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.

Classification of document page images based on visual similarity of layout structures

NASA Astrophysics Data System (ADS)

Shin, Christian K.; Doermann, David S.

1999-12-01

Searching for documents by their type or genre is a natural way to enhance the effectiveness of document retrieval. The layout of a document contains a significant amount of information that can be used to classify a document's type in the absence of domain specific models. A document type or genre can be defined by the user based primarily on layout structure. Our classification approach is based on 'visual similarity' of the layout structure by building a supervised classifier, given examples of the class. We use image features, such as the percentages of tex and non-text (graphics, image, table, and ruling) content regions, column structures, variations in the point size of fonts, the density of content area, and various statistics on features of connected components which can be derived from class samples without class knowledge. In order to obtain class labels for training samples, we conducted a user relevance test where subjects ranked UW-I document images with respect to the 12 representative images. We implemented our classification scheme using the OC1, a decision tree classifier, and report our findings.

Multiclass Bayes error estimation by a feature space sampling technique

NASA Technical Reports Server (NTRS)

Mobasseri, B. G.; Mcgillem, C. D.

1979-01-01

A general Gaussian M-class N-feature classification problem is defined. An algorithm is developed that requires the class statistics as its only input and computes the minimum probability of error through use of a combined analytical and numerical integration over a sequence simplifying transformations of the feature space. The results are compared with those obtained by conventional techniques applied to a 2-class 4-feature discrimination problem with results previously reported and 4-class 4-feature multispectral scanner Landsat data classified by training and testing of the available data.

Patient classification as an outlier detection problem: An application of the One-Class Support Vector Machine

PubMed Central

Mourão-Miranda, Janaina; Hardoon, David R.; Hahn, Tim; Marquand, Andre F.; Williams, Steve C.R.; Shawe-Taylor, John; Brammer, Michael

2011-01-01

Pattern recognition approaches, such as the Support Vector Machine (SVM), have been successfully used to classify groups of individuals based on their patterns of brain activity or structure. However these approaches focus on finding group differences and are not applicable to situations where one is interested in accessing deviations from a specific class or population. In the present work we propose an application of the one-class SVM (OC-SVM) to investigate if patterns of fMRI response to sad facial expressions in depressed patients would be classified as outliers in relation to patterns of healthy control subjects. We defined features based on whole brain voxels and anatomical regions. In both cases we found a significant correlation between the OC-SVM predictions and the patients' Hamilton Rating Scale for Depression (HRSD), i.e. the more depressed the patients were the more of an outlier they were. In addition the OC-SVM split the patient groups into two subgroups whose membership was associated with future response to treatment. When applied to region-based features the OC-SVM classified 52% of patients as outliers. However among the patients classified as outliers 70% did not respond to treatment and among those classified as non-outliers 89% responded to treatment. In addition 89% of the healthy controls were classified as non-outliers. PMID:21723950

Error minimizing algorithms for nearest eighbor classifiers

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

Porter, Reid B; Hush, Don; Zimmer, G. Beate

2011-01-03

Stack Filters define a large class of discrete nonlinear filter first introd uced in image and signal processing for noise removal. In recent years we have suggested their application to classification problems, and investigated their relationship to other types of discrete classifiers such as Decision Trees. In this paper we focus on a continuous domain version of Stack Filter Classifiers which we call Ordered Hypothesis Machines (OHM), and investigate their relationship to Nearest Neighbor classifiers. We show that OHM classifiers provide a novel framework in which to train Nearest Neighbor type classifiers by minimizing empirical error based loss functions. Wemore » use the framework to investigate a new cost sensitive loss function that allows us to train a Nearest Neighbor type classifier for low false alarm rate applications. We report results on both synthetic data and real-world image data.« less

Toward functional classification of neuronal types.

PubMed

Sharpee, Tatyana O

2014-09-17

How many types of neurons are there in the brain? This basic neuroscience question remains unsettled despite many decades of research. Classification schemes have been proposed based on anatomical, electrophysiological, or molecular properties. However, different schemes do not always agree with each other. This raises the question of whether one can classify neurons based on their function directly. For example, among sensory neurons, can a classification scheme be devised that is based on their role in encoding sensory stimuli? Here, theoretical arguments are outlined for how this can be achieved using information theory by looking at optimal numbers of cell types and paying attention to two key properties: correlations between inputs and noise in neural responses. This theoretical framework could help to map the hierarchical tree relating different neuronal classes within and across species. Copyright © 2014 Elsevier Inc. All rights reserved.

Search for correlation between asteroid families and classes

NASA Technical Reports Server (NTRS)

Hansen, O.

1977-01-01

A correlation between membership in a dynamically defined asteroid family and membership in a given asteroid spectral class is sought. Examination of 10 families each with five or more classified members indicates a correlation for the 4 families whose existence is best established, and no correlation for the remaining 6 families. This conclusion supports the break-up hypothesis for the origin of some families, while not contradicting that hypothesis for any family.

Physiological and morphological characterization of local interneurons in the Drosophila antennal lobe.

PubMed

Seki, Yoichi; Rybak, Jürgen; Wicher, Dieter; Sachse, Silke; Hansson, Bill S

2010-08-01

The Drosophila antennal lobe (AL) has become an excellent model for studying early olfactory processing mechanisms. Local interneurons (LNs) connect a large number of glomeruli and are ideally positioned to increase computational capabilities of odor information processing in the AL. Although the neural circuit of the Drosophila AL has been intensively studied at both the input and the output level, the internal circuit is not yet well understood. An unambiguous characterization of LNs is essential to remedy this lack of knowledge. We used whole cell patch-clamp recordings and characterized four classes of LNs in detail using electrophysiological and morphological properties at the single neuron level. Each class of LN displayed unique characteristics in intrinsic electrophysiological properties, showing differences in firing patterns, degree of spike adaptation, and amplitude of spike afterhyperpolarization. Notably, one class of LNs had characteristic burst firing properties, whereas the others were tonically active. Morphologically, neurons from three classes innervated almost all glomeruli, while LNs from one class innervated a specific subpopulation of glomeruli. Three-dimensional reconstruction analyses revealed general characteristics of LN morphology and further differences in dendritic density and distribution within specific glomeruli between the different classes of LNs. Additionally, we found that LNs labeled by a specific enhancer trap line (GAL4-Krasavietz), which had previously been reported as cholinergic LNs, were mostly GABAergic. The current study provides a systematic characterization of olfactory LNs in Drosophila and demonstrates that a variety of inhibitory LNs, characterized by class-specific electrophysiological and morphological properties, construct the neural circuit of the AL.

Wildlife management by habitat units: A preliminary plan of action

NASA Technical Reports Server (NTRS)

Frentress, C. D.; Frye, R. G.

1975-01-01

Procedures for yielding vegetation type maps were developed using LANDSAT data and a computer assisted classification analysis (LARSYS) to assist in managing populations of wildlife species by defined area units. Ground cover in Travis County, Texas was classified on two occasions using a modified version of the unsupervised approach to classification. The first classification produced a total of 17 classes. Examination revealed that further grouping was justified. A second analysis produced 10 classes which were displayed on printouts which were later color-coded. The final classification was 82 percent accurate. While the classification map appeared to satisfactorily depict the existing vegetation, two classes were determined to contain significant error. The major sources of error could have been eliminated by stratifying cluster sites more closely among previously mapped soil associations that are identified with particular plant associations and by precisely defining class nomenclature using established criteria early in the analysis.

Outcome in chronic inflammatory demyelinating polyneuropathy from a Malaysian centre over sixteen years.

PubMed

Hiew, Fu Liong; Ong, Jun-Jean; Viswanathan, Shanthi; Puvanarajah, Santhi

2018-04-01

Long-term outcome in Chronic Inflammatory Demyelinating Polyneuropathy (CIDP) is very limited, especially from Asian countries. We aimed to determine the outcome of our cohort of CIDP patients and to define the relevant clinical, electrophysiological and laboratory determinants of disease activity, progression and treatment response. We retrospectively reviewed records of 23 CIDP patients attending our Neurology service at Kuala Lumpur Hospital, Malaysia between January 2000 and December 2016. We analysed data on neurological deficits, electrophysiological and laboratory parameters to determine diagnostic characteristics, correlation with disease activity and clinical outcomes following treatment. Included were 15 (65%) males and 8 (35%) females with a mean age of 42.7 years (SD 14.4). Mean duration of follow-up visit was 66 months (range 6-134 months). The cohort consists of 19 classical (sensory-motor) CIDP and 4 MADSAM. Large majority of patients (66%) had either stable active disease (CDAS 3, 44%) or were in remission (CDAS class 2, 22%) following treatment with standard immunotherapies (Intravenous Immunoglobulins, steroids or immunosuppressants). The proportion of CIDP patients in each CDAS class was comparable to published cohorts from North America and Europe. Medical Research Council (MRC) sum score was the only clinical score that differed across CDAS classes (p = .010) with significant inverse correlation (Spearman's rho -0.664, p = .001). In conclusion, treatment outcomes of our CIDP cohort was comparable to those of published series. Further studies with larger cohort of patients from other parts of Asia are important to determine the long-term outcome of this heterogenous disease in this region. Copyright © 2018 Elsevier Ltd. All rights reserved.

Approach to a tremor patient

PubMed Central

Sharma, Soumya; Pandey, Sanjay

2016-01-01

Tremors are commonly encountered in clinical practice and are the most common movement disorders seen. It is defined as a rhythmic, involuntary oscillatory movement of a body part around one or more joints. In the majority of the population, tremor tends to be mild. They have varying etiology; hence, classifying them appropriately helps in identifying the underlying cause. Clinically, tremor is classified as occurring at rest or action. They can also be classified based on their frequency, amplitude, and body part involved. Parkinsonian tremor is the most common cause of rest tremor. Essential tremor (ET) and enhanced physiological tremor are the most common causes of action tremor. Isolated head tremor is more likely to be dystonic rather than ET. Isolated voice tremor could be considered to be a spectrum of ET. Psychogenic tremor is not a diagnosis of exclusion; rather, demonstration of various clinical signs is needed to establish the diagnosis. Severity of tremor and response to treatment can be assessed using clinical rating scales as well as using electrophysiological measurements. The treatment of tremor is symptomatic. Medications are effective in half the cases of essential hand tremor and in refractory patients; deep brain stimulation is an alternative therapy. Midline tremors benefit from botulinum toxin injections. It is also the treatment of choice in dystonic tremor and primary writing tremor. PMID:27994349

Idiopathic ventricular tachycardia and fibrillation.

PubMed

Belhassen, B; Viskin, S

1993-06-01

Important data have recently been added to our understanding of sustained ventricular tachyarrhythmias occurring in the absence of demonstrable heart disease. Idiopathic ventricular tachycardia (VT) is usually of monomorphic configuration and can be classified according to its site of origin as either right monomorphic (70% of all idiopathic VTs) or left monomorphic VT. Several physiopathological types of monomorphic VT can be presently individualized, according to their mode of presentation, their relationship to adrenergic stress, or their response to various drugs. The long-term prognosis is usually good. Idiopathic polymorphic VT is a much rarer type of arrhythmia with a less favorable prognosis. Idiopathic ventricular fibrillation may represent an underestimated cause of sudden cardiac death in ostensibly healty patients. A high incidence of inducibility of sustained polymorphic VT with programmed ventricular stimulation has been found by our group, but not by others. Long-term prognosis on Class IA antiarrhythmic medications that are highly effective at electrophysiologic study appears excellent.

Bayes estimation on parameters of the single-class classifier. [for remotely sensed crop data

NASA Technical Reports Server (NTRS)

Lin, G. C.; Minter, T. C.

1976-01-01

Normal procedures used for designing a Bayes classifier to classify wheat as the major crop of interest require not only training samples of wheat but also those of nonwheat. Therefore, ground truth must be available for the class of interest plus all confusion classes. The single-class Bayes classifier classifies data into the class of interest or the class 'other' but requires training samples only from the class of interest. This paper will present a procedure for Bayes estimation on the mean vector, covariance matrix, and a priori probability of the single-class classifier using labeled samples from the class of interest and unlabeled samples drawn from the mixture density function.

Sleep state classification using pressure sensor mats.

PubMed

Baran Pouyan, M; Nourani, M; Pompeo, M

2015-08-01

Sleep state detection is valuable in assessing patient's sleep quality and in-bed general behavior. In this paper, a novel classification approach of sleep states (sleep, pre-wake, wake) is proposed that uses only surface pressure sensors. In our method, a mobility metric is defined based on successive pressure body maps. Then, suitable statistical features are computed based on the mobility metric. Finally, a customized random forest classifier is employed to identify various classes including a new class for pre-wake state. Our algorithm achieves 96.1% and 88% accuracies for two (sleep, wake) and three (sleep, pre-wake, wake) class identification, respectively.

Classifying Radio Galaxies with the Convolutional Neural Network

NASA Astrophysics Data System (ADS)

Aniyan, A. K.; Thorat, K.

2017-06-01

We present the application of a deep machine learning technique to classify radio images of extended sources on a morphological basis using convolutional neural networks (CNN). In this study, we have taken the case of the Fanaroff-Riley (FR) class of radio galaxies as well as radio galaxies with bent-tailed morphology. We have used archival data from the Very Large Array (VLA)—Faint Images of the Radio Sky at Twenty Centimeters survey and existing visually classified samples available in the literature to train a neural network for morphological classification of these categories of radio sources. Our training sample size for each of these categories is ˜200 sources, which has been augmented by rotated versions of the same. Our study shows that CNNs can classify images of the FRI and FRII and bent-tailed radio galaxies with high accuracy (maximum precision at 95%) using well-defined samples and a “fusion classifier,” which combines the results of binary classifications, while allowing for a mechanism to find sources with unusual morphologies. The individual precision is highest for bent-tailed radio galaxies at 95% and is 91% and 75% for the FRI and FRII classes, respectively, whereas the recall is highest for FRI and FRIIs at 91% each, while the bent-tailed class has a recall of 79%. These results show that our results are comparable to that of manual classification, while being much faster. Finally, we discuss the computational and data-related challenges associated with the morphological classification of radio galaxies with CNNs.

A determination of the optimum time of year for remotely classifying marsh vegetation from LANDSAT multispectral scanner data. [Louisiana

NASA Technical Reports Server (NTRS)

Butera, M. K. (Principal Investigator)

1978-01-01

The author has identified the following significant results. A technique was used to determine the optimum time for classifying marsh vegetation from computer-processed LANDSAT MSS data. The technique depended on the analysis of data derived from supervised pattern recognition by maximum likelihood theory. A dispersion index, created by the ratio of separability among the class spectral means to variability within the classes, defined the optimum classification time. Data compared from seven LANDSAT passes acquired over the same area of Louisiana marsh indicated that June and September were optimum marsh mapping times to collectively classify Baccharis halimifolia, Spartina patens, Spartina alterniflora, Juncus roemericanus, and Distichlis spicata. The same technique was used to determine the optimum classification time for individual species. April appeared to be the best month to map Juncus roemericanus; May, Spartina alterniflora; June, Baccharis halimifolia; and September, Spartina patens and Distichlis spicata. This information is important, for instance, when a single species is recognized to indicate a particular environmental condition.

Beyond the frontiers of neuronal types

PubMed Central

Battaglia, Demian; Karagiannis, Anastassios; Gallopin, Thierry; Gutch, Harold W.; Cauli, Bruno

2012-01-01

Cortical neurons and, particularly, inhibitory interneurons display a large diversity of morphological, synaptic, electrophysiological, and molecular properties, as well as diverse embryonic origins. Various authors have proposed alternative classification schemes that rely on the concomitant observation of several multimodal features. However, a broad variability is generally observed even among cells that are grouped into a same class. Furthermore, the attribution of specific neurons to a single defined class is often difficult, because individual properties vary in a highly graded fashion, suggestive of continua of features between types. Going beyond the description of representative traits of distinct classes, we focus here on the analysis of atypical cells. We introduce a novel paradigm for neuronal type classification, assuming explicitly the existence of a structured continuum of diversity. Our approach, grounded on the theory of fuzzy sets, identifies a small optimal number of model archetypes. At the same time, it quantifies the degree of similarity between these archetypes and each considered neuron. This allows highlighting archetypal cells, which bear a clear similarity to a single model archetype, and edge cells, which manifest a convergence of traits from multiple archetypes. PMID:23403725

Class-modelling in food analytical chemistry: Development, sampling, optimisation and validation issues - A tutorial.

PubMed

Oliveri, Paolo

2017-08-22

Qualitative data modelling is a fundamental branch of pattern recognition, with many applications in analytical chemistry, and embraces two main families: discriminant and class-modelling methods. The first strategy is appropriate when at least two classes are meaningfully defined in the problem under study, while the second strategy is the right choice when the focus is on a single class. For this reason, class-modelling methods are also referred to as one-class classifiers. Although, in the food analytical field, most of the issues would be properly addressed by class-modelling strategies, the use of such techniques is rather limited and, in many cases, discriminant methods are forcedly used for one-class problems, introducing a bias in the outcomes. Key aspects related to the development, optimisation and validation of suitable class models for the characterisation of food products are critically analysed and discussed. Copyright © 2017 Elsevier B.V. All rights reserved.

A joint latent class model for classifying severely hemorrhaging trauma patients.

PubMed

Rahbar, Mohammad H; Ning, Jing; Choi, Sangbum; Piao, Jin; Hong, Chuan; Huang, Hanwen; Del Junco, Deborah J; Fox, Erin E; Rahbar, Elaheh; Holcomb, John B

2015-10-24

In trauma research, "massive transfusion" (MT), historically defined as receiving ≥10 units of red blood cells (RBCs) within 24 h of admission, has been routinely used as a "gold standard" for quantifying bleeding severity. Due to early in-hospital mortality, however, MT is subject to survivor bias and thus a poorly defined criterion to classify bleeding trauma patients. Using the data from a retrospective trauma transfusion study, we applied a latent-class (LC) mixture model to identify severely hemorrhaging (SH) patients. Based on the joint distribution of cumulative units of RBCs and binary survival outcome at 24 h of admission, we applied an expectation-maximization (EM) algorithm to obtain model parameters. Estimated posterior probabilities were used for patients' classification and compared with the MT rule. To evaluate predictive performance of the LC-based classification, we examined the role of six clinical variables as predictors using two separate logistic regression models. Out of 471 trauma patients, 211 (45 %) were MT, while our latent SH classifier identified only 127 (27 %) of patients as SH. The agreement between the two classification methods was 73 %. A non-ignorable portion of patients (17 out of 68, 25 %) who died within 24 h were not classified as MT but the SH group included 62 patients (91 %) who died during the same period. Our comparison of the predictive models based on MT and SH revealed significant differences between the coefficients of potential predictors of patients who may be in need of activation of the massive transfusion protocol. The traditional MT classification does not adequately reflect transfusion practices and outcomes during the trauma reception and initial resuscitation phase. Although we have demonstrated that joint latent class modeling could be used to correct for potential bias caused by misclassification of severely bleeding patients, improvement in this approach could be made in the presence of time to event data from prospective studies.

Research study: Space vehicle control systems

NASA Technical Reports Server (NTRS)

Likins, P. W.; Longman, R. W.

1979-01-01

From the control point of view, spacecraft are classified into two main groups: those for which the spacecraft is fully defined before the control system is designed; and those for which the control system must be specified before certain interchangeable parts of a multi-purpose spacecraft are selected for future missions. Consideration is given to both classes of problems.

Classifying Radio Galaxies with the Convolutional Neural Network

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

Aniyan, A. K.; Thorat, K.

We present the application of a deep machine learning technique to classify radio images of extended sources on a morphological basis using convolutional neural networks (CNN). In this study, we have taken the case of the Fanaroff–Riley (FR) class of radio galaxies as well as radio galaxies with bent-tailed morphology. We have used archival data from the Very Large Array (VLA)—Faint Images of the Radio Sky at Twenty Centimeters survey and existing visually classified samples available in the literature to train a neural network for morphological classification of these categories of radio sources. Our training sample size for each of these categoriesmore » is ∼200 sources, which has been augmented by rotated versions of the same. Our study shows that CNNs can classify images of the FRI and FRII and bent-tailed radio galaxies with high accuracy (maximum precision at 95%) using well-defined samples and a “fusion classifier,” which combines the results of binary classifications, while allowing for a mechanism to find sources with unusual morphologies. The individual precision is highest for bent-tailed radio galaxies at 95% and is 91% and 75% for the FRI and FRII classes, respectively, whereas the recall is highest for FRI and FRIIs at 91% each, while the bent-tailed class has a recall of 79%. These results show that our results are comparable to that of manual classification, while being much faster. Finally, we discuss the computational and data-related challenges associated with the morphological classification of radio galaxies with CNNs.« less

Is it Useful to Have a Clear-cut Definition of Life? On the Use of Fuzzy Logic in Prebiotic Chemistry

NASA Astrophysics Data System (ADS)

Bruylants, Gilles; Bartik, Kristin; Reisse, Jacques

2010-04-01

Many scientists, including one of the authors of the present paper, have devoted time to try to find a definition for life (Bersini and Reisse 2007). It is clear that a consensus will never be reached but, more importantly, it seems that the issue itself could be without major interest. It is indeed impossible to define a “natural” frontier between non-living and living systems and therefore also impossible to define dichotomic criteria which could be used in order to classify systems in one of these two classes (living or non-living). Fuzzy logic provides a natural way to deal with problems where class membership lacks sharply defined criteria. It also offers the possibility to avoid losing time with unnecessary controversies such as deciding whether a virus is, or is not, a living system.

Is it useful to have a clear-cut definition of life? On the use of fuzzy logic in prebiotic chemistry.

PubMed

Bruylants, Gilles; Bartik, Kristin; Reisse, Jacques

2010-04-01

Many scientists, including one of the authors of the present paper, have devoted time to try to find a definition for life (Bersini and Reisse 2007). It is clear that a consensus will never be reached but, more importantly, it seems that the issue itself could be without major interest. It is indeed impossible to define a "natural" frontier between non-living and living systems and therefore also impossible to define dichotomic criteria which could be used in order to classify systems in one of these two classes (living or non-living). Fuzzy logic provides a natural way to deal with problems where class membership lacks sharply defined criteria. It also offers the possibility to avoid losing time with unnecessary controversies such as deciding whether a virus is, or is not, a living system.

Altered LARK Expression Perturbs Development and Physiology of the Drosophila PDF Clock Neurons

PubMed Central

Huang, Yanmei; Howlett, Eric; Stern, Michael; Jackson, F. Rob

2009-01-01

The LARK RNA-binding protein (RBP) has well documented roles in the circadian systems of Drosophila and mammals. Recent studies have demonstrated that the Drosophila LARK RBP is associated with many mRNA targets, in vivo, including those that regulate either neurophysiology or development of the nervous system. In the present study, we have employed conditional expression techniques to distinguish developmental and physiological functions of LARK for a defined class of neurons: the Pigment Dispersing Factor (PDF)-containing LNv clock neurons. We found that increased LARK expression during development dramatically alters the small LNv class of neurons with no obvious effects on the large LNv cells. Conversely, conditional expression of LARK at the adult stage results in altered clock protein rhythms and circadian locomotor activity, even though neural morphology is normal in such animals. Electrophysiological analyses at the larval neuromuscular junction indicate a role for LARK in regulating neuronal excitability. Altogether, our results demonstrate that LARK activity is critical for neuronal development and physiology. PMID:19303442

Acceptability and characteristics of 124 human bioequivalence studies with active substances classified according to the Biopharmaceutic Classification System

PubMed Central

Ramirez, Elena; Laosa, Olga; Guerra, Pedro; Duque, Blanca; Mosquera, Beatriz; Borobia, Alberto M; Lei, Suhua H; Carcas, Antonio J; Frias, Jesus

2010-01-01

AIM The aim of this study was to evaluate the acceptability of 124 bioequivalence (BE) studies with 80 active substances categorized according to the Biopharmaceutics Classification System (BCS) in order to establish if there were different probabilities of proving BE between the different BCS classes. METHODS We evaluated the differences between pharmaceutical products with active substances from different BCS classes in terms of acceptability, number of subjects in the study (n), the point estimates, and intra- and inter-subject coefficients of variation data from BE studies with generic products. RESULTS Out of 124 BE studies 89 (71.77%) were performed with pharmaceutical products containing active substances classified by the BCS. In all BCS classes there were non-bioequivalent pharmaceutical products: 4 out of 26 (15.38%) in class 1, 14 out of 28 (50%) in class 2, 3 out of 22 (13.63%) in class 3 and 1 out of 13 (7.69%) in class 4. When we removed those pharmaceutical products in which intra-subject variability was higher than predicted (2 in class 1 active substances, 9 in class 2 and 2 in class 3) there were still non-BE pharmaceutical products in classes 1, 2 and 3. CONCLUSIONS Comparisons between pharmaceutical products with active substances from the four BCS classes have not allowed us to define differential characteristics of each class in terms of n, inter and intra-subject variability for Cmax or AUC. Despite the usually employed test dissolution methodology proposed as quality control, pharmaceutical products with active substances from the four classes of BCS showed non-BE studies. PMID:21039763

A hydrologically explicit, spatially exact, classification of landforms for Canada at 1:500,000 scale.

NASA Astrophysics Data System (ADS)

MacMillan, Robert A.; Geng, Xiaoyuan; Smith, Scott; Zawadzka, Joanna; Hengl, Tom

2016-04-01

A new approach for classifying landform types has been developed and applied to all of Canada using a 250 m DEM. The resulting LandMapR classification has been designed to provide a stable and consistent spatial fabric to act as initial proto-polygons to be used in updating the current 1:1 M scale Soil Landscapes of Canada map to 1:500,000 scale. There is a desire to make the current SLC polygon fabric more consistent across the country, more correctly aligned to observable hydrological and landscape features, more spatially exact, more detailed and more interpretable. The approach is essentially a modification of the Hammond (1954) criteria for classifying macro landform types as implemented for computerized analysis by Dikau (1989, 1991) and Brabyn (1998). The major modification is that the key input variables of local relief and relative position in the landscape are computed for specific hillslopes that occur between individual, explicitly defined, channels and divides. While most approaches, including Dikau et al., (1991) and SOTER (Dobos et al., 2005) compute relative relief and landscape position within a neighborhood analysis window (NAW) of some fixed size (9,600 m and 1 km respectively) the LandMapR method assesses these variables based on explicit analysis of flow paths between locally defined divides and channels (or lakes). We have modified the Hammond criteria by splitting the lowest relief class of 0-30 m into 4 classes of 0-0 m, 0-1 m, 1-10 m and 10-30 m) in order to be able to better differentiate subtle landform features in areas of low relief. Essentially this enables recognition of lakes and open water (0 relief and 0 slope), shorelines and littoral zones (0-1 m), nearly flat, low-relief landforms (1-10 m) and low relief undulating plains (10-30 m). We also modified the Hammond approach for separating upper versus lower landform positions used to differentiate flat areas in uplands from flat lowlands. We instead differentiate 3 relative slope positions of channel valley, toe slope and upper slope consistently and exhaustively and so can identify any flat areas that occur in any of these three landform positions. We did not find it necessary to use slope gradient as a criteria for defining and delineating classes because relief acts as a surrogate for slope and each relief class exhibits a narrow and definable range of slope gradients. Dominant slope gradient (or other attributes) can be computed, post classification, for each defined polygon, if there is a need to further classify by slope or other attribute. This simplifies classification and also reduces pixilation in the classification arising from considering too many local criteria in the class definitions. The resulting polygons provide an extremely detailed classification of relief and landform position at the level of individual hillslopes across all of Canada. The polygon boundaries explicitly follow major identifiable drainage networks and work their way upslope to delineate interfluves that occupy upslope positions at all levels of relief. The detailed LandMapR polygon classifications nest consistently within more general regions defined by the original Hammond-Dikau procedures. Initial visual analysis reveals a strong and consistent spatial relationship between observable changes in slope, vegetation and drainage regime and LandMapR landform polygon boundaries. More detailed quantitative assessment of the accuracy and utility of the LandMapR polygon classes is planned.

Mapping forest vegetation with ERTS-1 MSS data and automatic data processing techniques

NASA Technical Reports Server (NTRS)

Messmore, J.; Copeland, G. E.; Levy, G. F.

1975-01-01

This study was undertaken with the intent of elucidating the forest mapping capabilities of ERTS-1 MSS data when analyzed with the aid of LARS' automatic data processing techniques. The site for this investigation was the Great Dismal Swamp, a 210,000 acre wilderness area located on the Middle Atlantic coastal plain. Due to inadequate ground truth information on the distribution of vegetation within the swamp, an unsupervised classification scheme was utilized. Initially pictureprints, resembling low resolution photographs, were generated in each of the four ERTS-1 channels. Data found within rectangular training fields was then clustered into 13 spectral groups and defined statistically. Using a maximum likelihood classification scheme, the unknown data points were subsequently classified into one of the designated training classes. Training field data was classified with a high degree of accuracy (greater than 95%), and progress is being made towards identifying the mapped spectral classes.

Mapping forest vegetation with ERTS-1 MSS data and automatic data processing techniques

NASA Technical Reports Server (NTRS)

Messmore, J.; Copeland, G. E.; Levy, G. F.

1975-01-01

This study was undertaken with the intent of elucidating the forest mapping capabilities of ERTS-1 MSS data when analyzed with the aid of LARS' automatic data processing techniques. The site for this investigation was the Great Dismal Swamp, a 210,000 acre wilderness area located on the Middle Atlantic coastal plain. Due to inadequate ground truth information on the distribution of vegetation within the swamp, an unsupervised classification scheme was utilized. Initially pictureprints, resembling low resolution photographs, were generated in each of the four ERTS-1 channels. Data found within rectangular training fields was then clustered into 13 spectral groups and defined statistically. Using a maximum likelihood classification scheme, the unknown data points were subsequently classified into one of the designated training classes. Training field data was classified with a high degree of accuracy (greater than 95 percent), and progress is being made towards identifying the mapped spectral classes.

Evaluation of the membrane permeability (PAMPA and skin) of benzimidazoles with potential cannabinoid activity and their relation with the Biopharmaceutics Classification System (BCS).

PubMed

Alvarez-Figueroa, M Javiera; Pessoa-Mahana, C David; Palavecino-González, M Elisa; Mella-Raipán, Jaime; Espinosa-Bustos, Cristián; Lagos-Muñoz, Manuel E

2011-06-01

The permeability of five benzimidazole derivates with potential cannabinoid activity was determined in two models of membranes, parallel artificial membrane permeability assay (PAMPA) and skin, in order to study the relationship of the physicochemical properties of the molecules and characteristics of the membranes with the permeability defined by the Biopharmaceutics Classification System. It was established that the PAMPA intestinal absorption method is a good predictor for classifying these molecules as very permeable, independent of their thermodynamic solubility, if and only if these have a Log P(oct) value <3.0. In contrast, transdermal permeability is conditioned on the solubility of the molecule so that it can only serve as a model for classifying the permeability of molecules that possess high solubility (class I: high solubility, high permeability; class III: high solubility, low permeability).

Nanomaterial-Enabled Dry Electrodes for Electrophysiological Sensing: A Review

NASA Astrophysics Data System (ADS)

Yao, Shanshan; Zhu, Yong

2016-04-01

Long-term, continuous, and unsupervised tracking of physiological data is becoming increasingly attractive for health/wellness monitoring and ailment treatment. Nanomaterials have recently attracted extensive attention as building blocks for flexible/stretchable conductors and are thus promising candidates for electrophysiological electrodes. Here we provide a review on nanomaterial-enabled dry electrodes for electrophysiological sensing, focusing on electrocardiography (ECG). The dry electrodes can be classified into contact surface electrodes, contact-penetrating electrodes, and noncontact capacitive electrodes. Different types of electrodes including their corresponding equivalent electrode-skin interface models and the sources of the noise are first introduced, followed by a review on recent developments of dry ECG electrodes based on various nanomaterials, including metallic nanowires, metallic nanoparticles, carbon nanotubes, and graphene. Their fabrication processes and performances in terms of electrode-skin impedance, signal-to-noise ratio, resistance to motion artifacts, skin compatibility, and long-term stability are discussed.

Classification and analysis of the Rudaki's Area

NASA Astrophysics Data System (ADS)

Zambon, F.; De sanctis, M.; Capaccioni, F.; Filacchione, G.; Carli, C.; Ammannito, E.; Frigeri, A.

2011-12-01

During the first two MESSENGER flybys the Mercury Dual Imaging System (MDIS) has mapped 90% of the Mercury's surface. An effective way to study the different terrain on planetary surfaces is to apply classification methods. These are based on clustering algorithms and they can be divided in two categories: unsupervised and supervised. The unsupervised classifiers do not require the analyst feedback and the algorithm automatically organizes pixels values into classes. In the supervised method, instead, the analyst must choose the "training area" that define the pixels value of a given class. We applied an unsupervised classifier, ISODATA, to the WAC filter images of the Rudaki's area where several kind of terrain have been identified showing differences in albedo, topography and crater density. ISODATA classifier divides this region in four classes: 1) shadow regions, 2) rough regions, 3) smooth plane, 4) highest reflectance area. ISODATA can not distinguish the high albedo regions from highly reflective illuminated edge of the craters, however the algorithm identify four classes that can be considered different units mainly on the basis of their reflectances at the various wavelengths. Is not possible, instead, to extrapolate compositional information because of the absence of clear spectral features. An additional analysis was made using ISODATA to choose the "training area" for further supervised classifications. These approach would allow, for example, to separate more accurately the edge of the craters from the high reflectance areas and the low reflectance regions from the shadow areas.

Classification of Automated Search Traffic

NASA Astrophysics Data System (ADS)

Buehrer, Greg; Stokes, Jack W.; Chellapilla, Kumar; Platt, John C.

As web search providers seek to improve both relevance and response times, they are challenged by the ever-increasing tax of automated search query traffic. Third party systems interact with search engines for a variety of reasons, such as monitoring a web site’s rank, augmenting online games, or possibly to maliciously alter click-through rates. In this paper, we investigate automated traffic (sometimes referred to as bot traffic) in the query stream of a large search engine provider. We define automated traffic as any search query not generated by a human in real time. We first provide examples of different categories of query logs generated by automated means. We then develop many different features that distinguish between queries generated by people searching for information, and those generated by automated processes. We categorize these features into two classes, either an interpretation of the physical model of human interactions, or as behavioral patterns of automated interactions. Using the these detection features, we next classify the query stream using multiple binary classifiers. In addition, a multiclass classifier is then developed to identify subclasses of both normal and automated traffic. An active learning algorithm is used to suggest which user sessions to label to improve the accuracy of the multiclass classifier, while also seeking to discover new classes of automated traffic. Performance analysis are then provided. Finally, the multiclass classifier is used to predict the subclass distribution for the search query stream.

Minimum Information about a Cardiac Electrophysiology Experiment (MICEE): Standardised Reporting for Model Reproducibility, Interoperability, and Data Sharing

PubMed Central

Quinn, TA; Granite, S; Allessie, MA; Antzelevitch, C; Bollensdorff, C; Bub, G; Burton, RAB; Cerbai, E; Chen, PS; Delmar, M; DiFrancesco, D; Earm, YE; Efimov, IR; Egger, M; Entcheva, E; Fink, M; Fischmeister, R; Franz, MR; Garny, A; Giles, WR; Hannes, T; Harding, SE; Hunter, PJ; Iribe, G; Jalife, J; Johnson, CR; Kass, RS; Kodama, I; Koren, G; Lord, P; Markhasin, VS; Matsuoka, S; McCulloch, AD; Mirams, GR; Morley, GE; Nattel, S; Noble, D; Olesen, SP; Panfilov, AV; Trayanova, NA; Ravens, U; Richard, S; Rosenbaum, DS; Rudy, Y; Sachs, F; Sachse, FB; Saint, DA; Schotten, U; Solovyova, O; Taggart, P; Tung, L; Varró, A; Volders, PG; Wang, K; Weiss, JN; Wettwer, E; White, E; Wilders, R; Winslow, RL; Kohl, P

2011-01-01

Cardiac experimental electrophysiology is in need of a well-defined Minimum Information Standard for recording, annotating, and reporting experimental data. As a step toward establishing this, we present a draft standard, called Minimum Information about a Cardiac Electrophysiology Experiment (MICEE). The ultimate goal is to develop a useful tool for cardiac electrophysiologists which facilitates and improves dissemination of the minimum information necessary for reproduction of cardiac electrophysiology research, allowing for easier comparison and utilisation of findings by others. It is hoped that this will enhance the integration of individual results into experimental, computational, and conceptual models. In its present form, this draft is intended for assessment and development by the research community. We invite the reader to join this effort, and, if deemed productive, implement the Minimum Information about a Cardiac Electrophysiology Experiment standard in their own work. PMID:21745496

ElVisML: an open data format for the exchange and storage of electrophysiological data in ophthalmology.

PubMed

Strasser, Torsten; Peters, Tobias; Jägle, Herbert; Zrenner, Eberhart

2018-02-01

The ISCEV standards and recommendations for electrophysiological recordings in ophthalmology define a set of protocols with stimulus parameters, acquisition settings, and recording conditions, to unify the data and enable comparability of results across centers. Up to now, however, there are no standards to define the storage and exchange of such electrophysiological recordings. The aim of this study was to develop an open standard data format for the exchange and storage of visual electrophysiological data (ElVisML). We first surveyed existing data formats for biomedical signals and examined their suitability for electrophysiological data in ophthalmology. We then compared the suitability of text-based and binary formats, as well as encoding in Extensible Markup Language (XML) and character/comma-separated values. The results of the methodological consideration led to the development of ElVisML with an XML-encoded text-based format. This allows referential integrity, extensibility, the storing of accompanying units, as well as ensuring confidentiality and integrity of the data. A visualization of ElVisML documents (ElVisWeb) has additionally been developed, which facilitates the exchange of recordings on mailing lists and allows open access to data along with published articles. The open data format ElVisML ensures the quality, validity, and integrity of electrophysiological data transmission and storage as well as providing manufacturer-independent access and long-term archiving in a future-proof format. Standardization of the format of such neurophysiology data would promote the development of new techniques and open software for the use of neurophysiological data in both clinic and research.

Automated Classification of ROSAT Sources Using Heterogeneous Multiwavelength Source Catalogs

NASA Technical Reports Server (NTRS)

McGlynn, Thomas; Suchkov, A. A.; Winter, E. L.; Hanisch, R. J.; White, R. L.; Ochsenbein, F.; Derriere, S.; Voges, W.; Corcoran, M. F.

2004-01-01

We describe an on-line system for automated classification of X-ray sources, ClassX, and present preliminary results of classification of the three major catalogs of ROSAT sources, RASS BSC, RASS FSC, and WGACAT, into six class categories: stars, white dwarfs, X-ray binaries, galaxies, AGNs, and clusters of galaxies. ClassX is based on a machine learning technology. It represents a system of classifiers, each classifier consisting of a considerable number of oblique decision trees. These trees are built as the classifier is 'trained' to recognize various classes of objects using a training sample of sources of known object types. Each source is characterized by a preselected set of parameters, or attributes; the same set is then used as the classifier conducts classification of sources of unknown identity. The ClassX pipeline features an automatic search for X-ray source counterparts among heterogeneous data sets in on-line data archives using Virtual Observatory protocols; it retrieves from those archives all the attributes required by the selected classifier and inputs them to the classifier. The user input to ClassX is typically a file with target coordinates, optionally complemented with target IDs. The output contains the class name, attributes, and class probabilities for all classified targets. We discuss ways to characterize and assess the classifier quality and performance and present the respective validation procedures. Based on both internal and external validation, we conclude that the ClassX classifiers yield reasonable and reliable classifications for ROSAT sources and have the potential to broaden class representation significantly for rare object types.

The utility of rat jejunal permeability for biopharmaceutics classification system.

PubMed

Zakeri-Milani, Parvin; Valizadeh, Hadi; Tajerzadeh, Hosnieh; Islambulchilar, Ziba

2009-12-01

The biopharmaceutical classification system has been developed to provide a scientific approach for classifying drug compounds based on their dose/solubility ratio and human intestinal permeability. Therefore in this study a new classification is presented, which is based on a correlation between rat and human intestinal permeability values. In situ technique in rat jejunum was used to determine the effective intestinal permeability of tested drugs. Then three dimensionless parameters--dose number, absorption number, and dissolution number (D(o), A(n), and D(n))--were calculated for each drug. Four classes of drugs were defined, that is, class I, D(0) < 0.5, P(eff(rat)) > 5.09 x 10(-5) cm/s; class II, D(o) > 1, P(eff(rat)) > 5.09 x 10( -5) cm/s; class III, D(0) < 0.5, P(eff(rat)) < 4.2 x 10(-5) cm/s; and class IV, D(o) > 1, P(eff(rat)) < 4.2 x 10(-5) cm/s. A region of borderline drugs (0.5 < D(o) < 1, 4.2 x 10(-5) < P(eff(rat)) < 5.09 x 10(-5) cm/s) was also defined. According to obtained results and proposed classification for drugs, it is concluded that drugs could be categorized correctly based on dose number and their intestinal permeability values in rat model using single-pass intestinal perfusion technique. This classification enables us to remark defined characteristics for intestinal absorption of all four classes using suitable cutoff points for both dose number and rat effective intestinal permeability values.

Anatomical and Electrophysiological Clustering of Superficial Medial Entorhinal Cortex Interneurons

PubMed Central

2017-01-01

Abstract Local GABAergic interneurons regulate the activity of spatially-modulated principal cells in the medial entorhinal cortex (MEC), mediating stellate-to-stellate connectivity and possibly enabling grid formation via recurrent inhibitory circuitry. Despite the important role interneurons seem to play in the MEC cortical circuit, the combination of low cell counts and functional diversity has made systematic electrophysiological studies of these neurons difficult. For these reasons, there remains a paucity of knowledge on the electrophysiological profiles of superficial MEC interneuron populations. Taking advantage of glutamic acid decarboxylase 2 (GAD2)-IRES-tdTomato and PV-tdTomato transgenic mice, we targeted GABAergic interneurons for whole-cell patch-clamp recordings and characterized their passive membrane features, basic input/output properties and action potential (AP) shape. These electrophysiologically characterized cells were then anatomically reconstructed, with emphasis on axonal projections and pial depth. K-means clustering of interneuron anatomical and electrophysiological data optimally classified a population of 106 interneurons into four distinct clusters. The first cluster is comprised of layer 2- and 3-projecting, slow-firing interneurons. The second cluster is comprised largely of PV+ fast-firing interneurons that project mainly to layers 2 and 3. The third cluster contains layer 1- and 2-projecting interneurons, and the fourth cluster is made up of layer 1-projecting horizontal interneurons. These results, among others, will provide greater understanding of the electrophysiological characteristics of MEC interneurons, help guide future in vivo studies, and may aid in uncovering the mechanism of grid field formation. PMID:29085901

Transepithelial transport of biperiden hydrochloride in Caco-2 cell monolayers.

PubMed

Abalos, Ivana S; Rodríguez, Yanina I; Lozano, Verónica; Cereseto, Marina; Mussini, Maria V; Spinetto, Marta E; Chiale, Carlos; Pesce, Guido

2012-09-01

The aim of this research has been to determine the biperiden hydrochloride permeability in Caco-2 model, in order to classify it based on the Biopharmaceutics Classification System (BCS). The World Health Organization (WHO) as well as many other authors have provisionally assigned the drug as BCS class I (high solubility-high permeability) or III (high solubility-low permeability), based on different methods. We determined biperiden BCS class by comparing its permeability to 5 pre-defined compounds: atenolol and ranitidine hydrochloride (low permeability group) and metoprolol tartrate, sodium naproxen and theophylline (high permeability group). Since biperiden permeability was higher than those obtained for high permeability drugs, we classified it as a BCS class I compound. On the other hand, as no differences were obtained for permeability values when apical to basolateral and basolateral to apical fluxes were studied, this drug cannot act as a substrate of efflux transporters. As a consequence of our results, we suggest that the widely used antiparkinsonian drug, biperiden, should be candidate for a waiver of in vivo bioequivalence studies. Copyright © 2012 Elsevier B.V. All rights reserved.

Mapping raised bogs with an iterative one-class classification approach

NASA Astrophysics Data System (ADS)

Mack, Benjamin; Roscher, Ribana; Stenzel, Stefanie; Feilhauer, Hannes; Schmidtlein, Sebastian; Waske, Björn

2016-10-01

Land use and land cover maps are one of the most commonly used remote sensing products. In many applications the user only requires a map of one particular class of interest, e.g. a specific vegetation type or an invasive species. One-class classifiers are appealing alternatives to common supervised classifiers because they can be trained with labeled training data of the class of interest only. However, training an accurate one-class classification (OCC) model is challenging, particularly when facing a large image, a small class and few training samples. To tackle these problems we propose an iterative OCC approach. The presented approach uses a biased Support Vector Machine as core classifier. In an iterative pre-classification step a large part of the pixels not belonging to the class of interest is classified. The remaining data is classified by a final classifier with a novel model and threshold selection approach. The specific objective of our study is the classification of raised bogs in a study site in southeast Germany, using multi-seasonal RapidEye data and a small number of training sample. Results demonstrate that the iterative OCC outperforms other state of the art one-class classifiers and approaches for model selection. The study highlights the potential of the proposed approach for an efficient and improved mapping of small classes such as raised bogs. Overall the proposed approach constitutes a feasible approach and useful modification of a regular one-class classifier.

Cloud classification in polar regions using AVHRR textural and spectral signatures

NASA Technical Reports Server (NTRS)

Welch, R. M.; Sengupta, S. K.; Weger, R. C.; Christopher, S. A.; Kuo, K. S.; Carsey, F. D.

1990-01-01

Arctic clouds and ice-covered surfaces are classified on the basis of textural and spectral features obtained with AVHRR 1.1-km spatial resolution imagery over the Beaufort Sea during May-October, 1989. Scenes were acquired about every 5 days, for a total of 38 cases. A list comprising 20 arctic-surface and cloud classes is compiled using spectral measures defined by Garand (1988).

Characteristic classes of gauge systems

NASA Astrophysics Data System (ADS)

Lyakhovich, S. L.; Sharapov, A. A.

2004-12-01

We define and study invariants which can be uniformly constructed for any gauge system. By a gauge system we understand an (anti-)Poisson supermanifold provided with an odd Hamiltonian self-commuting vector field called a homological vector field. This definition encompasses all the cases usually included into the notion of a gauge theory in physics as well as some other similar (but different) structures like Lie or Courant algebroids. For Lagrangian gauge theories or Hamiltonian first class constrained systems, the homological vector field is identified with the classical BRST transformation operator. We define characteristic classes of a gauge system as universal cohomology classes of the homological vector field, which are uniformly constructed in terms of this vector field itself. Not striving to exhaustively classify all the characteristic classes in this work, we compute those invariants which are built up in terms of the first derivatives of the homological vector field. We also consider the cohomological operations in the space of all the characteristic classes. In particular, we show that the (anti-)Poisson bracket becomes trivial when applied to the space of all the characteristic classes, instead the latter space can be endowed with another Lie bracket operation. Making use of this Lie bracket one can generate new characteristic classes involving higher derivatives of the homological vector field. The simplest characteristic classes are illustrated by the examples relating them to anomalies in the traditional BV or BFV-BRST theory and to characteristic classes of (singular) foliations.

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.

Dissimilarity representations in lung parenchyma classification

NASA Astrophysics Data System (ADS)

Sørensen, Lauge; de Bruijne, Marleen

2009-02-01

A good problem representation is important for a pattern recognition system to be successful. The traditional approach to statistical pattern recognition is feature representation. More specifically, objects are represented by a number of features in a feature vector space, and classifiers are built in this representation. This is also the general trend in lung parenchyma classification in computed tomography (CT) images, where the features often are measures on feature histograms. Instead, we propose to build normal density based classifiers in dissimilarity representations for lung parenchyma classification. This allows for the classifiers to work on dissimilarities between objects, which might be a more natural way of representing lung parenchyma. In this context, dissimilarity is defined between CT regions of interest (ROI)s. ROIs are represented by their CT attenuation histogram and ROI dissimilarity is defined as a histogram dissimilarity measure between the attenuation histograms. In this setting, the full histograms are utilized according to the chosen histogram dissimilarity measure. We apply this idea to classification of different emphysema patterns as well as normal, healthy tissue. Two dissimilarity representation approaches as well as different histogram dissimilarity measures are considered. The approaches are evaluated on a set of 168 CT ROIs using normal density based classifiers all showing good performance. Compared to using histogram dissimilarity directly as distance in a emph{k} nearest neighbor classifier, which achieves a classification accuracy of 92.9%, the best dissimilarity representation based classifier is significantly better with a classification accuracy of 97.0% (text{emph{p" border="0" class="imgtopleft"> = 0.046).

Modulation of the electrophysiological correlates of retrieval cue processing by the specificity of task demands.

PubMed

Johnson, Jeffrey D; Rugg, Michael D

2006-02-03

Retrieval orientation refers to the differential processing of retrieval cues according to the type of information sought from memory (e.g., words vs. pictures). In the present study, event-related potentials (ERPs) were employed to investigate whether the neural correlates of differential retrieval orientations are sensitive to the specificity of the retrieval demands of the test task. In separate study-test phases, subjects encoded lists of intermixed words and pictures, and then undertook one of two retrieval tests, in both of which the retrieval cues were exclusively words. In the recognition test, subjects performed 'old/new' discriminations on the test items, and old items corresponded to only one class of studied material (words or pictures). In the exclusion test, old items corresponded to both classes of study material, and subjects were required to respond 'old' only to test items corresponding to a designated class of material. Thus, demands for retrieval specificity were greater in the exclusion test than during recognition. ERPs elicited by correctly classified new items in the two types of test were contrasted according to whether words or pictures were the sought-for material. Material-dependent ERP effects were evident in both tests, but the effects onset earlier and offset later in the exclusion test. The findings suggest that differential processing of retrieval cues, and hence the adoption of differential retrieval orientations, varies according to the specificity of the retrieval goal.

Bias in error estimation when using cross-validation for model selection.

PubMed

Varma, Sudhir; Simon, Richard

2006-02-23

Cross-validation (CV) is an effective method for estimating the prediction error of a classifier. Some recent articles have proposed methods for optimizing classifiers by choosing classifier parameter values that minimize the CV error estimate. We have evaluated the validity of using the CV error estimate of the optimized classifier as an estimate of the true error expected on independent data. We used CV to optimize the classification parameters for two kinds of classifiers; Shrunken Centroids and Support Vector Machines (SVM). Random training datasets were created, with no difference in the distribution of the features between the two classes. Using these "null" datasets, we selected classifier parameter values that minimized the CV error estimate. 10-fold CV was used for Shrunken Centroids while Leave-One-Out-CV (LOOCV) was used for the SVM. Independent test data was created to estimate the true error. With "null" and "non null" (with differential expression between the classes) data, we also tested a nested CV procedure, where an inner CV loop is used to perform the tuning of the parameters while an outer CV is used to compute an estimate of the error. The CV error estimate for the classifier with the optimal parameters was found to be a substantially biased estimate of the true error that the classifier would incur on independent data. Even though there is no real difference between the two classes for the "null" datasets, the CV error estimate for the Shrunken Centroid with the optimal parameters was less than 30% on 18.5% of simulated training data-sets. For SVM with optimal parameters the estimated error rate was less than 30% on 38% of "null" data-sets. Performance of the optimized classifiers on the independent test set was no better than chance. The nested CV procedure reduces the bias considerably and gives an estimate of the error that is very close to that obtained on the independent testing set for both Shrunken Centroids and SVM classifiers for "null" and "non-null" data distributions. We show that using CV to compute an error estimate for a classifier that has itself been tuned using CV gives a significantly biased estimate of the true error. Proper use of CV for estimating true error of a classifier developed using a well defined algorithm requires that all steps of the algorithm, including classifier parameter tuning, be repeated in each CV loop. A nested CV procedure provides an almost unbiased estimate of the true error.

A common class of transcripts with 5'-intron depletion, distinct early coding sequence features, and N1-methyladenosine modification.

PubMed

Cenik, Can; Chua, Hon Nian; Singh, Guramrit; Akef, Abdalla; Snyder, Michael P; Palazzo, Alexander F; Moore, Melissa J; Roth, Frederick P

2017-03-01

Introns are found in 5' untranslated regions (5'UTRs) for 35% of all human transcripts. These 5'UTR introns are not randomly distributed: Genes that encode secreted, membrane-bound and mitochondrial proteins are less likely to have them. Curiously, transcripts lacking 5'UTR introns tend to harbor specific RNA sequence elements in their early coding regions. To model and understand the connection between coding-region sequence and 5'UTR intron status, we developed a classifier that can predict 5'UTR intron status with >80% accuracy using only sequence features in the early coding region. Thus, the classifier identifies transcripts with 5 ' proximal- i ntron- m inus-like-coding regions ("5IM" transcripts). Unexpectedly, we found that the early coding sequence features defining 5IM transcripts are widespread, appearing in 21% of all human RefSeq transcripts. The 5IM class of transcripts is enriched for non-AUG start codons, more extensive secondary structure both preceding the start codon and near the 5' cap, greater dependence on eIF4E for translation, and association with ER-proximal ribosomes. 5IM transcripts are bound by the exon junction complex (EJC) at noncanonical 5' proximal positions. Finally, N 1 -methyladenosines are specifically enriched in the early coding regions of 5IM transcripts. Taken together, our analyses point to the existence of a distinct 5IM class comprising ∼20% of human transcripts. This class is defined by depletion of 5' proximal introns, presence of specific RNA sequence features associated with low translation efficiency, N 1 -methyladenosines in the early coding region, and enrichment for noncanonical binding by the EJC. © 2017 Cenik et al.; Published by Cold Spring Harbor Laboratory Press for the RNA Society.

Classification of California streams using combined deductive and inductive approaches: Setting the foundation for analysis of hydrologic alteration

USGS Publications Warehouse

Pyne, Matthew I.; Carlisle, Daren M.; Konrad, Christopher P.; Stein, Eric D.

2017-01-01

Regional classification of streams is an early step in the Ecological Limits of Hydrologic Alteration framework. Many stream classifications are based on an inductive approach using hydrologic data from minimally disturbed basins, but this approach may underrepresent streams from heavily disturbed basins or sparsely gaged arid regions. An alternative is a deductive approach, using watershed climate, land use, and geomorphology to classify streams, but this approach may miss important hydrological characteristics of streams. We classified all stream reaches in California using both approaches. First, we used Bayesian and hierarchical clustering to classify reaches according to watershed characteristics. Streams were clustered into seven classes according to elevation, sedimentary rock, and winter precipitation. Permutation-based analysis of variance and random forest analyses were used to determine which hydrologic variables best separate streams into their respective classes. Stream typology (i.e., the class that a stream reach is assigned to) is shaped mainly by patterns of high and mean flow behavior within the stream's landscape context. Additionally, random forest was used to determine which hydrologic variables best separate minimally disturbed reference streams from non-reference streams in each of the seven classes. In contrast to stream typology, deviation from reference conditions is more difficult to detect and is largely defined by changes in low-flow variables, average daily flow, and duration of flow. Our combined deductive/inductive approach allows us to estimate flow under minimally disturbed conditions based on the deductive analysis and compare to measured flow based on the inductive analysis in order to estimate hydrologic change.

Robust Framework to Combine Diverse Classifiers Assigning Distributed Confidence to Individual Classifiers at Class Level

PubMed Central

Arshad, Sannia; Rho, Seungmin

2014-01-01

We have presented a classification framework that combines multiple heterogeneous classifiers in the presence of class label noise. An extension of m-Mediods based modeling is presented that generates model of various classes whilst identifying and filtering noisy training data. This noise free data is further used to learn model for other classifiers such as GMM and SVM. A weight learning method is then introduced to learn weights on each class for different classifiers to construct an ensemble. For this purpose, we applied genetic algorithm to search for an optimal weight vector on which classifier ensemble is expected to give the best accuracy. The proposed approach is evaluated on variety of real life datasets. It is also compared with existing standard ensemble techniques such as Adaboost, Bagging, and Random Subspace Methods. Experimental results show the superiority of proposed ensemble method as compared to its competitors, especially in the presence of class label noise and imbalance classes. PMID:25295302

Robust framework to combine diverse classifiers assigning distributed confidence to individual classifiers at class level.

PubMed

Khalid, Shehzad; Arshad, Sannia; Jabbar, Sohail; Rho, Seungmin

2014-01-01

We have presented a classification framework that combines multiple heterogeneous classifiers in the presence of class label noise. An extension of m-Mediods based modeling is presented that generates model of various classes whilst identifying and filtering noisy training data. This noise free data is further used to learn model for other classifiers such as GMM and SVM. A weight learning method is then introduced to learn weights on each class for different classifiers to construct an ensemble. For this purpose, we applied genetic algorithm to search for an optimal weight vector on which classifier ensemble is expected to give the best accuracy. The proposed approach is evaluated on variety of real life datasets. It is also compared with existing standard ensemble techniques such as Adaboost, Bagging, and Random Subspace Methods. Experimental results show the superiority of proposed ensemble method as compared to its competitors, especially in the presence of class label noise and imbalance classes.

Classification of neocortical interneurons using affinity propagation.

PubMed

Santana, Roberto; McGarry, Laura M; Bielza, Concha; Larrañaga, Pedro; Yuste, Rafael

2013-01-01

In spite of over a century of research on cortical circuits, it is still unknown how many classes of cortical neurons exist. In fact, neuronal classification is a difficult problem because it is unclear how to designate a neuronal cell class and what are the best characteristics to define them. Recently, unsupervised classifications using cluster analysis based on morphological, physiological, or molecular characteristics, have provided quantitative and unbiased identification of distinct neuronal subtypes, when applied to selected datasets. However, better and more robust classification methods are needed for increasingly complex and larger datasets. Here, we explored the use of affinity propagation, a recently developed unsupervised classification algorithm imported from machine learning, which gives a representative example or exemplar for each cluster. As a case study, we applied affinity propagation to a test dataset of 337 interneurons belonging to four subtypes, previously identified based on morphological and physiological characteristics. We found that affinity propagation correctly classified most of the neurons in a blind, non-supervised manner. Affinity propagation outperformed Ward's method, a current standard clustering approach, in classifying the neurons into 4 subtypes. Affinity propagation could therefore be used in future studies to validly classify neurons, as a first step to help reverse engineer neural circuits.

Dissecting local circuits in vivo: integrated optogenetic and electrophysiology approaches for exploring inhibitory regulation of cortical activity.

PubMed

Cardin, Jessica A

2012-01-01

Local cortical circuit activity in vivo comprises a complex and flexible series of interactions between excitatory and inhibitory neurons. Our understanding of the functional interactions between these different neural populations has been limited by the difficulty of identifying and selectively manipulating the diverse and sparsely represented inhibitory interneuron classes in the intact brain. The integration of recently developed optical tools with traditional electrophysiological techniques provides a powerful window into the role of inhibition in regulating the activity of excitatory neurons. In particular, optogenetic targeting of specific cell classes reveals the distinct impacts of local inhibitory populations on other neurons in the surrounding local network. In addition to providing the ability to activate or suppress spiking in target cells, optogenetic activation identifies extracellularly recorded neurons by class, even when naturally occurring spike rates are extremely low. However, there are several important limitations on the use of these tools and the interpretation of resulting data. The purpose of this article is to outline the uses and limitations of optogenetic tools, along with current methods for achieving cell type-specific expression, and to highlight the advantages of an experimental approach combining optogenetics and electrophysiology to explore the role of inhibition in active networks. To illustrate the efficacy of these combined approaches, I present data comparing targeted manipulations of cortical fast-spiking, parvalbumin-expressing and low threshold-spiking, somatostatin-expressing interneurons in vivo. Copyright © 2011 Elsevier Ltd. All rights reserved.

