophysics Data System (ADS)

Dib, Gerges; Karpenko, Oleksii; Koricho, Ermias; Khomenko, Anton; Haq, Mahmoodul; Udpa, Lalita

2018-01-01

Guided wave structural health monitoring uses sparse sensor networks embedded in sophisticated structures for defect detection and characterization. The biggest challenge of those sensor networks is developing robust techniques for reliable damage detection under changing environmental and operating conditions (EOC). To address this challenge, we develop a novelty classifier for damage detection based on one class support vector machines. We identify appropriate features for damage detection and introduce a feature aggregation method which quadratically increases the number of available training observations. We adopt a two-level voting scheme by using an ensemble of classifiers and predictions. Each classifier is trained on a different segment of the guided wave signal, and each classifier makes an ensemble of predictions based on a single observation. Using this approach, the classifier can be trained using a small number of baseline signals. We study the performance using Monte-Carlo simulations of an analytical model and data from impact damage experiments on a glass fiber composite plate. We also demonstrate the classifier performance using two types of baseline signals: fixed and rolling baseline training set. The former requires prior knowledge of baseline signals from all EOC, while the latter does not and leverages the fact that EOC vary slowly over time and can be modeled as a Gaussian process.

Clinicopathological and immunohistochemical characterization of papillary proliferation of the endometrium: A single institutional experience.

PubMed

Park, Cheol Keun; Yoon, Gun; Cho, Yoon Ah; Kim, Hyun-Soo

2016-06-28

Papillary proliferation of the endometrium is an unusual lesion that is composed of papillae with fibrovascular stromal cores covered with benign-appearing glandular epithelium. We studied the clinicopathological and immunohistochemical features of four cases of endometrial papillary proliferations. All patients were postmenopausal. Two lesions were incidental findings in hysterectomy specimens, and two lesions were detected in endometrial curettage specimens. Based on the degree of architectural complexity and extent of proliferation, we classified papillary proliferations histopathologically into "simple" or "complex" growth patterns. Three cases were classified as simple papillary proliferation, and one case was classified as complex papillary proliferation. Simple papillary proliferations were characterized by slender papillae with delicate stromal cores. In contrast, complex papillary proliferations had intracystic papillary projections and cellular clusters with frequent branching and occasional cytological atypia. All cases showed coexistent metaplastic epithelial changes, including mucinous metaplasia, eosinophilic cell change, and ciliated cell metaplasia. One patient with simple papillary proliferations had coexistent well-differentiated endometrioid carcinoma. One patient had subsequent hyperplasia without atypia, and another patient had subsequent atypical hyperplasia/endometrioid intraepithelial neoplasia; both patients underwent total hysterectomy within four months. Our observations are consistent with previous data demonstrating that endometrial papillary proliferations coexist with or develop into atypical hyperplasia/endometrioid intraepithelial neoplasia or endometrioid carcinoma. It is very important for pathologists to discriminate papillary proliferations from neoplastic lesions (including atypical hyperplasia/endometrioid intraepithelial neoplasia and well-differentiated endometrioid carcinoma) and benign mimickers (including papillary syncytial metaplasia).

Clinicopathological and immunohistochemical characterization of papillary proliferation of the endometrium: A single institutional experience

PubMed Central

Park, Cheol Keun; Yoon, Gun; Cho, Yoon Ah; Kim, Hyun-Soo

2016-01-01

Papillary proliferation of the endometrium is an unusual lesion that is composed of papillae with fibrovascular stromal cores covered with benign-appearing glandular epithelium. We studied the clinicopathological and immunohistochemical features of four cases of endometrial papillary proliferations. All patients were postmenopausal. Two lesions were incidental findings in hysterectomy specimens, and two lesions were detected in endometrial curettage specimens. Based on the degree of architectural complexity and extent of proliferation, we classified papillary proliferations histopathologically into “simple” or “complex” growth patterns. Three cases were classified as simple papillary proliferation, and one case was classified as complex papillary proliferation. Simple papillary proliferations were characterized by slender papillae with delicate stromal cores. In contrast, complex papillary proliferations had intracystic papillary projections and cellular clusters with frequent branching and occasional cytological atypia. All cases showed coexistent metaplastic epithelial changes, including mucinous metaplasia, eosinophilic cell change, and ciliated cell metaplasia. One patient with simple papillary proliferations had coexistent well-differentiated endometrioid carcinoma. One patient had subsequent hyperplasia without atypia, and another patient had subsequent atypical hyperplasia/endometrioid intraepithelial neoplasia; both patients underwent total hysterectomy within four months. Our observations are consistent with previous data demonstrating that endometrial papillary proliferations coexist with or develop into atypical hyperplasia/endometrioid intraepithelial neoplasia or endometrioid carcinoma. It is very important for pathologists to discriminate papillary proliferations from neoplastic lesions (including atypical hyperplasia/endometrioid intraepithelial neoplasia and well-differentiated endometrioid carcinoma) and benign mimickers (including papillary syncytial metaplasia). PMID:27322430

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.

Prevalence of Ectopic Breast Tissue and Tumor: A 20-Year Single Center Experience.

PubMed

Famá, Fausto; Cicciú, Marco; Sindoni, Alessandro; Scarfó, Paola; Pollicino, Andrea; Giacobbe, Giuseppa; Buccheri, Giancarlo; Taranto, Filippo; Palella, Jessica; Gioffré-Florio, Maria

2016-08-01

Ectopic breast tissue, which includes both supernumerary breast and aberrant breast tissue, is the most common congenital breast abnormality. Ectopic breast cancers are rare neoplasms that occur in 0.3% to 0.6% of all cases of breast cancer. We retrospectively report, using a large series of breast abnormalities diagnosed and treated, our clinical experience on the management of the ectopic breast cancer. In 2 decades, we observed 327 (2.7%) patients with ectopic breast tissue out of a total of 12,177 subjects undergoing a breast visit for lesions. All patients were classified into 8 classes, according to the classification of Kajava, and assessed by a physician examination, ultrasounds, and, when appropriate, further studies with fine needle aspiration cytology and mammography. All specimens were submitted to the anatomo-pathologist. The most frequent benign histological diagnosis was fibrocystic disease. A rare granulosa cell tumor was also found in the right anterior thoracic wall of 1 patient. Four malignancies were also diagnosed in 4 women: an infiltrating lobular cancer in 1 patient with a lesion classified as class I, and an infiltrating apocrine carcinoma, an infiltrating ductal cancer, and an infiltrating ductal cancer with tubular pattern, occurring in 3 patients with lesions classified as class IV. Only 1 recurrence was observed. We recommend an earlier surgical approach for patients with lesions from class I to IV. Copyright © 2016 Elsevier Inc. All rights reserved.

STACCATO: a novel solution to supernova photometric classification with biased training sets

NASA Astrophysics Data System (ADS)

Revsbech, E. A.; Trotta, R.; van Dyk, D. A.

2018-01-01

We present a new solution to the problem of classifying Type Ia supernovae from their light curves alone given a spectroscopically confirmed but biased training set, circumventing the need to obtain an observationally expensive unbiased training set. We use Gaussian processes (GPs) to model the supernovae's (SN's) light curves, and demonstrate that the choice of covariance function has only a small influence on the GPs ability to accurately classify SNe. We extend and improve the approach of Richards et al. - a diffusion map combined with a random forest classifier - to deal specifically with the case of biased training sets. We propose a novel method called Synthetically Augmented Light Curve Classification (STACCATO) that synthetically augments a biased training set by generating additional training data from the fitted GPs. Key to the success of the method is the partitioning of the observations into subgroups based on their propensity score of being included in the training set. Using simulated light curve data, we show that STACCATO increases performance, as measured by the area under the Receiver Operating Characteristic curve (AUC), from 0.93 to 0.96, close to the AUC of 0.977 obtained using the 'gold standard' of an unbiased training set and significantly improving on the previous best result of 0.88. STACCATO also increases the true positive rate for SNIa classification by up to a factor of 50 for high-redshift/low-brightness SNe.

Association between parenting styles and own fruit and vegetable consumption among Portuguese mothers of school children.

PubMed

Franchini, Bela; Poínhos, Rui; Klepp, Knut-Inge; de Almeida, Maria Daniel Vaz

2011-09-01

The aim of the present study was to evaluate the association between parenting styles and own fruit and vegetable consumption among Portuguese mothers of school children. A cross-sectional study was performed in Portugal as part of the Pro Children cross-sectional European survey. Portuguese mothers (n 1601) of 11-13-year-old school children were included in the present study. A self-administered questionnaire was developed to assess fruit and vegetable consumption as well as the parenting styles. Fruit and vegetable consumption was assessed by a validated FFQ. Parenting styles based on two dimensions - strictness and involvement - were classified into authoritative, authoritarian, indulgent and neglectful. The higher mean intakes of fruit, vegetables and total fruit and vegetables were observed for mothers classified as indulgent, whereas the lower mean intakes were observed for mothers classified as neglectful. Differences in intake among parenting styles were significant for fruit, vegetables and total fruit and vegetables. When partial correlations were calculated between the two dimensions, strictness and involvement (controlled one for the other), and intakes, only involvement was positively associated with fruit, vegetables and total fruit and vegetable intake. Findings from the present study show that fruit and vegetable consumption of Portuguese mothers of school children seems to be related to their own parenting styles, especially with the dimension involvement. Future interventions to promote fruit and vegetable intake should take into account these variables.

Novel classification system of rib fractures observed in infants.

PubMed

Love, Jennifer C; Derrick, Sharon M; Wiersema, Jason M; Pinto, Deborrah C; Greeley, Christopher; Donaruma-Kwoh, Marcella; Bista, Bibek

2013-03-01

Rib fractures are considered highly suspicious for nonaccidental injury in the pediatric clinical literature; however, a rib fracture classification system has not been developed. As an aid and impetus for rib fracture research, we developed a concise schema for classifying rib fracture types and fracture location that is applicable to infants. The system defined four fracture types (sternal end, buckle, transverse, and oblique) and four regions of the rib (posterior, posterolateral, anterolateral, and anterior). It was applied to all rib fractures observed during 85 consecutive infant autopsies. Rib fractures were found in 24 (28%) of the cases. A total of 158 rib fractures were identified. The proposed schema was adequate to classify 153 (97%) of the observed fractures. The results indicate that the classification system is sufficiently robust to classify rib fractures typically observed in infants and should be used by researchers investigating infant rib fractures. © 2013 American Academy of Forensic Sciences.

Predicting variations of perceptual performance across individuals from neural activity using pattern classifiers.

PubMed

Das, Koel; Giesbrecht, Barry; Eckstein, Miguel P

2010-07-15

Within the past decade computational approaches adopted from the field of machine learning have provided neuroscientists with powerful new tools for analyzing neural data. For instance, previous studies have applied pattern classification algorithms to electroencephalography data to predict the category of presented visual stimuli, human observer decision choices and task difficulty. Here, we quantitatively compare the ability of pattern classifiers and three ERP metrics (peak amplitude, mean amplitude, and onset latency of the face-selective N170) to predict variations across individuals' behavioral performance in a difficult perceptual task identifying images of faces and cars embedded in noise. We investigate three different pattern classifiers (Classwise Principal Component Analysis, CPCA; Linear Discriminant Analysis, LDA; and Support Vector Machine, SVM), five training methods differing in the selection of training data sets and three analyses procedures for the ERP measures. We show that all three pattern classifier algorithms surpass traditional ERP measurements in their ability to predict individual differences in performance. Although the differences across pattern classifiers were not large, the CPCA method with training data sets restricted to EEG activity for trials in which observers expressed high confidence about their decisions performed the highest at predicting perceptual performance of observers. We also show that the neural activity predicting the performance across individuals was distributed through time starting at 120ms, and unlike the face-selective ERP response, sustained for more than 400ms after stimulus presentation, indicating that both early and late components contain information correlated with observers' behavioral performance. Together, our results further demonstrate the potential of pattern classifiers compared to more traditional ERP techniques as an analysis tool for modeling spatiotemporal dynamics of the human brain and relating neural activity to behavior. Copyright 2010 Elsevier Inc. All rights reserved.

Evaluation of radiography as a screening method for detection and characterisation of congenital vertebral malformations in dogs.

PubMed

Brocal, Josep; De Decker, Steven; José-López, Roberto; Guevar, Julien; Ortega, Maria; Parkin, Tim; Ter Haar, Gert; Gutierrez-Quintana, Rodrigo

2018-05-19

Congenital vertebral malformations (CVM) are common in brachycephalic 'screw-tailed' dogs; they can be associated with neurological deficits and a genetic predisposition has been suggested. The purpose of this study was to evaluate radiography as a screening method for congenital thoracic vertebral malformations in brachycephalic 'screw-tailed' dogs by comparing it with CT. Forty-nine dogs that had both radiographic and CT evaluations of the thoracic vertebral column were included. Three observers retrospectively reviewed the images independently to detect CVMs. When identified, they were classified according to a previously published radiographic classification scheme. A CT consensus was then reached. All observers identified significantly more affected vertebrae when evaluating orthogonal radiographic views compared with lateral views alone; and more affected vertebrae with the CT consensus compared with orthogonal radiographic views. Given the high number of CVMs per dog, the number of dogs classified as being CVM free was not significantly different between CT and radiography. Significantly more midline closure defects were also identified with CT compared with radiography. Malformations classified as symmetrical or ventral hypoplasias on radiography were frequently classified as ventral and medial aplasias on CT images. Our results support that CT is better than radiography for the classification of CVMs and this will be important when further evidence of which are the most clinically relevant CVMs is identified. These findings are of particular importance for designing screening schemes of CVMs that could help selective breeding programmes based on phenotype and future studies. © British Veterinary Association (unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Issues and considerations in the use of serologic biomarkers for classifying vaccination history in household surveys.

PubMed

MacNeil, Adam; Lee, Chung-Won; Dietz, Vance

2014-09-03

Accurate estimates of vaccination coverage are crucial for assessing routine immunization program performance. Community based household surveys are frequently used to assess coverage within a country. In household surveys to assess routine immunization coverage, a child's vaccination history is classified on the basis of observation of the immunization card, parental recall of receipt of vaccination, or both; each of these methods has been shown to commonly be inaccurate. The use of serologic data as a biomarker of vaccination history is a potential additional approach to improve accuracy in classifying vaccination history. However, potential challenges, including the accuracy of serologic methods in classifying vaccination history, varying vaccine types and dosing schedules, and logistical and financial implications must be considered. We provide historic and scientific context for the potential use of serologic data to assess vaccination history and discuss in detail key areas of importance for consideration in the context of using serologic data for classifying vaccination history in household surveys. Further studies are needed to directly evaluate the performance of serologic data compared with use of immunization cards or parental recall for classification of vaccination history in household surveys, as well assess the impact of age at the time of sample collection on serologic titers, the predictive value of serology to identify a fully vaccinated child for multi-dose vaccines, and the cost impact and logistical issues on outcomes associated with different types of biological samples for serologic testing. Published by Elsevier Ltd.

Towards Autonomous Agriculture: Automatic Ground Detection Using Trinocular Stereovision

PubMed Central

Reina, Giulio; Milella, Annalisa

2012-01-01

Autonomous driving is a challenging problem, particularly when the domain is unstructured, as in an outdoor agricultural setting. Thus, advanced perception systems are primarily required to sense and understand the surrounding environment recognizing artificial and natural structures, topology, vegetation and paths. In this paper, a self-learning framework is proposed to automatically train a ground classifier for scene interpretation and autonomous navigation based on multi-baseline stereovision. The use of rich 3D data is emphasized where the sensor output includes range and color information of the surrounding environment. Two distinct classifiers are presented, one based on geometric data that can detect the broad class of ground and one based on color data that can further segment ground into subclasses. The geometry-based classifier features two main stages: an adaptive training stage and a classification stage. During the training stage, the system automatically learns to associate geometric appearance of 3D stereo-generated data with class labels. Then, it makes predictions based on past observations. It serves as well to provide training labels to the color-based classifier. Once trained, the color-based classifier is able to recognize similar terrain classes in stereo imagery. The system is continuously updated online using the latest stereo readings, thus making it feasible for long range and long duration navigation, over changing environments. Experimental results, obtained with a tractor test platform operating in a rural environment, are presented to validate this approach, showing an average classification precision and recall of 91.0% and 77.3%, respectively.

Non-seismic tsunamis: filling the forecast gap

NASA Astrophysics Data System (ADS)

Moore, C. W.; Titov, V. V.; Spillane, M. C.

2015-12-01

Earthquakes are the generation mechanism in over 85% of tsunamis. However, non-seismic tsunamis, including those generated by meteorological events, landslides, volcanoes, and asteroid impacts, can inundate significant area and have a large far-field effect. The current National Oceanographic and Atmospheric Administration (NOAA) tsunami forecast system falls short in detecting these phenomena. This study attempts to classify the range of effects possible from these non-seismic threats, and to investigate detection methods appropriate for use in a forecast system. Typical observation platforms are assessed, including DART bottom pressure recorders and tide gauges. Other detection paths include atmospheric pressure anomaly algorithms for detecting meteotsunamis and the early identification of asteroids large enough to produce a regional hazard. Real-time assessment of observations for forecast use can provide guidance to mitigate the effects of a non-seismic tsunami.

A Hybrid Template-Based Composite Classification System

DTIC Science & Technology

2009-02-01

Hybrid Classifier: Forced Decision . . . . 116 5.3.2 Forced Decision Experimental Results . . . . . 119 5.3.3 Test for Statistical Significance ...Results . . . . . . . . . . 127 5.4.2 Test for Statistical Significance : NDEC Option 129 5.5 Implementing the Hyrid Classifier with OOL Targets . 130...comple- mentary in nature . Complementary classifiers are observed by finding an optimal method for partitioning the problem space. For example, the

Evaluation of damage in reinforced concrete bridge beams using acoustic emission technique

NASA Astrophysics Data System (ADS)

Vidya Sagar, R.; Raghu Prasad, B. K.; Sharma, Reema

2012-06-01

Acoustic emission (AE) testing is a well-known method for damage identification of various concrete structures including bridges. This article presents a method to assess damage in reinforced concrete (RC) bridge beams subjected to incremental cyclic loading. The specifications in the standard NDIS-2421 were used to classify the damage in RC bridge beams. Earlier researchers classified the damage occurring in bridge beams by using crack mouth opening displacement (CMOD) and AE released and proposed a standard (NDIS-2421: the Japanese Society for NonDestructive Inspection). In general, multiple cracks take place in RC beams under bending; therefore, utilisation of CMOD for crack detection may not be appropriate. In the present study, the damage in RC beams is classified by using the AE released, deflection, strains in steel and concrete, because the measurement of the strains in steel and concrete is easy and the codes of practice are specified for different limit states (IS-456:2000). The observations made in the present experimental study have some important practical applications in assessing the state of damage of concrete structural members.

Establishing the pharmaceutical quality of Chinese herbal medicine: a provisional BCS classification.

PubMed

Fong, Sophia Y K; Liu, Mary; Wei, Hai; Löbenberg, Raimar; Kanfer, Isadore; Lee, Vincent H L; Amidon, Gordon L; Zuo, Zhong

2013-05-06

The Biopharmaceutical Classification System (BCS), which is a scientific approach to categorize active drug ingredient based on its solubility and intestinal permeability into one of the four classes, has been used to set the pharmaceutical quality standards for drug products in western society. However, it has received little attention in the area of Chinese herbal medicine (CHM). This is likely, in part, due to the presence of multiple active components as well as lack of standardization of CHM. In this report, we apply BCS classification to CHMs provisionally as a basis for establishing improved in vitro quality standards. Based on a top-200 drugs selling list in China, a total of 31 CHM products comprising 50 official active marker compounds (AMCs) were provisionally classified according to BCS. Information on AMC content and doses of these CHM products were retrieved from the Chinese Pharmacopoeia. BCS parameters including solubility and permeability of the AMCs were predicted in silico (ACD/Laboratories). A BCS classification of CHMs according to biopharmaceutical properties of their AMCs is demonstrated to be feasible in the current study and can be used to provide a minimum set of quality standards. Our provisional results showed that 44% of the included AMCs were classified as Class III (high solubility, low permeability), followed by Class II (26%), Class I (18%), and Class IV (12%). A similar trend was observed when CHMs were classified in accordance with the BCS class of AMCs. Most (45%) of the included CHMs were classified as Class III, followed by Class II (16%), Class I (10%), and Class IV (6%); whereas 23% of the CHMs were of mixed class due to the presence of multiple individual AMCs with different BCS classifications. Moreover, about 60% of the AMCs were classified as high-solubility compounds (Class I and Class III), suggesting an important role for an in vitro dissolution test in setting quality control standards ensuring consistent biopharmaceutical quality for the commercially available CHM products. That is, provisionally, more than half of the AMCs of the top-selling CHMs included in this study would be candidates for a bioequivalence (BE) biowaiver, based on WHO recommendations and EMEA guidelines. Thus a dissolution requirement on these AMCs would represent a significant advance in the pharmaceutical quality of CHM today.

Uncertain Classification of Variable Stars: Handling Observational GAPS and Noise

NASA Astrophysics Data System (ADS)

Castro, Nicolás; Protopapas, Pavlos; Pichara, Karim

2018-01-01

Automatic classification methods applied to sky surveys have revolutionized the astronomical target selection process. Most surveys generate a vast amount of time series, or “lightcurves,” that represent the brightness variability of stellar objects in time. Unfortunately, lightcurves’ observations take several years to be completed, producing truncated time series that generally remain without the application of automatic classifiers until they are finished. This happens because state-of-the-art methods rely on a variety of statistical descriptors or features that present an increasing degree of dispersion when the number of observations decreases, which reduces their precision. In this paper, we propose a novel method that increases the performance of automatic classifiers of variable stars by incorporating the deviations that scarcity of observations produces. Our method uses Gaussian process regression to form a probabilistic model of each lightcurve’s observations. Then, based on this model, bootstrapped samples of the time series features are generated. Finally, a bagging approach is used to improve the overall performance of the classification. We perform tests on the MAssive Compact Halo Object (MACHO) and Optical Gravitational Lensing Experiment (OGLE) catalogs, results show that our method effectively classifies some variability classes using a small fraction of the original observations. For example, we found that RR Lyrae stars can be classified with ~80% accuracy just by observing the first 5% of the whole lightcurves’ observations in the MACHO and OGLE catalogs. We believe these results prove that, when studying lightcurves, it is important to consider the features’ error and how the measurement process impacts it.

On Algorithms for Generating Computationally Simple Piecewise Linear Classifiers

DTIC Science & Technology

1989-05-01

suffers. - Waveform classification, e.g. speech recognition, seismic analysis (i.e. discrimination between earthquakes and nuclear explosions), target...assuming Gaussian distributions (B-G) d) Bayes classifier with probability densities estimated with the k-N-N method (B- kNN ) e) The -arest neighbour...range of classifiers are chosen including a fast, easy computable and often used classifier (B-G), reliable and complex classifiers (B- kNN and NNR

Data fusion with artificial neural networks (ANN) for classification of earth surface from microwave satellite measurements

NASA Technical Reports Server (NTRS)

Lure, Y. M. Fleming; Grody, Norman C.; Chiou, Y. S. Peter; Yeh, H. Y. Michael

1993-01-01

A data fusion system with artificial neural networks (ANN) is used for fast and accurate classification of five earth surface conditions and surface changes, based on seven SSMI multichannel microwave satellite measurements. The measurements include brightness temperatures at 19, 22, 37, and 85 GHz at both H and V polarizations (only V at 22 GHz). The seven channel measurements are processed through a convolution computation such that all measurements are located at same grid. Five surface classes including non-scattering surface, precipitation over land, over ocean, snow, and desert are identified from ground-truth observations. The system processes sensory data in three consecutive phases: (1) pre-processing to extract feature vectors and enhance separability among detected classes; (2) preliminary classification of Earth surface patterns using two separate and parallely acting classifiers: back-propagation neural network and binary decision tree classifiers; and (3) data fusion of results from preliminary classifiers to obtain the optimal performance in overall classification. Both the binary decision tree classifier and the fusion processing centers are implemented by neural network architectures. The fusion system configuration is a hierarchical neural network architecture, in which each functional neural net will handle different processing phases in a pipelined fashion. There is a total of around 13,500 samples for this analysis, of which 4 percent are used as the training set and 96 percent as the testing set. After training, this classification system is able to bring up the detection accuracy to 94 percent compared with 88 percent for back-propagation artificial neural networks and 80 percent for binary decision tree classifiers. The neural network data fusion classification is currently under progress to be integrated in an image processing system at NOAA and to be implemented in a prototype of a massively parallel and dynamically reconfigurable Modular Neural Ring (MNR).

10 CFR 1045.34 - Designation of restricted data classifiers.

Code of Federal Regulations, 2014 CFR

2014-01-01

... shall ensure that persons who derivatively classify RD or FRD documents are designated by position or by name as RD classifiers. (b) All contractor organizations with access to RD and FRD, including DoD...

10 CFR 1045.34 - Designation of restricted data classifiers.

Code of Federal Regulations, 2012 CFR

2012-01-01

... shall ensure that persons who derivatively classify RD or FRD documents are designated by position or by name as RD classifiers. (b) All contractor organizations with access to RD and FRD, including DoD...

10 CFR 1045.34 - Designation of restricted data classifiers.

Code of Federal Regulations, 2013 CFR

2013-01-01

... shall ensure that persons who derivatively classify RD or FRD documents are designated by position or by name as RD classifiers. (b) All contractor organizations with access to RD and FRD, including DoD...

10 CFR 1045.34 - Designation of restricted data classifiers.

Code of Federal Regulations, 2011 CFR

2011-01-01

... shall ensure that persons who derivatively classify RD or FRD documents are designated by position or by name as RD classifiers. (b) All contractor organizations with access to RD and FRD, including DoD...

10 CFR 1045.34 - Designation of restricted data classifiers.

Code of Federal Regulations, 2010 CFR

2010-01-01

... shall ensure that persons who derivatively classify RD or FRD documents are designated by position or by name as RD classifiers. (b) All contractor organizations with access to RD and FRD, including DoD...

Contextual classification on the massively parallel processor

NASA Technical Reports Server (NTRS)

Tilton, James C.

1987-01-01

Classifiers are often used to produce land cover maps from multispectral Earth observation imagery. Conventionally, these classifiers have been designed to exploit the spectral information contained in the imagery. Very few classifiers exploit the spatial information content of the imagery, and the few that do rarely exploit spatial information content in conjunction with spectral and/or temporal information. A contextual classifier that exploits spatial and spectral information in combination through a general statistical approach was studied. Early test results obtained from an implementation of the classifier on a VAX-11/780 minicomputer were encouraging, but they are of limited meaning because they were produced from small data sets. An implementation of the contextual classifier is presented on the Massively Parallel Processor (MPP) at Goddard that for the first time makes feasible the testing of the classifier on large data sets.

Equating an expert system to a classifier in order to evaluate the expert system

NASA Technical Reports Server (NTRS)

Odell, Patrick L.

1989-01-01

A strategy to evaluate an expert system is formulated. The strategy proposed is based on finding an equivalent classifier to an expert system and evaluate that classifier with respect to an optimal classifier, a Bayes classifier. Here it is shown that for the rules considered an equivalent classifier exists. Also, a brief consideration of meta and meta-meta rules is included. Also, a taxonomy of expert systems is presented and an assertion made that an equivalent classifier exists for each type of expert system in the taxonomy with associated sets of underlying assumptions.

Ensembles of novelty detection classifiers for structural health monitoring using guided waves

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

Dib, Gerges; Karpenko, Oleksii; Koricho, Ermias

Guided wave structural health monitoring uses sparse sensor networks embedded in sophisticated structures for defect detection and characterization. The biggest challenge of those sensor networks is developing robust techniques for reliable damage detection under changing environmental and operating conditions. To address this challenge, we develop a novelty classifier for damage detection based on one class support vector machines. We identify appropriate features for damage detection and introduce a feature aggregation method which quadratically increases the number of available training observations.We adopt a two-level voting scheme by using an ensemble of classifiers and predictions. Each classifier is trained on a differentmore » segment of the guided wave signal, and each classifier makes an ensemble of predictions based on a single observation. Using this approach, the classifier can be trained using a small number of baseline signals. We study the performance using monte-carlo simulations of an analytical model and data from impact damage experiments on a glass fiber composite plate.We also demonstrate the classifier performance using two types of baseline signals: fixed and rolling baseline training set. The former requires prior knowledge of baseline signals from all environmental and operating conditions, while the latter does not and leverages the fact that environmental and operating conditions vary slowly over time and can be modeled as a Gaussian process.« less

Role of erosions typical of rheumatoid arthritis in the 2010 ACR/EULAR rheumatoid arthritis classification criteria: results from a very early arthritis cohort.

PubMed

Brinkmann, Gina Hetland; Norli, Ellen S; Bøyesen, Pernille; van der Heijde, Désirée; Grøvle, Lars; Haugen, Anne J; Nygaard, Halvor; Bjørneboe, Olav; Thunem, Cathrine; Kvien, Tore K; Mjaavatten, Maria D; Lie, Elisabeth

2017-11-01

To determine how the European League Against Rheumatism (EULAR) definition of erosive disease (erosion criterion) contributes to the number of patients classified as rheumatoid arthritis (RA) according to the 2010 American College of Rheumatology/EULAR RA classification criteria (2010 RA criteria) in an early arthritis cohort. Patients from the observational study Norwegian Very Early Arthritis Clinic with joint swelling ≤16 weeks, a clinical diagnosis of RA or undifferentiated arthritis, and radiographs of hands and feet were included. Erosive disease was defined according to the EULAR definition accompanying the 2010 RA criteria. We calculated the additional number of patients being classified as RA based on the erosion criteria at baseline and during follow-up. Of the 289 included patients, 120 (41.5%) fulfilled the 2010 RA criteria, whereas 15 (5.2%) fulfilled only the erosion criterion at baseline. 118 patients had radiographic follow-up at 2 years, of whom 6.8% fulfilled the 2010 RA criteria and only one patient fulfilled solely the erosion criterion during follow-up. Few patients with early arthritis were classified as RA based on solely the erosion criteria, and of those who did almost all did so at baseline. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

A new map of standardized terrestrial ecosystems of Africa

USGS Publications Warehouse

Sayre, Roger G.; Comer, Patrick; Hak, Jon; Josse, Carmen; Bow, Jacquie; Warner, Harumi; Larwanou, Mahamane; Kelbessa, Ensermu; Bekele, Tamrat; Kehl, Harald; Amena, Ruba; Andriamasimanana, Rado; Ba, Taibou; Benson, Laurence; Boucher, Timothy; Brown, Matthew; Cress, Jill J.; Dassering, Oueddo; Friesen, Beverly A.; Gachathi, Francis; Houcine, Sebei; Keita, Mahamadou; Khamala, Erick; Marangu, Dan; Mokua, Fredrick; Morou, Boube; Mucina, Ladislav; Mugisha, Samuel; Mwavu, Edward; Rutherford, Michael; Sanou, Patrice; Syampungani, Stephen; Tomor, Bojoi; Vall, Abdallahi Ould Mohamed; Vande Weghe, Jean Pierre; Wangui, Eunice; Waruingi, Lucy

2013-01-01

Terrestrial ecosystems and vegetation of Africa were classified and mapped as part of a larger effort and global protocol (GEOSS – the Global Earth Observation System of Systems), which includes an activity to map terrestrial ecosystems of the earth in a standardized, robust, and practical manner, and at the finest possible spatial resolution. To model the potential distribution of ecosystems, new continental datasets for several key physical environment datalayers (including coastline, landforms, surficial lithology, and bioclimates) were developed at spatial and classification resolutions finer than existing similar datalayers. A hierarchical vegetation classification was developed by African ecosystem scientists and vegetation geographers, who also provided sample locations of the newly classified vegetation units. The vegetation types and ecosystems were then mapped across the continent using a classification and regression tree (CART) inductive model, which predicted the potential distribution of vegetation types from a suite of biophysical environmental attributes including bioclimate region, biogeographic region, surficial lithology, landform, elevation and land cover. Multi-scale ecosystems were classified and mapped in an increasingly detailed hierarchical framework using vegetation-based concepts of class, subclass, formation, division, and macrogroup levels. The finest vegetation units (macrogroups) classified and mapped in this effort are defined using diagnostic plant species and diagnostic growth forms that reflect biogeographic differences in composition and sub-continental to regional differences in mesoclimate, geology, substrates, hydrology, and disturbance regimes (FGDC, 2008). The macrogroups are regarded as meso-scale (100s to 10,000s of hectares) ecosystems. A total of 126 macrogroup types were mapped, each with multiple, repeating occurrences on the landscape. The modeling effort was implemented at a base spatial resolution of 90 m. In addition to creating several rich, new continent-wide biophysical datalayers describing African vegetation and ecosystems, our intention was to explore feasible approaches to rapidly moving this type of standardized, continent-wide, ecosystem classification and mapping effort forward.

Classifying Microorganisms.

ERIC Educational Resources Information Center

Baker, William P.; Leyva, Kathryn J.; Lang, Michael; Goodmanis, Ben

2002-01-01

Focuses on an activity in which students sample air at school and generate ideas about how to classify the microorganisms they observe. The results are used to compare air quality among schools via the Internet. Supports the development of scientific inquiry and technology skills. (DDR)

[Application of single-retainer all-ceramic resin-bonded fixed partial denture in replacing single anterior tooth].

PubMed

Lili, Yang; Debiao, Du; Ruoyu, Ning; Deying, Chen; Junling, Wu

2017-08-01

Objective In this study, we aimed to evaluate the clinical effect of single-retainer all-ceramic resin-bonded fixed partial denture (RBFPD) on the single anterior tooth loss patients. Methods A total of 20 single-retainer all-ceramic RBFPD
were fabricated and evaluated in a two-year follow-up observation. The restorations were examined on the basis of the American Public Health Association (APHA) criteria. Results A total of 20 single-retainer all-ceramic RBFPD achieved class A evaluation after a six-month follow-up observation. One single-retainer all-ceramic RBFPD was classified as class B for secondary caries after a one-year follow-up observation. After a two-year follow-up observation, one single-retainer all-ceramic RBFPD was classified as class B because of secondary caries, and one single-retainer all-ceramic RBFPD was classified as class B because of fracture. Conclusion Single-retainer all-ceramic RBFPD is a promising and optional method in replacing single anterior tooth.

Spacelab Science Results Study. Volume 1; External Observations

NASA Technical Reports Server (NTRS)

Naumann, Robert J. (Compiler)

1999-01-01

Some of the 36 Spacelab missions were more or less dedicated to specific scientific disciplines, while other carried a eclectic mixture of experiments ranging from astrophysics to life sciences. However, the experiments can be logically classified into two general categories; those that make use of the Shuttle as an observing platform for external phenomena (including those which use the Shuttle in an interactive mode) and those which use the Shuttle as a microgravity laboratory. This first volume of this Spacelab Science Results study will be devoted to experiments of the first category. The disciplines included are Astrophysics, Solar Physics, Space Plasma Physics, Atmospheric Sciences, and Earth Sciences. Because of the large number of microgravity investigations, Volume 2 will be devoted to Microgravity Sciences, which includes Fluid Physics, Combustion Science, Materials Science, and Biotechnology, and Volume 3 will be devoted to Space Life Sciences, which studies the response and adaptability of living organisms to the microgravity environment.

Differentiation of several interstitial lung disease patterns in HRCT images using support vector machine: role of databases on performance

NASA Astrophysics Data System (ADS)

Kale, Mandar; Mukhopadhyay, Sudipta; Dash, Jatindra K.; Garg, Mandeep; Khandelwal, Niranjan

2016-03-01

Interstitial lung disease (ILD) is complicated group of pulmonary disorders. High Resolution Computed Tomography (HRCT) considered to be best imaging technique for analysis of different pulmonary disorders. HRCT findings can be categorised in several patterns viz. Consolidation, Emphysema, Ground Glass Opacity, Nodular, Normal etc. based on their texture like appearance. Clinician often find it difficult to diagnosis these pattern because of their complex nature. In such scenario computer-aided diagnosis system could help clinician to identify patterns. Several approaches had been proposed for classification of ILD patterns. This includes computation of textural feature and training /testing of classifier such as artificial neural network (ANN), support vector machine (SVM) etc. In this paper, wavelet features are calculated from two different ILD database, publically available MedGIFT ILD database and private ILD database, followed by performance evaluation of ANN and SVM classifiers in terms of average accuracy. It is found that average classification accuracy by SVM is greater than ANN where trained and tested on same database. Investigation continued further to test variation in accuracy of classifier when training and testing is performed with alternate database and training and testing of classifier with database formed by merging samples from same class from two individual databases. The average classification accuracy drops when two independent databases used for training and testing respectively. There is significant improvement in average accuracy when classifiers are trained and tested with merged database. It infers dependency of classification accuracy on training data. It is observed that SVM outperforms ANN when same database is used for training and testing.

22 CFR 125.4 - Exemptions of general applicability.

Code of Federal Regulations, 2010 CFR

2010-04-01

..., except detailed design, development, production or manufacturing information; (7) Technical data..., production, or manufacture of any defense article; (4) Copies of technical data, including classified..., development, production, or manufacture of any defense article; (9) Technical data, including classified...

Thermal bioaerosol cloud tracking with Bayesian classification

NASA Astrophysics Data System (ADS)

Smith, Christian W.; Dupuis, Julia R.; Schundler, Elizabeth C.; Marinelli, William J.

2017-05-01

The development of a wide area, bioaerosol early warning capability employing existing uncooled thermal imaging systems used for persistent perimeter surveillance is discussed. The capability exploits thermal imagers with other available data streams including meteorological data and employs a recursive Bayesian classifier to detect, track, and classify observed thermal objects with attributes consistent with a bioaerosol plume. Target detection is achieved based on similarity to a phenomenological model which predicts the scene-dependent thermal signature of bioaerosol plumes. Change detection in thermal sensor data is combined with local meteorological data to locate targets with the appropriate thermal characteristics. Target motion is tracked utilizing a Kalman filter and nearly constant velocity motion model for cloud state estimation. Track management is performed using a logic-based upkeep system, and data association is accomplished using a combinatorial optimization technique. Bioaerosol threat classification is determined using a recursive Bayesian classifier to quantify the threat probability of each tracked object. The classifier can accept additional inputs from visible imagers, acoustic sensors, and point biological sensors to improve classification confidence. This capability was successfully demonstrated for bioaerosol simulant releases during field testing at Dugway Proving Grounds. Standoff detection at a range of 700m was achieved for as little as 500g of anthrax simulant. Developmental test results will be reviewed for a range of simulant releases, and future development and transition plans for the bioaerosol early warning platform will be discussed.

A computational pipeline for the development of multi-marker bio-signature panels and ensemble classifiers

PubMed Central

2012-01-01

Background Biomarker panels derived separately from genomic and proteomic data and with a variety of computational methods have demonstrated promising classification performance in various diseases. An open question is how to create effective proteo-genomic panels. The framework of ensemble classifiers has been applied successfully in various analytical domains to combine classifiers so that the performance of the ensemble exceeds the performance of individual classifiers. Using blood-based diagnosis of acute renal allograft rejection as a case study, we address the following question in this paper: Can acute rejection classification performance be improved by combining individual genomic and proteomic classifiers in an ensemble? Results The first part of the paper presents a computational biomarker development pipeline for genomic and proteomic data. The pipeline begins with data acquisition (e.g., from bio-samples to microarray data), quality control, statistical analysis and mining of the data, and finally various forms of validation. The pipeline ensures that the various classifiers to be combined later in an ensemble are diverse and adequate for clinical use. Five mRNA genomic and five proteomic classifiers were developed independently using single time-point blood samples from 11 acute-rejection and 22 non-rejection renal transplant patients. The second part of the paper examines five ensembles ranging in size from two to 10 individual classifiers. Performance of ensembles is characterized by area under the curve (AUC), sensitivity, and specificity, as derived from the probability of acute rejection for individual classifiers in the ensemble in combination with one of two aggregation methods: (1) Average Probability or (2) Vote Threshold. One ensemble demonstrated superior performance and was able to improve sensitivity and AUC beyond the best values observed for any of the individual classifiers in the ensemble, while staying within the range of observed specificity. The Vote Threshold aggregation method achieved improved sensitivity for all 5 ensembles, but typically at the cost of decreased specificity. Conclusion Proteo-genomic biomarker ensemble classifiers show promise in the diagnosis of acute renal allograft rejection and can improve classification performance beyond that of individual genomic or proteomic classifiers alone. Validation of our results in an international multicenter study is currently underway. PMID:23216969

A computational pipeline for the development of multi-marker bio-signature panels and ensemble classifiers.

PubMed

Günther, Oliver P; Chen, Virginia; Freue, Gabriela Cohen; Balshaw, Robert F; Tebbutt, Scott J; Hollander, Zsuzsanna; Takhar, Mandeep; McMaster, W Robert; McManus, Bruce M; Keown, Paul A; Ng, Raymond T

2012-12-08

Biomarker panels derived separately from genomic and proteomic data and with a variety of computational methods have demonstrated promising classification performance in various diseases. An open question is how to create effective proteo-genomic panels. The framework of ensemble classifiers has been applied successfully in various analytical domains to combine classifiers so that the performance of the ensemble exceeds the performance of individual classifiers. Using blood-based diagnosis of acute renal allograft rejection as a case study, we address the following question in this paper: Can acute rejection classification performance be improved by combining individual genomic and proteomic classifiers in an ensemble? The first part of the paper presents a computational biomarker development pipeline for genomic and proteomic data. The pipeline begins with data acquisition (e.g., from bio-samples to microarray data), quality control, statistical analysis and mining of the data, and finally various forms of validation. The pipeline ensures that the various classifiers to be combined later in an ensemble are diverse and adequate for clinical use. Five mRNA genomic and five proteomic classifiers were developed independently using single time-point blood samples from 11 acute-rejection and 22 non-rejection renal transplant patients. The second part of the paper examines five ensembles ranging in size from two to 10 individual classifiers. Performance of ensembles is characterized by area under the curve (AUC), sensitivity, and specificity, as derived from the probability of acute rejection for individual classifiers in the ensemble in combination with one of two aggregation methods: (1) Average Probability or (2) Vote Threshold. One ensemble demonstrated superior performance and was able to improve sensitivity and AUC beyond the best values observed for any of the individual classifiers in the ensemble, while staying within the range of observed specificity. The Vote Threshold aggregation method achieved improved sensitivity for all 5 ensembles, but typically at the cost of decreased specificity. Proteo-genomic biomarker ensemble classifiers show promise in the diagnosis of acute renal allograft rejection and can improve classification performance beyond that of individual genomic or proteomic classifiers alone. Validation of our results in an international multicenter study is currently underway.

SU-F-R-17: Advancing Glioblastoma Multiforme (GBM) Recurrence Detection with MRI Image Texture Feature Extraction and Machine Learning

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

Yu, V; Ruan, D; Nguyen, D

Purpose: To test the potential of early Glioblastoma Multiforme (GBM) recurrence detection utilizing image texture pattern analysis in serial MR images post primary treatment intervention. Methods: MR image-sets of six time points prior to the confirmed recurrence diagnosis of a GBM patient were included in this study, with each time point containing T1 pre-contrast, T1 post-contrast, T2-Flair, and T2-TSE images. Eight Gray-level co-occurrence matrix (GLCM) texture features including Contrast, Correlation, Dissimilarity, Energy, Entropy, Homogeneity, Sum-Average, and Variance were calculated from all images, resulting in a total of 32 features at each time point. A confirmed recurrent volume was contoured, alongmore » with an adjacent non-recurrent region-of-interest (ROI) and both volumes were propagated to all prior time points via deformable image registration. A support vector machine (SVM) with radial-basis-function kernels was trained on the latest time point prior to the confirmed recurrence to construct a model for recurrence classification. The SVM model was then applied to all prior time points and the volumes classified as recurrence were obtained. Results: An increase in classified volume was observed over time as expected. The size of classified recurrence maintained at a stable level of approximately 0.1 cm{sup 3} up to 272 days prior to confirmation. Noticeable volume increase to 0.44 cm{sup 3} was demonstrated at 96 days prior, followed by significant increase to 1.57 cm{sup 3} at 42 days prior. Visualization of the classified volume shows the merging of recurrence-susceptible region as the volume change became noticeable. Conclusion: Image texture pattern analysis in serial MR images appears to be sensitive to detecting the recurrent GBM a long time before the recurrence is confirmed by a radiologist. The early detection may improve the efficacy of targeted intervention including radiosurgery. More patient cases will be included to create a generalizable classification model applicable to a larger patient cohort. NIH R43CA183390 and R01CA188300.NSF Graduate Research Fellowship DGE-1144087.« less

The behavior limestone under explosive load

NASA Astrophysics Data System (ADS)

Orlov, M. Yu; Orlova, Yu N.; Bogomolov, G. N.

2016-11-01

Limestone behavior under explosive loading was investigated. The behavior of the limestone by the action of the three types of explosives, including granular, ammonite and emulsion explosives was studied in detail. The shape and diameter of the explosion craters were obtained. The observed fragments after the blast have been classified as large, medium and small fragments. Three full-scale experiments were carried out. The research results can be used as a qualitative test for the approbation of numerical methods.

A translational platform for prototyping closed-loop neuromodulation systems

PubMed Central

Afshar, Pedram; Khambhati, Ankit; Stanslaski, Scott; Carlson, David; Jensen, Randy; Linde, Dave; Dani, Siddharth; Lazarewicz, Maciej; Cong, Peng; Giftakis, Jon; Stypulkowski, Paul; Denison, Tim

2013-01-01

While modulating neural activity through stimulation is an effective treatment for neurological diseases such as Parkinson's disease and essential tremor, an opportunity for improving neuromodulation therapy remains in automatically adjusting therapy to continuously optimize patient outcomes. Practical issues associated with achieving this include the paucity of human data related to disease states, poorly validated estimators of patient state, and unknown dynamic mappings of optimal stimulation parameters based on estimated states. To overcome these challenges, we present an investigational platform including: an implanted sensing and stimulation device to collect data and run automated closed-loop algorithms; an external tool to prototype classifier and control-policy algorithms; and real-time telemetry to update the implanted device firmware and monitor its state. The prototyping system was demonstrated in a chronic large animal model studying hippocampal dynamics. We used the platform to find biomarkers of the observed states and transfer functions of different stimulation amplitudes. Data showed that moderate levels of stimulation suppress hippocampal beta activity, while high levels of stimulation produce seizure-like after-discharge activity. The biomarker and transfer function observations were mapped into classifier and control-policy algorithms, which were downloaded to the implanted device to continuously titrate stimulation amplitude for the desired network effect. The platform is designed to be a flexible prototyping tool and could be used to develop improved mechanistic models and automated closed-loop systems for a variety of neurological disorders. PMID:23346048

A translational platform for prototyping closed-loop neuromodulation systems.

PubMed

Afshar, Pedram; Khambhati, Ankit; Stanslaski, Scott; Carlson, David; Jensen, Randy; Linde, Dave; Dani, Siddharth; Lazarewicz, Maciej; Cong, Peng; Giftakis, Jon; Stypulkowski, Paul; Denison, Tim

2012-01-01

While modulating neural activity through stimulation is an effective treatment for neurological diseases such as Parkinson's disease and essential tremor, an opportunity for improving neuromodulation therapy remains in automatically adjusting therapy to continuously optimize patient outcomes. Practical issues associated with achieving this include the paucity of human data related to disease states, poorly validated estimators of patient state, and unknown dynamic mappings of optimal stimulation parameters based on estimated states. To overcome these challenges, we present an investigational platform including: an implanted sensing and stimulation device to collect data and run automated closed-loop algorithms; an external tool to prototype classifier and control-policy algorithms; and real-time telemetry to update the implanted device firmware and monitor its state. The prototyping system was demonstrated in a chronic large animal model studying hippocampal dynamics. We used the platform to find biomarkers of the observed states and transfer functions of different stimulation amplitudes. Data showed that moderate levels of stimulation suppress hippocampal beta activity, while high levels of stimulation produce seizure-like after-discharge activity. The biomarker and transfer function observations were mapped into classifier and control-policy algorithms, which were downloaded to the implanted device to continuously titrate stimulation amplitude for the desired network effect. The platform is designed to be a flexible prototyping tool and could be used to develop improved mechanistic models and automated closed-loop systems for a variety of neurological disorders.

Methods, systems and devices for detecting threatening objects and for classifying magnetic data

DOEpatents

Kotter, Dale K [Shelley, ID; Roybal, Lyle G [Idaho Falls, ID; Rohrbaugh, David T [Idaho Falls, ID; Spencer, David F [Idaho Falls, ID

2012-01-24

A method for detecting threatening objects in a security screening system. The method includes a step of classifying unique features of magnetic data as representing a threatening object. Another step includes acquiring magnetic data. Another step includes determining if the acquired magnetic data comprises a unique feature.

Lateral Radiograph of the Hip in Fracture Neck of Femur: Is it a Ritual?

PubMed

Kumar, Dheerendra S; Gubbi, Shivarathre D; Abdul, Bari; Bisalahalli, Muddu

2008-10-01

Historically routine work up of a patient with a fracture neck of femur has always included an antero-posterior (AP) and a lateral view of the hip. The aim of the study was to know whether a lateral view of hip influenced the decision of an Orthopedic Surgeon regarding management at a District General Hospital. A prospective study was conducted from February 2005 to September 2005 at Tameside General Hospital. X-rays of patients admitted with fracture neck of femur were shown to two independent observers in the daily trauma meeting. AP view of the hip was shown initially to observers and their classification and intended treatment was recorded. They were asked if they needed a lateral view to decide on management option and answers were recorded. The observers were then showed a lateral view of same hip and asked to comment on quality of film and also whether it would change their classification or intended management. There were 100 patients over six months. On AP view 56 were classified to have extra-capsular fracture, 37 were classified as displaced subcapital fracture and seven were classified undisplaced subcapital fracture. There was an interobserver variation in one patient between undisplaced or displaced subcapital fracture. The observers felt they would need a lateral X-ray on three occasions and there was a change in classification from undisplaced subcapital to displaced subcapital fracture on first occasion. There was no change in management plan in all the 100 patients after looking at a lateral X-ray. We can conclude that unless required for management a lateral X-ray of hip should be avoided routinely in all patients with fracture neck of femur as it would not only be cost effective but will also reduce radiation exposure to patient and relieve work pressure on radiographers, nursing and portering staff.

Retinal phenotypic characterization of patients with ABCA4 retinopathydue to the homozygous p.Ala1773Val mutation

PubMed Central

López-Rubio, Salvador; Chacon-Camacho, Oscar F.; Matsui, Rodrigo; Guadarrama-Vallejo, Dalia; Astiazarán, Mirena C.

2018-01-01

Purpose To describe the retinal clinical features of a group of Mexican patients with Stargardt disease carrying the uncommon p.Ala1773Val founder mutation in ABCA4. Methods Ten patients carrying the p.Ala1773Val mutation, nine of them homozygously, were included. Visual function studies included best-corrected visual acuity, electroretinography, Goldmann kinetic visual fields, and full-field electroretinography (ERG). In addition, imaging studies, such as optical coherence tomography (OCT), short-wave autofluorescence imaging, and quantitative analyses of hypofluorescence, were performed in each patient. Results Best-corrected visual acuities ranged from 20/200 to 4/200. The median age of the patients at diagnosis was 23.3 years. The majority of the patients had photophobia and nyctalopia, and were classified as Fishman stage 4 (widespread choriocapillaris atrophy, resorption of flecks, and greatly reduced ERG amplitudes). An atypical retinal pigmentation pattern was observed in the patients, and the majority showed cone-rod dystrophy on full-field ERG. In vivo retinal microstructure assessment with OCT demonstrated central retinal thinning, variable loss of photoreceptors, and three different patterns of structural retinal degeneration. Two dissimilar patterns of abnormal autofluorescence were observed. No apparent age-related differences in the pattern of retinal degeneration were observed. Conclusions The results indicate that this particular mutation in ABCA4 is associated with a severe retinal phenotype and thus, could be classified as null. Careful phenotyping of patients carrying specific mutations in ABCA4 is essential to enhance our understanding of disease expression linked to particular mutations and the resulting genotype–phenotype correlations. PMID:29422768

3200 Phaethon, Asteroid or Comet Nucleus?

NASA Astrophysics Data System (ADS)

Boice, Daniel C.; Benkhoff, Johannes

2015-08-01

Physico-chemical modeling is central to understand the important physical processes in small solar system bodies. We have developed a computer simulation, SUISEI, that includes the physico-chemical processes relevant to comets within a global modeling framework. Our goals are to gain valuable insights into the intrinsic properties of cometary nuclei so we can better understand observations and in situ measurements. SUISEI includes a 3-D model of gas and heat transport in porous sub-surface layers in the interior of the nucleus.We present results on the application of SUISEI to the near-Sun object, Phaethon. Discovered in 1983 and classified as an asteroid, it has recently exhibited an active dust coma. Phaethon has long been associated as the source of the Geminids meteor shower so the dust activity provides a clear link to the meteor shower. The observed dust activity would traditionally lead to Phaethon being also classified as a comet (e.g., 2060-95P/Chiron, 133P/Elst-Pizarro). This is unusual since the orbit of Phaethon has a perihelion of 0.14 AU, resulting in surface temperatures of more than 1025K, much too hot for water ice or other volatiles to exist near the surface and drive the activity. This situation and others such as the “Active Asteroids” necessitates a revision of how we understand and classify these small asteroid-comet transition objects.We conclude the following for Phaethon:1. It is likely to contain relatively pristine volatiles in its interior despite repeated near perihelion passages of 0.14 AU during its history in its present orbit,2. Steady water gas fluxes at perihelion and throughout its orbit are insufficient to entrain the currently observed dust production,3. Thermal gradients into the surface as well as those caused by diurnal rotation are consistent with the mechanism of dust release due to thermal fracture,4. The initial large gas release during the first perihelion passage may be sufficient to produce enough dust to explain the entire meteor stream.Acknowledgements: We greatly appreciate support from the NSF Planetary Astronomy Program under Grant No. 0908529 and the ESA/ESTEC Visiting Scientist Program.

Development of a noninvasive system for monitoring dairy cattle sleep.

PubMed

Klefot, J M; Murphy, J L; Donohue, K D; O'Hara, B F; Lhamon, M E; Bewley, J M

2016-10-01

Limited research has been conducted to assess sleep in production livestock primarily because of limitations with monitoring capabilities. Consequently, biological understanding of production circumstances and facility options that affect sleep is limited. The objective of this study was to assess if data collected from a proof-of-concept, noninvasive 3-axis accelerometer device are correlated with sleep and wake-like behaviors in dairy cattle. Four Holstein dairy cows housed at the University of Kentucky Coldstream Dairy in September 2013 were visually observed for 2 consecutive 24-h periods. The accelerometer device was attached to a harness positioned on the right side of each cow's neck. Times of classified behaviors of wake (standing, head up, alert, eyes open) or sleep-like behaviors (lying, still, head resting on ground, eyes closed) were recorded continuously by 2 observers who each watched 2 cows at a time. The radial signal was extracted from 3 different axes of the accelerometer to obtain a motion signal independent of direction of movement. Radial signal features were examined for maximizing the performance of detecting sleep-like behaviors using a Fisher's linear discriminant analysis classifier. The study included 652min of high-activity wake behaviors and 107min of sleep-like behavior among 4 cows. Results from a bootstrapping analysis showed an agreement between human observation and the linear discriminant analysis classifier, with an accuracy of 93.7±0.7% for wake behavior and 92.2±0.8% for sleep-like behavior (±95% confidence interval).This prototype shows promise in measuring sleep-like behaviors. Improvements to both hardware and software should allow more accurate determinations of subtle head movements and respiratory movements that will further improve the assessment of these sleep-like behaviors, including estimates of deep, light, and rapid eye movement sleep. These future studies will require simultaneous electroencephalography and electromyography measures and perhaps additional measures of arousal thresholds to validate this system for measuring true sleep. Copyright © 2016 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Classifying features in CT imagery: accuracy for some single- and multiple-species classifiers

Treesearch

Daniel L. Schmoldt; Jing He; A. Lynn Abbott

1998-01-01

Our current approach to automatically label features in CT images of hardwood logs classifies each pixel of an image individually. These feature classifiers use a back-propagation artificial neural network (ANN) and feature vectors that include a small, local neighborhood of pixels and the distance of the target pixel to the center of the log. Initially, this type of...

Integrated Sensing Processor, Phase 2

DTIC Science & Technology

2005-12-01

performance analysis for several baseline classifiers including neural nets, linear classifiers, and kNN classifiers. Use of CCDR as a preprocessing step...below the level of the benchmark non-linear classifier for this problem ( kNN ). Furthermore, the CCDR preconditioned kNN achieved a 10% improvement over...the benchmark kNN without CCDR. Finally, we found an important connection between intrinsic dimension estimation via entropic graphs and the optimal

Attention to local and global levels of hierarchical Navon figures affects rapid scene categorization.

PubMed

Brand, John; Johnson, Aaron P

2014-01-01

In four experiments, we investigated how attention to local and global levels of hierarchical Navon figures affected the selection of diagnostic spatial scale information used in scene categorization. We explored this issue by asking observers to classify hybrid images (i.e., images that contain low spatial frequency (LSF) content of one image, and high spatial frequency (HSF) content from a second image) immediately following global and local Navon tasks. Hybrid images can be classified according to either their LSF, or HSF content; thus, making them ideal for investigating diagnostic spatial scale preference. Although observers were sensitive to both spatial scales (Experiment 1), they overwhelmingly preferred to classify hybrids based on LSF content (Experiment 2). In Experiment 3, we demonstrated that LSF based hybrid categorization was faster following global Navon tasks, suggesting that LSF processing associated with global Navon tasks primed the selection of LSFs in hybrid images. In Experiment 4, replicating Experiment 3 but suppressing the LSF information in Navon letters by contrast balancing the stimuli examined this hypothesis. Similar to Experiment 3, observers preferred to classify hybrids based on LSF content; however and in contrast, LSF based hybrid categorization was slower following global than local Navon tasks.

Automated computer-based detection of encounter behaviours in groups of honeybees.

PubMed

Blut, Christina; Crespi, Alessandro; Mersch, Danielle; Keller, Laurent; Zhao, Linlin; Kollmann, Markus; Schellscheidt, Benjamin; Fülber, Carsten; Beye, Martin

2017-12-15

Honeybees form societies in which thousands of members integrate their behaviours to act as a single functional unit. We have little knowledge on how the collaborative features are regulated by workers' activities because we lack methods that enable collection of simultaneous and continuous behavioural information for each worker bee. In this study, we introduce the Bee Behavioral Annotation System (BBAS), which enables the automated detection of bees' behaviours in small observation hives. Continuous information on position and orientation were obtained by marking worker bees with 2D barcodes in a small observation hive. We computed behavioural and social features from the tracking information to train a behaviour classifier for encounter behaviours (interaction of workers via antennation) using a machine learning-based system. The classifier correctly detected 93% of the encounter behaviours in a group of bees, whereas 13% of the falsely classified behaviours were unrelated to encounter behaviours. The possibility of building accurate classifiers for automatically annotating behaviours may allow for the examination of individual behaviours of worker bees in the social environments of small observation hives. We envisage that BBAS will be a powerful tool for detecting the effects of experimental manipulation of social attributes and sub-lethal effects of pesticides on behaviour.

Attention to local and global levels of hierarchical Navon figures affects rapid scene categorization

PubMed Central

Brand, John; Johnson, Aaron P.

2014-01-01

In four experiments, we investigated how attention to local and global levels of hierarchical Navon figures affected the selection of diagnostic spatial scale information used in scene categorization. We explored this issue by asking observers to classify hybrid images (i.e., images that contain low spatial frequency (LSF) content of one image, and high spatial frequency (HSF) content from a second image) immediately following global and local Navon tasks. Hybrid images can be classified according to either their LSF, or HSF content; thus, making them ideal for investigating diagnostic spatial scale preference. Although observers were sensitive to both spatial scales (Experiment 1), they overwhelmingly preferred to classify hybrids based on LSF content (Experiment 2). In Experiment 3, we demonstrated that LSF based hybrid categorization was faster following global Navon tasks, suggesting that LSF processing associated with global Navon tasks primed the selection of LSFs in hybrid images. In Experiment 4, replicating Experiment 3 but suppressing the LSF information in Navon letters by contrast balancing the stimuli examined this hypothesis. Similar to Experiment 3, observers preferred to classify hybrids based on LSF content; however and in contrast, LSF based hybrid categorization was slower following global than local Navon tasks. PMID:25520675

Nutritional status of children and adolescents based on body mass index: agreement between World Health Organization and International Obesity Task Force

PubMed Central

Cavazzotto, Timothy Gustavo; Brasil, Marcos Roberto; Oliveira, Vinicius Machado; da Silva, Schelyne Ribas; Ronque, Enio Ricardo V.; Queiroga, Marcos Roberto; Serassuelo, Helio

2014-01-01

Objective: To investigate the agreement between two international criteria for classification of children and adolescents nutritional status. Methods: The study included 778 girls and 863 boys aged from six to 13 years old. Body mass and height were measured and used to calculate the body mass index. Nutritional status was classified according to the cut-off points defined by the World Health Organization and the International Obesity Task Force. The agreement was evaluated using Kappa statistic and weighted Kappa. Results: In order to classify the nutritional status, the agreement between the criteria was higher for the boys (Kappa 0.77) compared to girls (Kappa 0.61). The weighted Kappa was also higher for boys (0.85) in comparison to girls (0.77). Kappa index varied according to age. When the nutritional status was classified in only two categories - appropriate (thinness + accentuated thinness + eutrophy) and overweight (overweight + obesity + severe obesity) -, the Kappa index presented higher values than those related to the classification in six categories. Conclusions: A substantial agreement was observed between the criteria, being higher in males and varying according to the age. PMID:24676189

CATCh, an Ensemble Classifier for Chimera Detection in 16S rRNA Sequencing Studies

PubMed Central

Mysara, Mohamed; Saeys, Yvan; Leys, Natalie; Raes, Jeroen

2014-01-01

In ecological studies, microbial diversity is nowadays mostly assessed via the detection of phylogenetic marker genes, such as 16S rRNA. However, PCR amplification of these marker genes produces a significant amount of artificial sequences, often referred to as chimeras. Different algorithms have been developed to remove these chimeras, but efforts to combine different methodologies are limited. Therefore, two machine learning classifiers (reference-based and de novo CATCh) were developed by integrating the output of existing chimera detection tools into a new, more powerful method. When comparing our classifiers with existing tools in either the reference-based or de novo mode, a higher performance of our ensemble method was observed on a wide range of sequencing data, including simulated, 454 pyrosequencing, and Illumina MiSeq data sets. Since our algorithm combines the advantages of different individual chimera detection tools, our approach produces more robust results when challenged with chimeric sequences having a low parent divergence, short length of the chimeric range, and various numbers of parents. Additionally, it could be shown that integrating CATCh in the preprocessing pipeline has a beneficial effect on the quality of the clustering in operational taxonomic units. PMID:25527546

A standardized non-instrumental tool for characterizing workstations concerned with exposure to engineered nanomaterials

NASA Astrophysics Data System (ADS)

Canu I, Guseva; C, Ducros; S, Ducamp; L, Delabre; S, Audignon-Durand; C, Durand; Y, Iwatsubo; D, Jezewski-Serra; Bihan O, Le; S, Malard; A, Radauceanu; M, Reynier; M, Ricaud; O, Witschger

2015-05-01

The French national epidemiological surveillance program EpiNano aims at surveying mid- and long-term health effects possibly related with occupational exposure to either carbon nanotubes or titanium dioxide nanoparticles (TiO2). EpiNano is limited to workers potentially exposed to these nanomaterials including their aggregates and agglomerates. In order to identify those workers during the in-field industrial hygiene visits, a standardized non-instrumental method is necessary especially for epidemiologists and occupational physicians unfamiliar with nanoparticle and nanomaterial exposure metrology. A working group, Quintet ExpoNano, including national experts in nanomaterial metrology and occupational hygiene reviewed available methods, resources and their practice in order to develop a standardized tool for conducting company industrial hygiene visits and collecting necessary information. This tool, entitled “Onsite technical logbook”, includes 3 parts: company, workplace, and workstation allowing a detailed description of each task, process and exposure surrounding conditions. This logbook is intended to be completed during the company industrial hygiene visit. Each visit is conducted jointly by an industrial hygienist and an epidemiologist of the program and lasts one or two days depending on the company size. When all collected information is computerized using friendly-using software, it is possible to classify workstations with respect to their potential direct and/or indirect exposure. Workers appointed to workstations classified as concerned with exposure are considered as eligible for EpiNano program and invited to participate. Since January 2014, the Onsite technical logbook has been used in ten company visits. The companies visited were mostly involved in research and development. A total of 53 workstations with potential exposure to nanomaterials were pre-selected and observed: 5 with TiO2, 16 with single-walled carbon nanotubes, 27 multiwalled carbon nanotubes. Among the tasks observed there were: nanomaterial characterisation analysis (8), weighing (7), synthesis (6), functionalization (5), and transfer (5). The manipulated quantities were usually very small. After analysis of the data gathered in logbooks, 30 workstations have been classified as concerned with exposure to carbon nanotubes or TiO2. Additional tool validity as well as inter-and intra-evaluator reproducibility studies are ongoing. The first results are promising.

Combination of support vector machine, artificial neural network and random forest for improving the classification of convective and stratiform rain using spectral features of SEVIRI data

NASA Astrophysics Data System (ADS)

Lazri, Mourad; Ameur, Soltane

2018-05-01

A model combining three classifiers, namely Support vector machine, Artificial neural network and Random forest (SAR) is designed for improving the classification of convective and stratiform rain. This model (SAR model) has been trained and then tested on a datasets derived from MSG-SEVIRI (Meteosat Second Generation-Spinning Enhanced Visible and Infrared Imager). Well-classified, mid-classified and misclassified pixels are determined from the combination of three classifiers. Mid-classified and misclassified pixels that are considered unreliable pixels are reclassified by using a novel training of the developed scheme. In this novel training, only the input data corresponding to the pixels in question to are used. This whole process is repeated a second time and applied to mid-classified and misclassified pixels separately. Learning and validation of the developed scheme are realized against co-located data observed by ground radar. The developed scheme outperformed different classifiers used separately and reached 97.40% of overall accuracy of classification.

10 CFR 95.37 - Classification and preparation of documents.

Code of Federal Regulations, 2014 CFR

2014-01-01

... Information must contain the identity of the source document or the classification guide, including the agency.../Exemption) Classifier: (Name/Title/Number) (2) For Restricted Data documents: (i) Identity of the classifier. The identity of the classifier must be shown by completion of the “Derivative Classifier” line. The...

10 CFR 95.37 - Classification and preparation of documents.

Code of Federal Regulations, 2012 CFR

2012-01-01

... Information must contain the identity of the source document or the classification guide, including the agency.../Exemption) Classifier: (Name/Title/Number) (2) For Restricted Data documents: (i) Identity of the classifier. The identity of the classifier must be shown by completion of the “Derivative Classifier” line. The...

10 CFR 95.37 - Classification and preparation of documents.

Code of Federal Regulations, 2013 CFR

2013-01-01

... Information must contain the identity of the source document or the classification guide, including the agency.../Exemption) Classifier: (Name/Title/Number) (2) For Restricted Data documents: (i) Identity of the classifier. The identity of the classifier must be shown by completion of the “Derivative Classifier” line. The...

Classifying clinical notes with pain assessment using machine learning.