Classification and global distribution of ocean precipitation types based on satellite passive microwave signatures

NASA Astrophysics Data System (ADS)

Gautam, Nitin

The main objectives of this thesis are to develop a robust statistical method for the classification of ocean precipitation based on physical properties to which the SSM/I is sensitive and to examine how these properties vary globally and seasonally. A two step approach is adopted for the classification of oceanic precipitation classes from multispectral SSM/I data: (1)we subjectively define precipitation classes using a priori information about the precipitating system and its possible distinct signature on SSM/I data such as scattering by ice particles aloft in the precipitating cloud, emission by liquid rain water below freezing level, the difference of polarization at 19 GHz-an indirect measure of optical depth, etc.; (2)we then develop an objective classification scheme which is found to reproduce the subjective classification with high accuracy. This hybrid strategy allows us to use the characteristics of the data to define and encode classes and helps retain the physical interpretation of classes. The classification methods based on k-nearest neighbor and neural network are developed to objectively classify six precipitation classes. It is found that the classification method based neural network yields high accuracy for all precipitation classes. An inversion method based on minimum variance approach was used to retrieve gross microphysical properties of these precipitation classes such as column integrated liquid water path, column integrated ice water path, and column integrated min water path. This classification method is then applied to 2 years (1991-92) of SSM/I data to examine and document the seasonal and global distribution of precipitation frequency corresponding to each of these objectively defined six classes. The characteristics of the distribution are found to be consistent with assumptions used in defining these six precipitation classes and also with well known climatological patterns of precipitation regions. The seasonal and global distribution of these six classes is also compared with the earlier results obtained from Comprehensive Ocean Atmosphere Data Sets (COADS). It is found that the gross pattern of the distributions obtained from SSM/I and COADS data match remarkably well with each other.

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

Automatic Adviser on Mobile Objects Status Identification and Classification

NASA Astrophysics Data System (ADS)

Shabelnikov, A. N.; Liabakh, N. N.; Gibner, Ya M.; Saryan, A. S.

2018-05-01

A mobile object status identification task is defined within the image discrimination theory. It is proposed to classify objects into three classes: object operation status; its maintenance is required and object should be removed from the production process. Two methods were developed to construct the separating boundaries between the designated classes: a) using statistical information on the research objects executed movement, b) basing on regulatory documents and expert commentary. Automatic Adviser operation simulation and the operation results analysis complex were synthesized. Research results are commented using a specific example of cuts rolling from the hump yard. The work was supported by Russian Fundamental Research Fund, project No. 17-20-01040.

Application of the SNoW machine learning paradigm to a set of transportation imaging problems

NASA Astrophysics Data System (ADS)

Paul, Peter; Burry, Aaron M.; Wang, Yuheng; Kozitsky, Vladimir

2012-01-01

Machine learning methods have been successfully applied to image object classification problems where there is clear distinction between classes and where a comprehensive set of training samples and ground truth are readily available. The transportation domain is an area where machine learning methods are particularly applicable, since the classification problems typically have well defined class boundaries and, due to high traffic volumes in most applications, massive roadway data is available. Though these classes tend to be well defined, the particular image noises and variations can be challenging. Another challenge is the extremely high accuracy typically required in most traffic applications. Incorrect assignment of fines or tolls due to imaging mistakes is not acceptable in most applications. For the front seat vehicle occupancy detection problem, classification amounts to determining whether one face (driver only) or two faces (driver + passenger) are detected in the front seat of a vehicle on a roadway. For automatic license plate recognition, the classification problem is a type of optical character recognition problem encompassing multiple class classification. The SNoW machine learning classifier using local SMQT features is shown to be successful in these two transportation imaging applications.

Improved classification of drainage networks using junction angles and secondary tributary lengths

NASA Astrophysics Data System (ADS)

Jung, Kichul; Marpu, Prashanth R.; Ouarda, Taha B. M. J.

2015-06-01

River networks in different regions have distinct characteristics generated by geological processes. These differences enable classification of drainage networks using several measures with many features of the networks. In this study, we propose a new approach that only uses the junction angles with secondary tributary lengths to directly classify different network types. This methodology is based on observations on 50 predefined channel networks. The cumulative distributions of secondary tributary lengths for different ranges of junction angles are used to obtain the descriptive values that are defined using a power-law representation. The averages of the values for the known networks are used to represent the classes, and any unclassified network can be classified based on the similarity of the representative values to those of the known classes. The methodology is applied to 10 networks in the United Arab Emirates and Oman and five networks in the USA, and the results are validated using the classification obtained with other methods.

Use of Latent Class Analysis to define groups based on validity, cognition, and emotional functioning.

PubMed

Morin, Ruth T; Axelrod, Bradley N

Latent Class Analysis (LCA) was used to classify a heterogeneous sample of neuropsychology data. In particular, we used measures of performance validity, symptom validity, cognition, and emotional functioning to assess and describe latent groups of functioning in these areas. A data-set of 680 neuropsychological evaluation protocols was analyzed using a LCA. Data were collected from evaluations performed for clinical purposes at an urban medical center. A four-class model emerged as the best fitting model of latent classes. The resulting classes were distinct based on measures of performance validity and symptom validity. Class A performed poorly on both performance and symptom validity measures. Class B had intact performance validity and heightened symptom reporting. The remaining two Classes performed adequately on both performance and symptom validity measures, differing only in cognitive and emotional functioning. In general, performance invalidity was associated with worse cognitive performance, while symptom invalidity was associated with elevated emotional distress. LCA appears useful in identifying groups within a heterogeneous sample with distinct performance patterns. Further, the orthogonal nature of performance and symptom validities is supported.

Interaction of Language, Culture and Cognition in Group Dynamics for Understanding the Adversary

DTIC Science & Technology

2010-07-01

is particularly evident in noun class b. described above, which mixes women with what European cultures would classify as “inanimate” entities, as...languages emerge gradually from an ancient -root prototypical language. For example, when modern Italian and modern French emerged (and diverged) from...Rather, CGT relates to what an individual might conceptualize as ingroup and outgroup in a given context. In CGT, two kinds of groups are defined

The effect of sample size and disease prevalence on supervised machine learning of narrative data.

PubMed Central

McKnight, Lawrence K.; Wilcox, Adam; Hripcsak, George

2002-01-01

This paper examines the independent effects of outcome prevalence and training sample sizes on inductive learning performance. We trained 3 inductive learning algorithms (MC4, IB, and Naïve-Bayes) on 60 simulated datasets of parsed radiology text reports labeled with 6 disease states. Data sets were constructed to define positive outcome states at 4 prevalence rates (1, 5, 10, 25, and 50%) in training set sizes of 200 and 2,000 cases. We found that the effect of outcome prevalence is significant when outcome classes drop below 10% of cases. The effect appeared independent of sample size, induction algorithm used, or class label. Work is needed to identify methods of improving classifier performance when output classes are rare. PMID:12463878

Beyond "implementation strategies": classifying the full range of strategies used in implementation science and practice.

PubMed

Leeman, Jennifer; Birken, Sarah A; Powell, Byron J; Rohweder, Catherine; Shea, Christopher M

2017-11-03

Strategies are central to the National Institutes of Health's definition of implementation research as "the study of strategies to integrate evidence-based interventions into specific settings." Multiple scholars have proposed lists of the strategies used in implementation research and practice, which they increasingly are classifying under the single term "implementation strategies." We contend that classifying all strategies under a single term leads to confusion, impedes synthesis across studies, and limits advancement of the full range of strategies of importance to implementation. To address this concern, we offer a system for classifying implementation strategies that builds on Proctor and colleagues' (2013) reporting guidelines, which recommend that authors not only name and define their implementation strategies but also specify who enacted the strategy (i.e., the actor) and the level and determinants that were targeted (i.e., the action targets). We build on Wandersman and colleagues' Interactive Systems Framework to distinguish strategies based on whether they are enacted by actors functioning as part of a Delivery, Support, or Synthesis and Translation System. We build on Damschroder and colleague's Consolidated Framework for Implementation Research to distinguish the levels that strategies target (intervention, inner setting, outer setting, individual, and process). We then draw on numerous resources to identify determinants, which are conceptualized as modifiable factors that prevent or enable the adoption and implementation of evidence-based interventions. Identifying actors and targets resulted in five conceptually distinct classes of implementation strategies: dissemination, implementation process, integration, capacity-building, and scale-up. In our descriptions of each class, we identify the level of the Interactive System Framework at which the strategy is enacted (actors), level and determinants targeted (action targets), and outcomes used to assess strategy effectiveness. We illustrate how each class would apply to efforts to improve colorectal cancer screening rates in Federally Qualified Health Centers. Structuring strategies into classes will aid reporting of implementation research findings, alignment of strategies with relevant theories, synthesis of findings across studies, and identification of potential gaps in current strategy listings. Organizing strategies into classes also will assist users in locating the strategies that best match their needs.

A method of neighbor classes based SVM classification for optical printed Chinese character recognition.

PubMed

Zhang, Jie; Wu, Xiaohong; Yu, Yanmei; Luo, Daisheng

2013-01-01

In optical printed Chinese character recognition (OPCCR), many classifiers have been proposed for the recognition. Among the classifiers, support vector machine (SVM) might be the best classifier. However, SVM is a classifier for two classes. When it is used for multi-classes in OPCCR, its computation is time-consuming. Thus, we propose a neighbor classes based SVM (NC-SVM) to reduce the computation consumption of SVM. Experiments of NC-SVM classification for OPCCR have been done. The results of the experiments have shown that the NC-SVM we proposed can effectively reduce the computation time in OPCCR.

Relation of the activities of the IPDF/INPE project (reforestation subproject) during the year 1979. [Mato Grosso do Sul, Brazil

NASA Technical Reports Server (NTRS)

Dejesusparada, N. (Principal Investigator); Filho, P. H.; Shimabukuro, Y. E.; Demedeiros, J. S.; Desantana, C. C.; Alves, E. C. M.

1981-01-01

The state of Mato Grosso do Sul was selected as the study area to define the recognizable classes of Eucalyptus spp. and Pinus spp. by visual and automatic analyses. For visual analysis, a preliminary interpretation key and a legend of 6 groups were derived. Based on these six groups, three final classes were defined for analysis: (1) area prepared for reforestation; (2) area reforested with Eucalyptus spp.; and (3) area reforested with Pinus spp. For automatic interpretation the area along the highway from Ribas do Rio Pardo to Agua Clara was classified into the following classes: eucalytus, bare soil, plowed soil, pine and "cerrado". The results of visual analysis show that 67% of the reforested farms have relative differences in area estimate below 5%, 22%, between 5% and 10%; and 11% between 10% and 20%. The reforested eucalyptus area is 17 times greater than the area of reforested pine. Automatic classification of eucalyptus ranged from 73.03% to 92.30% in the training areas.

Certification of international allied professionals in cardiac pacing and electrophysiology: Opportunities?

PubMed Central

Chiu, Christine

2010-01-01

Allied professionals with diverse backgrounds and training are essential to the delivery of quality care to patients with heart rhythm disorders. There is a growing worldwide demand for defined educational requirements and certification pathways to ensure uniformity of knowledge and competence of those practicing in electrophysiology. The present viewpoint article reviews the current deficiencies of education and training, and advocates for the establishment of certification pathways by professional societies. PMID:20101363

[Poverty profile regarding households participating in a food assistance program].

PubMed

Álvarez-Uribe, Martha C; Aguirre-Acevedo, Daniel C

2012-06-01

This study was aimed at establishing subgroups having specific socioeconomic characteristics by using latent class analysis as a method for segmenting target population members of the MANA-ICBF supplementary food program in the Antioquia department of Colombia and determine their differences regarding poverty and health conditions in efficiently addressing pertinent resources, programs and policies. The target population consisted of 200,000 children and their households involved in the MANA food assistance program; a representative sample by region was used. Latent class analysis was used, as were the expectation-maximization and Newton Raphson algorithms for identifying the appropriate number of classes. The final model classified the households into four clusters or classes, differing according to well-defined socio-demographic conditions affecting children's health. Some homes had a greater depth of poverty, therefore lowering the families' quality of life and affecting the health of the children in this age group.

41 CFR 102-34.45 - How are passenger automobiles classified?

Code of Federal Regulations, 2013 CFR

2013-07-01

... automobiles classified? 102-34.45 Section 102-34.45 Public Contracts and Property Management Federal Property... MANAGEMENT Obtaining Fuel Efficient Motor Vehicles § 102-34.45 How are passenger automobiles classified? Passenger automobiles are classified in the following table: Sedan class Station wagon class Descriptive...

41 CFR 102-34.45 - How are passenger automobiles classified?

Code of Federal Regulations, 2014 CFR

2014-01-01

... automobiles classified? 102-34.45 Section 102-34.45 Public Contracts and Property Management Federal Property... MANAGEMENT Obtaining Fuel Efficient Motor Vehicles § 102-34.45 How are passenger automobiles classified? Passenger automobiles are classified in the following table: Sedan class Station wagon class Descriptive...

41 CFR 102-34.45 - How are passenger automobiles classified?

Code of Federal Regulations, 2011 CFR

2011-01-01

... automobiles classified? 102-34.45 Section 102-34.45 Public Contracts and Property Management Federal Property... MANAGEMENT Obtaining Fuel Efficient Motor Vehicles § 102-34.45 How are passenger automobiles classified? Passenger automobiles are classified in the following table: Sedan class Station wagon class Descriptive...

41 CFR 102-34.45 - How are passenger automobiles classified?

Code of Federal Regulations, 2012 CFR

2012-01-01

... automobiles classified? 102-34.45 Section 102-34.45 Public Contracts and Property Management Federal Property... MANAGEMENT Obtaining Fuel Efficient Motor Vehicles § 102-34.45 How are passenger automobiles classified? Passenger automobiles are classified in the following table: Sedan class Station wagon class Descriptive...

41 CFR 102-34.45 - How are passenger automobiles classified?

Code of Federal Regulations, 2010 CFR

2010-07-01

... automobiles classified? 102-34.45 Section 102-34.45 Public Contracts and Property Management Federal Property... MANAGEMENT Obtaining Fuel Efficient Motor Vehicles § 102-34.45 How are passenger automobiles classified? Passenger automobiles are classified in the following table: Sedan class Station wagon class Descriptive...

Satellite inventory of Minnesota forest resources

NASA Technical Reports Server (NTRS)

Bauer, Marvin E.; Burk, Thomas E.; Ek, Alan R.; Coppin, Pol R.; Lime, Stephen D.; Walsh, Terese A.; Walters, David K.; Befort, William; Heinzen, David F.

1993-01-01

The methods and results of using Landsat Thematic Mapper (TM) data to classify and estimate the acreage of forest covertypes in northeastern Minnesota are described. Portions of six TM scenes covering five counties with a total area of 14,679 square miles were classified into six forest and five nonforest classes. The approach involved the integration of cluster sampling, image processing, and estimation. Using cluster sampling, 343 plots, each 88 acres in size, were photo interpreted and field mapped as a source of reference data for classifier training and calibration of the TM data classifications. Classification accuracies of up to 75 percent were achieved; most misclassification was between similar or related classes. An inverse method of calibration, based on the error rates obtained from the classifications of the cluster plots, was used to adjust the classification class proportions for classification errors. The resulting area estimates for total forest land in the five-county area were within 3 percent of the estimate made independently by the USDA Forest Service. Area estimates for conifer and hardwood forest types were within 0.8 and 6.0 percent respectively, of the Forest Service estimates. A trial of a second method of estimating the same classes as the Forest Service resulted in standard errors of 0.002 to 0.015. A study of the use of multidate TM data for change detection showed that forest canopy depletion, canopy increment, and no change could be identified with greater than 90 percent accuracy. The project results have been the basis for the Minnesota Department of Natural Resources and the Forest Service to define and begin to implement an annual system of forest inventory which utilizes Landsat TM data to detect changes in forest cover.

Multi-Scale Molecular Deconstruction of the Serotonin Neuron System.

PubMed

Okaty, Benjamin W; Freret, Morgan E; Rood, Benjamin D; Brust, Rachael D; Hennessy, Morgan L; deBairos, Danielle; Kim, Jun Chul; Cook, Melloni N; Dymecki, Susan M

2015-11-18

Serotonergic (5HT) neurons modulate diverse behaviors and physiology and are implicated in distinct clinical disorders. Corresponding diversity in 5HT neuronal phenotypes is becoming apparent and is likely rooted in molecular differences, yet a comprehensive approach characterizing molecular variation across the 5HT system is lacking, as is concomitant linkage to cellular phenotypes. Here we combine intersectional fate mapping, neuron sorting, and genome-wide RNA-seq to deconstruct the mouse 5HT system at multiple levels of granularity-from anatomy, to genetic sublineages, to single neurons. Our unbiased analyses reveal principles underlying system organization, 5HT neuron subtypes, constellations of differentially expressed genes distinguishing subtypes, and predictions of subtype-specific functions. Using electrophysiology, subtype-specific neuron silencing, and conditional gene knockout, we show that these molecularly defined 5HT neuron subtypes are functionally distinct. Collectively, this resource classifies molecular diversity across the 5HT system and discovers sertonergic subtypes, markers, organizing principles, and subtype-specific functions with potential disease relevance. Copyright © 2015 Elsevier Inc. All rights reserved.

Sensory Guillain-Barré syndrome and related disorders: an attempt at systematization.

PubMed

Uncini, Antonino; Yuki, Nobuhiro

2012-04-01

The possibility that some patients diagnosed with an acute sensory neuropathy could actually have Guillain-Barré syndrome (GBS) has been repeatedly advanced in the literature, but the number of cases reported is small. The reports have shown different clinical presentations and electrophysiological findings and are variously named, thus generating terminological and nosological confusion. We operatively defined sensory GBS as an acute, monophasic, widespread neuropathy characterized clinically by exclusive sensory symptoms and signs that reach their nadir in a maximum of 6 weeks without related systemic disorders and other diseases or conditions. We reviewed the literature through searches of PubMed from 1980 to March 2011 and our own files. On the basis of the size of fibers involved and the possible site of primary damage, we propose tentatively classifying sensory GBS and related disorders into three subtypes: acute sensory demyelinating polyneuropathy; acute sensory large-fiber axonopathy-ganglionopathy; and acute sensory small-fiber neuropathy-ganglionopathy. Copyright © 2011 Wiley Periodicals, Inc.

Multi-Scale Molecular Deconstruction of the Serotonin Neuron System

PubMed Central

Okaty, Benjamin W.; Freret, Morgan E.; Rood, Benjamin D.; Brust, Rachael D.; Hennessy, Morgan L.; deBairos, Danielle; Kim, Jun Chul; Cook, Melloni N.; Dymecki, Susan M.

2016-01-01

Summary Serotonergic (5HT) neurons modulate diverse behaviors and physiology and are implicated in distinct clinical disorders. Corresponding diversity in 5HT neuronal phenotypes is becoming apparent and is likely rooted in molecular differences, yet a comprehensive approach characterizing molecular variation across the 5HT system is lacking, as is concomitant linkage to cellular phenotypes. Here we combine intersectional fate mapping, neuron sorting, and genome-wide RNA-Seq to deconstruct the mouse 5HT system at multiple levels of granularity—from anatomy, to genetic sublineages, to single neurons. Our unbiased analyses reveal: principles underlying system organization, novel 5HT neuron subtypes, constellations of differentially expressed genes distinguishing subtypes, and predictions of subtype-specific functions. Using electrophysiology, subtype-specific neuron silencing, and conditional gene knockout, we show that these molecularly defined 5HT neuron subtypes are functionally distinct. Collectively, this resource classifies molecular diversity across the 5HT system and discovers new subtypes, markers, organizing principles, and subtype-specific functions with potential disease relevance. PMID:26549332

Classifying seismic waveforms from scratch: a case study in the alpine environment

NASA Astrophysics Data System (ADS)

Hammer, C.; Ohrnberger, M.; Fäh, D.

2013-01-01

Nowadays, an increasing amount of seismic data is collected by daily observatory routines. The basic step for successfully analyzing those data is the correct detection of various event types. However, the visually scanning process is a time-consuming task. Applying standard techniques for detection like the STA/LTA trigger still requires the manual control for classification. Here, we present a useful alternative. The incoming data stream is scanned automatically for events of interest. A stochastic classifier, called hidden Markov model, is learned for each class of interest enabling the recognition of highly variable waveforms. In contrast to other automatic techniques as neural networks or support vector machines the algorithm allows to start the classification from scratch as soon as interesting events are identified. Neither the tedious process of collecting training samples nor a time-consuming configuration of the classifier is required. An approach originally introduced for the volcanic task force action allows to learn classifier properties from a single waveform example and some hours of background recording. Besides a reduction of required workload this also enables to detect very rare events. Especially the latter feature provides a milestone point for the use of seismic devices in alpine warning systems. Furthermore, the system offers the opportunity to flag new signal classes that have not been defined before. We demonstrate the application of the classification system using a data set from the Swiss Seismological Survey achieving very high recognition rates. In detail we document all refinements of the classifier providing a step-by-step guide for the fast set up of a well-working classification system.

Learn ++.NC: combining ensemble of classifiers with dynamically weighted consult-and-vote for efficient incremental learning of new classes.

PubMed

Muhlbaier, Michael D; Topalis, Apostolos; Polikar, Robi

2009-01-01

We have previously introduced an incremental learning algorithm Learn(++), which learns novel information from consecutive data sets by generating an ensemble of classifiers with each data set, and combining them by weighted majority voting. However, Learn(++) suffers from an inherent "outvoting" problem when asked to learn a new class omega(new) introduced by a subsequent data set, as earlier classifiers not trained on this class are guaranteed to misclassify omega(new) instances. The collective votes of earlier classifiers, for an inevitably incorrect decision, then outweigh the votes of the new classifiers' correct decision on omega(new) instances--until there are enough new classifiers to counteract the unfair outvoting. This forces Learn(++) to generate an unnecessarily large number of classifiers. This paper describes Learn(++).NC, specifically designed for efficient incremental learning of multiple new classes using significantly fewer classifiers. To do so, Learn (++).NC introduces dynamically weighted consult and vote (DW-CAV), a novel voting mechanism for combining classifiers: individual classifiers consult with each other to determine which ones are most qualified to classify a given instance, and decide how much weight, if any, each classifier's decision should carry. Experiments on real-world problems indicate that the new algorithm performs remarkably well with substantially fewer classifiers, not only as compared to its predecessor Learn(++), but also as compared to several other algorithms recently proposed for similar problems.

A Method of Neighbor Classes Based SVM Classification for Optical Printed Chinese Character Recognition

PubMed Central

Zhang, Jie; Wu, Xiaohong; Yu, Yanmei; Luo, Daisheng

2013-01-01

In optical printed Chinese character recognition (OPCCR), many classifiers have been proposed for the recognition. Among the classifiers, support vector machine (SVM) might be the best classifier. However, SVM is a classifier for two classes. When it is used for multi-classes in OPCCR, its computation is time-consuming. Thus, we propose a neighbor classes based SVM (NC-SVM) to reduce the computation consumption of SVM. Experiments of NC-SVM classification for OPCCR have been done. The results of the experiments have shown that the NC-SVM we proposed can effectively reduce the computation time in OPCCR. PMID:23536777

A comparative study of nonparametric methods for pattern recognition

NASA Technical Reports Server (NTRS)

Hahn, S. F.; Nelson, G. D.

1972-01-01

The applied research discussed in this report determines and compares the correct classification percentage of the nonparametric sign test, Wilcoxon's signed rank test, and K-class classifier with the performance of the Bayes classifier. The performance is determined for data which have Gaussian, Laplacian and Rayleigh probability density functions. The correct classification percentage is shown graphically for differences in modes and/or means of the probability density functions for four, eight and sixteen samples. The K-class classifier performed very well with respect to the other classifiers used. Since the K-class classifier is a nonparametric technique, it usually performed better than the Bayes classifier which assumes the data to be Gaussian even though it may not be. The K-class classifier has the advantage over the Bayes in that it works well with non-Gaussian data without having to determine the probability density function of the data. It should be noted that the data in this experiment was always unimodal.

Age-Related Changes in 1/f Neural Electrophysiological Noise

PubMed Central

Kramer, Mark A.; Case, John; Lepage, Kyle Q.; Tempesta, Zechari R.; Knight, Robert T.; Gazzaley, Adam

2015-01-01

Aging is associated with performance decrements across multiple cognitive domains. The neural noise hypothesis, a dominant view of the basis of this decline, posits that aging is accompanied by an increase in spontaneous, noisy baseline neural activity. Here we analyze data from two different groups of human subjects: intracranial electrocorticography from 15 participants over a 38 year age range (15–53 years) and scalp EEG data from healthy younger (20–30 years) and older (60–70 years) adults to test the neural noise hypothesis from a 1/f noise perspective. Many natural phenomena, including electrophysiology, are characterized by 1/f noise. The defining characteristic of 1/f is that the power of the signal frequency content decreases rapidly as a function of the frequency (f) itself. The slope of this decay, the noise exponent (χ), is often <−1 for electrophysiological data and has been shown to approach white noise (defined as χ = 0) with increasing task difficulty. We observed, in both electrophysiological datasets, that aging is associated with a flatter (more noisy) 1/f power spectral density, even at rest, and that visual cortical 1/f noise statistically mediates age-related impairments in visual working memory. These results provide electrophysiological support for the neural noise hypothesis of aging. SIGNIFICANCE STATEMENT Understanding the neurobiological origins of age-related cognitive decline is of critical scientific, medical, and public health importance, especially considering the rapid aging of the world's population. We find, in two separate human studies, that 1/f electrophysiological noise increases with aging. In addition, we observe that this age-related 1/f noise statistically mediates age-related working memory decline. These results significantly add to this understanding and contextualize a long-standing problem in cognition by encapsulating age-related cognitive decline within a neurocomputational model of 1/f noise-induced deficits in neural communication. PMID:26400953

Earthquake Hazard Class Mapping by Parcel in Las Vegas Valley

NASA Astrophysics Data System (ADS)

Pancha, A.; Pullammanappallil, S.; Louie, J. N.; Hellmer, W. K.

2011-12-01

Clark County, Nevada completed the very first effort in the United States to map earthquake hazard class systematically through an entire urban area. The map is used in development and disaster response planning, in addition to its direct use for building code implementation and enforcement. The County contracted with the Nevada System of Higher Education to classify about 500 square miles including urban Las Vegas Valley, and exurban areas considered for future development. The Parcel Map includes over 10,000 surface-wave array measurements accomplished over three years using Optim's SeisOpt° ReMi measurement and processing techniques adapted for large scale data. These array measurements classify individual parcels on the NEHRP hazard scale. Parallel "blind" tests were conducted at 93 randomly selected sites. The rms difference between the Vs30 values yielded by the blind data and analyses and the Parcel Map analyses is 4.92%. Only six of the blind-test sites showed a difference with a magnitude greater than 10%. We describe a "C+" Class for sites with Class B average velocities but soft surface soil. The measured Parcel Map shows a clearly definable C+ to C boundary on the west side of the Valley. The C to D boundary is much more complex. Using the parcel map in computing shaking in the Valley for scenario earthquakes is crucial for obtaining realistic predictions of ground motions.

Motor imagery-induced EEG patterns in individuals with spinal cord injury and their impact on brain-computer interface accuracy

NASA Astrophysics Data System (ADS)

Müller-Putz, G. R.; Daly, I.; Kaiser, V.

2014-06-01

Objective. Assimilating the diagnosis complete spinal cord injury (SCI) takes time and is not easy, as patients know that there is no ‘cure' at the present time. Brain-computer interfaces (BCIs) can facilitate daily living. However, inter-subject variability demands measurements with potential user groups and an understanding of how they differ to healthy users BCIs are more commonly tested with. Thus, a three-class motor imagery (MI) screening (left hand, right hand, feet) was performed with a group of 10 able-bodied and 16 complete spinal-cord-injured people (paraplegics, tetraplegics) with the objective of determining what differences were present between the user groups and how they would impact upon the ability of these user groups to interact with a BCI. Approach. Electrophysiological differences between patient groups and healthy users are measured in terms of sensorimotor rhythm deflections from baseline during MI, electroencephalogram microstate scalp maps and strengths of inter-channel phase synchronization. Additionally, using a common spatial pattern algorithm and a linear discriminant analysis classifier, the classification accuracy was calculated and compared between groups. Main results. It is seen that both patient groups (tetraplegic and paraplegic) have some significant differences in event-related desynchronization strengths, exhibit significant increases in synchronization and reach significantly lower accuracies (mean (M) = 66.1%) than the group of healthy subjects (M = 85.1%). Significance. The results demonstrate significant differences in electrophysiological correlates of motor control between healthy individuals and those individuals who stand to benefit most from BCI technology (individuals with SCI). They highlight the difficulty in directly translating results from healthy subjects to participants with SCI and the challenges that, therefore, arise in providing BCIs to such individuals.

Many Specialists for Suppressing Cortical Excitation

PubMed Central

Burkhalter, Andreas

2008-01-01

Cortical computations are critically dependent on GABA-releasing neurons for dynamically balancing excitation with inhibition that is proportional to the overall level of activity. Although it is widely accepted that there are multiple types of interneurons, defining their identities based on qualitative descriptions of morphological, molecular and physiological features has failed to produce a universally accepted ‘parts list’, which is needed to understand the roles that interneurons play in cortical processing. A list of features has been published by the Petilla Interneurons Nomenclature Group, which represents an important step toward an unbiased classification of interneurons. To this end some essential features have recently been studied quantitatively and their association was examined using multidimensional cluster analyses. These studies revealed at least 3 distinct electrophysiological, 6 morphological and 15 molecular phenotypes. This is a conservative estimate of the number of interneuron types, which almost certainly will be revised as more quantitative studies will be performed and similarities will be defined objectively. It is clear that interneurons are organized with physiological attributes representing the most general, molecular characteristics the most detailed and morphological features occupying the middle ground. By themselves, none of these features are sufficient to define classes of interneurons. The challenge will be to determine which features belong together and how cell type-specific feature combinations are genetically specified. PMID:19225588

Automatic threshold selection for multi-class open set recognition

NASA Astrophysics Data System (ADS)

Scherreik, Matthew; Rigling, Brian

2017-05-01

Multi-class open set recognition is the problem of supervised classification with additional unknown classes encountered after a model has been trained. An open set classifer often has two core components. The first component is a base classifier which estimates the most likely class of a given example. The second component consists of open set logic which estimates if the example is truly a member of the candidate class. Such a system is operated in a feed-forward fashion. That is, a candidate label is first estimated by the base classifier, and the true membership of the example to the candidate class is estimated afterward. Previous works have developed an iterative threshold selection algorithm for rejecting examples from classes which were not present at training time. In those studies, a Platt-calibrated SVM was used as the base classifier, and the thresholds were applied to class posterior probabilities for rejection. In this work, we investigate the effectiveness of other base classifiers when paired with the threshold selection algorithm and compare their performance with the original SVM solution.

Coastal change analysis program implemented in Louisiana

USGS Publications Warehouse

Ramsey, Elijah W.; Nelson, G.A.; Sapkota, S.K.

2001-01-01

Landsat Thematic Mapper images from 1990 to 1996 and collateral data sources were used to classify the land cover of the Mermentau River Basin (MRB) within the Chenier Plain of coastal Louisiana. Landcover classes followed the definition of the National Oceanic and Atmospheric Administration's Coastal Change Analysis Program; however, classification methods had to be developed as part of this study for attainment of these national classification standards. Classification method developments were especially important when classes were spectrally inseparable, when classes were part of spatial and spectral continuums, when the spatial resolution of the sensor included more than one landcover type, and when human activities caused abnormal transitions in the landscape. Most classification problems were overcome by using one or a combination of techniques, such as separating the MRB into subregions of commonality, applying masks to specific land mixtures, and highlighting class transitions between years that were highly unlikely. Overall, 1990, 1993, and 1996 classification accuracy percentages (associated kappa statistics) were 80% (0.79), 78% (0.76), and 86% (0.84), respectively. Most classification errors were associated with confusion between managed (cultivated land) and unmanaged grassland classes; scrub shrub, grasslands and forest classes; water, unconsolidated shore and bare land classes; and especially in 1993, between water and floating vegetation classes. Combining cultivated land and grassland classes and water and floating vegetation classes into single classes accuracies for 1990, 1993, and 1996 increased to 82%, 83%, and 90%, respectively. To improve the interpretation of landcover change, three indicators of landcover class stability were formulated. Location stability was defined as the percentage of a landcover class that remained as the same class in the same location at the beginning and the end of the monitoring period. Residence stability was defined as the percent change in each class within the entire MRB during the monitoring period. Turnover was defined as the addition of other landcover classes to the target landcover class during the defined monitoring period. These indicators allowed quick assessment of the dynamic nature of landcover classes, both in reference to a spatial location and to retaining their presence throughout the MRB. Examining the landcover changes between 1990 to 1993 and 1993 to 1996, led us to five principal findings: (1) Landcover turnover is maintaining a near stable logging cycle, although the locations of grassland, scrub shrub, and forest areas involved in the cycle appeared to change. (2) Planting of seedlings is critical to maintaining cycle stability. (3) Logging activities tend to replace woody land mixed forests with woody land evergreen forests. (4) Wetland estuarine marshes are expanding slightly. (5) Wetland palustrine marshes and mature forested wetlands in the MRB are relatively stable.

The effect of class imbalance on case selection for case-based classifiers: An empirical study in the context of medical decision support

PubMed Central

Malof, Jordan M.; Mazurowski, Maciej A.; Tourassi, Georgia D.

2013-01-01

Case selection is a useful approach for increasing the efficiency and performance of case-based classifiers. Multiple techniques have been designed to perform case selection. This paper empirically investigates how class imbalance in the available set of training cases can impact the performance of the resulting classifier as well as properties of the selected set. In this study, the experiments are performed using a dataset for the problem of detecting breast masses in screening mammograms. The classification problem was binary and we used a k-nearest neighbor classifier. The classifier’s performance was evaluated using the Receiver Operating Characteristic (ROC) area under the curve (AUC) measure. The experimental results indicate that although class imbalance reduces the performance of the derived classifier and the effectiveness of selection at improving overall classifier performance, case selection can still be beneficial, regardless of the level of class imbalance. PMID:21820273

Cr-pyrope garnets in the lithospheric mantle 2. Compositional populations and their distribution in time and space

NASA Astrophysics Data System (ADS)

Griffin, W. L.; Fisher, N. I.; Friedman, J. H.; O'Reilly, Suzanne Y.; Ryan, C. G.

2002-12-01

Three novel statistical approaches (Cluster Analysis by Regressive Partitioning [CARP], Patient Rule Induction Method [PRIM], and ModeMap) have been used to define compositional populations within a large database (n > 13,000) of Cr-pyrope garnets from the subcontinental lithospheric mantle (SCLM). The variables used are the major oxides and proton-microprobe data for Zn, Ga, Sr, Y, and Zr. Because the rules defining these populations (classes) are expressed in simple compositional variables, they are easily applied to new samples and other databases. The classes defined by the three methods show strong similarities and correlations, suggesting that they are statistically meaningful. The geological significance of the classes has been tested by classifying garnets from 184 mantle-derived peridotite xenoliths and from a smaller database (n > 5400) of garnets analyzed for >20 trace elements by laser ablation microprobe-inductively coupled plasma-mass spectrometry (LAM-ICPMS). The relative abundances of these classes in the lithospheric mantle vary widely across different tectonic settings, and some classes are absent or very rare in either Archean or Phanerozoic SCLM. Their distribution with depth also varies widely within individual lithospheric sections and between different sections of similar tectonothermal age. These garnet classes therefore are a useful tool for mapping the geology of the SCLM. Archean SCLM sections show high degrees of depletion and varying degrees of metasomatism, and they are commonly strongly layered. Several Proterozoic SCLM sections show a concentration of more depleted material near their base, grading upward into more fertile lherzolites. The distribution of garnet classes reflecting low-T phlogopite-related metasomatism and high-T melt-related metasomatism suggests that many of these Proterozoic SCLM sections consist of strongly metasomatized Archean SCLM. The garnet-facies SCLM beneath Phanerozoic terrains is only mildly depleted relative to Primitive Upper Mantle (PUM) compositions. These data emphasize the secular evolution of SCLM composition defined earlier [Griffin et al., 1998, 1999a] and suggest that at least part of this evolutionary trend reflects reworking and refertilization of SCLM formed in the Archean time.

A multi-criteria inference approach for anti-desertification management.

PubMed

Tervonen, Tommi; Sepehr, Adel; Kadziński, Miłosz

2015-10-01

We propose an approach for classifying land zones into categories indicating their resilience against desertification. Environmental management support is provided by a multi-criteria inference method that derives a set of value functions compatible with the given classification examples, and applies them to define, for the rest of the zones, their possible classes. In addition, a representative value function is inferred to explain the relative importance of the criteria to the stakeholders. We use the approach for classifying 28 administrative regions of the Khorasan Razavi province in Iran into three equilibrium classes: collapsed, transition, and sustainable zones. The model is parameterized with enhanced vegetation index measurements from 2005 to 2012, and 7 other natural and anthropogenic indicators for the status of the region in 2012. Results indicate that grazing density and land use changes are the main anthropogenic factors affecting desertification in Khorasan Razavi. The inference procedure suggests that the classification model is underdetermined in terms of attributes, but the approach itself is promising for supporting the management of anti-desertification efforts. Copyright © 2015 Elsevier Ltd. All rights reserved.

Atmosphere-based image classification through luminance and hue

NASA Astrophysics Data System (ADS)

Xu, Feng; Zhang, Yujin

2005-07-01

In this paper a novel image classification system is proposed. Atmosphere serves an important role in generating the scene"s topic or in conveying the message behind the scene"s story, which belongs to abstract attribute level in semantic levels. At first, five atmosphere semantic categories are defined according to rules of photo and film grammar, followed by global luminance and hue features. Then the hierarchical SVM classifiers are applied. In each classification stage, corresponding features are extracted and the trained linear SVM is implemented, resulting in two classes. After three stages of classification, five atmosphere categories are obtained. At last, the text annotation of the atmosphere semantics and the corresponding features by Extensible Markup Language (XML) in MPEG-7 is defined, which can be integrated into more multimedia applications (such as searching, indexing and accessing of multimedia content). The experiment is performed on Corel images and film frames. The classification results prove the effectiveness of the definition of atmosphere semantic classes and the corresponding features.

Multicategory Composite Least Squares Classifiers

PubMed Central

Park, Seo Young; Liu, Yufeng; Liu, Dacheng; Scholl, Paul

2010-01-01

Classification is a very useful statistical tool for information extraction. In particular, multicategory classification is commonly seen in various applications. Although binary classification problems are heavily studied, extensions to the multicategory case are much less so. In view of the increased complexity and volume of modern statistical problems, it is desirable to have multicategory classifiers that are able to handle problems with high dimensions and with a large number of classes. Moreover, it is necessary to have sound theoretical properties for the multicategory classifiers. In the literature, there exist several different versions of simultaneous multicategory Support Vector Machines (SVMs). However, the computation of the SVM can be difficult for large scale problems, especially for problems with large number of classes. Furthermore, the SVM cannot produce class probability estimation directly. In this article, we propose a novel efficient multicategory composite least squares classifier (CLS classifier), which utilizes a new composite squared loss function. The proposed CLS classifier has several important merits: efficient computation for problems with large number of classes, asymptotic consistency, ability to handle high dimensional data, and simple conditional class probability estimation. Our simulated and real examples demonstrate competitive performance of the proposed approach. PMID:21218128

Automatic classification and detection of clinically relevant images for diabetic retinopathy

NASA Astrophysics Data System (ADS)

Xu, Xinyu; Li, Baoxin

2008-03-01

We proposed a novel approach to automatic classification of Diabetic Retinopathy (DR) images and retrieval of clinically-relevant DR images from a database. Given a query image, our approach first classifies the image into one of the three categories: microaneurysm (MA), neovascularization (NV) and normal, and then it retrieves DR images that are clinically-relevant to the query image from an archival image database. In the classification stage, the query DR images are classified by the Multi-class Multiple-Instance Learning (McMIL) approach, where images are viewed as bags, each of which contains a number of instances corresponding to non-overlapping blocks, and each block is characterized by low-level features including color, texture, histogram of edge directions, and shape. McMIL first learns a collection of instance prototypes for each class that maximizes the Diverse Density function using Expectation- Maximization algorithm. A nonlinear mapping is then defined using the instance prototypes and maps every bag to a point in a new multi-class bag feature space. Finally a multi-class Support Vector Machine is trained in the multi-class bag feature space. In the retrieval stage, we retrieve images from the archival database who bear the same label with the query image, and who are the top K nearest neighbors of the query image in terms of similarity in the multi-class bag feature space. The classification approach achieves high classification accuracy, and the retrieval of clinically-relevant images not only facilitates utilization of the vast amount of hidden diagnostic knowledge in the database, but also improves the efficiency and accuracy of DR lesion diagnosis and assessment.

Handling Imbalanced Data Sets in Multistage Classification

NASA Astrophysics Data System (ADS)

López, M.

Multistage classification is a logical approach, based on a divide-and-conquer solution, for dealing with problems with a high number of classes. The classification problem is divided into several sequential steps, each one associated to a single classifier that works with subgroups of the original classes. In each level, the current set of classes is split into smaller subgroups of classes until they (the subgroups) are composed of only one class. The resulting chain of classifiers can be represented as a tree, which (1) simplifies the classification process by using fewer categories in each classifier and (2) makes it possible to combine several algorithms or use different attributes in each stage. Most of the classification algorithms can be biased in the sense of selecting the most populated class in overlapping areas of the input space. This can degrade a multistage classifier performance if the training set sample frequencies do not reflect the real prevalence in the population. Several techniques such as applying prior probabilities, assigning weights to the classes, or replicating instances have been developed to overcome this handicap. Most of them are designed for two-class (accept-reject) problems. In this article, we evaluate several of these techniques as applied to multistage classification and analyze how they can be useful for astronomy. We compare the results obtained by classifying a data set based on Hipparcos with and without these methods.

Development of the diabetes typology model for discerning Type 2 diabetes mellitus with national survey data.

PubMed

Bellatorre, Anna; Jackson, Sharon H; Choi, Kelvin

2017-01-01

To classify individuals with diabetes mellitus (DM) into DM subtypes using population-based studies. Population-based survey. Individuals participated in 2003-2004, 2005-2006, or 2009-2010 the National Health and Nutrition Examination Survey (NHANES), and 2010 Coronary Artery Risk Development in Young Adults (CARDIA) survey (research materials obtained from the National Heart, Lung, and Blood Institute Biologic Specimen and Data Repository Information Coordinating Center). 3084, 3040 and 3318 US adults from the 2003-2004, 2005-2006 and 2009-2010 NHANES samples respectively, and 5,115 US adults in the CARDIA cohort. We proposed the Diabetes Typology Model (DTM) through the use of six composite measures based on the Homeostatic Model Assessment (HOMA-IR, HOMA-%β, high HOMA-%S), insulin and glucose levels, and body mass index and conducted latent class analyses to empirically classify individuals into different classes. Three empirical latent classes consistently emerged across studies (entropy = 0.81-0.998). These three classes were likely Type 1 DM, likely Type 2 DM, and atypical DM. The classification has high sensitivity (75.5%), specificity (83.3%), and positive predictive value (97.4%) when validated against C-peptide level. Correlates of Type 2 DM were significantly associated with model-identified Type 2 DM. Compared to regression analysis on known correlates of Type 2 DM using all diabetes cases as outcomes, using DTM to remove likely Type 1 DM and atypical DM cases results in a 2.5-5.3% r-square improvement in the regression analysis, as well as model fits as indicated by significant improvement in -2 log likelihood (p<0.01). Lastly, model-defined likely Type 2 DM was significantly associated with known correlates of Type 2 DM (e.g., age, waist circumference), which provide additional validation of the DTM-defined classes. Our Diabetes Typology Model reflects a promising first step toward discerning likely DM types from population-based data. This novel tool will improve how large population-based studies can be used to examine behavioral and environmental factors associated with different types of DM.

Classification in Astronomy: Past and Present

NASA Astrophysics Data System (ADS)

Feigelson, Eric

2012-03-01

Astronomers have always classified celestial objects. The ancient Greeks distinguished between asteros, the fixed stars, and planetos, the roving stars. The latter were associated with the Gods and, starting with Plato in his dialog Timaeus, provided the first mathematical models of celestial phenomena. Giovanni Hodierna classified nebulous objects, seen with a Galilean refractor telescope in the mid-seventeenth century into three classes: "Luminosae," "Nebulosae," and "Occultae." A century later, Charles Messier compiled a larger list of nebulae, star clusters and galaxies, but did not attempt a classification. Classification of comets was a significant enterprise in the 19th century: Alexander (1850) considered two groups based on orbit sizes, Lardner (1853) proposed three groups of orbits, and Barnard (1891) divided them into two classes based on morphology. Aside from the segmentation of the bright stars into constellations, most stellar classifications were based on colors and spectral properties. During the 1860s, the pioneering spectroscopist Angelo Secchi classified stars into five classes: white, yellow, orange, carbon stars, and emission line stars. After many debates, the stellar spectral sequence was refined by the group at Harvard into the familiar OBAFGKM spectral types, later found to be a sequence on surface temperature (Cannon 1926). The spectral classification is still being extended with recent additions of O2 hot stars (Walborn et al. 2002) and L and T brown dwarfs (Kirkpatrick 2005). Townley (1913) reviews 30 years of variable star classification, emerging with six classes with five subclasses. The modern classification of variable stars has about 80 (sub)classes, and is still under debate (Samus 2009). Shortly after his confirmation that some nebulae are external galaxies, Edwin Hubble (1926) proposed his famous bifurcated classification of galaxy morphologies with three classes: ellipticals, spirals, and irregulars. These classes are still used today with many refinements by Gerard de Vaucouleurs and others. Supernovae, nearly all of which are found in external galaxies, have a complicated classification scheme:Type I with subtypes Ia, Ib, Ic, Ib/c pec and Type II with subtypes IIb, IIL, IIP, and IIn (Turatto 2003). The classification is based on elemental abundances in optical spectra and on optical light curve shapes. Tadhunter (2009) presents a three-dimensional classification of active galactic nuclei involving radio power, emission line width, and nuclear luminosity. These taxonomies have played enormously important roles in the development of astronomy, yet all were developed using heuristic methods. Many are based on qualitative and subjective assessments of spatial, temporal, or spectral properties. A qualitative, morphological approach to astronomical studies was explicitly promoted by Zwicky (1957). Other classifications are based on quantitative criteria, but these criteria were developed by subjective examination of training datasets. For example, starburst galaxies are discriminated from narrow-line Seyfert galaxies by a curved line in a diagramof the ratios of four emission lines (Veilleux and Osterbrock 1987). Class II young stellar objects have been defined by a rectangular region in a mid-infrared color-color diagram (Allen et al. 2004). Short and hard gamma-ray bursts are discriminated by a dip in the distribution of burst durations (Kouveliotou et al. 2000). In no case was a statistical or algorithmic procedure used to define the classes.

Encoding of Fundamental Chemical Entities of Organic Reactivity Interest using chemical ontology and XML.

PubMed

Durairaj, Vijayasarathi; Punnaivanam, Sankar

2015-09-01

Fundamental chemical entities are identified in the context of organic reactivity and classified as appropriate concept classes namely ElectronEntity, AtomEntity, AtomGroupEntity, FunctionalGroupEntity and MolecularEntity. The entity classes and their subclasses are organized into a chemical ontology named "ChemEnt" for the purpose of assertion, restriction and modification of properties through entity relations. Individual instances of entity classes are defined and encoded as a library of chemical entities in XML. The instances of entity classes are distinguished with a unique notation and identification values in order to map them with the ontology definitions. A model GUI named Entity Table is created to view graphical representations of all the entity instances. The detection of chemical entities in chemical structures is achieved through suitable algorithms. The possibility of asserting properties to the entities at different levels and the mechanism of property flow within the hierarchical entity levels is outlined. Copyright © 2015 Elsevier Inc. All rights reserved.

Classifier performance prediction for computer-aided diagnosis using a limited dataset.

PubMed

Sahiner, Berkman; Chan, Heang-Ping; Hadjiiski, Lubomir

2008-04-01

In a practical classifier design problem, the true population is generally unknown and the available sample is finite-sized. A common approach is to use a resampling technique to estimate the performance of the classifier that will be trained with the available sample. We conducted a Monte Carlo simulation study to compare the ability of the different resampling techniques in training the classifier and predicting its performance under the constraint of a finite-sized sample. The true population for the two classes was assumed to be multivariate normal distributions with known covariance matrices. Finite sets of sample vectors were drawn from the population. The true performance of the classifier is defined as the area under the receiver operating characteristic curve (AUC) when the classifier designed with the specific sample is applied to the true population. We investigated methods based on the Fukunaga-Hayes and the leave-one-out techniques, as well as three different types of bootstrap methods, namely, the ordinary, 0.632, and 0.632+ bootstrap. The Fisher's linear discriminant analysis was used as the classifier. The dimensionality of the feature space was varied from 3 to 15. The sample size n2 from the positive class was varied between 25 and 60, while the number of cases from the negative class was either equal to n2 or 3n2. Each experiment was performed with an independent dataset randomly drawn from the true population. Using a total of 1000 experiments for each simulation condition, we compared the bias, the variance, and the root-mean-squared error (RMSE) of the AUC estimated using the different resampling techniques relative to the true AUC (obtained from training on a finite dataset and testing on the population). Our results indicated that, under the study conditions, there can be a large difference in the RMSE obtained using different resampling methods, especially when the feature space dimensionality is relatively large and the sample size is small. Under this type of conditions, the 0.632 and 0.632+ bootstrap methods have the lowest RMSE, indicating that the difference between the estimated and the true performances obtained using the 0.632 and 0.632+ bootstrap will be statistically smaller than those obtained using the other three resampling methods. Of the three bootstrap methods, the 0.632+ bootstrap provides the lowest bias. Although this investigation is performed under some specific conditions, it reveals important trends for the problem of classifier performance prediction under the constraint of a limited dataset.

Evaluation of reforested areas using LANDSAT imagery

NASA Technical Reports Server (NTRS)

Dejesusparada, N. (Principal Investigator); Filho, P. H.; Shimabukuro, Y. E.

1978-01-01

The author has identified the following significant results. Visual and automatic interpretation of LANDSAT imagery was used to classify the general Pinus and Eucalyptus according to their age and species. A methodology was derived, based on training areas, to define the legend and spectral characteristics of the analyzed classes. Imager analysis of the training areas show that Pinus taeda is separable from the other Pinus species based on JM distance measurement. No difference of JM measurements was observed among Eucalyptus species. Two classes of Eucalyptus were separated according to their ages: those under and those over two years of age. Channel 6 and 7 were suitable for the discrimination of the reforested classes. Channel 5 was efficient to separated reforested areas from nonforested targets in the region. The automatic analysis shows the highest classification precision was obtained for Eucalyptus over two years of age (95.12 percent).

Novelty Detection Classifiers in Weed Mapping: Silybum marianum Detection on UAV Multispectral Images.

PubMed

Alexandridis, Thomas K; Tamouridou, Afroditi Alexandra; Pantazi, Xanthoula Eirini; Lagopodi, Anastasia L; Kashefi, Javid; Ovakoglou, Georgios; Polychronos, Vassilios; Moshou, Dimitrios

2017-09-01

In the present study, the detection and mapping of Silybum marianum (L.) Gaertn. weed using novelty detection classifiers is reported. A multispectral camera (green-red-NIR) on board a fixed wing unmanned aerial vehicle (UAV) was employed for obtaining high-resolution images. Four novelty detection classifiers were used to identify S. marianum between other vegetation in a field. The classifiers were One Class Support Vector Machine (OC-SVM), One Class Self-Organizing Maps (OC-SOM), Autoencoders and One Class Principal Component Analysis (OC-PCA). As input features to the novelty detection classifiers, the three spectral bands and texture were used. The S. marianum identification accuracy using OC-SVM reached an overall accuracy of 96%. The results show the feasibility of effective S. marianum mapping by means of novelty detection classifiers acting on multispectral UAV imagery.

A Novel Design of 4-Class BCI Using Two Binary Classifiers and Parallel Mental Tasks

PubMed Central

Geng, Tao; Gan, John Q.; Dyson, Matthew; Tsui, Chun SL; Sepulveda, Francisco

2008-01-01

A novel 4-class single-trial brain computer interface (BCI) based on two (rather than four or more) binary linear discriminant analysis (LDA) classifiers is proposed, which is called a “parallel BCI.” Unlike other BCIs where mental tasks are executed and classified in a serial way one after another, the parallel BCI uses properly designed parallel mental tasks that are executed on both sides of the subject body simultaneously, which is the main novelty of the BCI paradigm used in our experiments. Each of the two binary classifiers only classifies the mental tasks executed on one side of the subject body, and the results of the two binary classifiers are combined to give the result of the 4-class BCI. Data was recorded in experiments with both real movement and motor imagery in 3 able-bodied subjects. Artifacts were not detected or removed. Offline analysis has shown that, in some subjects, the parallel BCI can generate a higher accuracy than a conventional 4-class BCI, although both of them have used the same feature selection and classification algorithms. PMID:18584040

Two subgroups of antipsychotic-naive, first-episode schizophrenia patients identified with a Gaussian mixture model on cognition and electrophysiology

PubMed Central

Bak, N; Ebdrup, B H; Oranje, B; Fagerlund, B; Jensen, M H; Düring, S W; Nielsen, M Ø; Glenthøj, B Y; Hansen, L K

2017-01-01

Deficits in information processing and cognition are among the most robust findings in schizophrenia patients. Previous efforts to translate group-level deficits into clinically relevant and individualized information have, however, been non-successful, which is possibly explained by biologically different disease subgroups. We applied machine learning algorithms on measures of electrophysiology and cognition to identify potential subgroups of schizophrenia. Next, we explored subgroup differences regarding treatment response. Sixty-six antipsychotic-naive first-episode schizophrenia patients and sixty-five healthy controls underwent extensive electrophysiological and neurocognitive test batteries. Patients were assessed on the Positive and Negative Syndrome Scale (PANSS) before and after 6 weeks of monotherapy with the relatively selective D2 receptor antagonist, amisulpride (280.3±159 mg per day). A reduced principal component space based on 19 electrophysiological variables and 26 cognitive variables was used as input for a Gaussian mixture model to identify subgroups of patients. With support vector machines, we explored the relation between PANSS subscores and the identified subgroups. We identified two statistically distinct subgroups of patients. We found no significant baseline psychopathological differences between these subgroups, but the effect of treatment in the groups was predicted with an accuracy of 74.3% (P=0.003). In conclusion, electrophysiology and cognition data may be used to classify subgroups of schizophrenia patients. The two distinct subgroups, which we identified, were psychopathologically inseparable before treatment, yet their response to dopaminergic blockade was predicted with significant accuracy. This proof of principle encourages further endeavors to apply data-driven, multivariate and multimodal models to facilitate progress from symptom-based psychiatry toward individualized treatment regimens. PMID:28398342

Electrophysiological evidence for differential processing of numerical quantity and order in humans.

PubMed

Turconi, Eva; Jemel, Boutheina; Rossion, Bruno; Seron, Xavier

2004-09-01

It is yet unclear whether the processing of number magnitude and order rely on common or different functional processes and neural substrates. On the one hand, recent neuroimaging studies show that quantity and order coding activate the same areas in the parietal and prefrontal cortices. On the other hand, evidence from developmental and neuropsychological studies suggest dissociated mechanisms for processing quantity and order information. To clarify this issue, the present study investigated the spatio-temporal course of quantity and order coding operations using event-related potentials (ERPs). Twenty-four subjects performed a quantity task (classifying numbers as smaller or larger than 15) and an order task on the same material (classifying numbers as coming before or after 15), as well as a control order task on letters (classifying letters as coming before or after M). Behavioral results showed a classical distance effect (decreasing reaction times [RTs] with increasing distance from the standard) for all tasks. In agreement with previous electrophysiological evidence, this effect was significant on a P2 parietal component for numerical material. However, the difference between processing numbers close or far from the target appeared earlier and was larger on the left hemisphere for quantity processing, while it was delayed and bilateral for order processing. There was also a significant distance effect in all tasks on parietal sites for the following P3 component elicited by numbers, but this effect was larger on prefrontal areas for the order judgment. In conclusion, both quantity and order show similar behavioral effects, but they are associated with different spatio-temporal courses in parietal and prefrontal cortices.

Site classification for National Strong Motion Observation Network System (NSMONS) stations in China using an empirical H/V spectral ratio method

NASA Astrophysics Data System (ADS)

Ji, Kun; Ren, Yefei; Wen, Ruizhi

2017-10-01

Reliable site classification of the stations of the China National Strong Motion Observation Network System (NSMONS) has not yet been assigned because of lacking borehole data. This study used an empirical horizontal-to-vertical (H/V) spectral ratio (hereafter, HVSR) site classification method to overcome this problem. First, according to their borehole data, stations selected from KiK-net in Japan were individually assigned a site class (CL-I, CL-II, or CL-III), which is defined in the Chinese seismic code. Then, the mean HVSR curve for each site class was computed using strong motion recordings captured during the period 1996-2012. These curves were compared with those proposed by Zhao et al. (2006a) for four types of site classes (SC-I, SC-II, SC-III, and SC-IV) defined in the Japanese seismic code (JRA, 1980). It was found that an approximate range of the predominant period Tg could be identified by the predominant peak of the HVSR curve for the CL-I and SC-I sites, CL-II and SC-II sites, and CL-III and SC-III + SC-IV sites. Second, an empirical site classification method was proposed based on comprehensive consideration of peak period, amplitude, and shape of the HVSR curve. The selected stations from KiK-net were classified using the proposed method. The results showed that the success rates of the proposed method in identifying CL-I, CL-II, and CL-III sites were 63%, 64%, and 58% respectively. Finally, the HVSRs of 178 NSMONS stations were computed based on recordings from 2007 to 2015 and the sites classified using the proposed method. The mean HVSR curves were re-calculated for three site classes and compared with those from KiK-net data. It was found that both the peak period and the amplitude were similar for the mean HVSR curves derived from NSMONS classification results and KiK-net borehole data, implying the effectiveness of the proposed method in identifying different site classes. The classification results have good agreement with site classes based on borehole data of 81 stations in China, which indicates that our site classification results are acceptable and that the proposed method is practicable.

Latent Class Analysis of Antisocial Behavior: Interaction of Serotonin Transporter Genotype and Maltreatment

PubMed Central

Li, James J.

2010-01-01

To improve understanding about genetic and environmental influences on antisocial behavior (ASB), we tested the association of the 44-base pair polymorphism of the serotonin transporter gene (5-HTTLPR) and maltreatment using latent class analysis in 2,488 boys and girls from Wave 1 of the National Longitudinal Study of Adolescent Health. In boys, ASB was defined by three classes (Exclusive Covert, Mixed Covert and Overt, and No Problems) whereas in girls, ASB was defined by two classes (Exclusive Covert, No Problems). In boys, 5-HTTLPR and maltreatment were not significantly related to ASB. However, in girls, maltreatment, but not 5-HTTLPR, was significantly associated with ASB. A significant interaction between 5-HTTLPR and maltreatment was also observed, where maltreated girls homozygous for the short allele were 12 times more likely to be classified in the Exclusive Covert group than in the No Problems group. Structural differences in the latent structure of ASB at Wave 2 and Wave 3 prevented repeat LCA modeling. However, using counts of ASB, 5-HTTLPR, maltreatment, and its interaction were unrelated to overt and covert ASB at Wave 2 and only maltreatment was related to covert ASB at Wave 3. We discuss these findings within the context of sex differences in ASB and relevant models of gene-environment interplay across developmental periods. PMID:20405199

Combining multiple decisions: applications to bioinformatics

NASA Astrophysics Data System (ADS)

Yukinawa, N.; Takenouchi, T.; Oba, S.; Ishii, S.

2008-01-01

Multi-class classification is one of the fundamental tasks in bioinformatics and typically arises in cancer diagnosis studies by gene expression profiling. This article reviews two recent approaches to multi-class classification by combining multiple binary classifiers, which are formulated based on a unified framework of error-correcting output coding (ECOC). The first approach is to construct a multi-class classifier in which each binary classifier to be aggregated has a weight value to be optimally tuned based on the observed data. In the second approach, misclassification of each binary classifier is formulated as a bit inversion error with a probabilistic model by making an analogy to the context of information transmission theory. Experimental studies using various real-world datasets including cancer classification problems reveal that both of the new methods are superior or comparable to other multi-class classification methods.

An algorithm for generating modular hierarchical neural network classifiers: a step toward larger scale applications

NASA Astrophysics Data System (ADS)

Roverso, Davide

2003-08-01

Many-class learning is the problem of training a classifier to discriminate among a large number of target classes. Together with the problem of dealing with high-dimensional patterns (i.e. a high-dimensional input space), the many class problem (i.e. a high-dimensional output space) is a major obstacle to be faced when scaling-up classifier systems and algorithms from small pilot applications to large full-scale applications. The Autonomous Recursive Task Decomposition (ARTD) algorithm is here proposed as a solution to the problem of many-class learning. Example applications of ARTD to neural classifier training are also presented. In these examples, improvements in training time are shown to range from 4-fold to more than 30-fold in pattern classification tasks of both static and dynamic character.

Liquid-Based Medium Used to Prepare Cytological Breast Nipple Fluid Improves the Quality of Cellular Samples Automatic Collection

PubMed Central

Zonta, Marco Antonio; Velame, Fernanda; Gema, Samara; Filassi, Jose Roberto; Longatto-Filho, Adhemar

2014-01-01

Background Breast cancer is the second cause of death in women worldwide. The spontaneous breast nipple discharge may contain cells that can be analyzed for malignancy. Halo® Mamo Cyto Test (HMCT) was recently developed as an automated system indicated to aspirate cells from the breast ducts. The objective of this study was to standardize the methodology of sampling and sample preparation of nipple discharge obtained by the automated method Halo breast test and perform cytological evaluation in samples preserved in liquid medium (SurePath™). Methods We analyzed 564 nipple fluid samples, from women between 20 and 85 years old, without history of breast disease and neoplasia, no pregnancy, and without gynecologic medical history, collected by HMCT method and preserved in two different vials with solutions for transport. Results From 306 nipple fluid samples from method 1, 199 (65%) were classified as unsatisfactory (class 0), 104 (34%) samples were classified as benign findings (class II), and three (1%) were classified as undetermined to neoplastic cells (class III). From 258 samples analyzed in method 2, 127 (49%) were classified as class 0, 124 (48%) were classified as class II, and seven (2%) were classified as class III. Conclusion Our study suggests an improvement in the quality and quantity of cellular samples when the association of the two methodologies is performed, Halo breast test and the method in liquid medium. PMID:29147397

Epigenetic silencing of MLH1 in endometrial cancers is associated with larger tumor volume, increased rate of lymph node positivity and reduced recurrence-free survival.

PubMed

Cosgrove, Casey M; Cohn, David E; Hampel, Heather; Frankel, Wendy L; Jones, Dan; McElroy, Joseph P; Suarez, Adrian A; Zhao, Weiqiang; Chen, Wei; Salani, Ritu; Copeland, Larry J; O'Malley, David M; Fowler, Jeffrey M; Yilmaz, Ahmet; Chassen, Alexis S; Pearlman, Rachel; Goodfellow, Paul J; Backes, Floor J

2017-09-01

To determine the relationship between mismatch repair (MMR) classification and clinicopathologic features including tumor volume, and explore outcomes by MMR class in a contemporary cohort. Single institution cohort evaluating MMR classification for endometrial cancers (EC). MMR immunohistochemistry (IHC)±microsatellite instability (MSI) testing and reflex MLH1 methylation testing was performed. Tumors with MMR abnormalities by IHC or MSI and MLH1 methylation were classified as epigenetic MMR deficiency while those without MLH1 methylation were classified as probable MMR mutations. Clinicopathologic characteristics were analyzed. 466 endometrial cancers were classified; 75% as MMR proficient, 20% epigenetic MMR defects, and 5% as probable MMR mutations. Epigenetic MMR defects were associated with advanced stage, higher grade, presence of lymphovascular space invasion, and older age. MMR class was significantly associated with tumor volume, an association not previously reported. The epigenetic MMR defect tumors median volume was 10,220mm 3 compared to 3321mm 3 and 2,846mm 3 , for MMR proficient and probable MMR mutations respectively (P<0.0001). Higher tumor volume was associated with lymph node involvement. Endometrioid EC cases with epigenetic MMR defects had significantly reduced recurrence-free survival (RFS). Among advanced stage (III/IV) endometrioid EC the epigenetic MMR defect group was more likely to recur compared to the MMR proficient group (47.7% vs 3.4%) despite receiving similar adjuvant therapy. In contrast, there was no difference in the number of early stage recurrences for the different MMR classes. MMR testing that includes MLH1 methylation analysis defines a subset of tumors that have worse prognostic features and reduced RFS. Copyright © 2017 Elsevier Inc. All rights reserved.

Lithologic analysis from multispectral thermal infrared data of the alkalic rock complex at Iron Hill, Colorado

USGS Publications Warehouse

Watson, K.; Rowan, L.C.; Bowers, T.L.; Anton-Pacheco, C.; Gumiel, P.; Miller, S.H.

1996-01-01

Airborne thermal-infrared multispectral scanner (TIMS) data of the Iron Hill carbonatite-alkalic igneous rock complex in south-central Colorado are analyzed using a new spectral emissivity ratio algorithm and confirmed by field examination using existing 1:24 000-scale geologic maps and petrographic studies. Color composite images show that the alkalic rocks could be clearly identified and that differences existed among alkalic rocks in several parts of the complex. An unsupervised classification algorithm defines four alkalic rock classes within the complex: biotitic pyroxenite, uncompahgrite, augitic pyroxenite, and fenite + nepheline syenite. Felsic rock classes defined in the surrounding country rock are an extensive class consisting of tuff, granite, and felsite, a less extensive class of granite and felsite, and quartzite. The general composition of the classes can be determined from comparisons of the TIMS spectra with laboratory spectra. Carbonatite rocks are not classified, and we attribute that to the fact that dolomite, the predominant carbonate mineral in the complex, has a spectral feature that falls between TIMS channels 5 and 6. Mineralogical variability in the fenitized granite contributed to the nonuniform pattern of the fenite-nepheline syenite class. The biotitic pyroxenite, which resulted from alteration of the pyroxenite, is spatially associated and appears to be related to narrow carbonatite dikes and sills. Results from a linear unmixing algorithm suggest that the detected spatial extent of the two mixed felsic rock classes was sensitive to the amount of vegetation cover. These results illustrate that spectral thermal infrared data can be processed to yield compositional information that can be a cost-effective tool to target mineral exploration, particularly in igneous terranes.

LBM-EP: Lattice-Boltzmann method for fast cardiac electrophysiology simulation from 3D images.

PubMed

Rapaka, S; Mansi, T; Georgescu, B; Pop, M; Wright, G A; Kamen, A; Comaniciu, Dorin

2012-01-01

Current treatments of heart rhythm troubles require careful planning and guidance for optimal outcomes. Computational models of cardiac electrophysiology are being proposed for therapy planning but current approaches are either too simplified or too computationally intensive for patient-specific simulations in clinical practice. This paper presents a novel approach, LBM-EP, to solve any type of mono-domain cardiac electrophysiology models at near real-time that is especially tailored for patient-specific simulations. The domain is discretized on a Cartesian grid with a level-set representation of patient's heart geometry, previously estimated from images automatically. The cell model is calculated node-wise, while the transmembrane potential is diffused using Lattice-Boltzmann method within the domain defined by the level-set. Experiments on synthetic cases, on a data set from CESC'10 and on one patient with myocardium scar showed that LBM-EP provides results comparable to an FEM implementation, while being 10 - 45 times faster. Fast, accurate, scalable and requiring no specific meshing, LBM-EP paves the way to efficient and detailed models of cardiac electrophysiology for therapy planning.

Simultaneous profiling of activity patterns in multiple neuronal subclasses.

PubMed

Parrish, R Ryley; Grady, John; Codadu, Neela K; Trevelyan, Andrew J; Racca, Claudia

2018-06-01

Neuronal networks typically comprise heterogeneous populations of neurons. A core objective when seeking to understand such networks, therefore, is to identify what roles these different neuronal classes play. Acquiring single cell electrophysiology data for multiple cell classes can prove to be a large and daunting task. Alternatively, Ca 2+ network imaging provides activity profiles of large numbers of neurons simultaneously, but without distinguishing between cell classes. We therefore developed a strategy for combining cellular electrophysiology, Ca 2+ network imaging, and immunohistochemistry to provide activity profiles for multiple cell classes at once. This involves cross-referencing easily identifiable landmarks between imaging of the live and fixed tissue, and then using custom MATLAB functions to realign the two imaging data sets, to correct for distortions of the tissue introduced by the fixation or immunohistochemical processing. We illustrate the methodology for analyses of activity profiles during epileptiform events recorded in mouse brain slices. We further demonstrate the activity profile of a population of parvalbumin-positive interneurons prior, during, and following a seizure-like event. Current approaches to Ca 2+ network imaging analyses are severely limited in their ability to subclassify neurons, and often rely on transgenic approaches to identify cell classes. In contrast, our methodology is a generic, affordable, and flexible technique to characterize neuronal behaviour with respect to classification based on morphological and neurochemical identity. We present a new approach for analysing Ca 2+ network imaging datasets, and use this to explore the parvalbumin-positive interneuron activity during epileptiform events. Copyright © 2018 Elsevier B.V. All rights reserved.

Non-invasive continuous blood pressure monitoring of tachycardic episodes during interventional electrophysiology.

PubMed

Maggi, Roberto; Viscardi, Valentina; Furukawa, Toshiyuki; Brignole, Michele

2010-11-01

We thought to evaluate feasibility of continuous non-invasive blood pressure monitoring during procedures of interventional electrophysiology. We evaluated continuous non-invasive finger blood pressure (BP) monitoring by means of the Nexfin device in 22 patients (mean age 70 ± 24 years), undergoing procedures of interventional electrophysiology, in critical situations of hypotension caused by tachyarrhythmias or by intermittent incremental ventricular temporary pacing till to the maximum tolerated systolic BP fall (mean 61 ± 14 mmHg per patient at a rate of 195 ± 37 bpm). In all patients, Nexfin was able to detect immediately, at the onset of tachyarrythmia, the changes in BP and recorded reliable waveforms. The quality of the signal was arbitrarily classified as excellent in 11 cases, good in 10 cases, and sufficient in 1 case. In basal conditions, calibrations of the signal occurred every 49.2 ± 24.3 s and accounted for 4% of total monitoring time; during tachyarrhythmias their frequency increased to one every 12.7 s and accounted for 19% of total recording duration. A linear correlation for a range of BP values from 41 to 190 mmHg was found between non-invasive and intra-arterial BP among a total of 1055 beats from three patients who underwent simultaneous recordings with both methods (coefficient of correlation of 0.81, P < 0.0001). In conclusion, continuous non-invasive BP monitoring is feasible in the clinical practise of an interventional electrophysiology laboratory without the need of utilization of an intra-arterial BP line.

Evaluation of Radiographic Instability of the Trapeziometacarpal Joint in Women With Carpal Tunnel Syndrome.

PubMed

Kim, Jeong Hwan; Gong, Hyun Sik; Kim, Youn Ho; Rhee, Seung Hwan; Kim, Jihyoung; Baek, Goo Hyun

2015-07-01

To determine whether median nerve dysfunction measured by electrophysiologic studies in carpal tunnel syndrome (CTS) is associated with thumb trapeziometacarpal (TMC) joint instability. We evaluated 71 women with CTS and 31 asymptomatic control women. Patients with generalized laxity or TMC joint osteoarthritis were excluded. We classified the electrophysiologic severity of CTS based on nerve conduction time and amplitude and assessed radiographic instability of the TMC joint based on TMC joint stress radiographs. We compared subluxation ratio between patients with CTS and controls and performed correlation analysis of the relationship between the electrophysiologic grade and subluxation ratio. Thirty-one patients were categorized into the mild CTS subgroup and 41 into the severe CTS subgroup. There was no significant difference in subluxation ratio between the control group and CTS patients or between the control group and CTS subgroup patients. Furthermore, there was no significant correlation between electrophysiologic grade and subluxation ratio. This study demonstrated that patients with CTS did not have greater radiographic TMC joint instability compared with controls, and suggests that TMC joint stability is not affected by impaired median nerve function. Further studies could investigate how to better evaluate proprioceptive function of TMC joint and whether other nerves have effects on TMC joint motor/proprioceptive function, to elucidate the relationship between neuromuscular control of the TMC joint, its stability, and its progression to osteoarthritis. Diagnostic II. Copyright © 2015 American Society for Surgery of the Hand. Published by Elsevier Inc. All rights reserved.

Why Does Rebalancing Class-Unbalanced Data Improve AUC for Linear Discriminant Analysis?

PubMed

Xue, Jing-Hao; Hall, Peter

2015-05-01

Many established classifiers fail to identify the minority class when it is much smaller than the majority class. To tackle this problem, researchers often first rebalance the class sizes in the training dataset, through oversampling the minority class or undersampling the majority class, and then use the rebalanced data to train the classifiers. This leads to interesting empirical patterns. In particular, using the rebalanced training data can often improve the area under the receiver operating characteristic curve (AUC) for the original, unbalanced test data. The AUC is a widely-used quantitative measure of classification performance, but the property that it increases with rebalancing has, as yet, no theoretical explanation. In this note, using Gaussian-based linear discriminant analysis (LDA) as the classifier, we demonstrate that, at least for LDA, there is an intrinsic, positive relationship between the rebalancing of class sizes and the improvement of AUC. We show that the largest improvement of AUC is achieved, asymptotically, when the two classes are fully rebalanced to be of equal sizes.

Higher frequencies of HLA DQB1*05:01 and anti-glycosphingolipid antibodies in a cluster of severe Guillain-Barré syndrome.

PubMed

Schirmer, L; Worthington, V; Solloch, U; Loleit, V; Grummel, V; Lakdawala, N; Grant, D; Wassmuth, R; Schmidt, A H; Gebhardt, F; Andlauer, T F M; Sauter, J; Berthele, A; Lunn, M P; Hemmer, Bernhard

2016-10-01

Few regional and seasonal Guillain-Barré syndrome (GBS) clusters have been reported so far. It is unknown whether patients suffering from sporadic GBS differ from GBS clusters with respect to clinical and paraclinical parameters, HLA association and antibody response to glycosphingolipids and Campylobacter jejuni (Cj). We examined 40 consecutive patients with GBS from the greater Munich area in Germany with 14 of those admitted within a period of 3 months in fall 2010 defining a cluster of GBS. Sequencing-based HLA typing of the HLA genes DRB1, DQB1, and DPB1 was performed, and ELISA for anti-glycosphingolipid antibodies was carried out. Clinical and paraclinical findings (Cj seroreactivity, cerebrospinal fluid parameters, and electrophysiology) were obtained and analyzed. GBS cluster patients were characterized by a more severe clinical phenotype with more patients requiring mechanical ventilation and higher frequencies of autoantibodies against sulfatide, GalC and certain ganglioside epitopes (54 %) as compared to sporadic GBS cases (13 %, p = 0.017). Cj seropositivity tended to be higher within GBS cluster patients (69 %) as compared to sporadic cases (46 %, p = 0.155). We noted higher frequencies of HLA class II allele DQB1*05:01 in the cluster cohort (23 %) as compared to sporadic GBS patients (3 %, p = 0.019). Cluster of severe GBS was defined by higher frequencies of autoantibodies against glycosphingolipids. HLA class II allele DQB1*05:01 might contribute to clinical worsening in the cluster patients.

Premature Ventricular Contraction Coupling Interval Variability Destabilizes Cardiac Neuronal and Electrophysiological Control: Insights From Simultaneous Cardioneural Mapping.

PubMed

Hamon, David; Rajendran, Pradeep S; Chui, Ray W; Ajijola, Olujimi A; Irie, Tadanobu; Talebi, Ramin; Salavatian, Siamak; Vaseghi, Marmar; Bradfield, Jason S; Armour, J Andrew; Ardell, Jeffrey L; Shivkumar, Kalyanam

2017-04-01

Variability in premature ventricular contraction (PVC) coupling interval (CI) increases the risk of cardiomyopathy and sudden death. The autonomic nervous system regulates cardiac electrical and mechanical indices, and its dysregulation plays an important role in cardiac disease pathogenesis. The impact of PVCs on the intrinsic cardiac nervous system, a neural network on the heart, remains unknown. The objective was to determine the effect of PVCs and CI on intrinsic cardiac nervous system function in generating cardiac neuronal and electric instability using a novel cardioneural mapping approach. In a porcine model (n=8), neuronal activity was recorded from a ventricular ganglion using a microelectrode array, and cardiac electrophysiological mapping was performed. Neurons were functionally classified based on their response to afferent and efferent cardiovascular stimuli, with neurons that responded to both defined as convergent (local reflex processors). Dynamic changes in neuronal activity were then evaluated in response to right ventricular outflow tract PVCs with fixed short, fixed long, and variable CI. PVC delivery elicited a greater neuronal response than all other stimuli ( P <0.001). Compared with fixed short and long CI, PVCs with variable CI had a greater impact on neuronal response ( P <0.05 versus short CI), particularly on convergent neurons ( P <0.05), as well as neurons receiving sympathetic ( P <0.05) and parasympathetic input ( P <0.05). The greatest cardiac electric instability was also observed after variable (short) CI PVCs. Variable CI PVCs affect critical populations of intrinsic cardiac nervous system neurons and alter cardiac repolarization. These changes may be critical for arrhythmogenesis and remodeling, leading to cardiomyopathy. © 2017 American Heart Association, Inc.

Premature Ventricular Contraction Coupling Interval Variability Destabilizes Cardiac Neuronal and Electrophysiological Control: Insights from Simultaneous Cardio-Neural Mapping

PubMed Central

Hamon, David; Rajendran, Pradeep S.; Chui, Ray W.; Ajijola, Olujimi A.; Irie, Tadanobu; Talebi, Ramin; Salavatian, Siamak; Vaseghi, Marmar; Bradfield, Jason S.; Armour, J. Andrew; Ardell, Jeffrey L.; Shivkumar, Kalyanam

2017-01-01

Background Variability in premature ventricular contraction (PVC) coupling interval (CI) increases the risk of cardiomyopathy and sudden death. The autonomic nervous system regulates cardiac electrical and mechanical indices, and its dysregulation plays an important role in cardiac disease pathogenesis. The impact of PVCs on the intrinsic cardiac nervous system (ICNS), a neural network on the heart, remains unknown. The objective was to determine the effect of PVCs and CI on ICNS function in generating cardiac neuronal and electrical instability using a novel cardio-neural mapping approach. Methods and Results In a porcine model (n=8) neuronal activity was recorded from a ventricular ganglion using a microelectrode array, and cardiac electrophysiological mapping was performed. Neurons were functionally classified based on their response to afferent and efferent cardiovascular stimuli, with neurons that responded to both defined as convergent (local reflex processors). Dynamic changes in neuronal activity were then evaluated in response to right ventricular outflow tract PVCs with fixed short, fixed long, and variable CI. PVC delivery elicited a greater neuronal response than all other stimuli (P<0.001). Compared to fixed short and long CI, PVCs with variable CI had a greater impact on neuronal response (P<0.05 versus short CI), particularly on convergent neurons (P<0.05), as well as neurons receiving sympathetic (P<0.05) and parasympathetic input (P<0.05). The greatest cardiac electrical instability was also observed following variable (short) CI PVCs. Conclusions Variable CI PVCs affect critical populations of ICNS neurons and alter cardiac repolarization. These changes may be critical for arrhythmogenesis and remodeling leading to cardiomyopathy. PMID:28408652

Multiobjective GAs, quantitative indices, and pattern classification.

PubMed

Bandyopadhyay, Sanghamitra; Pal, Sankar K; Aruna, B

2004-10-01

The concept of multiobjective optimization (MOO) has been integrated with variable length chromosomes for the development of a nonparametric genetic classifier which can overcome the problems, like overfitting/overlearning and ignoring smaller classes, as faced by single objective classifiers. The classifier can efficiently approximate any kind of linear and/or nonlinear class boundaries of a data set using an appropriate number of hyperplanes. While designing the classifier the aim is to simultaneously minimize the number of misclassified training points and the number of hyperplanes, and to maximize the product of class wise recognition scores. The concepts of validation set (in addition to training and test sets) and validation functional are introduced in the multiobjective classifier for selecting a solution from a set of nondominated solutions provided by the MOO algorithm. This genetic classifier incorporates elitism and some domain specific constraints in the search process, and is called the CEMOGA-Classifier (constrained elitist multiobjective genetic algorithm based classifier). Two new quantitative indices, namely, the purity and minimal spacing, are developed for evaluating the performance of different MOO techniques. These are used, along with classification accuracy, required number of hyperplanes and the computation time, to compare the CEMOGA-Classifier with other related ones.

Analysis of MASTER Thermal Data in the Greeley Area of the Front Range Urban Corridor, Colorado--Delineation of Sites for Infrastructure Resource Characterization

USGS Publications Warehouse

Livo, K. Eric; Watson, Ken

2002-01-01

Sand and soils southwest of Greeley, Colorado, were characterized for mineral composition and industrial quality. Radi-ance data from the thermal channels of the MASTER simulator were calibrated using estimated atmospheric parameters. Chan-nel emissivities were approximated using an estimated ground temperature. Subsequently, a decorrelation algorithm was used to calculate inverse wave emissivity images. Six soil classes, one vegetation class, water, and several small classes were defined using an unsupervised classification algorithm. Ground covered by each of the derived emissivity spectral classes was studied using color-infrared air photos, color-infrared composite MAS-TER data, geologic maps, NASA/JPL Airborne Visible and Infra-red Imaging Spectrometer (AVIRIS) data, and field examination. Spectral classes were characterized by their responses and related to their mineral content through field examination. Classes with a minimum at channel 44, and having a similar spectral shape to quartz, field checked as containing abundant quartz. Classes with a minimum at channel 45, and having a spectral shape similar to the sheet minerals, were found in the field to contain abundant mica and clay. Sandy soil was found to have a positive slope at the longer wavelengths; the more clay rich soils had a negative slope. Spectra with a strong downturn at channel 50 generally indicated low vegetation cover, whereas an upturn indicated more vegetation cover. Mapping revealed a range of classified soils with varying amounts of quartz, silt, clay, and plant humus. Sand and gravel operations along the St. Vrain River, gravel lots, and some fields spectrally classified as quartz-rich sands were confirmed through field examination. Other fields mapped as sandy soils, ranging from quartz-rich sandy soil to quartz-rich silt-sand soil with clay. Flood plains mapped as sandy-silty-organic-rich clay. The city of Greeley contained all classes of materials, with the sand classes mapping as various types of asphalt. Abundant quartz gravel was apparent within the asphalt during field check-ing. The clay classes mapped silt-clay soils in areas of irrigated grass landscaping, some fields, and roofing materials.

Carbon nuclear magnetic resonance spectroscopic fingerprinting of commercial gasoline: pattern-recognition analyses for screening quality control purposes.

PubMed

Flumignan, Danilo Luiz; Boralle, Nivaldo; Oliveira, José Eduardo de

2010-06-30

In this work, the combination of carbon nuclear magnetic resonance ((13)C NMR) fingerprinting with pattern-recognition analyses provides an original and alternative approach to screening commercial gasoline quality. Soft Independent Modelling of Class Analogy (SIMCA) was performed on spectroscopic fingerprints to classify representative commercial gasoline samples, which were selected by Hierarchical Cluster Analyses (HCA) over several months in retails services of gas stations, into previously quality-defined classes. Following optimized (13)C NMR-SIMCA algorithm, sensitivity values were obtained in the training set (99.0%), with leave-one-out cross-validation, and external prediction set (92.0%). Governmental laboratories could employ this method as a rapid screening analysis to discourage adulteration practices. Copyright 2010 Elsevier B.V. All rights reserved.

Electrophysiological study for comparing the effect of biological activity between type A botulinum toxins in rat gastrocnemius muscle.

PubMed

Kim, C-S; Jang, W S; Son, I P; Nam, S H; Kim, Y I; Park, K Y; Kim, B J; Kim, M N

2013-09-01

New cosmetic applications and products based on the effects of botulinum toxin (BTX) treatment have stimulated demand for this class of natural compounds. This demand generates the need for appropriate standardized protocols to test and compare the effectiveness of new BTX preparations. Based on the previously described electrophysiological methods, we measured and compared the inhibitory effects of two BTX type A (BTX-A) preparations on neuromuscular transmission through split-body test. The effectiveness was evaluated in terms of the compound muscle action potential (CMAP) and conduction velocity after BTX-A injection. We used a split-body method to compare two different BTX-As in the rat. Based on the changes in the CMAP, the two different BTX-As induced paralytic effect on the rat tibialis anterior muscle. However, the two different BTX-A preparations did not differ significantly in effectiveness and did not induce a delay in conduction velocity. The new BTX-A preparation used in this electrophysiological study had similar effect compared with the previously marketed BTX-A.[AQ: Please approve the edits made to the sentence "The new BTX-A preparation…") We propose that a split-body electrophysiological protocol will be useful in establishing the comparative effectiveness of new BTX products.

Neuropharmacology of New Psychoactive Substances (NPS): Focus on the Rewarding and Reinforcing Properties of Cannabimimetics and Amphetamine-Like Stimulants

PubMed Central

Miliano, Cristina; Serpelloni, Giovanni; Rimondo, Claudia; Mereu, Maddalena; Marti, Matteo; De Luca, Maria Antonietta

2016-01-01

New psychoactive substances (NPS) are a heterogeneous and rapidly evolving class of molecules available on the global illicit drug market (e.g smart shops, internet, “dark net”) as a substitute for controlled substances. The use of NPS, mainly consumed along with other drugs of abuse and/or alcohol, has resulted in a significantly growing number of mortality and emergency admissions for overdoses, as reported by several poison centers from all over the world. The fact that the number of NPS have more than doubled over the last 10 years, is a critical challenge to governments, the scientific community, and civil society [EMCDDA (European Drug Report), 2014; UNODC, 2014b; Trends and developments]. The chemical structure (phenethylamines, piperazines, cathinones, tryptamines, synthetic cannabinoids) of NPS and their pharmacological and clinical effects (hallucinogenic, anesthetic, dissociative, depressant) help classify them into different categories. In the recent past, 50% of newly identified NPS have been classified as synthetic cannabinoids followed by new phenethylamines (17%) (UNODC, 2014b). Besides peripheral toxicological effects, many NPS seem to have addictive properties. Behavioral, neurochemical, and electrophysiological evidence can help in detecting them. This manuscript will review existing literature about the addictive and rewarding properties of the most popular NPS classes: cannabimimetics (JWH, HU, CP series) and amphetamine-like stimulants (amphetamine, methamphetamine, methcathinone, and MDMA analogs). Moreover, the review will include recent data from our lab which links JWH-018, a CB1 and CB2 agonist more potent than Δ9-THC, to other cannabinoids with known abuse potential, and to other classes of abused drugs that increase dopamine signaling in the Nucleus Accumbens (NAc) shell. Thus the neurochemical mechanisms that produce the rewarding properties of JWH-018, which most likely contributes to the greater incidence of dependence associated with “Spice” use, will be described (De Luca et al., 2015a). Considering the growing evidence of a widespread use of NPS, this review will be useful to understand the new trends in the field of drug reward and drug addiction by revealing the rewarding properties of NPS, and will be helpful to gather reliable data regarding the abuse potential of these compounds. PMID:27147945

Sotalol: An important new antiarrhythmic.

PubMed

Anderson, J L; Prystowsky, E N

1999-03-01

Sotalol, the most recently approved oral antiarrhythmic drug, has a unique pharmacologic profile. Its electrophysiology is explained by nonselective beta-blocking action as well as class III antiarrhythmic activity (including fast-activating cardiac membrane-delayed rectifier current blockade), which leads to increases in action potential duration and refractory period throughout the heart and in QT interval on the surface electrocardiogram. Its better hemodynamic tolerance than other beta-blockers may be a result of enhanced inotropy associated with class III activity. Sotalol's ability to suppress ventricular ectopy is similar to that of class I agents and better than that of standard beta-blockers. Unlike class I agents, its use in a postinfarction trial was not associated with increased mortality rate. Therapeutically, it has shown superior efficacy for prevention of recurrent ventricular tachycardia and ventricular fibrillation, which was the basis for its approval. In a randomized study, the Electrophysiologic Study Versus Electrocardiographic Monitoring (ESVEM) trial, sotalol was associated with an increased in-hospital efficacy prediction rate (by Holter monitor or electrophysiologic study), reduced long-term arrhythmic recurrence rate with superior tolerance, and lower mortality rate than class I ("standard") antiarrhythmic drugs. Sotalol was 1 of 2 drugs selected for comparison with implantable defibrillators in the recent National Institutes of Health Antiarrhythmics versus Implantable Defibrillator (AVID) study. Sotalol appears to be a preferred drug for use with implantable defibrillators; unlike some other agents (eg, amiodarone) it does not elevate and, indeed, may lower defibrillation threshold. Although unapproved for this use, sotalol is active against atrial arrhythmias. It has shown efficacy equivalent to propafenone and quinidine in preventing atrial fibrillation recurrence, but it is better tolerated than quinidine and provides excellent rate control during recurrence. Sotalol's major side effects are related to beta-blockade and the risk of torsades de pointes (acceptably small if appropriate precautions are taken). Unlike several other antiarrhythmics (eg, amiodarone), it has no pharmacokinetic drug-drug interactions, is not metabolized, and is entirely renally excreted. Initial dose is 80 mg twice daily, with gradual titration to 240 to 360 mg/day as needed. The daily dose must be reduced in renal failure. On the basis of favorable clinical trials and practice experience, sotalol has shown a steadily growing impact on the treatment of arrhythmias during its 5 years of market availability, a trend that is likely to continue.

High throughput screening technologies for ion channels

PubMed Central

Yu, Hai-bo; Li, Min; Wang, Wei-ping; Wang, Xiao-liang

2016-01-01

Ion channels are involved in a variety of fundamental physiological processes, and their malfunction causes numerous human diseases. Therefore, ion channels represent a class of attractive drug targets and a class of important off-targets for in vitro pharmacological profiling. In the past decades, the rapid progress in developing functional assays and instrumentation has enabled high throughput screening (HTS) campaigns on an expanding list of channel types. Chronologically, HTS methods for ion channels include the ligand binding assay, flux-based assay, fluorescence-based assay, and automated electrophysiological assay. In this review we summarize the current HTS technologies for different ion channel classes and their applications. PMID:26657056

Intracellular recordings of subnucleus reticularis dorsalis neurones revealed novel electrophysiological properties and windup mechanisms

PubMed Central

Soto, Cristina; Canedo, Antonio

2011-01-01

Abstract Aδ- and/or C-fibre nociceptive inputs drive subnucleus reticularis dorsalis (SRD) neurones projecting to a variety of regions including the spinal cord and the nucleus reticularis gigantocellularis (NRGc), but their electrophysiological properties are largely unknown. Here we intracellularly recorded the SRD neuronal responses to injection of polarising current pulses as well as to electrical stimulation of the cervical spinal posterior quadrant (PQ) and the NRGc. Three different classes of neurones with distinct electrophysiological properties were found: type I were characterised by the absence of a fast postspike hyperpolarisation, type II by the presence of a postspike hyperpolarisation followed by a depolarisation resembling low threshold calcium spikes (LTSs), and type III (lacking LTSs) had a fast postspike hyperpolarisation deinactivating A-like potassium channels leading to enlarged interspike intervals. All three classes generated depolarising sags to hyperpolarising current pulses and showed 3–4.5 Hz subthreshold oscillatory activity leading to windup when intracellularly injecting low-frequency repetitive depolarising pulses as well as in response to 0.5–2 Hz NRGc and PQ electrical stimulation. About half of the 132 sampled neurones responded antidromically to NRGc stimulation with more than 65% of the NRGc-antidromic cells, pertaining to all three types, also responding antidromically to PQ stimulation. NRGc stimulation induced exclusively excitatory first-synaptic-responses whilst PQ stimulation induced first-response excitation in most cases, but inhibitory postsynaptic potentials in a few type II and type III neurones not projecting to the spinal cord that also displayed cumulative inhibitory effects (inverse windup). The results show that SRD cells (i) can actively regulate different temporal firing patterns due to their intrinsic electrophysiological properties, (ii) generate windup upon gradual membrane depolarisation produced by low-frequency intracellular current injection and by C-fibre tonic input, both processes leading subthreshold oscillations to threshold, and (iii) collateralise to the NRGc and the spinal cord, potentially providing simultaneous regulation of ascending noxious information and motor reactions to pain. PMID:21746779

Intracellular recordings of subnucleus reticularis dorsalis neurones revealed novel electrophysiological properties and windup mechanisms.

PubMed

Soto, Cristina; Canedo, Antonio

2011-09-01

Aδ- and/or C-fibre nociceptive inputs drive subnucleus reticularis dorsalis (SRD) neurones projecting to a variety of regions including the spinal cord and the nucleus reticularis gigantocellularis (NRGc), but their electrophysiological properties are largely unknown. Here we intracellularly recorded the SRD neuronal responses to injection of polarising current pulses as well as to electrical stimulation of the cervical spinal posterior quadrant (PQ) and the NRGc. Three different classes of neurones with distinct electrophysiological properties were found: type I were characterised by the absence of a fast postspike hyperpolarisation, type II by the presence of a postspike hyperpolarisation followed by a depolarisation resembling low threshold calcium spikes (LTSs), and type III (lacking LTSs) had a fast postspike hyperpolarisation deinactivating A-like potassium channels leading to enlarged interspike intervals. All three classes generated depolarising sags to hyperpolarising current pulses and showed 3-4.5 Hz subthreshold oscillatory activity leading to windup when intracellularly injecting low-frequency repetitive depolarising pulses as well as in response to 0.5-2 Hz NRGc and PQ electrical stimulation. About half of the 132 sampled neurones responded antidromically to NRGc stimulation with more than 65% of the NRGc-antidromic cells, pertaining to all three types, also responding antidromically to PQ stimulation. NRGc stimulation induced exclusively excitatory first-synaptic-responses whilst PQ stimulation induced first-response excitation in most cases, but inhibitory postsynaptic potentials in a few type II and type III neurones not projecting to the spinal cord that also displayed cumulative inhibitory effects (inverse windup). The results show that SRD cells (i) can actively regulate different temporal firing patterns due to their intrinsic electrophysiological properties, (ii) generate windup upon gradual membrane depolarisation produced by low-frequency intracellular current injection and by C-fibre tonic input, both processes leading subthreshold oscillations to threshold, and (iii) collateralise to the NRGc and the spinal cord, potentially providing simultaneous regulation of ascending noxious information and motor reactions to pain.

Pathogenesis and treatment of immune-mediated neuropathies.

PubMed

Lehmann, Helmar C; Meyer Zu Horste, Gerd; Kieseier, Bernd C; Hartung, Hans-Peter

2009-07-01

Immune-mediated neuropathies represent a heterogeneous spectrum of peripheral nerve disorders that can be classified according to time course, predominant involvement of motor/sensory fibers, distribution of deficits and paraclinical parameters such as electrophysiology and serum antibodies. In the last few years, significant advances have been achieved in elucidating underlying pathomechanisms, which made it possible to identify potential therapeutic targets. In this review, we discuss the latest development in pathogenesis and treatment of immune-mediated neuropathies.

Classification of volcanic ash particles using a convolutional neural network and probability.

PubMed

Shoji, Daigo; Noguchi, Rina; Otsuki, Shizuka; Hino, Hideitsu

2018-05-25

Analyses of volcanic ash are typically performed either by qualitatively classifying ash particles by eye or by quantitatively parameterizing its shape and texture. While complex shapes can be classified through qualitative analyses, the results are subjective due to the difficulty of categorizing complex shapes into a single class. Although quantitative analyses are objective, selection of shape parameters is required. Here, we applied a convolutional neural network (CNN) for the classification of volcanic ash. First, we defined four basal particle shapes (blocky, vesicular, elongated, rounded) generated by different eruption mechanisms (e.g., brittle fragmentation), and then trained the CNN using particles composed of only one basal shape. The CNN could recognize the basal shapes with over 90% accuracy. Using the trained network, we classified ash particles composed of multiple basal shapes based on the output of the network, which can be interpreted as a mixing ratio of the four basal shapes. Clustering of samples by the averaged probabilities and the intensity is consistent with the eruption type. The mixing ratio output by the CNN can be used to quantitatively classify complex shapes in nature without categorizing forcibly and without the need for shape parameters, which may lead to a new taxonomy.

Electrophysiological Correlates of Stimulus Equivalence Processes

ERIC Educational Resources Information Center

Haimson, Barry; Wilkinson, Krista M.; Rosenquist, Celia; Ouimet, Carolyn; McIlvane, William J.

2009-01-01

Research reported here concerns neural processes relating to stimulus equivalence class formation. In Experiment 1, two types of word pairs were presented successively to normally capable adults. In one type, the words had related usage in English (e.g., uncle, aunt). In the other, the two words were not typically related in their usage (e.g.,…

Classifying Patients with Chronic Pelvic Pain into Levels of Biopsychosocial Dysfunction Using Latent Class Modeling of Patient Reported Outcome Measures

PubMed Central

Fenton, Bradford W.; Grey, Scott F.; Tossone, Krystel; McCarroll, Michele; Von Gruenigen, Vivian E.

2015-01-01

Chronic pelvic pain affects multiple aspects of a patient's physical, social, and emotional functioning. Latent class analysis (LCA) of Patient Reported Outcome Measures Information System (PROMIS) domains has the potential to improve clinical insight into these patients' pain. Based on the 11 PROMIS domains applied to n=613 patients referred for evaluation in a chronic pelvic pain specialty center, exploratory factor analysis (EFA) was used to identify unidimensional superdomains. Latent profile analysis (LPA) was performed to identify the number of homogeneous classes present and to further define the pain classification system. The EFA combined the 11 PROMIS domains into four unidimensional superdomains of biopsychosocial dysfunction: Pain, Negative Affect, Fatigue, and Social Function. Based on multiple fit criteria, a latent class model revealed four distinct classes of CPP: No dysfunction (3.2%); Low Dysfunction (17.8%); Moderate Dysfunction (53.2%); and High Dysfunction (25.8%). This study is the first description of a novel approach to the complex disease process such as chronic pelvic pain and was validated by demographic, medical, and psychosocial variables. In addition to an essentially normal class, three classes of increasing biopsychosocial dysfunction were identified. The LCA approach has the potential for application to other complex multifactorial disease processes. PMID:26355825

Ensemble of One-Class Classifiers for Personal Risk Detection Based on Wearable Sensor Data.

PubMed

Rodríguez, Jorge; Barrera-Animas, Ari Y; Trejo, Luis A; Medina-Pérez, Miguel Angel; Monroy, Raúl

2016-09-29

This study introduces the One-Class K-means with Randomly-projected features Algorithm (OCKRA). OCKRA is an ensemble of one-class classifiers built over multiple projections of a dataset according to random feature subsets. Algorithms found in the literature spread over a wide range of applications where ensembles of one-class classifiers have been satisfactorily applied; however, none is oriented to the area under our study: personal risk detection. OCKRA has been designed with the aim of improving the detection performance in the problem posed by the Personal RIsk DEtection(PRIDE) dataset. PRIDE was built based on 23 test subjects, where the data for each user were captured using a set of sensors embedded in a wearable band. The performance of OCKRA was compared against support vector machine and three versions of the Parzen window classifier. On average, experimental results show that OCKRA outperformed the other classifiers for at least 0.53% of the area under the curve (AUC). In addition, OCKRA achieved an AUC above 90% for more than 57% of the users.

Ensemble of One-Class Classifiers for Personal Risk Detection Based on Wearable Sensor Data

PubMed Central

Rodríguez, Jorge; Barrera-Animas, Ari Y.; Trejo, Luis A.; Medina-Pérez, Miguel Angel; Monroy, Raúl

2016-01-01

This study introduces the One-Class K-means with Randomly-projected features Algorithm (OCKRA). OCKRA is an ensemble of one-class classifiers built over multiple projections of a dataset according to random feature subsets. Algorithms found in the literature spread over a wide range of applications where ensembles of one-class classifiers have been satisfactorily applied; however, none is oriented to the area under our study: personal risk detection. OCKRA has been designed with the aim of improving the detection performance in the problem posed by the Personal RIsk DEtection(PRIDE) dataset. PRIDE was built based on 23 test subjects, where the data for each user were captured using a set of sensors embedded in a wearable band. The performance of OCKRA was compared against support vector machine and three versions of the Parzen window classifier. On average, experimental results show that OCKRA outperformed the other classifiers for at least 0.53% of the area under the curve (AUC). In addition, OCKRA achieved an AUC above 90% for more than 57% of the users. PMID:27690054

The Use of BDDCS in Classifying the Permeability of Marketed Drugs1

PubMed Central

Benet, Leslie Z.; Amidon, Gordon L.; Barends, Dirk M.; Lennernäs, Hans; Polli, James E.; Shah, Vinod P.; Stavchansky, Salomon A.; Yu, Lawrence X.

2013-01-01

We recommend that regulatory agencies add the extent of drug metabolism (i.e., ≥90% metabolized) as an alternate method in defining Class 1 marketed drugs suitable for a waiver of in vivo studies of bioequivalence. That is, ≥90% metabolized is an additional methodology that may be substituted for ≥90% absorbed. We propose that the following criteria be used to define ≥ 90% metabolized for marketed drugs: Following a single oral dose to humans, administered at the highest dose strength, mass balance of the Phase 1 oxidative and Phase 2 conjugative drug metabolites in the urine and feces, measured either as unlabeled, radioactive labeled or nonradioactive labeled substances, account for ≥ 90% of the drug dosed. This is the strictest definition for a waiver based on metabolism. For an orally administered drug to be ≥ 90% metabolized by Phase 1 oxidative and Phase 2 conjugative processes, it is obvious that the drug must be absorbed. This proposal, which strictly conforms to the present ≥90% criteria, is a suggested modification to facilitate a number of marketed drugs being appropriately assigned to Class 1. PMID:18236138

Layer-specific chromatin accessibility landscapes reveal regulatory networks in adult mouse visual cortex

PubMed Central

Gray, Lucas T; Yao, Zizhen; Nguyen, Thuc Nghi; Kim, Tae Kyung; Zeng, Hongkui; Tasic, Bosiljka

2017-01-01

Mammalian cortex is a laminar structure, with each layer composed of a characteristic set of cell types with different morphological, electrophysiological, and connectional properties. Here, we define chromatin accessibility landscapes of major, layer-specific excitatory classes of neurons, and compare them to each other and to inhibitory cortical neurons using the Assay for Transposase-Accessible Chromatin with high-throughput sequencing (ATAC-seq). We identify a large number of layer-specific accessible sites, and significant association with genes that are expressed in specific cortical layers. Integration of these data with layer-specific transcriptomic profiles and transcription factor binding motifs enabled us to construct a regulatory network revealing potential key layer-specific regulators, including Cux1/2, Foxp2, Nfia, Pou3f2, and Rorb. This dataset is a valuable resource for identifying candidate layer-specific cis-regulatory elements in adult mouse cortex. DOI: http://dx.doi.org/10.7554/eLife.21883.001 PMID:28112643

Modular prediction of protein structural classes from sequences of twilight-zone identity with predicting sequences.

PubMed

Mizianty, Marcin J; Kurgan, Lukasz

2009-12-13

Knowledge of structural class is used by numerous methods for identification of structural/functional characteristics of proteins and could be used for the detection of remote homologues, particularly for chains that share twilight-zone similarity. In contrast to existing sequence-based structural class predictors, which target four major classes and which are designed for high identity sequences, we predict seven classes from sequences that share twilight-zone identity with the training sequences. The proposed MODular Approach to Structural class prediction (MODAS) method is unique as it allows for selection of any subset of the classes. MODAS is also the first to utilize a novel, custom-built feature-based sequence representation that combines evolutionary profiles and predicted secondary structure. The features quantify information relevant to the definition of the classes including conservation of residues and arrangement and number of helix/strand segments. Our comprehensive design considers 8 feature selection methods and 4 classifiers to develop Support Vector Machine-based classifiers that are tailored for each of the seven classes. Tests on 5 twilight-zone and 1 high-similarity benchmark datasets and comparison with over two dozens of modern competing predictors show that MODAS provides the best overall accuracy that ranges between 80% and 96.7% (83.5% for the twilight-zone datasets), depending on the dataset. This translates into 19% and 8% error rate reduction when compared against the best performing competing method on two largest datasets. The proposed predictor provides accurate predictions at 58% accuracy for membrane proteins class, which is not considered by majority of existing methods, in spite that this class accounts for only 2% of the data. Our predictive model is analyzed to demonstrate how and why the input features are associated with the corresponding classes. The improved predictions stem from the novel features that express collocation of the secondary structure segments in the protein sequence and that combine evolutionary and secondary structure information. Our work demonstrates that conservation and arrangement of the secondary structure segments predicted along the protein chain can successfully predict structural classes which are defined based on the spatial arrangement of the secondary structures. A web server is available at http://biomine.ece.ualberta.ca/MODAS/.

Modular prediction of protein structural classes from sequences of twilight-zone identity with predicting sequences

PubMed Central

2009-01-01

Background Knowledge of structural class is used by numerous methods for identification of structural/functional characteristics of proteins and could be used for the detection of remote homologues, particularly for chains that share twilight-zone similarity. In contrast to existing sequence-based structural class predictors, which target four major classes and which are designed for high identity sequences, we predict seven classes from sequences that share twilight-zone identity with the training sequences. Results The proposed MODular Approach to Structural class prediction (MODAS) method is unique as it allows for selection of any subset of the classes. MODAS is also the first to utilize a novel, custom-built feature-based sequence representation that combines evolutionary profiles and predicted secondary structure. The features quantify information relevant to the definition of the classes including conservation of residues and arrangement and number of helix/strand segments. Our comprehensive design considers 8 feature selection methods and 4 classifiers to develop Support Vector Machine-based classifiers that are tailored for each of the seven classes. Tests on 5 twilight-zone and 1 high-similarity benchmark datasets and comparison with over two dozens of modern competing predictors show that MODAS provides the best overall accuracy that ranges between 80% and 96.7% (83.5% for the twilight-zone datasets), depending on the dataset. This translates into 19% and 8% error rate reduction when compared against the best performing competing method on two largest datasets. The proposed predictor provides accurate predictions at 58% accuracy for membrane proteins class, which is not considered by majority of existing methods, in spite that this class accounts for only 2% of the data. Our predictive model is analyzed to demonstrate how and why the input features are associated with the corresponding classes. Conclusions The improved predictions stem from the novel features that express collocation of the secondary structure segments in the protein sequence and that combine evolutionary and secondary structure information. Our work demonstrates that conservation and arrangement of the secondary structure segments predicted along the protein chain can successfully predict structural classes which are defined based on the spatial arrangement of the secondary structures. A web server is available at http://biomine.ece.ualberta.ca/MODAS/. PMID:20003388

Patterns of perceived barriers to medical care in older adults: a latent class analysis.

PubMed

Thorpe, Joshua M; Thorpe, Carolyn T; Kennelty, Korey A; Pandhi, Nancy

2011-08-03

This study examined multiple dimensions of healthcare access in order to develop a typology of perceived barriers to healthcare access in community-dwelling elderly. Secondary aims were to define distinct classes of older adults with similar perceived healthcare access barriers and to examine predictors of class membership to identify risk factors for poor healthcare access. A sample of 5,465 community-dwelling elderly was drawn from the 2004 wave of the Wisconsin Longitudinal Study. Perceived barriers to healthcare access were measured using items from the Group Health Association of America Consumer Satisfaction Survey. We used latent class analysis to assess the constellation of items measuring perceived barriers in access and multinomial logistic regression to estimate how risk factors affected the probability of membership in the latent barrier classes. Latent class analysis identified four classes of older adults. Class 1 (75% of sample) consisted of individuals with an overall low level of risk for perceived access problems (No Barriers). Class 2 (5%) perceived problems with the availability/accessibility of healthcare providers such as specialists or mental health providers (Availability/Accessibility Barriers). Class 3 (18%) perceived problems with how well their providers' operations arise organized to accommodate their needs and preferences (Accommodation Barriers). Class 4 (2%) perceived problems with all dimension of access (Severe Barriers). Results also revealed that healthcare affordability is a problem shared by members of all three barrier groups, suggesting that older adults with perceived barriers tend to face multiple, co-occurring problems. Compared to those classified into the No Barriers group, those in the Severe Barrier class were more likely to live in a rural county, have no health insurance, have depressive symptomatology, and speech limitations. Those classified into the Availability/Accessibility Barriers group were more likely to live in rural and micropolitan counties, have depressive symptomatology, more chronic conditions, and hearing limitations. Those in the Accommodation group were more likely to have depressive symptomatology and cognitive limitations. The current study identified a typology of perceived barriers in healthcare access in older adults. The identified risk factors for membership in perceived barrier classes could potentially assist healthcare organizations and providers with targeting polices and interventions designed to improve access in their most vulnerable older adult populations, particularly those in rural areas, with functional disabilities, or in poor mental health.

Are there socioeconomic differences in responses to a commonly used self report measure of chronic illness?

PubMed

Macintyre, Sally; Der, Geoff; Norrie, John

2005-12-01

Single questions on self-reported morbidity are commonly used in social or health surveys. It has been suggested that these may underestimate socioeconomic gradients in health because more disadvantaged social groups may have higher thresholds for defining illness. Method Face-to-face interviews by research nurses with community-based respondents in the West of Scotland, using a specially designed suite of prompts following up on responses to the UK General Household Survey (GHS) long-standing illness question. Participants were 858 respondents born in the early 1930s and 852 respondents born in the early 1950s (mean age at interview 59 and 40, respectively) classified by occupational social class and area deprivation. Adjusted for age and sex, the Relative Index of Inequality (RII) for reporting any condition in response to the GHS question was 2.14 (95% CIs 1.49-3.08) for social class and 2.01 (1.41-2.87) for Depcat. Among those not reporting any conditions to the GHS question, the RII for reporting conditions to any further prompts was 1.54 (0.87-2.70) for social class and 0.86 (0.50-1.46) for Depcat. The RIIs for reporting any condition after the initial question and all prompts were 2.16 (1.40-3.33) for social class and 1.50 (0.98-2.29) for Depcat. Across a range of conditions defined as more serious, and conditions classified by different ICD categories, socioeconomic status (SES) gradients after the initial question and all prompts were similar to, or less steep than, those produced by the GHS question alone. These data do not support the hypothesis that poorer social groups are more stoical and more likely to need detailed prompting in order to elicit morbidity. Nor do they support the hypothesis that SES gradients in morbidity are underestimated by using the GHS question rather than more detailed questioning. This suggests that responses to this type of question can be used in epidemiology and health needs assessment without major socioeconomic bias.

A new algorithm for reducing the workload of experts in performing systematic reviews.

PubMed

Matwin, Stan; Kouznetsov, Alexandre; Inkpen, Diana; Frunza, Oana; O'Blenis, Peter

2010-01-01

To determine whether a factorized version of the complement naïve Bayes (FCNB) classifier can reduce the time spent by experts reviewing journal articles for inclusion in systematic reviews of drug class efficacy for disease treatment. The proposed classifier was evaluated on a test collection built from 15 systematic drug class reviews used in previous work. The FCNB classifier was constructed to classify each article as containing high-quality, drug class-specific evidence or not. Weight engineering (WE) techniques were added to reduce underestimation for Medical Subject Headings (MeSH)-based and Publication Type (PubType)-based features. Cross-validation experiments were performed to evaluate the classifier's parameters and performance. Work saved over sampling (WSS) at no less than a 95% recall was used as the main measure of performance. The minimum workload reduction for a systematic review for one topic, achieved with a FCNB/WE classifier, was 8.5%; the maximum was 62.2% and the average over the 15 topics was 33.5%. This is 15.0% higher than the average workload reduction obtained using a voting perceptron-based automated citation classification system. The FCNB/WE classifier is simple, easy to implement, and produces significantly better results in reducing the workload than previously achieved. The results support it being a useful algorithm for machine-learning-based automation of systematic reviews of drug class efficacy for disease treatment.

Classification of Dark Modified KdV Equation

NASA Astrophysics Data System (ADS)

Xiong, Na; Lou, Sen-Yue; Li, Biao; Chen, Yong

2017-07-01

The dark Korteweg-de Vries (KdV) systems are defined and classified by Kupershmidt sixteen years ago. However, there is no other classifications for other kinds of nonlinear systems. In this paper, a complete scalar classification for dark modified KdV (MKdV) systems is obtained by requiring the existence of higher order differential polynomial symmetries. Different to the nine classes of the dark KdV case, there exist twelve independent classes of the dark MKdV equations. Furthermore, for the every class of dark MKdV system, there is a free parameter. Only for a fixed parameter, the dark MKdV can be related to dark KdV via suitable Miura transformation. The recursion operators of two classes of dark MKdV systems are also given. Supported by the Global Change Research Program of China under Grant No. 2015Cb953904, National Natural Science Foundation of China under Grant Nos. 11675054, 11435005, 11175092, and 11205092 and Shanghai Knowledge Service Platform for Trustworthy Internet of Things (No. ZF1213) and K. C. Wong Magna Fund in Ningbo University

Ferromagnetic, folded electrode composite as a soft interface to the skin for long-term electrophysiological recording.

PubMed

Jang, Kyung-In; Jung, Han Na; Lee, Jung Woo; Xu, Sheng; Liu, Yu Hao; Ma, Yinji; Jeong, Jae-Woong; Song, Young Min; Kim, Jeonghyun; Kim, Bong Hun; Banks, Anthony; Kwak, Jean Won; Yang, Yiyuan; Shi, Dawei; Wei, Zijun; Feng, Xue; Paik, Ungyu; Huang, Yonggang; Ghaffari, Roozbeh; Rogers, John A

2016-10-25

This paper introduces a class of ferromagnetic, folded, soft composite material for skin-interfaced electrodes with releasable interfaces to stretchable, wireless electronic measurement systems. These electrodes establish intimate, adhesive contacts to the skin, in dimensionally stable formats compatible with multiple days of continuous operation, with several key advantages over conventional hydrogel based alternatives. The reported studies focus on aspects ranging from ferromagnetic and mechanical behavior of the materials systems, to electrical properties associated with their skin interface, to system-level integration for advanced electrophysiological monitoring applications. The work combines experimental measurement and theoretical modeling to establish the key design considerations. These concepts have potential uses across a diverse set of skin-integrated electronic technologies.

Differentiation of functional constipation and constipation predominant irritable bowel syndrome based on Rome III criteria: a population-based study.

PubMed

Koloski, N A; Jones, M; Young, M; Talley, N J

2015-05-01

While the Rome III classification recognises functional constipation (FC) and constipation predominant IBS (IBS-C) as distinct disorders, recent evidence has suggested that these disorders are difficult to separate in clinical practice. To identify whether clinical and lifestyle factors differentiate Rome III-defined IBS-C from FC based on gastrointestinal symptoms and lifestyle characteristics. 3260 people randomly selected from the Australian population returned a postal survey. FC and IBS-C were defined according to Rome III. The first model used logistic regression to differentiate IBS-C from FC based on lifestyle, quality-of-life and psychological characteristics. The second approach was data-driven employing latent class analysis (LCA) to identify naturally occurring clusters in the data considering all symptoms involved in the Rome III criteria for IBS-C and FC. We found n = 206 (6.5%; 95% CI 5.7-7.4%) people met strict Rome III FC whereas n = 109 (3.5%; 95% CI 2.8-4.1%) met strict Rome III IBS-C. The case-control approach indicated that FC patients reported an older age at onset of constipation, were less likely to exercise, had higher mental QoL and less health care seeking than IBS-C. LCA yielded one latent class that was predominantly (75%) FC, while the other class was approximately half IBS-C and half FC. The FC-dominated latent class had clearly lower levels of symptoms used to classify IBS (pain-related symptoms) and was more likely to be male (P = 0.046) but was otherwise similar in distribution of lifestyle factors to the mixed class. The latent class analysis approach suggests a differentiation based more on symptom severity rather than the Rome III view. © 2015 John Wiley & Sons Ltd.

Two dimensional electrophysiological characterization of human pluripotent stem cell-derived cardiomyocyte system

PubMed Central

Zhu, Huanqi; Scharnhorst, Kelsey S.; Stieg, Adam Z.; Gimzewski, James K.; Minami, Itsunari; Nakatsuji, Norio; Nakano, Haruko; Nakano, Atsushi

2017-01-01

Stem cell-derived cardiomyocytes provide a promising tool for human developmental biology, regenerative therapies, disease modeling, and drug discovery. As human pluripotent stem cell-derived cardiomyocytes remain functionally fetal-type, close monitoring of electrophysiological maturation is critical for their further application to biology and translation. However, to date, electrophysiological analyses of stem cell-derived cardiomyocytes has largely been limited by biologically undefined factors including 3D nature of embryoid body, sera from animals, and the feeder cells isolated from mouse. Large variability in the aforementioned systems leads to uncontrollable and irreproducible results, making conclusive studies difficult. In this report, a chemically-defined differentiation regimen and a monolayer cell culture technique was combined with multielectrode arrays for accurate, real-time, and flexible measurement of electrophysiological parameters in translation-ready human cardiomyocytes. Consistent with their natural counterpart, amplitude and dV/dtmax of field potential progressively increased during the course of maturation. Monolayer culture allowed for the identification of pacemaking cells using the multielectrode array platform and thereby the estimation of conduction velocity, which gradually increased during the differentiation of cardiomyocytes. Thus, the electrophysiological maturation of the human pluripotent stem cell-derived cardiomyocytes in our system recapitulates in vivo development. This system provides a versatile biological tool to analyze human heart development, disease mechanisms, and the efficacy/toxicity of chemicals. PMID:28266620

Where can pixel counting area estimates meet user-defined accuracy requirements?

NASA Astrophysics Data System (ADS)

Waldner, François; Defourny, Pierre

2017-08-01

Pixel counting is probably the most popular way to estimate class areas from satellite-derived maps. It involves determining the number of pixels allocated to a specific thematic class and multiplying it by the pixel area. In the presence of asymmetric classification errors, the pixel counting estimator is biased. The overarching objective of this article is to define the applicability conditions of pixel counting so that the estimates are below a user-defined accuracy target. By reasoning in terms of landscape fragmentation and spatial resolution, the proposed framework decouples the resolution bias and the classifier bias from the overall classification bias. The consequence is that prior to any classification, part of the tolerated bias is already committed due to the choice of the spatial resolution of the imagery. How much classification bias is affordable depends on the joint interaction of spatial resolution and fragmentation. The method was implemented over South Africa for cropland mapping, demonstrating its operational applicability. Particular attention was paid to modeling a realistic sensor's spatial response by explicitly accounting for the effect of its point spread function. The diagnostic capabilities offered by this framework have multiple potential domains of application such as guiding users in their choice of imagery and providing guidelines for space agencies to elaborate the design specifications of future instruments.

A dictionary learning approach for human sperm heads classification.