PubMed

Fodeh, Samah Jamal; Finch, Dezon; Bouayad, Lina; Luther, Stephen L; Ling, Han; Kerns, Robert D; Brandt, Cynthia

2017-12-26

Pain is a significant public health problem, affecting millions of people in the USA. Evidence has highlighted that patients with chronic pain often suffer from deficits in pain care quality (PCQ) including pain assessment, treatment, and reassessment. Currently, there is no intelligent and reliable approach to identify PCQ indicators inelectronic health records (EHR). Hereby, we used unstructured text narratives in the EHR to derive pain assessment in clinical notes for patients with chronic pain. Our dataset includes patients with documented pain intensity rating ratings > = 4 and initial musculoskeletal diagnoses (MSD) captured by (ICD-9-CM codes) in fiscal year 2011 and a minimal 1 year of follow-up (follow-up period is 3-yr maximum); with complete data on key demographic variables. A total of 92 patients with 1058 notes was used. First, we manually annotated qualifiers and descriptors of pain assessment using the annotation schema that we previously developed. Second, we developed a reliable classifier for indicators of pain assessment in clinical note. Based on our annotation schema, we found variations in documenting the subclasses of pain assessment. In positive notes, providers mostly documented assessment of pain site (67%) and intensity of pain (57%), followed by persistence (32%). In only 27% of positive notes, did providers document a presumed etiology for the pain complaint or diagnosis. Documentation of patients' reports of factors that aggravate pain was only present in 11% of positive notes. Random forest classifier achieved the best performance labeling clinical notes with pain assessment information, compared to other classifiers; 94, 95, 94, and 94% was observed in terms of accuracy, PPV, F1-score, and AUC, respectively. Despite the wide spectrum of research that utilizes machine learning in many clinical applications, none explored using these methods for pain assessment research. In addition, previous studies using large datasets to detect and analyze characteristics of patients with various types of pain have relied exclusively on billing and coded data as the main source of information. This study, in contrast, harnessed unstructured narrative text data from the EHR to detect pain assessment clinical notes. We developed a Random forest classifier to identify clinical notes with pain assessment information. Compared to other classifiers, ours achieved the best results in most of the reported metrics. Graphical abstract Framework for detecting pain assessment in clinical notes.

De novo immune complex deposition in kidney allografts: a series of 32 patients.

PubMed

Lloyd, Isaac E; Ahmed, Faris; Revelo, Monica P; Khalighi, Mazdak A

2018-01-01

Immune complex deposition in kidney allografts can include both recurrent and de novo processes. Recurrent glomerulonephritis is a well-recognized phenomenon and has been shown to be a common cause of allograft failure. De novo immune complex-mediated disease remains relatively poorly characterized, likely owing to the less frequent use of immunofluorescence and electron microscopy in the transplant setting. We performed a retrospective review of kidney allograft biopsies showing glomerular immune complex deposition. Cases with de novo deposits were identified and further organized into two groups depending on whether the immune complex deposition could be clinically and/or histologically classified. Thirty-two patients with de novo immune complex deposition were identified over a 7-year period. A broad range of immune complex-mediated injuries were observed, the majority (63%) of which could be readily classified either clinically or histologically. These included cases of membranous glomerulonephropathy, IgA nephropathy, infection-related glomerulonephritis and glomerulonephritis related to an underlying autoimmune process. A smaller subset of patients (37%) demonstrated immune complex deposition that was difficult to histologically or clinically classify. These patients typically showed mild mesangial immune complex deposition with co-dominant IgG and IgM staining by immunofluorescence microscopy. The presence of concurrent antibody-mediated rejection and donor-specific antibody positivity was significantly higher in the unclassifiable group. The significance of these deposits and their possible relationship to allograft rejection deserves further investigation. Copyright © 2017 Elsevier Inc. All rights reserved.

WND-CHARM: Multi-purpose image classification using compound image transforms

PubMed Central

Orlov, Nikita; Shamir, Lior; Macura, Tomasz; Johnston, Josiah; Eckley, D. Mark; Goldberg, Ilya G.

2008-01-01

We describe a multi-purpose image classifier that can be applied to a wide variety of image classification tasks without modifications or fine-tuning, and yet provide classification accuracy comparable to state-of-the-art task-specific image classifiers. The proposed image classifier first extracts a large set of 1025 image features including polynomial decompositions, high contrast features, pixel statistics, and textures. These features are computed on the raw image, transforms of the image, and transforms of transforms of the image. The feature values are then used to classify test images into a set of pre-defined image classes. This classifier was tested on several different problems including biological image classification and face recognition. Although we cannot make a claim of universality, our experimental results show that this classifier performs as well or better than classifiers developed specifically for these image classification tasks. Our classifier’s high performance on a variety of classification problems is attributed to (i) a large set of features extracted from images; and (ii) an effective feature selection and weighting algorithm sensitive to specific image classification problems. The algorithms are available for free download from openmicroscopy.org. PMID:18958301

Sitting Posture Monitoring System Based on a Low-Cost Load Cell Using Machine Learning

PubMed Central

Roh, Jongryun; Park, Hyeong-jun; Lee, Kwang Jin; Hyeong, Joonho; Kim, Sayup

2018-01-01

Sitting posture monitoring systems (SPMSs) help assess the posture of a seated person in real-time and improve sitting posture. To date, SPMS studies reported have required many sensors mounted on the backrest plate and seat plate of a chair. The present study, therefore, developed a system that measures a total of six sitting postures including the posture that applied a load to the backrest plate, with four load cells mounted only on the seat plate. Various machine learning algorithms were applied to the body weight ratio measured by the developed SPMS to identify the method that most accurately classified the actual sitting posture of the seated person. After classifying the sitting postures using several classifiers, average and maximum classification rates of 97.20% and 97.94%, respectively, were obtained from nine subjects with a support vector machine using the radial basis function kernel; the results obtained by this classifier showed a statistically significant difference from the results of multiple classifications using other classifiers. The proposed SPMS was able to classify six sitting postures including the posture with loading on the backrest and showed the possibility of classifying the sitting posture even though the number of sensors is reduced. PMID:29329261

[The historical materials of stomatology in the oracle bone inscriptions of the Yin-Shang Dynasties].

PubMed

Li, Xiaojun; Zhu, Lang

2015-07-01

Some oracle bone inscriptions of the Yin-Shang Dynasties were related to the stomatology, including special terms of diseases of the mouth, tongue and teeth which were classified, and proper nouns of some special diseases. Moreover, witch doctors' exploration for the causes of oral diseases, the observation on different stages of oral diseases, and the records of oral disease treatment were also involved. All of these reflected the sprouting stage of stomatology in the Yin-Shang Dynasties in ancient China.

Stackable differential mobility analyzer for aerosol measurement

DOEpatents

Cheng, Meng-Dawn [Oak Ridge, TN; Chen, Da-Ren [Creve Coeur, MO

2007-05-08

A multi-stage differential mobility analyzer (MDMA) for aerosol measurements includes a first electrode or grid including at least one inlet or injection slit for receiving an aerosol including charged particles for analysis. A second electrode or grid is spaced apart from the first electrode. The second electrode has at least one sampling outlet disposed at a plurality different distances along its length. A volume between the first and the second electrode or grid between the inlet or injection slit and a distal one of the plurality of sampling outlets forms a classifying region, the first and second electrodes for charging to suitable potentials to create an electric field within the classifying region. At least one inlet or injection slit in the second electrode receives a sheath gas flow into an upstream end of the classifying region, wherein each sampling outlet functions as an independent DMA stage and classifies different size ranges of charged particles based on electric mobility simultaneously.

Rotational Study of Ambiguous Taxonomic Classified Asteroids

NASA Astrophysics Data System (ADS)

Linder, Tyler R.; Sanchez, Rick; Wuerker, Wolfgang; Clayson, Timothy; Giles, Tucker

2017-01-01

The Sloan Digital Sky Survey (SDSS) moving object catalog (MOC4) provided the largest ever catalog of asteroid spectrophotometry observations. Carvano et al. (2010), while analyzing MOC4, discovered that individual observations of asteroids which were observed multiple times did not classify into the same photometric-based taxonomic class. A small subset of those asteroids were classified as having both the presence and absence of a 1um silicate absorption feature. If these variations are linked to differences in surface mineralogy, the prevailing assumption that an asteroid’s surface composition is predominantly homogenous would need to be reexamined. Furthermore, our understanding of the evolution of the asteroid belt, as well as the linkage between certain asteroids and meteorite types may need to be modified.This research is an investigation to determine the rotational rates of these taxonomically ambiguous asteroids. Initial questions to be answered:Do these asteroids have unique or nonstandard rotational rates?Is there any evidence in their light curve to suggest an abnormality?Observations were taken using PROMPT6 a 0.41-m telescope apart of the SKYNET network at Cerro Tololo Inter-American Observatory (CTIO). Observations were calibrated and analyzed using Canopus software. Initial results will be presented at AAS.

Molecular activity prediction by means of supervised subspace projection based ensembles of classifiers.

PubMed

Cerruela García, G; García-Pedrajas, N; Luque Ruiz, I; Gómez-Nieto, M Á

2018-03-01

This paper proposes a method for molecular activity prediction in QSAR studies using ensembles of classifiers constructed by means of two supervised subspace projection methods, namely nonparametric discriminant analysis (NDA) and hybrid discriminant analysis (HDA). We studied the performance of the proposed ensembles compared to classical ensemble methods using four molecular datasets and eight different models for the representation of the molecular structure. Using several measures and statistical tests for classifier comparison, we observe that our proposal improves the classification results with respect to classical ensemble methods. Therefore, we show that ensembles constructed using supervised subspace projections offer an effective way of creating classifiers in cheminformatics.

A 3D Active Learning Application for NeMO-Net, the NASA Neural Multi-Modal Observation and Training Network for Global Coral Reef Assessment

NASA Technical Reports Server (NTRS)

van den Bergh, Jarrett; Schutz, Joey; Li, Alan; Chirayath, Ved

2017-01-01

NeMO-Net, the NASA neural multi-modal observation and training network for global coral reef assessment, is an open-source deep convolutional neural network and interactive active learning training software aiming to accurately assess the present and past dynamics of coral reef ecosystems through determination of percent living cover and morphology as well as mapping of spatial distribution. We present an interactive video game prototype for tablet and mobile devices where users interactively label morphology classifications over mm-scale 3D coral reef imagery captured using fluid lensing to create a dataset that will be used to train NeMO-Nets convolutional neural network. The application currently allows for users to classify preselected regions of coral in the Pacific and will be expanded to include additional regions captured using our NASA FluidCam instrument, presently the highest-resolution remote sensing benthic imaging technology capable of removing ocean wave distortion, as well as lower-resolution airborne remote sensing data from the ongoing NASA CORAL campaign. Active learning applications present a novel methodology for efficiently training large-scale Neural Networks wherein variances in identification can be rapidly mitigated against control data. NeMO-Net periodically checks users input against pre-classified coral imagery to gauge their accuracy and utilize in-game mechanics to provide classification training. Users actively communicate with a server and are requested to classify areas of coral for which other users had conflicting classifications and contribute their input to a larger database for ranking. In partnering with Mission Blue and IUCN, NeMO-Net leverages an international consortium of subject matter experts to classify areas of confusion identified by NeMO-Net and generate additional labels crucial for identifying decision boundary locations in coral reef assessment.

A 3D Active Learning Application for NeMO-Net, the NASA Neural Multi-Modal Observation and Training Network for Global Coral Reef Assessment

NASA Astrophysics Data System (ADS)

van den Bergh, J.; Schutz, J.; Chirayath, V.; Li, A.

2017-12-01

NeMO-Net, the NASA neural multi-modal observation and training network for global coral reef assessment, is an open-source deep convolutional neural network and interactive active learning training software aiming to accurately assess the present and past dynamics of coral reef ecosystems through determination of percent living cover and morphology as well as mapping of spatial distribution. We present an interactive video game prototype for tablet and mobile devices where users interactively label morphology classifications over mm-scale 3D coral reef imagery captured using fluid lensing to create a dataset that will be used to train NeMO-Net's convolutional neural network. The application currently allows for users to classify preselected regions of coral in the Pacific and will be expanded to include additional regions captured using our NASA FluidCam instrument, presently the highest-resolution remote sensing benthic imaging technology capable of removing ocean wave distortion, as well as lower-resolution airborne remote sensing data from the ongoing NASA CORAL campaign.Active learning applications present a novel methodology for efficiently training large-scale Neural Networks wherein variances in identification can be rapidly mitigated against control data. NeMO-Net periodically checks users' input against pre-classified coral imagery to gauge their accuracy and utilizes in-game mechanics to provide classification training. Users actively communicate with a server and are requested to classify areas of coral for which other users had conflicting classifications and contribute their input to a larger database for ranking. In partnering with Mission Blue and IUCN, NeMO-Net leverages an international consortium of subject matter experts to classify areas of confusion identified by NeMO-Net and generate additional labels crucial for identifying decision boundary locations in coral reef assessment.

Onboard Classifiers for Science Event Detection on a Remote Sensing Spacecraft

NASA Technical Reports Server (NTRS)

Castano, Rebecca; Mazzoni, Dominic; Tang, Nghia; Greeley, Ron; Doggett, Thomas; Cichy, Ben; Chien, Steve; Davies, Ashley

2006-01-01

Typically, data collected by a spacecraft is downlinked to Earth and pre-processed before any analysis is performed. We have developed classifiers that can be used onboard a spacecraft to identify high priority data for downlink to Earth, providing a method for maximizing the use of a potentially bandwidth limited downlink channel. Onboard analysis can also enable rapid reaction to dynamic events, such as flooding, volcanic eruptions or sea ice break-up. Four classifiers were developed to identify cryosphere events using hyperspectral images. These classifiers include a manually constructed classifier, a Support Vector Machine (SVM), a Decision Tree and a classifier derived by searching over combinations of thresholded band ratios. Each of the classifiers was designed to run in the computationally constrained operating environment of the spacecraft. A set of scenes was hand-labeled to provide training and testing data. Performance results on the test data indicate that the SVM and manual classifiers outperformed the Decision Tree and band-ratio classifiers with the SVM yielding slightly better classifications than the manual classifier.

Long Duration X-ray Bursts Observed by MAXI

NASA Astrophysics Data System (ADS)

Serino, Motoko; Iwakiri, Wataru; Tamagawa, Toru; Sakamoto, Takanori; Nakahira, Satoshi; Matsuoka, Masaru; Yamaoka, Kazutaka; Negoro, Hitoshi

Monitor of All-sky X-ray Image (MAXI) is X-ray mission on the International Space Station. MAXI scans all sky every 92 min and detects various X-ray transient events including X-ray bursts. Among the X-ray bursts observed by MAXI, eleven had long duration and were observed more than one scan. Six out of eleven long bursts have the e-folding time of >1 h, that should be classified as "superbursts", while the rest are "intermediate-duration bursts". The total emitted energy of these long X-ray bursts range from 1041 to 1042 ergs. The lower limits of the superburst recurrence time of 4U 0614+091 and Ser X-1 are calculated as 4400 and 59 days, which may be consistent with the observed recurrence time of 3523 and 1148 days, respectively.

Phylogenetic Relationships of Citrus and Its Relatives Based on matK Gene Sequences

PubMed Central

Penjor, Tshering; Uehara, Miki; Ide, Manami; Matsumoto, Natsumi; Matsumoto, Ryoji

2013-01-01

The genus Citrus includes mandarin, orange, lemon, grapefruit and lime, which have high economic and nutritional value. The family Rutaceae can be divided into 7 subfamilies, including Aurantioideae. The genus Citrus belongs to the subfamily Aurantioideae. In this study, we sequenced the chloroplast matK genes of 135 accessions from 22 genera of Aurantioideae and analyzed them phylogenetically. Our study includes many accessions that have not been examined in other studies. The subfamily Aurantioideae has been classified into 2 tribes, Clauseneae and Citreae, and our current molecular analysis clearly discriminate Citreae from Clauseneae by using only 1 chloroplast DNA sequence. Our study confirms previous observations on the molecular phylogeny of Aurantioideae in many aspects. However, we have provided novel information on these genetic relationships. For example, inconsistent with the previous observation, and consistent with our preliminary study using the chloroplast rbcL genes, our analysis showed that Feroniella oblata is not nested in Citrus species and is closely related with Feronia limonia. Furthermore, we have shown that Murraya paniculata is similar to Merrillia caloxylon and is dissimilar to Murraya koenigii. We found that “true citrus fruit trees” could be divided into 2 subclusters. One subcluster included Citrus, Fortunella, and Poncirus, while the other cluster included Microcitrus and Eremocitrus. Compared to previous studies, our current study is the most extensive phylogenetic study of Citrus species since it includes 93 accessions. The results indicate that Citrus species can be classified into 3 clusters: a citron cluster, a pummelo cluster, and a mandarin cluster. Although most mandarin accessions belonged to the mandarin cluster, we found some exceptions. We also obtained the information on the genetic background of various species of acid citrus grown in Japan. Because the genus Citrus contains many important accessions, we have comprehensively discussed the classification of this genus. PMID:23638116

Influence of etiology of heart failure on the obesity paradox.

PubMed

Arena, Ross; Myers, Jonathan; Abella, Joshua; Pinkstaff, Sherry; Brubaker, Peter; Moore, Brian; Kitzman, Dalane; Peberdy, Mary Ann; Bensimhon, Daniel; Chase, Paul; Forman, Daniel; West, Erin; Guazzi, Marco

2009-10-15

Several investigations have demonstrated that higher body weight, as assessed by the body mass index, is associated with improved prognosis in patients with heart failure (HF). The purpose of the present investigation was to assess the influence of HF etiology on the prognostic ability of the body mass index in a cohort undergoing cardiopulmonary exercise testing. A total of 1,160 subjects were included in the analysis. All subjects underwent cardiopulmonary exercise testing, at which the minute ventilation/carbon dioxide production slope and peak oxygen consumption were determined. In the overall group, 193 cardiac deaths occurred during a mean follow-up of 30.7 +/- 25.6 months (annual event rate 6.0%). The subjects classified as obese consistently had improved survival compared to those classified as normal weight (overall survival rate 88.0% vs or=43.4, p <0.001) for both etiologies, and the body mass index added prognostic value (residual chi-square >or=4.7, p <0.05). In conclusion, these results further support the notion that obesity confers improved prognosis in patients with HF, irrespective of the HF etiology. Moreover, the body mass index appears to add predictive value during the cardiopulmonary exercise testing assessment. However, survival appears to differ according to HF etiology in subjects classified as overweight.

Evaluation of artificial neural network algorithms for predicting METs and activity type from accelerometer data: validation on an independent sample.

PubMed

Freedson, Patty S; Lyden, Kate; Kozey-Keadle, Sarah; Staudenmayer, John

2011-12-01

Previous work from our laboratory provided a "proof of concept" for use of artificial neural networks (nnets) to estimate metabolic equivalents (METs) and identify activity type from accelerometer data (Staudenmayer J, Pober D, Crouter S, Bassett D, Freedson P, J Appl Physiol 107: 1330-1307, 2009). The purpose of this study was to develop new nnets based on a larger, more diverse, training data set and apply these nnet prediction models to an independent sample to evaluate the robustness and flexibility of this machine-learning modeling technique. The nnet training data set (University of Massachusetts) included 277 participants who each completed 11 activities. The independent validation sample (n = 65) (University of Tennessee) completed one of three activity routines. Criterion measures were 1) measured METs assessed using open-circuit indirect calorimetry; and 2) observed activity to identify activity type. The nnet input variables included five accelerometer count distribution features and the lag-1 autocorrelation. The bias and root mean square errors for the nnet MET trained on University of Massachusetts and applied to University of Tennessee were +0.32 and 1.90 METs, respectively. Seventy-seven percent of the activities were correctly classified as sedentary/light, moderate, or vigorous intensity. For activity type, household and locomotion activities were correctly classified by the nnet activity type 98.1 and 89.5% of the time, respectively, and sport was correctly classified 23.7% of the time. Use of this machine-learning technique operates reasonably well when applied to an independent sample. We propose the creation of an open-access activity dictionary, including accelerometer data from a broad array of activities, leading to further improvements in prediction accuracy for METs, activity intensity, and activity type.

A new strategy for snow-cover mapping using remote sensing data and ensemble based systems techniques

NASA Astrophysics Data System (ADS)

Roberge, S.; Chokmani, K.; De Sève, D.

2012-04-01

The snow cover plays an important role in the hydrological cycle of Quebec (Eastern Canada). Consequently, evaluating its spatial extent interests the authorities responsible for the management of water resources, especially hydropower companies. The main objective of this study is the development of a snow-cover mapping strategy using remote sensing data and ensemble based systems techniques. Planned to be tested in a near real-time operational mode, this snow-cover mapping strategy has the advantage to provide the probability of a pixel to be snow covered and its uncertainty. Ensemble systems are made of two key components. First, a method is needed to build an ensemble of classifiers that is diverse as much as possible. Second, an approach is required to combine the outputs of individual classifiers that make up the ensemble in such a way that correct decisions are amplified, and incorrect ones are cancelled out. In this study, we demonstrate the potential of ensemble systems to snow-cover mapping using remote sensing data. The chosen classifier is a sequential thresholds algorithm using NOAA-AVHRR data adapted to conditions over Eastern Canada. Its special feature is the use of a combination of six sequential thresholds varying according to the day in the winter season. Two versions of the snow-cover mapping algorithm have been developed: one is specific for autumn (from October 1st to December 31st) and the other for spring (from March 16th to May 31st). In order to build the ensemble based system, different versions of the algorithm are created by varying randomly its parameters. One hundred of the versions are included in the ensemble. The probability of a pixel to be snow, no-snow or cloud covered corresponds to the amount of votes the pixel has been classified as such by all classifiers. The overall performance of ensemble based mapping is compared to the overall performance of the chosen classifier, and also with ground observations at meteorological stations.

Food and Drug Administration criteria for the diagnosis of drug-induced valvular heart disease in patients previously exposed to benfluorex: a prospective multicentre study.

PubMed

Maréchaux, Sylvestre; Rusinaru, Dan; Jobic, Yannick; Ederhy, Stéphane; Donal, Erwan; Réant, Patricia; Arnalsteen, Elise; Boulanger, Jacques; Garban, Thierry; Ennezat, Pierre-Vladimir; Jeu, Antoine; Szymanski, Catherine; Tribouilloy, Christophe

2015-02-01

The Food and Drug Administration (FDA) criteria for diagnosis of drug-induced valvular heart disease (DIVHD) are only based on the observation of aortic regurgitation ≥ mild and/or mitral regurgitation ≥ moderate. We sought to evaluate the diagnostic value of FDA criteria in a cohort of control patients and in a cohort of patients exposed to a drug (benfluorex) known to induce VHD. This prospective, multicentre study included 376 diabetic control patients not exposed to valvulopathic drugs and 1000 subjects previously exposed to benfluorex. Diagnosis of mitral or aortic DIVHD was based on a combined functional and morphological echocardiographic analysis of cardiac valves. Patients were classified according to the FDA criteria [mitral or aortic-FDA(+) and mitral or aortic-FDA(-)]. Among the 376 control patients, 2 were wrongly classified as mitral-FDA(+) and 17 as aortic-FDA(+) (0.53 and 4.5% of false positives, respectively). Of those exposed to benfluorex, 48 of 58 with a diagnosis of mitral DIVHD (83%) were classified as mitral-FDA(-), and 901 of the 910 patients (99%) without a diagnosis of the mitral DIVHD group were classified as mitral-FDA(-). All 40 patients with a diagnosis of aortic DIVHD were classified as aortic-FDA(+), and 105 of the 910 patients without a diagnosis of aortic DIVHD (12%) were classified aortic-FDA(+). Older age and lower BMI were independent predictors of disagreement between FDA criteria and the diagnosis of DIVHD in patients exposed to benfluorex (both P ≤ 0.001). FDA criteria solely based on the Doppler detection of cardiac valve regurgitation underestimate for the mitral valve and overestimate for the aortic valve the frequency of DIVHD. Therefore, the diagnosis of DIVHD must be based on a combined echocardiographic and Doppler morphological and functional analysis of cardiac valves. Published on behalf of the European Society of Cardiology. All rights reserved. © The Author 2014. For permissions please email: journals.permissions@oup.com.

Simulated Real-Life Experiences Using Classified Ads in the Classroom.

ERIC Educational Resources Information Center

Hechler, Ellen

This guide contains activities to help teachers give middle school students experience in practical life skills. Techniques include role playing and using classified advertisements from newspapers. The five lessons include teacher tips on conducting the activities. Lessons contain objectives, materials needed, discussion, and suggested dialogue.…

Front-Line Educators: The Impact of Classified Staff Interactions on the Student Experience

ERIC Educational Resources Information Center

Schmitt, Mary Ann; Duggan, Molly H.; Williams, Mitchell R.; McMillan, Judy B.

2015-01-01

This multiple case study explored classified staff interactions with students as a strategy for increasing success. Interviews, observations, and focus groups examined interactions from the staff perspective. Findings indicate staff members enhance the educational process by providing a human connection, offering practical strategies for success,…

Bayesian Redshift Classification of Emission-line Galaxies with Photometric Equivalent Widths

NASA Astrophysics Data System (ADS)

Leung, Andrew S.; Acquaviva, Viviana; Gawiser, Eric; Ciardullo, Robin; Komatsu, Eiichiro; Malz, A. I.; Zeimann, Gregory R.; Bridge, Joanna S.; Drory, Niv; Feldmeier, John J.; Finkelstein, Steven L.; Gebhardt, Karl; Gronwall, Caryl; Hagen, Alex; Hill, Gary J.; Schneider, Donald P.

2017-07-01

We present a Bayesian approach to the redshift classification of emission-line galaxies when only a single emission line is detected spectroscopically. We consider the case of surveys for high-redshift Lyα-emitting galaxies (LAEs), which have traditionally been classified via an inferred rest-frame equivalent width (EW {W}{Lyα }) greater than 20 Å. Our Bayesian method relies on known prior probabilities in measured emission-line luminosity functions and EW distributions for the galaxy populations, and returns the probability that an object in question is an LAE given the characteristics observed. This approach will be directly relevant for the Hobby-Eberly Telescope Dark Energy Experiment (HETDEX), which seeks to classify ˜106 emission-line galaxies into LAEs and low-redshift [{{O}} {{II}}] emitters. For a simulated HETDEX catalog with realistic measurement noise, our Bayesian method recovers 86% of LAEs missed by the traditional {W}{Lyα } > 20 Å cutoff over 2 < z < 3, outperforming the EW cut in both contamination and incompleteness. This is due to the method’s ability to trade off between the two types of binary classification error by adjusting the stringency of the probability requirement for classifying an observed object as an LAE. In our simulations of HETDEX, this method reduces the uncertainty in cosmological distance measurements by 14% with respect to the EW cut, equivalent to recovering 29% more cosmological information. Rather than using binary object labels, this method enables the use of classification probabilities in large-scale structure analyses. It can be applied to narrowband emission-line surveys as well as upcoming large spectroscopic surveys including Euclid and WFIRST.

An assessment of support vector machines for land cover classification

USGS Publications Warehouse

Huang, C.; Davis, L.S.; Townshend, J.R.G.

2002-01-01

The support vector machine (SVM) is a group of theoretically superior machine learning algorithms. It was found competitive with the best available machine learning algorithms in classifying high-dimensional data sets. This paper gives an introduction to the theoretical development of the SVM and an experimental evaluation of its accuracy, stability and training speed in deriving land cover classifications from satellite images. The SVM was compared to three other popular classifiers, including the maximum likelihood classifier (MLC), neural network classifiers (NNC) and decision tree classifiers (DTC). The impacts of kernel configuration on the performance of the SVM and of the selection of training data and input variables on the four classifiers were also evaluated in this experiment.

Naïve observers' perceptions of family drawings by 7-year-olds with disorganized attachment histories.

PubMed

Madigan, Sheri; Goldberg, Susan; Moran, Greg; Pederson, David R

2004-09-01

Previous research has succeeded in distinguishing among drawings made by children with histories of organized attachment relationships (secure, avoidant, and resistant); however, drawings of children with histories of disorganized attachment have yet to be systematically investigated. The purpose of this study was to determine whether naïve observers would respond differentially to family drawings of 7-year-olds who were classified in infancy as disorganized vs. organized. Seventy-three undergraduate students from one university and 78 from a second viewed 50 family drawings of 7-year-olds (25 by children with organized infant attachment and 25 by children with disorganized infant attachment). Participants were asked to (1) circle the emotion that best described their reaction to the drawings and (2) rate the drawings on 6 bipolar scales. Drawings from children classified as disorganized in infancy evoked positive emotion labels less often and negative emotion labels more often than those children classified as organized. Furthermore, drawings from children classified as disorganized in infancy received higher ratings on scales for disorganization, carelessness, family chaos, bizarreness, uneasiness, and dysfunction. These data indicate that naive observers are relatively successful in distinguishing selected features of drawings by children with histories of disorganized vs. organized attachment.

AO Distal Radius Fracture Classification: Global Perspective on Observer Agreement.

PubMed

Jayakumar, Prakash; Teunis, Teun; Giménez, Beatriz Bravo; Verstreken, Frederik; Di Mascio, Livio; Jupiter, Jesse B

2017-02-01

Background  The primary objective of this study was to test interobserver reliability when classifying fractures by consensus by AO types and groups among a large international group of surgeons. Secondarily, we assessed the difference in inter- and intraobserver agreement of the AO classification in relation to geographical location, level of training, and subspecialty. Methods  A randomized set of radiographic and computed tomographic images from a consecutive series of 96 distal radius fractures (DRFs), treated between October 2010 and April 2013, was classified using an electronic web-based portal by an invited group of participants on two occasions. Results  Interobserver reliability was substantial when classifying AO type A fractures but fair and moderate for type B and C fractures, respectively. No difference was observed by location, except for an apparent difference between participants from India and Australia classifying type B fractures. No statistically significant associations were observed comparing interobserver agreement by level of training and no differences were shown comparing subspecialties. Intra-rater reproducibility was "substantial" for fracture types and "fair" for fracture groups with no difference accounting for location, training level, or specialty. Conclusion  Improved definition of reliability and reproducibility of this classification may be achieved using large international groups of raters, empowering decision making on which system to utilize. Level of Evidence  Level III.

AO Distal Radius Fracture Classification: Global Perspective on Observer Agreement

PubMed Central

Jayakumar, Prakash; Teunis, Teun; Giménez, Beatriz Bravo; Verstreken, Frederik; Di Mascio, Livio; Jupiter, Jesse B.

2016-01-01

Background The primary objective of this study was to test interobserver reliability when classifying fractures by consensus by AO types and groups among a large international group of surgeons. Secondarily, we assessed the difference in inter- and intraobserver agreement of the AO classification in relation to geographical location, level of training, and subspecialty. Methods A randomized set of radiographic and computed tomographic images from a consecutive series of 96 distal radius fractures (DRFs), treated between October 2010 and April 2013, was classified using an electronic web-based portal by an invited group of participants on two occasions. Results Interobserver reliability was substantial when classifying AO type A fractures but fair and moderate for type B and C fractures, respectively. No difference was observed by location, except for an apparent difference between participants from India and Australia classifying type B fractures. No statistically significant associations were observed comparing interobserver agreement by level of training and no differences were shown comparing subspecialties. Intra-rater reproducibility was “substantial” for fracture types and “fair” for fracture groups with no difference accounting for location, training level, or specialty. Conclusion Improved definition of reliability and reproducibility of this classification may be achieved using large international groups of raters, empowering decision making on which system to utilize. Level of Evidence Level III PMID:28119795

On-Line Detection and Segmentation of Sports Motions Using a Wearable Sensor.

PubMed

Kim, Woosuk; Kim, Myunggyu

2018-03-19

In sports motion analysis, observation is a prerequisite for understanding the quality of motions. This paper introduces a novel approach to detect and segment sports motions using a wearable sensor for supporting systematic observation. The main goal is, for convenient analysis, to automatically provide motion data, which are temporally classified according to the phase definition. For explicit segmentation, a motion model is defined as a sequence of sub-motions with boundary states. A sequence classifier based on deep neural networks is designed to detect sports motions from continuous sensor inputs. The evaluation on two types of motions (soccer kicking and two-handed ball throwing) verifies that the proposed method is successful for the accurate detection and segmentation of sports motions. By developing a sports motion analysis system using the motion model and the sequence classifier, we show that the proposed method is useful for observation of sports motions by automatically providing relevant motion data for analysis.

Classification of urban features using airborne hyperspectral data

NASA Astrophysics Data System (ADS)

Ganesh Babu, Bharath

Accurate mapping and modeling of urban environments are critical for their efficient and successful management. Superior understanding of complex urban environments is made possible by using modern geospatial technologies. This research focuses on thematic classification of urban land use and land cover (LULC) using 248 bands of 2.0 meter resolution hyperspectral data acquired from an airborne imaging spectrometer (AISA+) on 24th July 2006 in and near Terre Haute, Indiana. Three distinct study areas including two commercial classes, two residential classes, and two urban parks/recreational classes were selected for classification and analysis. Four commonly used classification methods -- maximum likelihood (ML), extraction and classification of homogeneous objects (ECHO), spectral angle mapper (SAM), and iterative self organizing data analysis (ISODATA) - were applied to each data set. Accuracy assessment was conducted and overall accuracies were compared between the twenty four resulting thematic maps. With the exception of SAM and ISODATA in a complex commercial area, all methods employed classified the designated urban features with more than 80% accuracy. The thematic classification from ECHO showed the best agreement with ground reference samples. The residential area with relatively homogeneous composition was classified consistently with highest accuracy by all four of the classification methods used. The average accuracy amongst the classifiers was 93.60% for this area. When individually observed, the complex recreational area (Deming Park) was classified with the highest accuracy by ECHO, with an accuracy of 96.80% and 96.10% Kappa. The average accuracy amongst all the classifiers was 92.07%. The commercial area with relatively high complexity was classified with the least accuracy by all classifiers. The lowest accuracy was achieved by SAM at 63.90% with 59.20% Kappa. This was also the lowest accuracy in the entire analysis. This study demonstrates the potential for using the visible and near infrared (VNIR) bands from AISA+ hyperspectral data in urban LULC classification. Based on their performance, the need for further research using ECHO and SAM is underscored. The importance incorporating imaging spectrometer data in high resolution urban feature mapping is emphasized.

Pairwise Classifier Ensemble with Adaptive Sub-Classifiers for fMRI Pattern Analysis.

PubMed

Kim, Eunwoo; Park, HyunWook

2017-02-01

The multi-voxel pattern analysis technique is applied to fMRI data for classification of high-level brain functions using pattern information distributed over multiple voxels. In this paper, we propose a classifier ensemble for multiclass classification in fMRI analysis, exploiting the fact that specific neighboring voxels can contain spatial pattern information. The proposed method converts the multiclass classification to a pairwise classifier ensemble, and each pairwise classifier consists of multiple sub-classifiers using an adaptive feature set for each class-pair. Simulated and real fMRI data were used to verify the proposed method. Intra- and inter-subject analyses were performed to compare the proposed method with several well-known classifiers, including single and ensemble classifiers. The comparison results showed that the proposed method can be generally applied to multiclass classification in both simulations and real fMRI analyses.

Extending the Functionality of Behavioural Change-Point Analysis with k-Means Clustering: A Case Study with the Little Penguin (Eudyptula minor)

PubMed Central

Zhang, Jingjing; Dennis, Todd E.

2015-01-01

We present a simple framework for classifying mutually exclusive behavioural states within the geospatial lifelines of animals. This method involves use of three sequentially applied statistical procedures: (1) behavioural change point analysis to partition movement trajectories into discrete bouts of same-state behaviours, based on abrupt changes in the spatio-temporal autocorrelation structure of movement parameters; (2) hierarchical multivariate cluster analysis to determine the number of different behavioural states; and (3) k-means clustering to classify inferred bouts of same-state location observations into behavioural modes. We demonstrate application of the method by analysing synthetic trajectories of known ‘artificial behaviours’ comprised of different correlated random walks, as well as real foraging trajectories of little penguins (Eudyptula minor) obtained by global-positioning-system telemetry. Our results show that the modelling procedure correctly classified 92.5% of all individual location observations in the synthetic trajectories, demonstrating reasonable ability to successfully discriminate behavioural modes. Most individual little penguins were found to exhibit three unique behavioural states (resting, commuting/active searching, area-restricted foraging), with variation in the timing and locations of observations apparently related to ambient light, bathymetry, and proximity to coastlines and river mouths. Addition of k-means clustering extends the utility of behavioural change point analysis, by providing a simple means through which the behaviours inferred for the location observations comprising individual movement trajectories can be objectively classified. PMID:25922935

Extending the Functionality of Behavioural Change-Point Analysis with k-Means Clustering: A Case Study with the Little Penguin (Eudyptula minor).

PubMed

Zhang, Jingjing; O'Reilly, Kathleen M; Perry, George L W; Taylor, Graeme A; Dennis, Todd E

2015-01-01

We present a simple framework for classifying mutually exclusive behavioural states within the geospatial lifelines of animals. This method involves use of three sequentially applied statistical procedures: (1) behavioural change point analysis to partition movement trajectories into discrete bouts of same-state behaviours, based on abrupt changes in the spatio-temporal autocorrelation structure of movement parameters; (2) hierarchical multivariate cluster analysis to determine the number of different behavioural states; and (3) k-means clustering to classify inferred bouts of same-state location observations into behavioural modes. We demonstrate application of the method by analysing synthetic trajectories of known 'artificial behaviours' comprised of different correlated random walks, as well as real foraging trajectories of little penguins (Eudyptula minor) obtained by global-positioning-system telemetry. Our results show that the modelling procedure correctly classified 92.5% of all individual location observations in the synthetic trajectories, demonstrating reasonable ability to successfully discriminate behavioural modes. Most individual little penguins were found to exhibit three unique behavioural states (resting, commuting/active searching, area-restricted foraging), with variation in the timing and locations of observations apparently related to ambient light, bathymetry, and proximity to coastlines and river mouths. Addition of k-means clustering extends the utility of behavioural change point analysis, by providing a simple means through which the behaviours inferred for the location observations comprising individual movement trajectories can be objectively classified.

Characteristics of bursts observed by the SMM Gamma-Ray Spectrometer

NASA Technical Reports Server (NTRS)

Share, G. H.; Messina, D. C.; Iadicicco, A.; Matz, S. M.; Rieger, E.; Forrest, D. J.

1992-01-01

The Gamma Ray Spectrometer (GRS) on the SMM completed close to 10 years of highly successful operation when the spacecraft reentered the atmosphere on December 2, 1989. During this period the GRS detected 177 events above 300 keV which have been classified as cosmic gamma-ray bursts. A catalog of these events is in preparation which will include time profiles and spectra for all events. Visual inspection of the spectra indicates that emission typically extends into the MeV range, without any evidence for a high-energy cutoff; 17 of these events are also observed above 10 MeV. We find no convincing evidence for line-like emission features in any of the time-integrated spectra.

Short communication: Prevalence of digital dermatitis in Canadian dairy cattle classified as high, average, or low antibody- and cell-mediated immune responders.

PubMed

Cartwright, S L; Malchiodi, F; Thompson-Crispi, K; Miglior, F; Mallard, B A

2017-10-01

Lameness is a major animal welfare issue affecting Canadian dairy producers, and it can lead to production, reproduction, and health problems in dairy cattle herds. Although several different lesions affect dairy cattle hooves, studies show that digital dermatitis is the most common lesion identified in Canadian dairy herds. It has also been shown that dairy cattle classified as having high immune response (IR) have lower incidence of disease compared with those animals with average and low IR; therefore, it has been hypothesized that IR plays a role in preventing infectious hoof lesions. The objective of this study was to compare the prevalence of digital dermatitis in Canadian dairy cattle that were classified for antibody-mediated (AMIR) and cell-mediated (CMIR) immune response. Cattle (n = 329) from 5 commercial dairy farms in Ontario were evaluated for IR using a patented test protocol that captures both AMIR and CMIR. Individuals were classified as high, average, or low responders based on standardized residuals for AMIR and CMIR. Residuals were calculated using a general linear model that included the effects of herd, parity, stage of lactation, and stage of pregnancy. Hoof health data were collected from 2011 to 2013 by the farm's hoof trimmer using Hoof Supervisor software (KS Dairy Consulting Inc., Dresser, WI). All trim events were included for each animal, and lesions were assessed as a binary trait at each trim event. Hoof health data were analyzed using a mixed model that included the effects of herd, stage of lactation (at trim date), parity (at trim date), IR category (high, average, and low), and the random effect of animal. All data were presented as prevalence within IR category. Results showed that cows with high AMIR had significantly lower prevalence of digital dermatitis than cattle with average and low AMIR. No significant difference in prevalence of digital dermatitis was observed between high, average, and low CMIR cows. These results indicate that having more robust AMIR is associated with lower prevalence of digital dermatitis hoof lesions. Copyright © 2017 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Automated classification of self-grooming in mice using open-source software.

PubMed

van den Boom, Bastijn J G; Pavlidi, Pavlina; Wolf, Casper J H; Mooij, Adriana H; Willuhn, Ingo

2017-09-01

Manual analysis of behavior is labor intensive and subject to inter-rater variability. Although considerable progress in automation of analysis has been made, complex behavior such as grooming still lacks satisfactory automated quantification. We trained a freely available, automated classifier, Janelia Automatic Animal Behavior Annotator (JAABA), to quantify self-grooming duration and number of bouts based on video recordings of SAPAP3 knockout mice (a mouse line that self-grooms excessively) and wild-type animals. We compared the JAABA classifier with human expert observers to test its ability to measure self-grooming in three scenarios: mice in an open field, mice on an elevated plus-maze, and tethered mice in an open field. In each scenario, the classifier identified both grooming and non-grooming with great accuracy and correlated highly with results obtained by human observers. Consistently, the JAABA classifier confirmed previous reports of excessive grooming in SAPAP3 knockout mice. Thus far, manual analysis was regarded as the only valid quantification method for self-grooming. We demonstrate that the JAABA classifier is a valid and reliable scoring tool, more cost-efficient than manual scoring, easy to use, requires minimal effort, provides high throughput, and prevents inter-rater variability. We introduce the JAABA classifier as an efficient analysis tool for the assessment of rodent self-grooming with expert quality. In our "how-to" instructions, we provide all information necessary to implement behavioral classification with JAABA. Copyright © 2017 Elsevier B.V. All rights reserved.

A distributed approach for optimizing cascaded classifier topologies in real-time stream mining systems.

PubMed

Foo, Brian; van der Schaar, Mihaela

2010-11-01

In this paper, we discuss distributed optimization techniques for configuring classifiers in a real-time, informationally-distributed stream mining system. Due to the large volume of streaming data, stream mining systems must often cope with overload, which can lead to poor performance and intolerable processing delay for real-time applications. Furthermore, optimizing over an entire system of classifiers is a difficult task since changing the filtering process at one classifier can impact both the feature values of data arriving at classifiers further downstream and thus, the classification performance achieved by an ensemble of classifiers, as well as the end-to-end processing delay. To address this problem, this paper makes three main contributions: 1) Based on classification and queuing theoretic models, we propose a utility metric that captures both the performance and the delay of a binary filtering classifier system. 2) We introduce a low-complexity framework for estimating the system utility by observing, estimating, and/or exchanging parameters between the inter-related classifiers deployed across the system. 3) We provide distributed algorithms to reconfigure the system, and analyze the algorithms based on their convergence properties, optimality, information exchange overhead, and rate of adaptation to non-stationary data sources. We provide results using different video classifier systems.

Pairwise diversity ranking of polychotomous features for ensemble physiological signal classifiers.

PubMed

Gupta, Lalit; Kota, Srinivas; Molfese, Dennis L; Vaidyanathan, Ravi

2013-06-01

It is well known that fusion classifiers for physiological signal classification with diverse components (classifiers or data sets) outperform those with less diverse components. Determining component diversity, therefore, is of the utmost importance in the design of fusion classifiers that are often employed in clinical diagnostic and numerous other pattern recognition problems. In this article, a new pairwise diversity-based ranking strategy is introduced to select a subset of ensemble components, which when combined will be more diverse than any other component subset of the same size. The strategy is unified in the sense that the components can be classifiers or data sets. Moreover, the classifiers and data sets can be polychotomous. Classifier-fusion and data-fusion systems are formulated based on the diversity-based selection strategy, and the application of the two fusion strategies are demonstrated through the classification of multichannel event-related potentials. It is observed that for both classifier and data fusion, the classification accuracy tends to increase/decrease when the diversity of the component ensemble increases/decreases. For the four sets of 14-channel event-related potentials considered, it is shown that data fusion outperforms classifier fusion. Furthermore, it is demonstrated that the combination of data components that yield the best performance, in a relative sense, can be determined through the diversity-based selection strategy.

The EB factory project. I. A fast, neural-net-based, general purpose light curve classifier optimized for eclipsing binaries

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

Paegert, Martin; Stassun, Keivan G.; Burger, Dan M.

2014-08-01

We describe a new neural-net-based light curve classifier and provide it with documentation as a ready-to-use tool for the community. While optimized for identification and classification of eclipsing binary stars, the classifier is general purpose, and has been developed for speed in the context of upcoming massive surveys such as the Large Synoptic Survey Telescope. A challenge for classifiers in the context of neural-net training and massive data sets is to minimize the number of parameters required to describe each light curve. We show that a simple and fast geometric representation that encodes the overall light curve shape, together withmore » a chi-square parameter to capture higher-order morphology information results in efficient yet robust light curve classification, especially for eclipsing binaries. Testing the classifier on the ASAS light curve database, we achieve a retrieval rate of 98% and a false-positive rate of 2% for eclipsing binaries. We achieve similarly high retrieval rates for most other periodic variable-star classes, including RR Lyrae, Mira, and delta Scuti. However, the classifier currently has difficulty discriminating between different sub-classes of eclipsing binaries, and suffers a relatively low (∼60%) retrieval rate for multi-mode delta Cepheid stars. We find that it is imperative to train the classifier's neural network with exemplars that include the full range of light curve quality to which the classifier will be expected to perform; the classifier performs well on noisy light curves only when trained with noisy exemplars. The classifier source code, ancillary programs, a trained neural net, and a guide for use, are provided.« less

Fault detection and multiclassifier fusion for unmanned aerial vehicles (UAVs)

NASA Astrophysics Data System (ADS)

Yan, Weizhong

2001-03-01

UAVs demand more accurate fault accommodation for their mission manager and vehicle control system in order to achieve a reliability level that is comparable to that of a pilot aircraft. This paper attempts to apply multi-classifier fusion techniques to achieve the necessary performance of the fault detection function for the Lockheed Martin Skunk Works (LMSW) UAV Mission Manager. Three different classifiers that meet the design requirements of the fault detection of the UAAV are employed. The binary decision outputs from the classifiers are then aggregated using three different classifier fusion schemes, namely, majority vote, weighted majority vote, and Naieve Bayes combination. All of the three schemes are simple and need no retraining. The three fusion schemes (except the majority vote that gives an average performance of the three classifiers) show the classification performance that is better than or equal to that of the best individual. The unavoidable correlation between the classifiers with binary outputs is observed in this study. We conclude that it is the correlation between the classifiers that limits the fusion schemes to achieve an even better performance.

Solving a Higgs optimization problem with quantum annealing for machine learning.

PubMed

Mott, Alex; Job, Joshua; Vlimant, Jean-Roch; Lidar, Daniel; Spiropulu, Maria

2017-10-18

The discovery of Higgs-boson decays in a background of standard-model processes was assisted by machine learning methods. The classifiers used to separate signals such as these from background are trained using highly unerring but not completely perfect simulations of the physical processes involved, often resulting in incorrect labelling of background processes or signals (label noise) and systematic errors. Here we use quantum and classical annealing (probabilistic techniques for approximating the global maximum or minimum of a given function) to solve a Higgs-signal-versus-background machine learning optimization problem, mapped to a problem of finding the ground state of a corresponding Ising spin model. We build a set of weak classifiers based on the kinematic observables of the Higgs decay photons, which we then use to construct a strong classifier. This strong classifier is highly resilient against overtraining and against errors in the correlations of the physical observables in the training data. We show that the resulting quantum and classical annealing-based classifier systems perform comparably to the state-of-the-art machine learning methods that are currently used in particle physics. However, in contrast to these methods, the annealing-based classifiers are simple functions of directly interpretable experimental parameters with clear physical meaning. The annealer-trained classifiers use the excited states in the vicinity of the ground state and demonstrate some advantage over traditional machine learning methods for small training datasets. Given the relative simplicity of the algorithm and its robustness to error, this technique may find application in other areas of experimental particle physics, such as real-time decision making in event-selection problems and classification in neutrino physics.

Solving a Higgs optimization problem with quantum annealing for machine learning

NASA Astrophysics Data System (ADS)

Mott, Alex; Job, Joshua; Vlimant, Jean-Roch; Lidar, Daniel; Spiropulu, Maria

2017-10-01

The discovery of Higgs-boson decays in a background of standard-model processes was assisted by machine learning methods. The classifiers used to separate signals such as these from background are trained using highly unerring but not completely perfect simulations of the physical processes involved, often resulting in incorrect labelling of background processes or signals (label noise) and systematic errors. Here we use quantum and classical annealing (probabilistic techniques for approximating the global maximum or minimum of a given function) to solve a Higgs-signal-versus-background machine learning optimization problem, mapped to a problem of finding the ground state of a corresponding Ising spin model. We build a set of weak classifiers based on the kinematic observables of the Higgs decay photons, which we then use to construct a strong classifier. This strong classifier is highly resilient against overtraining and against errors in the correlations of the physical observables in the training data. We show that the resulting quantum and classical annealing-based classifier systems perform comparably to the state-of-the-art machine learning methods that are currently used in particle physics. However, in contrast to these methods, the annealing-based classifiers are simple functions of directly interpretable experimental parameters with clear physical meaning. The annealer-trained classifiers use the excited states in the vicinity of the ground state and demonstrate some advantage over traditional machine learning methods for small training datasets. Given the relative simplicity of the algorithm and its robustness to error, this technique may find application in other areas of experimental particle physics, such as real-time decision making in event-selection problems and classification in neutrino physics.

"JCE" Classroom Activity #105. A Sticky Situation: Chewing Gum and Solubility

ERIC Educational Resources Information Center

Montes-Gonzalez, Ingrid; Cintron-Maldonado, Jose A.; Perez-Medina, Ilia E.; Montes-Berrios, Veronica; Roman-Lopez, Saurie N.

2010-01-01

In this Activity, students perform several solubility tests using common food items such as chocolate, chewing gum, water, sugar, and oil. From their observations during the Activity, students will initially classify the substances tested as soluble or insoluble. They will then use their understanding of the chemistry of solubility to classify the…

The Preservation of Two Infant Temperaments into Adolescence

ERIC Educational Resources Information Center

Kagan, Jerome; Snidman, Nancy; Kahn, Vali; Towsley, Sara

2007-01-01

This "Monograph" reports theoretically relevant behavioral, biological, and self-report assessments of a sample of 14-17-year-olds who had been classified into one of four temperamental groups at 4 months of age. The infant temperamental categories were based on observed behavior to a battery of unfamiliar stimuli. The infants classified as high…

Novel probabilistic neuroclassifier

NASA Astrophysics Data System (ADS)

Hong, Jiang; Serpen, Gursel

2003-09-01

A novel probabilistic potential function neural network classifier algorithm to deal with classes which are multi-modally distributed and formed from sets of disjoint pattern clusters is proposed in this paper. The proposed classifier has a number of desirable properties which distinguish it from other neural network classifiers. A complete description of the algorithm in terms of its architecture and the pseudocode is presented. Simulation analysis of the newly proposed neuro-classifier algorithm on a set of benchmark problems is presented. Benchmark problems tested include IRIS, Sonar, Vowel Recognition, Two-Spiral, Wisconsin Breast Cancer, Cleveland Heart Disease and Thyroid Gland Disease. Simulation results indicate that the proposed neuro-classifier performs consistently better for a subset of problems for which other neural classifiers perform relatively poorly.

48 CFR 8.608 - Protection of classified and sensitive information.

Code of Federal Regulations, 2011 CFR

2011-10-01

... Prison Industries, Inc. 8.608 Protection of classified and sensitive information. Agencies shall not... about any individual private citizen, including information relating to such person's real property...

48 CFR 8.608 - Protection of classified and sensitive information.

Code of Federal Regulations, 2013 CFR

2013-10-01

... Prison Industries, Inc. 8.608 Protection of classified and sensitive information. Agencies shall not... about any individual private citizen, including information relating to such person's real property...

48 CFR 8.608 - Protection of classified and sensitive information.

Code of Federal Regulations, 2010 CFR

2010-10-01

... Prison Industries, Inc. 8.608 Protection of classified and sensitive information. Agencies shall not... about any individual private citizen, including information relating to such person's real property...

48 CFR 8.608 - Protection of classified and sensitive information.

Code of Federal Regulations, 2014 CFR

2014-10-01

... Prison Industries, Inc. 8.608 Protection of classified and sensitive information. Agencies shall not... about any individual private citizen, including information relating to such person's real property...

48 CFR 8.608 - Protection of classified and sensitive information.

Code of Federal Regulations, 2012 CFR

2012-10-01

... Prison Industries, Inc. 8.608 Protection of classified and sensitive information. Agencies shall not... about any individual private citizen, including information relating to such person's real property...

Principles of assembly reveal a periodic table of protein complexes.

PubMed

Ahnert, Sebastian E; Marsh, Joseph A; Hernández, Helena; Robinson, Carol V; Teichmann, Sarah A

2015-12-11

Structural insights into protein complexes have had a broad impact on our understanding of biological function and evolution. In this work, we sought a comprehensive understanding of the general principles underlying quaternary structure organization in protein complexes. We first examined the fundamental steps by which protein complexes can assemble, using experimental and structure-based characterization of assembly pathways. Most assembly transitions can be classified into three basic types, which can then be used to exhaustively enumerate a large set of possible quaternary structure topologies. These topologies, which include the vast majority of observed protein complex structures, enable a natural organization of protein complexes into a periodic table. On the basis of this table, we can accurately predict the expected frequencies of quaternary structure topologies, including those not yet observed. These results have important implications for quaternary structure prediction, modeling, and engineering. Copyright © 2015, American Association for the Advancement of Science.

Stackable differential mobility analyzer for aerosol measurement

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

Cheng, Meng-Dawn; Chen, Da-Ren

2007-05-08

A multi-stage differential mobility analyzer (MDMA) for aerosol measurements includes a first electrode or grid including at least one inlet or injection slit for receiving an aerosol including charged particles for analysis. A second electrode or grid is spaced apart from the first electrode. The second electrode has at least one sampling outlet disposed at a plurality different distances along its length. A volume between the first and the second electrode or grid between the inlet or injection slit and a distal one of the plurality of sampling outlets forms a classifying region, the first and second electrodes for chargingmore » to suitable potentials to create an electric field within the classifying region. At least one inlet or injection slit in the second electrode receives a sheath gas flow into an upstream end of the classifying region, wherein each sampling outlet functions as an independent DMA stage and classifies different size ranges of charged particles based on electric mobility simultaneously.« less

Label-free identification of white blood cell using optical diffraction tomography (Conference Presentation)

NASA Astrophysics Data System (ADS)

Yoon, Jonghee; Kim, Kyoohyun; Kim, Min-hyeok; Kang, Suk-Jo; Park, YongKeun

2016-03-01

White blood cells (WBC) have crucial roles in immune systems which defend the host against from disease conditions and harmful invaders. Various WBC subsets have been characterized and reported to be involved in many pathophysiologic conditions. It is crucial to isolate a specific WBC subset to study its pathophysiological roles in diseases. Identification methods for a specific WBC population are rely on invasive approaches, including Wright-Gimesa staining for observing cellular morphologies and fluorescence staining for specific protein markers. While these methods enable precise classification of WBC populations, they could disturb cellular viability or functions. In order to classify WBC populations in a non-invasive manner, we exploited optical diffraction tomography (ODT). ODT is a three-dimensional (3-D) quantitative phase imaging technique that measures 3-D refractive index (RI) distributions of individual WBCs. To test feasibility of label-free classification of WBC populations using ODT, we measured four subtypes of WBCs, including B cell, CD4 T cell, CD8 T cell, and natural killer (NK) cell. From measured 3-D RI tomograms of WBCs, we obtain quantitative structural and biochemical information and classify each WBC population using a machine learning algorithm.

Observational constraints on the inter-binary stellar flare hypothesis for the gamma-ray bursts

NASA Astrophysics Data System (ADS)

Rao, A. R.; Vahia, M. N.

1994-01-01

The Gamma Ray Observatory/Burst and Transient Source Experiment (GRO/BATSE) results on the Gamma Ray Bursts (GRBs) have given an internally consistent set of observations of about 260 GRBs which have been released for analysis by the BATSE team. Using this database we investigate our earlier suggestion (Vahia and Rao, 1988) that GRBs are inter-binary stellar flares from a group of objects classified as Magnetically Active Stellar Systems (MASS) which includes flare stars, RS CVn binaries and cataclysmic variables. We show that there exists an observationally consistent parameter space for the number density, scale height and flare luminosity of MASS which explains the complete log(N) - log(P) distribution of GRBs as also the observed isotropic distribution. We further use this model to predict anisotropy in the GRB distribution at intermediate luminosities. We make definite predictions under the stellar flare hypothesis that can be tested in the near future.

Movement Processes as Observable Behavior.

ERIC Educational Resources Information Center

Harrington, Wilma M.

The operations for achieving skill in motor performance are perceiving, patterning, adapting, refining, varying, improvising, and composing. These operations are readily observable in physical education classes. An observation record containing the seven catagories was used to classify teacher feedback to students. The teachers observed were…

A proposed ethogram of large-carnivore predatory behavior, exemplified by the wolf

USGS Publications Warehouse

MacNulty, D.R.; Mech, L.D.; Smith, D.W.

2007-01-01

Although predatory behavior is traditionally described by a basic ethogram composed of 3 phases (search, pursue, and capture), behavioral studies of large terrestrial carnivores generally use the concept of a "hunt" to classify and measure foraging. This approach is problematic because there is no consensus on what behaviors constitute a hunt. We therefore examined how the basic ethogram could be used as a common framework for classifying large-carnivore behavior. We used >2,150 h of observed wolf (Canis lupus) behavior in Yellowstone National Park, including 517 and 134 encounters with elk (Cervus elaphus) and American bison (Bison bison), respectively, to demonstrate the functional importance of several frequently described, but rarely quantified, patterns of large-carnivore behavior not explicitly described by the basic ethogram (approaching, watching, and attacking groups). To account for these additionally important behaviors we propose a modified form of the basic ethogram (search, approach, watch, attack-group, attack-individual, and capture). We tested the applicability of this ethogram by comparing it to 31 previous classifications and descriptions involving 7 other species and 5 other wolf populations. Close correspondence among studies suggests that this ethogram may provide a generally useful scheme for classifying large-carnivore predatory behavior that is behaviorally less ambiguous than the concept of a hunt. ?? 2007 American Society of Mammalogists.

36 CFR § 1256.80 - How does NARA provide classified access to historical researchers and former Presidential...

Code of Federal Regulations, 2013 CFR

2013-07-01

... and former Presidential appointees? (a) In accordance with the requirements of section 4.4 of E.O... under section 4.4 of E.O. 12958, you must first qualify under special access provisions of 44 U.S.C... information, including classified information originated by the White House or classified information in the...

Physical characteristics of faint meteors by light curve and high-resolution observations, and the implications for parent bodies

NASA Astrophysics Data System (ADS)

Subasinghe, Dilini; Campbell-Brown, Margaret D.; Stokan, Edward

2016-04-01

Optical observations of faint meteors (10-7 < mass < 10-4 kg) were collected by the Canadian Automated Meteor Observatory between 2010 April and 2014 May. These high-resolution (metre scale) observations were combined with two-station light-curve observations and the meteoroid orbit to classify meteors and attempt to answer questions related to meteoroid fragmentation, strength, and light-curve shape. The F parameter was used to classify the meteor light-curve shape; the observed morphology was used to classify the fragmentation mode; and the Tisserand parameter described the origin of the meteoroid. We find that most meteor light curves are symmetric (mean F parameter 0.49), show long distinct trails (continuous fragmentation), and are cometary in origin. Meteors that show no obvious fragmentation (presumably single body objects) show mostly symmetric light curves, surprisingly, and this indicates that light-curve shape is not an indication of fragility or fragmentation behaviour. Approximately 90 per cent of meteors observed with high-resolution video cameras show some form of fragmentation. Our results also show, unexpectedly, that meteors which show negligible fragmentation are more often on high-inclination orbits (I > 60°) than low-inclination ones. We also find that dynamically asteroidal meteors fragment as often as dynamically cometary meteors, which may suggest mixing in the early Solar system, or contamination between the dynamic groups.

Accurate Classification of Diminutive Colorectal Polyps Using Computer-Aided Analysis.

PubMed

Chen, Peng-Jen; Lin, Meng-Chiung; Lai, Mei-Ju; Lin, Jung-Chun; Lu, Henry Horng-Shing; Tseng, Vincent S

2018-02-01

Narrow-band imaging is an image-enhanced form of endoscopy used to observed microstructures and capillaries of the mucosal epithelium which allows for real-time prediction of histologic features of colorectal polyps. However, narrow-band imaging expertise is required to differentiate hyperplastic from neoplastic polyps with high levels of accuracy. We developed and tested a system of computer-aided diagnosis with a deep neural network (DNN-CAD) to analyze narrow-band images of diminutive colorectal polyps. We collected 1476 images of neoplastic polyps and 681 images of hyperplastic polyps, obtained from the picture archiving and communications system database in a tertiary hospital in Taiwan. Histologic findings from the polyps were also collected and used as the reference standard. The images and data were used to train the DNN. A test set of images (96 hyperplastic and 188 neoplastic polyps, smaller than 5 mm), obtained from patients who underwent colonoscopies from March 2017 through August 2017, was then used to test the diagnostic ability of the DNN-CAD vs endoscopists (2 expert and 4 novice), who were asked to classify the images of the test set as neoplastic or hyperplastic. Their classifications were compared with findings from histologic analysis. The primary outcome measures were diagnostic accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and diagnostic time. The accuracy, sensitivity, specificity, PPV, NPV, and diagnostic time were compared among DNN-CAD, the novice endoscopists, and the expert endoscopists. The study was designed to detect a difference of 10% in accuracy by a 2-sided McNemar test. In the test set, the DNN-CAD identified neoplastic or hyperplastic polyps with 96.3% sensitivity, 78.1% specificity, a PPV of 89.6%, and a NPV of 91.5%. Fewer than half of the novice endoscopists classified polyps with a NPV of 90% (their NPVs ranged from 73.9% to 84.0%). DNN-CAD classified polyps as neoplastic or hyperplastic in 0.45 ± 0.07 seconds-shorter than the time required by experts (1.54 ± 1.30 seconds) and nonexperts (1.77 ± 1.37 seconds) (both P < .001). DNN-CAD classified polyps with perfect intra-observer agreement (kappa score of 1). There was a low level of intra-observer and inter-observer agreement in classification among endoscopists. We developed a system called DNN-CAD to identify neoplastic or hyperplastic colorectal polyps less than 5 mm. The system classified polyps with a PPV of 89.6%, and a NPV of 91.5%, and in a shorter time than endoscopists. This deep-learning model has potential for not only endoscopic image recognition but for other forms of medical image analysis, including sonography, computed tomography, and magnetic resonance images. Copyright © 2018 AGA Institute. Published by Elsevier Inc. All rights reserved.

Online and unsupervised face recognition for continuous video stream

NASA Astrophysics Data System (ADS)

Huo, Hongwen; Feng, Jufu

2009-10-01

We present a novel online face recognition approach for video stream in this paper. Our method includes two stages: pre-training and online training. In the pre-training phase, our method observes interactions, collects batches of input data, and attempts to estimate their distributions (Box-Cox transformation is adopted here to normalize rough estimates). In the online training phase, our method incrementally improves classifiers' knowledge of the face space and updates it continuously with incremental eigenspace analysis. The performance achieved by our method shows its great potential in video stream processing.

[Updates on rickets and osteomalacia: FGF23-mediated hypophosphatemic rickets/osteomalacia].

PubMed

Michigami, Toshimi

2013-10-01

Some of the hypophosphatemic rickets/osteomalacia are caused by the increased bioactivity of FGF23, and classified into FGF23-mediated hypophosphatemic rickets/osteomalacia. This group includes various disorders such as X-linked, autosomal dominant and autosomal recessive hypophosphatemic rickets/osteomalacia, tumor-induced osteomalacia, and rickets/osteomalacia caused by the administration of iron polymaltose or saccharated ferric oxide. Measurement of serum levels of FGF23 is useful for diagnosis of these conditions. In the adult patients with FGF23-mediated hypophosphatemic rickets/osteomalacia, mineralizing enthesoopathy is an often observed complication.

Accurate vehicle classification including motorcycles using piezoelectric sensors.

DOT National Transportation Integrated Search

2013-03-01

State and federal departments of transportation are charged with classifying vehicles and monitoring mileage traveled. Accurate data reporting enables suitable roadway design for safety and capacity. Vehicle classifiers currently employ inductive loo...

Chaotic particle swarm optimization with mutation for classification.

PubMed

Assarzadeh, Zahra; Naghsh-Nilchi, Ahmad Reza

2015-01-01

In this paper, a chaotic particle swarm optimization with mutation-based classifier particle swarm optimization is proposed to classify patterns of different classes in the feature space. The introduced mutation operators and chaotic sequences allows us to overcome the problem of early convergence into a local minima associated with particle swarm optimization algorithms. That is, the mutation operator sharpens the convergence and it tunes the best possible solution. Furthermore, to remove the irrelevant data and reduce the dimensionality of medical datasets, a feature selection approach using binary version of the proposed particle swarm optimization is introduced. In order to demonstrate the effectiveness of our proposed classifier, mutation-based classifier particle swarm optimization, it is checked out with three sets of data classifications namely, Wisconsin diagnostic breast cancer, Wisconsin breast cancer and heart-statlog, with different feature vector dimensions. The proposed algorithm is compared with different classifier algorithms including k-nearest neighbor, as a conventional classifier, particle swarm-classifier, genetic algorithm, and Imperialist competitive algorithm-classifier, as more sophisticated ones. The performance of each classifier was evaluated by calculating the accuracy, sensitivity, specificity and Matthews's correlation coefficient. The experimental results show that the mutation-based classifier particle swarm optimization unequivocally performs better than all the compared algorithms.

Comparison of clinical causes of death with autopsy diagnosis using discrepency classification.

PubMed

Ullah, Khalil; Alamgir, Wasim

2006-12-01

To determine the usefulness of autopsy findings in the quality improvement of patients care. An observational study. Departments of Pathology and Medicine, Combined Military Hospital (CMH) Kharian, a tertiary care hospital, from January 2001 to December 2003. The clinical and necropsy findings of all the cases, who died in hospital and had undergone autopsy examination at CMH, Kharian, from January 2001 to December 2003, were retrieved from record of clinical case sheet data and autopsy record of the hospital. The two were analyzed and compared according to the discrepancy classification. The exclusion and inclusion criteria, the international classification of disease (ICD) to code deaths, the global burden of disease (GBD) system to classify and group diseases, and the Goldman discrepancy classification to compare clinical and autopsy diagnosis and classify the discrepancies, were used as described. The death rate varied from 0.94% to 1.29% and autopsy rate from 4.69% to 10.10% annually between January 2001 and December 2003. The number of cases classified according to GBD system was 3 (5%) in Group 1, 26 (43.33 %) in Group 2 and 31 (51.66 %) in Group 3. The discrepancy classes included 9 (15 %) class I major discrepancies and 3 (5 %) class II major discrepancies. Non-discrepant diagnosis was seen in 37 cases (61.66 %) and 11 cases (18.32 %) were non-classifiable. This study showed the usefulness of autopsy findings in the quality improvement of the diagnosis and management of the disease by showing only a minority of cases with discrepant diagnosis of the cause of death.