PubMed

Shaker, Fariba; Monadjemi, S Amirhassan; Alirezaie, Javad; Naghsh-Nilchi, Ahmad Reza

2017-12-01

To diagnose infertility in men, semen analysis is conducted in which sperm morphology is one of the factors that are evaluated. Since manual assessment of sperm morphology is time-consuming and subjective, automatic classification methods are being developed. Automatic classification of sperm heads is a complicated task due to the intra-class differences and inter-class similarities of class objects. In this research, a Dictionary Learning (DL) technique is utilized to construct a dictionary of sperm head shapes. This dictionary is used to classify the sperm heads into four different classes. Square patches are extracted from the sperm head images. Columnized patches from each class of sperm are used to learn class-specific dictionaries. The patches from a test image are reconstructed using each class-specific dictionary and the overall reconstruction error for each class is used to select the best matching class. Average accuracy, precision, recall, and F-score are used to evaluate the classification method. The method is evaluated using two publicly available datasets of human sperm head shapes. The proposed DL based method achieved an average accuracy of 92.2% on the HuSHeM dataset, and an average recall of 62% on the SCIAN-MorphoSpermGS dataset. The results show a significant improvement compared to a previously published shape-feature-based method. We have achieved high-performance results. In addition, our proposed approach offers a more balanced classifier in which all four classes are recognized with high precision and recall. In this paper, we use a Dictionary Learning approach in classifying human sperm heads. It is shown that the Dictionary Learning method is far more effective in classifying human sperm heads than classifiers using shape-based features. Also, a dataset of human sperm head shapes is introduced to facilitate future research. Copyright © 2017 Elsevier Ltd. All rights reserved.

High-throughput electrophysiological assays for voltage gated ion channels using SyncroPatch 768PE.

PubMed

Li, Tianbo; Lu, Gang; Chiang, Eugene Y; Chernov-Rogan, Tania; Grogan, Jane L; Chen, Jun

2017-01-01

Ion channels regulate a variety of physiological processes and represent an important class of drug target. Among the many methods of studying ion channel function, patch clamp electrophysiology is considered the gold standard by providing the ultimate precision and flexibility. However, its utility in ion channel drug discovery is impeded by low throughput. Additionally, characterization of endogenous ion channels in primary cells remains technical challenging. In recent years, many automated patch clamp (APC) platforms have been developed to overcome these challenges, albeit with varying throughput, data quality and success rate. In this study, we utilized SyncroPatch 768PE, one of the latest generation APC platforms which conducts parallel recording from two-384 modules with giga-seal data quality, to push these 2 boundaries. By optimizing various cell patching parameters and a two-step voltage protocol, we developed a high throughput APC assay for the voltage-gated sodium channel Nav1.7. By testing a group of Nav1.7 reference compounds' IC50, this assay was proved to be highly consistent with manual patch clamp (R > 0.9). In a pilot screening of 10,000 compounds, the success rate, defined by > 500 MΩ seal resistance and >500 pA peak current, was 79%. The assay was robust with daily throughput ~ 6,000 data points and Z' factor 0.72. Using the same platform, we also successfully recorded endogenous voltage-gated potassium channel Kv1.3 in primary T cells. Together, our data suggest that SyncroPatch 768PE provides a powerful platform for ion channel research and drug discovery.

Electrophysiological and Structural Remodeling in Heart Failure Modulate Arrhythmogenesis. 2D Simulation Study

PubMed Central

Gomez, Juan F.; Cardona, Karen; Martinez, Laura; Saiz, Javier; Trenor, Beatriz

2014-01-01

Background Heart failure is operationally defined as the inability of the heart to maintain blood flow to meet the needs of the body and it is the final common pathway of various cardiac pathologies. Electrophysiological remodeling, intercellular uncoupling and a pro-fibrotic response have been identified as major arrhythmogenic factors in heart failure. Objective In this study we investigate vulnerability to reentry under heart failure conditions by incorporating established electrophysiological and anatomical remodeling using computer simulations. Methods The electrical activity of human transmural ventricular tissue (5 cm×5 cm) was simulated using the human ventricular action potential model Grandi et al. under control and heart failure conditions. The MacCannell et al. model was used to model fibroblast electrical activity, and their electrotonic interactions with myocytes. Selected degrees of diffuse fibrosis and variations in intercellular coupling were considered and the vulnerable window (VW) for reentry was evaluated following cross-field stimulation. Results No reentry was observed in normal conditions or in the presence of HF ionic remodeling. However, defined amount of fibrosis and/or cellular uncoupling were sufficient to elicit reentrant activity. Under conditions where reentry was generated, HF electrophysiological remodeling did not alter the width of the VW. However, intermediate fibrosis and cellular uncoupling significantly widened the VW. In addition, biphasic behavior was observed, as very high fibrotic content or very low tissue conductivity hampered the development of reentry. Detailed phase analysis of reentry dynamics revealed an increase of phase singularities with progressive fibrotic components. Conclusion Structural remodeling is a key factor in the genesis of vulnerability to reentry. A range of intermediate levels of fibrosis and intercellular uncoupling can combine to favor reentrant activity. PMID:25054335

The discriminant pixel approach: a new tool for the rational interpretation of GCxGC-MS chromatograms.

PubMed

Vial, Jérôme; Pezous, Benoît; Thiébaut, Didier; Sassiat, Patrick; Teillet, Béatrice; Cahours, Xavier; Rivals, Isabelle

2011-01-30

GCxGC is now recognized as the most suited analytical technique for the characterization of complex mixtures of volatile compounds; it is implemented worldwide in academic and industrial laboratories. However, in the frame of comprehensive analysis of non-target analytes, going beyond the visual examination of the color plots remains challenging for most users. We propose a strategy that aims at classifying chromatograms according to the chemical composition of the samples while determining the origin of the discrimination between different classes of samples: the discriminant pixel approach. After data pre-processing and time-alignment, the discriminatory power of each chromatogram pixel for a given class was defined as its correlation with the membership to this class. Using a peak finding algorithm, the most discriminant pixels were then linked to chromatographic peaks. Finally, crosschecking with mass spectrometry data enabled to establish relationships with compounds that could consequently be considered as candidate class markers. This strategy was applied to a large experimental data set of 145 GCxGC-MS chromatograms of tobacco extracts corresponding to three distinct classes of tobacco. Copyright © 2010 Elsevier B.V. All rights reserved.

Dealing with contaminated datasets: An approach to classifier training

NASA Astrophysics Data System (ADS)

Homenda, Wladyslaw; Jastrzebska, Agnieszka; Rybnik, Mariusz

2016-06-01

The paper presents a novel approach to classification reinforced with rejection mechanism. The method is based on a two-tier set of classifiers. First layer classifies elements, second layer separates native elements from foreign ones in each distinguished class. The key novelty presented here is rejection mechanism training scheme according to the philosophy "one-against-all-other-classes". Proposed method was tested in an empirical study of handwritten digits recognition.

Distributions of Sensitivities to Three Sterol Demethylation Inhibitor Fungicides Among Populations of Uncinula necator Sensitive and Resistant to Triadimefon.

PubMed

Erickson, E O; Wilcox, W F

1997-08-01

ABSTRACT Single-conidial isolates of Uncinula necator from (i) a population representing two vineyards with no previous exposure to sterol demethylation inhibitor (DMI) fungicides ("unexposed," n = 77) and (ii) a population representing two vineyards in which powdery mildew was poorly controlled by triadimefon after prolonged DMI use ("selected," n = 82) were assayed to determine distributions of sensitivities to the DMI fungicides triadimenol (the active form of triadimefon), myclobutanil, and fenarimol. Median 50% effective dose (ED(50)) values (micrograms per milliliter) in the selected versus unexposed populations were 0.06 versus 1.9 for triadimenol, 0.03 versus 0.23 for myclobutanil, and 0.03 versus 0.07 for fenarimol, respectively. Isolates were grouped into sensitivity classes according to their ED(50) values, and those from the selected population were categorized as resistant if the frequency of their sensitivity class had increased significantly relative to levels found in the unexposed population (ED(50) values exceeding 0.56, 0.18, and 0.18 mug/ml for triadimenol, myclobutanil, and fenarimol, respectively). Of the 76 isolates defined as resistant to triadimenol, 64% were classified as cross-resistant to myclobutanil, 18% were classified as cross-resistant to fenarimol, and 17% were classified as resistant to all three fungicides; 25% of the isolates classified as resistant to myclobutanil also were classified as resistant to fenarimol. Similar cross-resistance relationships were revealed when all isolates were examined by regressing log ED(50) values for each fungicide against those for the remaining two fungicides to determine the correlation coefficients (e.g., r = 0.85 for triadimenol versus myclobutanil and 0.56 for triadimenol versus fenarimol). The restricted levels of cross-resistance indicated by these data, particularly between fenarimol and the other two fungicides, is in sharp contrast to the high levels of cross-resistance among DMIs reported for some other pathogens and has significant implications with respect to programs for managing grapevine powdery mildew and DMI resistance.

The Effects of Day-to-Day Variability of Physiological Data on Operator Functional State Classification

DTIC Science & Technology

2013-03-01

fMRI data (e.g. Kamitami & Tong, 2005). This approach has been remarkably successful in classifying mental workload in complex tasks (Berka, et al...1991). These previous studies relied upon spectral comparison rather than classification. In previous research examining the stability of fMRI ...chose to focus on electrophysiology, as the collection conditions may be more carefully controlled across days than fMRI and it is more amenable to

Complex Dynamics in the Basal Ganglia: Health and Disease Beyond the Motor System.

PubMed

Andres, Daniela S; Darbin, Olivier

2018-01-01

The rate and oscillatory hypotheses are the two main current frameworks of basal ganglia pathophysiology. Both hypotheses have emerged from research on movement disorders sharing similar conceptualizations. These pathological conditions are classified either as hypokinetic or hyperkinetic, and the electrophysiological hallmarks of basal ganglia dysfunction are categorized as prokinetic or antikinetic. Although nonmotor symptoms, including neurobehavioral symptoms, are a key manifestation of basal ganglia dysfunction, they are uncommonly accounted for in these models. In patients with Parkinson's disease, the broad spectrum of motor symptoms and neurobehavioral symptoms challenges the concept that basal ganglia disorders can be classified into two categories. The profile of symptoms of basal ganglia dysfunction is best characterized by a breakdown of information processing, accompanied at an electrophysiological level by complex alterations of spiking activity from basal ganglia neurons. The authors argue that the dynamics of the basal ganglia circuit cannot be fully characterized by linear properties such as the firing rate or oscillatory activity. In fact, the neuronal spiking stream of the basal ganglia circuit is irregular but has temporal structure. In this context, entropy was introduced as a measure of probabilistic irregularity in the temporal organization of neuronal activity of the basal ganglia, giving place to the entropy hypothesis of basal ganglia pathology. Obtaining a quantitative characterization of irregularity of spike trains from basal ganglia neurons is key to elaborating a new framework of basal ganglia pathophysiology.

Class D Management Implementation Approach of the First Orbital Mission of the Earth Venture Series

NASA Technical Reports Server (NTRS)

Wells, James E.; Scherrer, John; Law, Richard; Bonniksen, Chris

2013-01-01

A key element of the National Research Council's Earth Science and Applications Decadal Survey called for the creation of the Venture Class line of low-cost research and application missions within NASA (National Aeronautics and Space Administration). One key component of the architecture chosen by NASA within the Earth Venture line is a series of self-contained stand-alone spaceflight science missions called "EV-Mission". The first mission chosen for this competitively selected, cost and schedule capped, Principal Investigator-led opportunity is the CYclone Global Navigation Satellite System (CYGNSS). As specified in the defining Announcement of Opportunity, the Principal Investigator is held responsible for successfully achieving the science objectives of the selected mission and the management approach that he/she chooses to obtain those results has a significant amount of freedom as long as it meets the intent of key NASA guidance like NPR 7120.5 and 7123. CYGNSS is classified under NPR 7120.5E guidance as a Category 3 (low priority, low cost) mission and carries a Class D risk classification (low priority, high risk) per NPR 8705.4. As defined in the NPR guidance, Class D risk classification allows for a relatively broad range of implementation strategies. The management approach that will be utilized on CYGNSS is a streamlined implementation that starts with a higher risk tolerance posture at NASA and that philosophy flows all the way down to the individual part level.

An automatic method for skeletal patterns classification using craniomaxillary variables on a Colombian population.

PubMed

Niño-Sandoval, Tania Camila; Guevara Perez, Sonia V; González, Fabio A; Jaque, Robinson Andrés; Infante-Contreras, Clementina

2016-04-01

The mandibular bone is an important part of the forensic facial reconstruction and it has the possibility of getting lost in skeletonized remains; for this reason, it is necessary to facilitate the identification process simulating the mandibular position only through craniomaxillary measures, for this task, different modeling techniques have been performed, but they only contemplate a straight facial profile that belong to skeletal pattern Class I, but the 24.5% corresponding to the Colombian skeletal patterns Class II and III are not taking into account, besides, craniofacial measures do not follow a parametric trend or a normal distribution. The aim of this study was to employ an automatic non-parametric method as the Support Vector Machines to classify skeletal patterns through craniomaxillary variables, in order to simulate the natural mandibular position on a contemporary Colombian sample. Lateral cephalograms (229) of Colombian young adults of both sexes were collected. Landmark coordinates protocols were used to create craniomaxillary variables. A Support Vector Machine with a linear kernel classifier model was trained on a subset of the available data and evaluated over the remaining samples. The weights of the model were used to select the 10 best variables for classification accuracy. An accuracy of 74.51% was obtained, defined by Pr-A-N, N-Pr-A, A-N-Pr, A-Te-Pr, A-Pr-Rhi, Rhi-A-Pr, Pr-A-Te, Te-Pr-A, Zm-A-Pr and PNS-A-Pr angles. The Class Precision and the Class Recall showed a correct distinction of the Class II from the Class III and vice versa. Support Vector Machines created an important model of classification of skeletal patterns using craniomaxillary variables that are not commonly used in the literature and could be applicable to the 24.5% of the contemporary Colombian sample. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Non-invasive continuous blood pressure monitoring of tachycardic episodes during interventional electrophysiology

PubMed Central

Maggi, Roberto; Viscardi, Valentina; Furukawa, Toshiyuki; Brignole, Michele

2010-01-01

Aims We thought to evaluate feasibility of continuous non-invasive blood pressure monitoring during procedures of interventional electrophysiology. Methods and results We evaluated continuous non-invasive finger blood pressure (BP) monitoring by means of the Nexfin device in 22 patients (mean age 70 ± 24 years), undergoing procedures of interventional electrophysiology, in critical situations of hypotension caused by tachyarrhythmias or by intermittent incremental ventricular temporary pacing till to the maximum tolerated systolic BP fall (mean 61 ± 14 mmHg per patient at a rate of 195 ± 37 bpm). In all patients, Nexfin was able to detect immediately, at the onset of tachyarrythmia, the changes in BP and recorded reliable waveforms. The quality of the signal was arbitrarily classified as excellent in 11 cases, good in 10 cases, and sufficient in 1 case. In basal conditions, calibrations of the signal occurred every 49.2 ± 24.3 s and accounted for 4% of total monitoring time; during tachyarrhythmias their frequency increased to one every 12.7 s and accounted for 19% of total recording duration. A linear correlation for a range of BP values from 41 to 190 mmHg was found between non-invasive and intra-arterial BP among a total of 1055 beats from three patients who underwent simultaneous recordings with both methods (coefficient of correlation of 0.81, P < 0.0001). Conclusion In conclusion, continuous non-invasive BP monitoring is feasible in the clinical practise of an interventional electrophysiology laboratory without the need of utilization of an intra-arterial BP line. PMID:20837572

Comparison of electrophysiological findings in axonal and demyelinating Guillain-Barre syndrome

PubMed Central

Yadegari, Samira; Nafissi, Shahriar; Kazemi, Neda

2014-01-01

Background: Incidence and predominant subtype of Guillain-Barre syndrome (GBS) differs geographically. Electrophysiology has an important role in early diagnosis and prediction of prognosis. This study is conducted to determine the frequent subtype of GBS in a large group of patients in Iran and compare nerve conduction studies in axonal and demyelinating forms of GBS. Methods: We retrospectively evaluated the medical records and electrodiagnostic study (EDS) of 121 GBS patients who were managed in our hospital during 11 years. After regarding the exclusion criteria, patients classified as three groups: acute inflammatory demyelinating polyneuropathy (AIDP), acute motor axonal neuropathy (AMAN), and acute motor sensory axonal neuropathy (AMSAN). The most frequent subtype and then electrophysiological characteristic based on the time of EDS and their cerebrospinal fluid (CSF) profile were assessed. Results: Among 70 patients finally included in the study, 67% were men. About 63%, 23%, and 14% had AIDP, AMAN, and AMSAN, respectively. AIDP patients represented a wider range of ages compared with other groups. Higher levels of CSF protein, abnormal late responses and sural sparing were more frequent in AIDP subtype. Five AMSAN patients also revealed sural sparing. Conduction block (CB) was observed in one AMAN patient. Prolonged F-wave latency was observed only in AIDP cases. CB and inexcitable sensory nerves were more frequent after 2 weeks, but reduced F-wave persistency was more prominent in the early phase. Conclusion: AIDP was the most frequent subtype. Although the electrophysiology and CSF are important diagnostic tools, classification should not be made based on a distinct finding. PMID:25422732

Automatic classification of canine PRG neuronal discharge patterns using K-means clustering.

PubMed

Zuperku, Edward J; Prkic, Ivana; Stucke, Astrid G; Miller, Justin R; Hopp, Francis A; Stuth, Eckehard A

2015-02-01

Respiratory-related neurons in the parabrachial-Kölliker-Fuse (PB-KF) region of the pons play a key role in the control of breathing. The neuronal activities of these pontine respiratory group (PRG) neurons exhibit a variety of inspiratory (I), expiratory (E), phase spanning and non-respiratory related (NRM) discharge patterns. Due to the variety of patterns, it can be difficult to classify them into distinct subgroups according to their discharge contours. This report presents a method that automatically classifies neurons according to their discharge patterns and derives an average subgroup contour of each class. It is based on the K-means clustering technique and it is implemented via SigmaPlot User-Defined transform scripts. The discharge patterns of 135 canine PRG neurons were classified into seven distinct subgroups. Additional methods for choosing the optimal number of clusters are described. Analysis of the results suggests that the K-means clustering method offers a robust objective means of both automatically categorizing neuron patterns and establishing the underlying archetypical contours of subtypes based on the discharge patterns of group of neurons. Published by Elsevier B.V.

Automated grouping of action potentials of human embryonic stem cell-derived cardiomyocytes.

PubMed

Gorospe, Giann; Zhu, Renjun; Millrod, Michal A; Zambidis, Elias T; Tung, Leslie; Vidal, Rene

2014-09-01

Methods for obtaining cardiomyocytes from human embryonic stem cells (hESCs) are improving at a significant rate. However, the characterization of these cardiomyocytes (CMs) is evolving at a relatively slower rate. In particular, there is still uncertainty in classifying the phenotype (ventricular-like, atrial-like, nodal-like, etc.) of an hESC-derived cardiomyocyte (hESC-CM). While previous studies identified the phenotype of a CM based on electrophysiological features of its action potential, the criteria for classification were typically subjective and differed across studies. In this paper, we use techniques from signal processing and machine learning to develop an automated approach to discriminate the electrophysiological differences between hESC-CMs. Specifically, we propose a spectral grouping-based algorithm to separate a population of CMs into distinct groups based on the similarity of their action potential shapes. We applied this method to a dataset of optical maps of cardiac cell clusters dissected from human embryoid bodies. While some of the nine cell clusters in the dataset are presented with just one phenotype, the majority of the cell clusters are presented with multiple phenotypes. The proposed algorithm is generally applicable to other action potential datasets and could prove useful in investigating the purification of specific types of CMs from an electrophysiological perspective.

Automated Grouping of Action Potentials of Human Embryonic Stem Cell-Derived Cardiomyocytes

PubMed Central

Gorospe, Giann; Zhu, Renjun; Millrod, Michal A.; Zambidis, Elias T.; Tung, Leslie; Vidal, René

2015-01-01

Methods for obtaining cardiomyocytes from human embryonic stem cells (hESCs) are improving at a significant rate. However, the characterization of these cardiomyocytes is evolving at a relatively slower rate. In particular, there is still uncertainty in classifying the phenotype (ventricular-like, atrial-like, nodal-like, etc.) of an hESC-derived cardiomyocyte (hESC-CM). While previous studies identified the phenotype of a cardiomyocyte based on electrophysiological features of its action potential, the criteria for classification were typically subjective and differed across studies. In this paper, we use techniques from signal processing and machine learning to develop an automated approach to discriminate the electrophysiological differences between hESC-CMs. Specifically, we propose a spectral grouping-based algorithm to separate a population of cardiomyocytes into distinct groups based on the similarity of their action potential shapes. We applied this method to a dataset of optical maps of cardiac cell clusters dissected from human embryoid bodies (hEBs). While some of the 9 cell clusters in the dataset presented with just one phenotype, the majority of the cell clusters presented with multiple phenotypes. The proposed algorithm is generally applicable to other action potential datasets and could prove useful in investigating the purification of specific types of cardiomyocytes from an electrophysiological perspective. PMID:25148658

An ensemble of SVM classifiers based on gene pairs.

PubMed

Tong, Muchenxuan; Liu, Kun-Hong; Xu, Chungui; Ju, Wenbin

2013-07-01

In this paper, a genetic algorithm (GA) based ensemble support vector machine (SVM) classifier built on gene pairs (GA-ESP) is proposed. The SVMs (base classifiers of the ensemble system) are trained on different informative gene pairs. These gene pairs are selected by the top scoring pair (TSP) criterion. Each of these pairs projects the original microarray expression onto a 2-D space. Extensive permutation of gene pairs may reveal more useful information and potentially lead to an ensemble classifier with satisfactory accuracy and interpretability. GA is further applied to select an optimized combination of base classifiers. The effectiveness of the GA-ESP classifier is evaluated on both binary-class and multi-class datasets. Copyright © 2013 Elsevier Ltd. All rights reserved.

Improvement of training set structure in fusion data cleaning using Time-Domain Global Similarity method

NASA Astrophysics Data System (ADS)

Liu, J.; Lan, T.; Qin, H.

2017-10-01

Traditional data cleaning identifies dirty data by classifying original data sequences, which is a class-imbalanced problem since the proportion of incorrect data is much less than the proportion of correct ones for most diagnostic systems in Magnetic Confinement Fusion (MCF) devices. When using machine learning algorithms to classify diagnostic data based on class-imbalanced training set, most classifiers are biased towards the major class and show very poor classification rates on the minor class. By transforming the direct classification problem about original data sequences into a classification problem about the physical similarity between data sequences, the class-balanced effect of Time-Domain Global Similarity (TDGS) method on training set structure is investigated in this paper. Meanwhile, the impact of improved training set structure on data cleaning performance of TDGS method is demonstrated with an application example in EAST POlarimetry-INTerferometry (POINT) system.

Influenza antiviral therapeutics.

PubMed

Mayburd, Anatoly L

2010-01-01

In this review we conducted a landscaping study of the therapeutic anti-influenza agents, limiting the scope by exclusion of vaccines. The resulting 2800 patent publications were classified into 23 distinct technological sectors. The mechanism of action, the promise and drawbacks of the corresponding technological sectors were explored on comparative basis. A set of quantitative parameters was defined based on landscaping procedure that appears to correlate with the practical success of a given class of therapeutics. Thus, the sectors not considered promising from the mechanistic side were also displaying low value of the classifying parameters. The parameters were combined into a probabilistic Marketing Prediction Score, assessing a likelihood of a given sector to produce a marketable product. The proposed analytical methodology may be useful for automatic search and assessment of technologies for the goals of acquisition, investment and competitive bidding. While not being a substitute for an expert evaluation, it provides an initial assessment suitable for implementation with large-scale automated landscaping.

New Site Coefficients and Site Classification System Used in Recent Building Seismic Code Provisions

USGS Publications Warehouse

Dobry, R.; Borcherdt, R.D.; Crouse, C.B.; Idriss, I.M.; Joyner, W.B.; Martin, G.R.; Power, M.S.; Rinne, E.E.; Seed, R.B.

2000-01-01

Recent code provisions for buildings and other structures (1994 and 1997 NEHRP Provisions, 1997 UBC) have adopted new site amplification factors and a new procedure for site classification. Two amplitude-dependent site amplification factors are specified: Fa for short periods and Fv for longer periods. Previous codes included only a long period factor S and did not provide for a short period amplification factor. The new site classification system is based on definitions of five site classes in terms of a representative average shear wave velocity to a depth of 30 m (V?? s). This definition permits sites to be classified unambiguously. When the shear wave velocity is not available, other soil properties such as standard penetration resistance or undrained shear strength can be used. The new site classes denoted by letters A - E, replace site classes in previous codes denoted by S1 - S4. Site classes A and B correspond to hard rock and rock, Site Class C corresponds to soft rock and very stiff / very dense soil, and Site Classes D and E correspond to stiff soil and soft soil. A sixth site class, F, is defined for soils requiring site-specific evaluations. Both Fa and Fv are functions of the site class, and also of the level of seismic hazard on rock, defined by parameters such as Aa and Av (1994 NEHRP Provisions), Ss and S1 (1997 NEHRP Provisions) or Z (1997 UBC). The values of Fa and Fv decrease as the seismic hazard on rock increases due to soil nonlinearity. The greatest impact of the new factors Fa and Fv as compared with the old S factors occurs in areas of low-to-medium seismic hazard. This paper summarizes the new site provisions, explains the basis for them, and discusses ongoing studies of site amplification in recent earthquakes that may influence future code developments.

Minor Planet Science with the VISTA Hemisphere Survey

NASA Astrophysics Data System (ADS)

Popescu, M.; Licandro, J.; Morate, D.; de León, J.; Nedelcu, D. A.

2017-03-01

We have carried out a serendipitous search for Solar System objects imaged by the VISTA Hemisphere Survey (VHS) and have identified 230 375 valid detections for 39 947 objects. This information is available in three catalogues, entitled MOVIS. The distributions of the data in colour-colour plots show clusters identified with the different taxonomic asteroid types. Diagrams that use (Y-J) colour separate the spectral classes more effectively than any other method based on colours. In particular, the end-class members A-, D-, R-, and V-types occupy well-defined regions and can be easily identified. About 10 000 asteroids were classified taxonomically using a probabilistic approach. The distribution of basaltic asteroids across the Main Belt was characterised using the MOVIS colours: 477 V-type candidates were found, of which 244 are outside the Vesta dynamical family.

Nonparametric, Coupled ,Bayesian ,Dictionary ,and Classifier Learning for Hyperspectral Classification.

PubMed

Akhtar, Naveed; Mian, Ajmal

2017-10-03

We present a principled approach to learn a discriminative dictionary along a linear classifier for hyperspectral classification. Our approach places Gaussian Process priors over the dictionary to account for the relative smoothness of the natural spectra, whereas the classifier parameters are sampled from multivariate Gaussians. We employ two Beta-Bernoulli processes to jointly infer the dictionary and the classifier. These processes are coupled under the same sets of Bernoulli distributions. In our approach, these distributions signify the frequency of the dictionary atom usage in representing class-specific training spectra, which also makes the dictionary discriminative. Due to the coupling between the dictionary and the classifier, the popularity of the atoms for representing different classes gets encoded into the classifier. This helps in predicting the class labels of test spectra that are first represented over the dictionary by solving a simultaneous sparse optimization problem. The labels of the spectra are predicted by feeding the resulting representations to the classifier. Our approach exploits the nonparametric Bayesian framework to automatically infer the dictionary size--the key parameter in discriminative dictionary learning. Moreover, it also has the desirable property of adaptively learning the association between the dictionary atoms and the class labels by itself. We use Gibbs sampling to infer the posterior probability distributions over the dictionary and the classifier under the proposed model, for which, we derive analytical expressions. To establish the effectiveness of our approach, we test it on benchmark hyperspectral images. The classification performance is compared with the state-of-the-art dictionary learning-based classification methods.

Transfer Learning for Class Imbalance Problems with Inadequate Data.

PubMed

Al-Stouhi, Samir; Reddy, Chandan K

2016-07-01

A fundamental problem in data mining is to effectively build robust classifiers in the presence of skewed data distributions. Class imbalance classifiers are trained specifically for skewed distribution datasets. Existing methods assume an ample supply of training examples as a fundamental prerequisite for constructing an effective classifier. However, when sufficient data is not readily available, the development of a representative classification algorithm becomes even more difficult due to the unequal distribution between classes. We provide a unified framework that will potentially take advantage of auxiliary data using a transfer learning mechanism and simultaneously build a robust classifier to tackle this imbalance issue in the presence of few training samples in a particular target domain of interest. Transfer learning methods use auxiliary data to augment learning when training examples are not sufficient and in this paper we will develop a method that is optimized to simultaneously augment the training data and induce balance into skewed datasets. We propose a novel boosting based instance-transfer classifier with a label-dependent update mechanism that simultaneously compensates for class imbalance and incorporates samples from an auxiliary domain to improve classification. We provide theoretical and empirical validation of our method and apply to healthcare and text classification applications.

Class-specific Error Bounds for Ensemble Classifiers

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

Prenger, R; Lemmond, T; Varshney, K

2009-10-06

The generalization error, or probability of misclassification, of ensemble classifiers has been shown to be bounded above by a function of the mean correlation between the constituent (i.e., base) classifiers and their average strength. This bound suggests that increasing the strength and/or decreasing the correlation of an ensemble's base classifiers may yield improved performance under the assumption of equal error costs. However, this and other existing bounds do not directly address application spaces in which error costs are inherently unequal. For applications involving binary classification, Receiver Operating Characteristic (ROC) curves, performance curves that explicitly trade off false alarms and missedmore » detections, are often utilized to support decision making. To address performance optimization in this context, we have developed a lower bound for the entire ROC curve that can be expressed in terms of the class-specific strength and correlation of the base classifiers. We present empirical analyses demonstrating the efficacy of these bounds in predicting relative classifier performance. In addition, we specify performance regions of the ROC curve that are naturally delineated by the class-specific strengths of the base classifiers and show that each of these regions can be associated with a unique set of guidelines for performance optimization of binary classifiers within unequal error cost regimes.« less

Simultaneous Activation of Multiple Memory Systems during Learning: Insights from Electrophysiology and Modeling

DTIC Science & Technology

2010-06-01

autonomic and pain functions, and facilitating/inhibiting voluntary movements. The external segment of the globus pallidus (globus pallidus externa, GPe...or less responsive to pain stimuli. 1.2.4. Other cortico-basal ganglia loops Alexander, Strick and colleagues have additionally defined a number of... orofacial loop and loops through inferotemporal and posterior parietal cortical areas have also been defined. 1.2.5. Interactions between loops Once

Luminance- and Texture-Defined Information Processing in School-Aged Children with Autism

PubMed Central

Rivest, Jessica B.; Jemel, Boutheina; Bertone, Armando; McKerral, Michelle; Mottron, Laurent

2013-01-01

According to the complexity-specific hypothesis, the efficacy with which individuals with autism spectrum disorder (ASD) process visual information varies according to the extensiveness of the neural network required to process stimuli. Specifically, adults with ASD are less sensitive to texture-defined (or second-order) information, which necessitates the implication of several cortical visual areas. Conversely, the sensitivity to simple, luminance-defined (or first-order) information, which mainly relies on primary visual cortex (V1) activity, has been found to be either superior (static material) or intact (dynamic material) in ASD. It is currently unknown if these autistic perceptual alterations are present in childhood. In the present study, behavioural (threshold) and electrophysiological measures were obtained for static luminance- and texture-defined gratings presented to school-aged children with ASD and compared to those of typically developing children. Our behavioural and electrophysiological (P140) results indicate that luminance processing is likely unremarkable in autistic children. With respect to texture processing, there was no significant threshold difference between groups. However, unlike typical children, autistic children did not show reliable enhancements of brain activity (N230 and P340) in response to texture-defined gratings relative to luminance-defined gratings. This suggests reduced efficiency of neuro-integrative mechanisms operating at a perceptual level in autism. These results are in line with the idea that visual atypicalities mediated by intermediate-scale neural networks emerge before or during the school-age period in autism. PMID:24205355

Luminance- and texture-defined information processing in school-aged children with autism.

PubMed

Rivest, Jessica B; Jemel, Boutheina; Bertone, Armando; McKerral, Michelle; Mottron, Laurent

2013-01-01

According to the complexity-specific hypothesis, the efficacy with which individuals with autism spectrum disorder (ASD) process visual information varies according to the extensiveness of the neural network required to process stimuli. Specifically, adults with ASD are less sensitive to texture-defined (or second-order) information, which necessitates the implication of several cortical visual areas. Conversely, the sensitivity to simple, luminance-defined (or first-order) information, which mainly relies on primary visual cortex (V1) activity, has been found to be either superior (static material) or intact (dynamic material) in ASD. It is currently unknown if these autistic perceptual alterations are present in childhood. In the present study, behavioural (threshold) and electrophysiological measures were obtained for static luminance- and texture-defined gratings presented to school-aged children with ASD and compared to those of typically developing children. Our behavioural and electrophysiological (P140) results indicate that luminance processing is likely unremarkable in autistic children. With respect to texture processing, there was no significant threshold difference between groups. However, unlike typical children, autistic children did not show reliable enhancements of brain activity (N230 and P340) in response to texture-defined gratings relative to luminance-defined gratings. This suggests reduced efficiency of neuro-integrative mechanisms operating at a perceptual level in autism. These results are in line with the idea that visual atypicalities mediated by intermediate-scale neural networks emerge before or during the school-age period in autism.

Spike detection, characterization, and discrimination using feature analysis software written in LabVIEW.

PubMed

Stewart, C M; Newlands, S D; Perachio, A A

2004-12-01

Rapid and accurate discrimination of single units from extracellular recordings is a fundamental process for the analysis and interpretation of electrophysiological recordings. We present an algorithm that performs detection, characterization, discrimination, and analysis of action potentials from extracellular recording sessions. The program was entirely written in LabVIEW (National Instruments), and requires no external hardware devices or a priori information about action potential shapes. Waveform events are detected by scanning the digital record for voltages that exceed a user-adjustable trigger. Detected events are characterized to determine nine different time and voltage levels for each event. Various algebraic combinations of these waveform features are used as axis choices for 2-D Cartesian plots of events. The user selects axis choices that generate distinct clusters. Multiple clusters may be defined as action potentials by manually generating boundaries of arbitrary shape. Events defined as action potentials are validated by visual inspection of overlain waveforms. Stimulus-response relationships may be identified by selecting any recorded channel for comparison to continuous and average cycle histograms of binned unit data. The algorithm includes novel aspects of feature analysis and acquisition, including higher acquisition rates for electrophysiological data compared to other channels. The program confirms that electrophysiological data may be discriminated with high-speed and efficiency using algebraic combinations of waveform features derived from high-speed digital records.

Human Cerebrospinal Fluid Promotes Neuronal Viability and Activity of Hippocampal Neuronal Circuits In Vitro

PubMed Central

Perez-Alcazar, Marta; Culley, Georgia; Lyckenvik, Tim; Mobarrez, Kristoffer; Bjorefeldt, Andreas; Wasling, Pontus; Seth, Henrik; Asztely, Frederik; Harrer, Andrea; Iglseder, Bernhard; Aigner, Ludwig; Hanse, Eric; Illes, Sebastian

2016-01-01

For decades it has been hypothesized that molecules within the cerebrospinal fluid (CSF) diffuse into the brain parenchyma and influence the function of neurons. However, the functional consequences of CSF on neuronal circuits are largely unexplored and unknown. A major reason for this is the absence of appropriate neuronal in vitro model systems, and it is uncertain if neurons cultured in pure CSF survive and preserve electrophysiological functionality in vitro. In this article, we present an approach to address how human CSF (hCSF) influences neuronal circuits in vitro. We validate our approach by comparing the morphology, viability, and electrophysiological function of single neurons and at the network level in rat organotypic slice and primary neuronal cultures cultivated either in hCSF or in defined standard culture media. Our results demonstrate that rodent hippocampal slices and primary neurons cultured in hCSF maintain neuronal morphology and preserve synaptic transmission. Importantly, we show that hCSF increases neuronal viability and the number of electrophysiologically active neurons in comparison to the culture media. In summary, our data indicate that hCSF represents a physiological environment for neurons in vitro and a superior culture condition compared to the defined standard media. Moreover, this experimental approach paves the way to assess the functional consequences of CSF on neuronal circuits as well as suggesting a novel strategy for central nervous system (CNS) disease modeling. PMID:26973467

Cascaded discrimination of normal, abnormal, and confounder classes in histopathology: Gleason grading of prostate cancer

PubMed Central

2012-01-01

Background Automated classification of histopathology involves identification of multiple classes, including benign, cancerous, and confounder categories. The confounder tissue classes can often mimic and share attributes with both the diseased and normal tissue classes, and can be particularly difficult to identify, both manually and by automated classifiers. In the case of prostate cancer, they may be several confounding tissue types present in a biopsy sample, posing as major sources of diagnostic error for pathologists. Two common multi-class approaches are one-shot classification (OSC), where all classes are identified simultaneously, and one-versus-all (OVA), where a “target” class is distinguished from all “non-target” classes. OSC is typically unable to handle discrimination of classes of varying similarity (e.g. with images of prostate atrophy and high grade cancer), while OVA forces several heterogeneous classes into a single “non-target” class. In this work, we present a cascaded (CAS) approach to classifying prostate biopsy tissue samples, where images from different classes are grouped to maximize intra-group homogeneity while maximizing inter-group heterogeneity. Results We apply the CAS approach to categorize 2000 tissue samples taken from 214 patient studies into seven classes: epithelium, stroma, atrophy, prostatic intraepithelial neoplasia (PIN), and prostate cancer Gleason grades 3, 4, and 5. A series of increasingly granular binary classifiers are used to split the different tissue classes until the images have been categorized into a single unique class. Our automatically-extracted image feature set includes architectural features based on location of the nuclei within the tissue sample as well as texture features extracted on a per-pixel level. The CAS strategy yields a positive predictive value (PPV) of 0.86 in classifying the 2000 tissue images into one of 7 classes, compared with the OVA (0.77 PPV) and OSC approaches (0.76 PPV). Conclusions Use of the CAS strategy increases the PPV for a multi-category classification system over two common alternative strategies. In classification problems such as histopathology, where multiple class groups exist with varying degrees of heterogeneity, the CAS system can intelligently assign class labels to objects by performing multiple binary classifications according to domain knowledge. PMID:23110677

Medical devices; immunology and microbiology devices; classification of John Cunningham Virus serological reagents. Final order.

PubMed

2014-01-23

The Food and Drug Administration (FDA) is classifying John Cunningham Virus (JCV) serological reagents into class II (special controls). The Agency is classifying the device into class II (special controls) in order to provide a reasonable assurance of safety and effectiveness of the device.

Modeling Statistical Insensitivity: Sources of Suboptimal Behavior

ERIC Educational Resources Information Center

Gagliardi, Annie; Feldman, Naomi H.; Lidz, Jeffrey

2017-01-01

Children acquiring languages with noun classes (grammatical gender) have ample statistical information available that characterizes the distribution of nouns into these classes, but their use of this information to classify novel nouns differs from the predictions made by an optimal Bayesian classifier. We use rational analysis to investigate the…

Design of a hybrid model for cardiac arrhythmia classification based on Daubechies wavelet transform.

PubMed

Rajagopal, Rekha; Ranganathan, Vidhyapriya

2018-06-05

Automation in cardiac arrhythmia classification helps medical professionals make accurate decisions about the patient's health. The aim of this work was to design a hybrid classification model to classify cardiac arrhythmias. The design phase of the classification model comprises the following stages: preprocessing of the cardiac signal by eliminating detail coefficients that contain noise, feature extraction through Daubechies wavelet transform, and arrhythmia classification using a collaborative decision from the K nearest neighbor classifier (KNN) and a support vector machine (SVM). The proposed model is able to classify 5 arrhythmia classes as per the ANSI/AAMI EC57: 1998 classification standard. Level 1 of the proposed model involves classification using the KNN and the classifier is trained with examples from all classes. Level 2 involves classification using an SVM and is trained specifically to classify overlapped classes. The final classification of a test heartbeat pertaining to a particular class is done using the proposed KNN/SVM hybrid model. The experimental results demonstrated that the average sensitivity of the proposed model was 92.56%, the average specificity 99.35%, the average positive predictive value 98.13%, the average F-score 94.5%, and the average accuracy 99.78%. The results obtained using the proposed model were compared with the results of discriminant, tree, and KNN classifiers. The proposed model is able to achieve a high classification accuracy.

In vivo recordings of brain activity using organic transistors

PubMed Central

Khodagholy, Dion; Doublet, Thomas; Quilichini, Pascale; Gurfinkel, Moshe; Leleux, Pierre; Ghestem, Antoine; Ismailova, Esma; Hervé, Thierry; Sanaur, Sébastien; Bernard, Christophe; Malliaras, George G.

2013-01-01

In vivo electrophysiological recordings of neuronal circuits are necessary for diagnostic purposes and for brain-machine interfaces. Organic electronic devices constitute a promising candidate because of their mechanical flexibility and biocompatibility. Here we demonstrate the engineering of an organic electrochemical transistor embedded in an ultrathin organic film designed to record electrophysiological signals on the surface of the brain. The device, tested in vivo on epileptiform discharges, displayed superior signal-to-noise ratio due to local amplification compared with surface electrodes. The organic transistor was able to record on the surface low-amplitude brain activities, which were poorly resolved with surface electrodes. This study introduces a new class of biocompatible, highly flexible devices for recording brain activity with superior signal-to-noise ratio that hold great promise for medical applications. PMID:23481383

In vivo recordings of brain activity using organic transistors.

PubMed

Khodagholy, Dion; Doublet, Thomas; Quilichini, Pascale; Gurfinkel, Moshe; Leleux, Pierre; Ghestem, Antoine; Ismailova, Esma; Hervé, Thierry; Sanaur, Sébastien; Bernard, Christophe; Malliaras, George G

2013-01-01

In vivo electrophysiological recordings of neuronal circuits are necessary for diagnostic purposes and for brain-machine interfaces. Organic electronic devices constitute a promising candidate because of their mechanical flexibility and biocompatibility. Here we demonstrate the engineering of an organic electrochemical transistor embedded in an ultrathin organic film designed to record electrophysiological signals on the surface of the brain. The device, tested in vivo on epileptiform discharges, displayed superior signal-to-noise ratio due to local amplification compared with surface electrodes. The organic transistor was able to record on the surface low-amplitude brain activities, which were poorly resolved with surface electrodes. This study introduces a new class of biocompatible, highly flexible devices for recording brain activity with superior signal-to-noise ratio that hold great promise for medical applications.

Autosomal recessive type II hereditary motor and sensory neuropathy with acrodystrophy.

PubMed

Thomas, P K; Claus, D; King, R H

1999-02-01

A family is described with presumed autosomal recessive inheritance in which three siblings developed a progressive neuropathy that combined limb weakness and severe distal sensory loss leading to prominent mutilating changes. Electrophysiological and nerve biopsy findings indicated an axonopathy. The disorder is therefore classifiable as type II hereditary motor and sensory neuropathy (HMSN II). The clinical features differ from those reported in previously described cases of autosomal recessive HMSN II. This disorder may therefore represent a new variant.

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.

Command-line cellular electrophysiology for conventional and real-time closed-loop experiments.

PubMed

Linaro, Daniele; Couto, João; Giugliano, Michele

2014-06-15

Current software tools for electrophysiological experiments are limited in flexibility and rarely offer adequate support for advanced techniques such as dynamic clamp and hybrid experiments, which are therefore limited to laboratories with a significant expertise in neuroinformatics. We have developed lcg, a software suite based on a command-line interface (CLI) that allows performing both standard and advanced electrophysiological experiments. Stimulation protocols for classical voltage and current clamp experiments are defined by a concise and flexible meta description that allows representing complex waveforms as a piece-wise parametric decomposition of elementary sub-waveforms, abstracting the stimulation hardware. To perform complex experiments lcg provides a set of elementary building blocks that can be interconnected to yield a large variety of experimental paradigms. We present various cellular electrophysiological experiments in which lcg has been employed, ranging from the automated application of current clamp protocols for characterizing basic electrophysiological properties of neurons, to dynamic clamp, response clamp, and hybrid experiments. We finally show how the scripting capabilities behind a CLI are suited for integrating experimental trials into complex workflows, where actual experiment, online data analysis and computational modeling seamlessly integrate. We compare lcg with two open source toolboxes, RTXI and RELACS. We believe that lcg will greatly contribute to the standardization and reproducibility of both simple and complex experiments. Additionally, on the long run the increased efficiency due to a CLI will prove a great benefit for the experimental community. Copyright © 2014 Elsevier B.V. All rights reserved.

Clinical and electrophysiological characteristics of symmetric polyneuropathy in a cohort of systemic lupus erythematosus patients.

PubMed

Jasmin, R; Sockalingam, S; Ramanaidu, L P; Goh, K J

2015-03-01

Peripheral neuropathy in systemic lupus erythematosus (SLE) is heterogeneous and its commonest pattern is symmetrical polyneuropathy. The aim of this study was to describe the prevalence, clinical and electrophysiological features, disease associations and effects on function and quality of life of polyneuropathy in SLE patients, defined using combined clinical and electrophysiological diagnostic criteria. Consecutive SLE patients seen at the University of Malaya Medical Centre were included. Patients with medication and other disorders known to cause neuropathy were excluded. Demographic, clinical and laboratory data were obtained using a pre-defined questionnaire. Function and health-related quality of life was assessed using the modified Rankin scale and the SF-36 scores. Nerve conduction studies (NCS) were carried out in both upper and lower limbs. Polyneuropathy was defined as the presence of bilateral clinical symptoms and/or signs and bilateral abnormal NCS parameters. Of 150 patients, 23 (15.3%) had polyneuropathy. SLE-related polyneuropathy was mainly characterized by sensory symptoms of numbness/tingling and pain with mild signs of absent ankle reflexes and reduced pain sensation. Function was minimally affected and there were no differences in quality of life scores. NCS abnormalities suggested mild length-dependent axonal neuropathy, primarily in the distal lower limbs. Compared to those without polyneuropathy, SLE-related polyneuropathy patients were significantly older but had no other significant demographic or disease associations. SLE-related polyneuropathy is a chronic, axonal and predominantly sensory neuropathy, associated with older age. Its underlying pathogenetic mechanisms are unknown, although a possibility could be an increased susceptibility of peripheral nerves in SLE patients to effects of aging. © The Author(s) 2015 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav.

Predicting hepatotoxicity using ToxCast in vitro bioactivity and ...

EPA Pesticide Factsheets

Background: The U.S. EPA ToxCastTM program is screening thousands of environmental chemicals for bioactivity using hundreds of high-throughput in vitro assays to build predictive models of toxicity. We represented chemicals based on bioactivity and chemical structure descriptors then used supervised machine learning to predict their hepatotoxic effects.Results: A set of 677 chemicals were represented by 711 in vitro bioactivity descriptors (from ToxCast assays), 4,376 chemical structure descriptors (from QikProp, OpenBabel, PADEL, and PubChem), and three hepatotoxicity categories (from animal studies). Hepatotoxicants were defined by rat liver histopathology observed after chronic chemical testing and grouped into hypertrophy (161), injury (101) and proliferative lesions (99). Classifiers were built using six machine learning algorithms: linear discriminant analysis (LDA), Naïve Bayes (NB), support vector classification (SVM), classification and regression trees (CART), k-nearest neighbors (KNN) and an ensemble of classifiers (ENSMB). Classifiers of hepatotoxicity were built using chemical structure, ToxCast bioactivity, and a hybrid representation. Predictive performance was evaluated using 10-fold cross-validation testing and in-loop, filter-based, feature subset selection. Hybrid classifiers had the best balanced accuracy for predicting hypertrophy (0.78±0.08), injury (0.73±0.10) and proliferative lesions (0.72±0.09). Though chemical and bioactivity class

On Identifying Clusters Within the C-type Asteroids of the Sloan Digital Sky Survey

NASA Astrophysics Data System (ADS)

Poole, Renae; Ziffer, J.; Harvell, T.

2012-10-01

We applied AutoClass, a data mining technique based upon Bayesian Classification, to C-group asteroid colors in the Sloan Digital Sky Survey (SDSS). Previous taxonomic studies relied mostly on Principal Component Analysis (PCA) to differentiate asteroids within the C-group (e.g. B, G, F, Ch, Cg and Cb). AutoClass's advantage is that it calculates the most probable classification for us, removing the human factor from this part of the analysis. In our results, AutoClass divided the C-groups into two large classes and six smaller classes. The two large classes (n=4974 and 2033, respectively) display distinct regions with some overlap in color-vs-color plots. Each cluster's average spectrum is compared to 'typical' spectra of the C-group subtypes as defined by Tholen (1989) and each cluster's members are evaluated for consistency with previous taxonomies. Of the 117 asteroids classified as B-type in previous taxonomies, only 12 were found with SDSS colors that matched our criteria of having less than 0.1 magnitude error in u and 0.05 magnitude error in g, r, i, and z colors. Although this is a relatively small group, 11 of the 12 B-types were placed by AutoClass in the same cluster. By determining the C-group sub-classifications in the large SDSS database, this research furthers our understanding of the stratigraphy and composition of the main-belt.

On the classification of seawater intrusion

NASA Astrophysics Data System (ADS)

Werner, Adrian D.

2017-08-01

Seawater intrusion (SWI) arising from aquifer depletion is often classified as ;active; or ;passive;, depending on whether seawater moves in the same direction as groundwater flow or not. However, recent studies have demonstrated that alternative forms of active SWI show distinctly different characteristics, to the degree that the term ;active SWI; may be misleading without additional qualification. In response, this article proposes to modify hydrogeology lexicon by defining and characterizing three classes of SWI, namely passive SWI, passive-active SWI and active SWI. The threshold parameter combinations for the onset of each form of SWI are developed using sharp-interface, steady-state analytical solutions. Numerical simulation is then applied to a hypothetical case study to test the developed theory and to provide additional insights into dispersive SWI behavior. The results indicate that the three classes of SWI are readily predictable, with the exception of active SWI occurring in the presence of distributed recharge. The key characteristics of each SWI class are described to distinguish their most defining features. For example, active SWI occurring in aquifers receiving distributed recharge only creates watertable salinization downstream of the groundwater mound and only where dispersion effects are significant. The revised classification of SWI proposed in this article, along with the analysis of thresholds and SWI characteristics, provides coastal aquifer custodians with an improved basis upon which to expect salinization mechanisms to impact freshwater availability following aquifer depletion.

Poor-quality antimalarial drugs in southeast Asia and sub-Saharan Africa.

PubMed

Nayyar, Gaurvika M L; Breman, Joel G; Newton, Paul N; Herrington, James

2012-06-01

Poor-quality antimalarial drugs lead to drug resistance and inadequate treatment, which pose an urgent threat to vulnerable populations and jeopardise progress and investments in combating malaria. Emergence of artemisinin resistance or tolerance in Plasmodium falciparum on the Thailand-Cambodia border makes protection of the effectiveness of the drug supply imperative. We reviewed published and unpublished studies reporting chemical analyses and assessments of packaging of antimalarial drugs. Of 1437 samples of drugs in five classes from seven countries in southeast Asia, 497 (35%) failed chemical analysis, 423 (46%) of 919 failed packaging analysis, and 450 (36%) of 1260 were classified as falsified. In 21 surveys of drugs from six classes from 21 countries in sub-Saharan Africa, 796 (35%) of 2297 failed chemical analysis, 28 (36%) of 77 failed packaging analysis, and 79 (20%) of 389 were classified as falsified. Data were insufficient to identify the frequency of substandard (products resulting from poor manufacturing) antimalarial drugs, and packaging analysis data were scarce. Concurrent interventions and a multifaceted approach are needed to define and eliminate criminal production, distribution, and poor manufacturing of antimalarial drugs. Empowering of national medicine regulatory authorities to protect the global drug supply is more important than ever. Copyright © 2012 Elsevier Ltd. All rights reserved.

Atrial Model Development and Prototype Simulations: CRADA Final Report on Tasks 3 and 4

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

O'Hara, T.; Zhang, X.; Villongco, C.

2016-10-28

The goal of this CRADA was to develop essential tools needed to simulate human atrial electrophysiology in 3-dimensions using an anatomical image-based anatomy and physiologically detailed human cellular model. The atria were modeled as anisotropic, representing the preferentially longitudinal electrical coupling between myocytes. Across the entire anatomy, cellular electrophysiology was heterogeneous, with left and right atrial myocytes defined differently. Left and right cell types for the “control” case of sinus rhythm (SR) was compared with remodeled electrophysiology and calcium cycling characteristics of chronic atrial fibrillation (cAF). The effects of Isoproterenol (ISO), a beta-adrenergic agonist that represents the functional consequences ofmore » PKA phosphorylation of various ion channels and transporters, was also simulated in SR and cAF to represent atrial activity under physical or emotional stress. Results and findings from Tasks 3 & 4 are described. Tasks 3 and 4 are, respectively: Input parameters prepared for a Cardioid simulation; Report including recommendations for additional scenario development and post-processing analytic strategy.« less

Correlating multispectral imaging and compositional data from the Mars Exploration Rovers and implications for Mars Science Laboratory

NASA Astrophysics Data System (ADS)

Anderson, Ryan B.; Bell, James F.

2013-03-01

In an effort to infer compositional information about distant targets based on multispectral imaging data, we investigated methods of relating Mars Exploration Rover (MER) Pancam multispectral remote sensing observations to in situ alpha particle X-ray spectrometer (APXS)-derived elemental abundances and Mössbauer (MB)-derived abundances of Fe-bearing phases at the MER field sites in Gusev crater and Meridiani Planum. The majority of the partial correlation coefficients between these data sets were not statistically significant. Restricting the targets to those that were abraded by the rock abrasion tool (RAT) led to improved Pearson’s correlations, most notably between the red-blue ratio (673 nm/434 nm) and Fe3+-bearing phases, but partial correlations were not statistically significant. Partial Least Squares (PLS) calculations relating Pancam 11-color visible to near-IR (VNIR; ∼400-1000 nm) “spectra” to APXS and Mössbauer element or mineral abundances showed generally poor performance, although the presence of compositional outliers led to improved PLS results for data from Meridiani. When the Meridiani PLS model for pyroxene was tested by predicting the pyroxene content of Gusev targets, the results were poor, indicating that the PLS models for Meridiani are not applicable to data from other sites. Soft Independent Modeling of Class Analogy (SIMCA) classification of Gusev crater data showed mixed results. Of the 24 Gusev test regions of interest (ROIs) with known classes, 11 had >30% of the pixels in the ROI classified correctly, while others were mis-classified or unclassified. k-Means clustering of APXS and Mössbauer data was used to assign Meridiani targets to compositional classes. The clustering-derived classes corresponded to meaningful geologic and/or color unit differences, and SIMCA classification using these classes was somewhat successful, with >30% of pixels correctly classified in 9 of the 11 ROIs with known classes. This work shows that the relationship between SWIR multispectral imaging data and APXS- and Mössbauer-derived composition/mineralogy is often weak, a perhaps not entirely unexpected result given the different surface sampling depths of SWIR imaging (uppermost few microns) vs. APXS (tens of μm) and MB measurements (hundreds of μm). Results from the upcoming Mars Science Laboratory (MSL) rover’s ChemCam Laser Induced Breakdown Spectroscopy (LIBS) instrument may show a closer relationship to Mastcam SWIR multispectral observations, however, because the initial laser shots onto a target will analyze only the upper few micrometers of the surface. The clustering and classification methods used in this study can be applied to any data set to formalize the definition of classes and identify targets that do not fit in previously defined classes.

Correlating multispectral imaging and compositional data from the Mars Exploration Rovers and implications for Mars Science Laboratory

USGS Publications Warehouse

Anderson, Ryan B.; Bell, James F.

2013-01-01

In an effort to infer compositional information about distant targets based on multispectral imaging data, we investigated methods of relating Mars Exploration Rover (MER) Pancam multispectral remote sensing observations to in situ alpha particle X-ray spectrometer (APXS)-derived elemental abundances and Mössbauer (MB)-derived abundances of Fe-bearing phases at the MER field sites in Gusev crater and Meridiani Planum. The majority of the partial correlation coefficients between these data sets were not statistically significant. Restricting the targets to those that were abraded by the rock abrasion tool (RAT) led to improved Pearson’s correlations, most notably between the red–blue ratio (673 nm/434 nm) and Fe3+-bearing phases, but partial correlations were not statistically significant. Partial Least Squares (PLS) calculations relating Pancam 11-color visible to near-IR (VNIR; ∼400–1000 nm) “spectra” to APXS and Mössbauer element or mineral abundances showed generally poor performance, although the presence of compositional outliers led to improved PLS results for data from Meridiani. When the Meridiani PLS model for pyroxene was tested by predicting the pyroxene content of Gusev targets, the results were poor, indicating that the PLS models for Meridiani are not applicable to data from other sites. Soft Independent Modeling of Class Analogy (SIMCA) classification of Gusev crater data showed mixed results. Of the 24 Gusev test regions of interest (ROIs) with known classes, 11 had >30% of the pixels in the ROI classified correctly, while others were mis-classified or unclassified. k-Means clustering of APXS and Mössbauer data was used to assign Meridiani targets to compositional classes. The clustering-derived classes corresponded to meaningful geologic and/or color unit differences, and SIMCA classification using these classes was somewhat successful, with >30% of pixels correctly classified in 9 of the 11 ROIs with known classes. This work shows that the relationship between SWIR multispectral imaging data and APXS- and Mössbauer-derived composition/mineralogy is often weak, a perhaps not entirely unexpected result given the different surface sampling depths of SWIR imaging (uppermost few microns) vs. APXS (tens of μm) and MB measurements (hundreds of μm). Results from the upcoming Mars Science Laboratory (MSL) rover’s ChemCam Laser Induced Breakdown Spectroscopy (LIBS) instrument may show a closer relationship to Mastcam SWIR multispectral observations, however, because the initial laser shots onto a target will analyze only the upper few micrometers of the surface. The clustering and classification methods used in this study can be applied to any data set to formalize the definition of classes and identify targets that do not fit in previously defined classes.

Classifying human operator functional state based on electrophysiological and performance measures and fuzzy clustering method.

PubMed

Zhang, Jian-Hua; Peng, Xiao-Di; Liu, Hua; Raisch, Jörg; Wang, Ru-Bin

2013-12-01

The human operator's ability to perform their tasks can fluctuate over time. Because the cognitive demands of the task can also vary it is possible that the capabilities of the operator are not sufficient to satisfy the job demands. This can lead to serious errors when the operator is overwhelmed by the task demands. Psychophysiological measures, such as heart rate and brain activity, can be used to monitor operator cognitive workload. In this paper, the most influential psychophysiological measures are extracted to characterize Operator Functional State (OFS) in automated tasks under a complex form of human-automation interaction. The fuzzy c-mean (FCM) algorithm is used and tested for its OFS classification performance. The results obtained have shown the feasibility and effectiveness of the FCM algorithm as well as the utility of the selected input features for OFS classification. Besides being able to cope with nonlinearity and fuzzy uncertainty in the psychophysiological data it can provide information about the relative importance of the input features as well as the confidence estimate of the classification results. The OFS pattern classification method developed can be incorporated into an adaptive aiding system in order to enhance the overall performance of a large class of safety-critical human-machine cooperative systems.