The Thermodynamic Structure of Arctic Coastal Fog Occurring During the Melt Season over East Greenland

NASA Astrophysics Data System (ADS)

Gilson, Gaëlle F.; Jiskoot, Hester; Cassano, John J.; Gultepe, Ismail; James, Timothy D.

2018-05-01

An automated method to classify Arctic fog into distinct thermodynamic profiles using historic in-situ surface and upper-air observations is presented. This classification is applied to low-resolution Integrated Global Radiosonde Archive (IGRA) soundings and high-resolution Arctic Summer Cloud Ocean Study (ASCOS) soundings in low- and high-Arctic coastal and pack-ice environments. Results allow investigation of fog macrophysical properties and processes in coastal East Greenland during melt seasons 1980-2012. Integrated with fog observations from three synoptic weather stations, 422 IGRA soundings are classified into six fog thermodynamic types based on surface saturation ratio, type of temperature inversion, fog-top height relative to inversion-base height and stability using the virtual potential temperature gradient. Between 65-80% of fog observations occur with a low-level inversion, and statically neutral or unstable surface layers occur frequently. Thermodynamic classification is sensitive to the assigned dew-point depression threshold, but categorization is robust. Despite differences in the vertical resolution of radiosonde observations, IGRA and ASCOS soundings yield the same six fog classes, with fog-class distribution varying with latitude and environmental conditions. High-Arctic fog frequently resides within an elevated inversion layer, whereas low-Arctic fog is more often restricted to the mixed layer. Using supplementary time-lapse images, ASCOS microwave radiometer retrievals and airmass back-trajectories, we hypothesize that the thermodynamic classes represent different stages of advection fog formation, development, and dissipation, including stratus-base lowering and fog lifting. This automated extraction of thermodynamic boundary-layer and inversion structure can be applied to radiosonde observations worldwide to better evaluate fog conditions that affect transportation and lead to improvements in numerical models.

Computer-based coding of free-text job descriptions to efficiently identify occupations in epidemiological studies

PubMed Central

Russ, Daniel E.; Ho, Kwan-Yuet; Colt, Joanne S.; Armenti, Karla R.; Baris, Dalsu; Chow, Wong-Ho; Davis, Faith; Johnson, Alison; Purdue, Mark P.; Karagas, Margaret R.; Schwartz, Kendra; Schwenn, Molly; Silverman, Debra T.; Johnson, Calvin A.; Friesen, Melissa C.

2016-01-01

Background Mapping job titles to standardized occupation classification (SOC) codes is an important step in identifying occupational risk factors in epidemiologic studies. Because manual coding is time-consuming and has moderate reliability, we developed an algorithm called SOCcer (Standardized Occupation Coding for Computer-assisted Epidemiologic Research) to assign SOC-2010 codes based on free-text job description components. Methods Job title and task-based classifiers were developed by comparing job descriptions to multiple sources linking job and task descriptions to SOC codes. An industry-based classifier was developed based on the SOC prevalence within an industry. These classifiers were used in a logistic model trained using 14,983 jobs with expert-assigned SOC codes to obtain empirical weights for an algorithm that scored each SOC/job description. We assigned the highest scoring SOC code to each job. SOCcer was validated in two occupational data sources by comparing SOC codes obtained from SOCcer to expert assigned SOC codes and lead exposure estimates obtained by linking SOC codes to a job-exposure matrix. Results For 11,991 case-control study jobs, SOCcer-assigned codes agreed with 44.5% and 76.3% of manually assigned codes at the 6- and 2-digit level, respectively. Agreement increased with the score, providing a mechanism to identify assignments needing review. Good agreement was observed between lead estimates based on SOCcer and manual SOC assignments (kappa: 0.6–0.8). Poorer performance was observed for inspection job descriptions, which included abbreviations and worksite-specific terminology. Conclusions Although some manual coding will remain necessary, using SOCcer may improve the efficiency of incorporating occupation into large-scale epidemiologic studies. PMID:27102331

[Classification of disposable medical plastics and search for alternatives without polyvinyl chloride in the Hospital Virgen de las Nieves (Granada, Spain)].

PubMed

Sañudo Hacar, P; Blanco, M G; Martínez, E; Duarte, J A; González, A; Hernández, M; Martínez, M; Cueto, E; Navajas, J A; Navarrete, M J

2012-01-01

To identify and classify disposable hospital products containing polyvinyl chloride (PVC), including the search and evaluation of cost-effective sustainable alternative products free of PVC. A descriptive observational analysis was performed, after classifying the latest research in major databases, and disposable products that could contain PVC. These were divided into 5 groups: cannulas, catheters, tubes, bags, and equipment, purchased in the period 2008-2009, differentiating between the technical and economic assessment of the materials. In the analysis of the composition of 492 articles selected, 234 (47.5%) contained PVC, and 19.4% were considered PVC-free alternatives, with only 11.3% of these being economically viable. This study highlights the advantages of the classification of PVC products, by showing that safe and efficient alternatives exist for some product lines that are consistent with patient safety and quality in the work by doctors. Copyright © 2011 SECA. Published by Elsevier Espana. All rights reserved.

A Population-Based Assessment of the Agreement Between Grading of Goniophotographic Images and Gonioscopy in the Chinese-American Eye Study (CHES).

PubMed

Murakami, Yohko; Wang, Dandan; Burkemper, Bruce; Lin, Shan C; Varma, Rohit

2016-08-01

To compare grading of goniophotographic images and gonioscopy in assessing the iridocorneal angle. In a population-based, cross-sectional study, participants underwent gonioscopy and goniophotographic imaging during the same visit. The iridocorneal angle was classified as closed if the posterior trabecular meshwork could not be seen. A single masked observer graded the goniophotographic images, and each eye was classified as having angle closure based on the number of closed quadrants. Agreement between the methods was analyzed by calculating kappa (κ) and first-order agreement coefficient (AC1) statistics and comparison of area under receiver operating characteristic curves (AUC). A total of 4149 Chinese Americans (3994 eyes) were included in this study. The agreement for angle closure diagnosis between gonioscopy and EyeCam was moderate to excellent (κ = 0.60, AC1 0.90, AUC 0.76-0.80). Detection of iridocorneal angle closure based on goniophotographic imaging shows moderate to very good agreement with angle closure assessment using gonioscopy.

The Relations between Infant Negative Reactivity, Non-Maternal Childcare, and Children’s Interactions with Familiar and Unfamiliar Peers

PubMed Central

Almas, Alisa N.; Phillips, Deborah A.; Henderson, Heather A.; Hane, Amie Ashley; Degnan, Kathryn Amey; Moas, Olga L.; Fox, Nathan A.

2012-01-01

The present study examined the influence of children’s experiences during non-maternal childcare on their behavior towards unfamiliar peers. Participants included children classified as negatively reactive at 4 months of age (n = 52) and children not negatively reactive (n = 61), who were further divided into those who experienced non-maternal care and those who did not. Children were observed during childcare at 24 months of age and in the laboratory with an unfamiliar peer at 24 and 36 months of age, where their wariness, dysregulation and social engagement were assessed. Within the negatively reactive childcare group, children’s positive interactions with peers during childcare at 24 months predicted lower levels of wariness towards an unfamiliar peer at 36 months. This relation was not significant for children not classified as negatively reactive. The findings suggest that the influence of non-maternal childcare is dependent on a child’s temperament and the nature of peer interactions during care. PMID:22563147

Classification of cardiac patient states using artificial neural networks

PubMed Central

Kannathal, N; Acharya, U Rajendra; Lim, Choo Min; Sadasivan, PK; Krishnan, SM

2003-01-01

Electrocardiogram (ECG) is a nonstationary signal; therefore, the disease indicators may occur at random in the time scale. This may require the patient be kept under observation for long intervals in the intensive care unit of hospitals for accurate diagnosis. The present study examined the classification of the states of patients with certain diseases in the intensive care unit using their ECG and an Artificial Neural Networks (ANN) classification system. The states were classified into normal, abnormal and life threatening. Seven significant features extracted from the ECG were fed as input parameters to the ANN for classification. Three neural network techniques, namely, back propagation, self-organizing maps and radial basis functions, were used for classification of the patient states. The ANN classifier in this case was observed to be correct in approximately 99% of the test cases. This result was further improved by taking 13 features of the ECG as input for the ANN classifier. PMID:19649222

Proposed hybrid-classifier ensemble algorithm to map snow cover area

NASA Astrophysics Data System (ADS)

Nijhawan, Rahul; Raman, Balasubramanian; Das, Josodhir

2018-01-01

Metaclassification ensemble approach is known to improve the prediction performance of snow-covered area. The methodology adopted in this case is based on neural network along with four state-of-art machine learning algorithms: support vector machine, artificial neural networks, spectral angle mapper, K-mean clustering, and a snow index: normalized difference snow index. An AdaBoost ensemble algorithm related to decision tree for snow-cover mapping is also proposed. According to available literature, these methods have been rarely used for snow-cover mapping. Employing the above techniques, a study was conducted for Raktavarn and Chaturangi Bamak glaciers, Uttarakhand, Himalaya using multispectral Landsat 7 ETM+ (enhanced thematic mapper) image. The study also compares the results with those obtained from statistical combination methods (majority rule and belief functions) and accuracies of individual classifiers. Accuracy assessment is performed by computing the quantity and allocation disagreement, analyzing statistic measures (accuracy, precision, specificity, AUC, and sensitivity) and receiver operating characteristic curves. A total of 225 combinations of parameters for individual classifiers were trained and tested on the dataset and results were compared with the proposed approach. It was observed that the proposed methodology produced the highest classification accuracy (95.21%), close to (94.01%) that was produced by the proposed AdaBoost ensemble algorithm. From the sets of observations, it was concluded that the ensemble of classifiers produced better results compared to individual classifiers.

Radar observations of near-Earth asteroids from Arecibo Observatory

NASA Astrophysics Data System (ADS)

Rivera-Valentin, Edgard G.; Taylor, Patrick A.; Rodriguez-Ford, Linda A.; Zambrano Marin, Luisa Fernanda; Virkki, Anne; Aponte Hernandez, Betzaida

2016-10-01

The Arecibo S-Band (2.38 GHz, 12.6 cm, 1 MW) planetary radar system at the 305-m William E. Gordon Telescope in Arecibo, Puerto Rico is the most active and most sensitive planetary radar facility in the world. Since October 2015, we have detected 56 near-Earth asteroids, of which 17 are classified as potentially hazardous to Earth and 22 are compliant with the Near-Earth Object Human Space Flight Accessible Target Study (NHATS) as possible future robotic- or human-mission destinations. We will present a sampling of the asteroid zoo observed by the Arecibo radar since the 2015 DPS meeting. This includes press-noted asteroids 2015 TB145, the so-called "Great Pumpkin", and 2003 SD220, the so-called "Christmas Eve asteroid".

Publications of Los Alamos Research, 1983

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

Sheridan, C.J.; McClary, W.J.; Rich, J.A.

1984-10-01

This bibliography is a compilation of unclassified publications of work done at the Los Alamos National Laboratory for 1983. Papers published in 1982 are included regardless of when they were actually written. Publications received too late for inclusion in earlier compilations have also been listed. Declassification of previously classified reports is considered to constitute publication. All classified issuances are omitted - even those papers, themselves unclassified, which were published only as part of a classified document. If a paper was published more than once, all places of publication are included. The bibliography includes Los Alamos National Laboratory reports, papers releasedmore » as non-Laboratory reports, journal articles, books, chapters of books, conference papers either published separately or as part of conference proceedings issued as books or reports, papers publishd in congressional hearings, theses, and US patents. Publications by Los Alamos authors that are not records of Laboratory-sponsored work are included when the Library becomes aware of them.« less

A multimethod investigation including direct observation of 3751 patient visits to 120 dental offices

PubMed Central

Wotman, Stephen; Demko, Catherine A; Victoroff, Kristin; Sudano, Joseph J; Lalumandier, James A

2010-01-01

This report defines verbal interactions between practitioners and patients as core activities of dental practice. Trained teams spent four days in 120 Ohio dental practices observing 3751 patient encounters with dentists and hygienists. Direct observation of practice characteristics, procedures performed, and how procedure and nonprocedure time was utilized during patient visits was recorded using a modified Davis Observation Code that classified patient contact time into 24 behavioral categories. Dentist, hygienist, and patient characteristics were gathered by questionnaire. The most common nonprocedure behaviors observed for dentists were chatting, evaluation feedback, history taking, and answering patient questions. Hygienists added preventive counseling. We distinguish between preventive procedures and counseling in actual dental offices that are members of a practice-based research network. Almost a third of the dentist’s and half of the hygienist’s patient contact time is utilized for nonprocedure behaviors during patient encounters. These interactions may be linked to patient and practitioner satisfaction and effectiveness of self-care instruction. PMID:23662080

Strategies for setting occupational exposure limits for particles.

PubMed Central

Greim, H A; Ziegler-Skylakakis, K

1997-01-01

To set occupational exposure limits (OELs) for aerosol particles, dusts, or chemicals, one has to evaluate whether mechanistic considerations permit identification of a no observed effect level (NOEL). In the case of carcinogenic effects, this can be assumed if no genotoxicity is involved, and exposure is considered safe if it does not exceed the NOEL. If tumor induction is associated with genotoxicity, any exposure is considered to be of risk, although a NOEL may be identified in the animal or human exposure studies. This must also be assumed when no information on the carcinogenic mechanism, including genotoxicity, is available. Aerosol particles, especially fibrous dusts, which include man-made mineral fiber(s) (MMMF), present a challenge for toxicological evaluation. Many MMMF that have been investigated have induced tumors in animals and genotoxicity in vitro. Since these effects have been associated with long-thin fiber geometry and high durability in vivo, all fibers meeting such criteria are considered carcinogenic unless the opposite has been demonstrated. This approach is practicable. Investigations on fiber tumorigenicity/genotoxicity should include information on dose response, pathobiochemistry, particle clearance, and persistence of the material in the target organ. Such information will introduce quantitative aspects into the qualitative approach that has so far been used to classify fibrous dusts as carcinogens. The rationales for classifying the potential carcinogenicity of MMMF and for setting OELs used by the different European committees and regulatory agencies are described. PMID:9400750

Case base classification on digital mammograms: improving the performance of case base classifier

NASA Astrophysics Data System (ADS)

Raman, Valliappan; Then, H. H.; Sumari, Putra; Venkatesa Mohan, N.

2011-10-01

Breast cancer continues to be a significant public health problem in the world. Early detection is the key for improving breast cancer prognosis. The aim of the research presented here is in twofold. First stage of research involves machine learning techniques, which segments and extracts features from the mass of digital mammograms. Second level is on problem solving approach which includes classification of mass by performance based case base classifier. In this paper we build a case-based Classifier in order to diagnose mammographic images. We explain different methods and behaviors that have been added to the classifier to improve the performance of the classifier. Currently the initial Performance base Classifier with Bagging is proposed in the paper and it's been implemented and it shows an improvement in specificity and sensitivity.

Transportation-markings general table of contents with index

DOT National Transportation Integrated Search

2008-01-01

The T-M process outlined in these studies describes and classifies safety aids that assist operators of modes of transportation that process also includes technical and historical perspectives. However; a simple describing and classifying of safety a...

A system for classifying wood-using industries and recording statistics for automatic data processing.

Treesearch

E.W. Fobes; R.W. Rowe

1968-01-01

A system for classifying wood-using industries and recording pertinent statistics for automatic data processing is described. Forms and coding instructions for recording data of primary processing plants are included.

Maximum a posteriori classification of multifrequency, multilook, synthetic aperture radar intensity data

NASA Technical Reports Server (NTRS)

Rignot, E.; Chellappa, R.

1993-01-01

We present a maximum a posteriori (MAP) classifier for classifying multifrequency, multilook, single polarization SAR intensity data into regions or ensembles of pixels of homogeneous and similar radar backscatter characteristics. A model for the prior joint distribution of the multifrequency SAR intensity data is combined with a Markov random field for representing the interactions between region labels to obtain an expression for the posterior distribution of the region labels given the multifrequency SAR observations. The maximization of the posterior distribution yields Bayes's optimum region labeling or classification of the SAR data or its MAP estimate. The performance of the MAP classifier is evaluated by using computer-simulated multilook SAR intensity data as a function of the parameters in the classification process. Multilook SAR intensity data are shown to yield higher classification accuracies than one-look SAR complex amplitude data. The MAP classifier is extended to the case in which the radar backscatter from the remotely sensed surface varies within the SAR image because of incidence angle effects. The results obtained illustrate the practicality of the method for combining SAR intensity observations acquired at two different frequencies and for improving classification accuracy of SAR data.

Influence of season and type of restaurants on sashimi microbiota.

PubMed

Miguéis, S; Moura, A T; Saraiva, C; Esteves, A

2016-10-01

In recent years, an increase in the consumption of Japanese food in European countries has been verified, including in Portugal. These specialities made with raw fish, typical Japanese meals, have been prepared in typical and on non-typical restaurants, and represent a challenge to risk analysis on HACCP plans. The aim of this study was to evaluate the influence of the type of restaurant, season and type of fish used on sashimi microbiota. Sashimi samples (n = 114) were directly collected from 23 sushi restaurants and were classified as Winter and Summer Samples. They were also categorized according to the type of restaurant where they were obtained: as typical or non-typical. The samples were processed using international standards procedures. A middling seasonality influence was observed in microbiota using mesophilic aerobic bacteria, psychrotrophic microorganisms, Lactic acid bacteria, Pseudomonas spp., H 2 S positive bacteria, mould and Bacillus cereus counts parameters. During the Summer Season, samples classified as unacceptable or potentially Hazardous were observed. Non-typical restaurants had the most cases of Unacceptable/potentially hazardous samples 83.33%. These unacceptable results were obtained as a result of high values of pathogenic bacteria like Listeria monocytogenes and Staphylococcus aureus No significant differences were observed on microbiota counts from different fish species. The need to implement more accurate food safety systems was quite evident, especially in the warmer season, as well as in restaurants where other kinds of food, apart from Japanese meals, was prepared. © Crown copyright 2016.

Salaries and Fringe Benefits, Classified Personnel, 1974-1975. A Study of the Salary and Fringe Benefits for Selected Classified Personnel in Western New York.

ERIC Educational Resources Information Center

Western New York Regional Office for Educational Planning, Cheektowaga.

This survey is designed to provide comparative information for classified personnel in the six counties of western New York State. Data collection was more difficult this year than in previous years because of the increasing length of the negotiation process. This year 74 of 89 districts supplied data. This study includes two features that were…

Ensemble candidate classification for the LOTAAS pulsar survey

NASA Astrophysics Data System (ADS)

Tan, C. M.; Lyon, R. J.; Stappers, B. W.; Cooper, S.; Hessels, J. W. T.; Kondratiev, V. I.; Michilli, D.; Sanidas, S.

2018-03-01

One of the biggest challenges arising from modern large-scale pulsar surveys is the number of candidates generated. Here, we implemented several improvements to the machine learning (ML) classifier previously used by the LOFAR Tied-Array All-Sky Survey (LOTAAS) to look for new pulsars via filtering the candidates obtained during periodicity searches. To assist the ML algorithm, we have introduced new features which capture the frequency and time evolution of the signal and improved the signal-to-noise calculation accounting for broad profiles. We enhanced the ML classifier by including a third class characterizing RFI instances, allowing candidates arising from RFI to be isolated, reducing the false positive return rate. We also introduced a new training data set used by the ML algorithm that includes a large sample of pulsars misclassified by the previous classifier. Lastly, we developed an ensemble classifier comprised of five different Decision Trees. Taken together these updates improve the pulsar recall rate by 2.5 per cent, while also improving the ability to identify pulsars with wide pulse profiles, often misclassified by the previous classifier. The new ensemble classifier is also able to reduce the percentage of false positive candidates identified from each LOTAAS pointing from 2.5 per cent (˜500 candidates) to 1.1 per cent (˜220 candidates).

Automated assessment of cognitive health using smart home technologies.

PubMed

Dawadi, Prafulla N; Cook, Diane J; Schmitter-Edgecombe, Maureen; Parsey, Carolyn

2013-01-01

The goal of this work is to develop intelligent systems to monitor the wellbeing of individuals in their home environments. This paper introduces a machine learning-based method to automatically predict activity quality in smart homes and automatically assess cognitive health based on activity quality. This paper describes an automated framework to extract set of features from smart home sensors data that reflects the activity performance or ability of an individual to complete an activity which can be input to machine learning algorithms. Output from learning algorithms including principal component analysis, support vector machine, and logistic regression algorithms are used to quantify activity quality for a complex set of smart home activities and predict cognitive health of participants. Smart home activity data was gathered from volunteer participants (n=263) who performed a complex set of activities in our smart home testbed. We compare our automated activity quality prediction and cognitive health prediction with direct observation scores and health assessment obtained from neuropsychologists. With all samples included, we obtained statistically significant correlation (r=0.54) between direct observation scores and predicted activity quality. Similarly, using a support vector machine classifier, we obtained reasonable classification accuracy (area under the ROC curve=0.80, g-mean=0.73) in classifying participants into two different cognitive classes, dementia and cognitive healthy. The results suggest that it is possible to automatically quantify the task quality of smart home activities and perform limited assessment of the cognitive health of individual if smart home activities are properly chosen and learning algorithms are appropriately trained.

Automated Assessment of Cognitive Health Using Smart Home Technologies

PubMed Central

Dawadi, Prafulla N.; Cook, Diane J.; Schmitter-Edgecombe, Maureen; Parsey, Carolyn

2014-01-01

BACKGROUND The goal of this work is to develop intelligent systems to monitor the well being of individuals in their home environments. OBJECTIVE This paper introduces a machine learning-based method to automatically predict activity quality in smart homes and automatically assess cognitive health based on activity quality. METHODS This paper describes an automated framework to extract set of features from smart home sensors data that reflects the activity performance or ability of an individual to complete an activity which can be input to machine learning algorithms. Output from learning algorithms including principal component analysis, support vector machine, and logistic regression algorithms are used to quantify activity quality for a complex set of smart home activities and predict cognitive health of participants. RESULTS Smart home activity data was gathered from volunteer participants (n=263) who performed a complex set of activities in our smart home testbed. We compare our automated activity quality prediction and cognitive health prediction with direct observation scores and health assessment obtained from neuropsychologists. With all samples included, we obtained statistically significant correlation (r=0.54) between direct observation scores and predicted activity quality. Similarly, using a support vector machine classifier, we obtained reasonable classification accuracy (area under the ROC curve = 0.80, g-mean = 0.73) in classifying participants into two different cognitive classes, dementia and cognitive healthy. CONCLUSIONS The results suggest that it is possible to automatically quantify the task quality of smart home activities and perform limited assessment of the cognitive health of individual if smart home activities are properly chosen and learning algorithms are appropriately trained. PMID:23949177

Hybrid Collaborative Learning for Classification and Clustering in Sensor Networks

NASA Technical Reports Server (NTRS)

Wagstaff, Kiri L.; Sosnowski, Scott; Lane, Terran

2012-01-01

Traditionally, nodes in a sensor network simply collect data and then pass it on to a centralized node that archives, distributes, and possibly analyzes the data. However, analysis at the individual nodes could enable faster detection of anomalies or other interesting events as well as faster responses, such as sending out alerts or increasing the data collection rate. There is an additional opportunity for increased performance if learners at individual nodes can communicate with their neighbors. In previous work, methods were developed by which classification algorithms deployed at sensor nodes can communicate information about event labels to each other, building on prior work with co-training, self-training, and active learning. The idea of collaborative learning was extended to function for clustering algorithms as well, similar to ideas from penta-training and consensus clustering. However, collaboration between these learner types had not been explored. A new protocol was developed by which classifiers and clusterers can share key information about their observations and conclusions as they learn. This is an active collaboration in which learners of either type can query their neighbors for information that they then use to re-train or re-learn the concept they are studying. The protocol also supports broadcasts from the classifiers and clusterers to the rest of the network to announce new discoveries. Classifiers observe an event and assign it a label (type). Clusterers instead group observations into clusters without assigning them a label, and they collaborate in terms of pairwise constraints between two events [same-cluster (mustlink) or different-cluster (cannot-link)]. Fundamentally, these two learner types speak different languages. To bridge this gap, the new communication protocol provides four types of exchanges: hybrid queries for information, hybrid "broadcasts" of learned information, each specified for classifiers-to-clusterers, and clusterers-to-classifiers. The new capability has the potential to greatly expand the in situ analysis abilities of sensor networks. Classifiers seeking to categorize incoming data into different types of events can operate in tandem with clusterers that are sensitive to the occurrence of new kinds of events not known to the classifiers. In contrast to current approaches that treat these operations as independent components, a hybrid collaborative learning system can enable them to learn from each other.

An expert computer program for classifying stars on the MK spectral classification system

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

Gray, R. O.; Corbally, C. J.

2014-04-01

This paper describes an expert computer program (MKCLASS) designed to classify stellar spectra on the MK Spectral Classification system in a way similar to humans—by direct comparison with the MK classification standards. Like an expert human classifier, the program first comes up with a rough spectral type, and then refines that spectral type by direct comparison with MK standards drawn from a standards library. A number of spectral peculiarities, including barium stars, Ap and Am stars, λ Bootis stars, carbon-rich giants, etc., can be detected and classified by the program. The program also evaluates the quality of the delivered spectralmore » type. The program currently is capable of classifying spectra in the violet-green region in either the rectified or flux-calibrated format, although the accuracy of the flux calibration is not important. We report on tests of MKCLASS on spectra classified by human classifiers; those tests suggest that over the entire HR diagram, MKCLASS will classify in the temperature dimension with a precision of 0.6 spectral subclass, and in the luminosity dimension with a precision of about one half of a luminosity class. These results compare well with human classifiers.« less

Classifying Cereal Data

Cancer.gov

The DSQ includes questions about cereal intake and allows respondents up to two responses on which cereals they consume. We classified each cereal reported first by hot or cold, and then along four dimensions: density of added sugars, whole grains, fiber, and calcium.

Accuracy of Diagnostic Imaging Modalities for Classifying Pediatric Eyes as Papilledema Versus Pseudopapilledema.

PubMed

Chang, Melinda Y; Velez, Federico G; Demer, Joseph L; Bonelli, Laura; Quiros, Peter A; Arnold, Anthony C; Sadun, Alfredo A; Pineles, Stacy L

2017-12-01

To identify the most accurate diagnostic imaging modality for classifying pediatric eyes as papilledema (PE) or pseudopapilledema (PPE). Prospective observational study. Nineteen children between the ages of 5 and 18 years were recruited. Five children (10 eyes) with PE, 11 children (19 eyes) with PPE owing to suspected buried optic disc drusen (ODD), and 3 children (6 eyes) with PPE owing to superficial ODD were included. All subjects underwent imaging with B-scan ultrasonography, fundus photography, autofluorescence, fluorescein angiography (FA), optical coherence tomography (OCT) of the retinal nerve fiber layer (RNFL), and volumetric OCT scans through the optic nerve head with standard spectral-domain (SD OCT) and enhanced depth imaging (EDI OCT) settings. Images were read by 3 masked neuro-ophthalmologists, and the final image interpretation was based on 2 of 3 reads. Image interpretations were compared with clinical diagnosis to calculate accuracy and misinterpretation rates of each imaging modality. Accuracy of each imaging technique for classifying eyes as PE or PPE, and misinterpretation rates of each imaging modality for PE and PPE. Fluorescein angiography had the highest accuracy (97%, 34 of 35 eyes, 95% confidence interval 92%-100%) for classifying an eye as PE or PPE. FA of eyes with PE showed leakage of the optic nerve, whereas eyes with suspected buried ODD demonstrated no hyperfluorescence, and eyes with superficial ODD showed nodular staining. Other modalities had substantial likelihood (30%-70%) of misinterpretation of PE as PPE. The best imaging technique for correctly classifying pediatric eyes as PPE or PE is FA. Other imaging modalities, if used in isolation, are more likely to lead to misinterpretation of PE as PPE, which could potentially result in failure to identify a life-threatening disorder causing elevated intracranial pressure and papilledema. Copyright © 2017 American Academy of Ophthalmology. Published by Elsevier Inc. All rights reserved.

Chaotic Particle Swarm Optimization with Mutation for Classification

PubMed Central

Assarzadeh, Zahra; Naghsh-Nilchi, Ahmad Reza

2015-01-01

In this paper, a chaotic particle swarm optimization with mutation-based classifier particle swarm optimization is proposed to classify patterns of different classes in the feature space. The introduced mutation operators and chaotic sequences allows us to overcome the problem of early convergence into a local minima associated with particle swarm optimization algorithms. That is, the mutation operator sharpens the convergence and it tunes the best possible solution. Furthermore, to remove the irrelevant data and reduce the dimensionality of medical datasets, a feature selection approach using binary version of the proposed particle swarm optimization is introduced. In order to demonstrate the effectiveness of our proposed classifier, mutation-based classifier particle swarm optimization, it is checked out with three sets of data classifications namely, Wisconsin diagnostic breast cancer, Wisconsin breast cancer and heart-statlog, with different feature vector dimensions. The proposed algorithm is compared with different classifier algorithms including k-nearest neighbor, as a conventional classifier, particle swarm-classifier, genetic algorithm, and Imperialist competitive algorithm-classifier, as more sophisticated ones. The performance of each classifier was evaluated by calculating the accuracy, sensitivity, specificity and Matthews's correlation coefficient. The experimental results show that the mutation-based classifier particle swarm optimization unequivocally performs better than all the compared algorithms. PMID:25709937

Visual terrain mapping for traversable path planning of mobile robots

NASA Astrophysics Data System (ADS)

Shirkhodaie, Amir; Amrani, Rachida; Tunstel, Edward W.

2004-10-01

In this paper, we have primarily discussed technical challenges and navigational skill requirements of mobile robots for traversability path planning in natural terrain environments similar to Mars surface terrains. We have described different methods for detection of salient terrain features based on imaging texture analysis techniques. We have also presented three competing techniques for terrain traversability assessment of mobile robots navigating in unstructured natural terrain environments. These three techniques include: a rule-based terrain classifier, a neural network-based terrain classifier, and a fuzzy-logic terrain classifier. Each proposed terrain classifier divides a region of natural terrain into finite sub-terrain regions and classifies terrain condition exclusively within each sub-terrain region based on terrain visual clues. The Kalman Filtering technique is applied for aggregative fusion of sub-terrain assessment results. The last two terrain classifiers are shown to have remarkable capability for terrain traversability assessment of natural terrains. We have conducted a comparative performance evaluation of all three terrain classifiers and presented the results in this paper.

Soft computing-based terrain visual sensing and data fusion for unmanned ground robotic systems

NASA Astrophysics Data System (ADS)

Shirkhodaie, Amir

2006-05-01

In this paper, we have primarily discussed technical challenges and navigational skill requirements of mobile robots for traversability path planning in natural terrain environments similar to Mars surface terrains. We have described different methods for detection of salient terrain features based on imaging texture analysis techniques. We have also presented three competing techniques for terrain traversability assessment of mobile robots navigating in unstructured natural terrain environments. These three techniques include: a rule-based terrain classifier, a neural network-based terrain classifier, and a fuzzy-logic terrain classifier. Each proposed terrain classifier divides a region of natural terrain into finite sub-terrain regions and classifies terrain condition exclusively within each sub-terrain region based on terrain visual clues. The Kalman Filtering technique is applied for aggregative fusion of sub-terrain assessment results. The last two terrain classifiers are shown to have remarkable capability for terrain traversability assessment of natural terrains. We have conducted a comparative performance evaluation of all three terrain classifiers and presented the results in this paper.

ApoE4 effects on automated diagnostic classifiers for mild cognitive impairment and Alzheimer's disease

PubMed Central

Apostolova, Liana G.; Hwang, Kristy S.; Kohannim, Omid; Avila, David; Elashoff, David; Jack, Clifford R.; Shaw, Leslie; Trojanowski, John Q.; Weiner, Michael W.; Thompson, Paul M.

2014-01-01

Biomarkers are the only feasible way to detect and monitor presymptomatic Alzheimer's disease (AD). No single biomarker can predict future cognitive decline with an acceptable level of accuracy. In addition to designing powerful multimodal diagnostic platforms, a careful investigation of the major sources of disease heterogeneity and their influence on biomarker changes is needed. Here we investigated the accuracy of a novel multimodal biomarker classifier for differentiating cognitively normal (NC), mild cognitive impairment (MCI) and AD subjects with and without stratification by ApoE4 genotype. 111 NC, 182 MCI and 95 AD ADNI participants provided both structural MRI and CSF data at baseline. We used an automated machine-learning classifier to test the ability of hippocampal volume and CSF Aβ, t-tau and p-tau levels, both separately and in combination, to differentiate NC, MCI and AD subjects, and predict conversion. We hypothesized that the combined hippocampal/CSF biomarker classifier model would achieve the highest accuracy in differentiating between the three diagnostic groups and that ApoE4 genotype will affect both diagnostic accuracy and biomarker selection. The combined hippocampal/CSF classifier performed better than hippocampus-only classifier in differentiating NC from MCI and NC from AD. It also outperformed the CSF-only classifier in differentiating NC vs. AD. Our amyloid marker played a role in discriminating NC from MCI or AD but not for MCI vs. AD. Neurodegenerative markers contributed to accurate discrimination of AD from NC and MCI but not NC from MCI. Classifiers predicting MCI conversion performed well only after ApoE4 stratification. Hippocampal volume and sex achieved AUC = 0.68 for predicting conversion in the ApoE4-positive MCI, while CSF p-tau, education and sex achieved AUC = 0.89 for predicting conversion in ApoE4-negative MCI. These observations support the proposed biomarker trajectory in AD, which postulates that amyloid markers become abnormal early in the disease course while markers of neurodegeneration become abnormal later in the disease course and suggests that ApoE4 could be at least partially responsible for some of the observed disease heterogeneity. PMID:24634832

Pediatric Surgeon-Directed Wound Classification Improves Accuracy

PubMed Central

Zens, Tiffany J.; Rusy, Deborah A.; Gosain, Ankush

2015-01-01

Background Surgical wound classification (SWC) communicates the degree of contamination in the surgical field and is used to stratify risk of surgical site infection and compare outcomes amongst centers. We hypothesized that changing from nurse-directed to surgeon-directed SWC during a structured operative debrief we will improve accuracy of documentation. Methods An IRB-approved retrospective chart review was performed. Two time periods were defined: initially, SWC was determined and recorded by the circulating nurse (Pre-Debrief 6/2012-5/2013) and allowing six months for adoption and education, we implemented a structured operative debriefing including surgeon-directed SWC (Post-Debrief 1/2014-8/2014). Accuracy of SWC was determined for four commonly performed Pediatric General Surgery operations: inguinal hernia repair (clean), gastrostomy +/− Nissen fundoplication (clean-contaminated), appendectomy without perforation (contaminated), and appendectomy with perforation (dirty). Results 183 cases Pre-Debrief and 142 cases Post-Debrief met inclusion criteria. No differences between time periods were noted in regards to patient demographics, ASA class, or case mix. Accuracy of wound classification improved Post-Debrief (42% vs. 58.5%, p=0.003). Pre-Debrief, 26.8% of cases were overestimated or underestimated by more than one wound class, vs. 3.5% of cases Post-Debrief (p<0.001). Interestingly, the majority of Post-Debrief contaminated cases were incorrectly classified as clean-contaminated. Conclusions Implementation of a structured operative debrief including surgeon-directed SWC improves the percentage of correctly classified wounds and decreases the degree of inaccuracy in incorrectly classified cases. However, following implementation of the debriefing, we still observed a 41.5% rate of incorrect documentation, most notably in contaminated cases, indicating further education and process improvement is needed. PMID:27020829

Classification of ligand molecules in PDB with fast heuristic graph match algorithm COMPLIG.

PubMed

Saito, Mihoko; Takemura, Naomi; Shirai, Tsuyoshi

2012-12-14

A fast heuristic graph-matching algorithm, COMPLIG, was devised to classify the small-molecule ligands in the Protein Data Bank (PDB), which are currently not properly classified on structure basis. By concurrently classifying proteins and ligands, we determined the most appropriate parameter for categorizing ligands to be more than 60% identity of atoms and bonds between molecules, and we classified 11,585 types of ligands into 1946 clusters. Although the large clusters were composed of nucleotides or amino acids, a significant presence of drug compounds was also observed. Application of the system to classify the natural ligand status of human proteins in the current database suggested that, at most, 37% of the experimental structures of human proteins were in complex with natural ligands. However, protein homology- and/or ligand similarity-based modeling was implied to provide models of natural interactions for an additional 28% of the total, which might be used to increase the knowledge of intrinsic protein-metabolite interactions. Copyright © 2012 Elsevier Ltd. All rights reserved.

Learning to classify wakes from local sensory information

NASA Astrophysics Data System (ADS)

Alsalman, Mohamad; Colvert, Brendan; Kanso, Eva; Kanso Team

2017-11-01

Aquatic organisms exhibit remarkable abilities to sense local flow signals contained in their fluid environment and to surmise the origins of these flows. For example, fish can discern the information contained in various flow structures and utilize this information for obstacle avoidance and prey tracking. Flow structures created by flapping and swimming bodies are well characterized in the fluid dynamics literature; however, such characterization relies on classical methods that use an external observer to reconstruct global flow fields. The reconstructed flows, or wakes, are then classified according to the unsteady vortex patterns. Here, we propose a new approach for wake identification: we classify the wakes resulting from a flapping airfoil by applying machine learning algorithms to local flow information. In particular, we simulate the wakes of an oscillating airfoil in an incoming flow, extract the downstream vorticity information, and train a classifier to learn the different flow structures and classify new ones. This data-driven approach provides a promising framework for underwater navigation and detection in application to autonomous bio-inspired vehicles.

The performance of an automatic acoustic-based program classifier compared to hearing aid users' manual selection of listening programs.

PubMed

Searchfield, Grant D; Linford, Tania; Kobayashi, Kei; Crowhen, David; Latzel, Matthias

2018-03-01

To compare preference for and performance of manually selected programmes to an automatic sound classifier, the Phonak AutoSense OS. A single blind repeated measures study. Participants were fit with Phonak Virto V90 ITE aids; preferences for different listening programmes were compared across four different sound scenarios (speech in: quiet, noise, loud noise and a car). Following a 4-week trial preferences were reassessed and the users preferred programme was compared to the automatic classifier for sound quality and hearing in noise (HINT test) using a 12 loudspeaker array. Twenty-five participants with symmetrical moderate-severe sensorineural hearing loss. Participant preferences of manual programme for scenarios varied considerably between and within sessions. A HINT Speech Reception Threshold (SRT) advantage was observed for the automatic classifier over participant's manual selection for speech in quiet, loud noise and car noise. Sound quality ratings were similar for both manual and automatic selections. The use of a sound classifier is a viable alternative to manual programme selection.

On Burst Detection and Prediction in Retweeting Sequence

DTIC Science & Technology

2015-05-22

We conduct a comprehensive empirical analysis of a large microblogging dataset collected from the Sina Weibo and report our observations of burst...whether and how accurate we can predict bursts using classifiers based on the extracted features. Our empirical study of the Sina Weibo data shows the...feasibility of burst prediction using appropriately extracted features and classic classifiers. 1 Introduction Microblogging, such as Twitter and Sina

Classifying Cereal Data (Earlier Methods)

Cancer.gov

The DSQ includes questions about cereal intake and allows respondents up to two responses on which cereals they consume. We classified each cereal reported first by hot or cold, and then along four dimensions: density of added sugars, whole grains, fiber, and calcium.

Classifier Subset Selection for the Stacked Generalization Method Applied to Emotion Recognition in Speech

PubMed Central

Álvarez, Aitor; Sierra, Basilio; Arruti, Andoni; López-Gil, Juan-Miguel; Garay-Vitoria, Nestor

2015-01-01

In this paper, a new supervised classification paradigm, called classifier subset selection for stacked generalization (CSS stacking), is presented to deal with speech emotion recognition. The new approach consists of an improvement of a bi-level multi-classifier system known as stacking generalization by means of an integration of an estimation of distribution algorithm (EDA) in the first layer to select the optimal subset from the standard base classifiers. The good performance of the proposed new paradigm was demonstrated over different configurations and datasets. First, several CSS stacking classifiers were constructed on the RekEmozio dataset, using some specific standard base classifiers and a total of 123 spectral, quality and prosodic features computed using in-house feature extraction algorithms. These initial CSS stacking classifiers were compared to other multi-classifier systems and the employed standard classifiers built on the same set of speech features. Then, new CSS stacking classifiers were built on RekEmozio using a different set of both acoustic parameters (extended version of the Geneva Minimalistic Acoustic Parameter Set (eGeMAPS)) and standard classifiers and employing the best meta-classifier of the initial experiments. The performance of these two CSS stacking classifiers was evaluated and compared. Finally, the new paradigm was tested on the well-known Berlin Emotional Speech database. We compared the performance of single, standard stacking and CSS stacking systems using the same parametrization of the second phase. All of the classifications were performed at the categorical level, including the six primary emotions plus the neutral one. PMID:26712757

EEG error potentials detection and classification using time-frequency features for robot reinforcement learning.

PubMed

Boubchir, Larbi; Touati, Youcef; Daachi, Boubaker; Chérif, Arab Ali

2015-08-01

In thought-based steering of robots, error potentials (ErrP) can appear when the action resulting from the brain-machine interface (BMI) classifier/controller does not correspond to the user's thought. Using the Steady State Visual Evoked Potentials (SSVEP) techniques, ErrP, which appear when a classification error occurs, are not easily recognizable by only examining the temporal or frequency characteristics of EEG signals. A supplementary classification process is therefore needed to identify them in order to stop the course of the action and back up to a recovery state. This paper presents a set of time-frequency (t-f) features for the detection and classification of EEG ErrP in extra-brain activities due to misclassification observed by a user exploiting non-invasive BMI and robot control in the task space. The proposed features are able to characterize and detect ErrP activities in the t-f domain. These features are derived from the information embedded in the t-f representation of EEG signals, and include the Instantaneous Frequency (IF), t-f information complexity, SVD information, energy concentration and sub-bands' energies. The experiment results on real EEG data show that the use of the proposed t-f features for detecting and classifying EEG ErrP achieved an overall classification accuracy up to 97% for 50 EEG segments using 2-class SVM classifier.

Establishing Long-term Observations of Gas Hydrate Systems: Results from Ocean Networks Canada's NEPTUNE Observatory

NASA Astrophysics Data System (ADS)

Scherwath, M.; Riedel, M.; Roemer, M.; Heesemann, M.; Chun, J. H.; Moran, K.; Spence, G.; Thomsen, L.

2016-12-01

The key for a scientific understanding of natural environments and the determination of baselines is the long-term monitoring of environmental factors. For seafloor environments including gas hydrate systems, cabled ocean observatories are important platforms for the remote acquisition of a comprehensive suite of datasets. This is particularly critical for those datasets that are difficult to acquire with autonomous, battery-powered systems, such as cameras or high-bandwidth sonar because cable connections provide continuous power and communication from shore to the seafloor. Ocean Networks Canada is operating the NEPTUNE cabled undersea observatory in the Northeast Pacific with two nodes at gas hydrate sites, Barkley Canyon and Clayoquot Slope. With up to seven years of continuous data from these locations we are now beginning to understand the dynamics of the natural systems and are able to classify the variations within the gas hydrate system. For example, the long-term monitoring of gas vent activity has allowed us to classify phases of low, intermittent and high activity that seem to reoccur periodically. Or, by recording the speeds of bacterial mat growth or detecting periods of increased productivity of flora and fauna at hydrates sites we can start to classify benthic activity and relate that to outside environmental parameters. This will eventually allow us to do enhanced environmental monitoring, establish baselines, and potentially detect anthropogenic variations or events for example during gas hydrate production.

Satellite-based detection of global urban heat-island temperature influence

USGS Publications Warehouse

Gallo, K.P.; Adegoke, Jimmy O.; Owen, T.W.; Elvidge, C.D.

2002-01-01

This study utilizes a satellite-based methodology to assess the urban heat-island influence during warm season months for over 4400 stations included in the Global Historical Climatology Network of climate stations. The methodology includes local and regional satellite retrievals of an indicator of the presence green photosynthetically active vegetation at and around the stations. The difference in local and regional samples of the normalized difference vegetation index (NDVI) is used to estimate differences in mean air temperature. Stations classified as urban averaged 0.90??C (N. Hemisphere) and 0.92??C (S. Hemisphere) warmer than the surrounding environment on the basis of the NDVI-derived temperature estimates. Additionally, stations classified as rural averaged 0.19??C (N. Hemisphere) and 0.16??C (S. Hemisphere) warmer than the surrounding environment. The NDVI-derived temperature estimates were found to be in reasonable agreement with temperature differences observed between climate stations. The results suggest that satellite-derived data sets can be used to estimate the urban heat-island temperature influence on a global basis and that a more detailed analysis of rural stations and their surrounding environment may be necessary to assure that temperature trends derived from assumed rural environments are not influenced by changes in land use/land cover. Copyright 2002 by the American Geophysical Union.

Multipurpose spectral imager.

PubMed

Sigernes, F; Lorentzen, D A; Heia, K; Svenøe, T

2000-06-20

A small spectral imaging system is presented that images static or moving objects simultaneously as a function of wavelength. The main physical principle is outlined and demonstrated. The instrument is capable of resolving both spectral and spatial information from targets throughout the entire visible region. The spectral domain has a bandpass of 12 A. One can achieve the spatial domain by rotating the system's front mirror with a high-resolution stepper motor. The spatial resolution range from millimeters to several meters depends mainly on the front optics used and whether the target is fixed (static) or movable relative to the instrument. Different applications and examples are explored, including outdoor landscapes, industrial fish-related targets, and ground-level objects observed in the more traditional way from an airborne carrier (remote sensing). Through the examples, we found that the instrument correctly classifies whether a shrimp is peeled and whether it can disclose the spectral and spatial microcharacteristics of targets such as a fish nematode (parasite). In the macroregime, we were able to distinguish a marine vessel from the surrounding sea and sky. A study of the directional spectral albedo from clouds, mountains, snow cover, and vegetation has also been included. With the airborne experiment, the imager successfully classified snow cover, leads, and new and rafted ice, as seen from 10.000 ft (3.048 m).

Does the Implant Surgical Technique Affect the Primary and/or Secondary Stability of Dental Implants? A Systematic Review

PubMed Central

Shadid, Rola Muhammed; Sadaqah, Nasrin Rushdi; Othman, Sahar Abdo

2014-01-01

Background. A number of surgical techniques for implant site preparation have been advocated to enhance the implant of primary and secondary stability. However, there is insufficient scientific evidence to support the association between the surgical technique and implant stability. Purpose. This review aimed to investigate the influence of different surgical techniques including the undersized drilling, the osteotome, the piezosurgery, the flapless procedure, and the bone stimulation by low-level laser therapy on the primary and/or secondary stability of dental implants. Materials and methods. A search of PubMed, Cochrane Library, and grey literature was performed. The inclusion criteria comprised observational clinical studies and randomized controlled trials (RCTs) conducted in patients who received dental implants for rehabilitation, studies that evaluated the association between the surgical technique and the implant primary and/or secondary stability. The articles selected were carefully read and classified as low, moderate, and high methodological quality and data of interest were tabulated. Results. Eight clinical studies were included then they were classified as moderate or high methodological quality and control of bias. Conclusions. There is a weak evidence suggesting that any of previously mentioned surgical techniques could influence the primary and/or secondary implant stability. PMID:25126094

Observer performance in diagnosing osteoporosis by dental panoramic radiographs: results from the osteoporosis screening project in dentistry (OSPD).

PubMed

Taguchi, A; Asano, A; Ohtsuka, M; Nakamoto, T; Suei, Y; Tsuda, M; Kudo, Y; Inagaki, K; Noguchi, T; Tanimoto, K; Jacobs, R; Klemetti, E; White, S C; Horner, K

2008-07-01

Mandibular cortical erosion detected on dental panoramic radiographs (DPRs) may be useful for identifying women with osteoporosis, but little is known about the variation in diagnostic efficacy of observers worldwide. The purpose of this study was to measure the accuracy in identifying women at risk for osteoporosis in a worldwide group of observers using DPRs. We constructed a website that included background information about osteoporosis screening and instructions regarding the interpretation of mandibular cortical erosion. DPRs of 100 Japanese postmenopausal women aged 50 years or older who had completed skeletal bone mineral measurements by dual energy X-ray absorptiometry were digitized at 300 dpi. These were displayed on the website and used for the evaluation of diagnostic efficacy. Sixty observers aged 25 to 66 years recruited from 16 countries participated in this study. These observers classified cortical erosion into one of three groups (none, mild to moderate, and severe) on the website via the Internet, twice with an approximately 2-week interval. The diagnostic efficacy of the Osteoporosis Self-Assessment Tool (OST), a simple clinical decision rule based on age and weight, was also calculated and compared with that of cortical erosion. The overall mean sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of the 60 observers in identifying women with osteoporosis by cortical erosion on DPRs were 82.5, 46.2, 46.7, and 84.0%, respectively. Those same values by the OST index were 82.9, 43.1, 43.9, and 82.4%, respectively. The intra-observer agreement in classifying cortical erosion on DPRs was sufficient (weighted kappa values>0.6) in 36 (60%) observers. This was significantly increased in observers who specialized in oral radiology (P<0.05). In the 36 observers with sufficient intra-observer agreement, the overall mean sensitivity, specificity, PPV, and NPV in identifying women with osteoporosis by any cortical erosion were 83.5, 48.7, 48.3, and 85.7%, respectively. The mean PPV and NPV were significantly higher in the 36 observers with sufficient intra-observer agreement than in the 24 observers with insufficient intra-observer agreement. Our results reconfirm the efficacy of cortical erosion findings in identifying postmenopausal women at risk for osteoporosis, among observers with sufficient intra-observer agreement. Information gathered from radiographic examination is at least as useful as that gathered from the OST index.

Insects and associated arthropods analyzed during medicolegal death investigations in Harris County, Texas, USA: January 2013- April 2016

PubMed Central

2017-01-01

The application of insect and arthropod information to medicolegal death investigations is one of the more exacting applications of entomology. Historically limited to homicide investigations, the integration of full time forensic entomology services to the medical examiner’s office in Harris County has opened up the opportunity to apply entomology to a wide variety of manner of death classifications and types of scenes to make observations on a number of different geographical and species-level trends in Harris County, Texas, USA. In this study, a retrospective analysis was made of 203 forensic entomology cases analyzed during the course of medicolegal death investigations performed by the Harris County Institute of Forensic Sciences in Houston, TX, USA from January 2013 through April 2016. These cases included all manner of death classifications, stages of decomposition and a variety of different scene types that were classified into decedents transported from the hospital (typically associated with myiasis or sting allergy; 3.0%), outdoor scenes (32.0%) or indoor scenes (65.0%). Ambient scene air temperature at the time scene investigation was the only significantly different factor observed between indoor and outdoor scenes with average indoor scene temperature being slightly cooler (25.2°C) than that observed outdoors (28.0°C). Relative humidity was not found to be significantly different between scene types. Most of the indoor scenes were classified as natural (43.3%) whereas most of the outdoor scenes were classified as homicides (12.3%). All other manner of death classifications came from both indoor and outdoor scenes. Several species were found to be significantly associated with indoor scenes as indicated by a binomial test, including Blaesoxipha plinthopyga (Wiedemann) (Diptera: Sarcophagidae), all Sarcophagidae (including B. plinthopyga), Megaselia scalaris Loew (Diptera: Phoridae), Synthesiomyia nudiseta Wulp (Diptera: Muscidae) and Lucilia cuprina (Wiedemann) (Diptera: Calliphoridae). The only species that was a significant indicator of an outdoor scene was Lucilia eximia (Wiedemann) (Diptera: Calliphoridae). All other insect species that were collected in five or more cases were collected from both indoor and outdoor scenes. A species list with month of collection and basic scene characteristics with the length of the estimated time of colonization is also presented. The data presented here provide valuable casework related species data for Harris County, TX and nearby areas on the Gulf Coast that can be used to compare to other climate regions with other species assemblages and to assist in identifying new species introductions to the area. This study also highlights the importance of potential sources of uncertainty in preparation and interpretation of forensic entomology reports from different scene types. PMID:28604832

Insects and associated arthropods analyzed during medicolegal death investigations in Harris County, Texas, USA: January 2013- April 2016.

PubMed

Sanford, Michelle R

2017-01-01

The application of insect and arthropod information to medicolegal death investigations is one of the more exacting applications of entomology. Historically limited to homicide investigations, the integration of full time forensic entomology services to the medical examiner's office in Harris County has opened up the opportunity to apply entomology to a wide variety of manner of death classifications and types of scenes to make observations on a number of different geographical and species-level trends in Harris County, Texas, USA. In this study, a retrospective analysis was made of 203 forensic entomology cases analyzed during the course of medicolegal death investigations performed by the Harris County Institute of Forensic Sciences in Houston, TX, USA from January 2013 through April 2016. These cases included all manner of death classifications, stages of decomposition and a variety of different scene types that were classified into decedents transported from the hospital (typically associated with myiasis or sting allergy; 3.0%), outdoor scenes (32.0%) or indoor scenes (65.0%). Ambient scene air temperature at the time scene investigation was the only significantly different factor observed between indoor and outdoor scenes with average indoor scene temperature being slightly cooler (25.2°C) than that observed outdoors (28.0°C). Relative humidity was not found to be significantly different between scene types. Most of the indoor scenes were classified as natural (43.3%) whereas most of the outdoor scenes were classified as homicides (12.3%). All other manner of death classifications came from both indoor and outdoor scenes. Several species were found to be significantly associated with indoor scenes as indicated by a binomial test, including Blaesoxipha plinthopyga (Wiedemann) (Diptera: Sarcophagidae), all Sarcophagidae (including B. plinthopyga), Megaselia scalaris Loew (Diptera: Phoridae), Synthesiomyia nudiseta Wulp (Diptera: Muscidae) and Lucilia cuprina (Wiedemann) (Diptera: Calliphoridae). The only species that was a significant indicator of an outdoor scene was Lucilia eximia (Wiedemann) (Diptera: Calliphoridae). All other insect species that were collected in five or more cases were collected from both indoor and outdoor scenes. A species list with month of collection and basic scene characteristics with the length of the estimated time of colonization is also presented. The data presented here provide valuable casework related species data for Harris County, TX and nearby areas on the Gulf Coast that can be used to compare to other climate regions with other species assemblages and to assist in identifying new species introductions to the area. This study also highlights the importance of potential sources of uncertainty in preparation and interpretation of forensic entomology reports from different scene types.

Hard-Rock Stability Analysis for Span Design in Entry-Type Excavations with Learning Classifiers

PubMed Central

García-Gonzalo, Esperanza; Fernández-Muñiz, Zulima; García Nieto, Paulino José; Bernardo Sánchez, Antonio; Menéndez Fernández, Marta

2016-01-01

The mining industry relies heavily on empirical analysis for design and prediction. An empirical design method, called the critical span graph, was developed specifically for rock stability analysis in entry-type excavations, based on an extensive case-history database of cut and fill mining in Canada. This empirical span design chart plots the critical span against rock mass rating for the observed case histories and has been accepted by many mining operations for the initial span design of cut and fill stopes. Different types of analysis have been used to classify the observed cases into stable, potentially unstable and unstable groups. The main purpose of this paper is to present a new method for defining rock stability areas of the critical span graph, which applies machine learning classifiers (support vector machine and extreme learning machine). The results show a reasonable correlation with previous guidelines. These machine learning methods are good tools for developing empirical methods, since they make no assumptions about the regression function. With this software, it is easy to add new field observations to a previous database, improving prediction output with the addition of data that consider the local conditions for each mine. PMID:28773653

Hard-Rock Stability Analysis for Span Design in Entry-Type Excavations with Learning Classifiers.

PubMed

García-Gonzalo, Esperanza; Fernández-Muñiz, Zulima; García Nieto, Paulino José; Bernardo Sánchez, Antonio; Menéndez Fernández, Marta

2016-06-29

The mining industry relies heavily on empirical analysis for design and prediction. An empirical design method, called the critical span graph, was developed specifically for rock stability analysis in entry-type excavations, based on an extensive case-history database of cut and fill mining in Canada. This empirical span design chart plots the critical span against rock mass rating for the observed case histories and has been accepted by many mining operations for the initial span design of cut and fill stopes. Different types of analysis have been used to classify the observed cases into stable, potentially unstable and unstable groups. The main purpose of this paper is to present a new method for defining rock stability areas of the critical span graph, which applies machine learning classifiers (support vector machine and extreme learning machine). The results show a reasonable correlation with previous guidelines. These machine learning methods are good tools for developing empirical methods, since they make no assumptions about the regression function. With this software, it is easy to add new field observations to a previous database, improving prediction output with the addition of data that consider the local conditions for each mine.

Differentiation of Candida albicans, Candida glabrata, and Candida krusei by FT-IR and chemometrics by CHROMagar™ Candida.

PubMed

Wohlmeister, Denise; Vianna, Débora Renz Barreto; Helfer, Virginia Etges; Calil, Luciane Noal; Buffon, Andréia; Fuentefria, Alexandre Meneghello; Corbellini, Valeriano Antonio; Pilger, Diogo André

2017-10-01

Pathogenic Candida species are detected in clinical infections. CHROMagar™ is a phenotypical method used to identify Candida species, although it has limitations, which indicates the need for more sensitive and specific techniques. Infrared Spectroscopy (FT-IR) is an analytical vibrational technique used to identify patterns of metabolic fingerprint of biological matrixes, particularly whole microbial cell systems as Candida sp. in association of classificatory chemometrics algorithms. On the other hand, Soft Independent Modeling by Class Analogy (SIMCA) is one of the typical algorithms still little employed in microbiological classification. This study demonstrates the applicability of the FT-IR-technique by specular reflectance associated with SIMCA to discriminate Candida species isolated from vaginal discharges and grown on CHROMagar™. The differences in spectra of C. albicans, C. glabrata and C. krusei were suitable for use in the discrimination of these species, which was observed by PCA. Then, a SIMCA model was constructed with standard samples of three species and using the spectral region of 1792-1561cm -1 . All samples (n=48) were properly classified based on the chromogenic method using CHROMagar™ Candida. In total, 93.4% (n=45) of the samples were correctly and unambiguously classified (Class I). Two samples of C. albicans were classified correctly, though these could have been C. glabrata (Class II). Also, one C. glabrata sample could have been classified as C. krusei (Class II). Concerning these three samples, one triplicate of each was included in Class II and two in Class I. Therefore, FT-IR associated with SIMCA can be used to identify samples of C. albicans, C. glabrata, and C. krusei grown in CHROMagar™ Candida aiming to improve clinical applications of this technique. Copyright © 2017 Elsevier B.V. All rights reserved.

Classification of Malaysia aromatic rice using multivariate statistical analysis

NASA Astrophysics Data System (ADS)

Abdullah, A. H.; Adom, A. H.; Shakaff, A. Y. Md; Masnan, M. J.; Zakaria, A.; Rahim, N. A.; Omar, O.

2015-05-01

Aromatic rice (Oryza sativa L.) is considered as the best quality premium rice. The varieties are preferred by consumers because of its preference criteria such as shape, colour, distinctive aroma and flavour. The price of aromatic rice is higher than ordinary rice due to its special needed growth condition for instance specific climate and soil. Presently, the aromatic rice quality is identified by using its key elements and isotopic variables. The rice can also be classified via Gas Chromatography Mass Spectrometry (GC-MS) or human sensory panels. However, the uses of human sensory panels have significant drawbacks such as lengthy training time, and prone to fatigue as the number of sample increased and inconsistent. The GC-MS analysis techniques on the other hand, require detailed procedures, lengthy analysis and quite costly. This paper presents the application of in-house developed Electronic Nose (e-nose) to classify new aromatic rice varieties. The e-nose is used to classify the variety of aromatic rice based on the samples odour. The samples were taken from the variety of rice. The instrument utilizes multivariate statistical data analysis, including Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and K-Nearest Neighbours (KNN) to classify the unknown rice samples. The Leave-One-Out (LOO) validation approach is applied to evaluate the ability of KNN to perform recognition and classification of the unspecified samples. The visual observation of the PCA and LDA plots of the rice proves that the instrument was able to separate the samples into different clusters accordingly. The results of LDA and KNN with low misclassification error support the above findings and we may conclude that the e-nose is successfully applied to the classification of the aromatic rice varieties.

The Integrated Taxonomy of Health Care: Classifying Both Complementary and Biomedical Practices Using a Uniform Classification Protocol

PubMed Central

Porcino, Antony; MacDougall, Colleen

2009-01-01

Background: Since the late 1980s, several taxonomies have been developed to help map and describe the interrelationships of complementary and alternative medicine (CAM) modalities. In these taxonomies, several issues are often incompletely addressed: A simple categorization process that clearly isolates a modality to a single conceptual categoryClear delineation of verticality—that is, a differentiation of scale being observed from individually applied techniques, through modalities (therapies), to whole medical systemsRecognition of CAM as part of the general field of health care Methods: Development of the Integrated Taxonomy of Health Care (ITHC) involved three stages: Development of a precise, uniform health glossaryAnalysis of the extant taxonomiesUse of an iterative process of classifying modalities and medical systems into categories until a failure to singularly classify a modality occurred, requiring a return to the glossary and adjustment of the classifying protocol Results: A full vertical taxonomy was developed that includes and clearly differentiates between techniques, modalities, domains (clusters of similar modalities), systems of health care (coordinated care system involving multiple modalities), and integrative health care. Domains are the classical primary focus of taxonomies. The ITHC has eleven domains: chemical/substance-based work, device-based work, soft tissue–focused manipulation, skeletal manipulation, fitness/movement instruction, mind–body integration/classical somatics work, mental/emotional–based work, bio-energy work based on physical manipulation, bio-energy modulation, spiritual-based work, unique assessments. Modalities are assigned to the domains based on the primary mode of interaction with the client, according the literature of the practitioners. Conclusions: The ITHC has several strengths: little interpretation is used while successfully assigning modalities to single domains; the issue of taxonomic verticality is fully resolved; and the design fully integrates the complementary health care fields of biomedicine and CAM. PMID:21589735

Active Learning to Overcome Sample Selection Bias: Application to Photometric Variable Star Classification

NASA Astrophysics Data System (ADS)

Richards, Joseph W.; Starr, Dan L.; Brink, Henrik; Miller, Adam A.; Bloom, Joshua S.; Butler, Nathaniel R.; James, J. Berian; Long, James P.; Rice, John

2012-01-01

Despite the great promise of machine-learning algorithms to classify and predict astrophysical parameters for the vast numbers of astrophysical sources and transients observed in large-scale surveys, the peculiarities of the training data often manifest as strongly biased predictions on the data of interest. Typically, training sets are derived from historical surveys of brighter, more nearby objects than those from more extensive, deeper surveys (testing data). This sample selection bias can cause catastrophic errors in predictions on the testing data because (1) standard assumptions for machine-learned model selection procedures break down and (2) dense regions of testing space might be completely devoid of training data. We explore possible remedies to sample selection bias, including importance weighting, co-training, and active learning (AL). We argue that AL—where the data whose inclusion in the training set would most improve predictions on the testing set are queried for manual follow-up—is an effective approach and is appropriate for many astronomical applications. For a variable star classification problem on a well-studied set of stars from Hipparcos and Optical Gravitational Lensing Experiment, AL is the optimal method in terms of error rate on the testing data, beating the off-the-shelf classifier by 3.4% and the other proposed methods by at least 3.0%. To aid with manual labeling of variable stars, we developed a Web interface which allows for easy light curve visualization and querying of external databases. Finally, we apply AL to classify variable stars in the All Sky Automated Survey, finding dramatic improvement in our agreement with the ASAS Catalog of Variable Stars, from 65.5% to 79.5%, and a significant increase in the classifier's average confidence for the testing set, from 14.6% to 42.9%, after a few AL iterations.

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

Abdullah, A. H.; Adom, A. H.; Shakaff, A. Y. Md

Aromatic rice (Oryza sativa L.) is considered as the best quality premium rice. The varieties are preferred by consumers because of its preference criteria such as shape, colour, distinctive aroma and flavour. The price of aromatic rice is higher than ordinary rice due to its special needed growth condition for instance specific climate and soil. Presently, the aromatic rice quality is identified by using its key elements and isotopic variables. The rice can also be classified via Gas Chromatography Mass Spectrometry (GC-MS) or human sensory panels. However, the uses of human sensory panels have significant drawbacks such as lengthy trainingmore » time, and prone to fatigue as the number of sample increased and inconsistent. The GC–MS analysis techniques on the other hand, require detailed procedures, lengthy analysis and quite costly. This paper presents the application of in-house developed Electronic Nose (e-nose) to classify new aromatic rice varieties. The e-nose is used to classify the variety of aromatic rice based on the samples odour. The samples were taken from the variety of rice. The instrument utilizes multivariate statistical data analysis, including Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and K-Nearest Neighbours (KNN) to classify the unknown rice samples. The Leave-One-Out (LOO) validation approach is applied to evaluate the ability of KNN to perform recognition and classification of the unspecified samples. The visual observation of the PCA and LDA plots of the rice proves that the instrument was able to separate the samples into different clusters accordingly. The results of LDA and KNN with low misclassification error support the above findings and we may conclude that the e-nose is successfully applied to the classification of the aromatic rice varieties.« less

50 Years of Silent Service: Inside the CIA Library.

ERIC Educational Resources Information Center

Wright, Susan L.

1997-01-01

Explains some of the collections and operations of the library at the Central Intelligence Agency (CIA). Highlights include disseminating classified information from the classified collection, subject matter requirements, the unclassified Historical Intelligence Collection which deals with the intelligence profession, client confidentiality, and…

2 CFR 176.30 - Definitions.

Code of Federal Regulations, 2013 CFR

2013-01-01

... FOR ASSISTANCE AGREEMENTS THAT INCLUDE FUNDS UNDER THE AMERICAN RECOVERY AND REINVESTMENT ACT OF 2009.... Classified or “classified information” means any knowledge that can be communicated or any documentary... Recovery Act funds are funds made available through the appropriations of the American Recovery and...

2 CFR 176.30 - Definitions.

Code of Federal Regulations, 2012 CFR

2012-01-01

... FOR ASSISTANCE AGREEMENTS THAT INCLUDE FUNDS UNDER THE AMERICAN RECOVERY AND REINVESTMENT ACT OF 2009.... Classified or “classified information” means any knowledge that can be communicated or any documentary... Recovery Act funds are funds made available through the appropriations of the American Recovery and...

Applying six classifiers to airborne hyperspectral imagery for detecting giant reed

USDA-ARS?s Scientific Manuscript database

This study evaluated and compared six different image classifiers, including minimum distance (MD), Mahalanobis distance (MAHD), maximum likelihood (ML), spectral angle mapper (SAM), mixture tuned matched filtering (MTMF) and support vector machine (SVM), for detecting and mapping giant reed (Arundo...

22 CFR 124.10 - Nontransfer and use assurances.

Code of Federal Regulations, 2011 CFR

2011-04-01

... 124.10 Foreign Relations DEPARTMENT OF STATE INTERNATIONAL TRAFFIC IN ARMS REGULATIONS AGREEMENTS, OFF-SHORE PROCUREMENT AND OTHER DEFENSE SERVICES § 124.10 Nontransfer and use assurances. (a) Types of... Controls. With respect to all agreements involving classified articles, including classified technical data...

[Leptospirosis in animal reproduction: III. Role of the hardjo serovar in bovine leptospirosis in Rio de Janeiro, Brazil].

PubMed

Lilenbaum, W; Dos Santos, M R

1995-01-01

Four hundred and five serum samples were drawn from cows with reproductive problems which were not vaccinated against leptospirosis from 21 dairy farms. Three distinct geographic regions were determined and the farms were also classified considering the production system, based on technological, zootechnical and sanitary resources. A total of 277 positive reactions were observed, corresponding to 68.39% of the samples. The predominant serovar was hardjo, reactive on 85 samples (20.98%), predominant on nine farms and observed on 17 farms (80.95%). It was observed the predominance of hardjo in all studied regions and on properties classified as type "A" (22 samples) and type "B" (49 samples). The role of this serovar on bovine leptospirosis in Brazil compared with other countries is discussed.

Classification of atrophic mucosal patterns on Blue LASER Imaging for endoscopic diagnosis of Helicobacter pylori-related gastritis: A retrospective, observational study.

PubMed

Nishikawa, Yoshiyuki; Ikeda, Yoshio; Murakami, Hidehiro; Hori, Shin-Ichiro; Hino, Kaori; Sasaki, Chise; Nishikawa, Megumi

2018-01-01

Atrophic gastritis can be classified according to characteristic mucosal patterns observed by Blue LASER Imaging (BLI) in a medium-range to distant view. To facilitate the endoscopic diagnosis of Helicobacter pylori (HP)-related gastritis, we investigated whether atrophic mucosal patterns correlated with HP infection based on the image interpretations of three endoscopists blinded to clinical features. This study included 441 patients diagnosed as having atrophic gastritis by upper gastrointestinal endoscopy at Nishikawa Gastrointestinal Clinic between April 1, 2015 and March 31, 2016. The presence/absence of HP infection was not taken into consideration. Endoscopy was performed using a Fujifilm EG-L580NW scope. Atrophic mucosal patterns observed by BLI were classified into Spotty, Cracked and Mottled. Image interpretation results were that 89, 122 and 228 patients had the Spotty, Cracked and Mottled patterns, respectively, and 2 patients an undetermined pattern. Further analyses were performed on 439 patients, excluding the 2 with undetermined patterns. The numbers of patients testing negative/positive for HP infection in the Spotty, Cracked and Mottled pattern groups were 12/77, 105/17, and 138/90, respectively. The specificity, positive predictive value and positive likelihood ratio for endoscopic diagnosis with positive HP infection based on the Spotty pattern were 95.3%, 86.5% and 8.9, respectively. In all patients with the Spotty pattern before HP eradication, the Cracked pattern was observed on subsequent post-eradication endoscopy. The Spotty pattern may represent the presence of HP infection, the Cracked pattern, a post-inflammatory change as seen after HP eradication, and the Mottled pattern, intestinal metaplasia.

Multiple disturbances classifier for electric signals using adaptive structuring neural networks

NASA Astrophysics Data System (ADS)

Lu, Yen-Ling; Chuang, Cheng-Long; Fahn, Chin-Shyurng; Jiang, Joe-Air

2008-07-01

This work proposes a novel classifier to recognize multiple disturbances for electric signals of power systems. The proposed classifier consists of a series of pipeline-based processing components, including amplitude estimator, transient disturbance detector, transient impulsive detector, wavelet transform and a brand-new neural network for recognizing multiple disturbances in a power quality (PQ) event. Most of the previously proposed methods usually treated a PQ event as a single disturbance at a time. In practice, however, a PQ event often consists of various types of disturbances at the same time. Therefore, the performances of those methods might be limited in real power systems. This work considers the PQ event as a combination of several disturbances, including steady-state and transient disturbances, which is more analogous to the real status of a power system. Six types of commonly encountered power quality disturbances are considered for training and testing the proposed classifier. The proposed classifier has been tested on electric signals that contain single disturbance or several disturbances at a time. Experimental results indicate that the proposed PQ disturbance classification algorithm can achieve a high accuracy of more than 97% in various complex testing cases.

SCOPE - Stellar Classification Online Public Exploration

NASA Astrophysics Data System (ADS)

Harenberg, Steven

2010-01-01

The Astronomical Photographic Data Archive (APDA) has been established to be the primary North American archive for the collections of astronomical photographic plates. Located at the Pisgah Astronomical Research Institute (PARI) in Rosman, NC, the archive contains hundreds of thousands stellar spectra, many of which have never before been classified. To help classify the vast number of stars, the public is invited to participate in a distributed computing online environment called Stellar Classification Online - Public Exploration (SCOPE). Through a website, the participants will have a tutorial on stellar spectra and practice classifying. After practice, the participants classify spectra on photographic plates uploaded online from APDA. These classifications will be recorded in a database where the results from many users will be statistically analyzed. Stars with known spectral types will be included to test the reliability of classifications. The process of building the database of stars from APDA, which the citizen scientist will be able to classify, includes: scanning the photographic plates, orienting the plate to correct for the change in right ascension/declination using Aladin, stellar HD catalog identification using Simbad, marking the boundaries for each spectrum, and setting up the image for use on the website. We will describe the details of this process.

Enhancement of gesture recognition for contactless interface using a personalized classifier in the operating room.

PubMed

Cho, Yongwon; Lee, Areum; Park, Jongha; Ko, Bemseok; Kim, Namkug

2018-07-01

Contactless operating room (OR) interfaces are important for computer-aided surgery, and have been developed to decrease the risk of contamination during surgical procedures. In this study, we used Leap Motion™, with a personalized automated classifier, to enhance the accuracy of gesture recognition for contactless interfaces. This software was trained and tested on a personal basis that means the training of gesture per a user. We used 30 features including finger and hand data, which were computed, selected, and fed into a multiclass support vector machine (SVM), and Naïve Bayes classifiers and to predict and train five types of gestures including hover, grab, click, one peak, and two peaks. Overall accuracy of the five gestures was 99.58% ± 0.06, and 98.74% ± 3.64 on a personal basis using SVM and Naïve Bayes classifiers, respectively. We compared gesture accuracy across the entire dataset and used SVM and Naïve Bayes classifiers to examine the strength of personal basis training. We developed and enhanced non-contact interfaces with gesture recognition to enhance OR control systems. Copyright © 2018 Elsevier B.V. All rights reserved.

Dark matter candidates

NASA Technical Reports Server (NTRS)

Turner, Michael S.

1989-01-01

The types of particles which may provide the nonluminous mass required by big-bang cosmological models are listed and briefly characterized. The observational evidence for the existence of dark matter (outweighing the luminous component by at least a factor of 10) is reviewed; the theoretical arguments favoring mainly nonbaryonic dark matter are summarized; and particular attention is given to weakly interacting massive particles (WIMPs) remaining as relics from the early universe. The WIMPs are classified as thermal relics (heavy stable neutrinos and lighter neutralinos), asymmetric relics (including baryons), nonthermal relics (superheavy magnetic monopoles, axions, and soliton stars), and truly exotic relics (relativistic debris or vacuum energy). Explanations for the current apparent baryon/exotica ratio of about 0.1 in different theoretical scenarios are considered, and the problems of experimental and/or observational dark-matter detection are examined.

Probing the use of spectroscopy to determine the meteoritic analogues of meteors

NASA Astrophysics Data System (ADS)

Drouard, A.; Vernazza, P.; Loehle, S.; Gattacceca, J.; Vaubaillon, J.; Zanda, B.; Birlan, M.; Bouley, S.; Colas, F.; Eberhart, M.; Hermann, T.; Jorda, L.; Marmo, C.; Meindl, A.; Oefele, R.; Zamkotsian, F.; Zander, F.

2018-05-01

Context. Determining the source regions of meteorites is one of the major goals of current research in planetary science. Whereas asteroid observations are currently unable to pinpoint the source regions of most meteorite classes, observations of meteors with camera networks and the subsequent recovery of the meteorite may help make progress on this question. The main caveat of such an approach, however, is that the recovery rate of meteorite falls is low (<20%), implying that the meteoritic analogues of at least 80% of the observed falls remain unknown. Aims: Spectroscopic observations of incoming bolides may have the potential to mitigate this problem by classifying the incoming meteoritic material. Methods: To probe the use of spectroscopy to determine the meteoritic analogues of incoming bolides, we collected emission spectra in the visible range (320-880 nm) of five meteorite types (H, L, LL, CM, and eucrite) acquired in atmospheric entry-like conditions in a plasma wind tunnel at the Institute of Space Systems (IRS) at the University of Stuttgart (Germany). A detailed spectral analysis including a systematic line identification and mass ratio determinations (Mg/Fe, Na/Fe) was subsequently performed on all spectra. Results: It appears that spectroscopy, via a simple line identification, allows us to distinguish the three main meteorite classes (chondrites, achondrites and irons) but it does not have the potential to distinguish for example an H chondrite from a CM chondrite. Conclusions: The source location within the main belt of the different meteorite classes (H, L, LL, CM, CI, etc.) should continue to be investigated via fireball observation networks. Spectroscopy of incoming bolides only marginally helps precisely classify the incoming material (iron meteorites only). To reach a statistically significant sample of recovered meteorites along with accurate orbits (>100) within a reasonable time frame (10-20 years), the optimal solution may be the spatial extension of existing fireball observation networks. The movie associated to this article is available at http://www.aanda.org

Upward electrical discharges observed above Tropical Depression Dorian

PubMed Central

Liu, Ningyu; Spiva, Nicholas; Dwyer, Joseph R.; Rassoul, Hamid K.; Free, Dwayne; Cummer, Steven A.

2015-01-01

Observation of upward electrical discharges from thunderstorms has been sporadically reported in the scientific literature. According to their terminal altitudes, they are classified as starters (20–30 km), jets (40–50 km) and gigantic jets (70–90 km). They not only have a significant impact on the occupied atmospheric volumes but also electrically couple different atmospheric regions. However, as they are rare and unpredictable, our knowledge of them has been built on observations that typically record only one type of such discharges. Here we report a close-distance observation of seven upward discharges including one starter, two jets and four gigantic jets above Tropical Depression Dorian. Our optical and electromagnetic data indicate that all events are of negative polarity, suggesting they are initiated in the same thundercloud charge region. The data also indicate that the lightning-like discharge channel can extend above thunderclouds by about 30 km, but the discharge does not emit low-frequency electromagnetic radiation as normal lightning. PMID:25607345

Arecibo Radar Observations of Near-Earth Asteroids

NASA Astrophysics Data System (ADS)

Rivera-Valentin, Edgard G.; Taylor, Patrick A.; Virkki, Anne; Saran Bhiravarasu, Sriram; Venditti, Flaviane; Zambrano-Marin, Luisa Fernanda; Aponte-Hernandez, Betzaida

2017-10-01

The Arecibo S-Band (2.38 GHz, 12.6 cm; 1 MW) planetary radar system at the 305-m William E. Gordon Telescope in Arecibo, Puerto Rico is the most active, most powerful, and most sensitive planetary radar facility in the world. As such, Arecibo is vital for post-discovery characterization and orbital refinement of near-Earth asteroids. Since August 2016, the program has observed 100 near-Earth asteroids (NEAs), of which 38 are classified as potentially hazardous to Earth and 31 are compliant with the NASA Near-Earth Object Human Space Flight Accessible Targets Study (NHATS). Arecibo observations are critical for identifying NEAs that may be on a collision course with Earth in addition to providing detailed physical characterization of the objects themselves in terms of size, shape, spin, and surface properties, which are valuable for assessing impact mitigation strategies. Here, we will present a sampling of the asteroid zoo observed by Arecibo, including press-noted asteroids 2014 JO25 and the (163693) Atira binary system.

Plausibility assessment of a 2-state self-paced mental task-based BCI using the no-control performance analysis.

PubMed

Faradji, Farhad; Ward, Rabab K; Birch, Gary E

2009-06-15

The feasibility of having a self-paced brain-computer interface (BCI) based on mental tasks is investigated. The EEG signals of four subjects performing five mental tasks each are used in the design of a 2-state self-paced BCI. The output of the BCI should only be activated when the subject performs a specific mental task and should remain inactive otherwise. For each subject and each task, the feature coefficient and the classifier that yield the best performance are selected, using the autoregressive coefficients as the features. The classifier with a zero false positive rate and the highest true positive rate is selected as the best classifier. The classifiers tested include: linear discriminant analysis, quadratic discriminant analysis, Mahalanobis discriminant analysis, support vector machine, and radial basis function neural network. The results show that: (1) some classifiers obtained the desired zero false positive rate; (2) the linear discriminant analysis classifier does not yield acceptable performance; (3) the quadratic discriminant analysis classifier outperforms the Mahalanobis discriminant analysis classifier and performs almost as well as the radial basis function neural network; and (4) the support vector machine classifier has the highest true positive rates but unfortunately has nonzero false positive rates in most cases.

Objective categorization of interferential tear film lipid layer pattern: validation of the technique

NASA Astrophysics Data System (ADS)

García-Resúa, C.; Giráldez, M. J.; Barreira, N.; Penedo, M. G.; Yebra-Pimentel, E.

2011-05-01

Purpose: The lipid layer of the tear film limits evaporation during the inter-blink interval and also affects tear stability. This study was designed to validate a new software application designed to characterize the tear film lipid layer through texture and colour pattern recognition. Methods: Using the Tearscope-plus (slit lamp magnification 200X), the lipid layer was examined in 105 healthy young adults and interference photographs acquired with a Topcon DV-3 digital camera. The photographs were classified by the new software and by 2 further observers (observer 1 and observer 2) with experience in examining the eye surface. Results: Strong correlation was detected between the categories determined by the new application, observer 1 and observer 2 (Cramer's V, from 0.81 to 0.87, p<0.001). Best agreement (96.2%) was noted between the new method and observers 1 and 2 for recognizing meshwork patterns, whereas observers 1 and 2 showed greatest correspondence when classifying colour fringe patterns. Conclusions: The new application can objectively categorize LLPs using the Tearscope-plus.

Comparison of Hybrid Classifiers for Crop Classification Using Normalized Difference Vegetation Index Time Series: A Case Study for Major Crops in North Xinjiang, China

PubMed Central

Hao, Pengyu; Wang, Li; Niu, Zheng

2015-01-01

A range of single classifiers have been proposed to classify crop types using time series vegetation indices, and hybrid classifiers are used to improve discriminatory power. Traditional fusion rules use the product of multi-single classifiers, but that strategy cannot integrate the classification output of machine learning classifiers. In this research, the performance of two hybrid strategies, multiple voting (M-voting) and probabilistic fusion (P-fusion), for crop classification using NDVI time series were tested with different training sample sizes at both pixel and object levels, and two representative counties in north Xinjiang were selected as study area. The single classifiers employed in this research included Random Forest (RF), Support Vector Machine (SVM), and See 5 (C 5.0). The results indicated that classification performance improved (increased the mean overall accuracy by 5%~10%, and reduced standard deviation of overall accuracy by around 1%) substantially with the training sample number, and when the training sample size was small (50 or 100 training samples), hybrid classifiers substantially outperformed single classifiers with higher mean overall accuracy (1%~2%). However, when abundant training samples (4,000) were employed, single classifiers could achieve good classification accuracy, and all classifiers obtained similar performances. Additionally, although object-based classification did not improve accuracy, it resulted in greater visual appeal, especially in study areas with a heterogeneous cropping pattern. PMID:26360597

Can single classifiers be as useful as model ensembles to produce benthic seabed substratum maps?

NASA Astrophysics Data System (ADS)

Turner, Joseph A.; Babcock, Russell C.; Hovey, Renae; Kendrick, Gary A.

2018-05-01

Numerous machine-learning classifiers are available for benthic habitat map production, which can lead to different results. This study highlights the performance of the Random Forest (RF) classifier, which was significantly better than Classification Trees (CT), Naïve Bayes (NB), and a multi-model ensemble in terms of overall accuracy, Balanced Error Rate (BER), Kappa, and area under the curve (AUC) values. RF accuracy was often higher than 90% for each substratum class, even at the most detailed level of the substratum classification and AUC values also indicated excellent performance (0.8-1). Total agreement between classifiers was high at the broadest level of classification (75-80%) when differentiating between hard and soft substratum. However, this sharply declined as the number of substratum categories increased (19-45%) including a mix of rock, gravel, pebbles, and sand. The model ensemble, produced from the results of all three classifiers by majority voting, did not show any increase in predictive performance when compared to the single RF classifier. This study shows how a single classifier may be sufficient to produce benthic seabed maps and model ensembles of multiple classifiers.

Grounding by Attention Simulation in Peripersonal Space: Pupils Dilate to Pinch Grip But Not Big Size Nominal Classifier.

PubMed

Lobben, Marit; Bochynska, Agata

2018-03-01

Grammatical categories represent implicit knowledge, and it is not known if such abstract linguistic knowledge can be continuously grounded in real-life experiences, nor is it known what types of mental states can be simulated. A former study showed that attention bias in peripersonal space (PPS) affects reaction times in grammatical congruency judgments of nominal classifiers, suggesting that simulated semantics may include reenactment of attention. In this study, we contrasted a Chinese nominal classifier used with nouns denoting pinch grip objects with a classifier for nouns with big object referents in a pupil dilation experiment. Twenty Chinese native speakers read grammatical and ungrammatical classifier-noun combinations and made grammaticality judgment while their pupillary responses were measured. It was found that their pupils dilated significantly more to the pinch grip classifier than to the big object classifier, indicating attention simulation in PPS. Pupil dilations were also significantly larger with congruent trials on the whole than in incongruent trials, but crucially, congruency and classifier semantics were independent of each other. No such effects were found in controls. Copyright © 2017 Cognitive Science Society, Inc.

Therapeutic effect of immunoadsorption and subsequent immunoglobulin substitution in patients with dilated cardiomyopathy: Results from the observational prospective Bad Berka Registry.

PubMed

Ohlow, Marc-Alexander; Brunelli, Michele; Schreiber, Matthias; Lauer, Bernward

2017-02-01

Elimination of cardiac autoantibodies, frequently detected in patients with dilated cardiomyopathy (DCM), with immunoadsorption (IA) improves functional capacity and left ventricular (LV) function. This study aimed to prospectively address this issue in a large cohort of unselected patients. Consecutive patients undergoing IA followed by IgG substitution were included. Clinical and echocardiographic parameters were assessed at baseline (BL) and 12-month follow-up (FU). Patients were classified as IA responders when ≥2 of the following criteria were achieved: improvement in the Minnesota Living with Heart Failure Questionnaire (MLHFQ) ≥5 points, symptoms [≥1 New York Heart Association (NYHA) class], LV ejection fraction (EF) ≥10% or decrease in LV end-diastolic diameter (EDD) ≥10%, or N-terminal pro B-type natriuretic peptide (NT-pro-BNP) ≥50%. 93 patients (median age 61 years, LVEF 30%, duration of symptoms 14 months, 87% in NYHA class III/IV, >90% treated with β-blocker/angiotensin-converting enzyme inhibitor) were included. When the entire cohort was analyzed, a significant improvement in MLHFQ (50 vs. 26 points), NYHA-class (median 3.0 vs. 2.0), LVEF (30% vs. 38%), LVEDD (62 vs. 59mm), NT-pro-BNP (892 vs. 523pg/ml) was observed at FU (p<0.05 for all). 48% (n=43) were classified as responders. Those were characterized by a shorter disease duration (11 vs. 22 months), larger BL LVEDD (64 vs. 60mm), presence of >1 viral genome, and higher values of mononuclear inflammatory cells at endomyocardial biopsy. Sixteen (17.2%) patients experienced IA related complications. A positive response is observed in 48% of inflammatory DCM patients undergoing IA, and this translates into a significant improvement in clinical and echocardiographic parameters. Copyright © 2016 Japanese College of Cardiology. Published by Elsevier Ltd. All rights reserved.

Spectroscopic Classification of PS16ccj with Mayall/KOSMOS

NASA Astrophysics Data System (ADS)

Pan, Y.-C.; Foley, R. J.; Jha, S. W.; Rest, A.; Scolnic, D.

2016-05-01

We report the classification of PS16ccj from spectroscopic observation with KOSMOS on the Mayall telescope. The observation was made on 2016 May 05 UT. We classify PS16ccj as a SN Ia near maximum light.

Deep learning classification in asteroseismology using an improved neural network: results on 15 000 Kepler red giants and applications to K2 and TESS data

NASA Astrophysics Data System (ADS)

Hon, Marc; Stello, Dennis; Yu, Jie

2018-05-01

Deep learning in the form of 1D convolutional neural networks have previously been shown to be capable of efficiently classifying the evolutionary state of oscillating red giants into red giant branch stars and helium-core burning stars by recognizing visual features in their asteroseismic frequency spectra. We elaborate further on the deep learning method by developing an improved convolutional neural network classifier. To make our method useful for current and future space missions such as K2, TESS, and PLATO, we train classifiers that are able to classify the evolutionary states of lower frequency resolution spectra expected from these missions. Additionally, we provide new classifications for 8633 Kepler red giants, out of which 426 have previously not been classified using asteroseismology. This brings the total to 14983 Kepler red giants classified with our new neural network. We also verify that our classifiers are remarkably robust to suboptimal data, including low signal-to-noise and incorrect training truth labels.

Identifying and classifying hyperostosis frontalis interna via computerized tomography.

PubMed

May, Hila; Peled, Nathan; Dar, Gali; Hay, Ori; Abbas, Janan; Masharawi, Youssef; Hershkovitz, Israel

2010-12-01

The aim of this study was to recognize the radiological characteristics of hyperostosis frontalis interna (HFI) and to establish a valid and reliable method for its identification and classification. A reliability test was carried out on 27 individuals who had undergone a head computerized tomography (CT) scan. Intra-observer reliability was obtained by examining the images three times, by the same researcher, with a 2-week interval between each sample ranking. The inter-observer test was performed by three independent researchers. A validity test was carried out using two methods for identifying and classifying HFI: 46 cadaver skullcaps were ranked twice via computerized tomography scans and then by direct observation. Reliability and validity were calculated using Kappa test (SPSS 15.0). Reliability tests of ranking HFI via CT scans demonstrated good results (K > 0.7). As for validity, a very good consensus was obtained between the CT and direct observation, when moderate and advanced types of HFI were present (K = 0.82). The suggested classification method for HFI, using CT, demonstrated a sensitivity of 84%, specificity of 90.5%, and positive predictive value of 91.3%. In conclusion, volume rendering is a reliable and valid tool for identifying HFI. The suggested three-scale classification is most suitable for radiological diagnosis of the phenomena. Considering the increasing awareness of HFI as an early indicator of a developing malady, this study may assist radiologists in identifying and classifying the phenomena.

Dates fruits classification using SVM

NASA Astrophysics Data System (ADS)

Alzu'bi, Reem; Anushya, A.; Hamed, Ebtisam; Al Sha'ar, Eng. Abdelnour; Vincy, B. S. Angela

2018-04-01

In this paper, we used SVM in classifying various types of dates using their images. Dates have interesting different characteristics that can be valuable to distinguish and determine a particular date type. These characteristics include shape, texture, and color. A system that achieves 100% accuracy was built to classify the dates which can be eatable and cannot be eatable. The built system helps the food industry and customer in classifying dates depending on specific quality measures giving best performance with specific type of dates.

Release of (and lessons learned from mining) a pioneering large toxicogenomics database.

PubMed

Sandhu, Komal S; Veeramachaneni, Vamsi; Yao, Xiang; Nie, Alex; Lord, Peter; Amaratunga, Dhammika; McMillian, Michael K; Verheyen, Geert R

2015-07-01

We release the Janssen Toxicogenomics database. This rat liver gene-expression database was generated using Codelink microarrays, and has been used over the past years within Janssen to derive signatures for multiple end points and to classify proprietary compounds. The release consists of gene-expression responses to 124 compounds, selected to give a broad coverage of liver-active compounds. A selection of the compounds were also analyzed on Affymetrix microarrays. The release includes results of an in-house reannotation pipeline to Entrez gene annotations, to classify probes into different confidence classes. High confidence unambiguously annotated probes were used to create gene-level data which served as starting point for cross-platform comparisons. Connectivity map-based similarity methods show excellent agreement between Codelink and Affymetrix runs of the same samples. We also compared our dataset with the Japanese Toxicogenomics Project and observed reasonable agreement, especially for compounds with stronger gene signatures. We describe an R-package containing the gene-level data and show how it can be used for expression-based similarity searches. Comparing the same biological samples run on the Affymetrix and the Codelink platform, good correspondence is observed using connectivity mapping approaches. As expected, this correspondence is smaller when the data are compared with an independent dataset such as TG-GATE. We hope that this collection of gene-expression profiles will be incorporated in toxicogenomics pipelines of users.

Maternal sensitivity and infant attachment security in Korea: cross-cultural validation of the Strange Situation.

PubMed

Jin, Mi Kyoung; Jacobvitz, Deborah; Hazen, Nancy; Jung, Sung Hoon

2012-01-01

The present study sought to analyze infant and maternal behavior both during the Strange Situation Procedure (SSP) and a free play session in a Korean sample (N = 87) to help understand whether mother-infant attachment relationships are universal or culture-specific. Distributions of attachment classifications in the Korean sample were compared with a cross-national sample. Behavior of mothers and infants following the two separation episodes in the SSP, including mothers' proximity to their infants and infants' approach to the caregiver, was also observed, as was the association between maternal sensitivity observed during free play session and infant security. The percentage of Korean infants classified as secure versus insecure mirrored the global distribution, however, only one Korean baby was classified as avoidant. Following the separation episodes in the Strange Situation, Korean mothers were more likely than mothers in Ainsworth's Baltimore sample to approach their babies immediately and sit beside them throughout the reunion episodes, even when their babies were no longer distressed. Also, Korean babies less often approached their mothers during reunions than did infants in the Baltimore sample. Finally, the link between maternal sensitivity and infant security was significant. The findings support the idea that the basic secure base function of attachment is universal and the SSP is a valid measure of secure attachment, but cultural differences in caregiving may result in variations in how this function is manifested.

Identifying Effectiveness Criteria for Internet Payment Systems.

ERIC Educational Resources Information Center

Shon, Tae-Hwan; Swatman, Paula M. C.

1998-01-01

Examines Internet payment systems (IPS): third-party, card, secure Web server, electronic token, financial electronic data interchange (EDI), and micropayment based. Reports the results of a Delphi survey of experts identifying and classifying IPS effectiveness criteria and classifying types of IPS providers. Includes the survey invitation letter…

46 CFR 58.10-15 - Gas turbine installations.

Code of Federal Regulations, 2012 CFR

2012-10-01

... classified location. 1 1 Sections 108.171 to 108.175 of this Chapter define classified locations for mobile... are employed, data concerning their properties, including high temperature strength data, where... other than pipe is employed, the drawings and design data shall be submitted to substantiate suitability...

46 CFR 58.10-15 - Gas turbine installations.

Code of Federal Regulations, 2014 CFR

2014-10-01

... classified location. 1 1 Sections 108.171 to 108.175 of this chapter define classified locations for mobile... are employed, data concerning their properties, including high temperature strength data, where... other than pipe is employed, the drawings and design data shall be submitted to substantiate suitability...

46 CFR 58.10-15 - Gas turbine installations.

Code of Federal Regulations, 2013 CFR

2013-10-01

... classified location. 1 1 Sections 108.171 to 108.175 of this chapter define classified locations for mobile... are employed, data concerning their properties, including high temperature strength data, where... other than pipe is employed, the drawings and design data shall be submitted to substantiate suitability...

Association of Malignancy Prevalence With Test Properties and Performance of the Gene Expression Classifier in Indeterminate Thyroid Nodules.

PubMed

Al-Qurayshi, Zaid; Deniwar, Ahmed; Thethi, Tina; Mallik, Tilak; Srivastav, Sudesh; Murad, Fadi; Bhatia, Parisha; Moroz, Krzysztof; Sholl, Andrew B; Kandil, Emad

2017-04-01

It is crucial for clinicians to know the malignancy prevalence within each indeterminate cytologic category to estimate the performance of the gene expression classifier (GEC). To examine the variability in the performance of the GEC. This retrospective cohort study of patients with Bethesda category III and IV thyroid nodules used single-institution data from January 1, 2013, through February 29, 2016. Expected negative predictive value (NPV) was calculated by adopting published sensitivity and specificity. Observed NPV was calculated based on the true-negative rate. Outcomes were compared with pooled data from 11 studies published January 1, 2010, to January 31, 2016. A total of 145 patients with 154 thyroid nodules were included in the study (mean [SD] age, 56.0 [16.2] years; 106 females [73.1%]). Malignancy prevalence was 45%. On the basis of this prevalence, the expected NPV is 85% and the observed NPV is 69%. If the prevalence is assumed to be 25%, the expected NPV would be 94%, whereas the observed NPV would be 85%. Pooled data analysis of 11 studies comprising 1303 participants revealed a malignancy prevalence of 31% (95% CI, 29%-34%) and a pooled NPV of 92% (95% CI, 87%-96%). In this study, variability in the performance of the GEC was not solely a function of malignancy prevalence and may have been attributable to intrinsic variability of the test sensitivity and specificity. The utility of the GEC in practice is elusive because of this variability. A better definition of the GEC's intrinsic properties is needed.

Learning accurate and concise naïve Bayes classifiers from attribute value taxonomies and data

PubMed Central

Kang, D.-K.; Silvescu, A.; Honavar, V.

2009-01-01

In many application domains, there is a need for learning algorithms that can effectively exploit attribute value taxonomies (AVT)—hierarchical groupings of attribute values—to learn compact, comprehensible and accurate classifiers from data—including data that are partially specified. This paper describes AVT-NBL, a natural generalization of the naïve Bayes learner (NBL), for learning classifiers from AVT and data. Our experimental results show that AVT-NBL is able to generate classifiers that are substantially more compact and more accurate than those produced by NBL on a broad range of data sets with different percentages of partially specified values. We also show that AVT-NBL is more efficient in its use of training data: AVT-NBL produces classifiers that outperform those produced by NBL using substantially fewer training examples. PMID:20351793

Clinical performance of an objective methodology to categorize tear film lipid layer patterns

NASA Astrophysics Data System (ADS)

Garcia-Resua, Carlos; Pena-Verdeal, Hugo; Giraldez, Maria J.; Yebra-Pimentel, Eva

2017-08-01

Purpose: To validate the performance of a new objective application designated iDEAS (Dry Eye Assessment System) to categorize different zones of lipid layer patterns (LLPs) in one image. Material and methods: Using the Tearscopeplus and a digital camera attached to a slit-lamp, 50 images were captured and analyzed by 4 experienced optometrists. In each image the observers outlined tear film zones that they clearly identified as a specific LLP. Further, the categorization made by the 4 optometrists (called observer 1, 2, 3 and 4) was compared with the automatic system included in iDEAS (5th observer). Results: In general, observer 3 classified worse than all observers (observers 1, 2, 4 and automatic application, Wilcoxon test, <0.05). The automatic system behaved similar to the remaining three observers (observer 1, 2 and 4) showing differences only for Open meshwork LLP when comparing with observer 4 (Wilcoxon test, p=0.02). For the remaining two observers (observer 1 and 2) there was not found statistical differences (Wilcoxon test, >0.05). Furthermore, we obtained a set of photographs per LLP category for which all optometrists showed agreement by using the new tool. After examining them, we detected the more characteristic features for each LLP to enhance the description of the patterns implemented by Guillon. Conclusions: The automatic application included in the iDEAS framework is able to provide zones similar to the annotations made by experienced optometrists. Thus, the manual process done by experts can be automated with the benefits of being unaffected by subjective factors.

Multivariate analysis and visualization of soil quality data for no-till systems.

PubMed

Villamil, M B; Miguez, F E; Bollero, G A

2008-01-01

To evidence the multidimensionality of the soil quality concept, we propose the use of data visualization as a tool for exploratory data analyses, model building, and diagnostics. Our objective was to establish the best edaphic indicators for assessing soil quality in four no-till systems with regard to functioning as a medium for crop production and nutrient cycling across two Illinois locations. The compared situations were no-till corn-soybean rotations including either winter fallowing (C/S) or cover crops of rye (Secale cereale; C-R/S-R), hairy vetch (Vicia villosa; C-R/S-V), or their mixture (C-R/S-VR). The dataset included the variables bulk density (BD), penetration resistance (PR), water aggregate stability (WAS), soil reaction (pH), and the contents of soil organic matter (SOM), total nitrogen (TN), soil nitrates (NO(3)-N), and available phosphorus (P). Interactive data visualization along with canonical discriminant analysis (CDA) allowed us to show that WAS, BD, and the contents of P, TN, and SOM have the greatest potential as soil quality indicators in no-till systems in Illinois. It was more difficult to discriminate among WCC rotations than to separate these from C/S, considerably inflating the error rate associated with CDA. We predict that observations of no-till C/S will be classified correctly 51% of the time, while observations of no-till WCC rotations will be classified correctly 74% of the time. High error rates in CDA underscore the complexity of no-till systems and the need in this area for more long-term studies with larger datasets to increase accuracy to acceptable levels.

Cognitive reserve as a predictor of two year neuropsychological performance in early onset first-episode schizophrenia.

PubMed

de la Serna, Elena; Andrés-Perpiñá, Susana; Puig, Olga; Baeza, Inmaculada; Bombin, Igor; Bartrés-Faz, David; Arango, Celso; Gonzalez-Pinto, Ana; Parellada, Mara; Mayoral, María; Graell, Montserrat; Otero, Soraya; Guardia, Joan; Castro-Fornieles, Josefina

2013-01-01

The concept of cognitive reserve (CR) has been defined as individual differences in the efficient utilization of brain networks which allow some people to cope better than others with brain pathology. CR has been developed mainly in the field of aging and dementia after it was observed that there appears to be no direct relationship between the degree of brain pathology and the severity of clinical manifestations of this damage. The present study applies the concept of CR to a sample of children and adolescents with a first episode of schizophrenia, aiming to assess the possible influence of CR on neuropsychological performance after two year follow-up, controlling for the influence of clinical psychopathology. 35 patients meeting DSM-IV criteria for schizophrenia or schizoaffective disorder (SSD) and 98 healthy controls (HC) matched for age and gender were included. CR was assessed at baseline, taking into account premorbid IQ, educational-occupational level and leisure activities. Clinical and neuropsychological assessments were completed by all patients at two year follow-up. The CR proxy was able to predict working memory and attention at two year follow-up. Verbal memory and cognitive flexibility were not predicted by any of the variables included in the regression model. The SSD group obtained lower scores than HC on CR. CR measures correctly classified 79.8% of the sample as being SSD or HC. Lower scores on CR were observed in SSD than in HC and the CR measure correctly classified a high percentage of the sample into the two groups. CR may predict SSD performance on working memory and attention tasks. Copyright © 2012 Elsevier B.V. All rights reserved.

FT-Raman and chemometric tools for rapid determination of quality parameters in milk powder: Classification of samples for the presence of lactose and fraud detection by addition of maltodextrin.

PubMed

Rodrigues Júnior, Paulo Henrique; de Sá Oliveira, Kamila; de Almeida, Carlos Eduardo Rocha; De Oliveira, Luiz Fernando Cappa; Stephani, Rodrigo; Pinto, Michele da Silva; de Carvalho, Antônio Fernandes; Perrone, Ítalo Tuler

2016-04-01

FT-Raman spectroscopy has been explored as a quick screening method to evaluate the presence of lactose and identify milk powder samples adulterated with maltodextrin (2.5-50% w/w). Raman measurements can easily differentiate samples of milk powder, without the need for sample preparation, while traditional quality control methods, including high performance liquid chromatography, are cumbersome and slow. FT-Raman spectra were obtained from samples of whole lactose and low-lactose milk powder, both without and with addition of maltodextrin. Differences were observed between the spectra involved in identifying samples with low lactose content, as well as adulterated samples. Exploratory data analysis using Raman spectroscopy and multivariate analysis was also developed to classify samples with PCA and PLS-DA. The PLS-DA models obtained allowed to correctly classify all samples. These results demonstrate the utility of FT-Raman spectroscopy in combination with chemometrics to infer about the quality of milk powder. Copyright © 2015 Elsevier Ltd. All rights reserved.

Functional deficit of the medial prefrontal cortex during emotional sentence attribution in schizophrenia.

PubMed

Razafimandimby, Annick; Hervé, Pierre-Yves; Marzloff, Vincent; Brazo, Perrine; Tzourio-Mazoyer, Nathalie; Dollfus, Sonia

2016-12-01

Functional brain imaging research has already demonstrated that patients with schizophrenia had difficulties with emotion processing, namely in facial emotion perception and emotional prosody. However, the moderating effect of social context and the boundary of perceptual categories of emotion attribution remain unclear. This study investigated the neural bases of emotional sentence attribution in schizophrenia. Twenty-one schizophrenia patients and 25 healthy subjects underwent an event-related functional magnetic resonance imaging paradigm including two tasks: one to classify sentences according to their emotional content, and the other to classify neutral sentences according to their grammatical person. First, patients showed longer response times as compared to controls only during the emotion attribution task. Second, patients with schizophrenia showed reduction of activation in bilateral auditory areas irrespective of the presence of emotions. Lastly, during emotional sentences attribution, patients displayed less activation than controls in the medial prefrontal cortex (mPFC). We suggest that the functional abnormality observed in the mPFC during the emotion attribution task could provide a biological basis for social cognition deficits in patients with schizophrenia. Copyright © 2016 Elsevier B.V. All rights reserved.

A Population-Based Assessment of the Agreement Between Grading of Goniophotographic Images and Gonioscopy in the Chinese-American Eye Study (CHES)

PubMed Central

Murakami, Yohko; Wang, Dandan; Burkemper, Bruce; Lin, Shan C.; Varma, Rohit

2016-01-01

Purpose To compare grading of goniophotographic images and gonioscopy in assessing the iridocorneal angle. Methods In a population-based, cross-sectional study, participants underwent gonioscopy and goniophotographic imaging during the same visit. The iridocorneal angle was classified as closed if the posterior trabecular meshwork could not be seen. A single masked observer graded the goniophotographic images, and each eye was classified as having angle closure based on the number of closed quadrants. Agreement between the methods was analyzed by calculating kappa (κ) and first-order agreement coefficient (AC1) statistics and comparison of area under receiver operating characteristic curves (AUC). Results A total of 4149 Chinese Americans (3994 eyes) were included in this study. The agreement for angle closure diagnosis between gonioscopy and EyeCam was moderate to excellent (κ = 0.60, AC1 0.90, AUC 0.76–0.80). Conclusions Detection of iridocorneal angle closure based on goniophotographic imaging shows moderate to very good agreement with angle closure assessment using gonioscopy. PMID:27571018

CALIPSO Satellite Lidar Identification Of Elevated Dust Over Australia Compared With Air Quality Model PM60 Forecasts

NASA Technical Reports Server (NTRS)

Young, Stuart A.; Vaughan, Mark; Omar, Ali; Liu, Zhaoyan; Lee, Sunhee; Hu, Youngxiang; Cope, Martin

2008-01-01

Global measurements of the vertical distribution of clouds and aerosols have been recorded by the lidar on board the CALIPSO (Cloud Aerosol Lidar Infrared Pathfinder Satellite Observations) satellite since June 2006. Such extensive, height-resolved measurements provide a rare and valuable opportunity for developing, testing and validating various atmospheric models, including global climate, numerical weather prediction, chemical transport and air quality models. Here we report on the initial results of an investigation into the performance of the Australian Air Quality Forecast System (AAQFS) model in forecasting the distribution of elevated dust over the Australian region. The model forecasts of PM60 dust distribution are compared with the CALIPSO lidar Vertical Feature Mask (VFM) data product. The VFM classifies contiguous atmospheric regions of enhanced backscatter as either cloud or aerosols. Aerosols are further classified into six subtypes. By comparing forecast PM60 concentration profiles to the spatial distribution of dust reported in the CALIPSO VFM, we can assess the model s ability to predict the occurrence and the vertical and horizontal extents of dust events within the study area.

Discrimination of premalignant lesions and cancer tissues from normal gastric tissues using Raman spectroscopy

NASA Astrophysics Data System (ADS)

Luo, Shuwen; Chen, Changshui; Mao, Hua; Jin, Shaoqin

2013-06-01

The feasibility of early detection of gastric cancer using near-infrared (NIR) Raman spectroscopy (RS) by distinguishing premalignant lesions (adenomatous polyp, n=27) and cancer tissues (adenocarcinoma, n=33) from normal gastric tissues (n=45) is evaluated. Significant differences in Raman spectra are observed among the normal, adenomatous polyp, and adenocarcinoma gastric tissues at 936, 1003, 1032, 1174, 1208, 1323, 1335, 1450, and 1655 cm-1. Diverse statistical methods are employed to develop effective diagnostic algorithms for classifying the Raman spectra of different types of ex vivo gastric tissues, including principal component analysis (PCA), linear discriminant analysis (LDA), and naive Bayesian classifier (NBC) techniques. Compared with PCA-LDA algorithms, PCA-NBC techniques together with leave-one-out, cross-validation method provide better discriminative results of normal, adenomatous polyp, and adenocarcinoma gastric tissues, resulting in superior sensitivities of 96.3%, 96.9%, and 96.9%, and specificities of 93%, 100%, and 95.2%, respectively. Therefore, NIR RS associated with multivariate statistical algorithms has the potential for early diagnosis of gastric premalignant lesions and cancer tissues in molecular level.

A Nasal Brush-based Classifier of Asthma Identified by Machine Learning Analysis of Nasal RNA Sequence Data.

PubMed

Pandey, Gaurav; Pandey, Om P; Rogers, Angela J; Ahsen, Mehmet E; Hoffman, Gabriel E; Raby, Benjamin A; Weiss, Scott T; Schadt, Eric E; Bunyavanich, Supinda

2018-06-11

Asthma is a common, under-diagnosed disease affecting all ages. We sought to identify a nasal brush-based classifier of mild/moderate asthma. 190 subjects with mild/moderate asthma and controls underwent nasal brushing and RNA sequencing of nasal samples. A machine learning-based pipeline identified an asthma classifier consisting of 90 genes interpreted via an L2-regularized logistic regression classification model. This classifier performed with strong predictive value and sensitivity across eight test sets, including (1) a test set of independent asthmatic and control subjects profiled by RNA sequencing (positive and negative predictive values of 1.00 and 0.96, respectively; AUC of 0.994), (2) two independent case-control cohorts of asthma profiled by microarray, and (3) five cohorts with other respiratory conditions (allergic rhinitis, upper respiratory infection, cystic fibrosis, smoking), where the classifier had a low to zero misclassification rate. Following validation in large, prospective cohorts, this classifier could be developed into a nasal biomarker of asthma.

Low vacuum scanning electron microscopy for paraffin sections utilizing the differential stainability of cells and tissues with platinum blue.

PubMed

Inaga, Sumire; Hirashima, Sayuri; Tanaka, Keiichi; Katsumoto, Tetsuo; Kameie, Toshio; Nakane, Hironobu; Naguro, Tomonori

2009-07-01

The present study introduces a novel method for the direct observation of histological paraffin sections by low vacuum scanning electron microscopy (LVSEM) with platinum blue (Pt-blue) treatment. Pt-blue was applied not only as a backscattered electron (BSE) signal enhancer but also as a histologically specific stain. In this method, paraffin sections of the rat tongue prepared for conventional light microscopy (LM) were stained on glass slides with a Pt-blue staining solution (pH 9) and observed in a LVSEM using BSE detector. Under LVSEM, overviews of whole sections as well as three-dimensional detailed observations of individual cells and tissues could be easily made at magnifications from x40 to x10,000. Each kind of cell and tissue observed in the section could be clearly distinguished due to the different yields of BSE signals, which depended on the surface structures and different affinities to Pt-blue. Thus, we roughly classified cellular and tissue components into three groups according to the staining intensity of Pt-blue observed by LM and LVSEM: 1) a strongly stained (deep blue by LM and brightest by LVSEM) group which included epithelial tissue, endothelium and mast cells; 2) a moderately stained (light blue and bright) group which included muscular tissue and nervous tissue; 3) an unstained or weakly stained (colorless and dark) group which included elastic fibers and collagen fibers. We expect that this method will prove useful for the three-dimensional direct observation of histological paraffin sections of various tissues by LVSEM with higher resolutions than LM.

Double Ramp Loss Based Reject Option Classifier

DTIC Science & Technology

2015-05-22

choose 10% of these points uniformly at random and flip their labels. 2. Ionosphere Dataset [2] : This dataset describes the problem of discrimi- nating...good versus bad radars based on whether they send some useful infor- mation about the Ionosphere . There are 34 variables and 351 observations. 3... Ionosphere dataset (nonlinear classifiers using RBF kernel for both the approaches) d LDR (C = 2, γ = 0.125) LDH (C = 16, γ = 0.125) Risk RR Acc(unrej

Recent optical observations of NHATS target 2015 DP155

NASA Astrophysics Data System (ADS)

Reshetnyk, V.; Godunova, V.; Sergeev, O.; Simon, A.

2018-05-01

We report light curve observations of the near-Earth asteroid 2015 DP155 which is on the NASA's list of potential future space mission targets (NHATS). It was first observed at Pan-STARRS 1, Haleakala, on 2015, February 17 and has been classified by the Minor Planet Center as a potentially hazardous asteroid.

30 CFR 250.1628 - Design, installation, and operation of production systems.

Code of Federal Regulations, 2014 CFR

2014-07-01

... Systems (as incorporated by reference in § 250.198); (3) Electrical system information including a plan of... Practice for Classification of Locations for Electrical Installations at Petroleum Facilities Classified as... for Electrical Installations at Petroleum Facilities Classified as Class I, Zone 0, Zone 1, and Zone 2...

30 CFR 250.1628 - Design, installation, and operation of production systems.

Code of Federal Regulations, 2012 CFR

2012-07-01

... Systems (as incorporated by reference in § 250.198); (3) Electrical system information including a plan of... Practice for Classification of Locations for Electrical Installations at Petroleum Facilities Classified as... for Electrical Installations at Petroleum Facilities Classified as Class I, Zone 0, Zone 1, and Zone 2...

30 CFR 250.1628 - Design, installation, and operation of production systems.

Code of Federal Regulations, 2013 CFR

2013-07-01

... Systems (as incorporated by reference in § 250.198); (3) Electrical system information including a plan of... Practice for Classification of Locations for Electrical Installations at Petroleum Facilities Classified as... for Electrical Installations at Petroleum Facilities Classified as Class I, Zone 0, Zone 1, and Zone 2...

22 CFR 125.3 - Exports of classified technical data and classified defense articles.

Code of Federal Regulations, 2010 CFR

2010-04-01

... in the Department of Defense National Industrial Security Program Operating Manual (unless such.... It should also list the facility security clearance code of all U.S. parties on the license and include the Defense Security Service cognizant security office of the party responsible for packaging the...

Decision-Tree, Rule-Based, and Random Forest Classification of High-Resolution Multispectral Imagery for Wetland Mapping and Inventory

EPA Science Inventory

Efforts are increasingly being made to classify the world’s wetland resources, an important ecosystem and habitat that is diminishing in abundance. There are multiple remote sensing classification methods, including a suite of nonparametric classifiers such as decision-tree...

Classified Staff Perceptions of Behavior and Discipline: Implications for Schoolwide Positive Behavior Supports

ERIC Educational Resources Information Center

Feuerborn, Laura L.; Tyre, Ashli D.; Beaudoin, Kathleen

2018-01-01

Classified staff are important stakeholders in schools and commonly interact with students across grade levels, subject matter areas, and physical locations--making their involvement in the implementation of schoolwide positive behavior interventions and supports (SWPBIS) essential. However, their voice, including the intentional and systematic…

A Machine Learning Classifier for Fast Radio Burst Detection at the VLBA

NASA Astrophysics Data System (ADS)

Wagstaff, Kiri L.; Tang, Benyang; Thompson, David R.; Khudikyan, Shakeh; Wyngaard, Jane; Deller, Adam T.; Palaniswamy, Divya; Tingay, Steven J.; Wayth, Randall B.

2016-08-01

Time domain radio astronomy observing campaigns frequently generate large volumes of data. Our goal is to develop automated methods that can identify events of interest buried within the larger data stream. The V-FASTR fast transient system was designed to detect rare fast radio bursts within data collected by the Very Long Baseline Array. The resulting event candidates constitute a significant burden in terms of subsequent human reviewing time. We have trained and deployed a machine learning classifier that marks each candidate detection as a pulse from a known pulsar, an artifact due to radio frequency interference, or a potential new discovery. The classifier maintains high reliability by restricting its predictions to those with at least 90% confidence. We have also implemented several efficiency and usability improvements to the V-FASTR web-based candidate review system. Overall, we found that time spent reviewing decreased and the fraction of interesting candidates increased. The classifier now classifies (and therefore filters) 80%-90% of the candidates, with an accuracy greater than 98%, leaving only the 10%-20% most promising candidates to be reviewed by humans.

Minimum-Light Spectral Classifications for M-Type Mira Variables

NASA Astrophysics Data System (ADS)

Wing, Robert F.

2015-08-01

Many bright, well-known Mira variables, including most of the 378 stars for which the AAVSO publishes predicted dates of maximum and minimum in its annual Bulletins, have never been spectroscopically observed close to the time of minimum light, and consequently their catalogued ranges in spectral type are often grossly and misleadingly under-represented. In an effort to improve this situation, for the past 12 years I have been using my 6-color system of narrow-band classification photometry to observe Miras predicted to be near minimum light at the times of my biannual observing runs with the CTIO 0.9-m telescope (operated by the SMARTS consortium). The 6-color system measures the 7100 A band of TiO, which serves to classify stars in the interval K4 to M8, and the 1.06 micron band of VO, which is effective for stars of type M8 and later. To date I have made 431 observations of approximately 220 different (and mostly southern) Miras. Examples are shown of the observed 6-color spectra, and the classifications derived from them.

Intimate partner violence and breastfeeding practices: a systematic review of observational studies.

PubMed

Mezzavilla, Raquel de Souza; Ferreira, Marina de Figueiredo; Curioni, Cintia Chaves; Lindsay, Ana Cristina; Hasselmann, Maria Helena

To review the association between intimate partner violence and breastfeeding practices in the literature. The search was carried out in five databases, including MEDLINE, LILACS, SCOPUS, PsycoINFO, and Science Direct. The search strategy was carried out in February 2017. The authors included original studies with observational design, which investigated forms of intimate partner violence (including emotional, physical, and/or sexual) and breastfeeding practices. The quality of the studies was assessed based on the bias susceptibility through criteria specifically developed for this review. The study included 12 original articles (10 cross-sectional, one case-control, and one cohort study) carried out in different countries. The forms of intimate partner violence observed were emotional, physical, and/or sexual. Breastfeeding was investigated by different tools and only assessed children between 2 days and 6 months of life. Of the 12 studies included in this review, eight found a lower breastfeeding intention, breastfeeding initiation, and exclusive breastfeeding during the first six months of the child's life, and a higher likelihood of early termination of exclusive breastfeeding among women living at home where violence was present. The quality varied between the studies and six were classified as having low bias susceptibility based on the assessed items. Intimate partner violence is associated with inadequate breastfeeding practices of children aged 2 days to 6 months of life. Copyright © 2017 Sociedade Brasileira de Pediatria. Published by Elsevier Editora Ltda. All rights reserved.

A survey of supervised machine learning models for mobile-phone based pathogen identification and classification

NASA Astrophysics Data System (ADS)

Ceylan Koydemir, Hatice; Feng, Steve; Liang, Kyle; Nadkarni, Rohan; Tseng, Derek; Benien, Parul; Ozcan, Aydogan

2017-03-01

Giardia lamblia causes a disease known as giardiasis, which results in diarrhea, abdominal cramps, and bloating. Although conventional pathogen detection methods used in water analysis laboratories offer high sensitivity and specificity, they are time consuming, and need experts to operate bulky equipment and analyze the samples. Here we present a field-portable and cost-effective smartphone-based waterborne pathogen detection platform that can automatically classify Giardia cysts using machine learning. Our platform enables the detection and quantification of Giardia cysts in one hour, including sample collection, labeling, filtration, and automated counting steps. We evaluated the performance of three prototypes using Giardia-spiked water samples from different sources (e.g., reagent-grade, tap, non-potable, and pond water samples). We populated a training database with >30,000 cysts and estimated our detection sensitivity and specificity using 20 different classifier models, including decision trees, nearest neighbor classifiers, support vector machines (SVMs), and ensemble classifiers, and compared their speed of training and classification, as well as predicted accuracies. Among them, cubic SVM, medium Gaussian SVM, and bagged-trees were the most promising classifier types with accuracies of 94.1%, 94.2%, and 95%, respectively; we selected the latter as our preferred classifier for the detection and enumeration of Giardia cysts that are imaged using our mobile-phone fluorescence microscope. Without the need for any experts or microbiologists, this field-portable pathogen detection platform can present a useful tool for water quality monitoring in resource-limited-settings.

Classifying free-text triage chief complaints into syndromic categories with natural language processing.

PubMed

Chapman, Wendy W; Christensen, Lee M; Wagner, Michael M; Haug, Peter J; Ivanov, Oleg; Dowling, John N; Olszewski, Robert T

2005-01-01

Develop and evaluate a natural language processing application for classifying chief complaints into syndromic categories for syndromic surveillance. Much of the input data for artificial intelligence applications in the medical field are free-text patient medical records, including dictated medical reports and triage chief complaints. To be useful for automated systems, the free-text must be translated into encoded form. We implemented a biosurveillance detection system from Pennsylvania to monitor the 2002 Winter Olympic Games. Because input data was in free-text format, we used a natural language processing text classifier to automatically classify free-text triage chief complaints into syndromic categories used by the biosurveillance system. The classifier was trained on 4700 chief complaints from Pennsylvania. We evaluated the ability of the classifier to classify free-text chief complaints into syndromic categories with a test set of 800 chief complaints from Utah. The classifier produced the following areas under the ROC curve: Constitutional = 0.95; Gastrointestinal = 0.97; Hemorrhagic = 0.99; Neurological = 0.96; Rash = 1.0; Respiratory = 0.99; Other = 0.96. Using information stored in the system's semantic model, we extracted from the Respiratory classifications lower respiratory complaints and lower respiratory complaints with fever with a precision of 0.97 and 0.96, respectively. Results suggest that a trainable natural language processing text classifier can accurately extract data from free-text chief complaints for biosurveillance.

Proposing an adaptive mutation to improve XCSF performance to classify ADHD and BMD patients

NASA Astrophysics Data System (ADS)

Sadatnezhad, Khadijeh; Boostani, Reza; Ghanizadeh, Ahmad

2010-12-01

There is extensive overlap of clinical symptoms observed among children with bipolar mood disorder (BMD) and those with attention deficit hyperactivity disorder (ADHD). Thus, diagnosis according to clinical symptoms cannot be very accurate. It is therefore desirable to develop quantitative criteria for automatic discrimination between these disorders. This study is aimed at designing an efficient decision maker to accurately classify ADHD and BMD patients by analyzing their electroencephalogram (EEG) signals. In this study, 22 channels of EEGs have been recorded from 21 subjects with ADHD and 22 individuals with BMD. Several informative features, such as fractal dimension, band power and autoregressive coefficients, were extracted from the recorded signals. Considering the multimodal overlapping distribution of the obtained features, linear discriminant analysis (LDA) was used to reduce the input dimension in a more separable space to make it more appropriate for the proposed classifier. A piecewise linear classifier based on the extended classifier system for function approximation (XCSF) was modified by developing an adaptive mutation rate, which was proportional to the genotypic content of best individuals and their fitness in each generation. The proposed operator controlled the trade-off between exploration and exploitation while maintaining the diversity in the classifier's population to avoid premature convergence. To assess the effectiveness of the proposed scheme, the extracted features were applied to support vector machine, LDA, nearest neighbor and XCSF classifiers. To evaluate the method, a noisy environment was simulated with different noise amplitudes. It is shown that the results of the proposed technique are more robust as compared to conventional classifiers. Statistical tests demonstrate that the proposed classifier is a promising method for discriminating between ADHD and BMD patients.

voomDDA: discovery of diagnostic biomarkers and classification of RNA-seq data.

PubMed

Zararsiz, Gokmen; Goksuluk, Dincer; Klaus, Bernd; Korkmaz, Selcuk; Eldem, Vahap; Karabulut, Erdem; Ozturk, Ahmet

2017-01-01

RNA-Seq is a recent and efficient technique that uses the capabilities of next-generation sequencing technology for characterizing and quantifying transcriptomes. One important task using gene-expression data is to identify a small subset of genes that can be used to build diagnostic classifiers particularly for cancer diseases. Microarray based classifiers are not directly applicable to RNA-Seq data due to its discrete nature. Overdispersion is another problem that requires careful modeling of mean and variance relationship of the RNA-Seq data. In this study, we present voomDDA classifiers: variance modeling at the observational level (voom) extensions of the nearest shrunken centroids (NSC) and the diagonal discriminant classifiers. VoomNSC is one of these classifiers and brings voom and NSC approaches together for the purpose of gene-expression based classification. For this purpose, we propose weighted statistics and put these weighted statistics into the NSC algorithm. The VoomNSC is a sparse classifier that models the mean-variance relationship using the voom method and incorporates voom's precision weights into the NSC classifier via weighted statistics. A comprehensive simulation study was designed and four real datasets are used for performance assessment. The overall results indicate that voomNSC performs as the sparsest classifier. It also provides the most accurate results together with power-transformed Poisson linear discriminant analysis, rlog transformed support vector machines and random forests algorithms. In addition to prediction purposes, the voomNSC classifier can be used to identify the potential diagnostic biomarkers for a condition of interest. Through this work, statistical learning methods proposed for microarrays can be reused for RNA-Seq data. An interactive web application is freely available at http://www.biosoft.hacettepe.edu.tr/voomDDA/.

Proposing an adaptive mutation to improve XCSF performance to classify ADHD and BMD patients.

PubMed

Sadatnezhad, Khadijeh; Boostani, Reza; Ghanizadeh, Ahmad

2010-12-01

There is extensive overlap of clinical symptoms observed among children with bipolar mood disorder (BMD) and those with attention deficit hyperactivity disorder (ADHD). Thus, diagnosis according to clinical symptoms cannot be very accurate. It is therefore desirable to develop quantitative criteria for automatic discrimination between these disorders. This study is aimed at designing an efficient decision maker to accurately classify ADHD and BMD patients by analyzing their electroencephalogram (EEG) signals. In this study, 22 channels of EEGs have been recorded from 21 subjects with ADHD and 22 individuals with BMD. Several informative features, such as fractal dimension, band power and autoregressive coefficients, were extracted from the recorded signals. Considering the multimodal overlapping distribution of the obtained features, linear discriminant analysis (LDA) was used to reduce the input dimension in a more separable space to make it more appropriate for the proposed classifier. A piecewise linear classifier based on the extended classifier system for function approximation (XCSF) was modified by developing an adaptive mutation rate, which was proportional to the genotypic content of best individuals and their fitness in each generation. The proposed operator controlled the trade-off between exploration and exploitation while maintaining the diversity in the classifier's population to avoid premature convergence. To assess the effectiveness of the proposed scheme, the extracted features were applied to support vector machine, LDA, nearest neighbor and XCSF classifiers. To evaluate the method, a noisy environment was simulated with different noise amplitudes. It is shown that the results of the proposed technique are more robust as compared to conventional classifiers. Statistical tests demonstrate that the proposed classifier is a promising method for discriminating between ADHD and BMD patients.

Paracoccidioidomycosis: Current Perspectives from Brazil

PubMed Central

Mendes, Rinaldo Poncio; Cavalcante, Ricardo de Souza; Marques, Sílvio Alencar; Marques, Mariângela Esther Alencar; Venturini, James; Sylvestre, Tatiane Fernanda; Paniago, Anamaria Mello Miranda; Pereira, Ana Carla; da Silva, Julhiany de Fátima; Fabro, Alexandre Todorovic; Bosco, Sandra de Moraes Gimenes; Bagagli, Eduardo; Hahn, Rosane Christine; Levorato, Adriele Dandara

2017-01-01

Background: This review article summarizes and updates the knowledge on paracoccidioidomycosis. P lutzii and the cryptic species of P. brasiliensis and their geographical distribution in Latin America, explaining the difficulties observed in the serological diagnosis. Objectives: Emphasis has been placed on some genetic factors as predisposing condition for paracoccidioidomycosis. Veterinary aspects were focused, showing the wide distribution of infection among animals. The cell-mediated immunity was better characterized, incorporating the recent findings. Methods: Serological methods for diagnosis were also compared for their parameters of accuracy, including the analysis of relapse. Results: Clinical forms have been better classified in order to include the pictures less frequently observesiod. Conclusion: Itraconazole and the trimethoprim-sulfamethoxazole combination was compared regarding efficacy, effectiveness and safety, demonstrating that azole should be the first choice in the treatment of paracoccidioidomycosis. PMID:29204222

Acoustic classification of zooplankton

NASA Astrophysics Data System (ADS)

Martin Traykovski, Linda V.

1998-11-01

Work on the forward problem in zooplankton bioacoustics has resulted in the identification of three categories of acoustic scatterers: elastic-shelled (e.g. pteropods), fluid-like (e.g. euphausiids), and gas-bearing (e.g. siphonophores). The relationship between backscattered energy and animal biomass has been shown to vary by a factor of ~19,000 across these categories, so that to make accurate estimates of zooplankton biomass from acoustic backscatter measurements of the ocean, the acoustic characteristics of the species of interest must be well-understood. This thesis describes the development of both feature based and model based classification techniques to invert broadband acoustic echoes from individual zooplankton for scatterer type, as well as for particular parameters such as animal orientation. The feature based Empirical Orthogonal Function Classifier (EOFC) discriminates scatterer types by identifying characteristic modes of variability in the echo spectra, exploiting only the inherent characteristic structure of the acoustic signatures. The model based Model Parameterisation Classifier (MPC) classifies based on correlation of observed echo spectra with simplified parameterisations of theoretical scattering models for the three classes. The Covariance Mean Variance Classifiers (CMVC) are a set of advanced model based techniques which exploit the full complexity of the theoretical models by searching the entire physical model parameter space without employing simplifying parameterisations. Three different CMVC algorithms were developed: the Integrated Score Classifier (ISC), the Pairwise Score Classifier (PSC) and the Bayesian Probability Classifier (BPC); these classifiers assign observations to a class based on similarities in covariance, mean, and variance, while accounting for model ambiguity and validity. These feature based and model based inversion techniques were successfully applied to several thousand echoes acquired from broadband (~350 kHz-750 kHz) insonifications of live zooplankton collected on Georges Bank and the Gulf of Maine to determine scatterer class. CMVC techniques were also applied to echoes from fluid-like zooplankton (Antarctic krill) to invert for angle of orientation using generic and animal-specific theoretical and empirical models. Application of these inversion techniques in situ will allow correct apportionment of backscattered energy to animal biomass, significantly improving estimates of zooplankton biomass based on acoustic surveys. (Copies available exclusively from MIT Libraries, Rm. 14-0551, Cambridge, MA 02139-4307. Ph. 617-253-5668; Fax 617-253-1690.)

Motor vehicle fuel analyzer

DOEpatents

Hoffheins, B.S.; Lauf, R.J.

1997-08-05

A gas detecting system is described for classifying the type of liquid fuel in a container or tank. The system includes a plurality of semiconductor gas sensors, each of which differs from the other in its response to various organic vapors. The system includes a means of processing the responses of the plurality of sensors such that the responses to any particular organic substance or mixture is sufficiently distinctive to constitute a recognizable ``signature``. The signature of known substances are collected and divided into two classes based on some other known characteristic of the substances. A pattern recognition system classifies the signature of an unknown substance with reference to the two user-defined classes, thereby classifying the unknown substance with regard to the characteristic of interest, such as its suitability for a particular use. 14 figs.

Motor vehicle fuel analyzer

DOEpatents

Hoffheins, Barbara S.; Lauf, Robert J.

1997-01-01

A gas detecting system for classifying the type of liquid fuel in a container or tank. The system includes a plurality of semiconductor gas sensors, each of which differs from the other in its response to various organic vapors. The system includes a means of processing the responses of the plurality of sensors such that the responses to any particular organic substance or mixture is sufficiently distinctive to constitute a recognizable "signature". The signature of known substances are collected and divided into two classes based on some other known characteristic of the substances. A pattern recognition system classifies the signature of an unknown substance with reference to the two user-defined classes, thereby classifying the unknown substance with regard to the characteristic of interest, such as its suitability for a particular use.

Progressive Classification Using Support Vector Machines

NASA Technical Reports Server (NTRS)

Wagstaff, Kiri; Kocurek, Michael

2009-01-01

An algorithm for progressive classification of data, analogous to progressive rendering of images, makes it possible to compromise between speed and accuracy. This algorithm uses support vector machines (SVMs) to classify data. An SVM is a machine learning algorithm that builds a mathematical model of the desired classification concept by identifying the critical data points, called support vectors. Coarse approximations to the concept require only a few support vectors, while precise, highly accurate models require far more support vectors. Once the model has been constructed, the SVM can be applied to new observations. The cost of classifying a new observation is proportional to the number of support vectors in the model. When computational resources are limited, an SVM of the appropriate complexity can be produced. However, if the constraints are not known when the model is constructed, or if they can change over time, a method for adaptively responding to the current resource constraints is required. This capability is particularly relevant for spacecraft (or any other real-time systems) that perform onboard data analysis. The new algorithm enables the fast, interactive application of an SVM classifier to a new set of data. The classification process achieved by this algorithm is characterized as progressive because a coarse approximation to the true classification is generated rapidly and thereafter iteratively refined. The algorithm uses two SVMs: (1) a fast, approximate one and (2) slow, highly accurate one. New data are initially classified by the fast SVM, producing a baseline approximate classification. For each classified data point, the algorithm calculates a confidence index that indicates the likelihood that it was classified correctly in the first pass. Next, the data points are sorted by their confidence indices and progressively reclassified by the slower, more accurate SVM, starting with the items most likely to be incorrectly classified. The user can halt this reclassification process at any point, thereby obtaining the best possible result for a given amount of computation time. Alternatively, the results can be displayed as they are generated, providing the user with real-time feedback about the current accuracy of classification.

Three learning phases for radial-basis-function networks.

PubMed

Schwenker, F; Kestler, H A; Palm, G

2001-05-01

In this paper, learning algorithms for radial basis function (RBF) networks are discussed. Whereas multilayer perceptrons (MLP) are typically trained with backpropagation algorithms, starting the training procedure with a random initialization of the MLP's parameters, an RBF network may be trained in many different ways. We categorize these RBF training methods into one-, two-, and three-phase learning schemes. Two-phase RBF learning is a very common learning scheme. The two layers of an RBF network are learnt separately; first the RBF layer is trained, including the adaptation of centers and scaling parameters, and then the weights of the output layer are adapted. RBF centers may be trained by clustering, vector quantization and classification tree algorithms, and the output layer by supervised learning (through gradient descent or pseudo inverse solution). Results from numerical experiments of RBF classifiers trained by two-phase learning are presented in three completely different pattern recognition applications: (a) the classification of 3D visual objects; (b) the recognition hand-written digits (2D objects); and (c) the categorization of high-resolution electrocardiograms given as a time series (ID objects) and as a set of features extracted from these time series. In these applications, it can be observed that the performance of RBF classifiers trained with two-phase learning can be improved through a third backpropagation-like training phase of the RBF network, adapting the whole set of parameters (RBF centers, scaling parameters, and output layer weights) simultaneously. This, we call three-phase learning in RBF networks. A practical advantage of two- and three-phase learning in RBF networks is the possibility to use unlabeled training data for the first training phase. Support vector (SV) learning in RBF networks is a different learning approach. SV learning can be considered, in this context of learning, as a special type of one-phase learning, where only the output layer weights of the RBF network are calculated, and the RBF centers are restricted to be a subset of the training data. Numerical experiments with several classifier schemes including k-nearest-neighbor, learning vector quantization and RBF classifiers trained through two-phase, three-phase and support vector learning are given. The performance of the RBF classifiers trained through SV learning and three-phase learning are superior to the results of two-phase learning, but SV learning often leads to complex network structures, since the number of support vectors is not a small fraction of the total number of data points.

Beyond where to how: a machine learning approach for sensing mobility contexts using smartphone sensors.