Vegetation classification and distribution mapping report Mesa Verde National Park

USGS Publications Warehouse

Thomas, Kathryn A.; McTeague, Monica L.; Ogden, Lindsay; Floyd, M. Lisa; Schulz, Keith; Friesen, Beverly A.; Fancher, Tammy; Waltermire, Robert G.; Cully, Anne

2009-01-01

The classification and distribution mapping of the vegetation of Mesa Verde National Park (MEVE) and surrounding environment was achieved through a multi-agency effort between 2004 and 2007. The National Park Service’s Southern Colorado Plateau Network facilitated the team that conducted the work, which comprised the U.S. Geological Survey’s Southwest Biological Science Center, Fort Collins Research Center, and Rocky Mountain Geographic Science Center; Northern Arizona University; Prescott College; and NatureServe. The project team described 47 plant communities for MEVE, 34 of which were described from quantitative classification based on f eld-relevé data collected in 1993 and 2004. The team derived 13 additional plant communities from field observations during the photointerpretation phase of the project. The National Vegetation Classification Standard served as a framework for classifying these plant communities to the alliance and association level. Eleven of the 47 plant communities were classified as “park specials;” that is, plant communities with insufficient data to describe them as new alliances or associations. The project team also developed a spatial vegetation map database representing MEVE, with three different map-class schemas: base, group, and management map classes. The base map classes represent the fi nest level of spatial detail. Initial polygons were developed using Definiens Professional (at the time of our use, this software was called eCognition), assisted by interpretation of 1:12,000 true-color digital orthophoto quarter quadrangles (DOQQs). These polygons (base map classes) were labeled using manual photo interpretation of the DOQQs and 1:12,000 true-color aerial photography. Field visits verified interpretation concepts. The vegetation map database includes 46 base map classes, which consist of associations, alliances, and park specials classified with quantitative analysis, additional associations and park specials noted during photointerpretation, and non-vegetated land cover, such as infrastructure, land use, and geological land cover. The base map classes consist of 5,007 polygons in the project area. A field-based accuracy assessment of the base map classes showed overall accuracy to be 43.5%. Seven map classes comprise 89.1% of the park vegetated land cover. The group map classes represent aggregations of the base map classes, approximating the group level of the National Vegetation Classification Standard, version 2 (Federal Geographic Data Committee 2007), and reflecting physiognomy and floristics. Terrestrial ecological systems, as described by NatureServe (Comer et al. 2003), were used as the fi rst approximation of the group level. The project team identified 14 group map classes for this project. The overall accuracy of the group map classes was determined using the same accuracy assessment data as for the base map classes. The overall accuracy of the group representation of vegetation was 80.3%. In consultation with park staff , the team developed management map classes, consisting of park-defined groupings of base map classes intended to represent a balance between maintaining required accuracy and providing a focus on vegetation of particular interest or import to park managers. The 23 management map classes had an overall accuracy of 73.3%. While the main products of this project are the vegetation classification and the vegetation map database, a number of ancillary digital geographic information system and database products were also produced that can be used independently or to augment the main products. These products include shapefiles of the locations of field-collected data and relational databases of field-collected data.

76 FR 34845 - Medical Devices; Ear, Nose, and Throat Devices; Classification of the Wireless Air-Conduction...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-06-15

... control by other users with a similar medical device. Exposure to non-ionizing radiation Wireless... Administration (FDA) is classifying the wireless air-conduction hearing aid into class II (special controls). The Agency is classifying the device into class II (special controls) in order to provide a reasonable...

77 FR 8117 - Medical Devices; Cardiovascular Devices; Classification of the Endovascular Suturing System

Federal Register 2010, 2011, 2012, 2013, 2014

2012-02-14

... Administration (FDA) is classifying the endovascular suturing system into class II (special controls). The Agency is classifying the device into class II (special controls) in order to provide a reasonable assurance..., or FDA issues an order finding the device to be substantially equivalent, in accordance with section...

Incorporating spatial context into statistical classification of multidimensional image data

NASA Technical Reports Server (NTRS)

Bauer, M. E. (Principal Investigator); Tilton, J. C.; Swain, P. H.

1981-01-01

Compound decision theory is employed to develop a general statistical model for classifying image data using spatial context. The classification algorithm developed from this model exploits the tendency of certain ground-cover classes to occur more frequently in some spatial contexts than in others. A key input to this contextural classifier is a quantitative characterization of this tendency: the context function. Several methods for estimating the context function are explored, and two complementary methods are recommended. The contextural classifier is shown to produce substantial improvements in classification accuracy compared to the accuracy produced by a non-contextural uniform-priors maximum likelihood classifier when these methods of estimating the context function are used. An approximate algorithm, which cuts computational requirements by over one-half, is presented. The search for an optimal implementation is furthered by an exploration of the relative merits of using spectral classes or information classes for classification and/or context function estimation.

Multiple hypotheses image segmentation and classification with application to dietary assessment.

PubMed

Zhu, Fengqing; Bosch, Marc; Khanna, Nitin; Boushey, Carol J; Delp, Edward J

2015-01-01

We propose a method for dietary assessment to automatically identify and locate food in a variety of images captured during controlled and natural eating events. Two concepts are combined to achieve this: a set of segmented objects can be partitioned into perceptually similar object classes based on global and local features; and perceptually similar object classes can be used to assess the accuracy of image segmentation. These ideas are implemented by generating multiple segmentations of an image to select stable segmentations based on the classifier's confidence score assigned to each segmented image region. Automatic segmented regions are classified using a multichannel feature classification system. For each segmented region, multiple feature spaces are formed. Feature vectors in each of the feature spaces are individually classified. The final decision is obtained by combining class decisions from individual feature spaces using decision rules. We show improved accuracy of segmenting food images with classifier feedback.

Application of texture analysis method for mammogram density classification

NASA Astrophysics Data System (ADS)

Nithya, R.; Santhi, B.

2017-07-01

Mammographic density is considered a major risk factor for developing breast cancer. This paper proposes an automated approach to classify breast tissue types in digital mammogram. The main objective of the proposed Computer-Aided Diagnosis (CAD) system is to investigate various feature extraction methods and classifiers to improve the diagnostic accuracy in mammogram density classification. Texture analysis methods are used to extract the features from the mammogram. Texture features are extracted by using histogram, Gray Level Co-Occurrence Matrix (GLCM), Gray Level Run Length Matrix (GLRLM), Gray Level Difference Matrix (GLDM), Local Binary Pattern (LBP), Entropy, Discrete Wavelet Transform (DWT), Wavelet Packet Transform (WPT), Gabor transform and trace transform. These extracted features are selected using Analysis of Variance (ANOVA). The features selected by ANOVA are fed into the classifiers to characterize the mammogram into two-class (fatty/dense) and three-class (fatty/glandular/dense) breast density classification. This work has been carried out by using the mini-Mammographic Image Analysis Society (MIAS) database. Five classifiers are employed namely, Artificial Neural Network (ANN), Linear Discriminant Analysis (LDA), Naive Bayes (NB), K-Nearest Neighbor (KNN), and Support Vector Machine (SVM). Experimental results show that ANN provides better performance than LDA, NB, KNN and SVM classifiers. The proposed methodology has achieved 97.5% accuracy for three-class and 99.37% for two-class density classification.

Electrodiagnostic and clinical aspects of Guillain-Barré syndrome: an analysis of 142 cases.

PubMed

Gupta, Deepak; Deepak, Gupta; Nair, Muraleedharan; Muraleedharan, Nair; Baheti, Neeraj N; Sarma, P Sankara; Sarma, Sankara P; Kuruvilla, Abraham; Abraham, Kuruvilla

2008-12-01

The incidence of Guillain-Barré syndrome (GBS) and its subtypes varies throughout the world. We present a retrospective analysis of 142 GBS cases, treated at our center, aimed at classifying GBS electrophysiologically, to study the sequential electrophysiological changes in cases with acute inflammatory demyelinating polyradiculoneuropathy (AIDP), and to look for any clinical and cerebrospinal fluid parameters that can also help in distinguishing the subtypes. One hundred twenty-one (85.2%) cases had AIDP, 15 (10.6%) had acute motor axonal neuropathy, and 6 (4.2%) were unclassifiable. Motor conduction blocks and temporal dispersion could be observed from days 3 and 5 onward, respectively. Progression of motor conduction slowing in AIDP was most impressive in the median nerves. Varying affection of deep tendon reflexes, cranial nerves, and cerebrospinal fluid albuminocytological dissociation can also help make a distinction between AIDP and acute motor axonal neuropathy. Sural sparing, a marker of demyelinating neuropathy, is more commonly seen in later than in early stages of AIDP.

Management of pediatric tachyarrhythmias on mechanical support.

PubMed

Silva, Jennifer N A; Erickson, Christopher C; Carter, Christopher D; Greene, E Anne; Kantoch, Michal; Collins, Kathryn K; Miyake, Christina Y; Carboni, Michael P; Rhee, Edward K; Papez, Andrew; Anand, Vijay; Bowman, Tammy M; Van Hare, George F

2014-08-01

Pediatric patients with persistent arrhythmias may require mechanical cardiopulmonary support. We sought to classify the population, spectrum, and success of current treatment strategies. A multicenter retrospective chart review was undertaken at 11 sites. Inclusion criteria were (1) patients <21 years, (2) initiation of mechanical support for a primary diagnosis of arrhythmias, and (3) actively treated on mechanical support. A total of 39 patients were identified with a median age of 5.5 months and median weight of 6 kg. A total of 69% of patients were cannulated for supraventricular tachycardia with a median rate of 230 beats per minute. A total of 90% of patients were supported with extracorporeal membrane oxygenation for an average of 5 days. The remaining 10% were supported with ventricular assist devices for an average of 38 (20-60) days. A total of 95% of patients were treated with antiarrhythmics, with 43% requiring >1 antiarrhythmic. Amiodarone was the most frequently used medication alone or in combination. A total of 33% patients underwent electrophysiology study/transcatheter ablation. Radiofrequency ablation was successful in 9 patients on full flow extracorporeal membrane oxygenation with 3 radiofrequency-failures/conversion to cryoablation. One patient underwent primary cryoablation. A total of 15% of complications were related to electrophysiology study/ablation. At follow-up, 23 patients were alive, 8 expired, and 8 transplanted. Younger patients were more likely to require support in the presented population. Most patients were treated with antiarrhythmics and one third required electrophysiology study/ablation. Radiofrequency ablation is feasible without altering extracorporeal membrane oxygenation flows. There was a low frequency of acute adverse events in patients undergoing electrophysiology study/ablation, while on extracorporeal membrane oxygenation. © 2014 American Heart Association, Inc.

Vision based nutrient deficiency classification in maize plants using multi class support vector machines

NASA Astrophysics Data System (ADS)

Leena, N.; Saju, K. K.

2018-04-01

Nutritional deficiencies in plants are a major concern for farmers as it affects productivity and thus profit. The work aims to classify nutritional deficiencies in maize plant in a non-destructive mannerusing image processing and machine learning techniques. The colored images of the leaves are analyzed and classified with multi-class support vector machine (SVM) method. Several images of maize leaves with known deficiencies like nitrogen, phosphorous and potassium (NPK) are used to train the SVM classifier prior to the classification of test images. The results show that the method was able to classify and identify nutritional deficiencies.

A novel modular ANN architecture for efficient monitoring of gases/odours in real-time

NASA Astrophysics Data System (ADS)

Mishra, A.; Rajput, N. S.

2018-04-01

Data pre-processing is tremendously used for enhanced classification of gases. However, it suppresses the concentration variances of different gas samples. A classical solution of using single artificial neural network (ANN) architecture is also inefficient and renders degraded quantification. In this paper, a novel modular ANN design has been proposed to provide an efficient and scalable solution in real–time. Here, two separate ANN blocks viz. classifier block and quantifier block have been used to provide efficient and scalable gas monitoring in real—time. The classifier ANN consists of two stages. In the first stage, the Net 1-NDSRT has been trained to transform raw sensor responses into corresponding virtual multi-sensor responses using normalized difference sensor response transformation (NDSRT). These responses have been fed to the second stage (i.e., Net 2-classifier ). The Net 2-classifier has been trained to classify various gas samples to their respective class. Further, the quantifier block has parallel ANN modules, multiplexed to quantify each gas. Therefore, the classifier ANN decides class and quantifier ANN decides the exact quantity of the gas/odor present in the respective sample of that class.

Classification of asteroid spectra using a neural network

NASA Technical Reports Server (NTRS)

Howell, E. S.; Merenyi, E.; Lebofsky, L. A.

1994-01-01

The 52-color asteroid survey (Bell et al., 1988) together with the 8-color asteroid survey (Zellner et al., 1985) provide a data set of asteroid spectra spanning 0.3-2.5 micrometers. An artificial neural network clusters these asteroid spectra based on their similarity to each other. We have also trained the neural network with a categorization learning output layer in a supervised mode to associate the established clusters with taxonomic classes. Results of our classification agree with Tholen's classification based on the 8-color data alone. When extending the spectral range using the 52-color survey data, we find that some modification of the Tholen classes is indicated to produce a cleaner, self-consistent set of taxonomic classes. After supervised training using our modified classes, the network correctly classifies both the training examples, and additional spectra into the correct class with an average of 90% accuracy. Our classification supports the separation of the K class from the S class, as suggested by Bell et al. (1987), based on the near-infrared spectrum. We define two end-member subclasses which seem to have compositional significance within the S class: the So class, which is olivine-rich and red, and the Sp class, which is pyroxene-rich and less red. The remaining S-class asteroids have intermediate compositions of both olivine and pyroxene and moderately red continua. The network clustering suggests some additional structure within the E-, M-, and P-class asteroids, even in the absence of albedo information, which is the only discriminant between these in the Tholen classification. New relationships are seen between the C class and related G, B, and F classes. However, in both cases, the number of spectra is too small to interpret or determine the significance of these separations.

StrateGene: object-oriented programming in molecular biology.

PubMed

Carhart, R E; Cash, H D; Moore, J F

1988-03-01

This paper describes some of the ways that object-oriented programming methodologies have been used to represent and manipulate biological information in a working application. When running on a Xerox 1100 series computer, StrateGene functions as a genetic engineering workstation for the management of information about cloning experiments. It represents biological molecules, enzymes, fragments, and methods as classes, subclasses, and members in a hierarchy of objects. These objects may have various attributes, which themselves can be defined and classified. The attributes and their values can be passed from the classes of objects down to the subclasses and members. The user can modify the objects and their attributes while using them. New knowledge and changes to the system can be incorporated relatively easily. The operations on the biological objects are associated with the objects themselves. This makes it easier to invoke them correctly and allows generic operations to be customized for the particular object.

Mining big data sets of plankton images: a zero-shot learning approach to retrieve labels without training data

NASA Astrophysics Data System (ADS)

Orenstein, E. C.; Morgado, P. M.; Peacock, E.; Sosik, H. M.; Jaffe, J. S.

2016-02-01

Technological advances in instrumentation and computing have allowed oceanographers to develop imaging systems capable of collecting extremely large data sets. With the advent of in situ plankton imaging systems, scientists must now commonly deal with "big data" sets containing tens of millions of samples spanning hundreds of classes, making manual classification untenable. Automated annotation methods are now considered to be the bottleneck between collection and interpretation. Typically, such classifiers learn to approximate a function that predicts a predefined set of classes for which a considerable amount of labeled training data is available. The requirement that the training data span all the classes of concern is problematic for plankton imaging systems since they sample such diverse, rapidly changing populations. These data sets may contain relatively rare, sparsely distributed, taxa that will not have associated training data; a classifier trained on a limited set of classes will miss these samples. The computer vision community, leveraging advances in Convolutional Neural Networks (CNNs), has recently attempted to tackle such problems using "zero-shot" object categorization methods. Under a zero-shot framework, a classifier is trained to map samples onto a set of attributes rather than a class label. These attributes can include visual and non-visual information such as what an organism is made out of, where it is distributed globally, or how it reproduces. A second stage classifier is then used to extrapolate a class. In this work, we demonstrate a zero-shot classifier, implemented with a CNN, to retrieve out-of-training-set labels from images. This method is applied to data from two continuously imaging, moored instruments: the Scripps Plankton Camera System (SPCS) and the Imaging FlowCytobot (IFCB). Results from simulated deployment scenarios indicate zero-shot classifiers could be successful at recovering samples of rare taxa in image sets. This capability will allow ecologists to identify trends in the distribution of difficult to sample organisms in their data.

Lyme carditis. Electrophysiologic and histopathologic study

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

Reznick, J.W.; Braunstein, D.B.; Walsh, R.L.

1986-11-01

To further define the nature of Lyme carditis, electrophysiologic study and endomyocardial biopsy were performed in a patient with Lyme disease, whose principal cardiac manifestation was high-degree atrioventricular block. Intracardiac recording demonstrated supra-Hisian block and complete absence of an escape mechanism. Gallium 67 scanning demonstrated myocardial uptake, and right ventricular endomyocardial biopsy revealed active lymphocytic myocarditis. A structure compatible with a spirochetal organism was demonstrated in one biopsy specimen. It is concluded that Lyme disease can produce active myocarditis, as suggested by gallium 67 imaging and confirmed by endomyocardial biopsy. Furthermore, the presence of high-grade atrioventricular block in this diseasemore » requires aggressive management with temporary pacemaker and corticosteroid therapy.« less

Building gene expression profile classifiers with a simple and efficient rejection option in R.

PubMed

Benso, Alfredo; Di Carlo, Stefano; Politano, Gianfranco; Savino, Alessandro; Hafeezurrehman, Hafeez

2011-01-01

The collection of gene expression profiles from DNA microarrays and their analysis with pattern recognition algorithms is a powerful technology applied to several biological problems. Common pattern recognition systems classify samples assigning them to a set of known classes. However, in a clinical diagnostics setup, novel and unknown classes (new pathologies) may appear and one must be able to reject those samples that do not fit the trained model. The problem of implementing a rejection option in a multi-class classifier has not been widely addressed in the statistical literature. Gene expression profiles represent a critical case study since they suffer from the curse of dimensionality problem that negatively reflects on the reliability of both traditional rejection models and also more recent approaches such as one-class classifiers. This paper presents a set of empirical decision rules that can be used to implement a rejection option in a set of multi-class classifiers widely used for the analysis of gene expression profiles. In particular, we focus on the classifiers implemented in the R Language and Environment for Statistical Computing (R for short in the remaining of this paper). The main contribution of the proposed rules is their simplicity, which enables an easy integration with available data analysis environments. Since in the definition of a rejection model tuning of the involved parameters is often a complex and delicate task, in this paper we exploit an evolutionary strategy to automate this process. This allows the final user to maximize the rejection accuracy with minimum manual intervention. This paper shows how the use of simple decision rules can be used to help the use of complex machine learning algorithms in real experimental setups. The proposed approach is almost completely automated and therefore a good candidate for being integrated in data analysis flows in labs where the machine learning expertise required to tune traditional classifiers might not be available.

42 CFR 410.33 - Independent diagnostic testing facility.

Code of Federal Regulations, 2010 CFR

2010-10-01

... supplier of portable x-ray services, a nurse practitioner, or a clinical nurse specialist when he or she... furnished by a clinical psychologist or a qualified independent psychologist as defined in program... electrophysiologic clinical specialist and permitted to provide the service under State law. (b) Supervising...

An Efficient Data Partitioning to Improve Classification Performance While Keeping Parameters Interpretable.

PubMed

Korjus, Kristjan; Hebart, Martin N; Vicente, Raul

2016-01-01

Supervised machine learning methods typically require splitting data into multiple chunks for training, validating, and finally testing classifiers. For finding the best parameters of a classifier, training and validation are usually carried out with cross-validation. This is followed by application of the classifier with optimized parameters to a separate test set for estimating the classifier's generalization performance. With limited data, this separation of test data creates a difficult trade-off between having more statistical power in estimating generalization performance versus choosing better parameters and fitting a better model. We propose a novel approach that we term "Cross-validation and cross-testing" improving this trade-off by re-using test data without biasing classifier performance. The novel approach is validated using simulated data and electrophysiological recordings in humans and rodents. The results demonstrate that the approach has a higher probability of discovering significant results than the standard approach of cross-validation and testing, while maintaining the nominal alpha level. In contrast to nested cross-validation, which is maximally efficient in re-using data, the proposed approach additionally maintains the interpretability of individual parameters. Taken together, we suggest an addition to currently used machine learning approaches which may be particularly useful in cases where model weights do not require interpretation, but parameters do.

Building rooftop classification using random forests for large-scale PV deployment

NASA Astrophysics Data System (ADS)

Assouline, Dan; Mohajeri, Nahid; Scartezzini, Jean-Louis

2017-10-01

Large scale solar Photovoltaic (PV) deployment on existing building rooftops has proven to be one of the most efficient and viable sources of renewable energy in urban areas. As it usually requires a potential analysis over the area of interest, a crucial step is to estimate the geometric characteristics of the building rooftops. In this paper, we introduce a multi-layer machine learning methodology to classify 6 roof types, 9 aspect (azimuth) classes and 5 slope (tilt) classes for all building rooftops in Switzerland, using GIS processing. We train Random Forests (RF), an ensemble learning algorithm, to build the classifiers. We use (2 × 2) [m2 ] LiDAR data (considering buildings and vegetation) to extract several rooftop features, and a generalised footprint polygon data to localize buildings. The roof classifier is trained and tested with 1252 labeled roofs from three different urban areas, namely Baden, Luzern, and Winterthur. The results for roof type classification show an average accuracy of 67%. The aspect and slope classifiers are trained and tested with 11449 labeled roofs in the Zurich periphery area. The results for aspect and slope classification show different accuracies depending on the classes: while some classes are well identified, other under-represented classes remain challenging to detect.

Neural network ensemble based CAD system for focal liver lesions from B-mode ultrasound.

PubMed

Virmani, Jitendra; Kumar, Vinod; Kalra, Naveen; Khandelwal, Niranjan

2014-08-01

A neural network ensemble (NNE) based computer-aided diagnostic (CAD) system to assist radiologists in differential diagnosis between focal liver lesions (FLLs), including (1) typical and atypical cases of Cyst, hemangioma (HEM) and metastatic carcinoma (MET) lesions, (2) small and large hepatocellular carcinoma (HCC) lesions, along with (3) normal (NOR) liver tissue is proposed in the present work. Expert radiologists, visualize the textural characteristics of regions inside and outside the lesions to differentiate between different FLLs, accordingly texture features computed from inside lesion regions of interest (IROIs) and texture ratio features computed from IROIs and surrounding lesion regions of interests (SROIs) are taken as input. Principal component analysis (PCA) is used for reducing the dimensionality of the feature space before classifier design. The first step of classification module consists of a five class PCA-NN based primary classifier which yields probability outputs for five liver image classes. The second step of classification module consists of ten binary PCA-NN based secondary classifiers for NOR/Cyst, NOR/HEM, NOR/HCC, NOR/MET, Cyst/HEM, Cyst/HCC, Cyst/MET, HEM/HCC, HEM/MET and HCC/MET classes. The probability outputs of five class PCA-NN based primary classifier is used to determine the first two most probable classes for a test instance, based on which it is directed to the corresponding binary PCA-NN based secondary classifier for crisp classification between two classes. By including the second step of the classification module, classification accuracy increases from 88.7 % to 95 %. The promising results obtained by the proposed system indicate its usefulness to assist radiologists in differential diagnosis of FLLs.

A theoretical formulation of the electrophysiological inverse problem on the sphere

NASA Astrophysics Data System (ADS)

Riera, Jorge J.; Valdés, Pedro A.; Tanabe, Kunio; Kawashima, Ryuta

2006-04-01

The construction of three-dimensional images of the primary current density (PCD) produced by neuronal activity is a problem of great current interest in the neuroimaging community, though being initially formulated in the 1970s. There exist even now enthusiastic debates about the authenticity of most of the inverse solutions proposed in the literature, in which low resolution electrical tomography (LORETA) is a focus of attention. However, in our opinion, the capabilities and limitations of the electro and magneto encephalographic techniques to determine PCD configurations have not been extensively explored from a theoretical framework, even for simple volume conductor models of the head. In this paper, the electrophysiological inverse problem for the spherical head model is cast in terms of reproducing kernel Hilbert spaces (RKHS) formalism, which allows us to identify the null spaces of the implicated linear integral operators and also to define their representers. The PCD are described in terms of a continuous basis for the RKHS, which explicitly separates the harmonic and non-harmonic components. The RKHS concept permits us to bring LORETA into the scope of the general smoothing splines theory. A particular way of calculating the general smoothing splines is illustrated, avoiding a brute force discretization prematurely. The Bayes information criterion is used to handle dissimilarities in the signal/noise ratios and physical dimensions of the measurement modalities, which could affect the estimation of the amount of smoothness required for that class of inverse solution to be well specified. In order to validate the proposed method, we have estimated the 3D spherical smoothing splines from two data sets: electric potentials obtained from a skull phantom and magnetic fields recorded from subjects performing an experiment of human faces recognition.

Electrophysiological safety of DW-286a, a novel fluoroquinolone antibiotic agent.

PubMed

Kim, Eun-Joo; Kim, Ki-Suk; Shin, Won-Ho

2005-01-01

Inhibition of the potassium current I(Kr) and QT prolongation has been known to be associated with drug-induced torsades de pointes arrhythmias (TdP) and sudden cardiac death. We investigated the cardiac electrophysiological effects of DW-286a, a new class of fluoroquinolone antibiotics reported to prolong the QT interval. To investigate the electrophysiological safety of DW-286a, we used conventional microelectrode recording techniques in isolated guinea pig papillary muscles, whole-cell patch clamp techniques in human ether-à-go-go related gene (hERG)-transient transfected Chinese hamster ovary cells, and in vivo electrocardiogram (ECG) measurements in Sprague-Dawley (SD) rats by the use of a telemetry system. DW-286a at 300 microM significantly (P<0.01) prolonged action potentials at 50% repolarization (APD50) and 90% repolarization (APD90). For IHERG, the IC50 value was 89.00+/-37.85 microM with a Hill coefficient (nH) of -0.97+/-0.49. However, when DW-286a was orally administered to conscious SD rats at a high dose (1000 mg/kg), no significant effect on ECG in vivo was detected. From a previous study, we know that concentration at 19.8 microM is the antimicrobial end-point of DW-286a. Therefore, our data suggest that in the electrophysiological aspect, it can be thought that the effective concentrations of DW-286a are between 19.8 and 100 microM (concentration in serum).

Post-operative atrial fibrillation: a maze of mechanisms

PubMed Central

Maesen, Bart; Nijs, Jan; Maessen, Jos; Allessie, Maurits; Schotten, Ulrich

2012-01-01

Post-operative atrial fibrillation (POAF) is one of the most frequent complications of cardiac surgery and an important predictor of patient morbidity as well as of prolonged hospitalization. It significantly increases costs for hospitalization. Insights into the pathophysiological factors causing POAF have been provided by both experimental and clinical investigations and show that POAF is ‘multi-factorial’. Facilitating factors in the mechanism of the arrhythmia can be classified as acute factors caused by the surgical intervention and chronic factors related to structural heart disease and ageing of the heart. Furthermore, some proarrhythmic mechanisms specifically occur in the setting of POAF. For example, inflammation and beta-adrenergic activation have been shown to play a prominent role in POAF, while these mechanisms are less important in non-surgical AF. More recently, it has been shown that atrial fibrosis and the presence of an electrophysiological substrate capable of maintaining AF also promote the arrhythmia, indicating that POAF has some proarrhythmic mechanisms in common with other forms of AF. The clinical setting of POAF offers numerous opportunities to study its mechanisms. During cardiac surgery, biopsies can be taken and detailed electrophysiological measurements can be performed. Furthermore, the specific time course of POAF, with the delayed onset and the transient character of the arrhythmia, also provides important insight into its mechanisms. This review discusses the mechanistic interaction between predisposing factors and the electrophysiological mechanisms resulting in POAF and their therapeutic implications. PMID:21821851

Electrophysiological signatures of event words: Dissociating syntactic and semantic category effects in lexical processing.

PubMed

Lapinskaya, Natalia; Uzomah, Uchechukwu; Bedny, Marina; Lau, Ellen

2016-12-01

Numerous theories have been proposed regarding the brain's organization and retrieval of lexical information. Neurophysiological dissociations in processing different word classes, particularly nouns and verbs, have been extensively documented, supporting the contribution of grammatical class to lexical organization. However, the contribution of semantic properties to these processing differences is still unresolved. We aim to isolate this contribution by comparing ERPs to verbs (e.g. wade), object nouns (e.g. cookie), and event nouns (e.g. concert) in a paired similarity judgment task, as event nouns share grammatical category with object nouns but some semantic properties with verbs. We find that event nouns pattern with verbs in eliciting a more positive response than object nouns across left anterior electrodes 300-500ms after word presentation. This time-window has been strongly linked to lexical-semantic access by prior electrophysiological work. Thus, the similarity of the response to words referring to concepts with more complex participant structure and temporal continuity extends across grammatical class (event nouns and verbs), and contrasts with the words that refer to objects (object nouns). This contrast supports a semantic, as well as syntactic, contribution to the differential neural organization and processing of lexical items. We also observed a late (500-800ms post-stimulus) posterior positivity for object nouns relative to event nouns and verbs at the second word of each pair, which may reflect the impact of semantic properties on the similarity judgment task. Copyright © 2016 Elsevier Ltd. All rights reserved.

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...

Classifying and quantifying basins of attraction

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

Sprott, J. C.; Xiong, Anda

2015-08-15

A scheme is proposed to classify the basins for attractors of dynamical systems in arbitrary dimensions. There are four basic classes depending on their size and extent, and each class can be further quantified to facilitate comparisons. The calculation uses a Monte Carlo method and is applied to numerous common dissipative chaotic maps and flows in various dimensions.

Five-class height-weight mean and SD system applying Estonian reference values of height-weight mean and SD for systematization of seventeen-year-old conscripts' anthropometric data.

PubMed

Lintsi, Mart; Kaarma, Helje; Aunapuu, Marina; Arend, Andres

2007-03-01

A study of 739 conscripts aged 17 years from the town of Tartu and from the Tartu county was performed. Height, weight, 33 anthropometric measurements and 12 skinfolds were measured. The data were classified into five height-weight mean and SD-classes applying the Estonian reference values for this age and sex (Grünberg et al. 1998). There were 3 classes with conformity between height and weight class: 1--small (small height and small weight), 2--medium (medium height and medium weight), 3--large (large height and large weight), 4--weight class dominating (pyknomorphic) and 5--height class dominating (leptomorphic). It was found, that in classes 1, 2 and 3 the height and weight increase was in accordance with the increase in all heights, breadths and depths, circumferences, skinfolds, body fat, muscle and bone mass. In class 4 circumferences, skinfolds, body fat and muscle mass were bigger. In class 5 all heights and the relative bone mass were bigger. The present investigation confirms the assumption that the five height-weight mean and SD five-class system applying the Estonian reference values for classifying the anthropometric variables is suitable for seventeen-year-old conscripts. As well the border values of 5%, 50% and 95% for every anthropometrical variable in the five-classes were calculated, which may be helpful for practical classifying.

Automatic classification of spectra from the Infrared Astronomical Satellite (IRAS)

NASA Technical Reports Server (NTRS)

Cheeseman, Peter; Stutz, John; Self, Matthew; Taylor, William; Goebel, John; Volk, Kevin; Walker, Helen

1989-01-01

A new classification of Infrared spectra collected by the Infrared Astronomical Satellite (IRAS) is presented. The spectral classes were discovered automatically by a program called Auto Class 2. This program is a method for discovering (inducing) classes from a data base, utilizing a Bayesian probability approach. These classes can be used to give insight into the patterns that occur in the particular domain, in this case, infrared astronomical spectroscopy. The classified spectra are the entire Low Resolution Spectra (LRS) Atlas of 5,425 sources. There are seventy-seven classes in this classification and these in turn were meta-classified to produce nine meta-classes. The classification is presented as spectral plots, IRAS color-color plots, galactic distribution plots and class commentaries. Cross-reference tables, listing the sources by IRAS name and by Auto Class class, are also given. These classes show some of the well known classes, such as the black-body class, and silicate emission classes, but many other classes were unsuspected, while others show important subtle differences within the well known classes.

Adaptive exponential integrate-and-fire model as an effective description of neuronal activity.

PubMed

Brette, Romain; Gerstner, Wulfram

2005-11-01

We introduce a two-dimensional integrate-and-fire model that combines an exponential spike mechanism with an adaptation equation, based on recent theoretical findings. We describe a systematic method to estimate its parameters with simple electrophysiological protocols (current-clamp injection of pulses and ramps) and apply it to a detailed conductance-based model of a regular spiking neuron. Our simple model predicts correctly the timing of 96% of the spikes (+/-2 ms) of the detailed model in response to injection of noisy synaptic conductances. The model is especially reliable in high-conductance states, typical of cortical activity in vivo, in which intrinsic conductances were found to have a reduced role in shaping spike trains. These results are promising because this simple model has enough expressive power to reproduce qualitatively several electrophysiological classes described in vitro.

Diabetic foot surgery: classifying patients to predict complications.

PubMed

Bevilacqua, Nicholas J; Rogers, Lee C; Armstrong, David G

2008-01-01

The purpose of this article is to describe a classification of diabetic foot surgery performed in the absence of critical limb ischaemia. The basis of this classification is centred on three fundamental variables that are present in the assessment of risk and indication: (1) presence or absence of neuropathy (the loss of protective sensation); (2) presence or absence of an open wound; (3) presence or absence of acute limb-threatening infection. The conceptual framework for this classification is to define distinct classes of surgery in an order of theoretically increasing risk for high-level amputation. These include: Class I: elective diabetic foot surgery (procedures performed to treat a painful deformity in a patient without the loss of protective sensation); Class II: prophylactic (procedure performed to reduce the risk of ulceration or reulceration in a person with the loss of protective sensation but without an open wound); Class III: curative (procedure performed to assist in healing an open wound); and Class IV: emergency (procedure performed to limit the progression of acute infection). The presence of critical ischaemia in any of these classes of surgery should prompt a vascular evaluation to consider (1) the urgency of the procedure being considered and (2) possible revascularization prior to or temporally concomitant with the procedure. It is our hope that this system begins a dialogue amongst physicians and surgeons which can ultimately facilitate communication, enhance perspective, and improve care.

Evaluating the ecotourism potentials of Naharkhoran area in Gorgan using remote sensing and geographic information system

NASA Astrophysics Data System (ADS)

Oladi, Jafar; Bozorgnia, Delavar

2010-10-01

Ecotourism may be defined as voluntary travels to intact natural areas in order to enjoy the natural attractions as well as to get familiar with the culture of local communities. The main factor contributing to inappropriate land uses and natural resource destruction is overaggregation of ecotourists in some specific natural areas such as forests and rangelands; while other parts remain unvisited due to the lack of a proper propagation about those areas. Evaluating the ecotourism potentials of each area would lead to a wider participation of local people in natural resource conservation activities. In order to properly introduce the ecotourism potential areas, at first, we carried out land preparation practices using Geographic Information System (GIS) and Remote Sensing (RS) techniques; then, the maps of height, slope and orientation were produced using the digital elevation model (DEM) of the study area. Afterwards, we overlaid these maps and the ecotourism potential areas were identified on the map. These specified areas were classified into two land uses of mass and alternative ecotourism, with three subclasses (including class1, class2 and an inappropriate class) considered for each land use. To classify the image, the training areas determined on the ground using a GPS device (Ground Positioning System) were transferred on the RS image. Subsequently, the ecotourism potential areas were determined using a hybrid method. At the final phase, these areas were compared with the areas determined on the ecotourism potential map; as a result of this comparison, the overlaid ecotourism potential areas were distinguished on the Geographic information System.

MORFOMETRYKA—A NEW WAY OF ESTABLISHING MORPHOLOGICAL CLASSIFICATION OF GALAXIES

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

Ferrari, F.; Carvalho, R. R. de; Trevisan, M., E-mail: fabricio@ferrari.pro.br

We present an extended morphometric system to automatically classify galaxies from astronomical images. The new system includes the original and modified versions of the CASGM coefficients (Concentration C{sub 1}, Asymmetry A{sub 3}, and Smoothness S{sub 3}), and the new parameters entropy, H, and spirality σ{sub ψ}. The new parameters A{sub 3}, S{sub 3}, and H are better to discriminate galaxy classes than A{sub 1}, S{sub 1}, and G, respectively. The new parameter σ{sub ψ} captures the amount of non-radial pattern on the image and is almost linearly dependent on T-type. Using a sample of spiral and elliptical galaxies from themore » Galaxy Zoo project as a training set, we employed the Linear Discriminant Analysis (LDA) technique to classify EFIGI (Baillard et al. 4458 galaxies), Nair and Abraham (14,123 galaxies), and SDSS Legacy (779,235 galaxies) samples. The cross-validation test shows that we can achieve an accuracy of more than 90% with our classification scheme. Therefore, we are able to define a plane in the morphometric parameter space that separates the elliptical and spiral classes with a mismatch between classes smaller than 10%. We use the distance to this plane as a morphometric index (M{sub i}) and we show that it follows the human based T-type index very closely. We calculate morphometric index M{sub i} for ∼780k galaxies from SDSS Legacy Survey–DR7. We discuss how M{sub i} correlates with stellar population parameters obtained using the spectra available from SDSS–DR7.« less

Transgenic Mouse Lines Subdivide External Segment of the Globus Pallidus (GPe) Neurons and Reveal Distinct GPe Output Pathways

PubMed Central

Mastro, Kevin J.; Bouchard, Rachel S.; Holt, Hiromi A. K.

2014-01-01

Cell-type diversity in the brain enables the assembly of complex neural circuits, whose organization and patterns of activity give rise to brain function. However, the identification of distinct neuronal populations within a given brain region is often complicated by a lack of objective criteria to distinguish one neuronal population from another. In the external segment of the globus pallidus (GPe), neuronal populations have been defined using molecular, anatomical, and electrophysiological criteria, but these classification schemes are often not generalizable across preparations and lack consistency even within the same preparation. Here, we present a novel use of existing transgenic mouse lines, Lim homeobox 6 (Lhx6)–Cre and parvalbumin (PV)–Cre, to define genetically distinct cell populations in the GPe that differ molecularly, anatomically, and electrophysiologically. Lhx6–GPe neurons, which do not express PV, are concentrated in the medial portion of the GPe. They have lower spontaneous firing rates, narrower dynamic ranges, and make stronger projections to the striatum and substantia nigra pars compacta compared with PV–GPe neurons. In contrast, PV–GPe neurons are more concentrated in the lateral portions of the GPe. They have narrower action potentials, deeper afterhyperpolarizations, and make stronger projections to the subthalamic nucleus and parafascicular nucleus of the thalamus. These electrophysiological and anatomical differences suggest that Lhx6–GPe and PV–GPe neurons participate in different circuits with the potential to contribute to different aspects of motor function and dysfunction in disease. PMID:24501350

Automated extraction and classification of RNA tertiary structure cyclic motifs

PubMed Central

Lemieux, Sébastien; Major, François

2006-01-01

A minimum cycle basis of the tertiary structure of a large ribosomal subunit (LSU) X-ray crystal structure was analyzed. Most cycles are small, as they are composed of 3- to 5 nt, and repeated across the LSU tertiary structure. We used hierarchical clustering to quantify and classify the 4 nt cycles. One class is defined by the GNRA tetraloop motif. The inspection of the GNRA class revealed peculiar instances in sequence. First is the presence of UA, CA, UC and CC base pairs that substitute the usual sheared GA base pair. Second is the revelation of GNR(Xn)A tetraloops, where Xn is bulged out of the classical GNRA structure, and of GN/RA formed by the two strands of interior-loops. We were able to unambiguously characterize the cycle classes using base stacking and base pairing annotations. The cycles identified correspond to small and cyclic motifs that compose most of the LSU RNA tertiary structure and contribute to its thermodynamic stability. Consequently, the RNA minimum cycles could well be used as the basic elements of RNA tertiary structure prediction methods. PMID:16679452

Detection and classification of interstitial lung diseases and emphysema using a joint morphological-fuzzy approach

NASA Astrophysics Data System (ADS)

Chang Chien, Kuang-Che; Fetita, Catalin; Brillet, Pierre-Yves; Prêteux, Françoise; Chang, Ruey-Feng

2009-02-01

Multi-detector computed tomography (MDCT) has high accuracy and specificity on volumetrically capturing serial images of the lung. It increases the capability of computerized classification for lung tissue in medical research. This paper proposes a three-dimensional (3D) automated approach based on mathematical morphology and fuzzy logic for quantifying and classifying interstitial lung diseases (ILDs) and emphysema. The proposed methodology is composed of several stages: (1) an image multi-resolution decomposition scheme based on a 3D morphological filter is used to detect and analyze the different density patterns of the lung texture. Then, (2) for each pattern in the multi-resolution decomposition, six features are computed, for which fuzzy membership functions define a probability of association with a pathology class. Finally, (3) for each pathology class, the probabilities are combined up according to the weight assigned to each membership function and two threshold values are used to decide the final class of the pattern. The proposed approach was tested on 10 MDCT cases and the classification accuracy was: emphysema: 95%, fibrosis/honeycombing: 84% and ground glass: 97%.

Context-based automated defect classification system using multiple morphological masks

DOEpatents

Gleason, Shaun S.; Hunt, Martin A.; Sari-Sarraf, Hamed

2002-01-01

Automatic detection of defects during the fabrication of semiconductor wafers is largely automated, but the classification of those defects is still performed manually by technicians. This invention includes novel digital image analysis techniques that generate unique feature vector descriptions of semiconductor defects as well as classifiers that use these descriptions to automatically categorize the defects into one of a set of pre-defined classes. Feature extraction techniques based on multiple-focus images, multiple-defect mask images, and segmented semiconductor wafer images are used to create unique feature-based descriptions of the semiconductor defects. These feature-based defect descriptions are subsequently classified by a defect classifier into categories that depend on defect characteristics and defect contextual information, that is, the semiconductor process layer(s) with which the defect comes in contact. At the heart of the system is a knowledge database that stores and distributes historical semiconductor wafer and defect data to guide the feature extraction and classification processes. In summary, this invention takes as its input a set of images containing semiconductor defect information, and generates as its output a classification for the defect that describes not only the defect itself, but also the location of that defect with respect to the semiconductor process layers.

Texture and color features for tile classification

NASA Astrophysics Data System (ADS)

Baldrich, Ramon; Vanrell, Maria; Villanueva, Juan J.

1999-09-01

In this paper we present the results of a preliminary computer vision system to classify the production of a ceramic tile industry. We focus on the classification of a specific type of tiles whose production can be affected by external factors, such as humidity, temperature, origin of clays and pigments. Variations on these uncontrolled factors provoke small differences in the color and the texture of the tiles that force to classify all the production. A constant and non- subjective classification would allow avoiding devolution from customers and unnecessary stock fragmentation. The aim of this work is to simulate the human behavior on this classification task by extracting a set of features from tile images. These features are induced by definitions from experts. To compute them we need to mix color and texture information and to define global and local measures. In this work, we do not seek a general texture-color representation, we only deal with textures formed by non-oriented colored-blobs randomly distributed. New samples are classified using Discriminant Analysis functions derived from known class tile samples. The last part of the paper is devoted to explain the correction of acquired images in order to avoid time and geometry illumination changes.

Landslide susceptibility map: from research to application

NASA Astrophysics Data System (ADS)

Fiorucci, Federica; Reichenbach, Paola; Ardizzone, Francesca; Rossi, Mauro; Felicioni, Giulia; Antonini, Guendalina

2014-05-01

Susceptibility map is an important and essential tool in environmental planning, to evaluate landslide hazard and risk and for a correct and responsible management of the territory. Landslide susceptibility is the likelihood of a landslide occurring in an area on the basis of local terrain conditions. Can be expressed as the probability that any given region will be affected by landslides, i.e. an estimate of "where" landslides are likely to occur. In this work we present two examples of landslide susceptibility map prepared for the Umbria Region and for the Perugia Municipality. These two maps were realized following official request from the Regional and Municipal government to the Research Institute for the Hydrogeological Protection (CNR-IRPI). The susceptibility map prepared for the Umbria Region represents the development of previous agreements focused to prepare: i) a landslide inventory map that was included in the Urban Territorial Planning (PUT) and ii) a series of maps for the Regional Plan for Multi-risk Prevention. The activities carried out for the Umbria Region were focused to define and apply methods and techniques for landslide susceptibility zonation. Susceptibility maps were prepared exploiting a multivariate statistical model (linear discriminant analysis) for the five Civil Protection Alert Zones defined in the regional territory. The five resulting maps were tested and validated using the spatial distribution of recent landslide events that occurred in the region. The susceptibility map for the Perugia Municipality was prepared to be integrated as one of the cartographic product in the Municipal development plan (PRG - Piano Regolatore Generale) as required by the existing legislation. At strategic level, one of the main objectives of the PRG, is to establish a framework of knowledge and legal aspects for the management of geo-hydrological risk. At national level most of the susceptibility maps prepared for the PRG, were and still are obtained qualitatively classifying the territory according to slope classes. For the Perugia Municipality the susceptibility map was obtained combining results of statistical multivariate models and landslide density map. In particular, in the first phase a susceptibility zonation was prepared using different single and combined probability statistical multivariate techniques. The zonation was then combined and compared with the landslide density map in order to reclassify the false negative (portion of the territory classified by the model as stable affected by slope failures). The semi-quantitative resulting map was classified in five susceptibility classes. For each class a set of technical regulation was established to manage the territory.

Metal Oxide Gas Sensor Drift Compensation Using a Two-Dimensional Classifier Ensemble

PubMed Central

Liu, Hang; Chu, Renzhi; Tang, Zhenan

2015-01-01

Sensor drift is the most challenging problem in gas sensing at present. We propose a novel two-dimensional classifier ensemble strategy to solve the gas discrimination problem, regardless of the gas concentration, with high accuracy over extended periods of time. This strategy is appropriate for multi-class classifiers that consist of combinations of pairwise classifiers, such as support vector machines. We compare the performance of the strategy with those of competing methods in an experiment based on a public dataset that was compiled over a period of three years. The experimental results demonstrate that the two-dimensional ensemble outperforms the other methods considered. Furthermore, we propose a pre-aging process inspired by that applied to the sensors to improve the stability of the classifier ensemble. The experimental results demonstrate that the weight of each multi-class classifier model in the ensemble remains fairly static before and after the addition of new classifier models to the ensemble, when a pre-aging procedure is applied. PMID:25942640

Dynamic and scalable audio classification by collective network of binary classifiers framework: an evolutionary approach.

PubMed

Kiranyaz, Serkan; Mäkinen, Toni; Gabbouj, Moncef

2012-10-01

In this paper, we propose a novel framework based on a collective network of evolutionary binary classifiers (CNBC) to address the problems of feature and class scalability. The main goal of the proposed framework is to achieve a high classification performance over dynamic audio and video repositories. The proposed framework adopts a "Divide and Conquer" approach in which an individual network of binary classifiers (NBC) is allocated to discriminate each audio class. An evolutionary search is applied to find the best binary classifier in each NBC with respect to a given criterion. Through the incremental evolution sessions, the CNBC framework can dynamically adapt to each new incoming class or feature set without resorting to a full-scale re-training or re-configuration. Therefore, the CNBC framework is particularly designed for dynamically varying databases where no conventional static classifiers can adapt to such changes. In short, it is entirely a novel topology, an unprecedented approach for dynamic, content/data adaptive and scalable audio classification. A large set of audio features can be effectively used in the framework, where the CNBCs make appropriate selections and combinations so as to achieve the highest discrimination among individual audio classes. Experiments demonstrate a high classification accuracy (above 90%) and efficiency of the proposed framework over large and dynamic audio databases. Copyright © 2012 Elsevier Ltd. All rights reserved.

Heavy metals characteristics of settled particles of streets dust from Diwaniyah City- Qadisiyah Governorate - Southern Iraq

NASA Astrophysics Data System (ADS)

Al-Dabbas, Moutaz A.; Mahdi, Khalid H.; Al-Khafaji, Raad; Obayes, Kawthar H.

2018-05-01

Road-side dust samples were collected from selected areas of Diwaniyah city-Qadisiyah Governorate - Southern Iraq. The heavy metals (Fe, Co, Ni, Cu, Zn and Pb) in these streets dust samples were studied and used as indicator for pollution by using three of main indices (I-geo, CF, and PLI). Determination of heavy metal in the roadside dust is with XRD and XRF methods. I-geo for Co, Zn, Pb, and Ni in the studied sites shows relative values of class 1, which indicated the slightly polluted, while I-geo for Fe and Cu shows relative values of class 0, which indicated no pollution. The contamination factor for Co, Zn, Pb, and Ni classified as class 2, which indicate moderately contamination, while the contamination factor for Fe and Cu classified as class 1, which indicate low contamination. PLI values in the all of studied sites classified as class 2 (Deterioration on site quality) indicating local pollution, as well as denote perfection with (class 0) of no pollution. The distribution pattern of metals percentages was affected by gases emitted from transportation vehicles as well as the prevailing wind direction.

Effect of separate sampling on classification accuracy.

PubMed

Shahrokh Esfahani, Mohammad; Dougherty, Edward R

2014-01-15

Measurements are commonly taken from two phenotypes to build a classifier, where the number of data points from each class is predetermined, not random. In this 'separate sampling' scenario, the data cannot be used to estimate the class prior probabilities. Moreover, predetermined class sizes can severely degrade classifier performance, even for large samples. We employ simulations using both synthetic and real data to show the detrimental effect of separate sampling on a variety of classification rules. We establish propositions related to the effect on the expected classifier error owing to a sampling ratio different from the population class ratio. From these we derive a sample-based minimax sampling ratio and provide an algorithm for approximating it from the data. We also extend to arbitrary distributions the classical population-based Anderson linear discriminant analysis minimax sampling ratio derived from the discriminant form of the Bayes classifier. All the codes for synthetic data and real data examples are written in MATLAB. A function called mmratio, whose output is an approximation of the minimax sampling ratio of a given dataset, is also written in MATLAB. All the codes are available at: http://gsp.tamu.edu/Publications/supplementary/shahrokh13b.

[Consumer satisfaction study in philanthropic hospital health plans].

PubMed

Gerschman, Silvia; Veiga, Luciana; Guimarães, César; Ugá, Maria Alicia Dominguez; Portela, Margareth Crisóstomo; Vasconcellos, Miguel Murat; Barbosa, Pedro Ribeiro; Lima, Sheyla Maria Lemos

2007-01-01

This paper presents the findings of research aimed at identifying and analyzing the argumentation and rationale that justify the satisfaction of consumers with their health plans. The qualitative method applied used the focus group technique, for which the following aspects were defined: the criteria for choosing the health plans which were considered, the composition of the group and its distribution, recruitment strategy, and infrastructure and dynamics of the meetings. The health plan beneficiaries were classified into groups according to their social class, the place where they lived, mainly, the relationship that they established with the health plan operators which enabled us to develop a typology for the plan beneficiaries. Initially, we indicated how the health plan beneficiaries assess and use the Brazilian Unified Health System (SUS), and, then, considering the types of plans defined, we evaluated their degree of satisfaction with the different aspects of health care, and identified which aspects mostly contributed explain their satisfaction.

The evolving understanding of the construct of intellectual disability.

PubMed

Schalock, Robert L

2011-12-01

This article addresses two major areas concerned with the evolving understanding of the construct of intellectual disability. The first part of the article discusses current answers to five critical questions that have revolved around the general question, "What is Intellectual Disability?" These five are what to call the phenomenon, how to explain the phenomenon, how to define the phenomenon and determine who is a member of the class, how to classify persons so defined and identified, and how to establish public policy regarding such persons. The second part of the article discusses four critical issues that will impact both our future understanding of the construct and the approach taken to persons with intellectual disability. These four critical issues relate to the conceptualisation and measurement of intellectual functioning, the constitutive definition of intellectual disability, the alignment of clinical functions related to diagnosis, classification, and planning supports, and how the field resolves a number of emerging epistemological issues.

Invariant-feature-based adaptive automatic target recognition in obscured 3D point clouds

NASA Astrophysics Data System (ADS)

Khuon, Timothy; Kershner, Charles; Mattei, Enrico; Alverio, Arnel; Rand, Robert

2014-06-01

Target recognition and classification in a 3D point cloud is a non-trivial process due to the nature of the data collected from a sensor system. The signal can be corrupted by noise from the environment, electronic system, A/D converter, etc. Therefore, an adaptive system with a desired tolerance is required to perform classification and recognition optimally. The feature-based pattern recognition algorithm architecture as described below is particularly devised for solving a single-sensor classification non-parametrically. Feature set is extracted from an input point cloud, normalized, and classifier a neural network classifier. For instance, automatic target recognition in an urban area would require different feature sets from one in a dense foliage area. The figure above (see manuscript) illustrates the architecture of the feature based adaptive signature extraction of 3D point cloud including LIDAR, RADAR, and electro-optical data. This network takes a 3D cluster and classifies it into a specific class. The algorithm is a supervised and adaptive classifier with two modes: the training mode and the performing mode. For the training mode, a number of novel patterns are selected from actual or artificial data. A particular 3D cluster is input to the network as shown above for the decision class output. The network consists of three sequential functional modules. The first module is for feature extraction that extracts the input cluster into a set of singular value features or feature vector. Then the feature vector is input into the feature normalization module to normalize and balance it before being fed to the neural net classifier for the classification. The neural net can be trained by actual or artificial novel data until each trained output reaches the declared output within the defined tolerance. In case new novel data is added after the neural net has been learned, the training is then resumed until the neural net has incrementally learned with the new novel data. The associative memory capability of the neural net enables the incremental learning. The back propagation algorithm or support vector machine can be utilized for the classification and recognition.

Compositional modes in the ash fraction of some modern pyroclastic deposits: their determination and significance

NASA Astrophysics Data System (ADS)

De Rosa, R.

This paper illustrates some problems involved in the quantitative compositional study of pyroclastic deposits and proposes criteria for selecting the main petrographic and textural classes for modal analysis. The relative proportions of the different classes are obtained using a point-counting procedure applied to medium-coarse ash samples that reduces the dependence of the modal composition on grain size and avoids tedious counting of different grain-size fractions. The major purposes of a quantified measure of component distributions are to: (a) document the nature of the fragmenting magma; (b) define the eruptive dynamics of the eruptions on a detailed scale; and (c) ensure accuracy in classifying pyroclastic deposits. Compositional modes of the ash fraction of pyroclastic deposits vary systematically, and their graphical representation defines the compositional and textural characteristics of pyroclastic fragments associated with different eruptive styles. Textural features of the glass component can be very helpful for inferring aspects of eruptive dynamics. Four major parameters can be used to represent the component composition of pyroclastic ash deposits: (a) juvenile index (JI); (b) crystallinity index (CrI); (c) juvenile vesicularity index (JVI); and (d) free crystal index (FCrI). The FCrI is defined as the ratio between single and total crystal fragments in the juvenile component (single crystals+crystals in juvenile glass). This parameter may provide an effective estimate of the mechanical energy of eruptions. Variations in FCrI vs JVI discriminate among pyroclastic deposits of different origin and define compositional fields that represent ash derived from different fragmentation styles.

Fuzzy association rule mining and classification for the prediction of malaria in South Korea.

PubMed

Buczak, Anna L; Baugher, Benjamin; Guven, Erhan; Ramac-Thomas, Liane C; Elbert, Yevgeniy; Babin, Steven M; Lewis, Sheri H

2015-06-18

Malaria is the world's most prevalent vector-borne disease. Accurate prediction of malaria outbreaks may lead to public health interventions that mitigate disease morbidity and mortality. We describe an application of a method for creating prediction models utilizing Fuzzy Association Rule Mining to extract relationships between epidemiological, meteorological, climatic, and socio-economic data from Korea. These relationships are in the form of rules, from which the best set of rules is automatically chosen and forms a classifier. Two classifiers have been built and their results fused to become a malaria prediction model. Future malaria cases are predicted as Low, Medium or High, where these classes are defined as a total of 0-2, 3-16, and above 17 cases, respectively, for a region in South Korea during a two-week period. Based on user recommendations, HIGH is considered an outbreak. Model accuracy is described by Positive Predictive Value (PPV), Sensitivity, and F-score for each class, computed on test data not previously used to develop the model. For predictions made 7-8 weeks in advance, model PPV and Sensitivity are 0.842 and 0.681, respectively, for the HIGH classes. The F0.5 and F3 scores (which combine PPV and Sensitivity) are 0.804 and 0.694, respectively, for the HIGH classes. The overall FARM results (as measured by F-scores) are significantly better than those obtained by Decision Tree, Random Forest, Support Vector Machine, and Holt-Winters methods for the HIGH class. For the Medium class, Random Forest and FARM obtain comparable results, with FARM being better at F0.5, and Random Forest obtaining a higher F3. A previously described method for creating disease prediction models has been modified and extended to build models for predicting malaria. In addition, some new input variables were used, including indicators of intervention measures. The South Korea malaria prediction models predict Low, Medium or High cases 7-8 weeks in the future. This paper demonstrates that our data driven approach can be used for the prediction of different diseases.

Safety assessment of plant varieties using transcriptomics profiling and a one-class classifier.

PubMed

van Dijk, Jeroen P; de Mello, Carla Souza; Voorhuijzen, Marleen M; Hutten, Ronald C B; Arisi, Ana Carolina Maisonnave; Jansen, Jeroen J; Buydens, Lutgarde M C; van der Voet, Hilko; Kok, Esther J

2014-10-01

An important part of the current hazard identification of novel plant varieties is comparative targeted analysis of the novel and reference varieties. Comparative analysis will become much more informative with unbiased analytical approaches, e.g. omics profiling. Data analysis estimating the similarity of new varieties to a reference baseline class of known safe varieties would subsequently greatly facilitate hazard identification. Further biological and eventually toxicological analysis would then only be necessary for varieties that fall outside this reference class. For this purpose, a one-class classifier tool was explored to assess and classify transcriptome profiles of potato (Solanum tuberosum) varieties in a model study. Profiles of six different varieties, two locations of growth, two year of harvest and including biological and technical replication were used to build the model. Two scenarios were applied representing evaluation of a 'different' variety and a 'similar' variety. Within the model higher class distances resulted for the 'different' test set compared with the 'similar' test set. The present study may contribute to a more global hazard identification of novel plant varieties. Copyright © 2014 Elsevier Inc. All rights reserved.

7 CFR 27.43 - Validity of cotton class certificates.

Code of Federal Regulations, 2014 CFR

2014-01-01

... 7 Agriculture 2 2014-01-01 2014-01-01 false Validity of cotton class certificates. 27.43 Section... CONTAINER REGULATIONS COTTON CLASSIFICATION UNDER COTTON FUTURES LEGISLATION Regulations Cotton Class Certificates § 27.43 Validity of cotton class certificates. Each cotton class certificate for cotton classified...

7 CFR 27.43 - Validity of cotton class certificates.

Code of Federal Regulations, 2011 CFR

2011-01-01

... 7 Agriculture 2 2011-01-01 2011-01-01 false Validity of cotton class certificates. 27.43 Section... CONTAINER REGULATIONS COTTON CLASSIFICATION UNDER COTTON FUTURES LEGISLATION Regulations Cotton Class Certificates § 27.43 Validity of cotton class certificates. Each cotton class certificate for cotton classified...

7 CFR 27.43 - Validity of cotton class certificates.

Code of Federal Regulations, 2012 CFR

2012-01-01

... 7 Agriculture 2 2012-01-01 2012-01-01 false Validity of cotton class certificates. 27.43 Section... CONTAINER REGULATIONS COTTON CLASSIFICATION UNDER COTTON FUTURES LEGISLATION Regulations Cotton Class Certificates § 27.43 Validity of cotton class certificates. Each cotton class certificate for cotton classified...

7 CFR 27.43 - Validity of cotton class certificates.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 7 Agriculture 2 2010-01-01 2010-01-01 false Validity of cotton class certificates. 27.43 Section... CONTAINER REGULATIONS COTTON CLASSIFICATION UNDER COTTON FUTURES LEGISLATION Regulations Cotton Class Certificates § 27.43 Validity of cotton class certificates. Each cotton class certificate for cotton classified...

7 CFR 27.43 - Validity of cotton class certificates.

Code of Federal Regulations, 2013 CFR

2013-01-01

... 7 Agriculture 2 2013-01-01 2013-01-01 false Validity of cotton class certificates. 27.43 Section... CONTAINER REGULATIONS COTTON CLASSIFICATION UNDER COTTON FUTURES LEGISLATION Regulations Cotton Class Certificates § 27.43 Validity of cotton class certificates. Each cotton class certificate for cotton classified...

Seismic event classification system

DOEpatents

Dowla, F.U.; Jarpe, S.P.; Maurer, W.