PubMed

Guinness, Robert E

2015-04-28

This paper presents the results of research on the use of smartphone sensors (namely, GPS and accelerometers), geospatial information (points of interest, such as bus stops and train stations) and machine learning (ML) to sense mobility contexts. Our goal is to develop techniques to continuously and automatically detect a smartphone user's mobility activities, including walking, running, driving and using a bus or train, in real-time or near-real-time (<5 s). We investigated a wide range of supervised learning techniques for classification, including decision trees (DT), support vector machines (SVM), naive Bayes classifiers (NB), Bayesian networks (BN), logistic regression (LR), artificial neural networks (ANN) and several instance-based classifiers (KStar, LWLand IBk). Applying ten-fold cross-validation, the best performers in terms of correct classification rate (i.e., recall) were DT (96.5%), BN (90.9%), LWL (95.5%) and KStar (95.6%). In particular, the DT-algorithm RandomForest exhibited the best overall performance. After a feature selection process for a subset of algorithms, the performance was improved slightly. Furthermore, after tuning the parameters of RandomForest, performance improved to above 97.5%. Lastly, we measured the computational complexity of the classifiers, in terms of central processing unit (CPU) time needed for classification, to provide a rough comparison between the algorithms in terms of battery usage requirements. As a result, the classifiers can be ranked from lowest to highest complexity (i.e., computational cost) as follows: SVM, ANN, LR, BN, DT, NB, IBk, LWL and KStar. The instance-based classifiers take considerably more computational time than the non-instance-based classifiers, whereas the slowest non-instance-based classifier (NB) required about five-times the amount of CPU time as the fastest classifier (SVM). The above results suggest that DT algorithms are excellent candidates for detecting mobility contexts in smartphones, both in terms of performance and computational complexity.

Beyond Where to How: A Machine Learning Approach for Sensing Mobility Contexts Using Smartphone Sensors †

PubMed Central

Guinness, Robert E.

2015-01-01

This paper presents the results of research on the use of smartphone sensors (namely, GPS and accelerometers), geospatial information (points of interest, such as bus stops and train stations) and machine learning (ML) to sense mobility contexts. Our goal is to develop techniques to continuously and automatically detect a smartphone user's mobility activities, including walking, running, driving and using a bus or train, in real-time or near-real-time (<5 s). We investigated a wide range of supervised learning techniques for classification, including decision trees (DT), support vector machines (SVM), naive Bayes classifiers (NB), Bayesian networks (BN), logistic regression (LR), artificial neural networks (ANN) and several instance-based classifiers (KStar, LWLand IBk). Applying ten-fold cross-validation, the best performers in terms of correct classification rate (i.e., recall) were DT (96.5%), BN (90.9%), LWL (95.5%) and KStar (95.6%). In particular, the DT-algorithm RandomForest exhibited the best overall performance. After a feature selection process for a subset of algorithms, the performance was improved slightly. Furthermore, after tuning the parameters of RandomForest, performance improved to above 97.5%. Lastly, we measured the computational complexity of the classifiers, in terms of central processing unit (CPU) time needed for classification, to provide a rough comparison between the algorithms in terms of battery usage requirements. As a result, the classifiers can be ranked from lowest to highest complexity (i.e., computational cost) as follows: SVM, ANN, LR, BN, DT, NB, IBk, LWL and KStar. The instance-based classifiers take considerably more computational time than the non-instance-based classifiers, whereas the slowest non-instance-based classifier (NB) required about five-times the amount of CPU time as the fastest classifier (SVM). The above results suggest that DT algorithms are excellent candidates for detecting mobility contexts in smartphones, both in terms of performance and computational complexity. PMID:25928060

[Ultrasonic monitoring foam sclerotherapy for serious varicosis of lower extremity].

PubMed

Yin, Heng-hui; Pan, Fu-shun; Huang, Xue-ling; Chang, Guang-qi; Wang, Shen-ming

2013-11-19

To evaluate the efficacy and safety of foam sclerotherapy for lower extremity varicosis in C4 to C6 patients. A total of 32 patients (32 limbs) with serious lower extremity varicosis classified as C4 to C6 were enrolled. Ultrasonic monitoring of foam sclerotherapy was performed after subfascial endoscopic perforator suture and saphenous vein ligation. They were followed up monthly at outpatient department. Duplex Doppler scan was performed during each interview. All patients were treated successfully. An average of 3.2 perforators were ligated per leg (1-5 perforators). The average volume of foam sclerosing agent was 27.5 ml per leg. Mild chest tightness was observed in one patient but computed tomography (CT) scan excluded pulmonary embolism. Obvious local inflammatory reaction was observed in 4 patients. Residual vein mass without blood signal was seen in 3 patients. No such serious complication as cerebral ischemia was observed. The average follow-up period was 4.8 (1-10) months. Obvious varicose veins and clinical symptoms disappeared at 1 month. And venous ulcers in patients classified as C5 healed within 3 months. Ultrasonic monitoring of foam sclerotherapy, incorporation with saphenous vein ligation and subfascial endoscopic perforator suture, is both safe and effective in the treatment of serious lower extremity varicosis classified as C4 to C6.

Bayesian Decision Tree for the Classification of the Mode of Motion in Single-Molecule Trajectories

PubMed Central

Türkcan, Silvan; Masson, Jean-Baptiste

2013-01-01

Membrane proteins move in heterogeneous environments with spatially (sometimes temporally) varying friction and with biochemical interactions with various partners. It is important to reliably distinguish different modes of motion to improve our knowledge of the membrane architecture and to understand the nature of interactions between membrane proteins and their environments. Here, we present an analysis technique for single molecule tracking (SMT) trajectories that can determine the preferred model of motion that best matches observed trajectories. The method is based on Bayesian inference to calculate the posteriori probability of an observed trajectory according to a certain model. Information theory criteria, such as the Bayesian information criterion (BIC), the Akaike information criterion (AIC), and modified AIC (AICc), are used to select the preferred model. The considered group of models includes free Brownian motion, and confined motion in 2nd or 4th order potentials. We determine the best information criteria for classifying trajectories. We tested its limits through simulations matching large sets of experimental conditions and we built a decision tree. This decision tree first uses the BIC to distinguish between free Brownian motion and confined motion. In a second step, it classifies the confining potential further using the AIC. We apply the method to experimental Clostridium Perfingens -toxin (CPT) receptor trajectories to show that these receptors are confined by a spring-like potential. An adaptation of this technique was applied on a sliding window in the temporal dimension along the trajectory. We applied this adaptation to experimental CPT trajectories that lose confinement due to disaggregation of confining domains. This new technique adds another dimension to the discussion of SMT data. The mode of motion of a receptor might hold more biologically relevant information than the diffusion coefficient or domain size and may be a better tool to classify and compare different SMT experiments. PMID:24376584

Molecular-Directed Treatment of Differentiated Thyroid Cancer: Advances in Diagnosis and Treatment.

PubMed

Yip, Linwah; Sosa, Julie Ann

2016-07-01

Thyroid cancer incidence is increasing, and when fine-needle aspiration biopsy results are cytologically indeterminate, the diagnosis is often still established only after thyroidectomy. Molecular marker testing may be helpful in guiding patient-oriented and tailored management of thyroid nodules and thyroid cancer. To summarize available data on the use of molecular testing to improve the diagnosis and prognostication of thyroid cancer. A MEDLINE review was conducted using the primary search terms molecular, thyroid cancer, thyroid nodule, and gene expression classifier in search strings. Articles were restricted to those published between January 1, 2010, and June 1, 2015, inclusive of adult humans, and reported in the English language only. Of 867 titles screened, 67 articles were further identified for review of the full text. The 2 most studied molecular marker testing techniques for indeterminate thyroid nodules include gene expression classifier analysis and evaluation for somatic mutations or rearrangements that are commonly found in thyroid cancer (7-gene panel). Nodules with benign results on gene expression classifier analysis can be associated with less than a 5% risk of cancer and may be observed, while nodules with positive results on the 7-gene panel may have a higher risk of cancer (80%-100%) and definitive surgery can be recommended. However, cancer prevalence and geographic variations in histologic subtypes may affect accuracy and clinical applicability of both tests. Molecular marker tests such as ThyroSeq version 2.1 are more comprehensive, but they need further validation. Preoperative risk stratification using molecular markers also may be used to better define the optimal extent of thyroidectomy for patients with thyroid cancer. Molecular markers potentially can augment the diagnostic specificity of fine-needle aspiration biopsy to better differentiate cytologically indeterminate nodules that can be safely observed from cytologically indeterminate nodules that may be associated with differentiated thyroid cancer. Long-term follow-up data are still needed; in the end, patient preference regarding the relative risks and benefits of molecular testing is at the crux of decision making.

Computer-based coding of free-text job descriptions to efficiently identify occupations in epidemiological studies.

PubMed

Russ, Daniel E; Ho, Kwan-Yuet; Colt, Joanne S; Armenti, Karla R; Baris, Dalsu; Chow, Wong-Ho; Davis, Faith; Johnson, Alison; Purdue, Mark P; Karagas, Margaret R; Schwartz, Kendra; Schwenn, Molly; Silverman, Debra T; Johnson, Calvin A; Friesen, Melissa C

2016-06-01

Mapping job titles to standardised occupation classification (SOC) codes is an important step in identifying occupational risk factors in epidemiological studies. Because manual coding is time-consuming and has moderate reliability, we developed an algorithm called SOCcer (Standardized Occupation Coding for Computer-assisted Epidemiologic Research) to assign SOC-2010 codes based on free-text job description components. Job title and task-based classifiers were developed by comparing job descriptions to multiple sources linking job and task descriptions to SOC codes. An industry-based classifier was developed based on the SOC prevalence within an industry. These classifiers were used in a logistic model trained using 14 983 jobs with expert-assigned SOC codes to obtain empirical weights for an algorithm that scored each SOC/job description. We assigned the highest scoring SOC code to each job. SOCcer was validated in 2 occupational data sources by comparing SOC codes obtained from SOCcer to expert assigned SOC codes and lead exposure estimates obtained by linking SOC codes to a job-exposure matrix. For 11 991 case-control study jobs, SOCcer-assigned codes agreed with 44.5% and 76.3% of manually assigned codes at the 6-digit and 2-digit level, respectively. Agreement increased with the score, providing a mechanism to identify assignments needing review. Good agreement was observed between lead estimates based on SOCcer and manual SOC assignments (κ 0.6-0.8). Poorer performance was observed for inspection job descriptions, which included abbreviations and worksite-specific terminology. Although some manual coding will remain necessary, using SOCcer may improve the efficiency of incorporating occupation into large-scale epidemiological studies. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

Risk factors of learning disabilities in Chinese children in Wuhan.

PubMed

Yao, Bin; Wu, Han-Rong

2003-12-01

To investigate prevalence rate of learning disabilities (LD) in Chinese children, and to explore related risk factors, and to provide theoretical basis for preventing such disabilities. One thousand and one hundred fifty one children were randomly selected in primary schools. According to criteria set by ICD-10, 118 children diagnosed as LD were classified into the study group. Four hundred and ninety one children were classified into the normal control group. Five hundred and forty two children were classified into the excellent control group. The study instruments included PRS (The pupil rating scale revised screening for learning disabilities), Conners' children behavior check-list taken by parents and YG-WR character check-list. The prevalence rate of LD in Chinese children was 10.3%. Significant differences were observed between LD and normally learning children, and between the LD group and the excellent group, in terms of scores of Conners' behavior check-list (P < 0.05). The study further showed that individual differences in character between the LD group and the control groups still existed even after controlling individual differences in age, IQ, and gender. Some possible causal explanations contributing to LD were improper teaching by parents, low educational level of the parents, and children's characteristics and social relationships. These data underscore the fact that LD is a serious national public health problem in China. LD is resulted from a number of factors. Good studying and living environments should be created for LD children.

Effects of acidic precipitation on waterbirds in Maine

USGS Publications Warehouse

Longcore, J.R.; McAuley, D.G.; Stromborg, K.L.; Hensler, G.L.

1985-01-01

During 1982-84 waterbird use and numbers of waterbird broods were recorded for 29 wetlands on two study areas (25 and 77 km2) in east-central Maine underlain with bedrock having low, acid-neutralizing capacity (ANC). Twenty-nine wetlands over bedrock with high ANC (Class 3) and 31 wetlands over bedrock of low ANC (Class 1) were evaluated as predictors of wetland pH and alkalinity. Using the alkalinity value of 25 times was greater (P< ..0001) for downstream (84%) versus headwater (16%) wetlands during 1982-84. Avian use was similar when wetlands were classified either as beaver-created or glacial in origin. Headwater wetlands, which are most vulnerable to acidification within the low ANC areas, are used mostly by common goldeneye (Bucephala clangula), and common loon (Gavia immer). Common merganser (Mergus merganser), spotted sandpiper (Actitis macularia), and chimney swift (Chaetura pelagica) were associated with headwater wetlands about equally. The majority of species (16), including dabbling ducks, used, almost exclusively, wetlands classified as downstream or beaver-created. For all years, 87% of the 246 broods observed was on wetlands classified as either downstream or beaver-created. Our data suggest that avian use of wetlands is influenced more by the morphometric and vegetative characteristics of the wetland basin rather than by the wetland water chemistry. Nevertheless, large numbers of a variety of avian species are associated with wetlands underlain with bedrock that has little or no capacity to neutralize acidic depositions.

Featureless classification of light curves

NASA Astrophysics Data System (ADS)

Kügler, S. D.; Gianniotis, N.; Polsterer, K. L.

2015-08-01

In the era of rapidly increasing amounts of time series data, classification of variable objects has become the main objective of time-domain astronomy. Classification of irregularly sampled time series is particularly difficult because the data cannot be represented naturally as a vector which can be directly fed into a classifier. In the literature, various statistical features serve as vector representations. In this work, we represent time series by a density model. The density model captures all the information available, including measurement errors. Hence, we view this model as a generalization to the static features which directly can be derived, e.g. as moments from the density. Similarity between each pair of time series is quantified by the distance between their respective models. Classification is performed on the obtained distance matrix. In the numerical experiments, we use data from the OGLE (Optical Gravitational Lensing Experiment) and ASAS (All Sky Automated Survey) surveys and demonstrate that the proposed representation performs up to par with the best currently used feature-based approaches. The density representation preserves all static information present in the observational data, in contrast to a less-complete description by features. The density representation is an upper boundary in terms of information made available to the classifier. Consequently, the predictive power of the proposed classification depends on the choice of similarity measure and classifier, only. Due to its principled nature, we advocate that this new approach of representing time series has potential in tasks beyond classification, e.g. unsupervised learning.

Study on environmental indices and heat tolerance tests in hair sheep.

PubMed

Seixas, L; de Melo, C B; Menezes, A M; Ramos, A F; Paludo, G R; Peripolli, V; Tanure, C B; Costa Junior, J B G; McManus, C

2017-06-01

The ability to predict the effects of climatic factors on animals and their adaptability is important for livestock production. The aim of the present study was to analyze whether existing indices are suitable for evaluating heat stress in Santa Ines and Morada Nova sheep, which are locally adapted hair sheep breeds from northeastern Brazil, and if the limits used to classify thermal stress are suitable for these breeds. Therefore, climatic, physiological, and physical parameters, as well as thermographic images, were collected in 26 sheep, 1 1/2 years old, from two genetic groups (Santa Ines 12 males and 4 females; Morada Nov. 7 males and 3 females) for 3 days in both morning (4:00 a.m.) and afternoon (2:00 p.m.) with six repetitions, totalizing 156 repetitions. Statistical analysis included correlations and broken-line regressions. Iberia and Benezra indices were the tolerance tests that best correlated with the assessed parameters. High correlations between environmental indices and rectal or skin surface temperatures was observed, which indicates that these indices can be used for Santa Ines and Morada Nova sheep raised in central Brazil. However, some indicative values of thermal discomfort are different from the existing classification. Therefore, in order to classify appropriately, the model used needs to be carefully studied, because these classifying values can vary according to the species and model. Further research is necessary to establish indicators of thermal stress for sheep breeds raised in the region.

Integrating in-situ, Landsat, and MODIS data for mapping in Southern African savannas: experiences of LCCS-based land-cover mapping in the Kalahari in Namibia.

PubMed

Hüttich, Christian; Herold, Martin; Strohbach, Ben J; Dech, Stefan

2011-05-01

Integrated ecosystem assessment initiatives are important steps towards a global biodiversity observing system. Reliable earth observation data are key information for tracking biodiversity change on various scales. Regarding the establishment of standardized environmental observation systems, a key question is: What can be observed on each scale and how can land cover information be transferred? In this study, a land cover map from a dry semi-arid savanna ecosystem in Namibia was obtained based on the UN LCCS, in-situ data, and MODIS and Landsat satellite imagery. In situ botanical relevé samples were used as baseline data for the definition of a standardized LCCS legend. A standard LCCS code for savanna vegetation types is introduced. An object-oriented segmentation of Landsat imagery was used as intermediate stage for downscaling in-situ training data on a coarse MODIS resolution. MODIS time series metrics of the growing season 2004/2005 were used to classify Kalahari vegetation types using a tree-based ensemble classifier (Random Forest). The prevailing Kalahari vegetation types based on LCCS was open broadleaved deciduous shrubland with an herbaceous layer which differs from the class assignments of the global and regional land-cover maps. The separability analysis based on Bhattacharya distance measurements applied on two LCCS levels indicated a relationship of spectral mapping dependencies of annual MODIS time series features due to the thematic detail of the classification scheme. The analysis of LCCS classifiers showed an increased significance of life-form composition and soil conditions to the mapping accuracy. An overall accuracy of 92.48% was achieved. Woody plant associations proved to be most stable due to small omission and commission errors. The case study comprised a first suitability assessment of the LCCS classifier approach for a southern African savanna ecosystem.

Movement activity based classification of animal behaviour with an application to data from cheetah (Acinonyx jubatus).

PubMed

Grünewälder, Steffen; Broekhuis, Femke; Macdonald, David Whyte; Wilson, Alan Martin; McNutt, John Weldon; Shawe-Taylor, John; Hailes, Stephen

2012-01-01

We propose a new method, based on machine learning techniques, for the analysis of a combination of continuous data from dataloggers and a sampling of contemporaneous behaviour observations. This data combination provides an opportunity for biologists to study behaviour at a previously unknown level of detail and accuracy; however, continuously recorded data are of little use unless the resulting large volumes of raw data can be reliably translated into actual behaviour. We address this problem by applying a Support Vector Machine and a Hidden-Markov Model that allows us to classify an animal's behaviour using a small set of field observations to calibrate continuously recorded activity data. Such classified data can be applied quantitatively to the behaviour of animals over extended periods and at times during which observation is difficult or impossible. We demonstrate the usefulness of the method by applying it to data from six cheetah (Acinonyx jubatus) in the Okavango Delta, Botswana. Cumulative activity data scores were recorded every five minutes by accelerometers embedded in GPS radio-collars for around one year on average. Direct behaviour sampling of each of the six cheetah were collected in the field for comparatively short periods. Using this approach we are able to classify each five minute activity score into a set of three key behaviour (feeding, mobile and stationary), creating a continuous behavioural sequence for the entire period for which the collars were deployed. Evaluation of our classifier with cross-validation shows the accuracy to be 83%-94%, but that the accuracy for individual classes is reduced with decreasing sample size of direct observations. We demonstrate how these processed data can be used to study behaviour identifying seasonal and gender differences in daily activity and feeding times. Results given here are unlike any that could be obtained using traditional approaches in both accuracy and detail.

Movement Activity Based Classification of Animal Behaviour with an Application to Data from Cheetah (Acinonyx jubatus)

PubMed Central

Grünewälder, Steffen; Broekhuis, Femke; Macdonald, David Whyte; Wilson, Alan Martin; McNutt, John Weldon; Shawe-Taylor, John; Hailes, Stephen

2012-01-01

We propose a new method, based on machine learning techniques, for the analysis of a combination of continuous data from dataloggers and a sampling of contemporaneous behaviour observations. This data combination provides an opportunity for biologists to study behaviour at a previously unknown level of detail and accuracy; however, continuously recorded data are of little use unless the resulting large volumes of raw data can be reliably translated into actual behaviour. We address this problem by applying a Support Vector Machine and a Hidden-Markov Model that allows us to classify an animal's behaviour using a small set of field observations to calibrate continuously recorded activity data. Such classified data can be applied quantitatively to the behaviour of animals over extended periods and at times during which observation is difficult or impossible. We demonstrate the usefulness of the method by applying it to data from six cheetah (Acinonyx jubatus) in the Okavango Delta, Botswana. Cumulative activity data scores were recorded every five minutes by accelerometers embedded in GPS radio-collars for around one year on average. Direct behaviour sampling of each of the six cheetah were collected in the field for comparatively short periods. Using this approach we are able to classify each five minute activity score into a set of three key behaviour (feeding, mobile and stationary), creating a continuous behavioural sequence for the entire period for which the collars were deployed. Evaluation of our classifier with cross-validation shows the accuracy to be , but that the accuracy for individual classes is reduced with decreasing sample size of direct observations. We demonstrate how these processed data can be used to study behaviour identifying seasonal and gender differences in daily activity and feeding times. Results given here are unlike any that could be obtained using traditional approaches in both accuracy and detail. PMID:23185301

Classification of Sporting Activities Using Smartphone Accelerometers

PubMed Central

Mitchell, Edmond; Monaghan, David; O'Connor, Noel E.

2013-01-01

In this paper we present a framework that allows for the automatic identification of sporting activities using commonly available smartphones. We extract discriminative informational features from smartphone accelerometers using the Discrete Wavelet Transform (DWT). Despite the poor quality of their accelerometers, smartphones were used as capture devices due to their prevalence in today's society. Successful classification on this basis potentially makes the technology accessible to both elite and non-elite athletes. Extracted features are used to train different categories of classifiers. No one classifier family has a reportable direct advantage in activity classification problems to date; thus we examine classifiers from each of the most widely used classifier families. We investigate three classification approaches; a commonly used SVM-based approach, an optimized classification model and a fusion of classifiers. We also investigate the effect of changing several of the DWT input parameters, including mother wavelets, window lengths and DWT decomposition levels. During the course of this work we created a challenging sports activity analysis dataset, comprised of soccer and field-hockey activities. The average maximum F-measure accuracy of 87% was achieved using a fusion of classifiers, which was 6% better than a single classifier model and 23% better than a standard SVM approach. PMID:23604031

Use of collateral information to improve LANDSAT classification accuracies

NASA Technical Reports Server (NTRS)

Strahler, A. H. (Principal Investigator)

1981-01-01

Methods to improve LANDSAT classification accuracies were investigated including: (1) the use of prior probabilities in maximum likelihood classification as a methodology to integrate discrete collateral data with continuously measured image density variables; (2) the use of the logit classifier as an alternative to multivariate normal classification that permits mixing both continuous and categorical variables in a single model and fits empirical distributions of observations more closely than the multivariate normal density function; and (3) the use of collateral data in a geographic information system as exercised to model a desired output information layer as a function of input layers of raster format collateral and image data base layers.

Lunar and Planetary Science XXXV: Image Processing and Earth Observations

NASA Technical Reports Server (NTRS)

2004-01-01

The titles in this section include: 1) Expansion in Geographic Information Services for PIGWAD; 2) Modernization of the Integrated Software for Imagers and Spectrometers; 3) Science-based Region-of-Interest Image Compression; 4) Topographic Analysis with a Stereo Matching Tool Kit; 5) Central Avra Valley Storage and Recovery Project (CAVSARP) Site, Tucson, Arizona: Floodwater and Soil Moisture Investigations with Extraterrestrial Applications; 6) ASE Floodwater Classifier Development for EO-1 HYPERION Imagery; 7) Autonomous Sciencecraft Experiment (ASE) Operations on EO-1 in 2004; 8) Autonomous Vegetation Cover Scene Classification of EO-1 Hyperion Hyperspectral Data; 9) Long-Term Continental Areal Reduction Produced by Tectonic Processes.

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

Feature Selection for Chemical Sensor Arrays Using Mutual Information

PubMed Central

Wang, X. Rosalind; Lizier, Joseph T.; Nowotny, Thomas; Berna, Amalia Z.; Prokopenko, Mikhail; Trowell, Stephen C.

2014-01-01

We address the problem of feature selection for classifying a diverse set of chemicals using an array of metal oxide sensors. Our aim is to evaluate a filter approach to feature selection with reference to previous work, which used a wrapper approach on the same data set, and established best features and upper bounds on classification performance. We selected feature sets that exhibit the maximal mutual information with the identity of the chemicals. The selected features closely match those found to perform well in the previous study using a wrapper approach to conduct an exhaustive search of all permitted feature combinations. By comparing the classification performance of support vector machines (using features selected by mutual information) with the performance observed in the previous study, we found that while our approach does not always give the maximum possible classification performance, it always selects features that achieve classification performance approaching the optimum obtained by exhaustive search. We performed further classification using the selected feature set with some common classifiers and found that, for the selected features, Bayesian Networks gave the best performance. Finally, we compared the observed classification performances with the performance of classifiers using randomly selected features. We found that the selected features consistently outperformed randomly selected features for all tested classifiers. The mutual information filter approach is therefore a computationally efficient method for selecting near optimal features for chemical sensor arrays. PMID:24595058

A Framework for Identifying and Classifying Undergraduate Student Proof Errors

ERIC Educational Resources Information Center

Strickland, S.; Rand, B.

2016-01-01

This paper describes a framework for identifying, classifying, and coding student proofs, modified from existing proof-grading rubrics. The framework includes 20 common errors, as well as categories for interpreting the severity of the error. The coding scheme is intended for use in a classroom context, for providing effective student feedback. In…

40 CFR 63.2435 - Am I subject to the requirements in this subpart?

Code of Federal Regulations, 2013 CFR

2013-07-01

... MCPU includes equipment necessary to operate a miscellaneous organic chemical manufacturing process, as...)(1)(i), (ii), (iii), (iv), or (v) of this section. (i) An organic chemical(s) classified using the...)(5) of this section. (ii) An organic chemical(s) classified using the 1997 version of NAICS code 325...

40 CFR 63.2435 - Am I subject to the requirements in this subpart?

Code of Federal Regulations, 2012 CFR

2012-07-01

... MCPU includes equipment necessary to operate a miscellaneous organic chemical manufacturing process, as...)(1)(i), (ii), (iii), (iv), or (v) of this section. (i) An organic chemical(s) classified using the...)(5) of this section. (ii) An organic chemical(s) classified using the 1997 version of NAICS code 325...

40 CFR 63.2435 - Am I subject to the requirements in this subpart?

Code of Federal Regulations, 2014 CFR

2014-07-01

... MCPU includes equipment necessary to operate a miscellaneous organic chemical manufacturing process, as...)(1)(i), (ii), (iii), (iv), or (v) of this section. (i) An organic chemical(s) classified using the...)(5) of this section. (ii) An organic chemical(s) classified using the 1997 version of NAICS code 325...

Developing a radiomics framework for classifying non-small cell lung carcinoma subtypes

NASA Astrophysics Data System (ADS)

Yu, Dongdong; Zang, Yali; Dong, Di; Zhou, Mu; Gevaert, Olivier; Fang, Mengjie; Shi, Jingyun; Tian, Jie

2017-03-01

Patient-targeted treatment of non-small cell lung carcinoma (NSCLC) has been well documented according to the histologic subtypes over the past decade. In parallel, recent development of quantitative image biomarkers has recently been highlighted as important diagnostic tools to facilitate histological subtype classification. In this study, we present a radiomics analysis that classifies the adenocarcinoma (ADC) and squamous cell carcinoma (SqCC). We extract 52-dimensional, CT-based features (7 statistical features and 45 image texture features) to represent each nodule. We evaluate our approach on a clinical dataset including 324 ADCs and 110 SqCCs patients with CT image scans. Classification of these features is performed with four different machine-learning classifiers including Support Vector Machines with Radial Basis Function kernel (RBF-SVM), Random forest (RF), K-nearest neighbor (KNN), and RUSBoost algorithms. To improve the classifiers' performance, optimal feature subset is selected from the original feature set by using an iterative forward inclusion and backward eliminating algorithm. Extensive experimental results demonstrate that radiomics features achieve encouraging classification results on both complete feature set (AUC=0.89) and optimal feature subset (AUC=0.91).

Should OCD be classified as an anxiety disorder in DSM-V?

PubMed

Stein, Dan J; Fineberg, Naomi A; Bienvenu, O Joseph; Denys, Damiaan; Lochner, Christine; Nestadt, Gerald; Leckman, James F; Rauch, Scott L; Phillips, Katharine A

2010-06-01

In DSM-III, DSM-III-R, and DSM-IV, obsessive-compulsive disorder (OCD) was classified as an anxiety disorder. In ICD-10, OCD is classified separately from the anxiety disorders, although within the same larger category as anxiety disorders (as one of the "neurotic, stress-related, and somatoform disorders"). Ongoing advances in our understanding of OCD and other anxiety disorders have raised the question of whether OCD should continue to be classified with the anxiety disorders in DSM-V. This review presents a number of options and preliminary recommendations to be considered for DSM-V. Evidence is reviewed for retaining OCD in the category of anxiety disorders, and for moving OCD to a separate category of obsessive-compulsive (OC)-spectrum disorders, if such a category is included in DSM-V. Our preliminary recommendation is that OCD be retained in the category of anxiety disorders but that this category also includes OC-spectrum disorders along with OCD. If this change is made, the name of this category should be changed to reflect this proposed change. (c) 2010 Wiley-Liss, Inc.

Dark energy: A brief review

NASA Astrophysics Data System (ADS)

Li, Miao; Li, Xiao-Dong; Wang, Shuang; Wang, Yi

2013-12-01

The problem of dark energy is briefly reviewed in both theoretical and observational aspects. In the theoretical aspect, dark energy scenarios are classified into symmetry, anthropic principle, tuning mechanism, modified gravity, quantum cosmology, holographic principle, back-reaction and phenomenological types. In the observational aspect, we introduce cosmic probes, dark energy related projects, observational constraints on theoretical models and model independent reconstructions.

Limited receptive area neural classifier for recognition of swallowing sounds using continuous wavelet transform.

PubMed

Makeyev, Oleksandr; Sazonov, Edward; Schuckers, Stephanie; Lopez-Meyer, Paulo; Melanson, Ed; Neuman, Michael

2007-01-01

In this paper we propose a sound recognition technique based on the limited receptive area (LIRA) neural classifier and continuous wavelet transform (CWT). LIRA neural classifier was developed as a multipurpose image recognition system. Previous tests of LIRA demonstrated good results in different image recognition tasks including: handwritten digit recognition, face recognition, metal surface texture recognition, and micro work piece shape recognition. We propose a sound recognition technique where scalograms of sound instances serve as inputs of the LIRA neural classifier. The methodology was tested in recognition of swallowing sounds. Swallowing sound recognition may be employed in systems for automated swallowing assessment and diagnosis of swallowing disorders. The experimental results suggest high efficiency and reliability of the proposed approach.

Classification of earth terrain using polarimetric synthetic aperture radar images

NASA Technical Reports Server (NTRS)

Lim, H. H.; Swartz, A. A.; Yueh, H. A.; Kong, J. A.; Shin, R. T.; Van Zyl, J. J.

1989-01-01

Supervised and unsupervised classification techniques are developed and used to classify the earth terrain components from SAR polarimetric images of San Francisco Bay and Traverse City, Michigan. The supervised techniques include the Bayes classifiers, normalized polarimetric classification, and simple feature classification using discriminates such as the absolute and normalized magnitude response of individual receiver channel returns and the phase difference between receiver channels. An algorithm is developed as an unsupervised technique which classifies terrain elements based on the relationship between the orientation angle and the handedness of the transmitting and receiving polariation states. It is found that supervised classification produces the best results when accurate classifier training data are used, while unsupervised classification may be applied when training data are not available.

Probabilistic Multi-Person Tracking Using Dynamic Bayes Networks

NASA Astrophysics Data System (ADS)

Klinger, T.; Rottensteiner, F.; Heipke, C.

2015-08-01

Tracking-by-detection is a widely used practice in recent tracking systems. These usually rely on independent single frame detections that are handled as observations in a recursive estimation framework. If these observations are imprecise the generated trajectory is prone to be updated towards a wrong position. In contrary to existing methods our novel approach uses a Dynamic Bayes Network in which the state vector of a recursive Bayes filter, as well as the location of the tracked object in the image are modelled as unknowns. These unknowns are estimated in a probabilistic framework taking into account a dynamic model, and a state-of-the-art pedestrian detector and classifier. The classifier is based on the Random Forest-algorithm and is capable of being trained incrementally so that new training samples can be incorporated at runtime. This allows the classifier to adapt to the changing appearance of a target and to unlearn outdated features. The approach is evaluated on a publicly available benchmark. The results confirm that our approach is well suited for tracking pedestrians over long distances while at the same time achieving comparatively good geometric accuracy.

A new infectious encephalopathy syndrome, clinically mild encephalopathy associated with excitotoxicity (MEEX).

PubMed

Hirai, Nozomi; Yoshimaru, Daisuke; Moriyama, Yoko; Yasukawa, Kumi; Takanashi, Jun-Ichi

2017-09-15

Acute infectious encephalopathy is often observed in children in East Asia including Japan. More than 40% of the patients remain unclassified into specific syndromes. To investigate the underlying pathomechanisms in those with unclassified encephalopathy, we evaluated brain metabolism by MR spectroscopy. Among seven patients with acute encephalopathy admitted to our hospital from June 2016 to May 2017, three were classified into acute encephalopathy with biphasic seizures and late reduced diffusion (AESD). The other four showed consciousness disturbance lasting more than three days with no parenchymal lesion visible on MRI, which led to a diagnosis of unclassified encephalopathy. MR spectroscopy in these four patients, however, revealed an increase of glutamine with a normal N-acetyl aspartate level on days 5 to 8, which had normalized by follow-up studies on days 11 to 16. The four patients clinically recovered completely. Among 27 patients with encephalopathy, including the present seven patients, admitted to our hospital from January 2015 to March 2017, seven (26%) were classified into this type, which we propose is a new encephalopathy syndrome, clinically mild encephalopathy associated with excitotoxicity (MEEX). MEEX is the second most common subtype, following AESD (30%). This study suggests that excitotoxicity may be a common underlying pathomechanism of acute infectious encephalopathy, and prompt astrocytic neuroprotection from excitotoxicity may prevent progression of MEEX into AESD. Copyright © 2017 Elsevier B.V. All rights reserved.

Test-retest reliability of the proposed DSM-5 eating disorder diagnostic criteria

PubMed Central

Sysko, Robyn; Roberto, Christina A.; Barnes, Rachel D.; Grilo, Carlos M.; Attia, Evelyn; Walsh, B. Timothy

2012-01-01

The proposed DSM-5 classification scheme for eating disorders includes both major and minor changes to the existing DSM-IV diagnostic criteria. It is not known what effect these modifications will have on the ability to make reliable diagnoses. Two studies were conducted to evaluate the short-term test-retest reliability of the proposed DSM-5 eating disorder diagnoses: anorexia nervosa, bulimia nervosa, binge eating disorder, and feeding and eating conditions not elsewhere classified. Participants completed two independent telephone interviews with research assessors (n=70 Study 1; n=55 Study 2). Fair to substantial agreements (κ= 0.80 and 0.54) were observed across eating disorder diagnoses in Study 1 and Study 2, respectively. Acceptable rates of agreement were identified for the individual eating disorder diagnoses, including DSM-5 anorexia nervosa (κ’s of 0.81 to 0.97), bulimia nervosa (κ=0.84), binge eating disorder (κ’s of 0.75 and 0.61), and feeding and eating disorders not elsewhere classified (κ’s of 0.70 and 0.46). Further, improved short-term test-retest reliability was noted when using the DSM-5, in comparison to DSM-IV, criteria for binge eating disorder. Thus, these studies found that trained interviewers can reliably diagnose eating disorders using the proposed DSM-5 criteria; however, additional data from general practice settings and community samples are needed. PMID:22401974

Emotional behavior and arrhythmias induced in cats by hypothalamic stimulation.

PubMed

Tashiro, N; Tanaka, T; Fukumoto, T; Hirata, K; Nakao, H

1985-03-18

As the relationship between emotional behavior and electrocardiographic (ECG) change induced by hypothalamic stimulation is poorly understood, eighty-four points in various areas within the hypothalamus in conscious cats were stimulated electrically through chronically implanted electrodes, the objective being to clarify the behavior accompanying ECG changes, in particular poststimulus arrhythmias. Forty-one of 84 points elicited behavioral patterns such as defense reaction, pseudo-rage and restlessness (classified as group A), and in twenty-one (51%) of these 41 points arrhythmias occurred after cessation of stimulation. Forty-three of 84 points elicited behavioral patterns including predatory, exploratory and other behavioral responses (classified as group B), and in three (7%) of 43 points, poststimulus arrhythmias followed. Under light anesthesia, stimulations of twofold current intensity were applied at these points, and the incidences of the arrhythmias did not change in either group. The arrhythmia-inducing area in the cases of group A was found to lie dorsal and caudal to the optic chiasma and to extend caudally in the fornix. Three points in the cases of group B were located in the outer area of the aforementioned area. These studies showed that arrhythmias and group A behavior were observed mainly from stimulation of the anterior hypothalamus, whereas stimulation of other areas of the hypothalamus, including the lateral and the posterolateral hypothalamus, produced group B behavior and no arrhythmias.

Detection of motor execution using a hybrid fNIRS-biosignal BCI: a feasibility study

PubMed Central

2013-01-01

Background Brain-computer interfaces (BCIs) were recently recognized as a method to promote neuroplastic effects in motor rehabilitation. The core of a BCI is a decoding stage by which signals from the brain are classified into different brain-states. The goal of this paper was to test the feasibility of a single trial classifier to detect motor execution based on signals from cortical motor regions, measured by functional near-infrared spectroscopy (fNIRS), and the response of the autonomic nervous system. An approach that allowed for individually tuned classifier topologies was opted for. This promises to be a first step towards a novel form of active movement therapy that could be operated and controlled by paretic patients. Methods Seven healthy subjects performed repetitions of an isometric finger pinching task, while changes in oxy- and deoxyhemoglobin concentrations were measured in the contralateral primary motor cortex and ventral premotor cortex using fNIRS. Simultaneously, heart rate, breathing rate, blood pressure and skin conductance response were measured. Hidden Markov models (HMM) were used to classify between active isometric pinching phases and rest. The classification performance (accuracy, sensitivity and specificity) was assessed for two types of input data: (i) fNIRS-signals only and (ii) fNIRS- and biosignals combined. Results fNIRS data were classified with an average accuracy of 79.4%, which increased significantly to 88.5% when biosignals were also included (p=0.02). Comparable increases were observed for the sensitivity (from 78.3% to 87.2%, p=0.008) and specificity (from 80.5% to 89.9%, p=0.062). Conclusions This study showed, for the first time, promising classification results with hemodynamic fNIRS data obtained from motor regions and simultaneously acquired biosignals. Combining fNIRS data with biosignals has a beneficial effect, opening new avenues for the development of brain-body-computer interfaces for rehabilitation applications. Further research is required to identify the contribution of each modality to the decoding capability of the subject’s hemodynamic and physiological state. PMID:23336819

Association of RNA Biosignatures With Bacterial Infections in Febrile Infants Aged 60 Days or Younger.

PubMed

Mahajan, Prashant; Kuppermann, Nathan; Mejias, Asuncion; Suarez, Nicolas; Chaussabel, Damien; Casper, T Charles; Smith, Bennett; Alpern, Elizabeth R; Anders, Jennifer; Atabaki, Shireen M; Bennett, Jonathan E; Blumberg, Stephen; Bonsu, Bema; Borgialli, Dominic; Brayer, Anne; Browne, Lorin; Cohen, Daniel M; Crain, Ellen F; Cruz, Andrea T; Dayan, Peter S; Gattu, Rajender; Greenberg, Richard; Hoyle, John D; Jaffe, David M; Levine, Deborah A; Lillis, Kathleen; Linakis, James G; Muenzer, Jared; Nigrovic, Lise E; Powell, Elizabeth C; Rogers, Alexander J; Roosevelt, Genie; Ruddy, Richard M; Saunders, Mary; Tunik, Michael G; Tzimenatos, Leah; Vitale, Melissa; Dean, J Michael; Ramilo, Octavio

Young febrile infants are at substantial risk of serious bacterial infections; however, the current culture-based diagnosis has limitations. Analysis of host expression patterns ("RNA biosignatures") in response to infections may provide an alternative diagnostic approach. To assess whether RNA biosignatures can distinguish febrile infants aged 60 days or younger with and without serious bacterial infections. Prospective observational study involving a convenience sample of febrile infants 60 days or younger evaluated for fever (temperature >38° C) in 22 emergency departments from December 2008 to December 2010 who underwent laboratory evaluations including blood cultures. A random sample of infants with and without bacterial infections was selected for RNA biosignature analysis. Afebrile healthy infants served as controls. Blood samples were collected for cultures and RNA biosignatures. Bioinformatics tools were applied to define RNA biosignatures to classify febrile infants by infection type. RNA biosignatures compared with cultures for discriminating febrile infants with and without bacterial infections and infants with bacteremia from those without bacterial infections. Bacterial infection confirmed by culture. Performance of RNA biosignatures was compared with routine laboratory screening tests and Yale Observation Scale (YOS) scores. Of 1883 febrile infants (median age, 37 days; 55.7% boys), RNA biosignatures were measured in 279 randomly selected infants (89 with bacterial infections-including 32 with bacteremia and 15 with urinary tract infections-and 190 without bacterial infections), and 19 afebrile healthy infants. Sixty-six classifier genes were identified that distinguished infants with and without bacterial infections in the test set with 87% (95% CI, 73%-95%) sensitivity and 89% (95% CI, 81%-93%) specificity. Ten classifier genes distinguished infants with bacteremia from those without bacterial infections in the test set with 94% (95% CI, 70%-100%) sensitivity and 95% (95% CI, 88%-98%) specificity. The incremental C statistic for the RNA biosignatures over the YOS score was 0.37 (95% CI, 0.30-0.43). In this preliminary study, RNA biosignatures were defined to distinguish febrile infants aged 60 days or younger with vs without bacterial infections. Further research with larger populations is needed to refine and validate the estimates of test accuracy and to assess the clinical utility of RNA biosignatures in practice.

Association of RNA Biosignatures With Bacterial Infections in Febrile Infants Aged 60 Days or Younger

PubMed Central

Mahajan, Prashant; Kuppermann, Nathan; Mejias, Asuncion; Suarez, Nicolas; Chaussabel, Damien; Casper, T. Charles; Smith, Bennett; Alpern, Elizabeth R.; Anders, Jennifer; Atabaki, Shireen M.; Bennett, Jonathan E.; Blumberg, Stephen; Bonsu, Bema; Borgialli, Dominic; Brayer, Anne; Browne, Lorin; Cohen, Daniel M.; Crain, Ellen F.; Cruz, Andrea T.; Dayan, Peter S.; Gattu, Rajender; Greenberg, Richard; Hoyle, John D.; Jaffe, David M.; Levine, Deborah A.; Lillis, Kathleen; Linakis, James G.; Muenzer, Jared; Nigrovic, Lise E.; Powell, Elizabeth C.; Rogers, Alexander J.; Roosevelt, Genie; Ruddy, Richard M.; Saunders, Mary; Tunik, Michael G.; Tzimenatos, Leah; Vitale, Melissa; Dean, J. Michael; Ramilo, Octavio

2016-01-01

IMPORTANCE Young febrile infants are at substantial risk of serious bacterial infections; however, the current culture-based diagnosis has limitations. Analysis of host expression patterns (“RNA biosignatures”) in response to infections may provide an alternative diagnostic approach. OBJECTIVE To assess whether RNA biosignatures can distinguish febrile infants aged 60 days or younger with and without serious bacterial infections. DESIGN, SETTING, AND PARTICIPANTS Prospective observational study involving a convenience sample of febrile infants 60 days or younger evaluated for fever (temperature >38° C) in 22 emergency departments from December 2008 to December 2010 who underwent laboratory evaluations including blood cultures. A random sample of infants with and without bacterial infections was selected for RNA biosignature analysis. Afebrile healthy infants served as controls. Blood samples were collected for cultures and RNA biosignatures. Bioinformatics tools were applied to define RNA biosignatures to classify febrile infants by infection type. EXPOSURE RNA biosignatures compared with cultures for discriminating febrile infants with and without bacterial infections and infants with bacteremia from those without bacterial infections. MAIN OUTCOMES AND MEASURES Bacterial infection confirmed by culture. Performance of RNA biosignatures was compared with routine laboratory screening tests and Yale Observation Scale (YOS) scores. RESULTS Of 1883 febrile infants (median age, 37 days; 55.7%boys), RNA biosignatures were measured in 279 randomly selected infants (89 with bacterial infections—including 32 with bacteremia and 15 with urinary tract infections—and 190 without bacterial infections), and 19 afebrile healthy infants. Sixty-six classifier genes were identified that distinguished infants with and without bacterial infections in the test set with 87%(95%CI, 73%-95%) sensitivity and 89% (95%CI, 81%-93%) specificity. Ten classifier genes distinguished infants with bacteremia from those without bacterial infections in the test set with 94%(95%CI, 70%-100%) sensitivity and 95%(95%CI, 88%-98%) specificity. The incremental C statistic for the RNA biosignatures over the YOS score was 0.37 (95%CI, 0.30–0.43). CONCLUSIONS AND RELEVANCE In this preliminary study, RNA biosignatures were defined to distinguish febrile infants aged 60 days or younger with vs without bacterial infections. Further research with larger populations is needed to refine and validate the estimates of test accuracy and to assess the clinical utility of RNA biosignatures in practice. PMID:27552618

Characterizing Interference in Radio Astronomy Observations through Active and Unsupervised Learning

NASA Technical Reports Server (NTRS)

Doran, G.

2013-01-01

In the process of observing signals from astronomical sources, radio astronomers must mitigate the effects of manmade radio sources such as cell phones, satellites, aircraft, and observatory equipment. Radio frequency interference (RFI) often occurs as short bursts (< 1 ms) across a broad range of frequencies, and can be confused with signals from sources of interest such as pulsars. With ever-increasing volumes of data being produced by observatories, automated strategies are required to detect, classify, and characterize these short "transient" RFI events. We investigate an active learning approach in which an astronomer labels events that are most confusing to a classifier, minimizing the human effort required for classification. We also explore the use of unsupervised clustering techniques, which automatically group events into classes without user input. We apply these techniques to data from the Parkes Multibeam Pulsar Survey to characterize several million detected RFI events from over a thousand hours of observation.

Quantitative diagnosis of breast tumors by morphometric classification of microenvironmental myoepithelial cells using a machine learning approach

PubMed Central

Yamamoto, Yoichiro; Saito, Akira; Tateishi, Ayako; Shimojo, Hisashi; Kanno, Hiroyuki; Tsuchiya, Shinichi; Ito, Ken-ichi; Cosatto, Eric; Graf, Hans Peter; Moraleda, Rodrigo R.; Eils, Roland; Grabe, Niels

2017-01-01

Machine learning systems have recently received increased attention for their broad applications in several fields. In this study, we show for the first time that histological types of breast tumors can be classified using subtle morphological differences of microenvironmental myoepithelial cell nuclei without any direct information about neoplastic tumor cells. We quantitatively measured 11661 nuclei on the four histological types: normal cases, usual ductal hyperplasia and low/high grade ductal carcinoma in situ (DCIS). Using a machine learning system, we succeeded in classifying the four histological types with 90.9% accuracy. Electron microscopy observations suggested that the activity of typical myoepithelial cells in DCIS was lowered. Through these observations as well as meta-analytic database analyses, we developed a paracrine cross-talk-based biological mechanism of DCIS progressing to invasive cancer. Our observations support novel approaches in clinical computational diagnostics as well as in therapy development against progression. PMID:28440283

Operational use of Landsat data for timber inventory

NASA Technical Reports Server (NTRS)

Price, Curtis V.; Bowlin, Harry L.

1987-01-01

Landsat TM data, digital elevation model (DEM) data, and field observations were used to generate a timber type/structure and land-cover strata map of the Sequoia National Forest in California, U.S. and to create a classification data set. The spectral classes were identified as 32 information classes of land cover or timber type and structure. DEM data were used for the determination of major timber specie types by topographic regions of natural occurrence. The results suggest that, for inventories over large areas, traditional per-pixel classifiers are not appropriate for TM-resolution data sets over spatially complex regions such as forest lands; either resolution must be degraded, or more context-dependent classifiers, such as the ECHO classifier described by Landgrebe (1979), must be used.

Vegetation communities at Big Muddy National Fish and Wildlife Refuge, Missouri

USGS Publications Warehouse

Struckhoff, Matthew A.; Grabner, Keith W.; Stroh, Esther D.

2011-01-01

New and existing data were used to describe and map vegetation communities at Big Muddy National Fish and Wildlife Refuge. Existing data had been gathered during the growing seasons of 2002, 2003, and 2004. New data were collected in 2007 to describe previously unsampled communities and communities within which insufficient data had been collected. Plot data and field observations were used to describe 17 natural and semi-natural communities at the Association level of the National Vegetation Classification System (NVCS). Four ruderal communities not included in the NVCS are also described. Data were used to inform delineation of communities using aerial photos from 2000, 2002, 2003, 2005, 2006, and 2007. During this process, eleven additional land cover classes including cultural features, managed vegetation communities, and water features were identified. These features were mapped, some were described, but no vegetation data were collected. In 2009, nearly all community polygons were field visited and classified to the Association level. When necessary, polygon boundaries were adjusted based on field observations. The final map includes 482 polygons of 27 land cover classes encompassing 3,174 hectares on 5 units of the refuge. Data and information will inform the development of the refuge Comprehensive Conservation Plan.

Retrospective review of snake bite victims.

PubMed

Nazim, Muhammad H; Gupta, Sanjay; Hashmi, Syed; Zuberi, Jamshed; Wilson, Alison; Roberts, Lawrence; Karimi, Kamran

2008-01-01

Venomous snakebites are a rare but dangerous and potentially deadly condition in the U.S.. Most bites in the U. S. result from envenomation with snakes of the family Viperidae, subfamily Crotalinae, which includes rattlesnakes and copperheads. Treatment includes a comprehensive work-up to look for possible hematologic, neurologic, renal, and cardiovascular abnormalities, local wound care, systemic antivenom administration, tetanus prophylaxis, antibiotics in the presence of infection and surgical treatment if needed, which may include debridement, fasciotomy and rarely amputation. All these patients should be observed for a minimum of 8 hours. Any evidence of envenomation mandates a minimum of 24 hours of in-hospital observation. A grading system to classify the severity of envenomation is described. The most commonly used antivenom in the U.S. is CroFab, which has a much lower incidence of acute or delayed allergic reactions compared to the older antivenoms. When allergic reactions do occur, they are usually of mild to moderate severity. With the improved risk-benefit ratio of CroFab, antivenom is indicated with any grade of envenomation. In this a retrospective study, we will review our experience with 25 snakebite victims admitted to the West Virginia University over a five years period.

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.

Wavelet SVM in Reproducing Kernel Hilbert Space for hyperspectral remote sensing image classification

NASA Astrophysics Data System (ADS)

Du, Peijun; Tan, Kun; Xing, Xiaoshi

2010-12-01

Combining Support Vector Machine (SVM) with wavelet analysis, we constructed wavelet SVM (WSVM) classifier based on wavelet kernel functions in Reproducing Kernel Hilbert Space (RKHS). In conventional kernel theory, SVM is faced with the bottleneck of kernel parameter selection which further results in time-consuming and low classification accuracy. The wavelet kernel in RKHS is a kind of multidimensional wavelet function that can approximate arbitrary nonlinear functions. Implications on semiparametric estimation are proposed in this paper. Airborne Operational Modular Imaging Spectrometer II (OMIS II) hyperspectral remote sensing image with 64 bands and Reflective Optics System Imaging Spectrometer (ROSIS) data with 115 bands were used to experiment the performance and accuracy of the proposed WSVM classifier. The experimental results indicate that the WSVM classifier can obtain the highest accuracy when using the Coiflet Kernel function in wavelet transform. In contrast with some traditional classifiers, including Spectral Angle Mapping (SAM) and Minimum Distance Classification (MDC), and SVM classifier using Radial Basis Function kernel, the proposed wavelet SVM classifier using the wavelet kernel function in Reproducing Kernel Hilbert Space is capable of improving classification accuracy obviously.

Influence Analysis for the Area Under the Receiver Operating Characteristic Curve.

PubMed

Ke, Bo-Shiang; Chiang, An Jen; Chang, Yuan-Chin Ivan

2018-01-01

Classification measures play essential roles in the assessment and construction of classifiers. Hence, determining how to prevent these measures from being affected by individual observations has become an important problem. In this paper, we propose several indexes based on the influence function and the concept of local influence to identify influential observations that affect the estimate of the area under the receiver operating characteristic curve (AUC), an important and commonly used measure. Cumulative lift charts are also used to equipoise the disagreements among the proposed indexes. Both the AUC indexes and the graphical tools only rely on the classification scores, and both are applicable to classifiers that can produce real-valued classification scores. A real data set is used for illustration.

Self-reports of induced abortion: an empathetic setting can improve the quality of data.

PubMed Central

Rasch, V; Muhammad, H; Urassa, E; Bergström, S

2000-01-01

OBJECTIVES: This study estimated the proportion of incomplete abortions that are induced in hospital-based settings in Tanzania. METHODS: A cross-sectional questionnaire study was conducted in 2 phases at 3 hospitals in Tanzania. Phase 1 included 302 patients with a diagnosis of incomplete abortion, and phase 2 included 823 such patients. RESULTS: In phase 1, in which cases were classified by clinical criteria and information from the patient, 3.9% to 16.1% of the cases were classified as induced abortion. In phase 2, in which the structured interview was changed to an empathetic dialogue and previously used clinical criteria were omitted, 30.9% to 60.0% of the cases were classified as induced abortion. CONCLUSIONS: An empathetic dialogue improves the quality of data collected among women with induced abortion. PMID:10897196

Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2's q2-feature-classifier plugin.

PubMed

Bokulich, Nicholas A; Kaehler, Benjamin D; Rideout, Jai Ram; Dillon, Matthew; Bolyen, Evan; Knight, Rob; Huttley, Gavin A; Gregory Caporaso, J

2018-05-17

Taxonomic classification of marker-gene sequences is an important step in microbiome analysis. We present q2-feature-classifier ( https://github.com/qiime2/q2-feature-classifier ), a QIIME 2 plugin containing several novel machine-learning and alignment-based methods for taxonomy classification. We evaluated and optimized several commonly used classification methods implemented in QIIME 1 (RDP, BLAST, UCLUST, and SortMeRNA) and several new methods implemented in QIIME 2 (a scikit-learn naive Bayes machine-learning classifier, and alignment-based taxonomy consensus methods based on VSEARCH, and BLAST+) for classification of bacterial 16S rRNA and fungal ITS marker-gene amplicon sequence data. The naive-Bayes, BLAST+-based, and VSEARCH-based classifiers implemented in QIIME 2 meet or exceed the species-level accuracy of other commonly used methods designed for classification of marker gene sequences that were evaluated in this work. These evaluations, based on 19 mock communities and error-free sequence simulations, including classification of simulated "novel" marker-gene sequences, are available in our extensible benchmarking framework, tax-credit ( https://github.com/caporaso-lab/tax-credit-data ). Our results illustrate the importance of parameter tuning for optimizing classifier performance, and we make recommendations regarding parameter choices for these classifiers under a range of standard operating conditions. q2-feature-classifier and tax-credit are both free, open-source, BSD-licensed packages available on GitHub.

Heidelberg Retina Tomograph 3 machine learning classifiers for glaucoma detection

PubMed Central

Townsend, K A; Wollstein, G; Danks, D; Sung, K R; Ishikawa, H; Kagemann, L; Gabriele, M L; Schuman, J S

2010-01-01

Aims To assess performance of classifiers trained on Heidelberg Retina Tomograph 3 (HRT3) parameters for discriminating between healthy and glaucomatous eyes. Methods Classifiers were trained using HRT3 parameters from 60 healthy subjects and 140 glaucomatous subjects. The classifiers were trained on all 95 variables and smaller sets created with backward elimination. Seven types of classifiers, including Support Vector Machines with radial basis (SVM-radial), and Recursive Partitioning and Regression Trees (RPART), were trained on the parameters. The area under the ROC curve (AUC) was calculated for classifiers, individual parameters and HRT3 glaucoma probability scores (GPS). Classifier AUCs and leave-one-out accuracy were compared with the highest individual parameter and GPS AUCs and accuracies. Results The highest AUC and accuracy for an individual parameter were 0.848 and 0.79, for vertical cup/disc ratio (vC/D). For GPS, global GPS performed best with AUC 0.829 and accuracy 0.78. SVM-radial with all parameters showed significant improvement over global GPS and vC/ D with AUC 0.916 and accuracy 0.85. RPART with all parameters provided significant improvement over global GPS with AUC 0.899 and significant improvement over global GPS and vC/D with accuracy 0.875. Conclusions Machine learning classifiers of HRT3 data provide significant enhancement over current methods for detection of glaucoma. PMID:18523087

High Spatial resolution remote sensing for salt marsh change detection on Fire Island National Seashore

NASA Astrophysics Data System (ADS)

Campbell, A.; Wang, Y.

2017-12-01

Salt marshes are under increasing pressure due to anthropogenic stressors including sea level rise, nutrient enrichment, herbivory and disturbances. Salt marsh losses risk the important ecosystem services they provide including biodiversity, water filtration, wave attenuation, and carbon sequestration. This study determines salt marsh change on Fire Island National Seashore, a barrier island along the south shore of Long Island, New York. Object-based image analysis was used to classifying Worldview-2, high resolution satellite, and topobathymetric LiDAR. The site was impacted by Hurricane Sandy in October of 2012 causing a breach in the Barrier Island and extensive overwash. In situ training data from vegetation plots were used to train the Random Forest classifier. The object-based Worldview-2 classification achieved an overall classification accuracy of 92.75. Salt marsh change for the study site was determined by comparing the 2015 classification with a 1997 classification. The study found a shift from high marsh to low marsh and a reduction in Phragmites on Fire Island. Vegetation losses were observed along the edge of the marsh and in the marsh interior. The analysis agreed with many of the trends found throughout the region including the reduction of high marsh and decline of salt marsh. The reduction in Phragmites could be due to the species shrinking niche between rising seas and dune vegetation on barrier islands. The complex management issues facing salt marsh across the United States including sea level rise and eutrophication necessitate very high resolution classification and change detection of salt marsh to inform management decisions such as restoration, salt marsh migration, and nutrient inputs.

Perspectives on Unmanned Aircraft Classification for Civil Airworthiness Standards

NASA Technical Reports Server (NTRS)

Maddalon, Jeffrey M.; Hayhurst, Kelly J.; Koppen, Daniel M.; Upchurch, Jason M.; Morris, A. Terry; Verstynen, Harry A.

2013-01-01

The use of unmanned aircraft in the National Airspace System (NAS) has been characterized as the next great step forward in the evolution of civil aviation. Although use of unmanned aircraft systems (UAS) in military and public service operations is proliferating, civil use of UAS remains limited in the United States today. This report focuses on one particular regulatory challenge: classifying UAS to assign airworthiness standards. This paper provides observations related to how the current regulations for classifying manned aircraft could apply to UAS.

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

PubMed

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

2017-09-01

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

Classification Systems for Two-Year Colleges. New Directions for Community Colleges. The Jossey-Bass Higher and Adult Education Series.

ERIC Educational Resources Information Center

McCormick, Alexander C., Ed.; Cox, Rebecca D., Ed.

2003-01-01

This summer 2003 issue of New Directions for Community Colleges advances the conversation among researchers and practitioners about possible approaches to classifying two-year colleges. The 10 chapters include the following: (1) "Classifying Two-Year Colleges: Purposes, Possibilities and Pitfalls" (Alexander C. McCormick and Rebecca D.…

Accuracy and efficiency of area classifications based on tree tally

Treesearch

Michael S. Williams; Hans T. Schreuder; Raymond L. Czaplewski

2001-01-01

Inventory data are often used to estimate the area of the land base that is classified as a specific condition class. Examples include areas classified as old-growth forest, private ownership, or suitable habitat for a given species. Many inventory programs rely on classification algorithms of varying complexity to determine condition class. These algorithms can be...

A time-frequency classifier for human gait recognition

NASA Astrophysics Data System (ADS)

Mobasseri, Bijan G.; Amin, Moeness G.

2009-05-01

Radar has established itself as an effective all-weather, day or night sensor. Radar signals can penetrate walls and provide information on moving targets. Recently, radar has been used as an effective biometric sensor for classification of gait. The return from a coherent radar system contains a frequency offset in the carrier frequency, known as the Doppler Effect. The movements of arms and legs give rise to micro Doppler which can be clearly detailed in the time-frequency domain using traditional or modern time-frequency signal representation. In this paper we propose a gait classifier based on subspace learning using principal components analysis(PCA). The training set consists of feature vectors defined as either time or frequency snapshots taken from the spectrogram of radar backscatter. We show that gait signature is captured effectively in feature vectors. Feature vectors are then used in training a minimum distance classifier based on Mahalanobis distance metric. Results show that gait classification with high accuracy and short observation window is achievable using the proposed classifier.

Multicentre prospective validation of a urinary peptidome-based classifier for the diagnosis of type 2 diabetic nephropathy

PubMed Central

Siwy, Justyna; Schanstra, Joost P.; Argiles, Angel; Bakker, Stephan J.L.; Beige, Joachim; Boucek, Petr; Brand, Korbinian; Delles, Christian; Duranton, Flore; Fernandez-Fernandez, Beatriz; Jankowski, Marie-Luise; Al Khatib, Mohammad; Kunt, Thomas; Lajer, Maria; Lichtinghagen, Ralf; Lindhardt, Morten; Maahs, David M; Mischak, Harald; Mullen, William; Navis, Gerjan; Noutsou, Marina; Ortiz, Alberto; Persson, Frederik; Petrie, John R.; Roob, Johannes M.; Rossing, Peter; Ruggenenti, Piero; Rychlik, Ivan; Serra, Andreas L.; Snell-Bergeon, Janet; Spasovski, Goce; Stojceva-Taneva, Olivera; Trillini, Matias; von der Leyen, Heiko; Winklhofer-Roob, Brigitte M.; Zürbig, Petra; Jankowski, Joachim

2014-01-01

Background Diabetic nephropathy (DN) is one of the major late complications of diabetes. Treatment aimed at slowing down the progression of DN is available but methods for early and definitive detection of DN progression are currently lacking. The ‘Proteomic prediction and Renin angiotensin aldosterone system Inhibition prevention Of early diabetic nephRopathy In TYpe 2 diabetic patients with normoalbuminuria trial’ (PRIORITY) aims to evaluate the early detection of DN in patients with type 2 diabetes (T2D) using a urinary proteome-based classifier (CKD273). Methods In this ancillary study of the recently initiated PRIORITY trial we aimed to validate for the first time the CKD273 classifier in a multicentre (9 different institutions providing samples from 165 T2D patients) prospective setting. In addition we also investigated the influence of sample containers, age and gender on the CKD273 classifier. Results We observed a high consistency of the CKD273 classification scores across the different centres with areas under the curves ranging from 0.95 to 1.00. The classifier was independent of age (range tested 16–89 years) and gender. Furthermore, the use of different urine storage containers did not affect the classification scores. Analysis of the distribution of the individual peptides of the classifier over the nine different centres showed that fragments of blood-derived and extracellular matrix proteins were the most consistently found. Conclusion We provide for the first time validation of this urinary proteome-based classifier in a multicentre prospective setting and show the suitability of the CKD273 classifier to be used in the PRIORITY trial. PMID:24589724

The prevalence and determinants of overweight and obesity among French youths and adults with intellectual disabilities attending special education schools.

PubMed

Bégarie, Jérôme; Maïano, Christophe; Leconte, Pascale; Ninot, Grégory

2013-05-01

This study examines the prevalence of overweight and obesity and a panel of potential determinants among French youths and adults with an intellectual disability (ID). The sample used consisted of 1120 youths and adults with an ID, from 5 to 28 years old, attending a French special education school. The results indicated that 19.8% of the participants with an ID are classified as overweight and 8.6% as obese. Multivariate logistic regression analyses revealed that there are nearly three times more girls/women classified as overweight than boys/men. Additionally, they showed that there are nearly two times more participants from southern France classified as overweight than from northern France, and that the risk of being classified as overweight significantly increases with seniority in the school. Next, the interaction effects observed indicated first that there are nearly two times more boys/men on psychotropic medication classified as overweight than boys/men not on psychotropic medication. Second, they revealed that the odds of being classified as overweight for boys/men not on psychotropic medication are 47% lower than for girls/women not on psychotropic medication. Third, they indicated that there are nearly two times more boys/men from southern France classified as obese than boys/men from northern France. Fourth, they showed that the odds of being classified as obese for boys/men from northern France are 52% lower than for girls/women from northern France. In conclusion, these results should be viewed as preliminary and need to be replicated since, to our knowledge, this study is the first one to examine this topic while simultaneously controlling for all of the potential determinants and relying on a sample of youths and adults. Copyright © 2013 Elsevier Ltd. All rights reserved.

SPECTROSCOPY OF HIGH-REDSHIFT SUPERNOVAE FROM THE ESSENCE PROJECT: THE FIRST FOUR YEARS

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

Foley, R. J.; Chornock, R.; Silverman, J. M.

We present the results of spectroscopic observations from the ESSENCE high-redshift supernova (SN) survey during its first four years of operation. This sample includes spectra of all SNe Ia whose light curves were presented by Miknaitis et al. and used in the cosmological analyses of Davis et al. and Wood-Vasey et al. The sample represents 273 hr of spectroscopic observations with 6.5-10 m class telescopes of objects detected and selected for spectroscopy by the ESSENCE team. We present 184 spectra of 156 objects. Combining this sample with that of Matheson et al., we have a total sample of 329 spectramore » of 274 objects. From this, we are able to spectroscopically classify 118 Type Ia SNe. As the survey has matured, the efficiency of classifying SNe Ia has remained constant while we have observed both higher-redshift SNe Ia and SNe Ia farther from maximum brightness. Examining the subsample of SNe Ia with host-galaxy redshifts shows that redshifts derived from only the SN Ia spectra are consistent with redshifts found from host-galaxy spectra. Moreover, the phases derived from only the SN Ia spectra are consistent with those derived from light-curve fits. By comparing our spectra to local templates, we find that the rate of objects similar to the overluminous SN 1991T and the underluminous SN 1991bg in our sample are consistent with that of the local sample. We do note, however, that we detect no object spectroscopically or photometrically similar to SN 1991bg. Although systematic effects could reduce the high-redshift rate we expect based on the low-redshift surveys, it is possible that SN 1991bg-like SNe Ia are less prevalent at high redshift.« less

Cutaneous manifestations of the subtypes of polycystic ovary syndrome in Korean patients.

PubMed

Hong, J S; Kwon, H H; Park, S Y; Jung, J Y; Yoon, J Y; Min, S; Choi, Y M; Suh, D H

2015-01-01

Polycystic ovary syndrome (PCOS) is a common endocrinological disorder in women of childbearing-age. Although PCOS has common dermatological manifestations, including hirsutism, acne and androgenetic alopecia, little is known about the dermatological characteristics of PCOS patients in Asia. The goal of this study is to elucidate the dermatological characteristics and metabolic and hormonal parameters of Korean PCOS patients classified by the three ASRM/ESHERE criteria. We investigated 40 untreated PCOS patients who were newly diagnosed in the Department of Obstetrics & Gynecology of Seoul National University Hospital. Patients were classified according to the presence of irregular menstruation (IM), polycystic ovary morphology (PCOM) and hyperandrogenism (HA). Acne specific questionnaire, physical examination, and blood sampling were thoroughly conducted. Twenty four patients (60.0%) met the criteria for the IM/HA/PCOM group and sixteen (40.0%) belonged to the IM/PCOM group. Acne was the most commonly observed dermatological manifestation (95.0%) followed by hirsutism (60.0%), seoborrhea (47.5%), acanthosis nigricans (20.0%) and androgenetic alopecia (12.5%). Hirsutism was more frequently observed in the IM/HA/PCOM group; the prevalence of other cutaneous manifestations did not differ significantly. Acne was most often observed on the face and most acne lesions were distributed on the forehead and cheek. Serum dehydroepiandrosterone sulphate level was higher in IM/HA/PCOM group, while serum cholesterol and high density lipoprotein concentrations were higher in the IM/PCOM group. We described several dermatological manifestations and serum hormonal and metabolic parameters in Korean PCOS patients. Cutaneous manifestations might be the first signs of PCOS; therefore, dermatologists should be more aware of cutaneous manifestations of various ethnicities. © 2014 European Academy of Dermatology and Venereology.

Verbal Dominant Memory Impairment and Low Risk for Post-operative Memory Worsening in Both Left and Right Temporal Lobe Epilepsy Associated with Hippocampal Sclerosis.

PubMed

Khalil, Amr Farid; Iwasaki, Masaki; Nishio, Yoshiyuki; Jin, Kazutaka; Nakasato, Nobukazu; Tominaga, Teiji

2016-11-15

Post-operative memory changes after temporal lobe surgery have been established mainly by group analysis of cognitive outcome. This study investigated individual patient-based memory outcome in surgically-treated patients with mesial temporal lobe epilepsy (TLE). This study included 84 consecutive patients with intractable TLE caused by unilateral hippocampal sclerosis (HS) who underwent epilepsy surgery (47 females, 41 left [Lt] TLE). Memory functions were evaluated with the Wechsler Memory Scale-Revised before and at 1 year after surgery. Pre-operative memory function was classified into three patterns: verbal dominant memory impairment (Verb-D), visual dominant impairment (Vis-D), and no material-specific impairment. Post-operative changes in verbal and visual memory indices were classified into meaningful improvement, worsening, or no significant changes. Pre-operative patterns and post-operative changes in verbal and visual memory function were compared between the Lt and right (Rt) TLE groups. Pre-operatively, Verb-D was the most common type of impairment in both the Lt and Rt TLE groups (65.9 and 48.8%), and verbal memory indices were lower than visual memory indices, especially in the Lt compared with Rt TLE group. Vis-D was observed only in 11.6% of Rt and 7.3% of Lt TLE patients. Post-operatively, meaningful improvement of memory indices was observed in 23.3-36.6% of the patients, and the memory improvement was equivalent between Lt and Rt TLE groups and between verbal and visual materials. In conclusion, Verb-D is most commonly observed in patients with both the Lt and Rt TLE associated with HS. Hippocampectomy can improve memory indices in such patients regardless of the side of surgery and the function impaired.

RADARSAT-2 Polarimetric Radar Imaging for Lake Ice Mapping

NASA Astrophysics Data System (ADS)

Pan, F.; Kang, K.; Duguay, C. R.

2016-12-01

Changes in lake ice dates and duration are useful indicators for assessing long-term climate trends and variability in northern countries. Lake ice cover observations are also a valuable data source for predictions with numerical ice and weather forecasting models. In recent years, satellite remote sensing has assumed a greater role in providing observations of lake ice cover extent for both modeling and climate monitoring purposes. Polarimetric radar imaging has become a promising tool for lake ice mapping at high latitudes where meteorological conditions and polar darkness severely limit observations from optical sensors. In this study, we assessed and characterized the physical scattering mechanisms of lake ice from fully polarimetric RADARSAT-2 datasets obtained over Great Bear Lake, Canada, with the intent of classifying open water and different ice types during the freeze-up and break-up periods. Model-based and eigen-based decompositions were employed to construct the coherency matrix into deterministic scattering mechanisms. These procedures as well as basic polarimetric parameters were integrated into modified convolutional neural networks (CNN). The CNN were modified via introduction of a Markov random field into the higher iterative layers of networks for acquiring updated priors and classifying ice and open water areas over the lake. We show that the selected polarimetric parameters can help with interpretation of radar-ice/water interactions and can be used successfully for water-ice segmentation, including different ice types. As more satellite SAR sensors are being launched or planned, such as the Sentinel-1a/b series and the upcoming RADARSAT Constellation Mission, the rapid volume growth of data and their analysis require the development of robust automated algorithms. The approach developed in this study was therefore designed with the intent of moving towards fully automated mapping of lake ice for consideration by ice services.

Neurobiological Correlates in Internet Gaming Disorder: A Systematic Literature Review

PubMed Central

Kuss, Daria J.; Pontes, Halley M.; Griffiths, Mark D.

2018-01-01

Internet Gaming Disorder (IGD) is a potential mental disorder currently included in the third section of the latest (fifth) edition of the Diagnostic and Statistical Manual for Mental Disorders (DSM-5) as a condition that requires additional research to be included in the main manual. Although research efforts in the area have increased, there is a continuing debate about the respective criteria to use as well as the status of the condition as mental health concern. Rather than using diagnostic criteria which are based on subjective symptom experience, the National Institute of Mental Health advocates the use of Research Domain Criteria (RDoC) which may support classifying mental disorders based on dimensions of observable behavior and neurobiological measures because mental disorders are viewed as biological disorders that involve brain circuits that implicate specific domains of cognition, emotion, and behavior. Consequently, IGD should be classified on its underlying neurobiology, as well as its subjective symptom experience. Therefore, the aim of this paper is to review the neurobiological correlates involved in IGD based on the current literature base. Altogether, 853 studies on the neurobiological correlates were identified on ProQuest (in the following scholarly databases: ProQuest Psychology Journals, PsycARTICLES, PsycINFO, Applied Social Sciences Index and Abstracts, and ERIC) and on MEDLINE, with the application of the exclusion criteria resulting in reviewing a total of 27 studies, using fMRI, rsfMRI, VBM, PET, and EEG methods. The results indicate there are significant neurobiological differences between healthy controls and individuals with IGD. The included studies suggest that compared to healthy controls, gaming addicts have poorer response-inhibition and emotion regulation, impaired prefrontal cortex (PFC) functioning and cognitive control, poorer working memory and decision-making capabilities, decreased visual and auditory functioning, and a deficiency in their neuronal reward system, similar to those found in individuals with substance-related addictions. This suggests both substance-related addictions and behavioral addictions share common predisposing factors and may be part of an addiction syndrome. Future research should focus on replicating the reported findings in different cultural contexts, in support of a neurobiological basis of classifying IGD and related disorders. PMID:29867599

Neurobiological Correlates in Internet Gaming Disorder: A Systematic Literature Review.

PubMed

Kuss, Daria J; Pontes, Halley M; Griffiths, Mark D

2018-01-01

Internet Gaming Disorder (IGD) is a potential mental disorder currently included in the third section of the latest (fifth) edition of the Diagnostic and Statistical Manual for Mental Disorders (DSM-5) as a condition that requires additional research to be included in the main manual. Although research efforts in the area have increased, there is a continuing debate about the respective criteria to use as well as the status of the condition as mental health concern. Rather than using diagnostic criteria which are based on subjective symptom experience, the National Institute of Mental Health advocates the use of Research Domain Criteria (RDoC) which may support classifying mental disorders based on dimensions of observable behavior and neurobiological measures because mental disorders are viewed as biological disorders that involve brain circuits that implicate specific domains of cognition, emotion, and behavior. Consequently, IGD should be classified on its underlying neurobiology, as well as its subjective symptom experience. Therefore, the aim of this paper is to review the neurobiological correlates involved in IGD based on the current literature base. Altogether, 853 studies on the neurobiological correlates were identified on ProQuest (in the following scholarly databases: ProQuest Psychology Journals, PsycARTICLES, PsycINFO, Applied Social Sciences Index and Abstracts, and ERIC) and on MEDLINE, with the application of the exclusion criteria resulting in reviewing a total of 27 studies, using fMRI, rsfMRI, VBM, PET, and EEG methods. The results indicate there are significant neurobiological differences between healthy controls and individuals with IGD. The included studies suggest that compared to healthy controls, gaming addicts have poorer response-inhibition and emotion regulation, impaired prefrontal cortex (PFC) functioning and cognitive control, poorer working memory and decision-making capabilities, decreased visual and auditory functioning, and a deficiency in their neuronal reward system, similar to those found in individuals with substance-related addictions. This suggests both substance-related addictions and behavioral addictions share common predisposing factors and may be part of an addiction syndrome. Future research should focus on replicating the reported findings in different cultural contexts, in support of a neurobiological basis of classifying IGD and related disorders.

Photometry of the comet 2060 Chiron

NASA Technical Reports Server (NTRS)

Buratti, Bonnie J.; Marcialis, Robert L.; Dunbar, R. Scott

1991-01-01

The comet 2060 Chiron has proven to be an interesting and enigmatic object. Situated between the orbits of Saturn and Uranus, it was originally classified as the most distant asteroid. It began to show cometary behavior in 1987 by increasing a full magnitude in brightness and developing a coma; there is evidence also for similar earlier outbursts. A thorough study of Chiron is important for two reasons: (1) it is a transition object defining the relationship between comets, asteroids, and meteorites; and (2) a full description of its changes in brightness - particularly on time scale of hours - will provide an empirical foundation for understanding the physical mechanisms (including outgassing, sublimation of volatiles, and even significant mass ejections) driving the evolution of comets. Short term outbursts were observed in early 1989, and a rapid decrease in brightness of Chiron's coma was observed in 1990 in the V and R filters. Also, a rotational lightcurve was detected of the nucleus with an amplitude only 1/4 that observed in its quiescent state: this fact indicates the increased importance of the optically thin coma to the observed brightness.

Cooperative Robots to Observe Moving Targets: Review.

PubMed

Khan, Asif; Rinner, Bernhard; Cavallaro, Andrea

2018-01-01

The deployment of multiple robots for achieving a common goal helps to improve the performance, efficiency, and/or robustness in a variety of tasks. In particular, the observation of moving targets is an important multirobot application that still exhibits numerous open challenges, including the effective coordination of the robots. This paper reviews control techniques for cooperative mobile robots monitoring multiple targets. The simultaneous movement of robots and targets makes this problem particularly interesting, and our review systematically addresses this cooperative multirobot problem for the first time. We classify and critically discuss the control techniques: cooperative multirobot observation of multiple moving targets, cooperative search, acquisition, and track, cooperative tracking, and multirobot pursuit evasion. We also identify the five major elements that characterize this problem, namely, the coordination method, the environment, the target, the robot and its sensor(s). These elements are used to systematically analyze the control techniques. The majority of the studied work is based on simulation and laboratory studies, which may not accurately reflect real-world operational conditions. Importantly, while our systematic analysis is focused on multitarget observation, our proposed classification is useful also for related multirobot applications.

Micropulsations in the electric field near the plasmapause, observed by ISEE-1

NASA Technical Reports Server (NTRS)

Moe, T. E.; Maynard, N. C.; Heppner, J. P.

1979-01-01

The occurrence of micropulsations near and inside the plasmapause was surveyed. The observed pulsations, classified as Pc3 and Pi2, are discussed. In addition one single event of Pc1 was observed. The frequencies in the Pc3 and Pi2 bands, the amplitude ranges, and the direction of rotation for the electric field vector are reported.

Infant-mother and infant-sibling attachment in Zambia.

PubMed

Mooya, Haatembo; Sichimba, Francis; Bakermans-Kranenburg, Marian

2016-12-01

This study, the first in Zambia using the Strange Situation Procedure (SSP) to observe attachment relationships and the "very first" observational study of infant-sibling attachment, examined patterns of infant-mother and infant-sibling attachment, and tested their association. We included siblings who were substantially involved in caregiving activities with their younger siblings. We hypothesized that infants would develop attachment relationships to both mothers and siblings; the majority of infants would be classified as securely attached to both caregivers, and infant-mother and infant-sibling attachment would be unrelated. The sample included 88 low-income families in Lusaka, Zambia (average of 3.5 children; SD = 1.5). The SSP distributions (infant-mother) were 59% secure, 24% avoidant and 17% resistant, and 46% secure, 20% avoidant, 5% resistant and 29% disorganized for three- and four-way classifications, respectively. The infant-sibling classifications were 42% secure, 23% avoidant and 35% resistant, and 35% secure, 23% avoidant, 9% resistant and 33% disorganized for three- and four-way classifications, respectively. Infant-mother and infant-sibling attachment relationships were not associated.

Overview of Key Results from SDO Extreme ultraviolet Variability Experiment (EVE)

NASA Astrophysics Data System (ADS)

Woods, Tom; Eparvier, Frank; Jones, Andrew; Mason, James; Didkovsky, Leonid; Chamberlin, Phil

2016-10-01

The SDO Extreme ultraviolet Variability Experiment (EVE) includes several channels to observe the solar extreme ultraviolet (EUV) spectral irradiance from 1 to 106 nm. These channels include the Multiple EUV Grating Spectrograph (MEGS) A, B, and P channels from the University of Colorado (CU) and the EUV SpectroPhometer (ESP) channels from the University of Southern California (USC). The solar EUV spectrum is rich in many different emission lines from the corona, transition region, and chromosphere. The EVE full-disk irradiance spectra are important for studying the solar impacts in Earth's ionosphere and thermosphere and are useful for space weather operations. In addition, the EVE observations, with its high spectral resolution of 0.1 nm and in collaboration with AIA solar EUV images, have proven valuable for studying active region evolution and explosive energy release during flares and coronal eruptions. These SDO measurements have revealed interesting results such as understanding the flare variability over all wavelengths, discovering and classifying different flare phases, using coronal dimming measurements to predict CME properties of mass and velocity, and exploring the role of nano-flares in continual heating of active regions.

The endoscopic endonasal approach for the treatment of juvenile angiofibromas.

PubMed

Llorente, José Luis; López, Fernando

2018-05-12

Juvenile angiofibroma (JA) is a benign tumour, for which the treatment of choice is surgery. It may be associated with significant morbidity because of its anatomical location and its locally destructive growth pattern. Severe haemorrhage constitutes a high risk in JA and its surgical management can be complex. The management of JA remains a challenge. The objective of this study was to review a series of patients with JA treated via the endonasal/endoscopic approach. Medical records of patients operated for JA were reviewed. tumour stage, intraoperative blood loss, complications and persistence/recurrence rates. A total of 30 male patients and one female were included. The mean age was 17 years. Using the Radkowski classification, one JA was classified as stage I, 5 stage IIA, 9 stage IIB, 4 stage IIC, 10 stage IIIA and 2 stage IIIB. Thirty-nine percent of the JA was classified as advanced stage JA (IIIA and IIIB). The mean blood loss was 1.156mL Except in one case, no significant complications were observed. Tumour persistence/recurrence was observed in 2 JA (6%), at the end of the follow-up. Mean postoperative follow-up time was 86 months. This retrospective study supports the notion that endonasal endoscopic approaches for a JA are a feasible option associated with good long-term results. Copyright © 2018 Sociedad Española de Otorrinolaringología y Cirugía de Cabeza y Cuello. Publicado por Elsevier España, S.L.U. All rights reserved.

A Case for a Process Approach: The Warwick Experience.

ERIC Educational Resources Information Center

Screen, P.

1988-01-01

Describes the cyclical nature of a problem-solving sequence produced from observing children involved in the process. Discusses the generic qualities of science: (1) observing; (2) inferring; (3) classifying; (4) predicting; (5) controlling variables; and (6) hypothesizing. Explains the processes in use and advantages of a process-led course. (RT)

Preschool Children's Observed Disruptive Behavior: Variations across Sex, Interactional Context, and Disruptive Psychopathology

ERIC Educational Resources Information Center

Gray, Sarah A. O.; Carter, Alice S.; Briggs-Gowan, Margaret J.; Hill, Carri; Danis, Barbara; Keenan, Kate; Wakschlag, Lauren S.

2012-01-01

Sex differences in disruptive behavior and sensitivity to social context are documented, but the intersection between them is rarely examined empirically. This report focuses on sex differences in observed disruptive behavior across interactional contexts and diagnostic status. Preschoolers (n = 327) were classified as nondisruptive (51%),…

Impact of cardiac resynchronisation therapy on physical ability and quality of life in patients with chronic heart failure.

PubMed

Kloch Badełek, Małgorzata; Klocek, Marek; Czarnecka, Danuta; Wojciechowska, Wiktoria; Wiliński, Jerzy; Kawecka Jaszcz, Kalina

2012-01-01

Chronic heart failure (CHF) is a serious public health problem associated with high rates of morbidity and mortality. Cardiac resynchronisation therapy (CRT) is a well established treatment for selected patients who do not respond to optimal drug treatment of CHF. To assess the impact of CRT on the physical ability and quality of life (QoL) of patients with CHF. The study group consisted of 60 patients (mean age: 66.3 ± 8.7 years, 57 males and three females) with CHF classified as NYHA class III or IV (despite optimal pharmacotherapy for more than three months), a left ventricular end-diastolic diameter ≥ 55 mm, ejection fraction (LVEF) ≤ 35%, and a QRS duration ≥ 130 ms. Just before CRT, and three months after the procedure, patients were assessed using echocardiography and the 6-minute walk test (6-MWT), while their QoL was assessed by the Psychological General Well-Being index (PGWB). Three months after CRT, a 10% increase in baseline values of the 6-MWT constituted a positive response - patients who improved in this manner were classified as responders. Changes of at least ± 10% from baseline values of the PGWB total index were considered as improvement or worsening in QoL. During the follow-up, three men died, and so 57 patients were included in the final analysis. At the end of the study, an increase in the walking distance during the 6-MWT (298.0 ± 107.4 m vs 373.1 ± 127.2 m; p 〈 0.001) was observed. After three months, 38 (66.7%) patients were classified as responders while 19 (33.3%) subjects were classified as non-responders to CRT. Concurrently, after CRT we observed an improvement in QoL in 34 (59.6%) patients, while 23 (41.4%) patients showed no such effect. Patients who demonstrated an increased QoL at three months after CRT were characterised by lower baseline values of the total PGWB index as well as its dimensions (with the exception of the general health dimension). Improvement in QoL after CRT was observed only in the responders group (p 〈 0.01). The implementation of CRT leads to a reduction of heart failure related symptoms and an increase in physical ability in roughly two thirds of patients. Improvement in QoL after CRT pertains only to patients who demonstrate simultaneously an improvement in their 6-MWT. None of the other baseline clinical and echocardiographic parameters were useful in predicting better QoL and exercise capacity after CRT implementation.

Cropping Pattern Detection and Change Analysis in Central Luzon, Philippines Using Multi-Temporal MODIS Imagery and Artificial Neural Network Classifier

NASA Astrophysics Data System (ADS)

dela Torre, D. M.; Perez, G. J. P.

2016-12-01

Cropping practices in the Philippines has been intensifying with greater demand for food and agricultural supplies in view of an increasing population and advanced technologies for farming. This has not been monitored regularly using traditional methods but alternative methods using remote sensing has been promising yet underutilized. This study employed multi-temporal data from MODIS and neural network classifier to map annual land use in agricultural areas from 2001-2014 in Central Luzon, the primary rice growing area of the Philippines. Land use statistics derived from these maps were compared with historical El Nino events to examine how land area is affected by drought events. Fourteen maps of agricultural land use was produced, with the primary classes being single-cropping, double-cropping and perennial crops with secondary classes of forests, urban, bare, water and other classes. Primary classes were produced from the neural network classifier while secondary classes were derived from NDVI threshold masks. The overall accuracy for the 2014 map was 62.05% and a kappa statistic of 0.45. 155.56% increase in single-cropping systems from 2001 to 2014 was observed while double cropping systems decreased by 14.83%. Perennials increased by 76.21% while built-up areas decreased by 12.22% within the 14-year interval. There are several sources of error including mixed-pixels, scale-conversion problems and limited ground reference data. An analysis including El Niño events in 2004 and 2010 demonstrated that marginally irrigated areas that usually planted twice in a year resorted to single cropping, indicating that scarcity of water limited the intensification allowable in the area. Findings from this study can be used to predict future use of agricultural land in the country and also examine how farmlands have responded to climatic factors and stressors.

An efficient robust sound classification algorithm for hearing aids.

PubMed

Nordqvist, Peter; Leijon, Arne

2004-06-01

An efficient robust sound classification algorithm based on hidden Markov models is presented. The system would enable a hearing aid to automatically change its behavior for differing listening environments according to the user's preferences. This work attempts to distinguish between three listening environment categories: speech in traffic noise, speech in babble, and clean speech, regardless of the signal-to-noise ratio. The classifier uses only the modulation characteristics of the signal. The classifier ignores the absolute sound pressure level and the absolute spectrum shape, resulting in an algorithm that is robust against irrelevant acoustic variations. The measured classification hit rate was 96.7%-99.5% when the classifier was tested with sounds representing one of the three environment categories included in the classifier. False-alarm rates were 0.2%-1.7% in these tests. The algorithm is robust and efficient and consumes a small amount of instructions and memory. It is fully possible to implement the classifier in a DSP-based hearing instrument.

The influence of category coherence on inference about cross-classified entities.

PubMed

Patalano, Andrea L; Wengrovitz, Steven M; Sharpes, Kirsten M

2009-01-01

A critical function of categories is their use in property inference (Heit, 2000). However, one challenge to using categories in inference is that most entities in the world belong to multiple categories (e.g., Fido could be a dog, a pet, a mammal, or a security system). Building on Patalano, Chin-Parker, and Ross (2006), we tested the hypothesis that category coherence (the extent to which category features go together in light of prior knowledge) influences the selection of categories for use in property inference about cross-classified entities. In two experiments, we directly contrasted coherent and incoherent categories, both of which included cross-classified entities as members, and we found that the coherent categories were used more readily as the source of both property transfer and property extension. We conclude that category coherence, which has been found to be a potent influence on strength of inference for singly classified entities (Rehder & Hastie, 2004), is also central to category use in reasoning about novel cross-classified ones.

An ultra low power feature extraction and classification system for wearable seizure detection.

PubMed

Page, Adam; Pramod Tim Oates, Siddharth; Mohsenin, Tinoosh

2015-01-01

In this paper we explore the use of a variety of machine learning algorithms for designing a reliable and low-power, multi-channel EEG feature extractor and classifier for predicting seizures from electroencephalographic data (scalp EEG). Different machine learning classifiers including k-nearest neighbor, support vector machines, naïve Bayes, logistic regression, and neural networks are explored with the goal of maximizing detection accuracy while minimizing power, area, and latency. The input to each machine learning classifier is a 198 feature vector containing 9 features for each of the 22 EEG channels obtained over 1-second windows. All classifiers were able to obtain F1 scores over 80% and onset sensitivity of 100% when tested on 10 patients. Among five different classifiers that were explored, logistic regression (LR) proved to have minimum hardware complexity while providing average F-1 score of 91%. Both ASIC and FPGA implementations of logistic regression are presented and show the smallest area, power consumption, and the lowest latency when compared to the previous work.

A case study of microphysical structures and hydrometeor phase in convection using radar Doppler spectra at Darwin, Australia

NASA Astrophysics Data System (ADS)

Riihimaki, L. D.; Comstock, J. M.; Luke, E.; Thorsen, T. J.; Fu, Q.

2017-07-01

To understand the microphysical processes that impact diabatic heating and cloud lifetimes in convection, we need to characterize the spatial distribution of supercooled liquid water. To address this observational challenge, ground-based vertically pointing active sensors at the Darwin Atmospheric Radiation Measurement site are used to classify cloud phase within a deep convective cloud. The cloud cannot be fully observed by a lidar due to signal attenuation. Therefore, we developed an objective method for identifying hydrometeor classes, including mixed-phase conditions, using k-means clustering on parameters that describe the shape of the Doppler spectra from vertically pointing Ka-band cloud radar. This approach shows that multiple, overlapping mixed-phase layers exist within the cloud, rather than a single region of supercooled liquid. Diffusional growth calculations show that the conditions for the Wegener-Bergeron-Findeisen process exist within one of these mixed-phase microstructures.

HR 7578 - A K dwarf double-lined spectroscopic binary with peculiar abundances

NASA Technical Reports Server (NTRS)

Fekel, F. C., Jr.; Beavers, W. I.

1983-01-01

The number of double-lined K and M dwarf binaries which is currently known is quite small, only a dozen or less of each type. The HR 7578 system was classified as dK5 on the Mount Wilson system and as K2 V on the MK ystem. A summary of radial-velocity measurements including the observatory and weight of each observation is given in a table. The star with the stronger lines has been called component A. The final orbital element solution with all observations appropriately weighted was computed with a differential corrections computer program described by Barker et al. (1967). The program had been modified for the double-lined case. Of particular interest are the very large eccentricity of the system and the large minimum masses for each component. These large minimum masses suggest that eclipses may be detectable despite the relatively long period and small radii of the stars.

Toxic effects of phenol on grey mullet, Mugil auratus Risso

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

Krajnovic-Ozretic, M.; Ozretic, B.

1988-01-01

Phenolic compounds are frequently found as contaminants in surface waters, including marine coastal waters. Phenols are generally classified as nonspecific metabolic inhibitors, and the main toxic effects are manifested on the nervous system due to the dissolution of lipids, whereas in the circulatory system phenols act as hemolysing agents of the erythrocytes. Data about sublethal effects of phenol, particularly to marine organisms are rather scarce. In several fresh water fish species exposed to phenol, the number of erythrocytes and the amount of serum proteins were decreased while lesion of gill filaments with edema and blood infiltration with degenerative changes inmore » liver were also observed. These investigations concerned the identification of some physiological and biochemical changes in mullet blood as a consequence of exposure to phenol and some observations about the behavior and gross pathology of poisoned fish were also made.« less

Body adiposity and type 2 diabetes: increased risk with a high body fat percentage even having a normal BMI.

PubMed

Gómez-Ambrosi, Javier; Silva, Camilo; Galofré, Juan C; Escalada, Javier; Santos, Silvia; Gil, María J; Valentí, Victor; Rotellar, Fernando; Ramírez, Beatriz; Salvador, Javier; Frühbeck, Gema

2011-07-01

Obesity is the major risk factor for the development of prediabetes and type 2 diabetes. BMI is widely used as a surrogate measure of obesity, but underestimates the prevalence of obesity, defined as an excess of body fat. We assessed the presence of impaired glucose tolerance or impaired fasting glucose (both considered together as prediabetes) or type 2 diabetes in relation to the criteria used for the diagnosis of obesity using BMI as compared to body fat percentage (BF%). We performed a cross-sectional study including 4,828 (587 lean, 1,320 overweight, and 2,921 obese classified according to BMI) white subjects (66% females), aged 18-80 years. BMI, BF% determined by air-displacement plethysmography (ADP) and conventional blood markers of glucose metabolism and lipid profile were measured. We found a higher than expected number of subjects with prediabetes or type 2 diabetes in the obese category according to BF% when the sample was globally analyzed (P < 0.0001) and in the lean BMI-classified subjects (P < 0.0001), but not in the overweight or obese-classified individuals. Importantly, BF% was significantly higher in lean (by BMI) women with prediabetes or type 2 diabetes as compared to those with normoglycemia (NG) (35.5 ± 7.0 vs. 30.3 ± 7.7%, P < 0.0001), whereas no differences were observed for BMI. Similarly, increased BF% was found in lean BMI-classified men with prediabetes or type 2 diabetes (25.2 ± 9.0 vs. 19.9 ± 8.0%, P = 0.008), exhibiting no differences in BMI or waist circumference. In conclusion, assessing BF% may help to diagnose disturbed glucose tolerance beyond information provided by BMI and waist circumference in particular in male subjects with BMI <25 kg/m(2) and over the age of 40.

A multicenter hospital-based diagnosis study of automated breast ultrasound system in detecting breast cancer among Chinese women.

PubMed

Zhang, Xi; Lin, Xi; Tan, Yanjuan; Zhu, Ying; Wang, Hui; Feng, Ruimei; Tang, Guoxue; Zhou, Xiang; Li, Anhua; Qiao, Youlin

2018-04-01

The automated breast ultrasound system (ABUS) is a potential method for breast cancer detection; however, its diagnostic performance remains unclear. We conducted a hospital-based multicenter diagnostic study to evaluate the clinical performance of the ABUS for breast cancer detection by comparing it to handheld ultrasound (HHUS) and mammography (MG). Eligible participants underwent HHUS and ABUS testing; women aged 40-69 years additionally underwent MG. Images were interpreted using the Breast Imaging Reporting and Data System (BI-RADS). Women in the BI-RADS categories 1-2 were considered negative. Women classified as BI-RADS 3 underwent magnetic resonance imaging to distinguish true- and false-negative results. Core aspiration or surgical biopsy was performed in women classified as BI-RADS 4-5, followed by a pathological diagnosis. Kappa values and agreement rates were calculated between ABUS, HHUS and MG. A total of 1,973 women were included in the final analysis. Of these, 1,353 (68.6%) and 620 (31.4%) were classified as BI-RADS categories 1-3 and 4-5, respectively. In the older age group, the agreement rate and Kappa value between the ABUS and HHUS were 94.0% and 0.860 (P<0.001), respectively; they were 89.2% and 0.735 (P<0.001) between the ABUS and MG, respectively. Regarding consistency between imaging and pathology results, 78.6% of women classified as BI-RADS 4-5 based on the ABUS were diagnosed with precancerous lesions or cancer; which was 7.2% higher than that of women based on HHUS. For BI-RADS 1-2, the false-negative rates of the ABUS and HHUS were almost identical and were much lower than those of MG. We observed a good diagnostic reliability for the ABUS. Considering its performance for breast cancer detection in women with high-density breasts and its lower operator dependence, the ABUS is a promising option for breast cancer detection in China.

Prevalence of alveolar bone loss in healthy children treated at private pediatric dentistry clinics

PubMed Central

GUIMARÃES, Maria do Carmo Machado; de ARAÚJO, Valéria Martins; AVENA, Márcia Raquel; DUARTE, Daniel Rocha da Silva; FREITAS, Francisco Valter

2010-01-01

Objectives The purpose of this study was to evaluate the prevalence of alveolar bone loss (BL) in healthy children treated at private pediatric dentistry clinics in Brasília, Brazil. Material and Methods The research included 7,436 sites present in 885 radiographs from 450 children. The BL prevalence was estimated by measuring the distance from the cementoenamel junction (CEJ) to alveolar bone crest (ABC). Data were divided in groups: (I) No BL: distance from CEJ to ABC is ≤2 mm; (II) questionable BL (QBL): distance from CEJ to ABC is >2 and <3 mm; (III) definite BL (DBL): distance from CEJ to ABC ≥3 mm. Data were treated by the chi-square nonparametric test and Fisher's exact test (p<0.05). Results Among males, 89.31% were classified in group I, 9.82% were classified in group II and 0.85% in group III. Among females, 93.05%, 6.48% and 0.46% patients were classified in Group I, II and III, respectively. The differences between genders were not statistically significant (Chi-square test, p = 0.375). Group composition according to patients’ age showed that 91.11% of individuals were classified as group I, 8.22% in group II and 0.67% in group III. The differences among the age ranges were not statistically significant (Chi-square test, p = 0.418). The mesial and distal sites showed a higher prevalence of BL in the jaw, QBL (89.80%) and DBL (79.40%), and no significant difference was observed in the distribution of QBL (Fisher’s exact test p = 0.311) and DBL (Fisher’s exact test p = 0.672) in the dental arches. The distal sites exhibited higher prevalence of both QBL (77.56%) and DBL (58.82%). Conclusions The periodontal status of children should never be underestimated because BL occurs even in healthy populations, although in a lower frequency. PMID:20857009

Prevalence of alveolar bone loss in healthy children treated at private pediatric dentistry clinics.

PubMed

Guimarães, Maria do Carmo Machado; de Araújo, Valéria Martins; Avena, Márcia Raquel; Duarte, Daniel Rocha da Silva; Freitas, Francisco Valter

2010-01-01

The purpose of this study was to evaluate the prevalence of alveolar bone loss (BL) in healthy children treated at private pediatric dentistry clinics in Brasília, Brazil. The research included 7,436 sites present in 885 radiographs from 450 children. The BL prevalence was estimated by measuring the distance from the cementoenamel junction (CEJ) to alveolar bone crest (ABC). Data were divided in groups: (I) No BL: distance from CEJ to ABC is <2 mm; (II) questionable BL (QBL): distance from CEJ to ABC is >2 and <3 mm; (III) definite BL (DBL): distance from CEJ to ABC >3 mm. Data were treated by the chi-square nonparametric test and Fisher's exact test (p<0.05). Among males, 89.31% were classified in group I, 9.82% were classified in group II and 0.85% in group III. Among females, 93.05%, 6.48% and 0.46% patients were classified in Group I, II and III, respectively. The differences between genders were not statistically significant (Chi-square test, p = 0.375). Group composition according to patients' age showed that 91.11% of individuals were classified as group I, 8.22% in group II and 0.67% in group III. The differences among the age ranges were not statistically significant (Chi-square test, p = 0.418). The mesial and distal sites showed a higher prevalence of BL in the jaw, QBL (89.80%) and DBL (79.40%), and no significant difference was observed in the distribution of QBL (Fisher's exact test p = 0.311) and DBL (Fisher's exact test p = 0.672) in the dental arches. The distal sites exhibited higher prevalence of both QBL (77.56%) and DBL (58.82%). The periodontal status of children should never be underestimated because BL occurs even in healthy populations, although in a lower frequency.

From cognition to the system: developing a multilevel taxonomy of patient safety in general practice.

PubMed

Kostopoulou, O

The paper describes the process of developing a taxonomy of patient safety in general practice. The methodologies employed included fieldwork, task analysis and confidential reporting of patient-safety events in five West Midlands practices. Reported events were traced back to their root causes and contributing factors. The resulting taxonomy is based on a theoretical model of human cognition, includes multiple levels of classification to reflect the chain of causation and considers affective and physiological influences on performance. Events are classified at three levels. At level one, the information-processing model of cognition is used to classify errors. At level two, immediate causes are identified, internal and external to the individual. At level three, more remote causal factors are classified as either 'work organization' or 'technical' with subcategories. The properties of the taxonomy (validity, reliability, comprehensiveness) as well as its usability and acceptability remain to be tested with potential users.

Ischemic stroke lesion segmentation in multi-spectral MR images with support vector machine classifiers

NASA Astrophysics Data System (ADS)

Maier, Oskar; Wilms, Matthias; von der Gablentz, Janina; Krämer, Ulrike; Handels, Heinz

2014-03-01

Automatic segmentation of ischemic stroke lesions in magnetic resonance (MR) images is important in clinical practice and for neuroscientific trials. The key problem is to detect largely inhomogeneous regions of varying sizes, shapes and locations. We present a stroke lesion segmentation method based on local features extracted from multi-spectral MR data that are selected to model a human observer's discrimination criteria. A support vector machine classifier is trained on expert-segmented examples and then used to classify formerly unseen images. Leave-one-out cross validation on eight datasets with lesions of varying appearances is performed, showing our method to compare favourably with other published approaches in terms of accuracy and robustness. Furthermore, we compare a number of feature selectors and closely examine each feature's and MR sequence's contribution.

Ensemble Sparse Classification of Alzheimer’s Disease

PubMed Central

Liu, Manhua; Zhang, Daoqiang; Shen, Dinggang

2012-01-01

The high-dimensional pattern classification methods, e.g., support vector machines (SVM), have been widely investigated for analysis of structural and functional brain images (such as magnetic resonance imaging (MRI)) to assist the diagnosis of Alzheimer’s disease (AD) including its prodromal stage, i.e., mild cognitive impairment (MCI). Most existing classification methods extract features from neuroimaging data and then construct a single classifier to perform classification. However, due to noise and small sample size of neuroimaging data, it is challenging to train only a global classifier that can be robust enough to achieve good classification performance. In this paper, instead of building a single global classifier, we propose a local patch-based subspace ensemble method which builds multiple individual classifiers based on different subsets of local patches and then combines them for more accurate and robust classification. Specifically, to capture the local spatial consistency, each brain image is partitioned into a number of local patches and a subset of patches is randomly selected from the patch pool to build a weak classifier. Here, the sparse representation-based classification (SRC) method, which has shown effective for classification of image data (e.g., face), is used to construct each weak classifier. Then, multiple weak classifiers are combined to make the final decision. We evaluate our method on 652 subjects (including 198 AD patients, 225 MCI and 229 normal controls) from Alzheimer’s Disease Neuroimaging Initiative (ADNI) database using MR images. The experimental results show that our method achieves an accuracy of 90.8% and an area under the ROC curve (AUC) of 94.86% for AD classification and an accuracy of 87.85% and an AUC of 92.90% for MCI classification, respectively, demonstrating a very promising performance of our method compared with the state-of-the-art methods for AD/MCI classification using MR images. PMID:22270352

N-gram support vector machines for scalable procedure and diagnosis classification, with applications to clinical free text data from the intensive care unit.

PubMed

Marafino, Ben J; Davies, Jason M; Bardach, Naomi S; Dean, Mitzi L; Dudley, R Adams

2014-01-01

Existing risk adjustment models for intensive care unit (ICU) outcomes rely on manual abstraction of patient-level predictors from medical charts. Developing an automated method for abstracting these data from free text might reduce cost and data collection times. To develop a support vector machine (SVM) classifier capable of identifying a range of procedures and diagnoses in ICU clinical notes for use in risk adjustment. We selected notes from 2001-2008 for 4191 neonatal ICU (NICU) and 2198 adult ICU patients from the MIMIC-II database from the Beth Israel Deaconess Medical Center. Using these notes, we developed an implementation of the SVM classifier to identify procedures (mechanical ventilation and phototherapy in NICU notes) and diagnoses (jaundice in NICU and intracranial hemorrhage (ICH) in adult ICU). On the jaundice classification task, we also compared classifier performance using n-gram features to unigrams with application of a negation algorithm (NegEx). Our classifier accurately identified mechanical ventilation (accuracy=0.982, F1=0.954) and phototherapy use (accuracy=0.940, F1=0.912), as well as jaundice (accuracy=0.898, F1=0.884) and ICH diagnoses (accuracy=0.938, F1=0.943). Including bigram features improved performance on the jaundice (accuracy=0.898 vs 0.865) and ICH (0.938 vs 0.927) tasks, and outperformed NegEx-derived unigram features (accuracy=0.898 vs 0.863) on the jaundice task. Overall, a classifier using n-gram support vectors displayed excellent performance characteristics. The classifier generalizes to diverse patient populations, diagnoses, and procedures. SVM-based classifiers can accurately identify procedure status and diagnoses among ICU patients, and including n-gram features improves performance, compared to existing methods. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

Multivariate data analysis and machine learning in Alzheimer's disease with a focus on structural magnetic resonance imaging.

PubMed

Falahati, Farshad; Westman, Eric; Simmons, Andrew

2014-01-01

Machine learning algorithms and multivariate data analysis methods have been widely utilized in the field of Alzheimer's disease (AD) research in recent years. Advances in medical imaging and medical image analysis have provided a means to generate and extract valuable neuroimaging information. Automatic classification techniques provide tools to analyze this information and observe inherent disease-related patterns in the data. In particular, these classifiers have been used to discriminate AD patients from healthy control subjects and to predict conversion from mild cognitive impairment to AD. In this paper, recent studies are reviewed that have used machine learning and multivariate analysis in the field of AD research. The main focus is on studies that used structural magnetic resonance imaging (MRI), but studies that included positron emission tomography and cerebrospinal fluid biomarkers in addition to MRI are also considered. A wide variety of materials and methods has been employed in different studies, resulting in a range of different outcomes. Influential factors such as classifiers, feature extraction algorithms, feature selection methods, validation approaches, and cohort properties are reviewed, as well as key MRI-based and multi-modal based studies. Current and future trends are discussed.

Cognition research and constitutional classification in Chinese medicine.

PubMed

Wang, Ji; Li, Yingshuai; Ni, Cheng; Zhang, Huimin; Li, Lingru; Wang, Qi

2011-01-01

In the Western medicine system, scholars have explained individual differences in terms of behaviour and thinking, leading to the emergence of various classification theories on individual differences. Traditional Chinese medicine has long observed human constitutions. Modern Chinese medicine studies have also involved study of human constitutions; however, differences exist in the ways traditional and modern Chinese medicine explore individual constitutions. In the late 1970s, the constitutional theory of Chinese medicine was proposed. This theory takes a global and dynamic view of human differences (e.g., the shape of the human body, function, psychology, and other characteristics) based on arguments from traditional Chinese medicine. The establishment of a standard for classifying constitutions into nine modules was critical for clinical application of this theory. In this review, we describe the history and recent research progress of this theory, and compare it with related studies in the western medicine system. Several research methods, including philology, informatics, epidemiology, and molecular biology, in classifying constitutions used in the constitutional theory of Chinese medicine were discussed. In summary, this constitutional theory of Chinese medicine can be used in clinical practice and would contribute to health control of patients.

Fuzzy Nonlinear Proximal Support Vector Machine for Land Extraction Based on Remote Sensing Image

PubMed Central

Zhong, Xiaomei; Li, Jianping; Dou, Huacheng; Deng, Shijun; Wang, Guofei; Jiang, Yu; Wang, Yongjie; Zhou, Zebing; Wang, Li; Yan, Fei

2013-01-01

Currently, remote sensing technologies were widely employed in the dynamic monitoring of the land. This paper presented an algorithm named fuzzy nonlinear proximal support vector machine (FNPSVM) by basing on ETM+ remote sensing image. This algorithm is applied to extract various types of lands of the city Da’an in northern China. Two multi-category strategies, namely “one-against-one” and “one-against-rest” for this algorithm were described in detail and then compared. A fuzzy membership function was presented to reduce the effects of noises or outliers on the data samples. The approaches of feature extraction, feature selection, and several key parameter settings were also given. Numerous experiments were carried out to evaluate its performances including various accuracies (overall accuracies and kappa coefficient), stability, training speed, and classification speed. The FNPSVM classifier was compared to the other three classifiers including the maximum likelihood classifier (MLC), back propagation neural network (BPN), and the proximal support vector machine (PSVM) under different training conditions. The impacts of the selection of training samples, testing samples and features on the four classifiers were also evaluated in these experiments. PMID:23936016

Identical vs. Conceptual repetition FN400 and Parietal Old/New ERP components occur during encoding and predict subsequent memory

PubMed Central

Griffin, Michael; DeWolf, Melissa; Keinath, Alexander; Liu, Xiaonan; Reder, Lynne

2013-01-01

This Event-Related Potential (ERP) study investigated whether components commonly measured at test, such as the FN400 and the parietal old/new components, could be observed during encoding and, if so, whether they would predict different levels of accuracy on a subsequent memory test. ERPs were recorded while subjects classified pictures of objects as man-made or natural. Some objects were only classified once while others were classified twice during encoding, sometimes with an identical picture, and other times with a different exemplar from the same category. A subsequent surprise recognition test required subjects to judge whether each probe word corresponded to a picture shown earlier, and if so whether there were two identical pictures that corresponded to the word probe, two different pictures, or just one picture. When the second presentation showed a duplicate of an earlier picture, the FN400 effect (a significantly less negative deflection on the second presentation) was observed regardless of subsequent memory response; however, when the second presentation showed a different exemplar of the same concept, the FN400 effect was only marginally significant. In contrast, the parietal old/new effect was robust for the second presentation of conceptual repetitions when the test probe was subsequently recognized, but not for identical repetitions. These findings suggest that ERP components that are typically observed during an episodic memory test can be observed during an incidental encoding task, and that they are predictive of the degree of subsequent memory performance. PMID:23528265

Multi-categorical deep learning neural network to classify retinal images: A pilot study employing small database.

PubMed

Choi, Joon Yul; Yoo, Tae Keun; Seo, Jeong Gi; Kwak, Jiyong; Um, Terry Taewoong; Rim, Tyler Hyungtaek

2017-01-01

Deep learning emerges as a powerful tool for analyzing medical images. Retinal disease detection by using computer-aided diagnosis from fundus image has emerged as a new method. We applied deep learning convolutional neural network by using MatConvNet for an automated detection of multiple retinal diseases with fundus photographs involved in STructured Analysis of the REtina (STARE) database. Dataset was built by expanding data on 10 categories, including normal retina and nine retinal diseases. The optimal outcomes were acquired by using a random forest transfer learning based on VGG-19 architecture. The classification results depended greatly on the number of categories. As the number of categories increased, the performance of deep learning models was diminished. When all 10 categories were included, we obtained results with an accuracy of 30.5%, relative classifier information (RCI) of 0.052, and Cohen's kappa of 0.224. Considering three integrated normal, background diabetic retinopathy, and dry age-related macular degeneration, the multi-categorical classifier showed accuracy of 72.8%, 0.283 RCI, and 0.577 kappa. In addition, several ensemble classifiers enhanced the multi-categorical classification performance. The transfer learning incorporated with ensemble classifier of clustering and voting approach presented the best performance with accuracy of 36.7%, 0.053 RCI, and 0.225 kappa in the 10 retinal diseases classification problem. First, due to the small size of datasets, the deep learning techniques in this study were ineffective to be applied in clinics where numerous patients suffering from various types of retinal disorders visit for diagnosis and treatment. Second, we found that the transfer learning incorporated with ensemble classifiers can improve the classification performance in order to detect multi-categorical retinal diseases. Further studies should confirm the effectiveness of algorithms with large datasets obtained from hospitals.

Automatic Identification of Messages Related to Adverse Drug Reactions from Online User Reviews using Feature-based Classification.

PubMed

Liu, Jingfang; Zhang, Pengzhu; Lu, Yingjie

2014-11-01

User-generated medical messages on Internet contain extensive information related to adverse drug reactions (ADRs) and are known as valuable resources for post-marketing drug surveillance. The aim of this study was to find an effective method to identify messages related to ADRs automatically from online user reviews. We conducted experiments on online user reviews using different feature set and different classification technique. Firstly, the messages from three communities, allergy community, schizophrenia community and pain management community, were collected, the 3000 messages were annotated. Secondly, the N-gram-based features set and medical domain-specific features set were generated. Thirdly, three classification techniques, SVM, C4.5 and Naïve Bayes, were used to perform classification tasks separately. Finally, we evaluated the performance of different method using different feature set and different classification technique by comparing the metrics including accuracy and F-measure. In terms of accuracy, the accuracy of SVM classifier was higher than 0.8, the accuracy of C4.5 classifier or Naïve Bayes classifier was lower than 0.8; meanwhile, the combination feature sets including n-gram-based feature set and domain-specific feature set consistently outperformed single feature set. In terms of F-measure, the highest F-measure is 0.895 which was achieved by using combination feature sets and a SVM classifier. In all, we can get the best classification performance by using combination feature sets and SVM classifier. By using combination feature sets and SVM classifier, we can get an effective method to identify messages related to ADRs automatically from online user reviews.

Unsupervised machine-learning method for improving the performance of ambulatory fall-detection systems

PubMed Central

2012-01-01

Background Falls can cause trauma, disability and death among older people. Ambulatory accelerometer devices are currently capable of detecting falls in a controlled environment. However, research suggests that most current approaches can tend to have insufficient sensitivity and specificity in non-laboratory environments, in part because impacts can be experienced as part of ordinary daily living activities. Method We used a waist-worn wireless tri-axial accelerometer combined with digital signal processing, clustering and neural network classifiers. The method includes the application of Discrete Wavelet Transform, Regrouping Particle Swarm Optimization, Gaussian Distribution of Clustered Knowledge and an ensemble of classifiers including a multilayer perceptron and Augmented Radial Basis Function (ARBF) neural networks. Results Preliminary testing with 8 healthy individuals in a home environment yields 98.6% sensitivity to falls and 99.6% specificity for routine Activities of Daily Living (ADL) data. Single ARB and MLP classifiers were compared with a combined classifier. The combined classifier offers the greatest sensitivity, with a slight reduction in specificity for routine ADL and an increased specificity for exercise activities. In preliminary tests, the approach achieves 100% sensitivity on in-group falls, 97.65% on out-group falls, 99.33% specificity on routine ADL, and 96.59% specificity on exercise ADL. Conclusion The pre-processing and feature-extraction steps appear to simplify the signal while successfully extracting the essential features that are required to characterize a fall. The results suggest this combination of classifiers can perform better than MLP alone. Preliminary testing suggests these methods may be useful for researchers who are attempting to improve the performance of ambulatory fall-detection systems. PMID:22336100

Spectral Trends of Solar Bursts at Sub-THz Frequencies

NASA Astrophysics Data System (ADS)

Fernandes, L. O. T.; Kaufmann, P.; Correia, E.; Giménez de Castro, C. G.; Kudaka, A. S.; Marun, A.; Pereyra, P.; Raulin, J.-P.; Valio, A. B. M.

2017-01-01

Previous sub-THz studies were derived from single-event observations. We here analyze for the first time spectral trends for a larger collection of sub-THz bursts. The collection consists of a set of 16 moderate to small impulsive solar radio bursts observed at 0.2 and 0.4 THz by the Solar Submillimeter-wave Telescope (SST) in 2012 - 2014 at El Leoncito, in the Argentinean Andes. The peak burst spectra included data from new solar patrol radio telescopes (45 and 90 GHz), and were completed with microwave data obtained by the Radio Solar Telescope Network, when available. We critically evaluate errors and uncertainties in sub-THz flux estimates caused by calibration techniques and the corrections for atmospheric transmission, and introduce a new method to obtain a uniform flux scale criterion for all events. The sub-THz bursts were searched during reported GOES soft X-ray events of class C or larger, for periods common to SST observations. Seven out of 16 events exhibit spectral maxima in the range 5 - 40 GHz with fluxes decaying at sub-THz frequencies (three of them associated to GOES class X, and four to class M). Nine out of 16 events exhibited the sub-THz spectral component. In five of these events, the sub-THz emission fluxes increased with a separate frequency from that of the microwave spectral component (two classified as X and three as M), and four events have only been detected at sub-THz frequencies (three classified as M and one as C). The results suggest that the THz component might be present throughout, with the minimum turnover frequency increasing as a function of the energy of the emitting electrons. The peculiar nature of many sub-THz burst events requires further investigations of bursts that are examined from SST observations alone to better understand these phenomena.

Comparison of Random Forest, k-Nearest Neighbor, and Support Vector Machine Classifiers for Land Cover Classification Using Sentinel-2 Imagery

PubMed Central

Thanh Noi, Phan; Kappas, Martin

2017-01-01

In previous classification studies, three non-parametric classifiers, Random Forest (RF), k-Nearest Neighbor (kNN), and Support Vector Machine (SVM), were reported as the foremost classifiers at producing high accuracies. However, only a few studies have compared the performances of these classifiers with different training sample sizes for the same remote sensing images, particularly the Sentinel-2 Multispectral Imager (MSI). In this study, we examined and compared the performances of the RF, kNN, and SVM classifiers for land use/cover classification using Sentinel-2 image data. An area of 30 × 30 km2 within the Red River Delta of Vietnam with six land use/cover types was classified using 14 different training sample sizes, including balanced and imbalanced, from 50 to over 1250 pixels/class. All classification results showed a high overall accuracy (OA) ranging from 90% to 95%. Among the three classifiers and 14 sub-datasets, SVM produced the highest OA with the least sensitivity to the training sample sizes, followed consecutively by RF and kNN. In relation to the sample size, all three classifiers showed a similar and high OA (over 93.85%) when the training sample size was large enough, i.e., greater than 750 pixels/class or representing an area of approximately 0.25% of the total study area. The high accuracy was achieved with both imbalanced and balanced datasets. PMID:29271909

Comparison of Random Forest, k-Nearest Neighbor, and Support Vector Machine Classifiers for Land Cover Classification Using Sentinel-2 Imagery.

PubMed

Thanh Noi, Phan; Kappas, Martin

2017-12-22

In previous classification studies, three non-parametric classifiers, Random Forest (RF), k-Nearest Neighbor (kNN), and Support Vector Machine (SVM), were reported as the foremost classifiers at producing high accuracies. However, only a few studies have compared the performances of these classifiers with different training sample sizes for the same remote sensing images, particularly the Sentinel-2 Multispectral Imager (MSI). In this study, we examined and compared the performances of the RF, kNN, and SVM classifiers for land use/cover classification using Sentinel-2 image data. An area of 30 × 30 km² within the Red River Delta of Vietnam with six land use/cover types was classified using 14 different training sample sizes, including balanced and imbalanced, from 50 to over 1250 pixels/class. All classification results showed a high overall accuracy (OA) ranging from 90% to 95%. Among the three classifiers and 14 sub-datasets, SVM produced the highest OA with the least sensitivity to the training sample sizes, followed consecutively by RF and kNN. In relation to the sample size, all three classifiers showed a similar and high OA (over 93.85%) when the training sample size was large enough, i.e., greater than 750 pixels/class or representing an area of approximately 0.25% of the total study area. The high accuracy was achieved with both imbalanced and balanced datasets.

Elizabeth Brown and the Classification of Sunspots in the 19th Century

NASA Astrophysics Data System (ADS)

Larsen, Kristine

2014-06-01

British amateur astronomers collected solar observation data as members of organizations such as the British Astronomical Association (BAA) and Liverpool Astronomical Society (LAS) in the late 1800s. Amateur astronomer Elizabeth Brown (1830-99) served as Solar Section Director of both groups, and not only aggregated solar observations (including hand-drawn illustrations) from observers from around the globe, but worked closely with solar astronomer Edward Maunder and other professionals in an attempt to garner specific types of observations from BAA members in order to answer a number of astronomical questions of the day. For example, she encouraged the monitoring of the growth and decay of sunspot groups and published a number of her own observations of particular groups, urging observers to note whether faculae were seen before the birth of sunspots in a given region, a topic of controversy at that time. She also developed a system for classifying sunspots and sunspot groups based on their appearance, dividing then into 11 types: normal, compound, pairs, clusters, trains, streams, zigzags, elliptical, vertical, nebulous, and dots. This poster will summarize Brown’s important contributions to solar observing in the late 19th century and situate her classification scheme relative to those of A.L. Cortie (1901), M. Waldmeier (1938; 1947) and the modified Zurich system of McIntosh (1966; 1969; 1989).

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%.

The non-storm time corrugated upper thermosphere: What is beyond MSIS?

NASA Astrophysics Data System (ADS)

Liu, Huixin; Thayer, Jeff; Zhang, Yongliang; Lee, Woo Kyoung

2017-06-01

Observations in the recent decade have revealed many thermospheric density corrugations/perturbations under nonstorm conditions (Kp < 2). They are generally not captured by empirical models like Mass Spectrometer Incoherent Scatter (MSIS) but are operationally important for long-term orbital evolution of Low Earth Orbiting satellites and theoretically for coupling processes in the atmosphere-ionosphere system. We review these density corrugations by classifying them into three types which are driven respectively by the lower atmosphere, ionosphere, and solar wind/magnetosphere. Model capabilities in capturing these features are discussed. A summary table of these corrugations is included to provide a quick guide on their magnitudes, occurring latitude, local time, and season.

Predictive role of stress echocardiography before carotid endarterectomy in patients with coronary artery disease.

PubMed

Galyfos, George; Tsioufis, Constantinos; Theodorou, Dimitris; Katsaragakis, Stilianos; Zografos, Georgios; Filis, Konstantinos

2015-07-01

Our aim was to examine the predictive value of preoperative stress echocardiography regarding early myocardial ischemia and late cardiac events after carotid endarterectomy (CEA). Patients with coronary artery disease undergoing CEA were prospectively included in this study. All patients (n = 162) were classified into low, medium, and high cardiac risk group, according to preoperative stress echocardiography. Classification was based on the criteria of the American Society of Echocardiography. For all patients, cTnI was measured before surgery and on postoperative days 1, 3, and 7. Postoperative cTnI values ranging from 0.05 to 0.5 ng/mL were classified as myocardial ischemia; values >0.5 ng/mL were classified as myocardial infarction. Cardiac damage was defined as either myocardial ischemia or infarction. No deaths, strokes, or symptomatic coronary events were observed during the early postoperative period. There were 112 low cardiac risk patients, 42 medium-risk patients, and 8 high-risk patients, according to stress echocardiography findings. Overall, there were 22 patients (14%) that increased their cTnI values postoperatively (12 of low cardiac risk and 10 of medium cardiac risk), and all of them were asymptomatic. None of the high-risk patients showed any troponin increase. Late cardiac events were associated with cTnI increase, although no high-risk patients showed any late event. Preoperative stress echocardiography does not seem to independently recognize patients in high risk for asymptomatic cardiac damage after CEA. Postoperative troponin elevation seems to be more predictive for late adverse cardiac events than preoperative stress echocardiography. © 2014, Wiley Periodicals, Inc.

Morphology-based optical separation of subpopulations from a heterogeneous murine breast cancer cell line.

PubMed

Tamura, Masato; Sugiura, Shinji; Takagi, Toshiyuki; Satoh, Taku; Sumaru, Kimio; Kanamori, Toshiyuki; Okada, Tomoko; Matsui, Hirofumi

2017-01-01

Understanding tumor heterogeneity is an urgent and unmet need in cancer research. In this study, we used a morphology-based optical cell separation process to classify a heterogeneous cancer cell population into characteristic subpopulations. To classify the cell subpopulations, we assessed their morphology in hydrogel, a three-dimensional culture environment that induces morphological changes according to the characteristics of the cells (i.e., growth, migration, and invasion). We encapsulated the murine breast cancer cell line 4T1E, as a heterogeneous population that includes highly metastatic cells, in click-crosslinkable and photodegradable gelatin hydrogels, which we developed previously. We observed morphological changes within 3 days of encapsulating the cells in the hydrogel. We separated the 4T1E cell population into colony- and granular-type cells by optical separation, in which local UV-induced degradation of the photodegradable hydrogel around the target cells enabled us to collect those cells. The obtained colony- and granular-type cells were evaluated in vitro by using a spheroid assay and in vivo by means of a tumor growth and metastasis assay. The spheroid assay showed that the colony-type cells formed compact spheroids in 2 days, whereas the granular-type cells did not form spheroids. The tumor growth assay in mice revealed that the granular-type cells exhibited lower tumor growth and a different metastasis behavior compared with the colony-type cells. These results suggest that morphology-based optical cell separation is a useful technique to classify a heterogeneous cancer cell population according to its cellular characteristics.

Super-resolution mapping using multi-viewing CHRIS/PROBA data

NASA Astrophysics Data System (ADS)

Dwivedi, Manish; Kumar, Vinay

2016-04-01

High-spatial resolution Remote Sensing (RS) data provides detailed information which ensures high-definition visual image analysis of earth surface features. These data sets also support improved information extraction capabilities at a fine scale. In order to improve the spatial resolution of coarser resolution RS data, the Super Resolution Reconstruction (SRR) technique has become widely acknowledged which focused on multi-angular image sequences. In this study multi-angle CHRIS/PROBA data of Kutch area is used for SR image reconstruction to enhance the spatial resolution from 18 m to 6m in the hope to obtain a better land cover classification. Various SR approaches like Projection onto Convex Sets (POCS), Robust, Iterative Back Projection (IBP), Non-Uniform Interpolation and Structure-Adaptive Normalized Convolution (SANC) chosen for this study. Subjective assessment through visual interpretation shows substantial improvement in land cover details. Quantitative measures including peak signal to noise ratio and structural similarity are used for the evaluation of the image quality. It was observed that SANC SR technique using Vandewalle algorithm for the low resolution image registration outperformed the other techniques. After that SVM based classifier is used for the classification of SRR and data resampled to 6m spatial resolution using bi-cubic interpolation. A comparative analysis is carried out between classified data of bicubic interpolated and SR derived images of CHRIS/PROBA and SR derived classified data have shown a significant improvement of 10-12% in the overall accuracy. The results demonstrated that SR methods is able to improve spatial detail of multi-angle images as well as the classification accuracy.

Sensitivity and specificity of machine learning classifiers for glaucoma diagnosis using Spectral Domain OCT and standard automated perimetry.

PubMed

Silva, Fabrício R; Vidotti, Vanessa G; Cremasco, Fernanda; Dias, Marcelo; Gomi, Edson S; Costa, Vital P

2013-01-01

To evaluate the sensitivity and specificity of machine learning classifiers (MLCs) for glaucoma diagnosis using Spectral Domain OCT (SD-OCT) and standard automated perimetry (SAP). Observational cross-sectional study. Sixty two glaucoma patients and 48 healthy individuals were included. All patients underwent a complete ophthalmologic examination, achromatic standard automated perimetry (SAP) and retinal nerve fiber layer (RNFL) imaging with SD-OCT (Cirrus HD-OCT; Carl Zeiss Meditec Inc., Dublin, California). Receiver operating characteristic (ROC) curves were obtained for all SD-OCT parameters and global indices of SAP. Subsequently, the following MLCs were tested using parameters from the SD-OCT and SAP: Bagging (BAG), Naive-Bayes (NB), Multilayer Perceptron (MLP), Radial Basis Function (RBF), Random Forest (RAN), Ensemble Selection (ENS), Classification Tree (CTREE), Ada Boost M1(ADA),Support Vector Machine Linear (SVML) and Support Vector Machine Gaussian (SVMG). Areas under the receiver operating characteristic curves (aROC) obtained for isolated SAP and OCT parameters were compared with MLCs using OCT+SAP data. Combining OCT and SAP data, MLCs' aROCs varied from 0.777(CTREE) to 0.946 (RAN).The best OCT+SAP aROC obtained with RAN (0.946) was significantly larger the best single OCT parameter (p<0.05), but was not significantly different from the aROC obtained with the best single SAP parameter (p=0.19). Machine learning classifiers trained on OCT and SAP data can successfully discriminate between healthy and glaucomatous eyes. The combination of OCT and SAP measurements improved the diagnostic accuracy compared with OCT data alone.

Publications of LASL research, 1979

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

Willis, J.K.; Salazar, C.A.

1980-11-01

This bibliography is a compilation of unclassified publications of work done at the Los Alamos Scientific Laboratory for 1979. Papers published in 1979 are included regardless of when they were actually written. Declassification of previously classified reports is considered to constitute publication. All classified issuances are omitted. If a paper was published more than once, all places of publication are included. The bibliography includes Los Alamos Scientific Laboratory reports, papers released as non-LASL reports, journal articles, books, chapters of books, conference papers (whether published separately or as part of conference proceedings issued as books or reports), papers published in congressionalmore » hearings, theses, and US patents. The entries are arranged in sections by broad subject categories. (RWR)« less

Prevalence and Risk Factors for Musculoskeletal Pain in Keyboard Musicians: A Systematic Review.

PubMed

Amaral Corrêa, Leticia; Dos Santos, Luciano Teixeira; Nogueira Paranhos, Edmur Nelson; Minetti Albertini, Alfredo Ignacio; do Carmo Silva Parreira, Patrícia; Nogueira, Leandro Alberto Calazans

2018-04-26

To identify the prevalence and risk factors for musculoskeletal pain in keyboard musicians. Systematic review METHODS: A systematic review was conducted according to the MOOSE recommendations and it was registered with the PROSPERO database under registration number CRD42016042913. We included observational studies through the electronic databases PubMed, Scopus, ScienceDirect, Web of Science, Répertoire International de Littérature Musicale (RILM), Retrospective Index to Music Periodicals (RIPM), Scielo, and Google Scholar, with combinations of the keywords pianists, keyboard players, musculoskeletal pain, muscular disease, tendinitis, tendinopathy, observational, case-control, prevalence, and risk factors. Data from population, information about pain, and risk factors were extracted from studies that fulfilled the eligibility criteria. The methodological quality of the studies was classified through the Newcastle-Ottawa Scale. The risk of bias and quality of evidence was assessed using the GRADE system. Twelve articles (case-controls) were included for the qualitative synthesis. The quality of the studies was classified as fair (n = 6) and good (n = 6). Prevalence was observed between 25.8% and 77.0% of musculoskeletal pain among keyboard musicians, with a higher prevalence in wrists and hands (13.8%-65.8%), neck (9.8%-64.2%), and shoulders (9.8%-59.8%). The only consistent risk factor found in the 4 studies was being female, with OR ranging from 1.05-1.90. Age greater than 18 years; weekly training more than 20 hours; training for more than 60 minutes without a rest break; not having a habit of practicing sports; and playing despite the pain were also described as risks factors for musculoskeletal pain. It was not possible to perform the meta-analysis due to the heterogeneity of the studies. Keyboard musicians presented a high prevalence of musculoskeletal pain, especially in the upper extremity regions of the body. Female, ageing, playing behaviors, and sedentary lifestyle showed an increased likelihood to report musculoskeletal pain. Level II. Copyright © 2018 American Academy of Physical Medicine and Rehabilitation. Published by Elsevier Inc. All rights reserved.

Evaluation of novel scoring system named 5-5-5 exacerbation grading scale for allergic conjunctivitis disease.

PubMed

Shoji, Jun; Inada, Noriko; Sawa, Mitsuru

2009-12-01

The objective of this study is to evaluate the practical usefulness of a scoring system using the 5-5-5 exacerbation grading scale for allergic conjunctivitis disease (ACD). Subjects were 103 patients with ACD including 40 patients with vernal keratoconjunctivitis (VKC), 20 patients with atopic keratoconjunctivitis (AKC), and 43 patients with allergic conjunctivitis (AC). The 5-5-5 exacerbation grading scale consists of the following 3 graded groups of clinical observations: the 100-point-grade group (100 points for each observation) includes active giant papillae, gelatinous infiltrates of the limbus, exfoliative epithelial keratopathy, shield ulcer and papillary proliferation at lower palpebral conjunctiva; the 10-point-grade group (10 points for each observation) includes blepharitis, papillary proliferation with velvety appearance, Horner-Trantas spots, edema of bulbal conjunctiva, and superficial punctate keratopathy; and the 1-point-grade group (1 point for each observation) includes papillae at upper palpebral conjunctiva, follicular lesion at lower palpebral conjunctiva, hyperemia of palpebral conjunctiva, hyperemia of bulbal conjunctiva, and lacrimal effusion. The total points in each grade group were determined as the severity score of the 5-5-5 exacerbation grading scale. The median severity scores of the 5-5-5 exacerbation grading scale in VKC, AKC and AC were 243 (range: 12-444), 32.5 (11-344), and 13 (2-33), respectively. The severity score of each ACD disease type was significantly different (P < 0.001, Kruskal-Wallis test). The severity of each type of ACD was classified as severe, moderate, or mild according to the severity score. The 5-5-5 exacerbation grading scale is a useful clinical tool for grading the severity of each type of ACD.

Search Fermilab Insect Database

Science.gov Websites

data reflects observations at Fermilab. Search Clear Choices Find All Insects |Help| |Glossary | |Advanced Search| How it's named and classified: Common Name: Insect Order: equals contains begins with ends

Biweekly disturbance capture and attribution: case study in western Alberta grizzly bear habitat

NASA Astrophysics Data System (ADS)

Hilker, Thomas; Coops, Nicholas C.; Gaulton, Rachel; Wulder, Michael A.; Cranston, Jerome; Stenhouse, Gordon

2011-01-01

An increasing number of studies have demonstrated the impact of landscape disturbance on ecosystems. Satellite remote sensing can be used for mapping disturbances, and fusion techniques of sensors with complimentary characteristics can help to improve the spatial and temporal resolution of satellite-based mapping techniques. Classification of different disturbance types from satellite observations is difficult, yet important, especially in an ecological context as different disturbance types might have different impacts on vegetation recovery, wildlife habitats, and food resources. We demonstrate a possible approach for classifying common disturbance types by means of their spatial characteristics. First, landscape level change is characterized on a near biweekly basis through application of a data fusion model (spatial temporal adaptive algorithm for mapping reflectance change) and a number of spatial and temporal characteristics of the predicted disturbance patches are inferred. A regression tree approach is then used to classify disturbance events. Our results show that spatial and temporal disturbance characteristics can be used to classify disturbance events with an overall accuracy of 86% of the disturbed area observed. The date of disturbance was identified as the most powerful predictor of the disturbance type, together with the patch core area, patch size, and contiguity.