1994-12-13

In the computer interpretation of seismic data, the critical first step is to identify the general class of an unknown event. For example, the classification might be: teleseismic, regional, local, vehicular, or noise. Self-organizing neural networks (SONNs) can be used for classifying such events. Both Kohonen and Adaptive Resonance Theory (ART) SONNs are useful for this purpose. Given the detection of a seismic event and the corresponding signal, computation is made of: the time-frequency distribution, its binary representation, and finally a shift-invariant representation, which is the magnitude of the two-dimensional Fourier transform (2-D FFT) of the binary time-frequency distribution. This pre-processed input is fed into the SONNs. These neural networks are able to group events that look similar. The ART SONN has an advantage in classifying the event because the types of cluster groups do not need to be pre-defined. The results from the SONNs together with an expert seismologist's classification are then used to derive event classification probabilities. 21 figures.

Seismic event classification system

DOEpatents

Dowla, Farid U.; Jarpe, Stephen P.; Maurer, William

1994-01-01

In the computer interpretation of seismic data, the critical first step is to identify the general class of an unknown event. For example, the classification might be: teleseismic, regional, local, vehicular, or noise. Self-organizing neural networks (SONNs) can be used for classifying such events. Both Kohonen and Adaptive Resonance Theory (ART) SONNs are useful for this purpose. Given the detection of a seismic event and the corresponding signal, computation is made of: the time-frequency distribution, its binary representation, and finally a shift-invariant representation, which is the magnitude of the two-dimensional Fourier transform (2-D FFT) of the binary time-frequency distribution. This pre-processed input is fed into the SONNs. These neural networks are able to group events that look similar. The ART SONN has an advantage in classifying the event because the types of cluster groups do not need to be pre-defined. The results from the SONNs together with an expert seismologist's classification are then used to derive event classification probabilities.

Comparison of BMI, AHI, and apolipoprotein E ε4 (APOE-ε4) alleles among sleep apnea patients with different skeletal classifications.

PubMed

Roedig, Jason J; Phillips, Barbara A; Morford, Lorri A; Van Sickels, Joseph E; Falcao-Alencar, Gabriel; Fardo, David W; Hartsfield, James K; Ding, Xiuhua; Kluemper, G Thomas

2014-04-15

This case-control study investigated whether variations within the APOE-ε gene were associated with having a convex facial profile (skeletal Class II) compared to exhibiting a straight or concave facial profile (Class I or Class III) among patients with obstructive sleep apnea (OSA). Associations between the apnea-hypopnea index (AHI) and body mass index (BMI) scores for these OSA patients were also examined in the context of facial profile. OSA patients with an AHI ≥ 15 were recruited from a sleep clinic and classified by facial and dental occlusal relationships based on a profile facial analysis, lateral photographs, and dental examination. Saliva was collected as a source of DNA. The APOE-ε1-4 allele-defining single nucleotide polymorphisms (SNPs) rs429358 and rs7412 were genotyped. A χ(2) analysis was used to assess Hardy-Weinberg equilibrium and for association analysis (significance at p < 0.05). ANOVA and Fisher exact test were also used. Seventy-six Caucasian OSA patients participated in the study-25 Class II cases and 51 non-Class II cases. There was no association of the APOE-ε4 allele with facial profile among these OSA patients. Class II OSA patients had significantly lower BMIs (30.7 ± 5.78) than Class I (37.3 ± 6.14) or Class III (37.8 ± 6.17) patients (p < 0.001), although there was no statistical difference in AHI for Class II patients compared with other groups. OSA patients with Class II convex profile were more likely to have a lower BMI than those in other skeletal groups. In fact 20% of them were not obese, suggesting that a Class II convex profile may influence or be associated with OSA development independent of BMI.

Unsupervised classification of scattering behavior using radar polarimetry data

NASA Technical Reports Server (NTRS)

Van Zyl, Jakob J.

1989-01-01

The use of an imaging radar polarimeter data for unsupervised classification of scattering behavior is described by comparing the polarization properties of each pixel in a image to that of simple classes of scattering such as even number of reflections, odd number of reflections, and diffuse scattering. For example, when this algorithm is applied to data acquired over the San Francisco Bay area in California, it classifies scattering by the ocean as being similar to that predicted by the class of odd number of reflections, scattering by the urban area as being similar to that predicted by the class of even number of reflections, and scattering by the Golden Gate Park as being similar to that predicted by the diffuse scattering class. It also classifies the scattering by a lighthouse in the ocean and boats on the ocean surface as being similar to that predicted by the even number of reflections class, making it easy to identify these objects against the background of the surrounding ocean. The algorithm is also applied to forested areas and shows that scattering from clear-cut areas and agricultural fields is mostly similar to that predicted by the odd number of reflections class, while the scattering from tree-covered areas generally is classified as being a mixture of pixels exhibiting the characteristics of all three classes, although each pixel is identified with only a single class.

YOUNG STELLAR OBJECTS IN THE GOULD BELT

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

Dunham, Michael M.; Allen, Lori E.; Evans II, Neal J.

2015-09-15

We present the full catalog of Young Stellar Objects (YSOs) identified in the 18 molecular clouds surveyed by the Spitzer Space Telescope “cores to disks” (c2d) and “Gould Belt” (GB) Legacy surveys. Using standard techniques developed by the c2d project, we identify 3239 candidate YSOs in the 18 clouds, 2966 of which survive visual inspection and form our final catalog of YSOs in the GB. We compile extinction corrected spectral energy distributions for all 2966 YSOs and calculate and tabulate the infrared spectral index, bolometric luminosity, and bolometric temperature for each object. We find that 326 (11%), 210 (7%), 1248more » (42%), and 1182 (40%) are classified as Class 0 + I, Flat-spectrum, Class II, and Class III, respectively, and show that the Class III sample suffers from an overall contamination rate by background Asymptotic Giant Branch stars between 25% and 90%. Adopting standard assumptions, we derive durations of 0.40–0.78 Myr for Class 0 + I YSOs and 0.26–0.50 Myr for Flat-spectrum YSOs, where the ranges encompass uncertainties in the adopted assumptions. Including information from (sub)millimeter wavelengths, one-third of the Class 0 + I sample is classified as Class 0, leading to durations of 0.13–0.26 Myr (Class 0) and 0.27–0.52 Myr (Class I). We revisit infrared color–color diagrams used in the literature to classify YSOs and propose minor revisions to classification boundaries in these diagrams. Finally, we show that the bolometric temperature is a poor discriminator between Class II and Class III YSOs.« less

Distinguishing prostate cancer from benign confounders via a cascaded classifier on multi-parametric MRI

NASA Astrophysics Data System (ADS)

Litjens, G. J. S.; Elliott, R.; Shih, N.; Feldman, M.; Barentsz, J. O.; Hulsbergen-van de Kaa, C. A.; Kovacs, I.; Huisman, H. J.; Madabhushi, A.

2014-03-01

Learning how to separate benign confounders from prostate cancer is important because the imaging characteristics of these confounders are poorly understood. Furthermore, the typical representations of the MRI parameters might not be enough to allow discrimination. The diagnostic uncertainty this causes leads to a lower diagnostic accuracy. In this paper a new cascaded classifier is introduced to separate prostate cancer and benign confounders on MRI in conjunction with specific computer-extracted features to distinguish each of the benign classes (benign prostatic hyperplasia (BPH), inflammation, atrophy or prostatic intra-epithelial neoplasia (PIN). In this study we tried to (1) calculate different mathematical representations of the MRI parameters which more clearly express subtle differences between different classes, (2) learn which of the MRI image features will allow to distinguish specific benign confounders from prostate cancer, and (2) find the combination of computer-extracted MRI features to best discriminate cancer from the confounding classes using a cascaded classifier. One of the most important requirements for identifying MRI signatures for adenocarcinoma, BPH, atrophy, inflammation, and PIN is accurate mapping of the location and spatial extent of the confounder and cancer categories from ex vivo histopathology to MRI. Towards this end we employed an annotated prostatectomy data set of 31 patients, all of whom underwent a multi-parametric 3 Tesla MRI prior to radical prostatectomy. The prostatectomy slides were carefully co-registered to the corresponding MRI slices using an elastic registration technique. We extracted texture features from the T2-weighted imaging, pharmacokinetic features from the dynamic contrast enhanced imaging and diffusion features from the diffusion-weighted imaging for each of the confounder classes and prostate cancer. These features were selected because they form the mainstay of clinical diagnosis. Relevant features for each of the classes were selected using maximum relevance minimum redundancy feature selection, allowing us to perform classifier independent feature selection. The selected features were then incorporated in a cascading classifier, which can focus on easier sub-tasks at each stage, leaving the more difficult classification tasks for later stages. Results show that distinct features are relevant for each of the benign classes, for example the fraction of extra-vascular, extra-cellular space in a voxel is a clear discriminator for inflammation. Furthermore, the cascaded classifier outperforms both multi-class and one-shot classifiers in overall accuracy for discriminating confounders from cancer: 0.76 versus 0.71 and 0.62.

Automatic Fitting of Spiking Neuron Models to Electrophysiological Recordings

PubMed Central

Rossant, Cyrille; Goodman, Dan F. M.; Platkiewicz, Jonathan; Brette, Romain

2010-01-01

Spiking models can accurately predict the spike trains produced by cortical neurons in response to somatically injected currents. Since the specific characteristics of the model depend on the neuron, a computational method is required to fit models to electrophysiological recordings. The fitting procedure can be very time consuming both in terms of computer simulations and in terms of code writing. We present algorithms to fit spiking models to electrophysiological data (time-varying input and spike trains) that can run in parallel on graphics processing units (GPUs). The model fitting library is interfaced with Brian, a neural network simulator in Python. If a GPU is present it uses just-in-time compilation to translate model equations into optimized code. Arbitrary models can then be defined at script level and run on the graphics card. This tool can be used to obtain empirically validated spiking models of neurons in various systems. We demonstrate its use on public data from the INCF Quantitative Single-Neuron Modeling 2009 competition by comparing the performance of a number of neuron spiking models. PMID:20224819

Effects of heavy ions on visual function and electrophysiology of rodents: the ALTEA-MICE project

NASA Technical Reports Server (NTRS)

Sannita, W. G.; Acquaviva, M.; Ball, S. L.; Belli, F.; Bisti, S.; Bidoli, V.; Carozzo, S.; Casolino, M.; Cucinotta, F.; De Pascale, M. P.;

Generation and customization of biosynthetic excitable tissues for electrophysiological studies and cell-based therapies.

PubMed

Nguyen, Hung X; Kirkton, Robert D; Bursac, Nenad

2018-05-01

We describe a two-stage protocol to generate electrically excitable and actively conducting cell networks with stable and customizable electrophysiological phenotypes. Using this method, we have engineered monoclonally derived excitable tissues as a robust and reproducible platform to investigate how specific ion channels and mutations affect action potential (AP) shape and conduction. In the first stage of the protocol, we combine computational modeling, site-directed mutagenesis, and electrophysiological techniques to derive optimal sets of mammalian and/or prokaryotic ion channels that produce specific AP shape and conduction characteristics. In the second stage of the protocol, selected ion channels are stably expressed in unexcitable human cells by means of viral or nonviral delivery, followed by flow cytometry or antibiotic selection to purify the desired phenotype. This protocol can be used with traditional heterologous expression systems or primary excitable cells, and application of this method to primary fibroblasts may enable an alternative approach to cardiac cell therapy. Compared with existing methods, this protocol generates a well-defined, relatively homogeneous electrophysiological phenotype of excitable cells that facilitates experimental and computational studies of AP conduction and can decrease arrhythmogenic risk upon cell transplantation. Although basic cell culture and molecular biology techniques are sufficient to generate excitable tissues using the described protocol, experience with patch-clamp techniques is required to characterize and optimize derived cell populations.

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

Interactions between Defining, Explaining and Classifying: The Case of Increasing and Decreasing Sequences

ERIC Educational Resources Information Center

Alcock, Lara; Simpson, Adrian

2017-01-01

This paper describes a study in which we investigated relationships between defining mathematical concepts--increasing and decreasing infinite sequences--explaining their meanings and classifying consistently with formal definitions. We explored the effect of defining, explaining or studying a definition on subsequent classification, and the…

Leukocyte Recognition Using EM-Algorithm

NASA Astrophysics Data System (ADS)

Colunga, Mario Chirinos; Siordia, Oscar Sánchez; Maybank, Stephen J.

This document describes a method for classifying images of blood cells. Three different classes of cells are used: Band Neutrophils, Eosinophils and Lymphocytes. The image pattern is projected down to a lower dimensional sub space using PCA; the probability density function for each class is modeled with a Gaussian mixture using the EM-Algorithm. A new cell image is classified using the maximum a posteriori decision rule.

Minimum Expected Risk Estimation for Near-neighbor Classification

DTIC Science & Technology

2006-04-01

We consider the problems of class probability estimation and classification when using near-neighbor classifiers, such as k-nearest neighbors ( kNN ...estimate for weighted kNN classifiers with different prior information, for a broad class of risk functions. Theory and simulations show how significant...the difference is compared to the standard maximum likelihood weighted kNN estimates. Comparisons are made with uniform weights, symmetric weights

Characterization of agricultural land using singular value decomposition

NASA Astrophysics Data System (ADS)

Herries, Graham M.; Danaher, Sean; Selige, Thomas

1995-11-01

A method is defined and tested for the characterization of agricultural land from multi-spectral imagery, based on singular value decomposition (SVD) and key vector analysis. The SVD technique, which bears a close resemblance to multivariate statistic techniques, has previously been successfully applied to problems of signal extraction for marine data and forestry species classification. In this study the SVD technique is used as a classifier for agricultural regions, using airborne Daedalus ATM data, with 1 m resolution. The specific region chosen is an experimental research farm in Bavaria, Germany. This farm has a large number of crops, within a very small region and hence is not amenable to existing techniques. There are a number of other significant factors which render existing techniques such as the maximum likelihood algorithm less suitable for this area. These include a very dynamic terrain and tessellated pattern soil differences, which together cause large variations in the growth characteristics of the crops. The SVD technique is applied to this data set using a multi-stage classification approach, removing unwanted land-cover classes one step at a time. Typical classification accuracy's for SVD are of the order of 85-100%. Preliminary results indicate that it is a fast and efficient classifier with the ability to differentiate between crop types such as wheat, rye, potatoes and clover. The results of characterizing 3 sub-classes of Winter Wheat are also shown.

Exploiting ensemble learning for automatic cataract detection and grading.

PubMed

Yang, Ji-Jiang; Li, Jianqiang; Shen, Ruifang; Zeng, Yang; He, Jian; Bi, Jing; Li, Yong; Zhang, Qinyan; Peng, Lihui; Wang, Qing

2016-02-01

Cataract is defined as a lenticular opacity presenting usually with poor visual acuity. It is one of the most common causes of visual impairment worldwide. Early diagnosis demands the expertise of trained healthcare professionals, which may present a barrier to early intervention due to underlying costs. To date, studies reported in the literature utilize a single learning model for retinal image classification in grading cataract severity. We present an ensemble learning based approach as a means to improving diagnostic accuracy. Three independent feature sets, i.e., wavelet-, sketch-, and texture-based features, are extracted from each fundus image. For each feature set, two base learning models, i.e., Support Vector Machine and Back Propagation Neural Network, are built. Then, the ensemble methods, majority voting and stacking, are investigated to combine the multiple base learning models for final fundus image classification. Empirical experiments are conducted for cataract detection (two-class task, i.e., cataract or non-cataractous) and cataract grading (four-class task, i.e., non-cataractous, mild, moderate or severe) tasks. The best performance of the ensemble classifier is 93.2% and 84.5% in terms of the correct classification rates for cataract detection and grading tasks, respectively. The results demonstrate that the ensemble classifier outperforms the single learning model significantly, which also illustrates the effectiveness of the proposed approach. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Single-Pol Synthetic Aperture Radar Terrain Classification using Multiclass Confidence for One-Class Classifiers

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

Koch, Mark William; Steinbach, Ryan Matthew; Moya, Mary M

2015-10-01

Except in the most extreme conditions, Synthetic aperture radar (SAR) is a remote sensing technology that can operate day or night. A SAR can provide surveillance over a long time period by making multiple passes over a wide area. For object-based intelligence it is convenient to segment and classify the SAR images into objects that identify various terrains and man-made structures that we call “static features.” In this paper we introduce a novel SAR image product that captures how different regions decorrelate at different rates. Using superpixels and their first two moments we develop a series of one-class classification algorithmsmore » using a goodness-of-fit metric. P-value fusion is used to combine the results from different classes. We also show how to combine multiple one-class classifiers to get a confidence about a classification. This can be used by downstream algorithms such as a conditional random field to enforce spatial constraints.« less

[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.

New data clustering for RBF classifier of agriculture products from x-ray images

NASA Astrophysics Data System (ADS)

Casasent, David P.; Chen, Xuewen

1999-08-01

Classification of real-time x-ray images of randomly oriented touching pistachio nuts is discussed. The ultimate objective is the development of a subsystem for automated non-invasive detection of defective product items on a conveyor belt. We discuss the use of clustering and how it is vital to achieve useful classification. New clustering methods using class identify and new cluster classes are advanced and shown to be of use for this application. Radial basis function neural net classifiers are emphasized. We expect our results to be of use for other classifiers and applications.

Materials for multifunctional balloon catheters with capabilities in cardiac electrophysiological mapping and ablation therapy

NASA Astrophysics Data System (ADS)

Kim, Dae-Hyeong; Lu, Nanshu; Ghaffari, Roozbeh; Kim, Yun-Soung; Lee, Stephen P.; Xu, Lizhi; Wu, Jian; Kim, Rak-Hwan; Song, Jizhou; Liu, Zhuangjian; Viventi, Jonathan; de Graff, Bassel; Elolampi, Brian; Mansour, Moussa; Slepian, Marvin J.; Hwang, Sukwon; Moss, Joshua D.; Won, Sang-Min; Huang, Younggang; Litt, Brian; Rogers, John A.

2011-04-01

Developing advanced surgical tools for minimally invasive procedures represents an activity of central importance to improving human health. A key challenge is in establishing biocompatible interfaces between the classes of semiconductor device and sensor technologies that might be most useful in this context and the soft, curvilinear surfaces of the body. This paper describes a solution based on materials that integrate directly with the thin elastic membranes of otherwise conventional balloon catheters, to provide diverse, multimodal functionality suitable for clinical use. As examples, we present sensors for measuring temperature, flow, tactile, optical and electrophysiological data, together with radiofrequency electrodes for controlled, local ablation of tissue. Use of such ‘instrumented’ balloon catheters in live animal models illustrates their operation, as well as their specific utility in cardiac ablation therapy. The same concepts can be applied to other substrates of interest, such as surgical gloves.

Hybrid automata models of cardiac ventricular electrophysiology for real-time computational applications.

PubMed

Andalam, Sidharta; Ramanna, Harshavardhan; Malik, Avinash; Roop, Parthasarathi; Patel, Nitish; Trew, Mark L

2016-08-01

Virtual heart models have been proposed for closed loop validation of safety-critical embedded medical devices, such as pacemakers. These models must react in real-time to off-the-shelf medical devices. Real-time performance can be obtained by implementing models in computer hardware, and methods of compiling classes of Hybrid Automata (HA) onto FPGA have been developed. Models of ventricular cardiac cell electrophysiology have been described using HA which capture the complex nonlinear behavior of biological systems. However, many models that have been used for closed-loop validation of pacemakers are highly abstract and do not capture important characteristics of the dynamic rate response. We developed a new HA model of cardiac cells which captures dynamic behavior and we implemented the model in hardware. This potentially enables modeling the heart with over 1 million dynamic cells, making the approach ideal for closed loop testing of medical devices.

Action of ethanol low doses on heart rate variability following intravenous administration in rabbits.

PubMed

Khvedelidze, M; Chitanava, E; Nadareishvili, D; Jiqia, G; Gvasalia, M

2007-05-01

Effects of low ethanol doses on the vagosympathetic mechanisms of heart rate regulation were studied in rabbits. Analysis of heart rate variability showed that single intravenous administration of 0.5 mg/kg ethanol caused a higher probability of heart electrophysiological instability in sympathicotonics in contrast to vagotonics. This was associated with activation of the whole complex of regulatory mechanisms. In vagotonics, perturbations in power spectrum indicated on rapidly shunting of regulatory activity from lower to high levels of regulatory mechanisms to realize a "first class" undifferentiated response on stress induction. Sympathicotonics were unready to ethanol intravenous administration that resulted in reduction of all spectral component. Intravenous administration of ethanol caused a higher probability of heart electrophysiological instability in sympathicotonics then in vagotonics. It is important to consider these differences for therapeutic application of ethanol to some acute poisoning (methyl alcohol, ethylene glycol).

(Biphenyl-4-yl)methylammonium chlorides: potent anticonvulsants that modulate Na+ currents.

PubMed

Lee, Hyosung; Park, Ki Duk; Yang, Xiao-Fang; Dustrude, Erik T; Wilson, Sarah M; Khanna, Rajesh; Kohn, Harold

2013-07-25

We have reported that compounds containing a biaryl linked unit (Ar-X-Ar') modulated Na(+) currents by promoting slow inactivation and fast inactivation processes and by inducing frequency (use)-dependent inhibition of Na(+) currents. These electrophysiological properties have been associated with the mode of action of several antiepileptic drugs. In this study, we demonstrate that the readily accessible (biphenyl-4-yl)methylammonium chlorides (compound class B) exhibited a broad range of anticonvulsant activities in animal models, and in the maximal electroshock seizure test the activity of (3'-trifluoromethoxybiphenyl-4-yl)methylammonium chloride (8) exceeded that of phenobarbital and phenytoin upon oral administration to rats. Electrophysiological studies of 8 using mouse catecholamine A-differentiated cells and rat embryonic cortical neurons confirmed that 8 promoted slow and fast inactivation in both cell types but did not affect the frequency (use)-dependent block of Na(+) currents.

Electrophysiological effects of an anti-influenza drug oseltamivir on the guinea-pig atrium: comparison with those of pilsicainide.

PubMed

Takahara, Akira; Suzuki, Sanae; Hagiwara, Mihoko; Nozaki, Shuhei; Sugiyama, Atsushi

2013-01-01

We assessed the effects of oseltamivir on the conduction velocity and effective refractory period in the guinea-pig atrium in comparison with those of a class Ic antiarrhythmic drug pilsicainide. The recording and stimulating electrodes were attached on the epicardium close to the sinus nodal region and on the left atrial appendage. Oseltamivir (10-100 µM) as well as pilsicainide (1-10 µM) decreased the atrial conduction velocity in a frequency-dependent manner. Both drugs also increased the effective refractory period in both atria; but the frequency-dependent property of oseltamivir was lacking in the left atrium, and it was less obvious in the right atrium compared with that of pilsicainide. These results suggest that oseltamivir can directly modify the electrophysiological functions in the guinea-pig atrium possibly via combination of Na(+) and K(+) channel-blocking actions.

A methodological approach to characterise Landslide Periods based on historical series of rainfall and landslide damage

NASA Astrophysics Data System (ADS)

Petrucci, O.; Pasqua, A. A.

2009-10-01

Landslide Periods (LPs) are defined as periods, shorter than a hydrological year, during which one or more landslide damage events occur in one or more sectors of a study area. In this work, we present a methodological approach, based on the comparative analysis of historical series of landslide damage and daily rainfall data, aiming to characterise the main types of LPs affecting selected areas. Cumulative rainfall preceding landslide activation is assessed for short (1, 2, 3, and 5 days), medium (7, 10, and 30 days) and long (60, 90, and 180 days) durations, and their Return Periods (RPs) are assessed and ranked into three classes (Class 1: RP=5-10 years; Class 2: RP=11-15; Class 3: RP>15 years). To assess landslide damage, the Simplified Damage Index (SDI) is introduced. This represents classified landslide losses and is obtained by multiplying the value of the damaged element and the percentage of damage affecting it. The comparison of the RP of rainfall and the SDI allows us to indentify the different types of LPs that affected the study area in the past and that could affect it again in the future. The results of this activity can be used for practical purposes to define scenarios and strategies for risk management, to suggest priorities in policy towards disaster mitigation and preparedness and to predispose defensive measures and civil protection plans ranked according to the types of LPs that must be managed. We present an application, performed for a 39-year series of rainfall/landslide damage data and concerning a study area located in NE Calabria (Italy); in this case study, we identify four main types of LPs, which are ranked according to damage severity.

Mechanisms of termination and prevention of atrial fibrillation by drug therapy

PubMed Central

Workman, AJ; Smith, GL; Rankin, AC

2011-01-01

Atrial fibrillation (AF) is a disorder of the rhythm of electrical activation of the cardiac atria. It is the most common cardiac arrhythmia, has multiple aetiologies, and increases the risk of death from stroke. Pharmacological therapy is the mainstay of treatment for AF, but currently available anti-arrhythmic drugs have limited efficacy and safety. An improved understanding of how anti-arrhythmic drugs affect the electrophysiological mechanisms of AF initiation and maintenance, in the setting of the different cardiac diseases that predispose to AF, is therefore required. A variety of animal models of AF has been developed, to represent and control the pathophysiological causes and risk factors of AF, and to permit the measurement of detailed and invasive parameters relating to the associated electrophysiological mechanisms of AF. The purpose of this review is to examine, consolidate and compare available relevant data on in-vivo electrophysiological mechanisms of AF suppression by currently approved and investigational anti-arrhythmic drugs in such models. These include the Vaughan Williams class I-IV drugs, namely Na+ channel blockers, β-adrenoceptor antagonists, action potential prolonging drugs, and Ca2+ channel blockers; the “upstream therapies”, e.g., angiotensin converting enzyme inhibitors, statins and fish oils; and a variety of investigational drugs such as “atrial-selective” multiple ion channel blockers, gap junction-enhancers, and intracellular Ca2+-handling modulators. It is hoped that this will help to clarify the main electrophysiological mechanisms of action of different and related drug types in different disease settings, and the likely clinical significance and potential future exploitation of such mechanisms. PMID:21334377

Support vector inductive logic programming outperforms the naive Bayes classifier and inductive logic programming for the classification of bioactive chemical compounds.

PubMed

Cannon, Edward O; Amini, Ata; Bender, Andreas; Sternberg, Michael J E; Muggleton, Stephen H; Glen, Robert C; Mitchell, John B O

2007-05-01

We investigate the classification performance of circular fingerprints in combination with the Naive Bayes Classifier (MP2D), Inductive Logic Programming (ILP) and Support Vector Inductive Logic Programming (SVILP) on a standard molecular benchmark dataset comprising 11 activity classes and about 102,000 structures. The Naive Bayes Classifier treats features independently while ILP combines structural fragments, and then creates new features with higher predictive power. SVILP is a very recently presented method which adds a support vector machine after common ILP procedures. The performance of the methods is evaluated via a number of statistical measures, namely recall, specificity, precision, F-measure, Matthews Correlation Coefficient, area under the Receiver Operating Characteristic (ROC) curve and enrichment factor (EF). According to the F-measure, which takes both recall and precision into account, SVILP is for seven out of the 11 classes the superior method. The results show that the Bayes Classifier gives the best recall performance for eight of the 11 targets, but has a much lower precision, specificity and F-measure. The SVILP model on the other hand has the highest recall for only three of the 11 classes, but generally far superior specificity and precision. To evaluate the statistical significance of the SVILP superiority, we employ McNemar's test which shows that SVILP performs significantly (p < 5%) better than both other methods for six out of 11 activity classes, while being superior with less significance for three of the remaining classes. While previously the Bayes Classifier was shown to perform very well in molecular classification studies, these results suggest that SVILP is able to extract additional knowledge from the data, thus improving classification results further.

Random forests ensemble classifier trained with data resampling strategy to improve cardiac arrhythmia diagnosis.

PubMed

Ozçift, Akin

2011-05-01

Supervised classification algorithms are commonly used in the designing of computer-aided diagnosis systems. In this study, we present a resampling strategy based Random Forests (RF) ensemble classifier to improve diagnosis of cardiac arrhythmia. Random forests is an ensemble classifier that consists of many decision trees and outputs the class that is the mode of the class's output by individual trees. In this way, an RF ensemble classifier performs better than a single tree from classification performance point of view. In general, multiclass datasets having unbalanced distribution of sample sizes are difficult to analyze in terms of class discrimination. Cardiac arrhythmia is such a dataset that has multiple classes with small sample sizes and it is therefore adequate to test our resampling based training strategy. The dataset contains 452 samples in fourteen types of arrhythmias and eleven of these classes have sample sizes less than 15. Our diagnosis strategy consists of two parts: (i) a correlation based feature selection algorithm is used to select relevant features from cardiac arrhythmia dataset. (ii) RF machine learning algorithm is used to evaluate the performance of selected features with and without simple random sampling to evaluate the efficiency of proposed training strategy. The resultant accuracy of the classifier is found to be 90.0% and this is a quite high diagnosis performance for cardiac arrhythmia. Furthermore, three case studies, i.e., thyroid, cardiotocography and audiology, are used to benchmark the effectiveness of the proposed method. The results of experiments demonstrated the efficiency of random sampling strategy in training RF ensemble classification algorithm. Copyright © 2011 Elsevier Ltd. All rights reserved.

A new prognostic model for chemotherapy-induced febrile neutropenia.

PubMed

Ahn, Shin; Lee, Yoon-Seon; Lee, Jae-Lyun; Lim, Kyung Soo; Yoon, Sung-Cheol

2016-02-01

The objective of this study was to develop and validate a new prognostic model for febrile neutropenia (FN). This study comprised 1001 episodes of FN: 718 for the derivation set and 283 for the validation set. Multivariate logistic regression analysis was performed with unfavorable outcome as the primary endpoint and bacteremia as the secondary endpoint. In the derivation set, risk factors for adverse outcomes comprised age ≥ 60 years (2 points), procalcitonin ≥ 0.5 ng/mL (5 points), ECOG performance score ≥ 2 (2 points), oral mucositis grade ≥ 3 (3 points), systolic blood pressure <90 mmHg (3 points), and respiratory rate ≥ 24 breaths/min (3 points). The model stratified patients into three severity classes, with adverse event rates of 6.0 % in class I (score ≤ 2), 27.3 % in class II (score 3-8), and 67.9 % in class III (score ≥ 9). Bacteremia was present in 1.1, 11.5, and 29.8 % of patients in class I, II, and III, respectively. The outcomes of the validation set were similar in each risk class. When the derivation and validation sets were integrated, unfavorable outcomes occurred in 5.9 % of the low-risk group classified by the new prognostic model and in 12.2 % classified by the Multinational Association for Supportive Care in Cancer (MASCC) risk index. With the new prognostic model, we can classify patients with FN into three classes of increasing adverse outcomes and bacteremia. Early discharge would be possible for class I patients, short-term observation could safely manage class II patients, and inpatient admission is warranted for class III patients.

Spider Silk Constructs Enhance Axonal Regeneration and Remyelination in Long Nerve Defects in Sheep

PubMed Central

Radtke, Christine; Allmeling, Christina; Waldmann, Karl-Heinz; Reimers, Kerstin; Thies, Kerstin; Schenk, Henning C.; Hillmer, Anja; Guggenheim, Merlin; Brandes, Gudrun; Vogt, Peter M.

2011-01-01

Background Surgical reapposition of peripheral nerve results in some axonal regeneration and functional recovery, but the clinical outcome in long distance nerve defects is disappointing and research continues to utilize further interventional approaches to optimize functional recovery. We describe the use of nerve constructs consisting of decellularized vein grafts filled with spider silk fibers as a guiding material to bridge a 6.0 cm tibial nerve defect in adult sheep. Methodology/Principal Findings The nerve constructs were compared to autologous nerve grafts. Regeneration was evaluated for clinical, electrophysiological and histological outcome. Electrophysiological recordings were obtained at 6 months and 10 months post surgery in each group. Ten months later, the nerves were removed and prepared for immunostaining, electrophysiological and electron microscopy. Immunostaining for sodium channel (NaV 1.6) was used to define nodes of Ranvier on regenerated axons in combination with anti-S100 and neurofilament. Anti-S100 was used to identify Schwann cells. Axons regenerated through the constructs and were myelinated indicating migration of Schwann cells into the constructs. Nodes of Ranvier between myelin segments were observed and identified by intense sodium channel (NaV 1.6) staining on the regenerated axons. There was no significant difference in electrophysiological results between control autologous experimental and construct implantation indicating that our construct are an effective alternative to autologous nerve transplantation. Conclusions/Significance This study demonstrates that spider silk enhances Schwann cell migration, axonal regrowth and remyelination including electrophysiological recovery in a long-distance peripheral nerve gap model resulting in functional recovery. This improvement in nerve regeneration could have significant clinical implications for reconstructive nerve surgery. PMID:21364921

Neural dissociation in the production of lexical versus classifier signs in ASL: distinct patterns of hemispheric asymmetry.

PubMed

Hickok, Gregory; Pickell, Herbert; Klima, Edward; Bellugi, Ursula

2009-01-01

We examine the hemispheric organization for the production of two classes of ASL signs, lexical signs and classifier signs. Previous work has found strong left hemisphere dominance for the production of lexical signs, but several authors have speculated that classifier signs may involve the right hemisphere to a greater degree because they can represent spatial information in a topographic, non-categorical manner. Twenty-one unilaterally brain damaged signers (13 left hemisphere damaged, 8 right hemisphere damaged) were presented with a story narration task designed to elicit both lexical and classifier signs. Relative frequencies of the two types of errors were tabulated. Left hemisphere damaged signers produced significantly more lexical errors than did right hemisphere damaged signers, whereas the reverse pattern held for classifier signs. Our findings argue for different patterns of hemispheric asymmetry for these two classes of ASL signs. We suggest that the requirement to encode analogue spatial information in the production of classifier signs results in the increased involvement of the right hemisphere systems.

Using random forest for reliable classification and cost-sensitive learning for medical diagnosis.

PubMed

Yang, Fan; Wang, Hua-zhen; Mi, Hong; Lin, Cheng-de; Cai, Wei-wen

2009-01-30

Most machine-learning classifiers output label predictions for new instances without indicating how reliable the predictions are. The applicability of these classifiers is limited in critical domains where incorrect predictions have serious consequences, like medical diagnosis. Further, the default assumption of equal misclassification costs is most likely violated in medical diagnosis. In this paper, we present a modified random forest classifier which is incorporated into the conformal predictor scheme. A conformal predictor is a transductive learning scheme, using Kolmogorov complexity to test the randomness of a particular sample with respect to the training sets. Our method show well-calibrated property that the performance can be set prior to classification and the accurate rate is exactly equal to the predefined confidence level. Further, to address the cost sensitive problem, we extend our method to a label-conditional predictor which takes into account different costs for misclassifications in different class and allows different confidence level to be specified for each class. Intensive experiments on benchmark datasets and real world applications show the resultant classifier is well-calibrated and able to control the specific risk of different class. The method of using RF outlier measure to design a nonconformity measure benefits the resultant predictor. Further, a label-conditional classifier is developed and turn to be an alternative approach to the cost sensitive learning problem that relies on label-wise predefined confidence level. The target of minimizing the risk of misclassification is achieved by specifying the different confidence level for different class.

Multiple Spectral-Spatial Classification Approach for Hyperspectral Data

NASA Technical Reports Server (NTRS)

Tarabalka, Yuliya; Benediktsson, Jon Atli; Chanussot, Jocelyn; Tilton, James C.

2010-01-01

A .new multiple classifier approach for spectral-spatial classification of hyperspectral images is proposed. Several classifiers are used independently to classify an image. For every pixel, if all the classifiers have assigned this pixel to the same class, the pixel is kept as a marker, i.e., a seed of the spatial region, with the corresponding class label. We propose to use spectral-spatial classifiers at the preliminary step of the marker selection procedure, each of them combining the results of a pixel-wise classification and a segmentation map. Different segmentation methods based on dissimilar principles lead to different classification results. Furthermore, a minimum spanning forest is built, where each tree is rooted on a classification -driven marker and forms a region in the spectral -spatial classification: map. Experimental results are presented for two hyperspectral airborne images. The proposed method significantly improves classification accuracies, when compared to previously proposed classification techniques.

The association between acute mental stress and abnormal left atrial electrophysiology.

PubMed

O'Neal, Wesley T; Hammadah, Muhammad; Sandesara, Pratik B; Almuwaqqat, Zakaria; Samman-Tahhan, Ayman; Gafeer, Mohamad M; Abdelhadi, Naser; Wilmot, Kobina; Al Mheid, Ibhar; Bremner, Douglas J; Kutner, Michael; Soliman, Elsayed Z; Shah, Amit J; Quyyumi, Arshed A; Vaccarino, Viola

2017-10-01

Acute stress may trigger atrial fibrillation (AF), but the underlying mechanisms are unclear. We examined if acute mental stress results in abnormal left atrial electrophysiology as detected by more negative deflection of P-wave terminal force in lead V 1 (PTFV 1 ), a well-known marker of AF risk. We examined this hypothesis in 422 patients (mean age = 56 ± 10 years; 61% men; 44% white) with stable coronary heart disease who underwent mental (speech task) stress testing. PTFV 1 was defined as the duration (milliseconds) times the value of the depth (μV) of the downward deflection (terminal portion) of the P-wave in lead V 1 measured on digital electrocardiograms (ECG). Electrocardiographic left atrial abnormality was defined as PTFV 1 ≤ -4000 μV*ms. Mean PTFV 1 values during stress and recovery were compared with rest. The percentage of participants who developed left atrial abnormality during stress and recovery was compared with the percentage at rest. Compared with rest, PTFV 1 became more negative during mental stress (mean change =  -348, 95% CI = [-515, -182]; P < 0.001) and no change was observed at recovery (mean change = 12, 95%CI = [-148, 172]; P = 0.89). A larger percentage of participants showed left atrial abnormality on ECGs obtained at stress (n = 163, 39%) and recovery (n = 142, 34%) compared with rest (n = 127, 30%). Acute mental stress alters left atrial electrophysiology, suggesting that stressful situations promote adverse transient electrical changes to provide the necessary substrate for AF. © 2017 Wiley Periodicals, Inc.

Bayes Error Rate Estimation Using Classifier Ensembles

NASA Technical Reports Server (NTRS)

Tumer, Kagan; Ghosh, Joydeep

2003-01-01

The Bayes error rate gives a statistical lower bound on the error achievable for a given classification problem and the associated choice of features. By reliably estimating th is rate, one can assess the usefulness of the feature set that is being used for classification. Moreover, by comparing the accuracy achieved by a given classifier with the Bayes rate, one can quantify how effective that classifier is. Classical approaches for estimating or finding bounds for the Bayes error, in general, yield rather weak results for small sample sizes; unless the problem has some simple characteristics, such as Gaussian class-conditional likelihoods. This article shows how the outputs of a classifier ensemble can be used to provide reliable and easily obtainable estimates of the Bayes error with negligible extra computation. Three methods of varying sophistication are described. First, we present a framework that estimates the Bayes error when multiple classifiers, each providing an estimate of the a posteriori class probabilities, a recombined through averaging. Second, we bolster this approach by adding an information theoretic measure of output correlation to the estimate. Finally, we discuss a more general method that just looks at the class labels indicated by ensem ble members and provides error estimates based on the disagreements among classifiers. The methods are illustrated for artificial data, a difficult four-class problem involving underwater acoustic data, and two problems from the Problem benchmarks. For data sets with known Bayes error, the combiner-based methods introduced in this article outperform existing methods. The estimates obtained by the proposed methods also seem quite reliable for the real-life data sets for which the true Bayes rates are unknown.

Advances in Doppler recognition for ground moving target indication

NASA Astrophysics Data System (ADS)

Kealey, Paul G.; Jahangir, Mohammed

2006-05-01

Ground Moving Target Indication (GMTI) radar provides a day/night, all-weather, wide-area surveillance capability to detect moving vehicles and personnel. Current GMTI radar sensors are limited to only detecting and tracking targets. The exploitation of GMTI data would be greatly enhanced by a capability to recognize accurately the detections as significant classes of target. Doppler classification exploits the differential internal motion of targets, e.g. due to the tracks, limbs and rotors. Recently, the QinetiQ Bayesian Doppler classifier has been extended to include a helicopter class in addition to wheeled, tracked and personnel classes. This paper presents the performance for these four classes using a traditional low-resolution GMTI surveillance waveform with an experimental radar system. We have determined the utility of an "unknown output decision" for enhancing the accuracy of the declared target classes. A confidence method has been derived, using a threshold of the difference in certainties, to assign uncertain classifications into an "unknown class". The trade-off between fraction of targets declared and accuracy of the classifier has been measured. To determine the operating envelope of a Doppler classification algorithm requires a detailed understanding of the Signal-to-Noise Ratio (SNR) performance of the algorithm. In this study the SNR dependence of the QinetiQ classifier has been determined.

Classifying environmental pollutants: Part 3. External validation of the classification system.

PubMed

Verhaar, H J; Solbé, J; Speksnijder, J; van Leeuwen, C J; Hermens, J L

2000-04-01

In order to validate a classification system for the prediction of the toxic effect concentrations of organic environmental pollutants to fish, all available fish acute toxicity data were retrieved from the ECETOC database, a database of quality-evaluated aquatic toxicity measurements created and maintained by the European Centre for the Ecotoxicology and Toxicology of Chemicals. The individual chemicals for which these data were available were classified according to the rulebase under consideration and predictions of effect concentrations or ranges of possible effect concentrations were generated. These predictions were compared to the actual toxicity data retrieved from the database. The results of this comparison show that generally, the classification system provides adequate predictions of either the aquatic toxicity (class 1) or the possible range of toxicity (other classes) of organic compounds. A slight underestimation of effect concentrations occurs for some highly water soluble, reactive chemicals with low log K(ow) values. On the other end of the scale, some compounds that are classified as belonging to a relatively toxic class appear to belong to the so-called baseline toxicity compounds. For some of these, additional classification rules are proposed. Furthermore, some groups of compounds cannot be classified, although they should be amenable to predictions. For these compounds additional research as to class membership and associated prediction rules is proposed.

Photocoagulation in rabbits: optical coherence tomographic lesion classification, wound healing reaction, and retinal temperatures.

PubMed

Koinzer, Stefan; Hesse, Carola; Caliebe, Amke; Saeger, Mark; Baade, Alexander; Schlott, Kerstin; Brinkmann, Ralf; Roider, Johann

2013-09-01

The rabbit is the most common animal model to study retinal photocoagulation lesions. We present a classification of retinal lesions from rabbits, that is based on optical coherence tomographic (OCT) findings, temperature data, and OCT-follow-up data over 3 months. Four hundred eighty-six photocoagulation lesions (modified Zeiss Visulas® 532 nm CW laser, lesion diameter 133 µm, exposure duration 200  milliseconds or variable, power variable) were analyzed from six eyes of three chinchilla gray rabbits. During the irradiation of each lesion, we used an optoacoustics-based method to measure the retinal temperature profile. Two hours, 1 week, 1 month, and 3 months after the treatment, we obtained fundus color and OCT (Spectralis®) images of each lesion. We classified the lesions according to their OCT morphology and correlated the findings to ophthalmoscopic and OCT lesion diameters, and temperatures. Besides an undetectable lesion class 0, we discerned subthreshold lesions that were invisible on the fundus but detectable in OCT (classes 1 and 2), very mild lesions that were partly visible on the fundus (class 3), and 3 classes of suprathreshold lesions. OCT greatest linear diameters (GLDs) were larger than ophthalmoscopic lesion diameters, both increased for increasing classes, and GLDs decreased over 3 months within each class. Mean peak end temperatures for 200  milliseconds lesions ranged from 61°C in class 2 to 80°C in class 6. The seven step rabbit lesion classifier is distinct from a previously published human lesion classifier. Threshold lesions are generated at comparable temperatures in rabbits and humans, while more intense lesions are created at lower temperatures in rabbits. The OCT lesion classifier could replace routine histology in some studies, and the presented data may be used to estimate lesion end temperatures from OCT images. © 2013 Wiley Periodicals, Inc.

Privacy-Preserving Classifier Learning

NASA Astrophysics Data System (ADS)

Brickell, Justin; Shmatikov, Vitaly

We present an efficient protocol for the privacy-preserving, distributed learning of decision-tree classifiers. Our protocol allows a user to construct a classifier on a database held by a remote server without learning any additional information about the records held in the database. The server does not learn anything about the constructed classifier, not even the user’s choice of feature and class attributes.

Mining disease state converters for medical intervention of diseases.

PubMed

Dong, Guozhu; Duan, Lei; Tang, Changjie

2010-02-01

In applications such as gene therapy and drug design, a key goal is to convert the disease state of diseased objects from an undesirable state into a desirable one. Such conversions may be achieved by changing the values of some attributes of the objects. For example, in gene therapy one may convert cancerous cells to normal ones by changing some genes' expression level from low to high or from high to low. In this paper, we define the disease state conversion problem as the discovery of disease state converters; a disease state converter is a small set of attribute value changes that may change an object's disease state from undesirable into desirable. We consider two variants of this problem: personalized disease state converter mining mines disease state converters for a given individual patient with a given disease, and universal disease state converter mining mines disease state converters for all samples with a given disease. We propose a DSCMiner algorithm to discover small and highly effective disease state converters. Since real-life medical experiments on living diseased instances are expensive and time consuming, we use classifiers trained from the datasets of given diseases to evaluate the quality of discovered converter sets. The effectiveness of a disease state converter is measured by the percentage of objects that are successfully converted from undesirable state into desirable state as deemed by state-of-the-art classifiers. We use experiments to evaluate the effectiveness of our algorithm and to show its effectiveness. We also discuss possible research directions for extensions and improvements. We note that the disease state conversion problem also has applications in customer retention, criminal rehabilitation, and company turn-around, where the goal is to convert class membership of objects whose class is an undesirable class.

Novel Type V Staphylococcal Cassette Chromosome mec Driven by a Novel Cassette Chromosome Recombinase, ccrC

PubMed Central

Ito, Teruyo; Ma, Xiao Xue; Takeuchi, Fumihiko; Okuma, Keiko; Yuzawa, Harumi; Hiramatsu, Keiichi

2004-01-01

Staphylococcal cassette chromosome mec (SCCmec) is a mobile genetic element composed of the mec gene complex, which encodes methicillin resistance, and the ccr gene complex, which encodes the recombinases responsible for its mobility. The mec gene complex has been classified into four classes, and the ccr gene complex has been classified into three allotypes. Different combinations of mec gene complex classes and ccr gene complex types have so far defined four types of SCCmec elements. Now we introduce the fifth allotype of SCCmec, which was found on the chromosome of a community-acquired methicillin-resistant Staphylococcus aureus strain (strain WIS [WBG8318]) isolated in Australia. The element shared the same chromosomal integration site with the four extant types of SCCmec and the characteristic nucleotide sequences at the chromosome-SCCmec junction regions. The novel SCCmec carried mecA bracketed by IS431 (IS431-mecA-ΔmecR1-IS431), which is designated the class C2 mec gene complex; and instead of ccrA and ccrB genes, it carried a single copy of a gene homologue that encoded cassette chromosome recombinase. Since the open reading frame (ORF) was found to encode an enzyme which catalyzes the precise excision as well as site- and orientation-specific integration of the element, we designated the ORF cassette chromosome recombinase C (ccrC), and we designated the element type V SCCmec. Type V SCCmec is a small SCCmec element (28 kb) and does not carry any antibiotic resistance genes besides mecA. Unlike the extant SCCmec types, it carries a set of foreign genes encoding a restriction-modification system that might play a role in the stabilization of the element on the chromosome. PMID:15215121

EEG classification for motor imagery and resting state in BCI applications using multi-class Adaboost extreme learning machine

NASA Astrophysics Data System (ADS)

Gao, Lin; Cheng, Wei; Zhang, Jinhua; Wang, Jue

2016-08-01

Brain-computer interface (BCI) systems provide an alternative communication and control approach for people with limited motor function. Therefore, the feature extraction and classification approach should differentiate the relative unusual state of motion intention from a common resting state. In this paper, we sought a novel approach for multi-class classification in BCI applications. We collected electroencephalographic (EEG) signals registered by electrodes placed over the scalp during left hand motor imagery, right hand motor imagery, and resting state for ten healthy human subjects. We proposed using the Kolmogorov complexity (Kc) for feature extraction and a multi-class Adaboost classifier with extreme learning machine as base classifier for classification, in order to classify the three-class EEG samples. An average classification accuracy of 79.5% was obtained for ten subjects, which greatly outperformed commonly used approaches. Thus, it is concluded that the proposed method could improve the performance for classification of motor imagery tasks for multi-class samples. It could be applied in further studies to generate the control commands to initiate the movement of a robotic exoskeleton or orthosis, which finally facilitates the rehabilitation of disabled people.

Multi-class machine classification of suicide-related communication on Twitter.

PubMed

Burnap, Pete; Colombo, Gualtiero; Amery, Rosie; Hodorog, Andrei; Scourfield, Jonathan

2017-08-01

The World Wide Web, and online social networks in particular, have increased connectivity between people such that information can spread to millions of people in a matter of minutes. This form of online collective contagion has provided many benefits to society, such as providing reassurance and emergency management in the immediate aftermath of natural disasters. However, it also poses a potential risk to vulnerable Web users who receive this information and could subsequently come to harm. One example of this would be the spread of suicidal ideation in online social networks, about which concerns have been raised. In this paper we report the results of a number of machine classifiers built with the aim of classifying text relating to suicide on Twitter. The classifier distinguishes between the more worrying content, such as suicidal ideation, and other suicide-related topics such as reporting of a suicide, memorial, campaigning and support. It also aims to identify flippant references to suicide. We built a set of baseline classifiers using lexical, structural, emotive and psychological features extracted from Twitter posts. We then improved on the baseline classifiers by building an ensemble classifier using the Rotation Forest algorithm and a Maximum Probability voting classification decision method, based on the outcome of base classifiers. This achieved an F-measure of 0.728 overall (for 7 classes, including suicidal ideation) and 0.69 for the suicidal ideation class. We summarise the results by reflecting on the most significant predictive principle components of the suicidal ideation class to provide insight into the language used on Twitter to express suicidal ideation. Finally, we perform a 12-month case study of suicide-related posts where we further evaluate the classification approach - showing a sustained classification performance and providing anonymous insights into the trends and demographic profile of Twitter users posting content of this type.

An Efficient Data Partitioning to Improve Classification Performance While Keeping Parameters Interpretable

PubMed Central

Korjus, Kristjan; Hebart, Martin N.; Vicente, Raul

2016-01-01

Supervised machine learning methods typically require splitting data into multiple chunks for training, validating, and finally testing classifiers. For finding the best parameters of a classifier, training and validation are usually carried out with cross-validation. This is followed by application of the classifier with optimized parameters to a separate test set for estimating the classifier’s generalization performance. With limited data, this separation of test data creates a difficult trade-off between having more statistical power in estimating generalization performance versus choosing better parameters and fitting a better model. We propose a novel approach that we term “Cross-validation and cross-testing” improving this trade-off by re-using test data without biasing classifier performance. The novel approach is validated using simulated data and electrophysiological recordings in humans and rodents. The results demonstrate that the approach has a higher probability of discovering significant results than the standard approach of cross-validation and testing, while maintaining the nominal alpha level. In contrast to nested cross-validation, which is maximally efficient in re-using data, the proposed approach additionally maintains the interpretability of individual parameters. Taken together, we suggest an addition to currently used machine learning approaches which may be particularly useful in cases where model weights do not require interpretation, but parameters do. PMID:27564393

Comparisons and Selections of Features and Classifiers for Short Text Classification

NASA Astrophysics Data System (ADS)

Wang, Ye; Zhou, Zhi; Jin, Shan; Liu, Debin; Lu, Mi

2017-10-01

Short text is considerably different from traditional long text documents due to its shortness and conciseness, which somehow hinders the applications of conventional machine learning and data mining algorithms in short text classification. According to traditional artificial intelligence methods, we divide short text classification into three steps, namely preprocessing, feature selection and classifier comparison. In this paper, we have illustrated step-by-step how we approach our goals. Specifically, in feature selection, we compared the performance and robustness of the four methods of one-hot encoding, tf-idf weighting, word2vec and paragraph2vec, and in the classification part, we deliberately chose and compared Naive Bayes, Logistic Regression, Support Vector Machine, K-nearest Neighbor and Decision Tree as our classifiers. Then, we compared and analysed the classifiers horizontally with each other and vertically with feature selections. Regarding the datasets, we crawled more than 400,000 short text files from Shanghai and Shenzhen Stock Exchanges and manually labeled them into two classes, the big and the small. There are eight labels in the big class, and 59 labels in the small class.

Latent classes of polydrug and polyroute use and associations with human immunodeficiency virus risk behaviours and overdose among people who inject drugs in Tijuana, Baja California, Mexico.

PubMed

Meacham, Meredith C; Roesch, Scott C; Strathdee, Steffanie A; Lindsay, Suzanne; Gonzalez-Zuniga, Patricia; Gaines, Tommi L

2018-01-01

Patterns of polydrug use among people who inject drugs (PWID) may be differentially associated with overdose and unique human immunodeficiency virus (HIV) risk factors. Subgroups of PWID in Tijuana, Mexico, were identified based on substances used, route of administration, frequency of use and co-injection indicators. Participants were PWID residing in Tijuana age ≥18 years sampled from 2011 to 2012 who reported injecting an illicit substance in the past month (n = 735). Latent class analysis identified discrete classes of polydrug use characterised by 11 indicators of past 6 months substance use. Multinomial logistic regression examined class membership association with HIV risk behaviours, overdose and other covariates using an automated three-step procedure in mplus to account for classification error. Participants were classified into five subgroups. Two polydrug and polyroute classes were defined by use of multiple substances through several routes of administration and were primarily distinguished from each other by cocaine use (class 1: 5%) or no cocaine use (class 2: 29%). The other classes consisted primarily of injectors: cocaine, methamphetamine and heroin injection (class 3: 4%); methamphetamine and heroin injection (class 4: 10%); and heroin injection (class 5: 52%). Compared with the heroin-only injection class, memberships in the two polydrug and polyroute use classes were independently associated with both HIV injection and sexual risk behaviours. Substance use patterns among PWID in Tijuana are highly heterogeneous, and polydrug and polyroute users are a high-risk subgroup who may require more tailored prevention and treatment interventions. [Meacham MC, Roesch SC, Strathdee SA, Lindsay S, Gonzalez-Zuniga P, Gaines TL. Latent classes of polydrug and polyroute use and associations with human immunodeficiency virus risk behaviours and overdose among people who inject drugs in Tijuana, Baja California, Mexico. Drug Alcohol Rev 2018;37:128-136]. © 2017 Australasian Professional Society on Alcohol and other Drugs.

Linking sleep and general anesthesia mechanisms: this is no walkover.

PubMed

Bonhomme, V; Boveroux, P; Vanhaudenhuyse, A; Hans, P; Brichant, J F; Jaquet, O; Boly, M; Laureys, S

2011-01-01

This review aims at defining the link between physiological sleep and general anesthesia. Despite common behavioral and electrophysiological characteristics between both states, current literature suggests that the transition process between waking and sleep or anesthesia-induced alteration of consciousness is not driven by the same sequence of events. On the one hand, sleep originates in sub-cortical structures with subsequent repercussions on thalamo-cortical interactions and cortical activity. On the other hand, anesthesia seems to primarily affect the cortex with subsequent repercussions on the activity of sub-cortical networks. This discrepancy has yet to be confirmed by further functional brain imaging and electrophysiological experiments. The relationship between the observed functional modifications of brain activity during anesthesia and the known biochemical targets of hypnotic anesthetic agents also remains to be determined.

78 FR 5327 - Medical Devices; Ophthalmic Devices; Classification of the Scleral Plug

Federal Register 2010, 2011, 2012, 2013, 2014

2013-01-25

... Agency) is proposing to classify the scleral plug into class II (special controls), and proposing to... controls needed to provide reasonable assurance of their safety and effectiveness. The three categories of devices are class I (general controls), class II (special controls), and class III (premarket approval...

Di-codon Usage for Gene Classification

NASA Astrophysics Data System (ADS)

Nguyen, Minh N.; Ma, Jianmin; Fogel, Gary B.; Rajapakse, Jagath C.

Classification of genes into biologically related groups facilitates inference of their functions. Codon usage bias has been described previously as a potential feature for gene classification. In this paper, we demonstrate that di-codon usage can further improve classification of genes. By using both codon and di-codon features, we achieve near perfect accuracies for the classification of HLA molecules into major classes and sub-classes. The method is illustrated on 1,841 HLA sequences which are classified into two major classes, HLA-I and HLA-II. Major classes are further classified into sub-groups. A binary SVM using di-codon usage patterns achieved 99.95% accuracy in the classification of HLA genes into major HLA classes; and multi-class SVM achieved accuracy rates of 99.82% and 99.03% for sub-class classification of HLA-I and HLA-II genes, respectively. Furthermore, by combining codon and di-codon usages, the prediction accuracies reached 100%, 99.82%, and 99.84% for HLA major class classification, and for sub-class classification of HLA-I and HLA-II genes, respectively.

SVM-Fold: a tool for discriminative multi-class protein fold and superfamily recognition

PubMed Central

Melvin, Iain; Ie, Eugene; Kuang, Rui; Weston, Jason; Stafford, William Noble; Leslie, Christina

2007-01-01

Background Predicting a protein's structural class from its amino acid sequence is a fundamental problem in computational biology. Much recent work has focused on developing new representations for protein sequences, called string kernels, for use with support vector machine (SVM) classifiers. However, while some of these approaches exhibit state-of-the-art performance at the binary protein classification problem, i.e. discriminating between a particular protein class and all other classes, few of these studies have addressed the real problem of multi-class superfamily or fold recognition. Moreover, there are only limited software tools and systems for SVM-based protein classification available to the bioinformatics community. Results We present a new multi-class SVM-based protein fold and superfamily recognition system and web server called SVM-Fold, which can be found at . Our system uses an efficient implementation of a state-of-the-art string kernel for sequence profiles, called the profile kernel, where the underlying feature representation is a histogram of inexact matching k-mer frequencies. We also employ a novel machine learning approach to solve the difficult multi-class problem of classifying a sequence of amino acids into one of many known protein structural classes. Binary one-vs-the-rest SVM classifiers that are trained to recognize individual structural classes yield prediction scores that are not comparable, so that standard "one-vs-all" classification fails to perform well. Moreover, SVMs for classes at different levels of the protein structural hierarchy may make useful predictions, but one-vs-all does not try to combine these multiple predictions. To deal with these problems, our method learns relative weights between one-vs-the-rest classifiers and encodes information about the protein structural hierarchy for multi-class prediction. In large-scale benchmark results based on the SCOP database, our code weighting approach significantly improves on the standard one-vs-all method for both the superfamily and fold prediction in the remote homology setting and on the fold recognition problem. Moreover, our code weight learning algorithm strongly outperforms nearest-neighbor methods based on PSI-BLAST in terms of prediction accuracy on every structure classification problem we consider. Conclusion By combining state-of-the-art SVM kernel methods with a novel multi-class algorithm, the SVM-Fold system delivers efficient and accurate protein fold and superfamily recognition. PMID:17570145

Current State of the Regulatory Trajectory for Whole Slide Imaging Devices in the USA

PubMed Central

Abels, Esther; Pantanowitz, Liron

2017-01-01

The regulatory field for digital pathology (DP) has advanced significantly. A major milestone was accomplished when the FDA allowed the first vendor to market their device for primary diagnostic use in the USA and published in the classification order that this device, and substantially equivalent devices of this generic type, should be classified into class II instead of class III as previously proposed. The Digital Pathology Association (DPA) regulatory task force had a major role in the accomplishment of getting the application request for Whole Slide Imaging (WSI) Systems recommended for a de novo. This article reviews the past and emerging regulatory environment of WSI for clinical use in the USA. A WSI system with integrated subsystems is defined in the context of medical device regulations. The FDA technical performance assessment guideline is also discussed as well as parameters involved in analytical testing and clinical studies to demonstrate that WSI devices are safe and effective for clinical use. PMID:28584684

Current State of the Regulatory Trajectory for Whole Slide Imaging Devices in the USA.