Automatic detection of ischemic stroke based on scaling exponent electroencephalogram using extreme learning machine

NASA Astrophysics Data System (ADS)

Adhi, H. A.; Wijaya, S. K.; Prawito; Badri, C.; Rezal, M.

2017-03-01

Stroke is one of cerebrovascular diseases caused by the obstruction of blood flow to the brain. Stroke becomes the leading cause of death in Indonesia and the second in the world. Stroke also causes of the disability. Ischemic stroke accounts for most of all stroke cases. Obstruction of blood flow can cause tissue damage which results the electrical changes in the brain that can be observed through the electroencephalogram (EEG). In this study, we presented the results of automatic detection of ischemic stroke and normal subjects based on the scaling exponent EEG obtained through detrended fluctuation analysis (DFA) using extreme learning machine (ELM) as the classifier. The signal processing was performed with 18 channels of EEG in the range of 0-30 Hz. Scaling exponents of the subjects were used as the input for ELM to classify the ischemic stroke. The performance of detection was observed by the value of accuracy, sensitivity and specificity. The result showed, performance of the proposed method to classify the ischemic stroke was 84 % for accuracy, 82 % for sensitivity and 87 % for specificity with 120 hidden neurons and sine as the activation function of ELM.

Planetary and Stellar Data Products Expected From The Kepler Mission

NASA Technical Reports Server (NTRS)

Borucki, W. J.; Koch, David G.; Basri, Gibor; Cochran, William; Dunham, Edward W.; Gilliland, Ronald; Jenkins, Jon M.; Caldwell, Douglas; Kondo, Yoji; Latham, David;

Observation and Classification of Prehension in Preschool Children: A Reliability Study.

ERIC Educational Resources Information Center

Moss, S. C.; Hogg, J.

1981-01-01

The variety of hand grips of 12 children, most of whom were moderately or severely retarded, were classified in order to begin an analysis of hand function. Test reliability was not as great when items were presented to the children as compared to when children were observed or rated by videotape. (FG)

Actors, Observers, and the Attribution of Intent in Conversation.

ERIC Educational Resources Information Center

Ehrenhaus, Peter C.

A study examined the manner in which conversants and observers of conversants attribute intent to messages in ongoing information-seeking conversations. College students were used to evolve and test three scenarios, in which evasion was more or less likely, and a system of classifying intention in information seeking conversations. Fifty-four…

Assessing collaborative computing: development of the Collaborative-Computing Observation Instrument (C-COI)

NASA Astrophysics Data System (ADS)

Israel, Maya; Wherfel, Quentin M.; Shehab, Saadeddine; Ramos, Evan A.; Metzger, Adam; Reese, George C.

2016-07-01

This paper describes the development, validation, and uses of the Collaborative Computing Observation Instrument (C-COI), a web-based analysis instrument that classifies individual and/or collaborative behaviors of students during computing problem-solving (e.g. coding, programming). The C-COI analyzes data gathered through video and audio screen recording software that captures students' computer screens as they program, and their conversations with their peers or adults. The instrument allows researchers to organize and quantify these data to track behavioral patterns that could be further analyzed for deeper understanding of persistence and/or collaborative interactions. The article provides a rationale for the C-COI including the development of a theoretical framework for measuring collaborative interactions in computer-mediated environments. This theoretical framework relied on the computer-supported collaborative learning literature related to adaptive help seeking, the joint problem-solving space in which collaborative computing occurs, and conversations related to outcomes and products of computational activities. Instrument development and validation also included ongoing advisory board feedback from experts in computer science, collaborative learning, and K-12 computing as well as classroom observations to test out the constructs in the C-COI. These processes resulted in an instrument with rigorous validation procedures and a high inter-rater reliability.

Quantifying biopsychosocial aspects in everyday contexts: an integrative methodological approach from the behavioral sciences

PubMed Central

Portell, Mariona; Anguera, M Teresa; Hernández-Mendo, Antonio; Jonsson, Gudberg K

2015-01-01

Contextual factors are crucial for evaluative research in psychology, as they provide insights into what works, for whom, in what circumstances, in what respects, and why. Studying behavior in context, however, poses numerous methodological challenges. Although a comprehensive framework for classifying methods seeking to quantify biopsychosocial aspects in everyday contexts was recently proposed, this framework does not contemplate contributions from observational methodology. The aim of this paper is to justify and propose a more general framework that includes observational methodology approaches. Our analysis is rooted in two general concepts: ecological validity and methodological complementarity. We performed a narrative review of the literature on research methods and techniques for studying daily life and describe their shared properties and requirements (collection of data in real time, on repeated occasions, and in natural settings) and classification criteria (eg, variables of interest and level of participant involvement in the data collection process). We provide several examples that illustrate why, despite their higher costs, studies of behavior and experience in everyday contexts offer insights that complement findings provided by other methodological approaches. We urge that observational methodology be included in classifications of research methods and techniques for studying everyday behavior and advocate a renewed commitment to prioritizing ecological validity in behavioral research seeking to quantify biopsychosocial aspects. PMID:26089708

Mapping surface disturbance of energy-related infrastructure in southwest Wyoming--An assessment of methods

USGS Publications Warehouse

Germaine, Stephen S.; O'Donnell, Michael S.; Aldridge, Cameron L.; Baer, Lori; Fancher, Tammy; McBeth, Jamie; McDougal, Robert R.; Waltermire, Robert; Bowen, Zachary H.; Diffendorfer, James; Garman, Steven; Hanson, Leanne

2012-01-01

We evaluated how well three leading information-extraction software programs (eCognition, Feature Analyst, Feature Extraction) and manual hand digitization interpreted information from remotely sensed imagery of a visually complex gas field in Wyoming. Specifically, we compared how each mapped the area of and classified the disturbance features present on each of three remotely sensed images, including 30-meter-resolution Landsat, 10-meter-resolution SPOT (Satellite Pour l'Observation de la Terre), and 0.6-meter resolution pan-sharpened QuickBird scenes. Feature Extraction mapped the spatial area of disturbance features most accurately on the Landsat and QuickBird imagery, while hand digitization was most accurate on the SPOT imagery. Footprint non-overlap error was smallest on the Feature Analyst map of the Landsat imagery, the hand digitization map of the SPOT imagery, and the Feature Extraction map of the QuickBird imagery. When evaluating feature classification success against a set of ground-truthed control points, Feature Analyst, Feature Extraction, and hand digitization classified features with similar success on the QuickBird and SPOT imagery, while eCognition classified features poorly relative to the other methods. All maps derived from Landsat imagery classified disturbance features poorly. Using the hand digitized QuickBird data as a reference and making pixel-by-pixel comparisons, Feature Extraction classified features best overall on the QuickBird imagery, and Feature Analyst classified features best overall on the SPOT and Landsat imagery. Based on the entire suite of tasks we evaluated, Feature Extraction performed best overall on the Landsat and QuickBird imagery, while hand digitization performed best overall on the SPOT imagery, and eCognition performed worst overall on all three images. Error rates for both area measurements and feature classification were prohibitively high on Landsat imagery, while QuickBird was time and cost prohibitive for mapping large spatial extents. The SPOT imagery produced map products that were far more accurate than Landsat and did so at a far lower cost than QuickBird imagery. Consideration of degree of map accuracy required, costs associated with image acquisition, software, operator and computation time, and tradeoffs in the form of spatial extent versus resolution should all be considered when evaluating which combination of imagery and information-extraction method might best serve any given land use mapping project. When resources permit, attaining imagery that supports the highest classification and measurement accuracy possible is recommended.

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

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

Chang, Yongjun; Lim, Jonghyuck; Kim, Namkug

2013-05-15

Purpose: To investigate the effect of using different computed tomography (CT) scanners on the accuracy of high-resolution CT (HRCT) images in classifying regional disease patterns in patients with diffuse lung disease, support vector machine (SVM) and Bayesian classifiers were applied to multicenter data. Methods: Two experienced radiologists marked sets of 600 rectangular 20 Multiplication-Sign 20 pixel regions of interest (ROIs) on HRCT images obtained from two scanners (GE and Siemens), including 100 ROIs for each of local patterns of lungs-normal lung and five of regional pulmonary disease patterns (ground-glass opacity, reticular opacity, honeycombing, emphysema, and consolidation). Each ROI was assessedmore » using 22 quantitative features belonging to one of the following descriptors: histogram, gradient, run-length, gray level co-occurrence matrix, low-attenuation area cluster, and top-hat transform. For automatic classification, a Bayesian classifier and a SVM classifier were compared under three different conditions. First, classification accuracies were estimated using data from each scanner. Next, data from the GE and Siemens scanners were used for training and testing, respectively, and vice versa. Finally, all ROI data were integrated regardless of the scanner type and were then trained and tested together. All experiments were performed based on forward feature selection and fivefold cross-validation with 20 repetitions. Results: For each scanner, better classification accuracies were achieved with the SVM classifier than the Bayesian classifier (92% and 82%, respectively, for the GE scanner; and 92% and 86%, respectively, for the Siemens scanner). The classification accuracies were 82%/72% for training with GE data and testing with Siemens data, and 79%/72% for the reverse. The use of training and test data obtained from the HRCT images of different scanners lowered the classification accuracy compared to the use of HRCT images from the same scanner. For integrated ROI data obtained from both scanners, the classification accuracies with the SVM and Bayesian classifiers were 92% and 77%, respectively. The selected features resulting from the classification process differed by scanner, with more features included for the classification of the integrated HRCT data than for the classification of the HRCT data from each scanner. For the integrated data, consisting of HRCT images of both scanners, the classification accuracy based on the SVM was statistically similar to the accuracy of the data obtained from each scanner. However, the classification accuracy of the integrated data using the Bayesian classifier was significantly lower than the classification accuracy of the ROI data of each scanner. Conclusions: The use of an integrated dataset along with a SVM classifier rather than a Bayesian classifier has benefits in terms of the classification accuracy of HRCT images acquired with more than one scanner. This finding is of relevance in studies involving large number of images, as is the case in a multicenter trial with different scanners.« less

[Ultrasound diagnosis of congenital intrahepatic portosystemic shunt].

PubMed

Fu, Qiang; Tan, Shi; Cui, Li-gang; Zhang, Hua-bin; Bai, Zhi-yong; Jiang, Jie

2013-12-01

To investigate the ultrasonographic features of congenital intrahepatic portosystemic venous shunt (CIPSVS) and to assess the clinical value of ultrasonography in the diagnosis of CIPSVS. Six cases of CIPSVS diagnosed in our hospital between March 2010 and March 2012 and confirmed by enhanced computed tomography (CT) were retrospectively reviewed. Five of the six cases had follow-up data that was included in the analysis. Among the six CIPSVS cases, only one was classified as Park's type II and the rest were classified as Park's type III. Five cases involved the right lobe of the liver and only one case involved the left lobe. The lesion shapes included cystic, tubular, and irregular with clear contour and appeared to be anechoic on CT scan. The lesions ranged in size from 1.1*0.6 cm to 2.0*1.7 cm. For all cases, the color Doppler ultrasound images showed blood flowing from the portal vein to the hepatic vein, and single-phase spectrum was detected in the diversion channel. The differences observed in level of lesion size and blood flow velocity at the shunt from the time of examinations at diagnosis and subsequent follow-up did not reach statistical significance (P = 0.223 more than 0.05 and P = 0.930 more than 0.05 respectively). Although cases of CIPSVS are rare, they share some specific sonographic features that may help in diagnosis. Color Doppler ultrasound findings have high diagnostic accuracy and may represent a preferred modality for follow-up monitoring.

An fMRI Study of Grammatical Morpheme Processing Associated with Nouns and Verbs in Chinese

PubMed Central

Yu, Xi; Bi, Yanchao; Han, Zaizhu; Law, Sam-Po

2013-01-01

This study examined whether the degree of complexity of a grammatical component in a language would impact on its representation in the brain through identifying the neural correlates of grammatical morpheme processing associated with nouns and verbs in Chinese. In particular, the processing of Chinese nominal classifiers and verbal aspect markers were investigated in a sentence completion task and a grammaticality judgment task to look for converging evidence. The Chinese language constitutes a special case because it has no inflectional morphology per se and a larger classifier than aspect marker inventory, contrary to the pattern of greater verbal than nominal paradigmatic complexity in most European languages. The functional imaging results showed BA47 and left supplementary motor area and superior medial frontal gyrus more strongly activated for classifier processing, and the left posterior middle temporal gyrus more responsive to aspect marker processing. We attributed the activation in the left prefrontal cortex to greater processing complexity during classifier selection, analogous to the accounts put forth for European languages, and the left posterior middle temporal gyrus to more demanding verb semantic processing. The overall findings significantly contribute to cross-linguistic observations of neural substrates underlying processing of grammatical morphemes from an analytic and a classifier language, and thereby deepen our understanding of neurobiology of human language. PMID:24146745

Development of a quantitative tool to assess the content of physical therapy for infants.

PubMed

Blauw-Hospers, Cornill H; Dirks, Tineke; Hulshof, Lily J; Hadders-Algra, Mijna

2010-01-01

The study aim was to describe and quantify physical therapy interventions for infants at high risk for developmental disorders. An observation protocol was developed based on knowledge about infant physical therapy and analysis of directly observable physiotherapeutic (PT) actions. The protocol's psychometric quality was assessed. Videos of 42 infant physical therapy sessions at 4 or 6 months of corrected age were analyzed. The observation protocol classified PT actions into 8 mutually exclusive categories. Virtually all PT actions during treatment could be classified. Inter- and intrarater agreements were satisfactory (intraclass correlations, 0.68-1.00). Approximately 40% of treatment time was spent challenging the infant to produce motor behavior by themselves, whereas approximately 30% of time facilitation techniques were applied. Tradition-based sessions could be differentiated from function-oriented ones. It is possible to document PT actions during physical therapy treatment of infants at high risk for cerebral palsy in a systematic, standardized, and reliable way.

Hand gesture recognition in confined spaces with partial observability and occultation constraints

NASA Astrophysics Data System (ADS)

Shirkhodaie, Amir; Chan, Alex; Hu, Shuowen

2016-05-01

Human activity detection and recognition capabilities have broad applications for military and homeland security. These tasks are very complicated, however, especially when multiple persons are performing concurrent activities in confined spaces that impose significant obstruction, occultation, and observability uncertainty. In this paper, our primary contribution is to present a dedicated taxonomy and kinematic ontology that are developed for in-vehicle group human activities (IVGA). Secondly, we describe a set of hand-observable patterns that represents certain IVGA examples. Thirdly, we propose two classifiers for hand gesture recognition and compare their performance individually and jointly. Finally, we present a variant of Hidden Markov Model for Bayesian tracking, recognition, and annotation of hand motions, which enables spatiotemporal inference to human group activity perception and understanding. To validate our approach, synthetic (graphical data from virtual environment) and real physical environment video imagery are employed to verify the performance of these hand gesture classifiers, while measuring their efficiency and effectiveness based on the proposed Hidden Markov Model for tracking and interpreting dynamic spatiotemporal IVGA scenarios.

Gravitation, Symmetry and Undergraduates

NASA Astrophysics Data System (ADS)

Jorgensen, Jamie

2001-04-01

This talk will discuss "Project Petrov" Which is designed to investigate gravitational fields with symmetry. Project Petrov represents a collaboration involving physicists, mathematicians as well as graduate and undergraduate math and physics students. An overview of Project Petrov will be given, with an emphasis on students' contributions, including software to classify and generate Lie algebras, to classify isometry groups, and to compute the isometry group of a given metric.

Automated defect spatial signature analysis for semiconductor manufacturing process

DOEpatents

Tobin, Jr., Kenneth W.; Gleason, Shaun S.; Karnowski, Thomas P.; Sari-Sarraf, Hamed

1999-01-01

An apparatus and method for performing automated defect spatial signature alysis on a data set representing defect coordinates and wafer processing information includes categorizing data from the data set into a plurality of high level categories, classifying the categorized data contained in each high level category into user-labeled signature events, and correlating the categorized, classified signature events to a present or incipient anomalous process condition.

Optical Coherence Tomography Machine Learning Classifiers for Glaucoma Detection: A Preliminary Study

PubMed Central

Burgansky-Eliash, Zvia; Wollstein, Gadi; Chu, Tianjiao; Ramsey, Joseph D.; Glymour, Clark; Noecker, Robert J.; Ishikawa, Hiroshi; Schuman, Joel S.

2007-01-01

Purpose Machine-learning classifiers are trained computerized systems with the ability to detect the relationship between multiple input parameters and a diagnosis. The present study investigated whether the use of machine-learning classifiers improves optical coherence tomography (OCT) glaucoma detection. Methods Forty-seven patients with glaucoma (47 eyes) and 42 healthy subjects (42 eyes) were included in this cross-sectional study. Of the glaucoma patients, 27 had early disease (visual field mean deviation [MD] ≥ −6 dB) and 20 had advanced glaucoma (MD < −6 dB). Machine-learning classifiers were trained to discriminate between glaucomatous and healthy eyes using parameters derived from OCT output. The classifiers were trained with all 38 parameters as well as with only 8 parameters that correlated best with the visual field MD. Five classifiers were tested: linear discriminant analysis, support vector machine, recursive partitioning and regression tree, generalized linear model, and generalized additive model. For the last two classifiers, a backward feature selection was used to find the minimal number of parameters that resulted in the best and most simple prediction. The cross-validated receiver operating characteristic (ROC) curve and accuracies were calculated. Results The largest area under the ROC curve (AROC) for glaucoma detection was achieved with the support vector machine using eight parameters (0.981). The sensitivity at 80% and 95% specificity was 97.9% and 92.5%, respectively. This classifier also performed best when judged by cross-validated accuracy (0.966). The best classification between early glaucoma and advanced glaucoma was obtained with the generalized additive model using only three parameters (AROC = 0.854). Conclusions Automated machine classifiers of OCT data might be useful for enhancing the utility of this technology for detecting glaucomatous abnormality. PMID:16249492

A new catalogue of polar-ring galaxies selected from the Sloan Digital Sky Survey

NASA Astrophysics Data System (ADS)

Moiseev, Alexei V.; Smirnova, Ksenia I.; Smirnova, Aleksandrina A.; Reshetnikov, Vladimir P.

2011-11-01

Galaxies with polar rings (PRGs) are a unique class of extragalactic objects. Using these, we can investigate a wide range of problems, linked to the formation and evolution of galaxies, and we can study the properties of their dark haloes. The progress that has been made in the study of PRGs has been constrained by the small number of known objects of this type. The Polar Ring Catalogue (PRC) by Whitmore et al. and their photographic atlas of PRGs and related objects includes 157 galaxies. At present, there are only about two dozen kinematically confirmed galaxies in this PRG class, mostly from the PRC. We present a new catalogue of PRGs, supplementing the PRC and significantly increasing the number of known candidate PRGs. The catalogue is based on the results of the original Galaxy Zoo project. Within this project, volunteers performed visual classifications of nearly a million galaxies from the Sloan Digital Sky Survey (SDSS). Based on the preliminary classifications of the Galaxy Zoo, we viewed more than 40 000 images of the SDSS and selected 275 galaxies to include in our catalogue. Our SDSS-based Polar Ring Catalogue (SPRC) contains 70 galaxies that we have classified as 'the best candidates'. Among these, we expect to have a very high proportion of true PRGs, and 115 good PRG candidates. There are 53 galaxies classified as PRG-related objects (mostly galaxies with strongly warped discs, and mergers). In addition, we have identified 37 galaxies that have their presumed polar rings strongly inclined to the line of sight (seen almost face-on). The SPRC objects are, on average, fainter and are located further away than the galaxies from the PRC, although our catalogue does include dozens of new nearby candidate PRGs. The SPRC significantly increases the number of genuine PRG candidates. It might serve as a good basis for both a further detailed study of individual galaxies and a statistical analysis of PRGs as a separate class of objects. We have performed spectroscopic observations of six galaxies from the SPRC at the 6-m Big Telescope Alt-Azimuthal (BTA). The existence of polar rings was confirmed in five galaxies, and one object appeared to be a projection of a pair of galaxies. Adding the data from the literature, we can already classify 10 galaxies from our catalogue as kinematically confirmed PRGs. This paper is partly based on observations collected with the 6-m telescope of the Special Astrophysical Observatory of the Russian Academy of Sciences, which is operated under the financial support of the Science Department of Russia (registration number 01-43).

CardioClassifier: disease- and gene-specific computational decision support for clinical genome interpretation.

PubMed

Whiffin, Nicola; Walsh, Roddy; Govind, Risha; Edwards, Matthew; Ahmad, Mian; Zhang, Xiaolei; Tayal, Upasana; Buchan, Rachel; Midwinter, William; Wilk, Alicja E; Najgebauer, Hanna; Francis, Catherine; Wilkinson, Sam; Monk, Thomas; Brett, Laura; O'Regan, Declan P; Prasad, Sanjay K; Morris-Rosendahl, Deborah J; Barton, Paul J R; Edwards, Elizabeth; Ware, James S; Cook, Stuart A

2018-01-25

PurposeInternationally adopted variant interpretation guidelines from the American College of Medical Genetics and Genomics (ACMG) are generic and require disease-specific refinement. Here we developed CardioClassifier (http://www.cardioclassifier.org), a semiautomated decision-support tool for inherited cardiac conditions (ICCs).MethodsCardioClassifier integrates data retrieved from multiple sources with user-input case-specific information, through an interactive interface, to support variant interpretation. Combining disease- and gene-specific knowledge with variant observations in large cohorts of cases and controls, we refined 14 computational ACMG criteria and created three ICC-specific rules.ResultsWe benchmarked CardioClassifier on 57 expertly curated variants and show full retrieval of all computational data, concordantly activating 87.3% of rules. A generic annotation tool identified fewer than half as many clinically actionable variants (64/219 vs. 156/219, Fisher's P = 1.1  ×  10 -18 ), with important false positives, illustrating the critical importance of disease and gene-specific annotations. CardioClassifier identified putatively disease-causing variants in 33.7% of 327 cardiomyopathy cases, comparable with leading ICC laboratories. Through addition of manually curated data, variants found in over 40% of cardiomyopathy cases are fully annotated, without requiring additional user-input data.ConclusionCardioClassifier is an ICC-specific decision-support tool that integrates expertly curated computational annotations with case-specific data to generate fast, reproducible, and interactive variant pathogenicity reports, according to best practice guidelines.GENETICS in MEDICINE advance online publication, 25 January 2018; doi:10.1038/gim.2017.258.

Opponent Classification in Poker

NASA Astrophysics Data System (ADS)

Ahmad, Muhammad Aurangzeb; Elidrisi, Mohamed

Modeling games has a long history in the Artificial Intelligence community. Most of the games that have been considered solved in AI are perfect information games. Imperfect information games like Poker and Bridge represent a domain where there is a great deal of uncertainty involved and additional challenges with respect to modeling the behavior of the opponent etc. Techniques developed for playing imperfect games also have many real world applications like repeated online auctions, human computer interaction, opponent modeling for military applications etc. In this paper we explore different techniques for playing poker, the core of these techniques is opponent modeling via classifying the behavior of opponent according to classes provided by domain experts. We utilize windows of full observation in the game to classify the opponent. In Poker, the behavior of an opponent is classified into four standard poker-playing styles based on a subjective function.

Check-up and cardiovascular risk progression: is there a room for innovation?

PubMed Central

Conceição, Raquel Dilguerian de Oliveira; Laurinavicius, Antonio Gabriele; Kashiwagi, Nea Miwa; de Carvalho, José Antonio Maluf; Oliva, Carlos Alberto Garcia; Santos, Raul Dias

2015-01-01

ABSTRACT Objective: To evaluate the impact of traditional check-up appointment on the progression of the cardiovascular risk throughout time. Methods: This retrospective cohort study included 11,126 medical records of asymptomatic executives who were evaluated between January, 2005 and October, 2008. Variables included participants’ demographics characteristics, smoking habit, history of cardiovascular diseases, diabetes, dyslipidemia, total cholesterol, HDL, triglycerides, glucose, c-reactive protein, waist circumference, hepatic steatosis, Framingham score, metabolic syndrome, level of physical activity, stress, alcohol consumption, and body mass index. Results: A total of 3,150 patients was included in the final analysis. A worsening was observed in all risk factors, excepting in smoking habit, incidence of myocardial infarction or stroke and in the number of individuals classified as medium or high risk for cardiovascular events. In addition, a decrease in stress level and alcohol consumption was also seen. Conclusion: The adoption of consistent health policies by companies is imperative in order to reduce the risk factors and the future costs associated with illness and absenteeism. PMID:26154540

Two-Way Regularized Fuzzy Clustering of Multiple Correspondence Analysis.

PubMed

Kim, Sunmee; Choi, Ji Yeh; Hwang, Heungsun

2017-01-01

Multiple correspondence analysis (MCA) is a useful tool for investigating the interrelationships among dummy-coded categorical variables. MCA has been combined with clustering methods to examine whether there exist heterogeneous subclusters of a population, which exhibit cluster-level heterogeneity. These combined approaches aim to classify either observations only (one-way clustering of MCA) or both observations and variable categories (two-way clustering of MCA). The latter approach is favored because its solutions are easier to interpret by providing explicitly which subgroup of observations is associated with which subset of variable categories. Nonetheless, the two-way approach has been built on hard classification that assumes observations and/or variable categories to belong to only one cluster. To relax this assumption, we propose two-way fuzzy clustering of MCA. Specifically, we combine MCA with fuzzy k-means simultaneously to classify a subgroup of observations and a subset of variable categories into a common cluster, while allowing both observations and variable categories to belong partially to multiple clusters. Importantly, we adopt regularized fuzzy k-means, thereby enabling us to decide the degree of fuzziness in cluster memberships automatically. We evaluate the performance of the proposed approach through the analysis of simulated and real data, in comparison with existing two-way clustering approaches.

MOWGLI: prediction of protein-MannOse interacting residues With ensemble classifiers usinG evoLutionary Information.

PubMed

Pai, Priyadarshini P; Mondal, Sukanta

2016-10-01

Proteins interact with carbohydrates to perform various cellular interactions. Of the many carbohydrate ligands that proteins bind with, mannose constitute an important class, playing important roles in host defense mechanisms. Accurate identification of mannose-interacting residues (MIR) may provide important clues to decipher the underlying mechanisms of protein-mannose interactions during infections. This study proposes an approach using an ensemble of base classifiers for prediction of MIR using their evolutionary information in the form of position-specific scoring matrix. The base classifiers are random forests trained by different subsets of training data set Dset128 using 10-fold cross-validation. The optimized ensemble of base classifiers, MOWGLI, is then used to predict MIR on protein chains of the test data set Dtestset29 which showed a promising performance with 92.0% accurate prediction. An overall improvement of 26.6% in precision was observed upon comparison with the state-of-art. It is hoped that this approach, yielding enhanced predictions, could be eventually used for applications in drug design and vaccine development.

A method for classification of transient events in EEG recordings: application to epilepsy diagnosis.

PubMed

Tzallas, A T; Karvelis, P S; Katsis, C D; Fotiadis, D I; Giannopoulos, S; Konitsiotis, S

2006-01-01

The aim of the paper is to analyze transient events in inter-ictal EEG recordings, and classify epileptic activity into focal or generalized epilepsy using an automated method. A two-stage approach is proposed. In the first stage the observed transient events of a single channel are classified into four categories: epileptic spike (ES), muscle activity (EMG), eye blinking activity (EOG), and sharp alpha activity (SAA). The process is based on an artificial neural network. Different artificial neural network architectures have been tried and the network having the lowest error has been selected using the hold out approach. In the second stage a knowledge-based system is used to produce diagnosis for focal or generalized epileptic activity. The classification of transient events reported high overall accuracy (84.48%), while the knowledge-based system for epilepsy diagnosis correctly classified nine out of ten cases. The proposed method is advantageous since it effectively detects and classifies the undesirable activity into appropriate categories and produces a final outcome related to the existence of epilepsy.

Non-parametric transient classification using adaptive wavelets

NASA Astrophysics Data System (ADS)

Varughese, Melvin M.; von Sachs, Rainer; Stephanou, Michael; Bassett, Bruce A.

2015-11-01

Classifying transients based on multiband light curves is a challenging but crucial problem in the era of GAIA and Large Synoptic Sky Telescope since the sheer volume of transients will make spectroscopic classification unfeasible. We present a non-parametric classifier that predicts the transient's class given training data. It implements two novel components: the use of the BAGIDIS wavelet methodology - a characterization of functional data using hierarchical wavelet coefficients - as well as the introduction of a ranked probability classifier on the wavelet coefficients that handles both the heteroscedasticity of the data in addition to the potential non-representativity of the training set. The classifier is simple to implement while a major advantage of the BAGIDIS wavelets is that they are translation invariant. Hence, BAGIDIS does not need the light curves to be aligned to extract features. Further, BAGIDIS is non-parametric so it can be used effectively in blind searches for new objects. We demonstrate the effectiveness of our classifier against the Supernova Photometric Classification Challenge to correctly classify supernova light curves as Type Ia or non-Ia. We train our classifier on the spectroscopically confirmed subsample (which is not representative) and show that it works well for supernova with observed light-curve time spans greater than 100 d (roughly 55 per cent of the data set). For such data, we obtain a Ia efficiency of 80.5 per cent and a purity of 82.4 per cent, yielding a highly competitive challenge score of 0.49. This indicates that our `model-blind' approach may be particularly suitable for the general classification of astronomical transients in the era of large synoptic sky surveys.

Psychophysical Models for Signal Detection with Time Varying Uncertainty. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Gai, E.

1975-01-01

Psychophysical models for the behavior of the human operator in detection tasks which include change in detectability, correlation between observations and deferred decisions are developed. Classical Signal Detection Theory (SDT) is discussed and its emphasis on the sensory processes is contrasted to decision strategies. The analysis of decision strategies utilizes detection tasks with time varying signal strength. The classical theory is modified to include such tasks and several optimal decision strategies are explored. Two methods of classifying strategies are suggested. The first method is similar to the analysis of ROC curves, while the second is based on the relation between the criterion level (CL) and the detectability. Experiments to verify the analysis of tasks with changes of signal strength are designed. The results show that subjects are aware of changes in detectability and tend to use strategies that involve changes in the CL's.

Differentiation between intra-abdominal neoplasms and abscesses in horses, using clinical and laboratory data: 40 cases (1973-1988).

PubMed

Zicker, S C; Wilson, W D; Medearis, I

1990-04-01

The medical records of 25 horses with intra-abdominal neoplasms and 15 horses with intra-abdominal abscesses were reviewed. Common clinical signs of disease observed by owners of horses in both groups included anorexia, weight loss, fever, signs of colic, and depression. Clinical laboratory abnormalities included leukocytosis, hyperfibrinogenemia, hypoalbuminemia, and hypocalcemia. There was considerable overlap of laboratory test results within and between the 2 groups of horses. Peritoneal fluid was classified as an exudate in 12 of 15 horses with intra-abdominal abscesses and in 14 of 25 horses with intra-abdominal neoplasms. Cytologic examination of peritoneal fluid yielded an accurate diagnosis in 11 of 25 horses with neoplasia and in 3 of 15 horses with abscesses. A mean number of 1.45 cytologic analyses/horse was needed to diagnose neoplasms in the 11 horses in which the analysis was successful in definitively diagnosing the condition.

Pieces of the Past.

ERIC Educational Resources Information Center

Carlson, Kenneth W.

1994-01-01

Discusses and presents activities for learning about plant fossils. The first activity uses teacher-prepared "fossils" made from plaster of Paris. Students attempt to classify the plants. In the second activity, students observe actual plant fossils. (PR)

Galaxy Zoo: quantitative visual morphological classifications for 48 000 galaxies from CANDELS

NASA Astrophysics Data System (ADS)

Simmons, B. D.; Lintott, Chris; Willett, Kyle W.; Masters, Karen L.; Kartaltepe, Jeyhan S.; Häußler, Boris; Kaviraj, Sugata; Krawczyk, Coleman; Kruk, S. J.; McIntosh, Daniel H.; Smethurst, R. J.; Nichol, Robert C.; Scarlata, Claudia; Schawinski, Kevin; Conselice, Christopher J.; Almaini, Omar; Ferguson, Henry C.; Fortson, Lucy; Hartley, William; Kocevski, Dale; Koekemoer, Anton M.; Mortlock, Alice; Newman, Jeffrey A.; Bamford, Steven P.; Grogin, N. A.; Lucas, Ray A.; Hathi, Nimish P.; McGrath, Elizabeth; Peth, Michael; Pforr, Janine; Rizer, Zachary; Wuyts, Stijn; Barro, Guillermo; Bell, Eric F.; Castellano, Marco; Dahlen, Tomas; Dekel, Avishai; Ownsworth, Jamie; Faber, Sandra M.; Finkelstein, Steven L.; Fontana, Adriano; Galametz, Audrey; Grützbauch, Ruth; Koo, David; Lotz, Jennifer; Mobasher, Bahram; Mozena, Mark; Salvato, Mara; Wiklind, Tommy

2017-02-01

We present quantified visual morphologies of approximately 48 000 galaxies observed in three Hubble Space Telescope legacy fields by the Cosmic Assembly Near-infrared Deep Extragalactic Legacy Survey (CANDELS) and classified by participants in the Galaxy Zoo project. 90 per cent of galaxies have z ≤ 3 and are observed in rest-frame optical wavelengths by CANDELS. Each galaxy received an average of 40 independent classifications, which we combine into detailed morphological information on galaxy features such as clumpiness, bar instabilities, spiral structure, and merger and tidal signatures. We apply a consensus-based classifier weighting method that preserves classifier independence while effectively down-weighting significantly outlying classifications. After analysing the effect of varying image depth on reported classifications, we also provide depth-corrected classifications which both preserve the information in the deepest observations and also enable the use of classifications at comparable depths across the full survey. Comparing the Galaxy Zoo classifications to previous classifications of the same galaxies shows very good agreement; for some applications, the high number of independent classifications provided by Galaxy Zoo provides an advantage in selecting galaxies with a particular morphological profile, while in others the combination of Galaxy Zoo with other classifications is a more promising approach than using any one method alone. We combine the Galaxy Zoo classifications of `smooth' galaxies with parametric morphologies to select a sample of featureless discs at 1 ≤ z ≤ 3, which may represent a dynamically warmer progenitor population to the settled disc galaxies seen at later epochs.

Image processing for x-ray inspection of pistachio nuts

NASA Astrophysics Data System (ADS)

Casasent, David P.

2001-03-01

A review is provided of image processing techniques that have been applied to the inspection of pistachio nuts using X-ray images. X-ray sensors provide non-destructive internal product detail not available from other sensors. The primary concern in this data is detecting the presence of worm infestations in nuts, since they have been linked to the presence of aflatoxin. We describe new techniques for segmentation, feature selection, selection of product categories (clusters), classifier design, etc. Specific novel results include: a new segmentation algorithm to produce images of isolated product items; preferable classifier operation (the classifier with the best probability of correct recognition Pc is not best); higher-order discrimination information is present in standard features (thus, high-order features appear useful); classifiers that use new cluster categories of samples achieve improved performance. Results are presented for X-ray images of pistachio nuts; however, all techniques have use in other product inspection applications.

Breast Cancer Recognition Using a Novel Hybrid Intelligent Method

PubMed Central

Addeh, Jalil; Ebrahimzadeh, Ata

2012-01-01

Breast cancer is the second largest cause of cancer deaths among women. At the same time, it is also among the most curable cancer types if it can be diagnosed early. This paper presents a novel hybrid intelligent method for recognition of breast cancer tumors. The proposed method includes three main modules: the feature extraction module, the classifier module, and the optimization module. In the feature extraction module, fuzzy features are proposed as the efficient characteristic of the patterns. In the classifier module, because of the promising generalization capability of support vector machines (SVM), a SVM-based classifier is proposed. In support vector machine training, the hyperparameters have very important roles for its recognition accuracy. Therefore, in the optimization module, the bees algorithm (BA) is proposed for selecting appropriate parameters of the classifier. The proposed system is tested on Wisconsin Breast Cancer database and simulation results show that the recommended system has a high accuracy. PMID:23626945

Diagnosis of cervical cancer cell taken from scanning electron and atomic force microscope images of the same patients using discrete wavelet entropy energy and Jensen Shannon, Hellinger, Triangle Measure classifier.

PubMed

Aytac Korkmaz, Sevcan

2016-05-05

The aim of this article is to provide early detection of cervical cancer by using both Atomic Force Microscope (AFM) and Scanning Electron Microscope (SEM) images of same patient. When the studies in the literature are examined, it is seen that the AFM and SEM images of the same patient are not used together for early diagnosis of cervical cancer. AFM and SEM images can be limited when using only one of them for the early detection of cervical cancer. Therefore, multi-modality solutions which give more accuracy results than single solutions have been realized in this paper. Optimum feature space has been obtained by Discrete Wavelet Entropy Energy (DWEE) applying to the 3×180 AFM and SEM images. Then, optimum features of these images are classified with Jensen Shannon, Hellinger, and Triangle Measure (JHT) Classifier for early diagnosis of cervical cancer. However, between classifiers which are Jensen Shannon, Hellinger, and triangle distance have been validated the measures via relationships. Afterwards, accuracy diagnosis of normal, benign, and malign cervical cancer cell was found by combining mean success rates of Jensen Shannon, Hellinger, and Triangle Measure which are connected with each other. Averages of accuracy diagnosis for AFM and SEM images by averaging the results obtained from these 3 classifiers are found as 98.29% and 97.10%, respectively. It has been observed that AFM images for early diagnosis of cervical cancer have higher performance than SEM images. Also in this article, surface roughness of malign AFM images in the result of the analysis made for the AFM images, according to the normal and benign AFM images is observed as larger, If the volume of particles has found as smaller. Copyright © 2016 Elsevier B.V. All rights reserved.

Automatic Estimation of Osteoporotic Fracture Cases by Using Ensemble Learning Approaches.

PubMed

Kilic, Niyazi; Hosgormez, Erkan

2016-03-01

Ensemble learning methods are one of the most powerful tools for the pattern classification problems. In this paper, the effects of ensemble learning methods and some physical bone densitometry parameters on osteoporotic fracture detection were investigated. Six feature set models were constructed including different physical parameters and they fed into the ensemble classifiers as input features. As ensemble learning techniques, bagging, gradient boosting and random subspace (RSM) were used. Instance based learning (IBk) and random forest (RF) classifiers applied to six feature set models. The patients were classified into three groups such as osteoporosis, osteopenia and control (healthy), using ensemble classifiers. Total classification accuracy and f-measure were also used to evaluate diagnostic performance of the proposed ensemble classification system. The classification accuracy has reached to 98.85 % by the combination of model 6 (five BMD + five T-score values) using RSM-RF classifier. The findings of this paper suggest that the patients will be able to be warned before a bone fracture occurred, by just examining some physical parameters that can easily be measured without invasive operations.

Toward automated classification of consumers' cancer-related questions with a new taxonomy of expected answer types.

PubMed

McRoy, Susan; Jones, Sean; Kurmally, Adam

2016-09-01

This article examines methods for automated question classification applied to cancer-related questions that people have asked on the web. This work is part of a broader effort to provide automated question answering for health education. We created a new corpus of consumer-health questions related to cancer and a new taxonomy for those questions. We then compared the effectiveness of different statistical methods for developing classifiers, including weighted classification and resampling. Basic methods for building classifiers were limited by the high variability in the natural distribution of questions and typical refinement approaches of feature selection and merging categories achieved only small improvements to classifier accuracy. Best performance was achieved using weighted classification and resampling methods, the latter yielding an accuracy of F1 = 0.963. Thus, it would appear that statistical classifiers can be trained on natural data, but only if natural distributions of classes are smoothed. Such classifiers would be useful for automated question answering, for enriching web-based content, or assisting clinical professionals to answer questions. © The Author(s) 2015.

Comparison of Classifier Architectures for Online Neural Spike Sorting.

PubMed

Saeed, Maryam; Khan, Amir Ali; Kamboh, Awais Mehmood

2017-04-01

High-density, intracranial recordings from micro-electrode arrays need to undergo Spike Sorting in order to associate the recorded neuronal spikes to particular neurons. This involves spike detection, feature extraction, and classification. To reduce the data transmission and power requirements, on-chip real-time processing is becoming very popular. However, high computational resources are required for classifiers in on-chip spike-sorters, making scalability a great challenge. In this review paper, we analyze several popular classifiers to propose five new hardware architectures using the off-chip training with on-chip classification approach. These include support vector classification, fuzzy C-means classification, self-organizing maps classification, moving-centroid K-means classification, and Cosine distance classification. The performance of these architectures is analyzed in terms of accuracy and resource requirement. We establish that the neural networks based Self-Organizing Maps classifier offers the most viable solution. A spike sorter based on the Self-Organizing Maps classifier, requires only 7.83% of computational resources of the best-reported spike sorter, hierarchical adaptive means, while offering a 3% better accuracy at 7 dB SNR.

Neonatal Seizure Detection Using Deep Convolutional Neural Networks.

PubMed

Ansari, Amir H; Cherian, Perumpillichira J; Caicedo, Alexander; Naulaers, Gunnar; De Vos, Maarten; Van Huffel, Sabine

2018-04-02

Identifying a core set of features is one of the most important steps in the development of an automated seizure detector. In most of the published studies describing features and seizure classifiers, the features were hand-engineered, which may not be optimal. The main goal of the present paper is using deep convolutional neural networks (CNNs) and random forest to automatically optimize feature selection and classification. The input of the proposed classifier is raw multi-channel EEG and the output is the class label: seizure/nonseizure. By training this network, the required features are optimized, while fitting a nonlinear classifier on the features. After training the network with EEG recordings of 26 neonates, five end layers performing the classification were replaced with a random forest classifier in order to improve the performance. This resulted in a false alarm rate of 0.9 per hour and seizure detection rate of 77% using a test set of EEG recordings of 22 neonates that also included dubious seizures. The newly proposed CNN classifier outperformed three data-driven feature-based approaches and performed similar to a previously developed heuristic method.

A bench-top hyperspectral imaging system to classify beef from Nellore cattle based on tenderness

NASA Astrophysics Data System (ADS)

Nubiato, Keni Eduardo Zanoni; Mazon, Madeline Rezende; Antonelo, Daniel Silva; Calkins, Chris R.; Naganathan, Govindarajan Konda; Subbiah, Jeyamkondan; da Luz e Silva, Saulo

2018-03-01

The aim of this study was to evaluate the accuracy of classification of Nellore beef aged for 0, 7, 14, or 21 days and classification based on tenderness and aging period using a bench-top hyperspectral imaging system. A hyperspectral imaging system (λ = 928-2524 nm) was used to collect hyperspectral images of the Longissimus thoracis et lumborum (aging n = 376 and tenderness n = 345) of Nellore cattle. The image processing steps included selection of region of interest, extraction of spectra, and indentification and evalution of selected wavelengths for classification. Six linear discriminant models were developed to classify samples based on tenderness and aging period. The model using the first derivative of partial absorbance spectra (give wavelength range spectra) was able to classify steaks based on the tenderness with an overall accuracy of 89.8%. The model using the first derivative of full absorbance spectra was able to classify steaks based on aging period with an overall accuracy of 84.8%. The results demonstrate that the HIS may be a viable technology for classifying beef based on tenderness and aging period.

Consensus-Based Attributes for Identifying Patients With Spasmodic Dysphonia and Other Voice Disorders.

PubMed

Ludlow, Christy L; Domangue, Rickie; Sharma, Dinesh; Jinnah, H A; Perlmutter, Joel S; Berke, Gerald; Sapienza, Christine; Smith, Marshall E; Blumin, Joel H; Kalata, Carrie E; Blindauer, Karen; Johns, Michael; Hapner, Edie; Harmon, Archie; Paniello, Randal; Adler, Charles H; Crujido, Lisa; Lott, David G; Bansberg, Stephen F; Barone, Nicholas; Drulia, Teresa; Stebbins, Glenn

2018-06-21

A roadblock for research on adductor spasmodic dysphonia (ADSD), abductor SD (ABSD), voice tremor (VT), and muscular tension dysphonia (MTD) is the lack of criteria for selecting patients with these disorders. To determine the agreement among experts not using standard guidelines to classify patients with ABSD, ADSD, VT, and MTD, and develop expert consensus attributes for classifying patients for research. From 2011 to 2016, a multicenter observational study examined agreement among blinded experts when classifying patients with ADSD, ABSD, VT or MTD (first study). Subsequently, a 4-stage Delphi method study used reiterative stages of review by an expert panel and 46 community experts to develop consensus on attributes to be used for classifying patients with the 4 disorders (second study). The study used a convenience sample of 178 patients clinically diagnosed with ADSD, ABSD, VT MTD, vocal fold paresis/paralysis, psychogenic voice disorders, or hypophonia secondary to Parkinson disease. Participants were aged 18 years or older, without laryngeal structural disease or surgery for ADSD and underwent speech and nasolaryngoscopy video recordings following a standard protocol. Speech and nasolaryngoscopy video recordings following a standard protocol. Specialists at 4 sites classified 178 patients into 11 categories. Four international experts independently classified 75 patients using the same categories without guidelines after viewing speech and nasolaryngoscopy video recordings. Each member from the 4 sites also classified 50 patients from other sites after viewing video clips of voice/laryngeal tasks. Interrater κ less than 0.40 indicated poor classification agreement among rater pairs and across recruiting sites. Consequently, a Delphi panel of 13 experts identified and ranked speech and laryngeal movement attributes for classifying ADSD, ABSD, VT, and MTD, which were reviewed by 46 community specialists. Based on the median attribute rankings, a final attribute list was created for each disorder. When classifying patients without guidelines, raters differed in their classification distributions (likelihood ratio, χ2 = 107.66), had poor interrater agreement, and poor agreement with site categories. For 11 categories, the highest agreement was 34%, with no κ values greater than 0.26. In external rater pairs, the highest κ was 0.23 and the highest agreement was 38.5%. Using 6 categories, the highest percent agreement was 73.3% and the highest κ was 0.40. The Delphi method yielded 18 attributes for classifying disorders from speech and nasolaryngoscopic examinations. Specialists without guidelines had poor agreement when classifying patients for research, leading to a Delphi-based development of the Spasmodic Dysphonia Attributes Inventory for classifying patients with ADSD, ABSD, VT, and MTD for research.

Calculation and evaluation of sediment effect concentrations for the amphipod Hyalella azteca and the midge Chironomus riparius

USGS Publications Warehouse

Ingersoll, Christopher G.; Haverland, Pamela S.; Brunson, Eric L.; Canfield, Timothy J.; Dwyer, F. James; Henke, Chris; Kemble, Nile E.; Mount, David R.; Fox, Richard G.

1996-01-01

Procedures are described for calculating and evaluating sediment effect concentrations (SECs) using laboratory data on the toxicity of contaminants associated with field-collected sediment to the amphipod Hyalella azteca and the midge Chironomus riparius. SECs are defined as the concentrations of individual contaminants in sediment below which toxicity is rarely observed and above which toxicity is frequently observed. The objective of the present study was to develop SECs to classify toxicity data for Great Lake sediment samples tested with Hyalella azteca and Chironomus riparius. This SEC database included samples from additional sites across the United States in order to make the database as robust as possible. Three types of SECs were calculated from these data: (1) Effect Range Low (ERL) and Effect Range Median (ERM), (2) Threshold Effect Level (TEL) and Probable Effect Level (PEL), and (3) No Effect Concentration (NEC). We were able to calculate SECs primarily for total metals, simultaneously extracted metals, polychlorinated biphenyls (PCBs), and polycyclic aromatic hydrocarbons (PAHs). The ranges of concentrations in sediment were too narrow in our database to adequately evaluate SECs for butyltins, methyl mercury, polychlorinated dioxins and furans, or chlorinated pesticides. About 60 to 80% of the sediment samples in the database are correctly classified as toxic or not toxic depending on type of SEC evaluated. ERMs and ERLs are generally as reliable as paired PELs and TELs at classifying both toxic and non-toxic samples in our database. Reliability of the SECs in terms of correctly classifying sediment samples is similar between ERMs and NECs; however, ERMs minimize Type I error (false positives) relative to ERLs and minimize Type II error (false negatives) relative to NECs. Correct classification of samples can be improved by using only the most reliable individual SECs for chemicals (i.e., those with a higher percentage of correct classification). SECs calculated using sediment concentrations normalized to total organic carbon (TOC) concentrations did not improve the reliability compared to SECs calculated using dry-weight concentrations. The range of TOC concentrations in our database was relatively narrow compared to the ranges of contaminant concentrations. Therefore, normalizing dry-weight concentrations to a relatively narrow range of TOC concentrations had little influence on relative concentra of contaminants among samples. When SECs are used to conduct a preliminary screening to predict the potential for toxicity in the absence of actual toxicity testing, a low number of SEC exceedances should be used to minimize the potential for false negatives; however, the risk of accepting higher false positives is increased.

Improving the mapping of crop types in the Midwestern U.S. by fusing Landsat and MODIS satellite data

NASA Astrophysics Data System (ADS)

Zhu, Likai; Radeloff, Volker C.; Ives, Anthony R.

2017-06-01

Mapping crop types is of great importance for assessing agricultural production, land-use patterns, and the environmental effects of agriculture. Indeed, both radiometric and spatial resolution of Landsat's sensors images are optimized for cropland monitoring. However, accurate mapping of crop types requires frequent cloud-free images during the growing season, which are often not available, and this raises the question of whether Landsat data can be combined with data from other satellites. Here, our goal is to evaluate to what degree fusing Landsat with MODIS Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR) data can improve crop-type classification. Choosing either one or two images from all cloud-free Landsat observations available for the Arlington Agricultural Research Station area in Wisconsin from 2010 to 2014, we generated 87 combinations of images, and used each combination as input into the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) algorithm to predict Landsat-like images at the nominal dates of each 8-day MODIS NBAR product. Both the original Landsat and STARFM-predicted images were then classified with a support vector machine (SVM), and we compared the classification errors of three scenarios: 1) classifying the one or two original Landsat images of each combination only, 2) classifying the one or two original Landsat images plus all STARFM-predicted images, and 3) classifying the one or two original Landsat images together with STARFM-predicted images for key dates. Our results indicated that using two Landsat images as the input of STARFM did not significantly improve the STARFM predictions compared to using only one, and predictions using Landsat images between July and August as input were most accurate. Including all STARFM-predicted images together with the Landsat images significantly increased average classification error by 4% points (from 21% to 25%) compared to using only Landsat images. However, incorporating only STARFM-predicted images for key dates decreased average classification error by 2% points (from 21% to 19%) compared to using only Landsat images. In particular, if only a single Landsat image was available, adding STARFM predictions for key dates significantly decreased the average classification error by 4 percentage points from 30% to 26% (p < 0.05). We conclude that adding STARFM-predicted images can be effective for improving crop-type classification when only limited Landsat observations are available, but carefully selecting images from a full set of STARFM predictions is crucial. We developed an approach to identify the optimal subsets of all STARFM predictions, which gives an alternative method of feature selection for future research.

"In the Eye of the Beholder": Sex Bias in Observations and Ratings of Children's Aggression

ERIC Educational Resources Information Center

Pellegrini, Anthony D.

2011-01-01

The processes by which children are classified as aggressive have important educational and research implications. For example, aggression in childhood reliably predicts dropping out of school and incarceration. The author argues that the sex-role stereotypicality of aggression produces bias in both observers and raters of student behavior. The…

[Characteristic features of female murderers].

PubMed

Patla, Mariusz; Teleśnicki, Stanisław

2005-01-01

65 female murderers were observed in the Forensic Psychiatry Ward. In 61 cases the victims were closely connected with the victim. The intellectual capacity of these women was similar to the average population. 41 women were abused before murder. Only in 7% of cases pathological abnormalities in CNS were not observed. In the examined group 5% were classified as insane.

Towards Automatic Classification of Exoplanet-Transit-Like Signals: A Case Study on Kepler Mission Data

NASA Astrophysics Data System (ADS)

Valizadegan, Hamed; Martin, Rodney; McCauliff, Sean D.; Jenkins, Jon Michael; Catanzarite, Joseph; Oza, Nikunj C.

2015-08-01

Building new catalogues of planetary candidates, astrophysical false alarms, and non-transiting phenomena is a challenging task that currently requires a reviewing team of astrophysicists and astronomers. These scientists need to examine more than 100 diagnostic metrics and associated graphics for each candidate exoplanet-transit-like signal to classify it into one of the three classes. Considering that the NASA Explorer Program's TESS mission and ESA's PLATO mission survey even a larger area of space, the classification of their transit-like signals is more time-consuming for human agents and a bottleneck to successfully construct the new catalogues in a timely manner. This encourages building automatic classification tools that can quickly and reliably classify the new signal data from these missions. The standard tool for building automatic classification systems is the supervised machine learning that requires a large set of highly accurate labeled examples in order to build an effective classifier. This requirement cannot be easily met for classifying transit-like signals because not only are existing labeled signals very limited, but also the current labels may not be reliable (because the labeling process is a subjective task). Our experiments with using different supervised classifiers to categorize transit-like signals verifies that the labeled signals are not rich enough to provide the classifier with enough power to generalize well beyond the observed cases (e.g. to unseen or test signals). That motivated us to utilize a new category of learning techniques, so-called semi-supervised learning, that combines the label information from the costly labeled signals, and distribution information from the cheaply available unlabeled signals in order to construct more effective classifiers. Our study on the Kepler Mission data shows that semi-supervised learning can significantly improve the result of multiple base classifiers (e.g. Support Vector Machines, AdaBoost, and Decision Tree) and is a good technique for automatic classification of exoplanet-transit-like signal.

Diagnosis of Pneumocystis jirovecii Pneumonia in Immunocompromised Patients by Real-Time PCR: a 4-Year Prospective Study

PubMed Central

Belaz, Sorya; Revest, Matthieu; Tattevin, Pierre; Jouneau, Stéphane; Decaux, Olivier; Chevrier, Sylviane; Le Tulzo, Yves; Gangneux, Jean-Pierre

2014-01-01

Pneumocystis jirovecii pneumonia (PCP) is a life-threatening infection in immunocompromised patients. Quantitative real-time PCR (qPCR) is more sensitive than microscopic examination for the detection of P. jirovecii but also detects colonized patients. Hence, its positive predictive value (PPV) needs evaluation. In this 4-year prospective observational study, all immunocompromised patients with acute respiratory symptoms who were investigated for PCP were included, totaling 659 patients (814 bronchoalveolar lavage fluid samples). Patients with negative microscopy but positive qPCR were classified through medical chart review as having retained PCP, possible PCP, or colonization, and their clinical outcomes were compared to those of patients with microscopically proven PCP. Overall, 119 patients were included for analysis, of whom 35, 41, and 43 were classified as having retained PCP, possible PCP, and colonization, respectively. The 35 patients with retained PCP had clinical findings similar to those with microscopically proven PCP but lower fungal loads (P < 0.001) and were mainly non-HIV-infected patients (P < 0.05). Although the mean amplification threshold was higher in colonized patients, it was not possible to determine a discriminant qPCR cutoff. The PPV of qPCR in patients with negative microscopy were 29.4% and 63.8% when considering retained PCP and retained plus possible PCP, respectively. Patients with possible PCP had a higher mortality rate than patients with retained PCP or colonization (63% versus 3% and 16%, respectively); patients who died had not received co-trimoxazole. In conclusion, qPCR is a useful tool to diagnose PCP in non-HIV patients, and treatment might be better targeted through a multicomponent algorithm including both clinical/radiological parameters and qPCR results. PMID:25009050

Confidential reporting of patient safety events in primary care: results from a multilevel classification of cognitive and system factors.

PubMed

Kostopoulou, Olga; Delaney, Brendan

2007-04-01

To classify events of actual or potential harm to primary care patients using a multilevel taxonomy of cognitive and system factors. Observational study of patient safety events obtained via a confidential but not anonymous reporting system. Reports were followed up with interviews where necessary. Events were analysed for their causes and contributing factors using causal trees and were classified using the taxonomy. Five general medical practices in the West Midlands were selected to represent a range of sizes and types of patient population. All practice staff were invited to report patient safety events. Main outcome measures were frequencies of clinical types of events reported, cognitive types of error, types of detection and contributing factors; and relationship between types of error, practice size, patient consequences and detection. 78 reports were relevant to patient safety and analysable. They included 21 (27%) adverse events and 50 (64%) near misses. 16.7% (13/71) had serious patient consequences, including one death. 75.7% (59/78) had the potential for serious patient harm. Most reports referred to administrative errors (25.6%, 20/78). 60% (47/78) of the reports contained sufficient information to characterise cognition: "situation assessment and response selection" was involved in 45% (21/47) of these reports and was often linked to serious potential consequences. The most frequent contributing factor was work organisation, identified in 71 events. This included excessive task demands (47%, 37/71) and fragmentation (28%, 22/71). Even though most reported events were near misses, events with serious patient consequences were also reported. Failures in situation assessment and response selection, a cognitive activity that occurs in both clinical and administrative tasks, was related to serious potential harm.

Confidential reporting of patient safety events in primary care: results from a multilevel classification of cognitive and system factors

PubMed Central

Kostopoulou, Olga; Delaney, Brendan

2007-01-01

Objective To classify events of actual or potential harm to primary care patients using a multilevel taxonomy of cognitive and system factors. Methods Observational study of patient safety events obtained via a confidential but not anonymous reporting system. Reports were followed up with interviews where necessary. Events were analysed for their causes and contributing factors using causal trees and were classified using the taxonomy. Five general medical practices in the West Midlands were selected to represent a range of sizes and types of patient population. All practice staff were invited to report patient safety events. Main outcome measures were frequencies of clinical types of events reported, cognitive types of error, types of detection and contributing factors; and relationship between types of error, practice size, patient consequences and detection. Results 78 reports were relevant to patient safety and analysable. They included 21 (27%) adverse events and 50 (64%) near misses. 16.7% (13/71) had serious patient consequences, including one death. 75.7% (59/78) had the potential for serious patient harm. Most reports referred to administrative errors (25.6%, 20/78). 60% (47/78) of the reports contained sufficient information to characterise cognition: “situation assessment and response selection” was involved in 45% (21/47) of these reports and was often linked to serious potential consequences. The most frequent contributing factor was work organisation, identified in 71 events. This included excessive task demands (47%, 37/71) and fragmentation (28%, 22/71). Conclusions Even though most reported events were near misses, events with serious patient consequences were also reported. Failures in situation assessment and response selection, a cognitive activity that occurs in both clinical and administrative tasks, was related to serious potential harm. PMID:17403753

A Critical Review of Mode of Action (MOA) Assignment ...

EPA Pesticide Factsheets

There are various structure-based classification schemes to categorize chemicals based on mode of action (MOA) which have been applied for both eco and human health toxicology. With increasing calls to assess thousands of chemicals, some of which have little available information other than structure, clear understanding how each of these MOA schemes was devised, what information they are based on, and the limitations of each approach is critical. Several groups are developing low-tier methods to more easily classify or assess chemicals, using approaches such as the ecological threshold of concern (eco-TTC) and chemical-activity. Evaluation of these approaches and determination of their domain of applicability is partly dependent on the MOA classification that is used. The most commonly used MOA classification schemes for ecotoxicology include Verhaar and Russom (included in ASTER), both of which are used to predict acute aquatic toxicity MOA. Verhaar is a QSAR-based system that classifies chemicals into one of 4 classes, with a 5th class specified for those chemicals that are not classified in the other 4. ASTER/Russom includes 8 classifications: narcotics (3 groups), oxidative phosphorylation uncouplers, respiratory inhibitors, electrophiles/proelectrophiles, AChE inhibitors, or CNS seizure agents. Other methodologies include TEST (Toxicity Estimation Software Tool), a computational chemistry-based application that allows prediction to one of 5 broad MOA

[Nursing diagnosis "impaired walking" in elderly patients: integrative literature review].

PubMed

Marques-Vieira, Cristina Maria Alves; de Sousa, Luís Manuel Mota; de Matos Machado Carias, João Filipe; Caldeira, Sílvia Maria Alves

2015-03-01

The impaired walking nursing diagnosis has been included in NANDA International classification taxonomy in 1998, and this review aims to identify the defining characteristics and related factors in elderly patients in recent literature. Integrative literature review based on the following guiding question: Are there more defining characteristics and factors related to the nursing diagnosis impaired walking than those included in NANDA International classification taxonomy in elderly patients? Search conducted in 2007-2013 on international and Portuguese databases. Sample composed of 15 papers. Among the 6 defining characteristics classified at NANDA International, 3 were identified in the search results, but 13 were not included in the classification. Regarding the 14 related factors that are classified, 9 were identified in the sample and 12 were not included in the NANDA International taxonomy. This review allowed the identification of new elements not included in NANDA International Taxonomy and may contribute to the development of taxonomy and nursing knowledge.

Ensemble Methods for Classification of Physical Activities from Wrist Accelerometry.

PubMed

Chowdhury, Alok Kumar; Tjondronegoro, Dian; Chandran, Vinod; Trost, Stewart G

2017-09-01

To investigate whether the use of ensemble learning algorithms improve physical activity recognition accuracy compared to the single classifier algorithms, and to compare the classification accuracy achieved by three conventional ensemble machine learning methods (bagging, boosting, random forest) and a custom ensemble model comprising four algorithms commonly used for activity recognition (binary decision tree, k nearest neighbor, support vector machine, and neural network). The study used three independent data sets that included wrist-worn accelerometer data. For each data set, a four-step classification framework consisting of data preprocessing, feature extraction, normalization and feature selection, and classifier training and testing was implemented. For the custom ensemble, decisions from the single classifiers were aggregated using three decision fusion methods: weighted majority vote, naïve Bayes combination, and behavior knowledge space combination. Classifiers were cross-validated using leave-one subject out cross-validation and compared on the basis of average F1 scores. In all three data sets, ensemble learning methods consistently outperformed the individual classifiers. Among the conventional ensemble methods, random forest models provided consistently high activity recognition; however, the custom ensemble model using weighted majority voting demonstrated the highest classification accuracy in two of the three data sets. Combining multiple individual classifiers using conventional or custom ensemble learning methods can improve activity recognition accuracy from wrist-worn accelerometer data.

Contribution of Race, Primary Language, Family Structure and Pre-Kindergarten Attendance to the Odds of Being Classified as Having a Specific Learning Disability by the Third Grade

ERIC Educational Resources Information Center

Roring, Catherine Mary

2013-01-01

Many risk factors have been identified for children entering Kindergarten. Many at-risk children eventually get classified as having a Specific Learning disability. Some of these risk factors include having a primary home language other than English (Hosp & Reschly, 2004), having non-intact families (Pong, 1997), being of minority status…

Multimodal Signal Processing for Personnel Detection and Activity Classification for Indoor Surveillance

DTIC Science & Technology

2013-11-15

features and designed a classifier that achieves up to 95% classification accuracy on classifying the occupancy with indoor footstep data. MDL-based...information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and...maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other

Automatic Keyframe Summarization of User-Generated Video

DTIC Science & Technology

2014-06-01

using the framework presented in this paper. 12 Scenery Technology has been developed that classifies the genre of a video. Here, video genres are...types of videos that shares similarities in content and structure. Many genres of video footage exist. Some examples include news, sports, movies...cartoons, and commercials. Rasheed et al. [42] classify video genres (comedy, action, drama, and horror) with low-level video statistics, such as average

Shared genetic factors underlie migraine and depression

PubMed Central

Yang, Yuanhao; Zhao, Huiying; Heath, Andrew C; Madden, Pamela AF; Martin, Nicholas G; Nyholt, Dale R

2017-01-01

Migraine frequently co-occurs with depression. Using a large sample of Australian twin pairs, we aimed to characterise the extent to which shared genetic factors underlie these two disorders. Migraine was classified using three diagnostic measures, including self-reported migraine, the ID migraine™ screening tool, or migraine without aura (MO) and migraine with aura (MA) based on International Headache Society (IHS) diagnostic criteria. Major depressive disorder (MDD) and minor depressive disorder (MiDD) were classified using the Diagnostic and Statistical Manual of Mental Disorders (DSM) criteria. Univariate and bivariate twin models, with and without sex-limitation, were constructed to estimate the univariate and bivariate variance components and genetic correlation for migraine and depression. The univariate heritability of broad migraine (self-reported, ID migraine or IHS MO/MA) and broad depression (MiDD or MDD) was estimated at 56% (95% confidence interval [CI]: 53–60%) and 42% (95% CI: 37–46%), respectively. A significant additive genetic correlation (rG=0.36, 95% CI: 0.29–0.43) and bivariate heritability (h2=5.5%, 95% CI: 3.6–7.8%) was observed between broad migraine and depression using the bivariate Cholesky model. Notably, both the bivariate h2 (13.3%, 95% CI: 7.0–24.5%) and rG (0.51, 95% CI: 0.37–0.69) estimates significantly increased when analysing the more narrow clinically-accepted diagnoses of IHS MO/MA and MDD. Our results indicate that for both broad and narrow definitions, the observed comorbidity between migraine and depression can be explained almost entirely by shared underlying genetically determined disease mechanisms. PMID:27302564

Angle assessment by EyeCam, goniophotography, and gonioscopy.

PubMed

Baskaran, Mani; Perera, Shamira A; Nongpiur, Monisha E; Tun, Tin A; Park, Judy; Kumar, Rajesh S; Friedman, David S; Aung, Tin

2012-09-01

To compare EyeCam (Clarity Medical Systems, Pleasanton, CA) and goniophotography in detecting angle closure, using gonioscopy as the reference standard. In this hospital-based, prospective, cross-sectional study, participants underwent gonioscopy by a single observer, and EyeCam imaging and goniophotography by different operators. The anterior chamber angle in a quadrant was classified as closed if the posterior trabecular meshwork could not be seen. A masked observer categorized the eyes as per the number of closed quadrants, and an eye was classified as having angle closure if there were 2 or more quadrants of closure. Agreement between the methods was analyzed by κ statistic and comparison of area under receiver operating characteristic curves (AUC). Eighty-five participants (85 eyes) were included, the majority of whom were Chinese. Angle closure was detected in 38 eyes (45%) with gonioscopy, 40 eyes (47%) using EyeCam, and 40 eyes (47%) with goniophotography (P=0.69 in both comparisons, McNemar test). The agreement for angle closure diagnosis (by eye) between gonioscopy and the 2 imaging modalities was high (κ=0.86; 95% Confidence Interval (CI), 0.75-0.97), whereas the agreement between EyeCam and goniophotography was not as good (κ=0.72; 95% CI, 0.57-0.87); largely due to lack of agreement in the nasal and temporal quadrants (κ=0.55 to 0.67). The AUC for detecting eyes with gonioscopic angle closure was similar for goniophotography and EyeCam (AUC 0.93, sensitivity=94.7%, specificity=91.5%; P>0.95). EyeCam and goniophotography have similarly high sensitivity and specificity for the detection of gonioscopic angle closure.

Deep Stromvil Photometry for Star Formation in the Head of the Pelican Nebula

NASA Astrophysics Data System (ADS)

Boyle, Richard P.; J., S.; Stott, J.; J., S.; Janusz, R.; J., S.; Straizys, V.

2010-01-01

The North America and Pelican Nebulae, and specifically the dark cloud L935 contain regions of active star formation (Herbig, G. H. 1958, ApJ, 128,259). Previously we reported on Vatican telescope observations by Stromvil intermediate-band filters in a 12-arcmin field in the "Gulf of Mexico" region of L935. There we classify A, F, and G-type stars. However, the many faint K and M-type dwarf stars remain somewhat ambiguous in calibration and classification. But attaining reasonable progress, we turn to another part of L935 located near the Pelican head. This area includes the "bright rim" which is formed by dust and gas condensed by the light pressure of an unseen O-type star hidden behind the dense dark cloud. Straizys and Laugalys (2008 Baltic Astronomy, 17, 143 ) have identified this star to be one of the 2MASS objects with Av=23 mag. A few concentrations of faint stars, V 13 to 14 mag. are immersed in this dark region. Among these stars are a few known emission-line objects (T-Tauri or post T-Tauri stars). A half degree nearby are some photometric Vilnius standards we use to calibrate our new field. We call on 2MASS data for correlative information. Also the Stromvil photometry offers candidate stars for spectral observations. The aim of this study in the Vilnius and Stromvil photometric systems is to classify stars down to V = 18 mag., to confirm the existence of the young star clusters, and to determine the distance of the cloud covering the suspected hidden ionizing star.