PubMed

Abels, Esther; Pantanowitz, Liron

2017-01-01

The regulatory field for digital pathology (DP) has advanced significantly. A major milestone was accomplished when the FDA allowed the first vendor to market their device for primary diagnostic use in the USA and published in the classification order that this device, and substantially equivalent devices of this generic type, should be classified into class II instead of class III as previously proposed. The Digital Pathology Association (DPA) regulatory task force had a major role in the accomplishment of getting the application request for Whole Slide Imaging (WSI) Systems recommended for a de novo . This article reviews the past and emerging regulatory environment of WSI for clinical use in the USA. A WSI system with integrated subsystems is defined in the context of medical device regulations. The FDA technical performance assessment guideline is also discussed as well as parameters involved in analytical testing and clinical studies to demonstrate that WSI devices are safe and effective for clinical use.

Classifying quantum entanglement through topological links

NASA Astrophysics Data System (ADS)

Quinta, Gonçalo M.; André, Rui

2018-04-01

We propose an alternative classification scheme for quantum entanglement based on topological links. This is done by identifying a nonrigid ring to a particle, attributing the act of cutting and removing a ring to the operation of tracing out the particle, and associating linked rings to entangled particles. This analogy naturally leads us to a classification of multipartite quantum entanglement based on all possible distinct links for a given number of rings. To determine all different possibilities, we develop a formalism that associates any link to a polynomial, with each polynomial thereby defining a distinct equivalence class. To demonstrate the use of this classification scheme, we choose qubit quantum states as our example of physical system. A possible procedure to obtain qubit states from the polynomials is also introduced, providing an example state for each link class. We apply the formalism for the quantum systems of three and four qubits and demonstrate the potential of these tools in a context of qubit networks.

An Electro-Physiological Temporal Principal Component Analysis of Processing Stages of Number Comparison in Developmental Dyscalculia

ERIC Educational Resources Information Center

Soltesz, Fruzsina; Szucs, Denes

2009-01-01

Developmental dyscalculia (DD) still lacks a generally accepted definition. A major problem is that the cognitive component processes contributing to arithmetic performance are still poorly defined. By a reanalysis of our previous event-related brain potential (ERP) data (Soltesz et al., 2007) here our objective was to identify and compare…

Consensus Classification Using Non-Optimized Classifiers.

PubMed

Brownfield, Brett; Lemos, Tony; Kalivas, John H

2018-04-03

Classifying samples into categories is a common problem in analytical chemistry and other fields. Classification is usually based on only one method, but numerous classifiers are available with some being complex, such as neural networks, and others are simple, such as k nearest neighbors. Regardless, most classification schemes require optimization of one or more tuning parameters for best classification accuracy, sensitivity, and specificity. A process not requiring exact selection of tuning parameter values would be useful. To improve classification, several ensemble approaches have been used in past work to combine classification results from multiple optimized single classifiers. The collection of classifications for a particular sample are then combined by a fusion process such as majority vote to form the final classification. Presented in this Article is a method to classify a sample by combining multiple classification methods without specifically classifying the sample by each method, that is, the classification methods are not optimized. The approach is demonstrated on three analytical data sets. The first is a beer authentication set with samples measured on five instruments, allowing fusion of multiple instruments by three ways. The second data set is composed of textile samples from three classes based on Raman spectra. This data set is used to demonstrate the ability to classify simultaneously with different data preprocessing strategies, thereby reducing the need to determine the ideal preprocessing method, a common prerequisite for accurate classification. The third data set contains three wine cultivars for three classes measured at 13 unique chemical and physical variables. In all cases, fusion of nonoptimized classifiers improves classification. Also presented are atypical uses of Procrustes analysis and extended inverted signal correction (EISC) for distinguishing sample similarities to respective classes.

Imagining class: A study into material social class position, subjective identification, and voting behavior across Europe.

PubMed

D'Hooge, Lorenzo; Achterberg, Peter; Reeskens, Tim

2018-02-01

The traditional approach to class voting has largely ignored the question whether material class positions coincide with subjective class identification. Following Sosnaud et al. (2013), this study evaluates party preferences when Europeans' material and subjective social class do not coincide. Seminal studies on voting behavior have suggested that members of lower classes are more likely to vote for the economic left and cultural right and that higher classes demonstrate the opposite pattern. Yet, these studies have on the one hand overlooked the possibility that there is a mismatch between the material class people can be classified in and the class they think they are part of, and on the other hand the consequences of this discordant class identification on voting behavior. Analyzing the 2009 wave of the European Elections Study, we find that the majority of the Europeans discordantly identify with the middle class, whereas only a minority of the lower and higher classes concordantly identify with their material social class. Further, material class only seems to predict economic voting behavior when it coincides with subjective class; for instance, individuals who have an inflated class identification are more likely to vote for the economic left, even when they materially can be classified as middle or high class. We conclude this paper with a discussion on scholarly debates concerning class and politics. Copyright © 2017 Elsevier Inc. All rights reserved.

The Role of Physical Fitness in the Neurocognitive Performance of Task Switching in Older Persons with Mild Cognitive Impairment.

PubMed

Tsai, Chia-Liang; Pai, Ming-Chyi; Ukropec, Jozef; Ukropcová, Barbara

2016-04-23

Although elderly people with amnestic mild cognitive impairment (aMCI) have been found to show impaired behavioral performance in task switching, no research has yet explored the electrophysiological mechanisms and the potential correlation between physical fitness and neurocognitive (i.e., behavioral and electrophysiological) performance in aMCI. The present study was thus aimed to examine whether there are differences in electrophysiological (i.e., event-related potential) performance between aMCI participants and controls when performing a task-switching paradigm, and to investigate the role of physical fitness in the relationship between neurocognitive performance and aMCI. Sixty participants were classified into aMCI (n = 30) and control (n = 30) groups, and performed a task-switching paradigm with concomitant electrophysiological recording, as well as underwent senior functional physical fitness tests. The aMCI group showed comparable scores on most parts of the physical fitness tests, but reduced lower body flexibility and VO2max as compared to the control group. When performing the task-switching paradigm, the aMCI group showed slower reaction times in the heterogeneous condition and larger global switching costs, although no significant difference was observed in accuracy rates between the two groups. In addition, the aMCI group showed significantly prolonged P3 latencies in the homogeneous and heterogeneous conditions, and a smaller P3 amplitude only in the heterogeneous condition. The level of cardiorespiratory fitness was significantly correlated with P3 amplitude in the aMCI group, particularly in the heterogeneous condition of the task-switching paradigm. These results show that the aMCI group exhibited abnormalities in their neurocognitive performance when performing the task-switching paradigm and such a deficit was likely associated with reduced cardiorespiratory fitness, which was shown to be the important predictor of neurocognitive performance.

Associations Between Body Anthropometric Measures and Severity of Carpal Tunnel Syndrome.

PubMed

Mondelli, Mauro; Curti, Stefania; Mattioli, Stefano; Aretini, Alessandro; Ginanneschi, Federica; Greco, Giuseppe; Farioli, Andrea

2016-09-01

To assess the associations between carpal tunnel syndrome (CTS) severity and selected anthropometric and obesity indexes. We performed a case-control study. Clinical and electrophysiological severity of CTS was classified as mild, moderate, or severe based on validated scales. Body and hand anthropometric characteristics were measured at the time of the electrodiagnostic study. We estimated the relative risk ratios (RRRs) of CTS severity by fitting multinomial logistic regression models adjusted by age and sex. In addition, we fitted multivariable models, including age, sex, wrist ratio, hand ratio, body mass index (BMI), and waist/stature ratio. Electromyography laboratories. Consecutive patients (N=1087), those with CTS (n=340) and those without CTS (n=747), were enrolled. Not applicable. Associations between CTS severity and selected anthropometric and obesity indexes. We observed associations between many anthropometric indexes and CTS severity. Among obesity indexes, the waist/stature ratio, and among hand anthropometric indexes, the wrist/palm ratio, showed the highest RRRs for the clinical and electrophysiological severity scales. The RRRs of severe CTS (adjusted for age and sex) for the wrist/palm ratio were 3.5 for the clinical scale and 2.4 for the electrophysiological scale. The RRRs of severe CTS for the waist/stature ratio were 2.3 for the clinical scale and 2.0 for the electrophysiological scale. In the multivariable models, both BMI and the waist/stature ratio were associated with the outcomes. Different configurations of the body and, in particular, the hand and wrist system may influence the occurrence and severity of CTS. Multiple obesity indexes, possibly including the waist/stature ratio, should be considered when investigating the association between body composition and CTS. Future studies should determine whether in obese subjects with CTS the weight and waist circumference loss produces an improvement in CTS symptoms and recovery of distal conduction velocity of the median nerve. Copyright © 2016 American Congress of Rehabilitation Medicine. Published by Elsevier Inc. All rights reserved.

Using deep belief network modelling to characterize differences in brain morphometry in schizophrenia

PubMed Central

Pinaya, Walter H. L.; Gadelha, Ary; Doyle, Orla M.; Noto, Cristiano; Zugman, André; Cordeiro, Quirino; Jackowski, Andrea P.; Bressan, Rodrigo A.; Sato, João R.

2016-01-01

Neuroimaging-based models contribute to increasing our understanding of schizophrenia pathophysiology and can reveal the underlying characteristics of this and other clinical conditions. However, the considerable variability in reported neuroimaging results mirrors the heterogeneity of the disorder. Machine learning methods capable of representing invariant features could circumvent this problem. In this structural MRI study, we trained a deep learning model known as deep belief network (DBN) to extract features from brain morphometry data and investigated its performance in discriminating between healthy controls (N = 83) and patients with schizophrenia (N = 143). We further analysed performance in classifying patients with a first-episode psychosis (N = 32). The DBN highlighted differences between classes, especially in the frontal, temporal, parietal, and insular cortices, and in some subcortical regions, including the corpus callosum, putamen, and cerebellum. The DBN was slightly more accurate as a classifier (accuracy = 73.6%) than the support vector machine (accuracy = 68.1%). Finally, the error rate of the DBN in classifying first-episode patients was 56.3%, indicating that the representations learned from patients with schizophrenia and healthy controls were not suitable to define these patients. Our data suggest that deep learning could improve our understanding of psychiatric disorders such as schizophrenia by improving neuromorphometric analyses. PMID:27941946

Using deep belief network modelling to characterize differences in brain morphometry in schizophrenia

NASA Astrophysics Data System (ADS)

Pinaya, Walter H. L.; Gadelha, Ary; Doyle, Orla M.; Noto, Cristiano; Zugman, André; Cordeiro, Quirino; Jackowski, Andrea P.; Bressan, Rodrigo A.; Sato, João R.

2016-12-01

Neuroimaging-based models contribute to increasing our understanding of schizophrenia pathophysiology and can reveal the underlying characteristics of this and other clinical conditions. However, the considerable variability in reported neuroimaging results mirrors the heterogeneity of the disorder. Machine learning methods capable of representing invariant features could circumvent this problem. In this structural MRI study, we trained a deep learning model known as deep belief network (DBN) to extract features from brain morphometry data and investigated its performance in discriminating between healthy controls (N = 83) and patients with schizophrenia (N = 143). We further analysed performance in classifying patients with a first-episode psychosis (N = 32). The DBN highlighted differences between classes, especially in the frontal, temporal, parietal, and insular cortices, and in some subcortical regions, including the corpus callosum, putamen, and cerebellum. The DBN was slightly more accurate as a classifier (accuracy = 73.6%) than the support vector machine (accuracy = 68.1%). Finally, the error rate of the DBN in classifying first-episode patients was 56.3%, indicating that the representations learned from patients with schizophrenia and healthy controls were not suitable to define these patients. Our data suggest that deep learning could improve our understanding of psychiatric disorders such as schizophrenia by improving neuromorphometric analyses.

Multiple Hypotheses Image Segmentation and Classification With Application to Dietary Assessment

PubMed Central

Zhu, Fengqing; Bosch, Marc; Khanna, Nitin; Boushey, Carol J.; Delp, Edward J.

2016-01-01

We propose a method for dietary assessment to automatically identify and locate food in a variety of images captured during controlled and natural eating events. Two concepts are combined to achieve this: a set of segmented objects can be partitioned into perceptually similar object classes based on global and local features; and perceptually similar object classes can be used to assess the accuracy of image segmentation. These ideas are implemented by generating multiple segmentations of an image to select stable segmentations based on the classifier’s confidence score assigned to each segmented image region. Automatic segmented regions are classified using a multichannel feature classification system. For each segmented region, multiple feature spaces are formed. Feature vectors in each of the feature spaces are individually classified. The final decision is obtained by combining class decisions from individual feature spaces using decision rules. We show improved accuracy of segmenting food images with classifier feedback. PMID:25561457

Mapping Land Cover Types in Amazon Basin Using 1km JERS-1 Mosaic

NASA Technical Reports Server (NTRS)

Saatchi, Sassan S.; Nelson, Bruce; Podest, Erika; Holt, John

2000-01-01

In this paper, the 100 meter JERS-1 Amazon mosaic image was used in a new classifier to generate a I km resolution land cover map. The inputs to the classifier were 1 km resolution mean backscatter and seven first order texture measures derived from the 100 m data by using a 10 x 10 independent sampling window. The classification approach included two interdependent stages: 1) a supervised maximum a posteriori Bayesian approach to classify the mean backscatter image into 5 general land cover categories of forest, savannah, inundated, white sand, and anthropogenic vegetation classes, and 2) a texture measure decision rule approach to further discriminate subcategory classes based on taxonomic information and biomass levels. Fourteen classes were successfully separated at 1 km scale. The results were verified by examining the accuracy of the approach by comparison with the IBGE and the AVHRR 1 km resolution land cover maps.

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.

[Symptomatic sinus dysfunction. A new use of electrophysiology].

PubMed

Graux, P; Jacquemart, T; Carlioz, R; Lemaire, N; Dutoit, A; Croccel, L

1993-06-01

The authors undertook a prospective electrophysiological study of 950 patients: 53 subjects considered to be "controls" since they were free of any history of syncope or faintness were identified, as well as 39 symptomatic subjects with a strong suspicion of sinus dysfunction, since no other detectable cause of fainting episodes was found by extracardiac investigation, 24 hour ECG nor electrophysiology. Following the creation of a computerised tool enabling not only the entry of indirect tests, processing, averaging of results, printing and memorization, but also assistance in interpretation, several electrophysiological parameters were used: heart rate and existence of sinus arrhythmia, Strauss tests with adjusted data or not, effective nodal refractory period, Guize, Narula and Mandel tests, and an atropine (0.03 mg/kg) test which was performed only in the symptomatic group. These tests were studied by single-variate and correlative analysis to define their normal ranges, their critical values and their dependence or independence. The performance of each test (i.e. its efficiency, and the specificity and sensitivity of each critical value) was measured. Tests found to be most useful (specificity and efficiency > 90%) were as follows: Mandel test = CSRT > or = 534 ms, Narula test = TECASA > or = 339 ms, heart rate < or = 55/min, type II, IIa and chaotic Strauss curve associated with a pathological Guize test. The combination of these tests in this algorithm resulted in an increase in sensitivity to 84%, at the price of a very moderate fall in specificity to 87%.(ABSTRACT TRUNCATED AT 250 WORDS)

Classification without labels: learning from mixed samples in high energy physics

NASA Astrophysics Data System (ADS)

Metodiev, Eric M.; Nachman, Benjamin; Thaler, Jesse

2017-10-01

Modern machine learning techniques can be used to construct powerful models for difficult collider physics problems. In many applications, however, these models are trained on imperfect simulations due to a lack of truth-level information in the data, which risks the model learning artifacts of the simulation. In this paper, we introduce the paradigm of classification without labels (CWoLa) in which a classifier is trained to distinguish statistical mixtures of classes, which are common in collider physics. Crucially, neither individual labels nor class proportions are required, yet we prove that the optimal classifier in the CWoLa paradigm is also the optimal classifier in the traditional fully-supervised case where all label information is available. After demonstrating the power of this method in an analytical toy example, we consider a realistic benchmark for collider physics: distinguishing quark- versus gluon-initiated jets using mixed quark/gluon training samples. More generally, CWoLa can be applied to any classification problem where labels or class proportions are unknown or simulations are unreliable, but statistical mixtures of the classes are available.

Improving imbalanced scientific text classification using sampling strategies and dictionaries.

PubMed

Borrajo, L; Romero, R; Iglesias, E L; Redondo Marey, C M

2011-09-15

Many real applications have the imbalanced class distribution problem, where one of the classes is represented by a very small number of cases compared to the other classes. One of the systems affected are those related to the recovery and classification of scientific documentation. Sampling strategies such as Oversampling and Subsampling are popular in tackling the problem of class imbalance. In this work, we study their effects on three types of classifiers (Knn, SVM and Naive-Bayes) when they are applied to search on the PubMed scientific database. Another purpose of this paper is to study the use of dictionaries in the classification of biomedical texts. Experiments are conducted with three different dictionaries (BioCreative, NLPBA, and an ad-hoc subset of the UniProt database named Protein) using the mentioned classifiers and sampling strategies. Best results were obtained with NLPBA and Protein dictionaries and the SVM classifier using the Subsampling balancing technique. These results were compared with those obtained by other authors using the TREC Genomics 2005 public corpus. Copyright 2011 The Author(s). Published by Journal of Integrative Bioinformatics.

Classification without labels: learning from mixed samples in high energy physics

DOE PAGES

Metodiev, Eric M.; Nachman, Benjamin; Thaler, Jesse

2017-10-25

Modern machine learning techniques can be used to construct powerful models for difficult collider physics problems. In many applications, however, these models are trained on imperfect simulations due to a lack of truth-level information in the data, which risks the model learning artifacts of the simulation. In this paper, we introduce the paradigm of classification without labels (CWoLa) in which a classifier is trained to distinguish statistical mixtures of classes, which are common in collider physics. Crucially, neither individual labels nor class proportions are required, yet we prove that the optimal classifier in the CWoLa paradigm is also the optimalmore » classifier in the traditional fully-supervised case where all label information is available. After demonstrating the power of this method in an analytical toy example, we consider a realistic benchmark for collider physics: distinguishing quark- versus gluon-initiated jets using mixed quark/gluon training samples. More generally, CWoLa can be applied to any classification problem where labels or class proportions are unknown or simulations are unreliable, but statistical mixtures of the classes are available.« less

Classification without labels: learning from mixed samples in high energy physics

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

Metodiev, Eric M.; Nachman, Benjamin; Thaler, Jesse

Modern machine learning techniques can be used to construct powerful models for difficult collider physics problems. In many applications, however, these models are trained on imperfect simulations due to a lack of truth-level information in the data, which risks the model learning artifacts of the simulation. In this paper, we introduce the paradigm of classification without labels (CWoLa) in which a classifier is trained to distinguish statistical mixtures of classes, which are common in collider physics. Crucially, neither individual labels nor class proportions are required, yet we prove that the optimal classifier in the CWoLa paradigm is also the optimalmore » classifier in the traditional fully-supervised case where all label information is available. After demonstrating the power of this method in an analytical toy example, we consider a realistic benchmark for collider physics: distinguishing quark- versus gluon-initiated jets using mixed quark/gluon training samples. More generally, CWoLa can be applied to any classification problem where labels or class proportions are unknown or simulations are unreliable, but statistical mixtures of the classes are available.« less

Towards exaggerated emphysema stereotypes

NASA Astrophysics Data System (ADS)

Chen, C.; Sørensen, L.; Lauze, F.; Igel, C.; Loog, M.; Feragen, A.; de Bruijne, M.; Nielsen, M.

2012-03-01

Classification is widely used in the context of medical image analysis and in order to illustrate the mechanism of a classifier, we introduce the notion of an exaggerated image stereotype based on training data and trained classifier. The stereotype of some image class of interest should emphasize/exaggerate the characteristic patterns in an image class and visualize the information the employed classifier relies on. This is useful for gaining insight into the classification and serves for comparison with the biological models of disease. In this work, we build exaggerated image stereotypes by optimizing an objective function which consists of a discriminative term based on the classification accuracy, and a generative term based on the class distributions. A gradient descent method based on iterated conditional modes (ICM) is employed for optimization. We use this idea with Fisher's linear discriminant rule and assume a multivariate normal distribution for samples within a class. The proposed framework is applied to computed tomography (CT) images of lung tissue with emphysema. The synthesized stereotypes illustrate the exaggerated patterns of lung tissue with emphysema, which is underpinned by three different quantitative evaluation methods.

Predicting membrane protein types using various decision tree classifiers based on various modes of general PseAAC for imbalanced datasets.

PubMed

Sankari, E Siva; Manimegalai, D

2017-12-21

Predicting membrane protein types is an important and challenging research area in bioinformatics and proteomics. Traditional biophysical methods are used to classify membrane protein types. Due to large exploration of uncharacterized protein sequences in databases, traditional methods are very time consuming, expensive and susceptible to errors. Hence, it is highly desirable to develop a robust, reliable, and efficient method to predict membrane protein types. Imbalanced datasets and large datasets are often handled well by decision tree classifiers. Since imbalanced datasets are taken, the performance of various decision tree classifiers such as Decision Tree (DT), Classification And Regression Tree (CART), C4.5, Random tree, REP (Reduced Error Pruning) tree, ensemble methods such as Adaboost, RUS (Random Under Sampling) boost, Rotation forest and Random forest are analysed. Among the various decision tree classifiers Random forest performs well in less time with good accuracy of 96.35%. Another inference is RUS boost decision tree classifier is able to classify one or two samples in the class with very less samples while the other classifiers such as DT, Adaboost, Rotation forest and Random forest are not sensitive for the classes with fewer samples. Also the performance of decision tree classifiers is compared with SVM (Support Vector Machine) and Naive Bayes classifier. Copyright © 2017 Elsevier Ltd. All rights reserved.

Land Use on the Island of Oahu, Hawaii, 1998

USGS Publications Warehouse

Klasner, Frederick L.; Mikami, Clinton D.

2003-01-01

A hierarchical land-use classification system for Hawaii was developed, and land use on the island of Oahu was mapped. The land-use classification system emphasizes agriculture, developed (urban), and barren/mining uses. Areas with other land uses (conservation, forest reserve, natural areas, wetlands, water, and barren [sand, rock, or soil] regions, and unmanaged vegetation [native or exotic]) were defined as 'other.' Multiple sources of digital orthophotographs from 1998 and 1999 were used as source data. The 1998 island of Oahu land-use data are provided in digital format at http://water.usgs.gov/lookup/getspatial?oahu_lu98 for use in a Geographic Information System (GIS), at 1:24,000-scale with minimum mapping units of 2 hectares (4.9 acres) area and 30-meters (98.4 feet) feature width. In 1998, a total of 59,195 acres (15.4 percent) of the island of Oahu were classified as agricultural land use; 98,663 acres (25.7 percent) were classified as developed; 1,522 acres (0.4 percent) were classified as barren/mining; and 224,331 acres (58.5 percent) were classified as other. An accuracy assessment identified 98 percent accuracy for all land-use classes. In windward (moister) areas, dense vegetation and canopy cover along with rapid recolonization by vegetation potentially obscured land use from photo-interpretation. While in leeward (drier) areas, sparse vegetative cover and slower vegetation recolonization may have resulted in more frequent recognition of apparent land-use patterns.

Considerations for a Pediatric Biopharmaceutics Classification System (BCS): application to five drugs.

PubMed

Gandhi, Shivani V; Rodriguez, William; Khan, Mansoor; Polli, James E

2014-06-01

It has been advocated that biopharmaceutic risk assessment should be conducted early in pediatric product development and synchronized with the adult product development program. However, we are unaware of efforts to classify drugs into a Biopharmaceutics Classification System (BCS) framework for pediatric patients. The objective was to classify five drugs into a potential BCS. These five drugs were selected since both oral and intravenous pharmacokinetic data were available for each drug, and covered the four BCS classes in adults. Literature searches for each drug were conducted using Medline and applied to classify drugs with respect to solubility and permeability in pediatric subpopulations. Four pediatric subpopulations were considered: neonates, infants, children, and adolescents. Regarding solubility, dose numbers were calculated using a volume for each subpopulation based on body surface area (BSA) relative to 250 ml for a 1.73 m(2) adult. Dose numbers spanned a range of values, depending upon the pediatric dose formula and subpopulation. Regarding permeability, pharmacokinetic literature data required assumptions and decisions about data collection. Using a devised pediatric BCS framework, there was agreement in adult and pediatric BCS class for two drugs, azithromycin (class 3) and ciprofloxacin (class 4). There was discordance for the three drugs that have high adult permeability since all pediatric permeabilities were low: dolasetron (class 3 in pediatric), ketoprofen (class 4 in pediatric), and voriconazole (class 4 in pediatric). A main contribution of this work is the identification of critical factors required for a pediatric BCS.

Landscape object-based analysis of wetland plant functional types: the effects of spatial scale, vegetation classes and classifier methods

NASA Astrophysics Data System (ADS)

Dronova, I.; Gong, P.; Wang, L.; Clinton, N.; Fu, W.; Qi, S.

2011-12-01

Remote sensing-based vegetation classifications representing plant function such as photosynthesis and productivity are challenging in wetlands with complex cover and difficult field access. Recent advances in object-based image analysis (OBIA) and machine-learning algorithms offer new classification tools; however, few comparisons of different algorithms and spatial scales have been discussed to date. We applied OBIA to delineate wetland plant functional types (PFTs) for Poyang Lake, the largest freshwater lake in China and Ramsar wetland conservation site, from 30-m Landsat TM scene at the peak of spring growing season. We targeted major PFTs (C3 grasses, C3 forbs and different types of C4 grasses and aquatic vegetation) that are both key players in system's biogeochemical cycles and critical providers of waterbird habitat. Classification results were compared among: a) several object segmentation scales (with average object sizes 900-9000 m2); b) several families of statistical classifiers (including Bayesian, Logistic, Neural Network, Decision Trees and Support Vector Machines) and c) two hierarchical levels of vegetation classification, a generalized 3-class set and more detailed 6-class set. We found that classification benefited from object-based approach which allowed including object shape, texture and context descriptors in classification. While a number of classifiers achieved high accuracy at the finest pixel-equivalent segmentation scale, the highest accuracies and best agreement among algorithms occurred at coarser object scales. No single classifier was consistently superior across all scales, although selected algorithms of Neural Network, Logistic and K-Nearest Neighbors families frequently provided the best discrimination of classes at different scales. The choice of vegetation categories also affected classification accuracy. The 6-class set allowed for higher individual class accuracies but lower overall accuracies than the 3-class set because individual classes differed in scales at which they were best discriminated from others. Main classification challenges included a) presence of C3 grasses in C4-grass areas, particularly following harvesting of C4 reeds and b) mixtures of emergent, floating and submerged aquatic plants at sub-object and sub-pixel scales. We conclude that OBIA with advanced statistical classifiers offers useful instruments for landscape vegetation analyses, and that spatial scale considerations are critical in mapping PFTs, while multi-scale comparisons can be used to guide class selection. Future work will further apply fuzzy classification and field-collected spectral data for PFT analysis and compare results with MODIS PFT products.

78 FR 24061 - Minimum Technical Standards for Class II Gaming Systems and Equipment

Federal Register 2010, 2011, 2012, 2013, 2014

2013-04-24

... Register that established technical standards for ensuring the integrity of electronic Class II games and aids. 73 FR 60508, Oct. 10, 2008. The technical standards were designed to assist tribal gaming... Class II gaming systems. The standards did not classify which games were Class II games and which games...

Impact of childhood asthma on growth trajectories in early adolescence: Findings from the Childhood Asthma Prevention Study (CAPS).

PubMed

Movin, Maria; Garden, Frances L; Protudjer, Jennifer L P; Ullemar, Vilhelmina; Svensdotter, Frida; Andersson, David; Kruse, Andreas; Cowell, Chris T; Toelle, Brett G; Marks, Guy B; Almqvist, Catarina

2017-04-01

Understanding the associations between childhood asthma and growth in early adolescence by accounting for the heterogeneity of growth during puberty has been largely unexplored. The objective was to identify sex-specific classes of growth trajectories during early adolescence, using a method which takes the heterogeneity of growth into account and to evaluate the association between childhood asthma and different classes of growth trajectories in adolescence. Our longitudinal study included participants with a family history of asthma born during 1997-1999 in Sydney, Australia. Hence, all participants were at high risk for asthma. Asthma status was ascertained at 8 years of age using data from questionnaires and lung function tests. Growth trajectories between 11 and 14 years of age were classified using a latent basis growth mixture model. Multinomial regression analyses were used to evaluate the association between asthma and the categorized classes of growth trajectories. In total, 316 participants (51.6% boys), representing 51.3% of the entire cohort, were included. Sex-specific classes of growth trajectories were defined. Among boys, asthma was not associated with the classes of growth trajectories. Girls with asthma were more likely than girls without asthma to belong to a class with later growth (OR: 3.79, 95% CI: 1.33, 10.84). Excluding participants using inhaled corticosteroids or adjusting for confounders did not significantly change the results for either sex. We identified sex-specific heterogeneous classes of growth using growth mixture modelling. Associations between childhood asthma and different classes of growth trajectories were found for girls only. © 2016 Asian Pacific Society of Respirology.

Application of recurrence quantification analysis for the automated identification of epileptic EEG signals.

PubMed

Acharya, U Rajendra; Sree, S Vinitha; Chattopadhyay, Subhagata; Yu, Wenwei; Ang, Peng Chuan Alvin

2011-06-01

Epilepsy is a common neurological disorder that is characterized by the recurrence of seizures. Electroencephalogram (EEG) signals are widely used to diagnose seizures. Because of the non-linear and dynamic nature of the EEG signals, it is difficult to effectively decipher the subtle changes in these signals by visual inspection and by using linear techniques. Therefore, non-linear methods are being researched to analyze the EEG signals. In this work, we use the recorded EEG signals in Recurrence Plots (RP), and extract Recurrence Quantification Analysis (RQA) parameters from the RP in order to classify the EEG signals into normal, ictal, and interictal classes. Recurrence Plot (RP) is a graph that shows all the times at which a state of the dynamical system recurs. Studies have reported significantly different RQA parameters for the three classes. However, more studies are needed to develop classifiers that use these promising features and present good classification accuracy in differentiating the three types of EEG segments. Therefore, in this work, we have used ten RQA parameters to quantify the important features in the EEG signals.These features were fed to seven different classifiers: Support vector machine (SVM), Gaussian Mixture Model (GMM), Fuzzy Sugeno Classifier, K-Nearest Neighbor (KNN), Naive Bayes Classifier (NBC), Decision Tree (DT), and Radial Basis Probabilistic Neural Network (RBPNN). Our results show that the SVM classifier was able to identify the EEG class with an average efficiency of 95.6%, sensitivity and specificity of 98.9% and 97.8%, respectively.

Medical devices; hematology and pathology devices; classification of cord blood processing system and storage container. Final rule.

PubMed

2007-02-01

The Food and Drug Administration (FDA) is classifying a cord blood processing system and storage container into class II (special controls). The special control that will apply to this device is the guidance document entitled "Class II Special Controls Guidance Document: Cord Blood Processing System and Storage Container." FDA is classifying this device into class II (special controls) in order to provide a reasonable assurance of safety and effectiveness of this device. Elsewhere in this issue of the Federal Register, FDA is announcing the availability of the guidance document that will serve as the special control for this device.

Geology and forestry classification from ERTS-1 digital data

NASA Technical Reports Server (NTRS)

Lawrence, R. D.; Herzog, J. H.

1975-01-01

Computer classifications into seven and ten classes of two areas in central Oregon of interest to geology and forestry demonstrate the extraction of information from ERTS-1 data. The area around Newberry Caldera was classified into basalt, rhyolite obsidian, pumice flats, Newberry pumice, ponderosa pine, lodgepole pine, and water classes. The area around Mt. Washington was classified into two basalts, three forest, two clearcut, burn, snow, and water classes. Both also include an unclassified category. Significant details that cannot be extracted from photographic reconstitutions of the data emerge from these classifications, such as moraine locations and paleowind directions. Spectral signatures for the various rocks are comparable to those published elsewhere.

Instructor and peer bullying in college students: Distinct typologies based on Latent Class Analysis.

PubMed

Marraccini, Marisa E; Brick, Leslie Ann D; Weyandt, Lisa L

2018-03-22

Although bullying is traditionally considered within the context of primary and secondary school, recent evidence suggests that bullying continues into college and workplace settings. Participants/Method: Latent class analysis (LCA) was employed to classify college bullying involvement typologies among 325 college students attending a northeastern university. Four classes concerning bullying involvement were revealed: Non-involved (36%); Instructor victim (30%); Peer bully-victim (22%); and Peer bully-victim/ Instructor victim (12%). Findings from this study, which classified college bullying experiences by incorporating both peer and instructor (teacher and professor) bullying, add substantially to the literature by providing insight into patterns of relatively unexplored bullying behaviors.

Electrophysiological evidence of heterogeneity in visual statistical learning in young children with ASD.

PubMed

Jeste, Shafali S; Kirkham, Natasha; Senturk, Damla; Hasenstab, Kyle; Sugar, Catherine; Kupelian, Chloe; Baker, Elizabeth; Sanders, Andrew J; Shimizu, Christina; Norona, Amanda; Paparella, Tanya; Freeman, Stephanny F N; Johnson, Scott P

2015-01-01

Statistical learning is characterized by detection of regularities in one's environment without an awareness or intention to learn, and it may play a critical role in language and social behavior. Accordingly, in this study we investigated the electrophysiological correlates of visual statistical learning in young children with autism spectrum disorder (ASD) using an event-related potential shape learning paradigm, and we examined the relation between visual statistical learning and cognitive function. Compared to typically developing (TD) controls, the ASD group as a whole showed reduced evidence of learning as defined by N1 (early visual discrimination) and P300 (attention to novelty) components. Upon further analysis, in the ASD group there was a positive correlation between N1 amplitude difference and non-verbal IQ, and a positive correlation between P300 amplitude difference and adaptive social function. Children with ASD and a high non-verbal IQ and high adaptive social function demonstrated a distinctive pattern of learning. This is the first study to identify electrophysiological markers of visual statistical learning in children with ASD. Through this work we have demonstrated heterogeneity in statistical learning in ASD that maps onto non-verbal cognition and adaptive social function. © 2014 John Wiley & Sons Ltd.

Isolation and culture of adult mouse vestibular nucleus neurons

PubMed

Him, Aydın; Altuntaş, Serap; Öztürk, Gürkan; Erdoğan, Ender; Cengiz, Nureddin

2017-12-19

Background/aim: Isolated cell cultures are widely used to study neuronal properties due to their advantages. Although embryonic animals are preferred for culturing, their morphological or electrophysiological properties may not reflect adult neurons, which may be important in neurodegenerative diseases. This paper aims to develop a method for preparing isolated cell cultures of medial vestibular nucleus (MVN) from adult mice and describe its morphological and electrophysiological properties.Materials and methods: Vestibular nucleus neurons were mechanically and enzymatically isolated and cultured using a defined medium with known growth factors. Cell survival was measured with propidium iodide, and electrophysiological properties were investigated with current-clamp recording.Results: Vestibular neurons grew neurites in cultures, gaining adult-like morphological properties, and stayed viable for 3 days in culture. Adding bovine calf serum, nerve growth factor, or insulin-like growth factor into the culture medium enhanced neuronal viability. Current-clamp recording of the cultured neurons revealed tonic and phasic-type neurons with similar input resistance, resting membrane potential, action potential amplitude, and duration. Conclusion: Vestibular neurons from adult mice can be cultured, and regenerate axons in a medium containing appropriate growth factors. Culturing adult vestibular neurons provides a new method to study age-related pathologies of the vestibular system.

32 CFR 2001.46 - Transmission.

Code of Federal Regulations, 2011 CFR

2011-07-01

... an attention line. The following exceptions apply: (i) If the classified information is an internal... Class Mail. However, Confidential information shall not be transmitted to government contractor facilities via first class mail. When first class mail is used, the envelope or outer wrapper shall be marked...

Semantic classification of business images

NASA Astrophysics Data System (ADS)

Erol, Berna; Hull, Jonathan J.

2006-01-01

Digital cameras are becoming increasingly common for capturing information in business settings. In this paper, we describe a novel method for classifying images into the following semantic classes: document, whiteboard, business card, slide, and regular images. Our method is based on combining low-level image features, such as text color, layout, and handwriting features with high-level OCR output analysis. Several Support Vector Machine Classifiers are combined for multi-class classification of input images. The system yields 95% accuracy in classification.

Studying superconformal symmetry enhancement through indices

NASA Astrophysics Data System (ADS)

Evtikhiev, Mikhail

2018-04-01

In this note we classify the necessary and the sufficient conditions that an index of a superconformal theory in 3 ≤ d ≤ 6 must obey for the theory to have enhanced supersymmetry. We do that by noting that the index distinguishes a superconformal multiplet contribution to the index only up to a certain equivalence class it lies in. We classify the equivalence classes in d = 4 and build a correspondence between N=1 and N>1 equivalence classes. Using this correspondence, we find a set of necessary conditions and a sufficient condition on the d = 4 N=1 index for the theory to have N>1 SUSY. We also find a necessary and sufficient condition on a d = 4 N>1 index to correspond to a theory with N>2 . We then use our results to study some of the d = 4 theories described by Agarwal, Maruyoshi and Song, and find that the theories in question have only N=1 SUSY despite having rational central charges. In d = 3 we classify the equivalence classes, and build a correspondence between N>2 and N>2 equivalence classes. Using this correspondence, we classify all necessary or sufficient conditions on an 1≤N≤3 superconformal index in d = 3 to correspond to a theory with higher SUSY, and find a necessary and sufficient condition on an N=4 index to correspond to an N=4 theory. Finally, in d = 6 we find a necessary and sufficient condition for an N=1 index to correspond to an N>2 theory.

Support vector machines-based fault diagnosis for turbo-pump rotor

NASA Astrophysics Data System (ADS)

Yuan, Sheng-Fa; Chu, Fu-Lei

2006-05-01

Most artificial intelligence methods used in fault diagnosis are based on empirical risk minimisation principle and have poor generalisation when fault samples are few. Support vector machines (SVM) is a new general machine-learning tool based on structural risk minimisation principle that exhibits good generalisation even when fault samples are few. Fault diagnosis based on SVM is discussed. Since basic SVM is originally designed for two-class classification, while most of fault diagnosis problems are multi-class cases, a new multi-class classification of SVM named 'one to others' algorithm is presented to solve the multi-class recognition problems. It is a binary tree classifier composed of several two-class classifiers organised by fault priority, which is simple, and has little repeated training amount, and the rate of training and recognition is expedited. The effectiveness of the method is verified by the application to the fault diagnosis for turbo pump rotor.

Functional Architecture of the Retina: Development and Disease

PubMed Central

Hoon, Mrinalini; Okawa, Haruhisa; Santina, Luca Della; Wong, Rachel O.L.

2014-01-01

Structure and function are highly correlated in the vertebrate retina, a sensory tissue that is organized into cell layers with microcircuits working in parallel and together to encode visual information. All vertebrate retinas share a fundamental plan, comprising five major neuronal cell classes with cell body distributions and connectivity arranged in stereotypic patterns. Conserved features in retinal design have enabled detailed analysis and comparisons of structure, connectivity and function across species. Each species, however, can adopt structural and/or functional retinal specializations, implementing variations to the basic design in order to satisfy unique requirements in visual function. Recent advances in molecular tools, imaging and electrophysiological approaches have greatly facilitated identification of the cellular and molecular mechanisms that establish the fundamental organization of the retina and the specializations of its microcircuits during development. Here, we review advances in our understanding of how these mechanisms act to shape structure and function at the single cell level, to coordinate the assembly of cell populations, and to define their specific circuitry. We also highlight how structure is rearranged and function is disrupted in disease, and discuss current approaches to re-establish the intricate functional architecture of the retina. PMID:24984227

Functional architecture of the retina: development and disease.

PubMed

Hoon, Mrinalini; Okawa, Haruhisa; Della Santina, Luca; Wong, Rachel O L

2014-09-01

Structure and function are highly correlated in the vertebrate retina, a sensory tissue that is organized into cell layers with microcircuits working in parallel and together to encode visual information. All vertebrate retinas share a fundamental plan, comprising five major neuronal cell classes with cell body distributions and connectivity arranged in stereotypic patterns. Conserved features in retinal design have enabled detailed analysis and comparisons of structure, connectivity and function across species. Each species, however, can adopt structural and/or functional retinal specializations, implementing variations to the basic design in order to satisfy unique requirements in visual function. Recent advances in molecular tools, imaging and electrophysiological approaches have greatly facilitated identification of the cellular and molecular mechanisms that establish the fundamental organization of the retina and the specializations of its microcircuits during development. Here, we review advances in our understanding of how these mechanisms act to shape structure and function at the single cell level, to coordinate the assembly of cell populations, and to define their specific circuitry. We also highlight how structure is rearranged and function is disrupted in disease, and discuss current approaches to re-establish the intricate functional architecture of the retina. Copyright © 2014 Elsevier Ltd. All rights reserved.

Electrophysiological evaluation of Wolff-Parkinson-White Syndrome

PubMed Central

Brembilla-Perrot, Beatrice

2002-01-01

Sudden death might complicate the follow-up of symptomatic patients with the Wolff-Parkinson-White syndrome (WPW) and might be the first event in patients with asymptomatic WPW. The risk of sudden death is increased in some clinical situations. Generally, the noninvasive studies are unable to predict the risk of sudden death correctly . The electrophysiological study is the best means to detect the risk of sudden death and to evaluate the nature of symptoms. Methods used to define the prognosis of WPW are well-defined. At first the maximal rate of conduction through the accessory pathway is evaluated; programmed atrial stimulation using 1 and 2 extrastimuli delivered at different cycle lengths is then used to determine the accessory pathway refractory period and to induce a supraventricular tachycardia. These methods should be performed in the control state and repeated in adrenergic situations either during exercise test or more simply during a perfusion of small doses of isoproterenol. The induction of an atrial fibrillation with rapid conduction through the accessory pathway (> 240/min in control state, > 300/min after isoproterenol) is the sign of a form of WPW at risk of sudden death. PMID:16951730

Characterizing artifacts in RR stress test time series.

PubMed

Astudillo-Salinas, Fabian; Palacio-Baus, Kenneth; Solano-Quinde, Lizandro; Medina, Ruben; Wong, Sara

2016-08-01

Electrocardiographic stress test records have a lot of artifacts. In this paper we explore a simple method to characterize the amount of artifacts present in unprocessed RR stress test time series. Four time series classes were defined: Very good lead, Good lead, Low quality lead and Useless lead. 65 ECG, 8 lead, records of stress test series were analyzed. Firstly, RR-time series were annotated by two experts. The automatic methodology is based on dividing the RR-time series in non-overlapping windows. Each window is marked as noisy whenever it exceeds an established standard deviation threshold (SDT). Series are classified according to the percentage of windows that exceeds a given value, based upon the first manual annotation. Different SDT were explored. Results show that SDT close to 20% (as a percentage of the mean) provides the best results. The coincidence between annotators classification is 70.77% whereas, the coincidence between the second annotator and the automatic method providing the best matches is larger than 63%. Leads classified as Very good leads and Good leads could be combined to improve automatic heartbeat labeling.

Is Mitochondrial Donation Germ-Line Gene Therapy? Classifications and Ethical Implications.

PubMed

Newson, Ainsley J; Wrigley, Anthony

2017-01-01

The classification of techniques used in mitochondrial donation, including their role as purported germ-line gene therapies, is far from clear. These techniques exhibit characteristics typical of a variety of classifications that have been used in both scientific and bioethics scholarship. This raises two connected questions, which we address in this paper: (i) how should we classify mitochondrial donation techniques?; and (ii) what ethical implications surround such a classification? First, we outline how methods of genetic intervention, such as germ-line gene therapy, are typically defined or classified. We then consider whether techniques of mitochondrial donation fit into these, whether they might do so with some refinement of these categories, or whether they require some other approach to classification. To answer the second question, we discuss the relationship between classification and several key ethical issues arising from mitochondrial donation. We conclude that the properties characteristic of mitochondrial inheritance mean that most mitochondrial donation techniques belong to a new sub-class of genetic modification, which we call 'conditionally inheritable genomic modification' (CIGM). © 2017 John Wiley & Sons Ltd.

Different electrophysiological profiles and treatment response in 'typical' and 'atypical' chronic inflammatory demyelinating polyneuropathy.

PubMed

Kuwabara, Satoshi; Isose, Sagiri; Mori, Masahiro; Mitsuma, Satsuki; Sawai, Setsu; Beppu, Minako; Sekiguchi, Yukari; Misawa, Sonoko

2015-10-01

Chronic inflammatory demyelinating polyneuropathy (CIDP) is currently classified into 'typical' CIDP and 'atypical' subtypes such as multifocal acquired demyelinating sensory and motor neuropathy (MADSAM). To assess the frequency of CIDP subtypes, and to elucidate clinical and electrophysiological features, and treatment response in each subtype. We reviewed data from 100 consecutive patients fulfilling criteria for CIDP proposed by the European Federation of Neurological Societies and the Peripheral Nerve Society. The Kaplan-Meier curve was used to estimate long-term outcome. Patients were classified as having typical CIDP (60%), MADSAM (34%), demyelinating acquired distal symmetric neuropathy (8%) or pure sensory CIDP (1%). Compared with patients with MADSAM, patients with typical CIDP showed more rapid progression and severe disability, and demyelination predominant in the distal nerve segments. MADSAM was characterised by multifocal demyelination in the nerve trunks. Abnormal median-normal sural sensory responses were more frequently found for typical CIDP (53% vs 13%). Patients with typical CIDP invariably responded to corticosteroids, immunoglobulin or plasmapheresis, whereas patients with MADSAM were more refractory to these treatments. The Kaplan-Meier analyses showed that 64% of patients with typical CIDP and 41% of patients with MADSAM had a clinical remission 5 years later (p=0.02). Among the CIDP spectrum, typical CIDP and MADSAM are the major subtypes, and their pathophysiology appears to be distinct. In typical CIDP, the distal nerve terminals and possibly the nerve roots, where the blood-nerve barrier is anatomically deficient, are preferentially affected, raising the possibility of antibody-mediated demyelination, whereas cellular immunity with breakdown of the barrier may be important in MADSAM neuropathy. 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.

Single cell analysis of voltage-gated potassium channels that determines neuronal types of rat hypothalamic paraventricular nucleus neurons.

PubMed

Lee, S K; Lee, S; Shin, S Y; Ryu, P D; Lee, S Y

2012-03-15

The hypothalamic paraventricular nucleus (PVN), a site for the integration of both the neuroendocrine and autonomic systems, has heterogeneous cell composition. These neurons are classified into type I and type II neurons based on their electrophysiological properties. In the present study, we investigated the molecular identification of voltage-gated K+ (Kv) channels, which determines a distinctive characteristic of type I PVN neurons, by means of single-cell reverse transcription-polymerase chain reaction (RT-PCR) along with slice patch clamp recordings. In order to determine the mRNA expression profiles, firstly, the PVN neurons of male rats were classified into type I and type II neurons, and then, single-cell RT-PCR and single-cell real-time RT-PCR analysis were performed using the identical cell. The single-cell RT-PCR analysis revealed that Kv1.2, Kv1.3, Kv1.4, Kv4.1, Kv4.2, and Kv4.3 were expressed both in type I and in type II neurons, and several Kv channels were co-expressed in a single PVN neuron. However, we found that the expression densities of Kv4.2 and Kv4.3 were significantly higher in type I neurons than in type II neurons. Taken together, several Kv channels encoding A-type K+ currents are present both in type I and in type II neurons, and among those, Kv4.2 and Kv4.3 are the major Kv subunits responsible for determining the distinct electrophysiological properties. Thus these 2 Kv subunits may play important roles in determining PVN cell types and regulating PVN neuronal excitability. This study further provides key molecular mechanisms for differentiating type I and type II PVN neurons. Copyright © 2012 IBRO. Published by Elsevier Ltd. All rights reserved.

A zone-specific fish-based biotic index as a management tool for the Zeeschelde estuary (Belgium).

PubMed

Breine, Jan; Quataert, Paul; Stevens, Maarten; Ollevier, Frans; Volckaert, Filip A M; Van den Bergh, Ericia; Maes, Joachim

2010-07-01

Fish-based indices monitor changes in surface waters and are a valuable aid in communication by summarising complex information about the environment (Harrison and Whitfield, 2004). A zone-specific fish-based multimetric estuarine index of biotic integrity (Z-EBI) was developed based on a 13 year time series of fish surveys from the Zeeschelde estuary (Belgium). Sites were pre-classified using indicators of anthropogenic impact. Metrics showing a monotone response with pressure classes were selected for further analysis. Thresholds for the good ecological potential (GEP) were defined from references. A modified trisection was applied for the other thresholds. The Z-EBI is defined by the average of the metric scores calculated over a one year period and translated into an ecological quality ratio (EQR). The indices integrate structural and functional qualities of the estuarine fish communities. The Z-EBI performances were successfully validated for habitat degradation in the various habitat zones. Copyright 2010 Elsevier Ltd. All rights reserved.

Dynamics of Vegetatin Indices in Tropical and Subtropical Savannas Defined by Ecoregions and Moderate Resolution Imaging Spectoradiometer (MODIS) Land Cover

NASA Technical Reports Server (NTRS)

Hill, Michael J.; Roman, Miguel O.; Schaaf, Crytal B.

2011-01-01

In this study, we explored the capacity of vegetation indices derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance products to characterize global savannas in Australia, Africa and South America. The savannas were spatially defined and subdivided using the World Wildlife Fund (WWF) global ecoregions and MODIS land cover classes. Average annual profiles of Normalized Difference Vegetation Index, shortwave infrared ratio (SWIR32), White Sky Albedo (WSA) and the Structural Scattering Index (SSI) were created. Metrics derived from average annual profiles of vegetation indices were used to classify savanna ecoregions. The response spaces between vegetation indices were used to examine the potential to derive structural and fractional cover measures. The ecoregions showed distinct temporal profiles and formed groups with similar structural properties, including higher levels of woody vegetation, similar forest savanna mixtures and similar grassland predominance. The potential benefits from the use of combinations of indices to characterize savannas are discussed.

Evaluation of entropy and JM-distance criterions as features selection methods using spectral and spatial features derived from LANDSAT images

NASA Technical Reports Server (NTRS)

Parada, N. D. J. (Principal Investigator); Dutra, L. V.; Mascarenhas, N. D. A.; Mitsuo, Fernando Augusta, II

1984-01-01

A study area near Ribeirao Preto in Sao Paulo state was selected, with predominance in sugar cane. Eight features were extracted from the 4 original bands of LANDSAT image, using low-pass and high-pass filtering to obtain spatial features. There were 5 training sites in order to acquire the necessary parameters. Two groups of four channels were selected from 12 channels using JM-distance and entropy criterions. The number of selected channels was defined by physical restrictions of the image analyzer and computacional costs. The evaluation was performed by extracting the confusion matrix for training and tests areas, with a maximum likelihood classifier, and by defining performance indexes based on those matrixes for each group of channels. Results show that in spatial features and supervised classification, the entropy criterion is better in the sense that allows a more accurate and generalized definition of class signature. On the other hand, JM-distance criterion strongly reduces the misclassification within training areas.

TEMPORAL NEUROTRANSMITTER CONDITIONING RESTORES THE FUNCTIONAL ACTIVITY OF ADULT SPINAL-CORD NEURONS IN LONG-TERM CULTURE

PubMed Central

Das, Mainak; Bhargava, Neelima; Bhalkikar, Abhijeet; Kang, Jung Fong; Hickman, James J

2008-01-01

The ability to culture functional adult mammalian spinal-cord neurons represents an important step in the understanding and treatment of a spectrum of neurological disorders including spinal cord injury. Previously, the limited functional recovery of these cells, as characterized by a diminished ability to initiate action potentials and to exhibit repetitive firing patterns, has arisen as a major impediment to their physiological relevance. In this report we demonstrate that single temporal doses of the neurotransmitters serotonin, glutamate (N-acetyl-DL-glutamic acid) and acetylcholine-chloride leads to the full electrophysiological functional recovery of adult mammalian spinal-cord neurons, when they are cultured under defined serum-free conditions. Approximately 60% of the neurons treated regained their electrophysiological signature, often firing single, double and, most importantly, multiple action potentials. PMID:18005959

Graph theory and the Virasoro master equation

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

Obers, N.A.J.

1991-01-01

A brief history of affine Lie algebra, the Virasoro algebra and its culmination in the Virasoro master equation is given. By studying ansaetze of the master equation, the author obtains exact solutions and gains insight in the structure of large slices of affine-Virasoro space. He finds an isomorphism between the constructions in the ansatz SO(n){sub diag}, which is a set of unitary, generically irrational affine-Virasoro constructions on SO(n), and the unlabeled graphs of order n. On the one hand, the conformal constructions, are classified by the graphs, while, conversely, a group-theoretic and conformal field-theoretic identification is obtained for every graphmore » of graph theory. He also defines a class of magic Lie group bases in which the Virasoro master equation admits a simple metric ansatz {l brace}g{sub metric}{r brace}, whose structure is visible in the high-level expansion. When a magic basis is real on compact g, the corresponding g{sub metric} is a large system of unitary, generically irrational conformal field theories. Examples in this class include the graph-theory ansatz SO(n){sub diag} in the Cartesian basis of SO(n), and the ansatz SU(n){sub metric} in the Pauli-like basis of SU(n). Finally, he defines the sine-area graphs' of SU(n), which label the conformal field theories of SU(n){sub metric}, and he notes that, in similar fashion, each magic basis of g defines a generalized graph theory on g which labels the conformal field theories of g{sub metric}.« less

Design of Biomedical Robots for Phenotype Prediction Problems

PubMed Central

deAndrés-Galiana, Enrique J.; Sonis, Stephen T.

2016-01-01

Abstract Genomics has been used with varying degrees of success in the context of drug discovery and in defining mechanisms of action for diseases like cancer and neurodegenerative and rare diseases in the quest for orphan drugs. To improve its utility, accuracy, and cost-effectiveness optimization of analytical methods, especially those that translate to clinically relevant outcomes, is critical. Here we define a novel tool for genomic analysis termed a biomedical robot in order to improve phenotype prediction, identifying disease pathogenesis and significantly defining therapeutic targets. Biomedical robot analytics differ from historical methods in that they are based on melding feature selection methods and ensemble learning techniques. The biomedical robot mathematically exploits the structure of the uncertainty space of any classification problem conceived as an ill-posed optimization problem. Given a classifier, there exist different equivalent small-scale genetic signatures that provide similar predictive accuracies. We perform the sensitivity analysis to noise of the biomedical robot concept using synthetic microarrays perturbed by different kinds of noises in expression and class assignment. Finally, we show the application of this concept to the analysis of different diseases, inferring the pathways and the correlation networks. The final aim of a biomedical robot is to improve knowledge discovery and provide decision systems to optimize diagnosis, treatment, and prognosis. This analysis shows that the biomedical robots are robust against different kinds of noises and particularly to a wrong class assignment of the samples. Assessing the uncertainty that is inherent to any phenotype prediction problem is the right way to address this kind of problem. PMID:27347715

Design of Biomedical Robots for Phenotype Prediction Problems.

PubMed

deAndrés-Galiana, Enrique J; Fernández-Martínez, Juan Luis; Sonis, Stephen T

2016-08-01

Genomics has been used with varying degrees of success in the context of drug discovery and in defining mechanisms of action for diseases like cancer and neurodegenerative and rare diseases in the quest for orphan drugs. To improve its utility, accuracy, and cost-effectiveness optimization of analytical methods, especially those that translate to clinically relevant outcomes, is critical. Here we define a novel tool for genomic analysis termed a biomedical robot in order to improve phenotype prediction, identifying disease pathogenesis and significantly defining therapeutic targets. Biomedical robot analytics differ from historical methods in that they are based on melding feature selection methods and ensemble learning techniques. The biomedical robot mathematically exploits the structure of the uncertainty space of any classification problem conceived as an ill-posed optimization problem. Given a classifier, there exist different equivalent small-scale genetic signatures that provide similar predictive accuracies. We perform the sensitivity analysis to noise of the biomedical robot concept using synthetic microarrays perturbed by different kinds of noises in expression and class assignment. Finally, we show the application of this concept to the analysis of different diseases, inferring the pathways and the correlation networks. The final aim of a biomedical robot is to improve knowledge discovery and provide decision systems to optimize diagnosis, treatment, and prognosis. This analysis shows that the biomedical robots are robust against different kinds of noises and particularly to a wrong class assignment of the samples. Assessing the uncertainty that is inherent to any phenotype prediction problem is the right way to address this kind of problem.

Determining crop residue type and class using satellite acquired data. M.S. Thesis Progress Report, Jun. 1990

NASA Technical Reports Server (NTRS)

Zhuang, Xin

1990-01-01

LANDSAT Thematic Mapper (TM) data for March 23, 1987 with accompanying ground truth data for the study area in Miami County, IN were used to determine crop residue type and class. Principle components and spectral ratioing transformations were applied to the LANDSAT TM data. One graphic information system (GIS) layer of land ownership was added to each original image as the eighth band of data in an attempt to improve classification. Maximum likelihood, minimum distance, and neural networks were used to classify the original, transformed, and GIS-enhanced remotely sensed data. Crop residues could be separated from one another and from bare soil and other biomass. Two types of crop residue and four classes were identified from each LANDSAT TM image. The maximum likelihood classifier performed the best classification for each original image without need of any transformation. The neural network classifier was able to improve the classification by incorporating a GIS-layer of land ownership as an eighth band of data. The maximum likelihood classifier was unable to consider this eighth band of data and thus, its results could not be improved by its consideration.

32 CFR 1630.17 - Class 1-O-S: Conscientious objector to all military service (separated).

Code of Federal Regulations, 2010 CFR

2010-07-01

... 32 National Defense 6 2010-07-01 2010-07-01 false Class 1-O-S: Conscientious objector to all... National Defense SELECTIVE SERVICE SYSTEM CLASSIFICATION RULES § 1630.17 Class 1-O-S: Conscientious... and noncombatant training and service in the Armed Forces shall be classified in Class 1-O-S unless...

32 CFR 1630.16 - Class 1-O: Conscientious objector to all military service.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 32 National Defense 6 2010-07-01 2010-07-01 false Class 1-O: Conscientious objector to all... SELECTIVE SERVICE SYSTEM CLASSIFICATION RULES § 1630.16 Class 1-O: Conscientious objector to all military... and service in the Armed Forces shall be classified in Class 1-O. (b) Upon the written request of the...

Class Size and Student Performance at a Public Research University: A Cross-Classified Model

ERIC Educational Resources Information Center

Johnson, Iryna Y.

2010-01-01

This study addresses several methodological problems that have confronted prior research on the effect of class size on student achievement. Unlike previous studies, this analysis accounts for the hierarchical data structure of student achievement, where grades are nested within classes and students, and considers a wide range of class sizes…

Clinical impact of major bleeding in patients with venous thromboembolism treated with factor Xa inhibitors or vitamin K antagonists. An individual patient data meta-analysis.