Comparison of Classification Methods for Detecting Emotion from Mandarin Speech

NASA Astrophysics Data System (ADS)

Pao, Tsang-Long; Chen, Yu-Te; Yeh, Jun-Heng

It is said that technology comes out from humanity. What is humanity? The very definition of humanity is emotion. Emotion is the basis for all human expression and the underlying theme behind everything that is done, said, thought or imagined. Making computers being able to perceive and respond to human emotion, the human-computer interaction will be more natural. Several classifiers are adopted for automatically assigning an emotion category, such as anger, happiness or sadness, to a speech utterance. These classifiers were designed independently and tested on various emotional speech corpora, making it difficult to compare and evaluate their performance. In this paper, we first compared several popular classification methods and evaluated their performance by applying them to a Mandarin speech corpus consisting of five basic emotions, including anger, happiness, boredom, sadness and neutral. The extracted feature streams contain MFCC, LPCC, and LPC. The experimental results show that the proposed WD-MKNN classifier achieves an accuracy of 81.4% for the 5-class emotion recognition and outperforms other classification techniques, including KNN, MKNN, DW-KNN, LDA, QDA, GMM, HMM, SVM, and BPNN. Then, to verify the advantage of the proposed method, we compared these classifiers by applying them to another Mandarin expressive speech corpus consisting of two emotions. The experimental results still show that the proposed WD-MKNN outperforms others.

Recognition algorithm for assisting ovarian cancer diagnosis from coregistered ultrasound and photoacoustic images: ex vivo study

NASA Astrophysics Data System (ADS)

Alqasemi, Umar; Kumavor, Patrick; Aguirre, Andres; Zhu, Quing

2012-12-01

Unique features and the underlining hypotheses of how these features may relate to the tumor physiology in coregistered ultrasound and photoacoustic images of ex vivo ovarian tissue are introduced. The images were first compressed with wavelet transform. The mean Radon transform of photoacoustic images was then computed and fitted with a Gaussian function to find the centroid of a suspicious area for shift-invariant recognition process. Twenty-four features were extracted from a training set by several methods, including Fourier transform, image statistics, and different composite filters. The features were chosen from more than 400 training images obtained from 33 ex vivo ovaries of 24 patients, and used to train three classifiers, including generalized linear model, neural network, and support vector machine (SVM). The SVM achieved the best training performance and was able to exclusively separate cancerous from non-cancerous cases with 100% sensitivity and specificity. At the end, the classifiers were used to test 95 new images obtained from 37 ovaries of 20 additional patients. The SVM classifier achieved 76.92% sensitivity and 95.12% specificity. Furthermore, if we assume that recognizing one image as a cancer is sufficient to consider an ovary as malignant, the SVM classifier achieves 100% sensitivity and 87.88% specificity.

Deep feature classification of angiomyolipoma without visible fat and renal cell carcinoma in abdominal contrast-enhanced CT images with texture image patches and hand-crafted feature concatenation.

PubMed

Lee, Hansang; Hong, Helen; Kim, Junmo; Jung, Dae Chul

2018-04-01

To develop an automatic deep feature classification (DFC) method for distinguishing benign angiomyolipoma without visible fat (AMLwvf) from malignant clear cell renal cell carcinoma (ccRCC) from abdominal contrast-enhanced computer tomography (CE CT) images. A dataset including 80 abdominal CT images of 39 AMLwvf and 41 ccRCC patients was used. We proposed a DFC method for differentiating the small renal masses (SRM) into AMLwvf and ccRCC using the combination of hand-crafted and deep features, and machine learning classifiers. First, 71-dimensional hand-crafted features (HCF) of texture and shape were extracted from the SRM contours. Second, 1000-4000-dimensional deep features (DF) were extracted from the ImageNet pretrained deep learning model with the SRM image patches. In DF extraction, we proposed the texture image patches (TIP) to emphasize the texture information inside the mass in DFs and reduce the mass size variability. Finally, the two features were concatenated and the random forest (RF) classifier was trained on these concatenated features to classify the types of SRMs. The proposed method was tested on our dataset using leave-one-out cross-validation and evaluated using accuracy, sensitivity, specificity, positive predictive values (PPV), negative predictive values (NPV), and area under receiver operating characteristics curve (AUC). In experiments, the combinations of four deep learning models, AlexNet, VGGNet, GoogleNet, and ResNet, and four input image patches, including original, masked, mass-size, and texture image patches, were compared and analyzed. In qualitative evaluation, we observed the change in feature distributions between the proposed and comparative methods using tSNE method. In quantitative evaluation, we evaluated and compared the classification results, and observed that (a) the proposed HCF + DF outperformed HCF-only and DF-only, (b) AlexNet showed generally the best performances among the CNN models, and (c) the proposed TIPs not only achieved the competitive performances among the input patches, but also steady performance regardless of CNN models. As a result, the proposed method achieved the accuracy of 76.6 ± 1.4% for the proposed HCF + DF with AlexNet and TIPs, which improved the accuracy by 6.6%p and 8.3%p compared to HCF-only and DF-only, respectively. The proposed shape features and TIPs improved the HCFs and DFs, respectively, and the feature concatenation further enhanced the quality of features for differentiating AMLwvf from ccRCC in abdominal CE CT images. © 2018 American Association of Physicists in Medicine.

Experience of treatment of patients with granulomatous lobular mastitis.

PubMed

Hur, Sung Mo; Cho, Dong Hui; Lee, Se Kyung; Choi, Min-Young; Bae, Soo Youn; Koo, Min Young; Kim, Sangmin; Choe, Jun-Ho; Kim, Jung-Han; Kim, Jee Soo; Nam, Seok-Jin; Yang, Jung-Hyun; Lee, Jeong Eon

2013-07-01

To present the author's experience with various treatment methods of granulomatous lobular mastitis (GLM) and to determine effective treatment methods of GLM. Fifty patients who were diagnosed with GLM were classified into five groups based on the initial treatment methods they underwent, which included observation (n = 8), antibiotics (n = 3), steroid (n = 13), drainage (n = 14), and surgical excision (n = 12). The treatment processes in each group were examined and their clinical characteristics, treatment processes, and results were analyzed respectively. Success rates with each initial treatment were observation, 87.5%; antibiotics, 33.3%; steroids, 30.8%; drainage, 28.6%; and surgical excision, 91.7%. In most cases of observation, the lesions were small and the symptoms were mild. A total of 23 patients underwent surgical excision during treatment. Surgical excision showed particularly fast recovery, high success rate (90.3%) and low recurrence rate (8.7%). The clinical course of GLM is complex and the outcome of each treatment type are variable. Surgery may play an important role when a lesion is determined to be mass-forming or appears localized as an abscess pocket during breast examination or imaging study.

Numerical MHD study for plasmoid instability in uniform resistivity

NASA Astrophysics Data System (ADS)

Shimizu, Tohru; Kondoh, Koji; Zenitani, Seiji

2017-11-01

The plasmoid instability (PI) caused in uniform resistivity is numerically studied with a MHD numerical code of HLLD scheme. It is shown that the PI observed in numerical studies may often include numerical (non-physical) tearing instability caused by the numerical dissipations. By increasing the numerical resolutions, the numerical tearing instability gradually disappears and the physical tearing instability remains. Hence, the convergence of the numerical results is observed. Note that the reconnection rate observed in the numerical tearing instability can be higher than that of the physical tearing instability. On the other hand, regardless of the numerical and physical tearing instabilities, the tearing instability can be classified into symmetric and asymmetric tearing instability. The symmetric tearing instability tends to occur when the thinning of current sheet is stopped by the physical or numerical dissipations, often resulting in the drastic changes in plasmoid chain's structure and its activity. In this paper, by eliminating the numerical tearing instability, we could not specify the critical Lundquist number Sc beyond which PI is fully developed. It suggests that Sc does not exist, at least around S = 105.

Evaluation of Classifier Performance for Multiclass Phenotype Discrimination in Untargeted Metabolomics.

PubMed

Trainor, Patrick J; DeFilippis, Andrew P; Rai, Shesh N

2017-06-21

Statistical classification is a critical component of utilizing metabolomics data for examining the molecular determinants of phenotypes. Despite this, a comprehensive and rigorous evaluation of the accuracy of classification techniques for phenotype discrimination given metabolomics data has not been conducted. We conducted such an evaluation using both simulated and real metabolomics datasets, comparing Partial Least Squares-Discriminant Analysis (PLS-DA), Sparse PLS-DA, Random Forests, Support Vector Machines (SVM), Artificial Neural Network, k -Nearest Neighbors ( k -NN), and Naïve Bayes classification techniques for discrimination. We evaluated the techniques on simulated data generated to mimic global untargeted metabolomics data by incorporating realistic block-wise correlation and partial correlation structures for mimicking the correlations and metabolite clustering generated by biological processes. Over the simulation studies, covariance structures, means, and effect sizes were stochastically varied to provide consistent estimates of classifier performance over a wide range of possible scenarios. The effects of the presence of non-normal error distributions, the introduction of biological and technical outliers, unbalanced phenotype allocation, missing values due to abundances below a limit of detection, and the effect of prior-significance filtering (dimension reduction) were evaluated via simulation. In each simulation, classifier parameters, such as the number of hidden nodes in a Neural Network, were optimized by cross-validation to minimize the probability of detecting spurious results due to poorly tuned classifiers. Classifier performance was then evaluated using real metabolomics datasets of varying sample medium, sample size, and experimental design. We report that in the most realistic simulation studies that incorporated non-normal error distributions, unbalanced phenotype allocation, outliers, missing values, and dimension reduction, classifier performance (least to greatest error) was ranked as follows: SVM, Random Forest, Naïve Bayes, sPLS-DA, Neural Networks, PLS-DA and k -NN classifiers. When non-normal error distributions were introduced, the performance of PLS-DA and k -NN classifiers deteriorated further relative to the remaining techniques. Over the real datasets, a trend of better performance of SVM and Random Forest classifier performance was observed.

Genetics Home Reference: critical congenital heart disease

MedlinePlus

... into and out of the heart (including the aorta and pulmonary artery). Still others involve a combination ... defects classified as CCHD include coarctation of the aorta , double-outlet right ventricle, D-transposition of the ...

Image-Based Airborne Sensors: A Combined Approach for Spectral Signatures Classification through Deterministic Simulated Annealing

PubMed Central

Guijarro, María; Pajares, Gonzalo; Herrera, P. Javier

2009-01-01

The increasing technology of high-resolution image airborne sensors, including those on board Unmanned Aerial Vehicles, demands automatic solutions for processing, either on-line or off-line, the huge amountds of image data sensed during the flights. The classification of natural spectral signatures in images is one potential application. The actual tendency in classification is oriented towards the combination of simple classifiers. In this paper we propose a combined strategy based on the Deterministic Simulated Annealing (DSA) framework. The simple classifiers used are the well tested supervised parametric Bayesian estimator and the Fuzzy Clustering. The DSA is an optimization approach, which minimizes an energy function. The main contribution of DSA is its ability to avoid local minima during the optimization process thanks to the annealing scheme. It outperforms simple classifiers used for the combination and some combined strategies, including a scheme based on the fuzzy cognitive maps and an optimization approach based on the Hopfield neural network paradigm. PMID:22399989

Recognition and classification of colon cells applying the ensemble of classifiers.

PubMed

Kruk, M; Osowski, S; Koktysz, R

2009-02-01

The paper presents the application of an ensemble of classifiers for the recognition of colon cells on the basis of the microscope colon image. The solved task include: segmentation of the individual cells from the image using the morphological operations, the preprocessing stages, leading to the extraction of features, selection of the most important features, and the classification stage applying the classifiers arranged in the form of ensemble. The paper presents and discusses the results concerning the recognition of four most important colon cell types: eosinophylic granulocyte, neutrophilic granulocyte, lymphocyte and plasmocyte. The proposed system is able to recognize the cells with the accuracy comparable to the human expert (around 5% of discrepancy of both results).

Huygens’ clocks revisited

PubMed Central

Kitanov, Petko M.; Langford, William F.

2017-01-01

In 1665, Huygens observed that two identical pendulum clocks, weakly coupled through a heavy beam, soon synchronized with the same period and amplitude but with the two pendula swinging in opposite directions. This behaviour is now called anti-phase synchronization. This paper presents an analysis of the behaviour of a large class of coupled identical oscillators, including Huygens' clocks, using methods of equivariant bifurcation theory. The equivariant normal form for such systems is developed and the possible solutions are characterized. The transformation of the physical system parameters to the normal form parameters is given explicitly and applied to the physical values appropriate for Huygens' clocks, and to those of more recent studies. It is shown that Huygens' physical system could only exhibit anti-phase motion, explaining why Huygens observed exclusively this. By contrast, some more recent researchers have observed in-phase or other more complicated motion in their own experimental systems. Here, it is explained which physical characteristics of these systems allow for the existence of these other types of stable solutions. The present analysis not only accounts for these previously observed solutions in a unified framework, but also introduces behaviour not classified by other authors, such as a synchronized toroidal breather and a chaotic toroidal breather. PMID:28989780

Is hypochondriasis an anxiety disorder?

PubMed

Olatunji, Bunmi O; Deacon, Brett J; Abramowitz, Jonathan S

2009-06-01

Although hypochondriasis is currently classified as a somatoform disorder, the underlying cognitive processes may be more consistent with an anxiety disorder. This observation has important implications for treatment and subsequent revisions of the diagnostic classification of hypochondriasis.

Framework for Conducting Empirical Observations of Learning Processes.

ERIC Educational Resources Information Center

Fischer, Hans Ernst; von Aufschnaiter, Stephan

1993-01-01

Reviews four hypotheses about learning: Comenius's transmission-reception theory, information processing theory, Gestalt theory, and Piagetian theory. Uses the categories preunderstanding, conceptual change, and learning processes to classify and assess investigations on learning processes. (PR)

Treating Patients with High-Risk Smoldering Myeloma

Cancer.gov

In this phase III clinical trial, patients with smoldering myeloma classified as high risk for progression will be randomly assigned to undergo standard observation or six 4-week courses of treatment with the drug lenalidomide.

A comparison study between MLP and convolutional neural network models for character recognition

NASA Astrophysics Data System (ADS)

Ben Driss, S.; Soua, M.; Kachouri, R.; Akil, M.

2017-05-01

Optical Character Recognition (OCR) systems have been designed to operate on text contained in scanned documents and images. They include text detection and character recognition in which characters are described then classified. In the classification step, characters are identified according to their features or template descriptions. Then, a given classifier is employed to identify characters. In this context, we have proposed the unified character descriptor (UCD) to represent characters based on their features. Then, matching was employed to ensure the classification. This recognition scheme performs a good OCR Accuracy on homogeneous scanned documents, however it cannot discriminate characters with high font variation and distortion.3 To improve recognition, classifiers based on neural networks can be used. The multilayer perceptron (MLP) ensures high recognition accuracy when performing a robust training. Moreover, the convolutional neural network (CNN), is gaining nowadays a lot of popularity for its high performance. Furthermore, both CNN and MLP may suffer from the large amount of computation in the training phase. In this paper, we establish a comparison between MLP and CNN. We provide MLP with the UCD descriptor and the appropriate network configuration. For CNN, we employ the convolutional network designed for handwritten and machine-printed character recognition (Lenet-5) and we adapt it to support 62 classes, including both digits and characters. In addition, GPU parallelization is studied to speed up both of MLP and CNN classifiers. Based on our experimentations, we demonstrate that the used real-time CNN is 2x more relevant than MLP when classifying characters.

Selectivity in Genetic Association with Sub-classified Migraine in Women

PubMed Central

Chasman, Daniel I.; Anttila, Verneri; Buring, Julie E.; Ridker, Paul M.; Schürks, Markus; Kurth, Tobias

2014-01-01

Migraine can be sub-classified not only according to presence of migraine aura (MA) or absence of migraine aura (MO), but also by additional features accompanying migraine attacks, e.g. photophobia, phonophobia, nausea, etc. all of which are formally recognized by the International Classification of Headache Disorders. It remains unclear how aura status and the other migraine features may be related to underlying migraine pathophysiology. Recent genome-wide association studies (GWAS) have identified 12 independent loci at which single nucleotide polymorphisms (SNPs) are associated with migraine. Using a likelihood framework, we explored the selective association of these SNPs with migraine, sub-classified according to aura status and the other features in a large population-based cohort of women including 3,003 active migraineurs and 18,108 free of migraine. Five loci met stringent significance for association with migraine, among which four were selective for sub-classified migraine, including rs11172113 (LRP1) for MO. The number of loci associated with migraine increased to 11 at suggestive significance thresholds, including five additional selective associations for MO but none for MA. No two SNPs showed similar patterns of selective association with migraine characteristics. At one extreme, SNPs rs6790925 (near TGFBR2) and rs2274316 (MEF2D) were not associated with migraine overall, MA, or MO but were selective for migraine sub-classified by the presence of one or more of the additional migraine features. In contrast, SNP rs7577262 (TRPM8) was associated with migraine overall and showed little or no selectivity for any of the migraine characteristics. The results emphasize the multivalent nature of migraine pathophysiology and suggest that a complete understanding of the genetic influence on migraine may benefit from analyses that stratify migraine according to both aura status and the additional diagnostic features used for clinical characterization of migraine. PMID:24852292

Exploiting the systematic review protocol for classification of medical abstracts.

PubMed

Frunza, Oana; Inkpen, Diana; Matwin, Stan; Klement, William; O'Blenis, Peter

2011-01-01

To determine whether the automatic classification of documents can be useful in systematic reviews on medical topics, and specifically if the performance of the automatic classification can be enhanced by using the particular protocol of questions employed by the human reviewers to create multiple classifiers. The test collection is the data used in large-scale systematic review on the topic of the dissemination strategy of health care services for elderly people. From a group of 47,274 abstracts marked by human reviewers to be included in or excluded from further screening, we randomly selected 20,000 as a training set, with the remaining 27,274 becoming a separate test set. As a machine learning algorithm we used complement naïve Bayes. We tested both a global classification method, where a single classifier is trained on instances of abstracts and their classification (i.e., included or excluded), and a novel per-question classification method that trains multiple classifiers for each abstract, exploiting the specific protocol (questions) of the systematic review. For the per-question method we tested four ways of combining the results of the classifiers trained for the individual questions. As evaluation measures, we calculated precision and recall for several settings of the two methods. It is most important not to exclude any relevant documents (i.e., to attain high recall for the class of interest) but also desirable to exclude most of the non-relevant documents (i.e., to attain high precision on the class of interest) in order to reduce human workload. For the global method, the highest recall was 67.8% and the highest precision was 37.9%. For the per-question method, the highest recall was 99.2%, and the highest precision was 63%. The human-machine workflow proposed in this paper achieved a recall value of 99.6%, and a precision value of 17.8%. The per-question method that combines classifiers following the specific protocol of the review leads to better results than the global method in terms of recall. Because neither method is efficient enough to classify abstracts reliably by itself, the technology should be applied in a semi-automatic way, with a human expert still involved. When the workflow includes one human expert and the trained automatic classifier, recall improves to an acceptable level, showing that automatic classification techniques can reduce the human workload in the process of building a systematic review. Copyright © 2010 Elsevier B.V. All rights reserved.

Customized vs population-based growth charts to identify neonates at risk of adverse outcome: systematic review and Bayesian meta-analysis of observational studies.

PubMed

Chiossi, G; Pedroza, C; Costantine, M M; Truong, V T T; Gargano, G; Saade, G R

2017-08-01

To compare the effectiveness of customized vs population-based growth charts for the prediction of adverse pregnancy outcomes. MEDLINE, ClinicalTrials.gov and The Cochrane Library were searched up to 31 May 2016 to identify interventional and observational studies comparing adverse outcomes among large- (LGA) and small- (SGA) for-gestational-age neonates, when classified according to customized vs population-based growth charts. Perinatal mortality and admission to the neonatal intensive care unit (NICU) of both SGA and LGA neonates, intrauterine fetal demise (IUFD) and neonatal mortality of SGA neonates, and neonatal shoulder dystocia and hypoglycemia as well as maternal third- and fourth-degree perineal lacerations in LGA pregnancies were evaluated. The electronic search identified 237 records that were examined based on title and abstract, of which 27 full-text articles were examined for eligibility. After excluding seven articles, 20 observational studies were included in a Bayesian meta-analysis. Neonates classified as SGA according to customized growth charts had higher risks of IUFD (odds ratio (OR), 7.8 (95% CI, 4.2-12.3)), neonatal death (OR, 3.5 (95% CI, 1.1-8.0)), perinatal death (OR, 5.8 (95% CI, 3.8-7.8)) and NICU admission (OR, 3.6 (95% CI, 2.0-5.5)) than did non-SGA cases. Neonates classified as SGA according to population-based growth charts also had increased risk for adverse outcomes, albeit the point estimates of the pooled ORs were smaller: IUFD (OR, 3.3 (95% CI, 1.9-5.0)), neonatal death (OR, 2.9 (95% CI, 1.2-4.5)), perinatal death (OR, 4.0 (95% CI, 2.8-5.1)) and NICU admission (OR, 2.4 (95% CI, 1.7-3.2)). For LGA vs non-LGA, there were no differences in pooled ORs for perinatal death, NICU admission, hypoglycemia and maternal third- and fourth-degree perineal lacerations when classified according to either the customized or the population-based approach. In contrast, both approaches indicated that LGA neonates are at increased risk for shoulder dystocia than are non-LGA ones (OR, 7.4 (95% CI, 4.9-9.8) using customized charts; OR, 8.0 (95% CI, 5.3-10.1) using population-based charts). Both customized and population-based growth charts can identify SGA neonates at risk for adverse outcomes. Although the point estimates of the pooled ORs may differ for some outcomes, the overlapping CIs and lack of direct comparisons prevent conclusions from being drawn on the superiority of one method. Future clinical trials should compare directly the two approaches in the management of fetuses of abnormal size. Copyright © 2016 ISUOG. Published by John Wiley & Sons Ltd. Copyright © 2016 ISUOG. Published by John Wiley & Sons Ltd.

ELHnet: a convolutional neural network for classifying cochlear endolymphatic hydrops imaged with optical coherence tomography.

PubMed

Liu, George S; Zhu, Michael H; Kim, Jinkyung; Raphael, Patrick; Applegate, Brian E; Oghalai, John S

2017-10-01

Detection of endolymphatic hydrops is important for diagnosing Meniere's disease, and can be performed non-invasively using optical coherence tomography (OCT) in animal models as well as potentially in the clinic. Here, we developed ELHnet, a convolutional neural network to classify endolymphatic hydrops in a mouse model using learned features from OCT images of mice cochleae. We trained ELHnet on 2159 training and validation images from 17 mice, using only the image pixels and observer-determined labels of endolymphatic hydrops as the inputs. We tested ELHnet on 37 images from 37 mice that were previously not used, and found that the neural network correctly classified 34 of the 37 mice. This demonstrates an improvement in performance from previous work on computer-aided classification of endolymphatic hydrops. To the best of our knowledge, this is the first deep CNN designed for endolymphatic hydrops classification.

ELHnet: a convolutional neural network for classifying cochlear endolymphatic hydrops imaged with optical coherence tomography

PubMed Central

Liu, George S.; Zhu, Michael H.; Kim, Jinkyung; Raphael, Patrick; Applegate, Brian E.; Oghalai, John S.

2017-01-01

Detection of endolymphatic hydrops is important for diagnosing Meniere’s disease, and can be performed non-invasively using optical coherence tomography (OCT) in animal models as well as potentially in the clinic. Here, we developed ELHnet, a convolutional neural network to classify endolymphatic hydrops in a mouse model using learned features from OCT images of mice cochleae. We trained ELHnet on 2159 training and validation images from 17 mice, using only the image pixels and observer-determined labels of endolymphatic hydrops as the inputs. We tested ELHnet on 37 images from 37 mice that were previously not used, and found that the neural network correctly classified 34 of the 37 mice. This demonstrates an improvement in performance from previous work on computer-aided classification of endolymphatic hydrops. To the best of our knowledge, this is the first deep CNN designed for endolymphatic hydrops classification. PMID:29082086

Comparing cosmic web classifiers using information theory

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

Leclercq, Florent; Lavaux, Guilhem; Wandelt, Benjamin

We introduce a decision scheme for optimally choosing a classifier, which segments the cosmic web into different structure types (voids, sheets, filaments, and clusters). Our framework, based on information theory, accounts for the design aims of different classes of possible applications: (i) parameter inference, (ii) model selection, and (iii) prediction of new observations. As an illustration, we use cosmographic maps of web-types in the Sloan Digital Sky Survey to assess the relative performance of the classifiers T-WEB, DIVA and ORIGAMI for: (i) analyzing the morphology of the cosmic web, (ii) discriminating dark energy models, and (iii) predicting galaxy colors. Ourmore » study substantiates a data-supported connection between cosmic web analysis and information theory, and paves the path towards principled design of analysis procedures for the next generation of galaxy surveys. We have made the cosmic web maps, galaxy catalog, and analysis scripts used in this work publicly available.« less

The auxiliary use of LANDSAT data in estimating crop acreages: Results of the 1975 Illinois crop-acreage experiment

NASA Technical Reports Server (NTRS)

Gleason, C. (Principal Investigator); Starbuck, R. R.; Sigman, R. S.; Hanuschak, G. A.; Craig, M. E.; Cook, P. W.; Allen, R. D.

1977-01-01

The author has identified the following significant results. It was found that classifier performance was influenced by a number of temporal, methodological, and geographical factors. Best results were obtained when corn was tasselled and near the dough stage of development. Dates earlier or later in the growing season produced poor results. Atmospheric effects on results cannot be independently measured or completely separated from the effects due to the maturity stage of the crops. Poor classifier performance was observed in areas where considerable spectral confusion was present.

Geomorphology and depositional subenvironments of Gulf Islands National Seashore, Perdido Key and Santa Rosa Island, Florida

USGS Publications Warehouse

Morton, Robert A.; Montgomery, Marilyn C.

2010-01-01

The primary mapping procedures were supervised functions within a Geographic Information System (GIS) that were applied to delineate and classify depositional subenvironments and features, collectively referred to as map units. The delineated boundaries of the map units were exported to create one shapefile, and are differentiated by the field "Type" in the associated attribute table. Map units were delineated and classified based on differences in tonal patterns of features in contrast to adjacent features observed on orthophotography. Land elevations from recent lidar surveys served as supplementary data to assist in delineating the map unit boundaries.

[Ecological environmental quality assessment of Hangzhou urban area based on RS and GIS].

PubMed

Xu, Pengwei; Zhao, Duo

2006-06-01

In allusion to the shortage of traditional ecological environmental quality assessment, this paper studied the spatial distribution of assessing factors at a mid-small scale, and the conversion of integer character to girding assessing cells. The main assessing factors including natural environmental condition, environmental quality, natural landscape and urbanization pressure, which were classified into four types with about eleven assessing factors, were selected from RS images and GIS-spatial analyzing environmental quality vector graph. Based on GIS, a comprehensive assessment model for the ecological environmental quality in Hangzhou urban area was established. In comparison with observed urban heat island effects, the assessment results were in good agreement with the ecological environmental quality in the urban area of Hangzhou.

Macrolides in Chronic Inflammatory Skin Disorders

PubMed Central

Alzolibani, Abdullateef A.; Zedan, Khaled

2012-01-01

Long-term therapy with the macrolide antibiotic erythromycin was shown to alter the clinical course of diffuse panbronchiolitis in the late 1980s. Since that time, macrolides have been found to have a large number of anti-inflammatory properties in addition to being antimicrobials. These observations provided the rationale for many studies performed to assess the usefulness of macrolides in other inflammatory diseases including skin and hair disorders, such as rosacea, psoriasis, pityriasis rosea, alopecia areata, bullous pemphigoid, and pityriasis lichenoides. This paper summarizes a collection of clinical studies and case reports dealing with the potential benefits of macrolides antibiotics in the treatment of selected dermatoses which have primarily been classified as noninfectious and demonstrating their potential for being disease-modifying agents. PMID:22685371

Enhancing emotional-based target prediction

NASA Astrophysics Data System (ADS)

Gosnell, Michael; Woodley, Robert

2008-04-01

This work extends existing agent-based target movement prediction to include key ideas of behavioral inertia, steady states, and catastrophic change from existing psychological, sociological, and mathematical work. Existing target prediction work inherently assumes a single steady state for target behavior, and attempts to classify behavior based on a single emotional state set. The enhanced, emotional-based target prediction maintains up to three distinct steady states, or typical behaviors, based on a target's operating conditions and observed behaviors. Each steady state has an associated behavioral inertia, similar to the standard deviation of behaviors within that state. The enhanced prediction framework also allows steady state transitions through catastrophic change and individual steady states could be used in an offline analysis with additional modeling efforts to better predict anticipated target reactions.

X-ray induced mutations in jute (Corchorus capsularis L. and Corchorus olitorius L.)

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

Singh, D.P.; Sharma, B.K.; Banerjee, S.C.

1973-09-30

Dry dormant seeds of three varieties of jute (C. capsularis & b. olitorius), which yield commercial fibers, were irradiated with different doses of x-rays ranging from 10 kR to 100 kR at 10 kR intervals. The percentage of germination, survival rates, the resulting morphological abnormalities in different generations, the total abnormalities, the total mutation frequency including chlorophyll mutations, and the complete, mutation spectrum are described in detail. Mutations were classified into different groups and each mutant was briefly described. Several directly useful mutations were observed with emphasis on the fiber yield. Interesting results were obtained after crossing mutants, where themore » first high yielding hybrid was evolved by the senior author. (auth)« less

Quasi-Supervised Scoring of Human Sleep in Polysomnograms Using Augmented Input Variables

PubMed Central

Yaghouby, Farid; Sunderam, Sridhar

2015-01-01

The limitations of manual sleep scoring make computerized methods highly desirable. Scoring errors can arise from human rater uncertainty or inter-rater variability. Sleep scoring algorithms either come as supervised classifiers that need scored samples of each state to be trained, or as unsupervised classifiers that use heuristics or structural clues in unscored data to define states. We propose a quasi-supervised classifier that models observations in an unsupervised manner but mimics a human rater wherever training scores are available. EEG, EMG, and EOG features were extracted in 30s epochs from human-scored polysomnograms recorded from 42 healthy human subjects (18 to 79 years) and archived in an anonymized, publicly accessible database. Hypnograms were modified so that: 1. Some states are scored but not others; 2. Samples of all states are scored but not for transitional epochs; and 3. Two raters with 67% agreement are simulated. A framework for quasi-supervised classification was devised in which unsupervised statistical models—specifically Gaussian mixtures and hidden Markov models—are estimated from unlabeled training data, but the training samples are augmented with variables whose values depend on available scores. Classifiers were fitted to signal features incorporating partial scores, and used to predict scores for complete recordings. Performance was assessed using Cohen's K statistic. The quasi-supervised classifier performed significantly better than an unsupervised model and sometimes as well as a completely supervised model despite receiving only partial scores. The quasi-supervised algorithm addresses the need for classifiers that mimic scoring patterns of human raters while compensating for their limitations. PMID:25679475

Quasi-supervised scoring of human sleep in polysomnograms using augmented input variables.

PubMed

Yaghouby, Farid; Sunderam, Sridhar

2015-04-01

The limitations of manual sleep scoring make computerized methods highly desirable. Scoring errors can arise from human rater uncertainty or inter-rater variability. Sleep scoring algorithms either come as supervised classifiers that need scored samples of each state to be trained, or as unsupervised classifiers that use heuristics or structural clues in unscored data to define states. We propose a quasi-supervised classifier that models observations in an unsupervised manner but mimics a human rater wherever training scores are available. EEG, EMG, and EOG features were extracted in 30s epochs from human-scored polysomnograms recorded from 42 healthy human subjects (18-79 years) and archived in an anonymized, publicly accessible database. Hypnograms were modified so that: 1. Some states are scored but not others; 2. Samples of all states are scored but not for transitional epochs; and 3. Two raters with 67% agreement are simulated. A framework for quasi-supervised classification was devised in which unsupervised statistical models-specifically Gaussian mixtures and hidden Markov models--are estimated from unlabeled training data, but the training samples are augmented with variables whose values depend on available scores. Classifiers were fitted to signal features incorporating partial scores, and used to predict scores for complete recordings. Performance was assessed using Cohen's Κ statistic. The quasi-supervised classifier performed significantly better than an unsupervised model and sometimes as well as a completely supervised model despite receiving only partial scores. The quasi-supervised algorithm addresses the need for classifiers that mimic scoring patterns of human raters while compensating for their limitations. Copyright © 2015 Elsevier Ltd. All rights reserved.

On-Site Classification of Pansteatitis in Mozambique Tilapia (Oreochromis mossambicus) using a Portable Lipid-Based Analyzer

PubMed Central

Somerville, Stephen E.; Cantu, Theresa M.; Guillette, Matthew P.; Botha, Hannes; Boggs, Ashley S. P.; Luus-Powell, Wilmien; Guillette, Louis J.

2017-01-01

While no pansteatitis-related large-scale mortality events have occurred since 2008, the current status of pansteatitis (presence and pervasiveness) in the Olifants River system and other regions of South Africa remain largely unknown. In part, this is due to both a lack of known biological markers of pansteatitis and a lack of suitable non-invasive assays capable of rapidly classifying the disease. Here, we propose the application of a point-of-care (POC) device using lipid-based test strips (total cholesterol (TC) and total triglyceride (TG)), for classifying pansteatitis status in the whole blood of pre-spawning Mozambique tilapia (Oreochromis mossambicus). Using the TC strips, the POC device was able to non-lethally classify the tilapia as either healthy or pansteatitis-affected; the sexes were examined independently because sexual dimorphism was observed for TC (males p = 0.0364, females χ2 = 0.0007). No significant difference between diseased and pansteatitis-affected tilapia was observed using the TG strips. This is one of the first described applications of using POC devices for on-site environmental disease state testing. A discussion on the merits of using portable lipid-based analyzers as an in-field disease-state diagnostic tool is provided. PMID:28729886

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.

Higher sensitivity and lower specificity in post-fire mortality model validation of 11 western US tree species

USGS Publications Warehouse

Kane, Jeffrey M.; van Mantgem, Phillip J.; Lalemand, Laura; Keifer, MaryBeth

2017-01-01

Managers require accurate models to predict post-fire tree mortality to plan prescribed fire treatments and examine their effectiveness. Here we assess the performance of a common post-fire tree mortality model with an independent dataset of 11 tree species from 13 National Park Service units in the western USA. Overall model discrimination was generally strong, but performance varied considerably among species and sites. The model tended to have higher sensitivity (proportion of correctly classified dead trees) and lower specificity (proportion of correctly classified live trees) for many species, indicating an overestimation of mortality. Variation in model accuracy (percentage of live and dead trees correctly classified) among species was not related to sample size or percentage observed mortality. However, we observed a positive relationship between specificity and a species-specific bark thickness multiplier, indicating that overestimation was more common in thin-barked species. Accuracy was also quite low for thinner bark classes (<1 cm) for many species, leading to poorer model performance. Our results indicate that a common post-fire mortality model generally performs well across a range of species and sites; however, some thin-barked species and size classes would benefit from further refinement to improve model specificity.

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

Lepine, Sebastien; Bergeron, P.; Lanning, Howard H., E-mail: lepine@amnh.org

We present spectroscopic observations confirming the identification of hot white dwarfs among UV-bright sources from the Sandage Two-color Survey of the Galactic Plane and listed in the Lanning (Lan) catalog of such sources. A subsample of 213 UV-bright Lan sources have been identified as candidate white dwarfs based on the detection of a significant proper motion. Spectroscopic observations of 46 candidates with the KPNO 2.1 m telescope confirm 30 sources to be hydrogen white dwarfs with subtypes in the DA1-DA6 range, and with one of the stars (Lan 161) having an unresolved M dwarf as a companion. Five more sourcesmore » are confirmed to be helium white dwarfs, with subtypes from DB3 to DB6. One source (Lan 364) is identified as a DZ 3 white dwarf, with strong lines of calcium. Three more stars are found to have featureless spectra (to within detection limits) and are thus classified as DC white dwarfs. In addition, three sources are found to be hot subdwarfs: Lan 20 and Lan 480 are classified as sdOB, and Lan 432 is classified sdB. The remaining four objects are found to be field F star interlopers. Physical parameters of the DA and DB white dwarfs are derived from model fits.« less

MRI Characterizes the Progressive Course of AD and Predicts Conversion to Alzheimer's Dementia 24 Months Before Probable Diagnosis.

PubMed

Salvatore, Christian; Cerasa, Antonio; Castiglioni, Isabella

2018-01-01

There is no disease-modifying treatment currently available for AD, one of the more impacting neurodegenerative diseases affecting more than 47.5 million people worldwide. The definition of new approaches for the design of proper clinical trials is highly demanded in order to achieve non-confounding results and assess more effective treatment. In this study, a cohort of 200 subjects was obtained from the Alzheimer's Disease Neuroimaging Initiative. Subjects were followed-up for 24 months, and classified as AD (50), progressive-MCI to AD (50), stable-MCI (50), and cognitively normal (50). Structural T1-weighted MRI brain studies and neuropsychological measures of these subjects were used to train and optimize an artificial-intelligence classifier to distinguish mild-AD patients who need treatment (AD + pMCI) from subjects who do not need treatment (sMCI + CN). The classifier was able to distinguish between the two groups 24 months before AD definite diagnosis using a combination of MRI brain studies and specific neuropsychological measures, with 85% accuracy, 83% sensitivity, and 87% specificity. The combined-approach model outperformed the classification using MRI data alone (72% classification accuracy, 69% sensitivity, and 75% specificity). The patterns of morphological abnormalities localized in the temporal pole and medial-temporal cortex might be considered as biomarkers of clinical progression and evolution. These regions can be already observed 24 months before AD definite diagnosis. The best neuropsychological predictors mainly included measures of functional abilities, memory and learning, working memory, language, visuoconstructional reasoning, and complex attention, with a particular focus on some of the sub-scores of the FAQ and AVLT tests.

Spatial assessment of intertidal seagrass meadows using optical imaging systems and a lightweight drone

NASA Astrophysics Data System (ADS)

Duffy, James P.; Pratt, Laura; Anderson, Karen; Land, Peter E.; Shutler, Jamie D.

2018-01-01

Seagrass ecosystems are highly sensitive to environmental change. They are also in global decline and under threat from a variety of anthropogenic factors. There is now an urgency to establish robust monitoring methodologies so that changes in seagrass abundance and distribution in these sensitive coastal environments can be understood. Typical monitoring approaches have included remote sensing from satellites and airborne platforms, ground based ecological surveys and snorkel/scuba surveys. These techniques can suffer from temporal and spatial inconsistency, or are very localised making it hard to assess seagrass meadows in a structured manner. Here we present a novel technique using a lightweight (sub 7 kg) drone and consumer grade cameras to produce very high spatial resolution (∼4 mm pixel-1) mosaics of two intertidal sites in Wales, UK. We present a full data collection methodology followed by a selection of classification techniques to produce coverage estimates at each site. We trialled three classification approaches of varying complexity to investigate and illustrate the differing performance and capabilities of each. Our results show that unsupervised classifications perform better than object-based methods in classifying seagrass cover. We also found that the more sparsely vegetated of the two meadows studied was more accurately classified - it had lower root mean squared deviation (RMSD) between observed and classified coverage (9-9.5%) compared to a more densely vegetated meadow (RMSD 16-22%). Furthermore, we examine the potential to detect other biotic features, finding that lugworm mounds can be detected visually at coarser resolutions such as 43 mm pixel-1, whereas smaller features such as cockle shells within seagrass require finer grained data (<17 mm pixel-1).

Risk Stratification of Neck Lesions Detected Sonographically During the Follow-Up of Differentiated Thyroid Cancer.

PubMed

Lamartina, Livia; Grani, Giorgio; Biffoni, Marco; Giacomelli, Laura; Costante, Giuseppe; Lupo, Stefania; Maranghi, Marianna; Plasmati, Katia; Sponziello, Marialuisa; Trulli, Fabiana; Verrienti, Antonella; Filetti, Sebastiano; Durante, Cosimo

2016-08-01

The European Thyroid Association (ETA) has classified posttreatment cervical ultrasound findings in thyroid cancer patients based on their association with disease persistence/recurrence. The objective of the study was to assess this classification's ability to predict the growth and persistence of such lesions during active posttreatment surveillance of patients with differentiated thyroid cancer (DTC). This was a retrospective, observational study. The study was conducted at a thyroid cancer center in a large Italian teaching hospital. Center referrals (2005-2014) were reviewed and patients selected with pathologically-confirmed DTC; total thyroidectomy, with or without neck dissection and/or radioiodine remnant ablation; abnormal findings on two or more consecutive posttreatment neck sonograms; and subsequent follow-up consisting of active surveillance. Baseline ultrasound abnormalities (thyroid bed masses, lymph nodes) were classified according to the ETA system. Patients were divided into group S (those with one or more lesions classified as suspicious) and group I (indeterminate lesions only). We recorded baseline and follow-up clinical data through June 30, 2015. The main outcomes were patients with growth (>3 mm, largest diameter) of one or more lesions during follow-up and patients with one or more persistent lesions at the final visit. The cohort included 58 of the 637 DTC cases screened (9%). A total of 113 lesions were followed up (18 thyroid bed masses, 95 lymph nodes). During surveillance (median 3.7 y), group I had significantly lower rates than group S of lesion growth (8% vs 36%, P = .01) and persistence (64% vs 97%, P = .014). The median time to scan normalization was 2.9 years. The ETA's evidence-based classification of sonographically detected neck abnormalities can help identify papillary thyroid cancer patients eligible for more relaxed follow-up.

MRI Characterizes the Progressive Course of AD and Predicts Conversion to Alzheimer’s Dementia 24 Months Before Probable Diagnosis

PubMed Central

Salvatore, Christian; Cerasa, Antonio; Castiglioni, Isabella

2018-01-01

There is no disease-modifying treatment currently available for AD, one of the more impacting neurodegenerative diseases affecting more than 47.5 million people worldwide. The definition of new approaches for the design of proper clinical trials is highly demanded in order to achieve non-confounding results and assess more effective treatment. In this study, a cohort of 200 subjects was obtained from the Alzheimer’s Disease Neuroimaging Initiative. Subjects were followed-up for 24 months, and classified as AD (50), progressive-MCI to AD (50), stable-MCI (50), and cognitively normal (50). Structural T1-weighted MRI brain studies and neuropsychological measures of these subjects were used to train and optimize an artificial-intelligence classifier to distinguish mild-AD patients who need treatment (AD + pMCI) from subjects who do not need treatment (sMCI + CN). The classifier was able to distinguish between the two groups 24 months before AD definite diagnosis using a combination of MRI brain studies and specific neuropsychological measures, with 85% accuracy, 83% sensitivity, and 87% specificity. The combined-approach model outperformed the classification using MRI data alone (72% classification accuracy, 69% sensitivity, and 75% specificity). The patterns of morphological abnormalities localized in the temporal pole and medial-temporal cortex might be considered as biomarkers of clinical progression and evolution. These regions can be already observed 24 months before AD definite diagnosis. The best neuropsychological predictors mainly included measures of functional abilities, memory and learning, working memory, language, visuoconstructional reasoning, and complex attention, with a particular focus on some of the sub-scores of the FAQ and AVLT tests. PMID:29881340

Validation of mercury tip-switch and accelerometer activity sensors for identifying resting and active behavior in bears

USGS Publications Warehouse

Jasmine Ware,; Rode, Karyn D.; Pagano, Anthony M.; Bromaghin, Jeffrey F.; Robbins, Charles T.; Joy Erlenbach,; Shannon Jensen,; Amy Cutting,; Nicole Nicassio-Hiskey,; Amy Hash,; Owen, Megan A.; Heiko Jansen,

2015-01-01

Activity sensors are often included in wildlife transmitters and can provide information on the behavior and activity patterns of animals remotely. However, interpreting activity-sensor data relative to animal behavior can be difficult if animals cannot be continuously observed. In this study, we examined the performance of a mercury tip-switch and a tri-axial accelerometer housed in collars to determine whether sensor data can be accurately classified as resting and active behaviors and whether data are comparable for the 2 sensor types. Five captive bears (3 polar [Ursus maritimus] and 2 brown [U. arctos horribilis]) were fitted with a collar specially designed to internally house the sensors. The bears’ behaviors were recorded, classified, and then compared with sensor readings. A separate tri-axial accelerometer that sampled continuously at a higher frequency and provided raw acceleration values from 3 axes was also mounted on the collar to compare with the lower resolution sensors. Both accelerometers more accurately identified resting and active behaviors at time intervals ranging from 1 minute to 1 hour (≥91.1% accuracy) compared with the mercury tip-switch (range = 75.5–86.3%). However, mercury tip-switch accuracy improved when sampled at longer intervals (e.g., 30–60 min). Data from the lower resolution accelerometer, but not the mercury tip-switch, accurately predicted the percentage of time spent resting during an hour. Although the number of bears available for this study was small, our results suggest that these activity sensors can remotely identify resting versus active behaviors across most time intervals. We recommend that investigators consider both study objectives and the variation in accuracy of classifying resting and active behaviors reported here when determining sampling interval.

Histogenesis and prognostic value of myenteric spread in colorectal cancer: a Japanese multi-institutional study.

PubMed

Ueno, Hideki; Shirouzu, Kazuo; Shimazaki, Hideyuki; Kawachi, Hiroshi; Eishi, Yoshinobu; Ajioka, Yoichi; Okuno, Kiyotaka; Yamada, Kazutaka; Sato, Toshihiko; Kusumi, Takaya; Kushima, Ryoji; Ikegami, Masahiro; Kojima, Motohiro; Ochiai, Atsushi; Murata, Akihiko; Akagi, Yoshito; Nakamura, Takahiro; Sugihara, Kenichi

2014-03-01

The histogenesis of the pattern of cancer spread along Auerbach's plexus (myenteric spread: MS) remains unclear and its prognostic value in colorectal cancer (CRC) has not been thoroughly investigated. Pathology slides of 2845 pT2/pT3/pT4 CRCs stained with hematoxylin-eosin (H&E) were reviewed at 10 institutions. MS was classified into 2 groups depending on whether it was accompanied by the finding of perineural invasion (PN) within the lesion. In addition, immunohistochemical staining (D2-40, S100, CD56, synaptophysin) was performed for serially sectioned specimens from 50 CRCs diagnosed as having PN-negative MS. MS was observed in 504 patients (17.7 %); 360 patients were classified as having PN-positive MS and 144 as having PN-negative MS. The 5-year disease-free survival rate of patients with MS was lower than that of patients without MS (63.3 vs 82.7 %, P < 0.0001); however, there was no significant difference in survival outcome according to the presence or absence of intralesion PN in MS. Multivariate analysis showed that the prognostic impact of MS was independent of conventional prognosticators including T and N stages, vascular invasion and extramural PN. In all the tumors having PN-negative MS, remnants of neural tissue were identified within or around cancer nests located at the leading edge of MS. MS is an important prognostic factor for CRC. This feature is the result of cancer development with replacement of Auerbach's plexus and can be classified as intramural PN. The clinical significance of "Pn1" in the UICC/AJCC TNM classification could be enhanced by individual assessment both intramurally and extramurally.

Patterns of 11C-PIB cerebral retention in mild cognitive impairment patients.

PubMed

Banzo, I; Jiménez-Bonilla, J F; Martínez-Rodríguez, I; Quirce, R; de Arcocha-Torres, M; Bravo-Ferrer, Z; Lavado-Pérez, C; Sánchez-Juan, P; Rodríguez, E; Jiménez-Alonso, M; López-Defilló, J; Carril, J M

2016-01-01

To evaluate the patterns of cerebral cortical distribution of (11)C-PIB in patients with mild cognitive impairment (MCI). The study included 69 patients (37 male, age range 42-79 years) with MCI, sub-classified as 53 with amnestic-MCI (A-MCI), and 16 with non-amnestic-MCI (NA-MCI). Patients underwent (11)C-PIB PET/CT scan 60min after intravenous injection of the radiotracer. A visual analysis of the images was performed by 2 experienced physicians. (11)C-PIB-positive studies were considered when gray matter uptake was equal to or greater than white matter. According to the regions involved, (11)C-PIB-positive studies were classified into A-pattern (predominant retention in frontal, anterior cingulate, lateral temporal, and basal ganglia) and B-pattern (generalized retention). Thirty-nine of the 69 (56%) patients with MCI showed (11)C-PIB retention. Of the 53 A-MCI patients, 36 (68%) showed (11)C-PIB retention. Eleven out of 36 (30%) positive scans in A-MCI patients showed A-pattern, and 25 out of 36 (70%) patients had a B-pattern. Positive (11)C-PIB was observed in 3 out of 16 (19%) patients with NA-MCI. Regional distribution in these 3 patients showed A-pattern in 1, and B-pattern in 2 patients. Cortical retention of (11)C-PIB was more frequent in A-MCI than in NA-MCI patients, and also B-pattern than A-pattern in the (11)C-PIB positive group. The recognition of (11)C-PIB distribution patterns allows MCI patients to be classified, and the A-pattern may offer a therapeutic window for potential future treatments. Copyright © 2015 Elsevier España, S.L.U. and SEMNIM. All rights reserved.

Age, stage and senescence in plants

PubMed Central

Caswell, Hal; Salguero-Gómez, Roberto

2013-01-01

1. Senescence (an increase in the mortality rate or force of mortality, or a decrease in fertility, with increasing age) is a widespread phenomenon. Theories about the evolution of senescence have long focused on the age trajectories of the selection gradients on mortality and fertility. In purely age-classified models, these selection gradients are non-increasing with age, implying that traits expressed early in life have a greater impact on fitness than traits expressed later in life. This pattern leads inevitably to the evolution of senescence if there are trade-offs between early and late performance. 2. It has long been suspected that the stage- or size-dependent demography typical of plants might change these conclusions. In this paper, we develop a model that includes both stage- and age-dependence and derive the age-dependent, stage-dependent and age×stage-dependent selection gradients on mortality and fertility. 3. We applied this model to stage-classified population projection matrices for 36 species of plants, from a wide variety of growth forms (from mosses to trees) and habitats. 4. We found that the age-specific selection gradients within a life cycle stage can exhibit increases with age (we call these contra-senescent selection gradients). In later stages, often large size classes in plant demography, the duration of these contra-senescent gradients can exceed the life expectancy by several fold. 5. Synthesis. The interaction of age- and stage-dependence in plants leads to selection pressures on senescence fundamentally different from those found in previous, age-classified theories. This result may explain the observation that large plants seem less subject to senescence than most kinds of animals. The methods presented here can lead to improved analysis of both age-dependent and stage-dependent demographic properties of plant populations. PMID:23741075

Impaired perception of facial emotion in developmental prosopagnosia.

PubMed

Biotti, Federica; Cook, Richard

2016-08-01

Developmental prosopagnosia (DP) is a neurodevelopmental condition characterised by difficulties recognising faces. Despite severe difficulties recognising facial identity, expression recognition is typically thought to be intact in DP; case studies have described individuals who are able to correctly label photographic displays of facial emotion, and no group differences have been reported. This pattern of deficits suggests a locus of impairment relatively late in the face processing stream, after the divergence of expression and identity analysis pathways. To date, however, there has been little attempt to investigate emotion recognition systematically in a large sample of developmental prosopagnosics using sensitive tests. In the present study, we describe three complementary experiments that examine emotion recognition in a sample of 17 developmental prosopagnosics. In Experiment 1, we investigated observers' ability to make binary classifications of whole-face expression stimuli drawn from morph continua. In Experiment 2, observers judged facial emotion using only the eye-region (the rest of the face was occluded). Analyses of both experiments revealed diminished ability to classify facial expressions in our sample of developmental prosopagnosics, relative to typical observers. Imprecise expression categorisation was particularly evident in those individuals exhibiting apperceptive profiles, associated with problems encoding facial shape accurately. Having split the sample of prosopagnosics into apperceptive and non-apperceptive subgroups, only the apperceptive prosopagnosics were impaired relative to typical observers. In our third experiment, we examined the ability of observers' to classify the emotion present within segments of vocal affect. Despite difficulties judging facial emotion, the prosopagnosics exhibited excellent recognition of vocal affect. Contrary to the prevailing view, our results suggest that many prosopagnosics do experience difficulties classifying expressions, particularly those with apperceptive profiles. These individuals may have difficulties forming view-invariant structural descriptions at an early stage in the face processing stream, before identity and expression pathways diverge. Copyright © 2016 Elsevier Ltd. All rights reserved.

Validation of mothers' reports of dietary intake by four to seven year-old children.

PubMed Central

Basch, C E; Shea, S; Arliss, R; Contento, I R; Rips, J; Gutin, B; Irigoyen, M; Zybert, P

1990-01-01

The validity of mothers' recall of four to seven year-old children's diet was assessed among 46 first generation Latino immigrant families from the Dominican Republic by comparing intake recalled by the mother to unobtrusive home observations of children. Correlations were moderate to high for calories and for most nutrients. There were no differences in mean intake of total calories or in intake of most macronutrients and micronutrients assessed. At least two-thirds of the children in the lowest (or highest) quintile based on home observations were correctly classified into the lowest or second lowest (or highest) quintiles based on mother's reports for calories and most nutrients. For all food items that were both observed and reported, 51 percent of reported portion sizes were equivalent to observed portion sizes, 15.5 percent were smaller, and 33.5 percent were larger. There was fair to good agreement on the number of food items eaten, with the exception of vegetables. Mothers' recall appears to be useful for classifying children by intake of calories, macronutrients and micronutrients, but provides a somewhat less accurate measure of actual foods eaten, portion sizes, and nutrient levels consumed. PMID:2240296

Classifier ensemble based on feature selection and diversity measures for predicting the affinity of A(2B) adenosine receptor antagonists.

PubMed

Bonet, Isis; Franco-Montero, Pedro; Rivero, Virginia; Teijeira, Marta; Borges, Fernanda; Uriarte, Eugenio; Morales Helguera, Aliuska

2013-12-23

A(2B) adenosine receptor antagonists may be beneficial in treating diseases like asthma, diabetes, diabetic retinopathy, and certain cancers. This has stimulated research for the development of potent ligands for this subtype, based on quantitative structure-affinity relationships. In this work, a new ensemble machine learning algorithm is proposed for classification and prediction of the ligand-binding affinity of A(2B) adenosine receptor antagonists. This algorithm is based on the training of different classifier models with multiple training sets (composed of the same compounds but represented by diverse features). The k-nearest neighbor, decision trees, neural networks, and support vector machines were used as single classifiers. To select the base classifiers for combining into the ensemble, several diversity measures were employed. The final multiclassifier prediction results were computed from the output obtained by using a combination of selected base classifiers output, by utilizing different mathematical functions including the following: majority vote, maximum and average probability. In this work, 10-fold cross- and external validation were used. The strategy led to the following results: i) the single classifiers, together with previous features selections, resulted in good overall accuracy, ii) a comparison between single classifiers, and their combinations in the multiclassifier model, showed that using our ensemble gave a better performance than the single classifier model, and iii) our multiclassifier model performed better than the most widely used multiclassifier models in the literature. The results and statistical analysis demonstrated the supremacy of our multiclassifier approach for predicting the affinity of A(2B) adenosine receptor antagonists, and it can be used to develop other QSAR models.

Metrics and textural features of MRI diffusion to improve classification of pediatric posterior fossa tumors.

PubMed

Rodriguez Gutierrez, D; Awwad, A; Meijer, L; Manita, M; Jaspan, T; Dineen, R A; Grundy, R G; Auer, D P

2014-05-01

Qualitative radiologic MR imaging review affords limited differentiation among types of pediatric posterior fossa brain tumors and cannot detect histologic or molecular subtypes, which could help to stratify treatment. This study aimed to improve current posterior fossa discrimination of histologic tumor type by using support vector machine classifiers on quantitative MR imaging features. This retrospective study included preoperative MRI in 40 children with posterior fossa tumors (17 medulloblastomas, 16 pilocytic astrocytomas, and 7 ependymomas). Shape, histogram, and textural features were computed from contrast-enhanced T2WI and T1WI and diffusivity (ADC) maps. Combinations of features were used to train tumor-type-specific classifiers for medulloblastoma, pilocytic astrocytoma, and ependymoma types in separation and as a joint posterior fossa classifier. A tumor-subtype classifier was also produced for classic medulloblastoma. The performance of different classifiers was assessed and compared by using randomly selected subsets of training and test data. ADC histogram features (25th and 75th percentiles and skewness) yielded the best classification of tumor type (on average >95.8% of medulloblastomas, >96.9% of pilocytic astrocytomas, and >94.3% of ependymomas by using 8 training samples). The resulting joint posterior fossa classifier correctly assigned >91.4% of the posterior fossa tumors. For subtype classification, 89.4% of classic medulloblastomas were correctly classified on the basis of ADC texture features extracted from the Gray-Level Co-Occurence Matrix. Support vector machine-based classifiers using ADC histogram features yielded very good discrimination among pediatric posterior fossa tumor types, and ADC textural features show promise for further subtype discrimination. These findings suggest an added diagnostic value of quantitative feature analysis of diffusion MR imaging in pediatric neuro-oncology. © 2014 by American Journal of Neuroradiology.

Blood Based Biomarkers of Early Onset Breast Cancer

DTIC Science & Technology

2016-12-01

discretizes the data, and also using logistic elastic net – a form of linear regression - we were unable to build a classifier that could accurately...classifier for differentiating cases from controls off discretized data. The first pass analysis demonstrated a 35 gene signature that differentiated...to the discretized data for mRNA gene signature, the samples used to “train” were also included in the final samples used to “test” the algorithm

Advanced Methods for Passive Acoustic Detection, Classification, and Localization of Marine Mammals

DTIC Science & Technology

2015-09-30

floor 1176 Howell St. Newport, RI 02842 phone: (401) 832-5749 fax: (401) 832-4441 email: David.Moretti@navy.mil Steve W. Martin... Jarvis et al. 2008). This classifier both detects and classifies echolocation clicks from five species of odontocetes, including Blainville’s and...Cuvier’s beaked whales, Risso’s dolphins, short-finned pilot whales, and sperm whales. Here Moretti’s group, particularly S. Jarvis , is improving the

Analysis of Web Spam for Non-English Content: Toward More Effective Language-Based Classifiers

PubMed Central

Alsaleh, Mansour; Alarifi, Abdulrahman

2016-01-01

Web spammers aim to obtain higher ranks for their web pages by including spam contents that deceive search engines in order to include their pages in search results even when they are not related to the search terms. Search engines continue to develop new web spam detection mechanisms, but spammers also aim to improve their tools to evade detection. In this study, we first explore the effect of the page language on spam detection features and we demonstrate how the best set of detection features varies according to the page language. We also study the performance of Google Penguin, a newly developed anti-web spamming technique for their search engine. Using spam pages in Arabic as a case study, we show that unlike similar English pages, Google anti-spamming techniques are ineffective against a high proportion of Arabic spam pages. We then explore multiple detection features for spam pages to identify an appropriate set of features that yields a high detection accuracy compared with the integrated Google Penguin technique. In order to build and evaluate our classifier, as well as to help researchers to conduct consistent measurement studies, we collected and manually labeled a corpus of Arabic web pages, including both benign and spam pages. Furthermore, we developed a browser plug-in that utilizes our classifier to warn users about spam pages after clicking on a URL and by filtering out search engine results. Using Google Penguin as a benchmark, we provide an illustrative example to show that language-based web spam classifiers are more effective for capturing spam contents. PMID:27855179

Trichotillomania (hair pulling disorder), skin picking disorder, and stereotypic movement disorder: toward DSM-V.

PubMed

Stein, Dan J; Grant, Jon E; Franklin, Martin E; Keuthen, Nancy; Lochner, Christine; Singer, Harvey S; Woods, Douglas W

2010-06-01

In DSM-IV-TR, trichotillomania (TTM) is classified as an impulse control disorder (not classified elsewhere), skin picking lacks its own diagnostic category (but might be diagnosed as an impulse control disorder not otherwise specified), and stereotypic movement disorder is classified as a disorder usually first diagnosed in infancy, childhood, or adolescence. ICD-10 classifies TTM as a habit and impulse disorder, and includes stereotyped movement disorders in a section on other behavioral and emotional disorders with onset usually occurring in childhood and adolescence. This article provides a focused review of nosological issues relevant to DSM-V, given recent empirical findings. This review presents a number of options and preliminary recommendations to be considered for DSM-V: (1) Although TTM fits optimally into a category of body-focused repetitive behavioral disorders, in a nosology comprised of relatively few major categories it fits best within a category of motoric obsessive-compulsive spectrum disorders, (2) available evidence does not support continuing to include (current) diagnostic criteria B and C for TTM in DSM-V, (3) the text for TTM should be updated to describe subtypes and forms of hair pulling, (4) there are persuasive reasons for referring to TTM as "hair pulling disorder (trichotillomania)," (5) diagnostic criteria for skin picking disorder should be included in DSM-V or in DSM-Vs Appendix of Criteria Sets Provided for Further Study, and (6) the diagnostic criteria for stereotypic movement disorder should be clarified and simplified, bringing them in line with those for hair pulling and skin picking disorder. (c) 2010 Wiley-Liss, Inc.

Analysis of Web Spam for Non-English Content: Toward More Effective Language-Based Classifiers.

PubMed

Alsaleh, Mansour; Alarifi, Abdulrahman

2016-01-01

Web spammers aim to obtain higher ranks for their web pages by including spam contents that deceive search engines in order to include their pages in search results even when they are not related to the search terms. Search engines continue to develop new web spam detection mechanisms, but spammers also aim to improve their tools to evade detection. In this study, we first explore the effect of the page language on spam detection features and we demonstrate how the best set of detection features varies according to the page language. We also study the performance of Google Penguin, a newly developed anti-web spamming technique for their search engine. Using spam pages in Arabic as a case study, we show that unlike similar English pages, Google anti-spamming techniques are ineffective against a high proportion of Arabic spam pages. We then explore multiple detection features for spam pages to identify an appropriate set of features that yields a high detection accuracy compared with the integrated Google Penguin technique. In order to build and evaluate our classifier, as well as to help researchers to conduct consistent measurement studies, we collected and manually labeled a corpus of Arabic web pages, including both benign and spam pages. Furthermore, we developed a browser plug-in that utilizes our classifier to warn users about spam pages after clicking on a URL and by filtering out search engine results. Using Google Penguin as a benchmark, we provide an illustrative example to show that language-based web spam classifiers are more effective for capturing spam contents.

COOL CORE CLUSTERS FROM COSMOLOGICAL SIMULATIONS

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

Rasia, E.; Borgani, S.; Murante, G.

2015-11-01

We present results obtained from a set of cosmological hydrodynamic simulations of galaxy clusters, aimed at comparing predictions with observational data on the diversity between cool-core (CC) and non-cool-core (NCC) clusters. Our simulations include the effects of stellar and active galactic nucleus (AGN) feedback and are based on an improved version of the smoothed particle hydrodynamics code GADGET-3, which ameliorates gas mixing and better captures gas-dynamical instabilities by including a suitable artificial thermal diffusion. In this Letter, we focus our analysis on the entropy profiles, the primary diagnostic we used to classify the degree of cool-coreness of clusters, and themore » iron profiles. In keeping with observations, our simulated clusters display a variety of behaviors in entropy profiles: they range from steadily decreasing profiles at small radii, characteristic of CC systems, to nearly flat core isentropic profiles, characteristic of NCC systems. Using observational criteria to distinguish between the two classes of objects, we find that they occur in similar proportions in both simulations and observations. Furthermore, we also find that simulated CC clusters have profiles of iron abundance that are steeper than those of NCC clusters, which is also in agreement with observational results. We show that the capability of our simulations to generate a realistic CC structure in the cluster population is due to AGN feedback and artificial thermal diffusion: their combined action allows us to naturally distribute the energy extracted from super-massive black holes and to compensate for the radiative losses of low-entropy gas with short cooling time residing in the cluster core.« less

Cool Core Clusters from Cosmological Simulations

NASA Astrophysics Data System (ADS)

Rasia, E.; Borgani, S.; Murante, G.; Planelles, S.; Beck, A. M.; Biffi, V.; Ragone-Figueroa, C.; Granato, G. L.; Steinborn, L. K.; Dolag, K.

2015-11-01

We present results obtained from a set of cosmological hydrodynamic simulations of galaxy clusters, aimed at comparing predictions with observational data on the diversity between cool-core (CC) and non-cool-core (NCC) clusters. Our simulations include the effects of stellar and active galactic nucleus (AGN) feedback and are based on an improved version of the smoothed particle hydrodynamics code GADGET-3, which ameliorates gas mixing and better captures gas-dynamical instabilities by including a suitable artificial thermal diffusion. In this Letter, we focus our analysis on the entropy profiles, the primary diagnostic we used to classify the degree of cool-coreness of clusters, and the iron profiles. In keeping with observations, our simulated clusters display a variety of behaviors in entropy profiles: they range from steadily decreasing profiles at small radii, characteristic of CC systems, to nearly flat core isentropic profiles, characteristic of NCC systems. Using observational criteria to distinguish between the two classes of objects, we find that they occur in similar proportions in both simulations and observations. Furthermore, we also find that simulated CC clusters have profiles of iron abundance that are steeper than those of NCC clusters, which is also in agreement with observational results. We show that the capability of our simulations to generate a realistic CC structure in the cluster population is due to AGN feedback and artificial thermal diffusion: their combined action allows us to naturally distribute the energy extracted from super-massive black holes and to compensate for the radiative losses of low-entropy gas with short cooling time residing in the cluster core.

Multi-categorical deep learning neural network to classify retinal images: A pilot study employing small database

PubMed Central

Seo, Jeong Gi; Kwak, Jiyong; Um, Terry Taewoong; Rim, Tyler Hyungtaek

2017-01-01

Deep learning emerges as a powerful tool for analyzing medical images. Retinal disease detection by using computer-aided diagnosis from fundus image has emerged as a new method. We applied deep learning convolutional neural network by using MatConvNet for an automated detection of multiple retinal diseases with fundus photographs involved in STructured Analysis of the REtina (STARE) database. Dataset was built by expanding data on 10 categories, including normal retina and nine retinal diseases. The optimal outcomes were acquired by using a random forest transfer learning based on VGG-19 architecture. The classification results depended greatly on the number of categories. As the number of categories increased, the performance of deep learning models was diminished. When all 10 categories were included, we obtained results with an accuracy of 30.5%, relative classifier information (RCI) of 0.052, and Cohen’s kappa of 0.224. Considering three integrated normal, background diabetic retinopathy, and dry age-related macular degeneration, the multi-categorical classifier showed accuracy of 72.8%, 0.283 RCI, and 0.577 kappa. In addition, several ensemble classifiers enhanced the multi-categorical classification performance. The transfer learning incorporated with ensemble classifier of clustering and voting approach presented the best performance with accuracy of 36.7%, 0.053 RCI, and 0.225 kappa in the 10 retinal diseases classification problem. First, due to the small size of datasets, the deep learning techniques in this study were ineffective to be applied in clinics where numerous patients suffering from various types of retinal disorders visit for diagnosis and treatment. Second, we found that the transfer learning incorporated with ensemble classifiers can improve the classification performance in order to detect multi-categorical retinal diseases. Further studies should confirm the effectiveness of algorithms with large datasets obtained from hospitals. PMID:29095872