PubMed

Bleker, Suzanne M; Brekelmans, Marjolein P A; Eerenberg, Elise S; Cohen, Alexander T; Middeldorp, Saskia; Raskob, Gary; Büller, Harry R

2017-10-05

Factor Xa (fXa)-inhibitors are as effective and safer than vitamin-K-antagonists (VKA) in the treatment of venous thromboembolism (VTE). We previously classified the severity of clinical presentation and course of all major bleeding events from the EINSTEIN, AMPLIFY and HOKUSAI-VTE trials separately. The current aim was to combine these findings in order to increase precision, assess a class effect and analyse presentation and course for different types of bleeding, i. e. intracranial, gastro-intestinal, and other. We classified the clinical presentation and course of all major bleeding events using pre-defined criteria. Both classifications comprised four categories; one being the mildest, and four the most severe. Odds ratios (OR) were calculated for all events classified as category three or four between fXa-inhibitors and VKA recipients. Also, ORs were computed for different types of bleeding. Major bleeding occurred in 111 fXa-inhibitor recipients and in 187 LMWH/VKA recipients. The clinical presentation was classified as category three or four in 35 % and 48 % of the major bleeds in fXa inhibitor and VKA recipients, respectively (OR 0.59, 95 % CI 0.36-0.97). For intracranial, gastro-intestinal and other bleeding a trend towards a less severe presentation was observed for patients treated with fXa inhibitors. Clinical course was classified as severe in 22 % of the fXa inhibitor and 25 % of the VKA associated bleeds (OR 0.83, 95 % CI 0.47-1.46). In conclusion, FXa inhibitor associated major bleeding events had a significantly less severe presentation and a similar course compared to VKA. This finding was consistent for different types of bleeding.

Floquet Topological Order in Interacting Systems of Bosons and Fermions

NASA Astrophysics Data System (ADS)

Harper, Fenner; Roy, Rahul

2017-03-01

Periodically driven noninteracting systems may exhibit anomalous chiral edge modes, despite hosting bands with trivial topology. We find that these drives have surprising many-body analogs, corresponding to class A, which exhibit anomalous charge and information transport at the boundary. Drives of this form are applicable to generic systems of bosons, fermions, and spins, and may be characterized by the anomalous unitary operator that acts at the edge of an open system. We find that these operators are robust to all local perturbations and may be classified by a pair of coprime integers. This defines a notion of dynamical topological order that may be applied to general time-dependent systems, including many-body localized phases or time crystals.

Multilayer perceptron, fuzzy sets, and classification

NASA Technical Reports Server (NTRS)

Pal, Sankar K.; Mitra, Sushmita

1992-01-01

A fuzzy neural network model based on the multilayer perceptron, using the back-propagation algorithm, and capable of fuzzy classification of patterns is described. The input vector consists of membership values to linguistic properties while the output vector is defined in terms of fuzzy class membership values. This allows efficient modeling of fuzzy or uncertain patterns with appropriate weights being assigned to the backpropagated errors depending upon the membership values at the corresponding outputs. During training, the learning rate is gradually decreased in discrete steps until the network converges to a minimum error solution. The effectiveness of the algorithm is demonstrated on a speech recognition problem. The results are compared with those of the conventional MLP, the Bayes classifier, and the other related models.

Selective classification for improved robustness of myoelectric control under nonideal conditions.

PubMed

Scheme, Erik J; Englehart, Kevin B; Hudgins, Bernard S

2011-06-01

Recent literature in pattern recognition-based myoelectric control has highlighted a disparity between classification accuracy and the usability of upper limb prostheses. This paper suggests that the conventionally defined classification accuracy may be idealistic and may not reflect true clinical performance. Herein, a novel myoelectric control system based on a selective multiclass one-versus-one classification scheme, capable of rejecting unknown data patterns, is introduced. This scheme is shown to outperform nine other popular classifiers when compared using conventional classification accuracy as well as a form of leave-one-out analysis that may be more representative of real prosthetic use. Additionally, the classification scheme allows for real-time, independent adjustment of individual class-pair boundaries making it flexible and intuitive for clinical use.

Lunar terrain mapping and relative-roughness analysis

NASA Technical Reports Server (NTRS)

Rowan, L. C.; Mccauley, J. F.; Holm, E. A.

1971-01-01

Terrain maps of the equatorial zone were prepared at scales of 1:2,000,000 and 1:1,000,000 to classify lunar terrain with respect to roughness and to provide a basis for selecting sites for Surveyor and Apollo landings, as well as for Ranger and Lunar Orbiter photographs. Lunar terrain was described by qualitative and quantitative methods and divided into four fundamental classes: maria, terrae, craters, and linear features. Some 35 subdivisions were defined and mapped throughout the equatorial zone, and, in addition, most of the map units were illustrated by photographs. The terrain types were analyzed quantitatively to characterize and order their relative roughness characteristics. For some morphologically homogeneous mare areas, relative roughness can be extrapolated to the large scales from measurements at small scales.

Fisher classifier and its probability of error estimation

NASA Technical Reports Server (NTRS)

Chittineni, C. B.

1979-01-01

Computationally efficient expressions are derived for estimating the probability of error using the leave-one-out method. The optimal threshold for the classification of patterns projected onto Fisher's direction is derived. A simple generalization of the Fisher classifier to multiple classes is presented. Computational expressions are developed for estimating the probability of error of the multiclass Fisher classifier.

Medical Devices; Gastroenterology-Urology Devices; Classification of the Metallic Biliary Stent System for Benign Strictures. Final order.

PubMed

2016-07-13

The Food and Drug Administration (FDA) is classifying the metallic biliary stent system for benign strictures into class II (special controls). The special controls that will apply to the device are identified in this order and will be part of the codified language for the metallic biliary stent system for benign strictures' classification. The Agency is classifying the device into class II (special controls) in order to provide a reasonable assurance of safety and effectiveness of the device.

Microscale photo interpretation of forest and nonforest land classes

NASA Technical Reports Server (NTRS)

Aldrich, R. C.; Greentree, W. J.

1972-01-01

Remote sensing of forest and nonforest land classes are discussed, using microscale photointerpretation. Results include: (1.) Microscale IR color photography can be interpreted within reasonable limits of error to estimate forest area. (2.) Forest interpretation is best on winter photography with 97 percent or better accuracy. (3.) Broad forest types can be classified on microscale photography. (4.) Active agricultural land is classified most accurately on early summer photography. (5.) Six percent of all nonforest observations were misclassified as forest.

Studies of Sea Ice Thickness and Characteristics from an Arctic Submarine Cruise

DTIC Science & Technology

1991-01-31

decreasing slope. It is likely 12 that at the smallest lags, the autocovariance is artificially increased because the sonai " had a beamwidth of about...region. Class F: Narrow linear lines of very bright (white) return. Class G : The remaining area is ’matrix’, a mottled region of mid-grey and white...classified SAR feature map was digitised in the same way as the classified sidescan data. 15.8 SAR Statistics Statistics of the SAR features (A to G ) were

Application of an object-oriented programming paradigm in three-dimensional computer modeling of mechanically active gastrointestinal tissues.

PubMed

Rashev, P Z; Mintchev, M P; Bowes, K L

2000-09-01

The aim of this study was to develop a novel three-dimensional (3-D) object-oriented modeling approach incorporating knowledge of the anatomy, electrophysiology, and mechanics of externally stimulated excitable gastrointestinal (GI) tissues and emphasizing the "stimulus-response" principle of extracting the modeling parameters. The modeling method used clusters of class hierarchies representing GI tissues from three perspectives: 1) anatomical; 2) electrophysiological; and 3) mechanical. We elaborated on the first four phases of the object-oriented system development life-cycle: 1) analysis; 2) design; 3) implementation; and 4) testing. Generalized cylinders were used for the implementation of 3-D tissue objects modeling the cecum, the descending colon, and the colonic circular smooth muscle tissue. The model was tested using external neural electrical tissue excitation of the descending colon with virtual implanted electrodes and the stimulating current density distributions over the modeled surfaces were calculated. Finally, the tissue deformations invoked by electrical stimulation were estimated and represented by a mesh-surface visualization technique.

A Label Propagation Approach for Detecting Buried Objects in Handheld GPR Data

DTIC Science & Technology

2016-04-17

regions of interest that correspond to locations with anomalous signatures. Second, a classifier (or an ensemble of classifiers ) is used to assign a...investigated for almost two decades and several classifiers have been developed. Most of these methods are based on the supervised learning paradigm where...labeled target and clutter signatures are needed to train a classifier to discriminate between the two classes. Typically, large and diverse labeled

Sound Classification in Hearing Aids Inspired by Auditory Scene Analysis

NASA Astrophysics Data System (ADS)

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

2005-12-01

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

Bayes classifiers for imbalanced traffic accidents datasets.

PubMed

Mujalli, Randa Oqab; López, Griselda; Garach, Laura

2016-03-01

Traffic accidents data sets are usually imbalanced, where the number of instances classified under the killed or severe injuries class (minority) is much lower than those classified under the slight injuries class (majority). This, however, supposes a challenging problem for classification algorithms and may cause obtaining a model that well cover the slight injuries instances whereas the killed or severe injuries instances are misclassified frequently. Based on traffic accidents data collected on urban and suburban roads in Jordan for three years (2009-2011); three different data balancing techniques were used: under-sampling which removes some instances of the majority class, oversampling which creates new instances of the minority class and a mix technique that combines both. In addition, different Bayes classifiers were compared for the different imbalanced and balanced data sets: Averaged One-Dependence Estimators, Weightily Average One-Dependence Estimators, and Bayesian networks in order to identify factors that affect the severity of an accident. The results indicated that using the balanced data sets, especially those created using oversampling techniques, with Bayesian networks improved classifying a traffic accident according to its severity and reduced the misclassification of killed and severe injuries instances. On the other hand, the following variables were found to contribute to the occurrence of a killed causality or a severe injury in a traffic accident: number of vehicles involved, accident pattern, number of directions, accident type, lighting, surface condition, and speed limit. This work, to the knowledge of the authors, is the first that aims at analyzing historical data records for traffic accidents occurring in Jordan and the first to apply balancing techniques to analyze injury severity of traffic accidents. Copyright © 2015 Elsevier Ltd. All rights reserved.

Process management using component thermal-hydraulic function classes

DOEpatents

Morman, James A.; Wei, Thomas Y. C.; Reifman, Jaques

1999-01-01

A process management expert system where following malfunctioning of a component, such as a pump, for determining system realignment procedures such as for by-passing the malfunctioning component with on-line speeds to maintain operation of the process at full or partial capacity or to provide safe shut down of the system while isolating the malfunctioning component. The expert system uses thermal-hydraulic function classes at the component level for analyzing unanticipated as well as anticipated component malfunctions to provide recommended sequences of operator actions. Each component is classified according to its thermal-hydraulic function, and the generic and component-specific characteristics for that function. Using the diagnosis of the malfunctioning component and its thermal hydraulic class, the expert system analysis is carried out using generic thermal-hydraulic first principles. One aspect of the invention employs a qualitative physics-based forward search directed primarily downstream from the malfunctioning component in combination with a subsequent backward search directed primarily upstream from the serviced component. Generic classes of components are defined in the knowledge base according to the three thermal-hydraulic functions of mass, momentum and energy transfer and are used to determine possible realignment of component configurations in response to thermal-hydraulic function imbalance caused by the malfunctioning component. Each realignment to a new configuration produces the accompanying sequence of recommended operator actions. All possible new configurations are examined and a prioritized list of acceptable solutions is produced.

Process management using component thermal-hydraulic function classes

DOEpatents

Morman, J.A.; Wei, T.Y.C.; Reifman, J.

1999-07-27

A process management expert system where following malfunctioning of a component, such as a pump, for determining system realignment procedures such as for by-passing the malfunctioning component with on-line speeds to maintain operation of the process at full or partial capacity or to provide safe shut down of the system while isolating the malfunctioning component. The expert system uses thermal-hydraulic function classes at the component level for analyzing unanticipated as well as anticipated component malfunctions to provide recommended sequences of operator actions. Each component is classified according to its thermal-hydraulic function, and the generic and component-specific characteristics for that function. Using the diagnosis of the malfunctioning component and its thermal hydraulic class, the expert system analysis is carried out using generic thermal-hydraulic first principles. One aspect of the invention employs a qualitative physics-based forward search directed primarily downstream from the malfunctioning component in combination with a subsequent backward search directed primarily upstream from the serviced component. Generic classes of components are defined in the knowledge base according to the three thermal-hydraulic functions of mass, momentum and energy transfer and are used to determine possible realignment of component configurations in response to thermal-hydraulic function imbalance caused by the malfunctioning component. Each realignment to a new configuration produces the accompanying sequence of recommended operator actions. All possible new configurations are examined and a prioritized list of acceptable solutions is produced. 5 figs.

The decision tree classifier - Design and potential. [for Landsat-1 data

NASA Technical Reports Server (NTRS)

Hauska, H.; Swain, P. H.

1975-01-01

A new classifier has been developed for the computerized analysis of remote sensor data. The decision tree classifier is essentially a maximum likelihood classifier using multistage decision logic. It is characterized by the fact that an unknown sample can be classified into a class using one or several decision functions in a successive manner. The classifier is applied to the analysis of data sensed by Landsat-1 over Kenosha Pass, Colorado. The classifier is illustrated by a tree diagram which for processing purposes is encoded as a string of symbols such that there is a unique one-to-one relationship between string and decision tree.

Classifying Higher Education Institutions in Korea: A Performance-Based Approach

ERIC Educational Resources Information Center

Shin, Jung Cheol

2009-01-01

The purpose of this study was to classify higher education institutions according to institutional performance rather than predetermined benchmarks. Institutional performance was defined as research performance and classified using Hierarchical Cluster Analysis, a statistical method that classifies objects according to specified classification…

Learning time series for intelligent monitoring

NASA Technical Reports Server (NTRS)

Manganaris, Stefanos; Fisher, Doug

1994-01-01

We address the problem of classifying time series according to their morphological features in the time domain. In a supervised machine-learning framework, we induce a classification procedure from a set of preclassified examples. For each class, we infer a model that captures its morphological features using Bayesian model induction and the minimum message length approach to assign priors. In the performance task, we classify a time series in one of the learned classes when there is enough evidence to support that decision. Time series with sufficiently novel features, belonging to classes not present in the training set, are recognized as such. We report results from experiments in a monitoring domain of interest to NASA.

Computational modeling for cardiac safety pharmacology analysis: Contribution of fibroblasts.

PubMed

Gao, Xin; Engel, Tyler; Carlson, Brian E; Wakatsuki, Tetsuro

2017-09-01

Drug-induced proarrhythmic potential is an important regulatory criterion in safety pharmacology. The application of in silico approaches to predict proarrhythmic potential of new compounds is under consideration as part of future guidelines. Current approaches simulate the electrophysiology of a single human adult ventricular cardiomyocyte. However, drug-induced proarrhythmic potential can be different when cardiomyocytes are surrounded by non-muscle cells. Incorporating fibroblasts in models of myocardium is important particularly for predicting a drugs cardiac liability in the aging population - a growing population who take more medications and exhibit increased cardiac fibrosis. In this study, we used computational models to investigate the effects of fibroblast coupling on the electrophysiology and response to drugs of cardiomyocytes. A computational model of cardiomyocyte electrophysiology and ion handling (O'Hara, Virag, Varro, & Rudy, 2011) is coupled to a passive model of fibroblast electrophysiology to test the effects of three compounds that block cardiomyocyte ion channels. Results are compared to model results without fibroblast coupling to see how fibroblasts affect cardiomyocyte action potential duration at 90% repolarization (APD 90 ) and propensity for early after depolarization (EAD). Simulation results show changes in cardiomyocyte APD 90 with increasing concentration of three drugs that affect cardiac function (dofetilide, vardenafil and nebivolol) when no fibroblasts are coupled to the cardiomyocyte. Coupling fibroblasts to cardiomyocytes markedly shortens APD 90 . Moreover, increasing the number of fibroblasts can augment the shortening effect. Coupling cardiomyocytes and fibroblasts are predicted to decrease proarrhythmic susceptibility under dofetilide, vardenafil and nebivolol block. However, this result is sensitive to parameters which define the electrophysiological function of the fibroblast. Fibroblast membrane capacitance and conductance (C FB and G FB ) have less of an effect on APD 90 than the fibroblast resting membrane potential (E FB ). This study suggests that in both theoretical models and experimental tissue constructs that represent cardiac tissue, both cardiomyocytes and non-muscle cells should be considered when testing cardiac pharmacological agents. Copyright © 2017 Elsevier Inc. All rights reserved.

Segmentation of thalamus from MR images via task-driven dictionary learning

NASA Astrophysics Data System (ADS)

Liu, Luoluo; Glaister, Jeffrey; Sun, Xiaoxia; Carass, Aaron; Tran, Trac D.; Prince, Jerry L.

2016-03-01

Automatic thalamus segmentation is useful to track changes in thalamic volume over time. In this work, we introduce a task-driven dictionary learning framework to find the optimal dictionary given a set of eleven features obtained from T1-weighted MRI and diffusion tensor imaging. In this dictionary learning framework, a linear classifier is designed concurrently to classify voxels as belonging to the thalamus or non-thalamus class. Morphological post-processing is applied to produce the final thalamus segmentation. Due to the uneven size of the training data samples for the non-thalamus and thalamus classes, a non-uniform sampling scheme is pro- posed to train the classifier to better discriminate between the two classes around the boundary of the thalamus. Experiments are conducted on data collected from 22 subjects with manually delineated ground truth. The experimental results are promising in terms of improvements in the Dice coefficient of the thalamus segmentation overstate-of-the-art atlas-based thalamus segmentation algorithms.

A statistical approach to combining multisource information in one-class classifiers

DOE PAGES

Simonson, Katherine M.; Derek West, R.; Hansen, Ross L.; ...

2017-06-08

A new method is introduced in this paper for combining information from multiple sources to support one-class classification. The contributing sources may represent measurements taken by different sensors of the same physical entity, repeated measurements by a single sensor, or numerous features computed from a single measured image or signal. The approach utilizes the theory of statistical hypothesis testing, and applies Fisher's technique for combining p-values, modified to handle nonindependent sources. Classifier outputs take the form of fused p-values, which may be used to gauge the consistency of unknown entities with one or more class hypotheses. The approach enables rigorousmore » assessment of classification uncertainties, and allows for traceability of classifier decisions back to the constituent sources, both of which are important for high-consequence decision support. Application of the technique is illustrated in two challenge problems, one for skin segmentation and the other for terrain labeling. Finally, the method is seen to be particularly effective for relatively small training samples.« less

A statistical approach to combining multisource information in one-class classifiers

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

Simonson, Katherine M.; Derek West, R.; Hansen, Ross L.

A new method is introduced in this paper for combining information from multiple sources to support one-class classification. The contributing sources may represent measurements taken by different sensors of the same physical entity, repeated measurements by a single sensor, or numerous features computed from a single measured image or signal. The approach utilizes the theory of statistical hypothesis testing, and applies Fisher's technique for combining p-values, modified to handle nonindependent sources. Classifier outputs take the form of fused p-values, which may be used to gauge the consistency of unknown entities with one or more class hypotheses. The approach enables rigorousmore » assessment of classification uncertainties, and allows for traceability of classifier decisions back to the constituent sources, both of which are important for high-consequence decision support. Application of the technique is illustrated in two challenge problems, one for skin segmentation and the other for terrain labeling. Finally, the method is seen to be particularly effective for relatively small training samples.« less

Segmentation of Thalamus from MR images via Task-Driven Dictionary Learning.

PubMed

Liu, Luoluo; Glaister, Jeffrey; Sun, Xiaoxia; Carass, Aaron; Tran, Trac D; Prince, Jerry L

2016-02-27

Automatic thalamus segmentation is useful to track changes in thalamic volume over time. In this work, we introduce a task-driven dictionary learning framework to find the optimal dictionary given a set of eleven features obtained from T1-weighted MRI and diffusion tensor imaging. In this dictionary learning framework, a linear classifier is designed concurrently to classify voxels as belonging to the thalamus or non-thalamus class. Morphological post-processing is applied to produce the final thalamus segmentation. Due to the uneven size of the training data samples for the non-thalamus and thalamus classes, a non-uniform sampling scheme is proposed to train the classifier to better discriminate between the two classes around the boundary of the thalamus. Experiments are conducted on data collected from 22 subjects with manually delineated ground truth. The experimental results are promising in terms of improvements in the Dice coefficient of the thalamus segmentation over state-of-the-art atlas-based thalamus segmentation algorithms.

Automatic mapping of event landslides at basin scale in Taiwan using a Montecarlo approach and synthetic land cover fingerprints

NASA Astrophysics Data System (ADS)

Mondini, Alessandro C.; Chang, Kang-Tsung; Chiang, Shou-Hao; Schlögel, Romy; Notarnicola, Claudia; Saito, Hitoshi

2017-12-01

We propose a framework to systematically generate event landslide inventory maps from satellite images in southern Taiwan, where landslides are frequent and abundant. The spectral information is used to assess the pixel land cover class membership probability through a Maximum Likelihood classifier trained with randomly generated synthetic land cover spectral fingerprints, which are obtained from an independent training images dataset. Pixels are classified as landslides when the calculated landslide class membership probability, weighted by a susceptibility model, is higher than membership probabilities of other classes. We generated synthetic fingerprints from two FORMOSAT-2 images acquired in 2009 and tested the procedure on two other images, one in 2005 and the other in 2009. We also obtained two landslide maps through manual interpretation. The agreement between the two sets of inventories is given by the Cohen's k coefficients of 0.62 and 0.64, respectively. This procedure can now classify a new FORMOSAT-2 image automatically facilitating the production of landslide inventory maps.

Heterogeneous Ensemble Combination Search Using Genetic Algorithm for Class Imbalanced Data Classification.

PubMed

Haque, Mohammad Nazmul; Noman, Nasimul; Berretta, Regina; Moscato, Pablo

2016-01-01

Classification of datasets with imbalanced sample distributions has always been a challenge. In general, a popular approach for enhancing classification performance is the construction of an ensemble of classifiers. However, the performance of an ensemble is dependent on the choice of constituent base classifiers. Therefore, we propose a genetic algorithm-based search method for finding the optimum combination from a pool of base classifiers to form a heterogeneous ensemble. The algorithm, called GA-EoC, utilises 10 fold-cross validation on training data for evaluating the quality of each candidate ensembles. In order to combine the base classifiers decision into ensemble's output, we used the simple and widely used majority voting approach. The proposed algorithm, along with the random sub-sampling approach to balance the class distribution, has been used for classifying class-imbalanced datasets. Additionally, if a feature set was not available, we used the (α, β) - k Feature Set method to select a better subset of features for classification. We have tested GA-EoC with three benchmarking datasets from the UCI-Machine Learning repository, one Alzheimer's disease dataset and a subset of the PubFig database of Columbia University. In general, the performance of the proposed method on the chosen datasets is robust and better than that of the constituent base classifiers and many other well-known ensembles. Based on our empirical study we claim that a genetic algorithm is a superior and reliable approach to heterogeneous ensemble construction and we expect that the proposed GA-EoC would perform consistently in other cases.

Heterogeneous Ensemble Combination Search Using Genetic Algorithm for Class Imbalanced Data Classification

PubMed Central

Haque, Mohammad Nazmul; Noman, Nasimul; Berretta, Regina; Moscato, Pablo

2016-01-01

Classification of datasets with imbalanced sample distributions has always been a challenge. In general, a popular approach for enhancing classification performance is the construction of an ensemble of classifiers. However, the performance of an ensemble is dependent on the choice of constituent base classifiers. Therefore, we propose a genetic algorithm-based search method for finding the optimum combination from a pool of base classifiers to form a heterogeneous ensemble. The algorithm, called GA-EoC, utilises 10 fold-cross validation on training data for evaluating the quality of each candidate ensembles. In order to combine the base classifiers decision into ensemble’s output, we used the simple and widely used majority voting approach. The proposed algorithm, along with the random sub-sampling approach to balance the class distribution, has been used for classifying class-imbalanced datasets. Additionally, if a feature set was not available, we used the (α, β) − k Feature Set method to select a better subset of features for classification. We have tested GA-EoC with three benchmarking datasets from the UCI-Machine Learning repository, one Alzheimer’s disease dataset and a subset of the PubFig database of Columbia University. In general, the performance of the proposed method on the chosen datasets is robust and better than that of the constituent base classifiers and many other well-known ensembles. Based on our empirical study we claim that a genetic algorithm is a superior and reliable approach to heterogeneous ensemble construction and we expect that the proposed GA-EoC would perform consistently in other cases. PMID:26764911

Transthyretin amyloid polyneuropathies mimicking a demyelinating polyneuropathy.

PubMed

Lozeron, Pierre; Mariani, Louise-Laure; Dodet, Pauline; Beaudonnet, Guillemette; Théaudin, Marie; Adam, Clovis; Arnulf, Bertrand; Adams, David

2018-06-15

To clearly define transthyretin familial amyloid polyneuropathies (TTR-FAPs) fulfilling definite clinical and electrophysiologic European Federation of Neurological Societies/Peripheral Nerve Society criteria for chronic inflammatory demyelinating polyneuropathy (CIDP). From a cohort of 194 patients with FAP, 13 of 84 patients (15%) of French ancestry had late-onset demyelinating TTR-FAP. We compared clinical presentation and electrophysiology to a cohort with CIDP and POEMS (polyneuropathy, organomegaly, endocrinopathy, monoclonal protein, and skin changes) syndrome. We assessed nerve histology and the correlation between motor/sensory amplitudes/velocities. Predictors of demyelinating TTR-FAP were identified from clinical and electrophysiologic data. Pain, dysautonomia, small fiber sensory loss above the wrists, upper limb weakness, and absence of ataxia were predictors of demyelinating TTR-FAP ( p < 0.01). The most frequent demyelinating features were prolonged distal motor latency of the median nerve and reduced sensory conduction velocity of the median and ulnar nerves. Motor axonal loss was severe and frequent in the median, ulnar, and tibial nerves ( p < 0.05) in demyelinating FAP. Ulnar nerve motor amplitude <5.4 mV and sural nerve amplitude <3.95 μV were distinguishing characteristics of demyelinating TTR-FAP. Nerve biopsy showed severe axonal loss and occasional segmental demyelination-remyelination. Misleading features of TTR-FAP fulfilling criteria for CIDP are not uncommon in sporadic late-onset TTR-FAP, which highlights the limits of European Federation of Neurological Societies/Peripheral Nerve Society criteria. Specific clinical aspects and marked electrophysiologic axonal loss are red flag symptoms that should alert to this diagnosis and prompt TTR gene sequencing. © 2018 American Academy of Neurology.

47 CFR 36.331 - Information origination/termination expenses-Account 6310 (Class B telephone companies); Accounts...

Code of Federal Regulations, 2010 CFR

2010-10-01

... telephone companies). (a) The expenses in this account are classified as follows: (1) Other Information... 47 Telecommunication 2 2010-10-01 2010-10-01 false Information origination/termination expenses-Account 6310 (Class B telephone companies); Accounts 6311, 6341, 6351, and 6362 (Class A telephone...

19 CFR 151.25 - Mixing classes of sugar.

Code of Federal Regulations, 2011 CFR

2011-04-01

... 19 Customs Duties 2 2011-04-01 2011-04-01 false Mixing classes of sugar. 151.25 Section 151.25... TREASURY (CONTINUED) EXAMINATION, SAMPLING, AND TESTING OF MERCHANDISE Sugars, Sirups, and Molasses § 151.25 Mixing classes of sugar. No regulations relative to the weighing, taring, sampling, classifying...

19 CFR 151.25 - Mixing classes of sugar.

Code of Federal Regulations, 2010 CFR

2010-04-01

... 19 Customs Duties 2 2010-04-01 2010-04-01 false Mixing classes of sugar. 151.25 Section 151.25... TREASURY (CONTINUED) EXAMINATION, SAMPLING, AND TESTING OF MERCHANDISE Sugars, Sirups, and Molasses § 151.25 Mixing classes of sugar. No regulations relative to the weighing, taring, sampling, classifying...

14 CFR 420.19 - Launch site location review-general.

Code of Federal Regulations, 2011 CFR

2011-01-01

... nm orbit Weight class Small Medium Medium large Large 28 degrees inclination * ≤4400 >4400 to ≤11100.... Orbital expendable launch vehicles are further classified by weight class, based on the weight of payload... class of orbital expendable launch vehicles flown from a launch point, the applicant shall demonstrate...

43 CFR 426.11 - Class 1 equivalency.

Code of Federal Regulations, 2011 CFR

2011-10-01

... will be allocated to one of these three classes on a case-by-case basis. (3) Once the Class 1... Reclamation has not classified, or for which Reclamation has not yet performed the necessary economic studies... such time as the necessary classifications or studies have been completed. (d) Determination of land...

43 CFR 426.11 - Class 1 equivalency.

Code of Federal Regulations, 2013 CFR

2013-10-01

... will be allocated to one of these three classes on a case-by-case basis. (3) Once the Class 1... Reclamation has not classified, or for which Reclamation has not yet performed the necessary economic studies... such time as the necessary classifications or studies have been completed. (d) Determination of land...

43 CFR 426.11 - Class 1 equivalency.

Code of Federal Regulations, 2012 CFR

2012-10-01

... will be allocated to one of these three classes on a case-by-case basis. (3) Once the Class 1... Reclamation has not classified, or for which Reclamation has not yet performed the necessary economic studies... such time as the necessary classifications or studies have been completed. (d) Determination of land...

43 CFR 426.11 - Class 1 equivalency.

Code of Federal Regulations, 2010 CFR

2010-10-01

... will be allocated to one of these three classes on a case-by-case basis. (3) Once the Class 1... Reclamation has not classified, or for which Reclamation has not yet performed the necessary economic studies... such time as the necessary classifications or studies have been completed. (d) Determination of land...

43 CFR 426.11 - Class 1 equivalency.

Code of Federal Regulations, 2014 CFR

2014-10-01

... will be allocated to one of these three classes on a case-by-case basis. (3) Once the Class 1... Reclamation has not classified, or for which Reclamation has not yet performed the necessary economic studies... such time as the necessary classifications or studies have been completed. (d) Determination of land...

40 CFR 600.315-82 - Classes of comparable automobiles.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 40 Protection of Environment 30 2011-07-01 2011-07-01 false Classes of comparable automobiles. 600... 1977 and Later Model Year Automobiles-Labeling § 600.315-82 Classes of comparable automobiles. (a) The Secretary will classify automobiles as passenger automobiles or light trucks (nonpassenger automobiles) in...

40 CFR 600.315-82 - Classes of comparable automobiles.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 40 Protection of Environment 29 2010-07-01 2010-07-01 false Classes of comparable automobiles. 600... 1977 and Later Model Year Automobiles-Labeling § 600.315-82 Classes of comparable automobiles. (a) The Secretary will classify automobiles as passenger automobiles or light trucks (nonpassenger automobiles) in...

19 CFR 151.25 - Mixing classes of sugar.

Code of Federal Regulations, 2014 CFR

2014-04-01

... 19 Customs Duties 2 2014-04-01 2014-04-01 false Mixing classes of sugar. 151.25 Section 151.25... TREASURY (CONTINUED) EXAMINATION, SAMPLING, AND TESTING OF MERCHANDISE Sugars, Sirups, and Molasses § 151.25 Mixing classes of sugar. No regulations relative to the weighing, taring, sampling, classifying...

19 CFR 151.25 - Mixing classes of sugar.

Code of Federal Regulations, 2012 CFR

2012-04-01

... 19 Customs Duties 2 2012-04-01 2012-04-01 false Mixing classes of sugar. 151.25 Section 151.25... TREASURY (CONTINUED) EXAMINATION, SAMPLING, AND TESTING OF MERCHANDISE Sugars, Sirups, and Molasses § 151.25 Mixing classes of sugar. No regulations relative to the weighing, taring, sampling, classifying...

19 CFR 151.25 - Mixing classes of sugar.

Code of Federal Regulations, 2013 CFR

2013-04-01

... 19 Customs Duties 2 2013-04-01 2013-04-01 false Mixing classes of sugar. 151.25 Section 151.25... TREASURY (CONTINUED) EXAMINATION, SAMPLING, AND TESTING OF MERCHANDISE Sugars, Sirups, and Molasses § 151.25 Mixing classes of sugar. No regulations relative to the weighing, taring, sampling, classifying...

Characteristics, treatment, and outcomes of periprocedural cerebrovascular accidents during electrophysiologic procedures.

PubMed

Harb, Serge C; Thomas, George; Saliba, Walid I; Nakhoul, Georges N; Hussein, Ayman A; Duarte, Valeria E; Bhargava, Mandeep; Baranowski, Bryan; Tchou, Patrick; Dresing, Thomas; Callahan, Thomas; Kanj, Mohamed; Natale, Andrea; Lindsay, Bruce D; Wazni, Oussama M

2013-06-01

We sought to identify the characteristics, treatment, and outcomes of periprocedural cerebrovascular accident (PCVA) during electrophysiologic (EP) procedures. Periprocedural cerebrovascular accident is one of the most feared complications during EP procedures with very few data regarding its characteristics, management, and outcomes. Between January 1998 and December 2008, we reviewed 30,032 invasive EP procedures for PCVA occurrence and characteristics. Management and outcomes were also determined. Thirty-eight CVAs were identified. Twenty (53 %) were intraprocedural and 18 (47 %) postprocedural. Thirty-two (84 %) were classified as strokes and six (16 %) as transient ischemic attacks. All CVAs except one (37, 97 %) were ischemic and the vast majority occurred during ablation procedures (36, 95 %). Among the 31 patients with ischemic stroke, 11 (35 %) were treated with reperfusion (eight catheter-based therapy and three intravenous t-PA) of whom five (46 %) had complete recovery, three (27 %) had partial recovery, and three (27 %) had no recovery. No hemorrhagic transformations occurred. Periprocedural cerebrovascular accident during EP procedures is rare and is almost always ischemic. It occurs more frequently during ablation procedures. Reperfusion therapy is feasible and safe.

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.

Neural Dissociation in the Production of Lexical versus Classifier Signs in ASL: Distinct Patterns of Hemispheric Asymmetry

ERIC Educational Resources Information Center

Hickok, Gregory; Pickell, Herbert; Klima, Edward; Bellugi, Ursula

2009-01-01

We examine the hemispheric organization for the production of two classes of ASL signs, lexical signs and classifier signs. Previous work has found strong left hemisphere dominance for the production of lexical signs, but several authors have speculated that classifier signs may involve the right hemisphere to a greater degree because they can…

Dataset of quantitative spectral EEG of different stages of kindling acquisition in rats.

PubMed

Jalilifar, Mostafa; Yadollahpour, Ali

2018-02-01

The data represented here are in relation with the manuscript "Quantitative assessments of extracellular EEG to classify specific features of main phases of seizure acquisition based on kindling model in Rat" (Jalilifar et al., 2017) [1] which quantitatively classified different main stages of the kindling process based on their electrophysiological characteristics using EEG signal processing. The data in the graphical form reported the contribution of different sub bands of EEG in different stages of kindling- induced epileptogenesis. Only EEG signals related to stages 1-2 (initial seizure stages (ISSs)), 3 (localized seizure stage (LSS)), and 4-5 (generalized seizure stages (GSSs) were transferred into frequency function by Fast Fourier Transform (FFT) and their power spectrum and power of each sub bands including delta (1-4 Hz), Theta (4-8 Hz), alpha (8-12 Hz), beta (12-28 Hz), gamma (28-40 Hz) were calculated with MATLAB 2013b. Accordingly, all results were obtained quantitatively which can contribute to reduce the errors in the behavioral assessments.

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

PubMed Central

Breit, Marc; Netzer, Michael

2015-01-01

The objectives of this work were the classification of dynamic metabolic biomarker candidates and the modeling and characterization of kinetic regulatory mechanisms in human metabolism with response to external perturbations by physical activity. Longitudinal metabolic concentration data of 47 individuals from 4 different groups were examined, obtained from a cycle ergometry cohort study. In total, 110 metabolites (within the classes of acylcarnitines, amino acids, and sugars) were measured through a targeted metabolomics approach, combining tandem mass spectrometry (MS/MS) with the concept of stable isotope dilution (SID) for metabolite quantitation. Biomarker candidates were selected by combined analysis of maximum fold changes (MFCs) in concentrations and P-values resulting from statistical hypothesis testing. Characteristic kinetic signatures were identified through a mathematical modeling approach utilizing polynomial fitting. Modeled kinetic signatures were analyzed for groups with similar behavior by applying hierarchical cluster analysis. Kinetic shape templates were characterized, defining different forms of basic kinetic response patterns, such as sustained, early, late, and other forms, that can be used for metabolite classification. Acetylcarnitine (C2), showing a late response pattern and having the highest values in MFC and statistical significance, was classified as late marker and ranked as strong predictor (MFC = 1.97, P < 0.001). In the class of amino acids, highest values were shown for alanine (MFC = 1.42, P < 0.001), classified as late marker and strong predictor. Glucose yields a delayed response pattern, similar to a hockey stick function, being classified as delayed marker and ranked as moderate predictor (MFC = 1.32, P < 0.001). These findings coincide with existing knowledge on central metabolic pathways affected in exercise physiology, such as β-oxidation of fatty acids, glycolysis, and glycogenolysis. The presented modeling approach demonstrates high potential for dynamic biomarker identification and the investigation of kinetic mechanisms in disease or pharmacodynamics studies using MS data from longitudinal cohort studies. PMID:26317529

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

You, D; Aryal, M; Samuels, S

Purpose: A previous study showed that large sub-volumes of tumor with low blood volume (BV) (poorly perfused) in head-and-neck (HN) cancers are significantly associated with local-regional failure (LRF) after chemoradiation therapy, and could be targeted with intensified radiation doses. This study aimed to develop an automated and scalable model to extract voxel-wise contrast-enhanced temporal features of dynamic contrastenhanced (DCE) MRI in HN cancers for predicting LRF. Methods: Our model development consists of training and testing stages. The training stage includes preprocessing of individual-voxel DCE curves from tumors for intensity normalization and temporal alignment, temporal feature extraction from the curves, featuremore » selection, and training classifiers. For feature extraction, multiresolution Haar discrete wavelet transformation is applied to each DCE curve to capture temporal contrast-enhanced features. The wavelet coefficients as feature vectors are selected. Support vector machine classifiers are trained to classify tumor voxels having either low or high BV, for which a BV threshold of 7.6% is previously established and used as ground truth. The model is tested by a new dataset. The voxel-wise DCE curves for training and testing were from 14 and 8 patients, respectively. A posterior probability map of the low BV class was created to examine the tumor sub-volume classification. Voxel-wise classification accuracy was computed to evaluate performance of the model. Results: Average classification accuracies were 87.2% for training (10-fold crossvalidation) and 82.5% for testing. The lowest and highest accuracies (patient-wise) were 68.7% and 96.4%, respectively. Posterior probability maps of the low BV class showed the sub-volumes extracted by our model similar to ones defined by the BV maps with most misclassifications occurred near the sub-volume boundaries. Conclusion: This model could be valuable to support adaptive clinical trials with further validation. The framework could be extendable and scalable to extract temporal contrastenhanced features of DCE-MRI in other tumors. We would like to acknowledge NIH for funding support: UO1 CA183848.« less

Voxel-Based Neighborhood for Spatial Shape Pattern Classification of Lidar Point Clouds with Supervised Learning.

PubMed

Plaza-Leiva, Victoria; Gomez-Ruiz, Jose Antonio; Mandow, Anthony; García-Cerezo, Alfonso

2017-03-15

Improving the effectiveness of spatial shape features classification from 3D lidar data is very relevant because it is largely used as a fundamental step towards higher level scene understanding challenges of autonomous vehicles and terrestrial robots. In this sense, computing neighborhood for points in dense scans becomes a costly process for both training and classification. This paper proposes a new general framework for implementing and comparing different supervised learning classifiers with a simple voxel-based neighborhood computation where points in each non-overlapping voxel in a regular grid are assigned to the same class by considering features within a support region defined by the voxel itself. The contribution provides offline training and online classification procedures as well as five alternative feature vector definitions based on principal component analysis for scatter, tubular and planar shapes. Moreover, the feasibility of this approach is evaluated by implementing a neural network (NN) method previously proposed by the authors as well as three other supervised learning classifiers found in scene processing methods: support vector machines (SVM), Gaussian processes (GP), and Gaussian mixture models (GMM). A comparative performance analysis is presented using real point clouds from both natural and urban environments and two different 3D rangefinders (a tilting Hokuyo UTM-30LX and a Riegl). Classification performance metrics and processing time measurements confirm the benefits of the NN classifier and the feasibility of voxel-based neighborhood.

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

NASA Astrophysics Data System (ADS)

Elhag, Mohamed; Boteva, Silvena

2016-10-01

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

Classifying galaxy spectra at 0.5 < z < 1 with self-organizing maps

NASA Astrophysics Data System (ADS)

Rahmani, S.; Teimoorinia, H.; Barmby, P.

2018-05-01

The spectrum of a galaxy contains information about its physical properties. Classifying spectra using templates helps elucidate the nature of a galaxy's energy sources. In this paper, we investigate the use of self-organizing maps in classifying galaxy spectra against templates. We trained semi-supervised self-organizing map networks using a set of templates covering the wavelength range from far ultraviolet to near infrared. The trained networks were used to classify the spectra of a sample of 142 galaxies with 0.5 < z < 1 and the results compared to classifications performed using K-means clustering, a supervised neural network, and chi-squared minimization. Spectra corresponding to quiescent galaxies were more likely to be classified similarly by all methods while starburst spectra showed more variability. Compared to classification using chi-squared minimization or the supervised neural network, the galaxies classed together by the self-organizing map had more similar spectra. The class ordering provided by the one-dimensional self-organizing maps corresponds to an ordering in physical properties, a potentially important feature for the exploration of large datasets.

Non-Mutually Exclusive Deep Neural Network Classifier for Combined Modes of Bearing Fault Diagnosis.

PubMed

Duong, Bach Phi; Kim, Jong-Myon

2018-04-07

The simultaneous occurrence of various types of defects in bearings makes their diagnosis more challenging owing to the resultant complexity of the constituent parts of the acoustic emission (AE) signals. To address this issue, a new approach is proposed in this paper for the detection of multiple combined faults in bearings. The proposed methodology uses a deep neural network (DNN) architecture to effectively diagnose the combined defects. The DNN structure is based on the stacked denoising autoencoder non-mutually exclusive classifier (NMEC) method for combined modes. The NMEC-DNN is trained using data for a single fault and it classifies both single faults and multiple combined faults. The results of experiments conducted on AE data collected through an experimental test-bed demonstrate that the DNN achieves good classification performance with a maximum accuracy of 95%. The proposed method is compared with a multi-class classifier based on support vector machines (SVMs). The NMEC-DNN yields better diagnostic performance in comparison to the multi-class classifier based on SVM. The NMEC-DNN reduces the number of necessary data collections and improves the bearing fault diagnosis performance.

Solar System constraints on massless scalar-tensor gravity with positive coupling constant upon cosmological evolution of the scalar field

NASA Astrophysics Data System (ADS)

Anderson, David; Yunes, Nicolás

2017-09-01

Scalar-tensor theories of gravity modify general relativity by introducing a scalar field that couples nonminimally to the metric tensor, while satisfying the weak-equivalence principle. These theories are interesting because they have the potential to simultaneously suppress modifications to Einstein's theory on Solar System scales, while introducing large deviations in the strong field of neutron stars. Scalar-tensor theories can be classified through the choice of conformal factor, a scalar that regulates the coupling between matter and the metric in the Einstein frame. The class defined by a Gaussian conformal factor with a negative exponent has been studied the most because it leads to spontaneous scalarization (i.e. the sudden activation of the scalar field in neutron stars), which consequently leads to large deviations from general relativity in the strong field. This class, however, has recently been shown to be in conflict with Solar System observations when accounting for the cosmological evolution of the scalar field. We here study whether this remains the case when the exponent of the conformal factor is positive, as well as in another class of theories defined by a hyperbolic conformal factor. We find that in both of these scalar-tensor theories, Solar System tests are passed only in a very small subset of coupling parameter space, for a large set of initial conditions compatible with big bang nucleosynthesis. However, while we find that it is possible for neutron stars to scalarize, one must carefully select the coupling parameter to do so, and even then, the scalar charge is typically 2 orders of magnitude smaller than in the negative-exponent case. Our study suggests that future work on scalar-tensor gravity, for example in the context of tests of general relativity with gravitational waves from neutron star binaries, should be carried out within the positive coupling parameter class.

Novel 3D bio-macromolecular bilinear descriptors for protein science: Predicting protein structural classes.

PubMed

Marrero-Ponce, Yovani; Contreras-Torres, Ernesto; García-Jacas, César R; Barigye, Stephen J; Cubillán, Néstor; Alvarado, Ysaías J

2015-06-07

In the present study, we introduce novel 3D protein descriptors based on the bilinear algebraic form in the ℝ(n) space on the coulombic matrix. For the calculation of these descriptors, macromolecular vectors belonging to ℝ(n) space, whose components represent certain amino acid side-chain properties, were used as weighting schemes. Generalization approaches for the calculation of inter-amino acidic residue spatial distances based on Minkowski metrics are proposed. The simple- and double-stochastic schemes were defined as approaches to normalize the coulombic matrix. The local-fragment indices for both amino acid-types and amino acid-groups are presented in order to permit characterizing fragments of interest in proteins. On the other hand, with the objective of taking into account specific interactions among amino acids in global or local indices, geometric and topological cut-offs are defined. To assess the utility of global and local indices a classification model for the prediction of the major four protein structural classes, was built with the Linear Discriminant Analysis (LDA) technique. The developed LDA-model correctly classifies the 92.6% and 92.7% of the proteins on the training and test sets, respectively. The obtained model showed high values of the generalized square correlation coefficient (GC(2)) on both the training and test series. The statistical parameters derived from the internal and external validation procedures demonstrate the robustness, stability and the high predictive power of the proposed model. The performance of the LDA-model demonstrates the capability of the proposed indices not only to codify relevant biochemical information related to the structural classes of proteins, but also to yield suitable interpretability. It is anticipated that the current method will benefit the prediction of other protein attributes or functions. Copyright © 2015 Elsevier Ltd. All rights reserved.

Latent class analysis reveals clinically relevant atopy phenotypes in 2 birth cohorts.

PubMed

Hose, Alexander J; Depner, Martin; Illi, Sabina; Lau, Susanne; Keil, Thomas; Wahn, Ulrich; Fuchs, Oliver; Pfefferle, Petra Ina; Schmaußer-Hechfellner, Elisabeth; Genuneit, Jon; Lauener, Roger; Karvonen, Anne M; Roduit, Caroline; Dalphin, Jean-Charles; Riedler, Josef; Pekkanen, Juha; von Mutius, Erika; Ege, Markus J

2017-06-01

Phenotypes of childhood-onset asthma are characterized by distinct trajectories and functional features. For atopy, definition of phenotypes during childhood is less clear. We sought to define phenotypes of atopic sensitization over the first 6 years of life using a latent class analysis (LCA) integrating 3 dimensions of atopy: allergen specificity, time course, and levels of specific IgE (sIgE). Phenotypes were defined by means of LCA in 680 children of the Multizentrische Allergiestudie (MAS) and 766 children of the Protection against allergy: Study in Rural Environments (PASTURE) birth cohorts and compared with classical nondisjunctive definitions of seasonal, perennial, and food sensitization with respect to atopic diseases and lung function. Cytokine levels were measured in the PASTURE cohort. The LCA classified predominantly by type and multiplicity of sensitization (food vs inhalant), allergen combinations, and sIgE levels. Latent classes were related to atopic disease manifestations with higher sensitivity and specificity than the classical definitions. LCA detected consistently in both cohorts a distinct group of children with severe atopy characterized by high seasonal sIgE levels and a strong propensity for asthma; hay fever; eczema; and impaired lung function, also in children without an established asthma diagnosis. Severe atopy was associated with an increased IL-5/IFN-γ ratio. A path analysis among sensitized children revealed that among all features of severe atopy, only excessive sIgE production early in life affected asthma risk. LCA revealed a set of benign, symptomatic, and severe atopy phenotypes. The severe phenotype emerged as a latent condition with signs of a dysbalanced immune response. It determined high asthma risk through excessive sIgE production and directly affected impaired lung function. Copyright © 2016 American Academy of Allergy, Asthma & Immunology. Published by Elsevier Inc. All rights reserved.

The first step in the development of text mining technology for cancer risk assessment: identifying and organizing scientific evidence in risk assessment literature

PubMed Central

Korhonen, Anna; Silins, Ilona; Sun, Lin; Stenius, Ulla

2009-01-01

Background One of the most neglected areas of biomedical Text Mining (TM) is the development of systems based on carefully assessed user needs. We have recently investigated the user needs of an important task yet to be tackled by TM -- Cancer Risk Assessment (CRA). Here we take the first step towards the development of TM technology for the task: identifying and organizing the scientific evidence required for CRA in a taxonomy which is capable of supporting extensive data gathering from biomedical literature. Results The taxonomy is based on expert annotation of 1297 abstracts downloaded from relevant PubMed journals. It classifies 1742 unique keywords found in the corpus to 48 classes which specify core evidence required for CRA. We report promising results with inter-annotator agreement tests and automatic classification of PubMed abstracts to taxonomy classes. A simple user test is also reported in a near real-world CRA scenario which demonstrates along with other evaluation that the resources we have built are well-defined, accurate, and applicable in practice. Conclusion We present our annotation guidelines and a tool which we have designed for expert annotation of PubMed abstracts. A corpus annotated for keywords and document relevance is also presented, along with the taxonomy which organizes the keywords into classes defining core evidence for CRA. As demonstrated by the evaluation, the materials we have constructed provide a good basis for classification of CRA literature along multiple dimensions. They can support current manual CRA as well as facilitate the development of an approach based on TM. We discuss extending the taxonomy further via manual and machine learning approaches and the subsequent steps required to develop TM technology for the needs of CRA. PMID:19772619

Algorithms exploiting ultrasonic sensors for subject classification

NASA Astrophysics Data System (ADS)

Desai, Sachi; Quoraishee, Shafik

2009-09-01

Proposed here is a series of techniques exploiting micro-Doppler ultrasonic sensors capable of characterizing various detected mammalian targets based on their physiological movements captured a series of robust features. Employed is a combination of unique and conventional digital signal processing techniques arranged in such a manner they become capable of classifying a series of walkers. These processes for feature extraction develops a robust feature space capable of providing discrimination of various movements generated from bipeds and quadrupeds and further subdivided into large or small. These movements can be exploited to provide specific information of a given signature dividing it in a series of subset signatures exploiting wavelets to generate start/stop times. After viewing a series spectrograms of the signature we are able to see distinct differences and utilizing kurtosis, we generate an envelope detector capable of isolating each of the corresponding step cycles generated during a walk. The walk cycle is defined as one complete sequence of walking/running from the foot pushing off the ground and concluding when returning to the ground. This time information segments the events that are readily seen in the spectrogram but obstructed in the temporal domain into individual walk sequences. This walking sequence is then subsequently translated into a three dimensional waterfall plot defining the expected energy value associated with the motion at particular instance of time and frequency. The value is capable of being repeatable for each particular class and employable to discriminate the events. Highly reliable classification is realized exploiting a classifier trained on a candidate sample space derived from the associated gyrations created by motion from actors of interest. The classifier developed herein provides a capability to classify events as an adult humans, children humans, horses, and dogs at potentially high rates based on the tested sample space. The algorithm developed and described will provide utility to an underused sensor modality for human intrusion detection because of the current high-rate of generated false alarms. The active ultrasonic sensor coupled in a multi-modal sensor suite with binary, less descriptive sensors like seismic devices realizing a greater accuracy rate for detection of persons of interest for homeland purposes.

Analysis of composition-based metagenomic classification.

PubMed

Higashi, Susan; Barreto, André da Motta Salles; Cantão, Maurício Egidio; de Vasconcelos, Ana Tereza Ribeiro

2012-01-01

An essential step of a metagenomic study is the taxonomic classification, that is, the identification of the taxonomic lineage of the organisms in a given sample. The taxonomic classification process involves a series of decisions. Currently, in the context of metagenomics, such decisions are usually based on empirical studies that consider one specific type of classifier. In this study we propose a general framework for analyzing the impact that several decisions can have on the classification problem. Instead of focusing on any specific classifier, we define a generic score function that provides a measure of the difficulty of the classification task. Using this framework, we analyze the impact of the following parameters on the taxonomic classification problem: (i) the length of n-mers used to encode the metagenomic sequences, (ii) the similarity measure used to compare sequences, and (iii) the type of taxonomic classification, which can be conventional or hierarchical, depending on whether the classification process occurs in a single shot or in several steps according to the taxonomic tree. We defined a score function that measures the degree of separability of the taxonomic classes under a given configuration induced by the parameters above. We conducted an extensive computational experiment and found out that reasonable values for the parameters of interest could be (i) intermediate values of n, the length of the n-mers; (ii) any similarity measure, because all of them resulted in similar scores; and (iii) the hierarchical strategy, which performed better in all of the cases. As expected, short n-mers generate lower configuration scores because they give rise to frequency vectors that represent distinct sequences in a similar way. On the other hand, large values for n result in sparse frequency vectors that represent differently metagenomic fragments that are in fact similar, also leading to low configuration scores. Regarding the similarity measure, in contrast to our expectations, the variation of the measures did not change the configuration scores significantly. Finally, the hierarchical strategy was more effective than the conventional strategy, which suggests that, instead of using a single classifier, one should adopt multiple classifiers organized as a hierarchy.

Feature Selection Has a Large Impact on One-Class Classification Accuracy for MicroRNAs in Plants.

PubMed

Yousef, Malik; Saçar Demirci, Müşerref Duygu; Khalifa, Waleed; Allmer, Jens

2016-01-01

MicroRNAs (miRNAs) are short RNA sequences involved in posttranscriptional gene regulation. Their experimental analysis is complicated and, therefore, needs to be supplemented with computational miRNA detection. Currently computational miRNA detection is mainly performed using machine learning and in particular two-class classification. For machine learning, the miRNAs need to be parametrized and more than 700 features have been described. Positive training examples for machine learning are readily available, but negative data is hard to come by. Therefore, it seems prerogative to use one-class classification instead of two-class classification. Previously, we were able to almost reach two-class classification accuracy using one-class classifiers. In this work, we employ feature selection procedures in conjunction with one-class classification and show that there is up to 36% difference in accuracy among these feature selection methods. The best feature set allowed the training of a one-class classifier which achieved an average accuracy of ~95.6% thereby outperforming previous two-class-based plant miRNA detection approaches by about 0.5%. We believe that this can be improved upon in the future by rigorous filtering of the positive training examples and by improving current feature clustering algorithms to better target pre-miRNA feature selection.

The rat caudal nerves: a model for experimental neuropathies.

PubMed

Schaumburg, Herbert H; Zotova, Elena; Raine, Cedric S; Tar, Moses; Arezzo, Joseph

2010-06-01

This study provides a detailed investigation of the anatomy of the rat caudal nerve along its entire length, as well as correlated nerve conduction measures in both large and small diameter axons. It determines that rodent caudal nerves provide a simple, sensitive experimental model for evaluation of the pathophysiology of degeneration, recovery, and prevention of length-dependent distal axonopathy. After first defining the normal anatomy and electrophysiology of the rat caudal nerves, acrylamide monomer, a reliable axonal toxin, was administered at different doses for escalating time periods. Serial electrophysiological recordings were obtained, during intoxication, from multiple sites along caudal and distal sciatic nerves. Multiple sections of the caudal and sciatic nerves were examined with light and electron microscopy. The normal distribution of conduction velocities was determined and acrylamide-induced time- and dose-related slowing of velocities at the vulnerable ultraterminal region was documented. Degenerative morphological changes in the distal regions of the caudal nerves appeared well before changes in the distal sciatic nerves. Our study has shown that (1) rat caudal nerves have a complex neural structure that varies along a distal-to-proximal gradient and (2) correlative assessment of both morphology and electrophysiology of rat caudal nerves is easily achieved and provides a highly sensitive index of the onset and progression of the length-dependent distal axonopathy.

Medical devices; immunology and microbiology devices; classification of nucleic acid-based devices for the detection of Mycobacterium tuberculosis complex and the genetic mutations associated with antibiotic resistance. Final order.

PubMed

2014-10-22

The Food and Drug Administration (FDA) is classifying nucleic acid-based in vitro diagnostic devices for the detection of Mycobacterium tuberculosis complex (MTB-complex) and the genetic mutations associated with MTB-complex antibiotic resistance in respiratory specimens devices into class II (special controls). The Agency is classifying the device into class II (special controls) because special controls, in addition to general controls, will provide a reasonable assurance of safety and effectiveness of the device.

Medical Devices; General Hospital and Personal Use Devices; Classification of the Ultraviolet Radiation Chamber Disinfection Device. Final order.

PubMed

2015-11-20

The Food and Drug Administration (FDA or the Agency) is classifying the ultraviolet (UV) radiation chamber disinfection device into class II (special controls). The special controls that will apply to the device are identified in this order and will be part of the codified language for the UV radiation chamber disinfection device classification. The Agency is classifying the device into class II (special controls) in order to provide a reasonable assurance of safety and effectiveness of the device.

Medical Devices; Immunology and Microbiology Devices; Classification of the Assayed Quality Control Material for Clinical Microbiology Assays. Final order.

PubMed

2017-07-27

The Food and Drug Administration (FDA, Agency, or we) is classifying the assayed quality control material for clinical microbiology assays into class II (special controls). The special controls that will apply to the device are identified in this order and will be part of the codified language for the assayed quality control material for clinical microbiology assays' classification. The Agency is classifying the device into class II (special controls) to provide a reasonable assurance of safety and effectiveness of the device.

The relationship between RNA catalytic processes

NASA Astrophysics Data System (ADS)

Cedergren, Robert; Lang, B. Franz; Gravel, Denis

1988-09-01

Proposals that an RNA-based genetic system preceeded DNA, stem from the ability of RNA to store genetic information and to promote simple catalysis. However, to be a valid basis for the RNA world, RNA catalysis must demonstrate or be related to intrinsic chemical properties which could have existed in primordial times. We analyze this question by first classifying RNA catalysis and related processes according to their mechanism. We define: (A) thedisjunct nucleophile class which leads to 5'-phosphates. These include Group I and II intron splicing, nuclear mRNA splicing and RNase P reactions. Although Group I introns and its excision mechanism is likely to have existed in primordial times, present-day examples have arisen independently in different phyla much more recently. Comparative methodology indicates that RNase P catalysis originated before the divergence of the major kingdoms. In addition, alldisjunct nucleophile reactions can be interrelated by a proposed mechanism involving a distant 2-OH nucleophile. (B) theconjunct nucleophile class leading to 3'-phosphates. This class is composed of self-cleaving RNAs found in plant viruses and the newt. We propose that tRNA splicing is related to this mechanism rather than the previous one. The presence of introns in tRNA genes of eukaryotes and archaebacteria supports the idea that tRNA splicing predates the divergence of these cell types.

Classification of Unmanned Aircraft Systems. UAS Classification/Categorization for Certification

NASA Technical Reports Server (NTRS)

2004-01-01

Category, class, and type designations are primary means to identify appropriate aircraft certification basis, operating rules/limitations, and pilot qualifications to operate in the National Airspace System (NAS). The question is whether UAS fit into existing aircraft categories or classes, or are unique enough to justify the creation of a new category/class. In addition, the characteristics or capabilities, which define when an UAS becomes a regulated aircraft, must also be decided. This issue focuses on UAS classification for certification purposes. Several approaches have been considered for classifying UAS. They basically group into either using a weight/mass basis, or a safety risk basis, factoring in the performance of the UAS, including where the UAS would operate. Under existing standards, aircraft must have a Type Certificate and Certificate of Airworthiness, in order to be used for "compensation or hire", a major difference from model aircraft. Newer technologies may make it possible for very small UAS to conduct commercial services, but that is left for a future discussion to extend the regulated aircraft to a lower level. The Access 5 position is that UAS are aircraft and should be regulated above the weight threshold differentiating them from model airplanes. The recommended classification grouping is summarized in a chart.

Classes in the Balance: Latent Class Analysis and the Balance Scale Task

ERIC Educational Resources Information Center

Boom, Jan; ter Laak, Jan

2007-01-01

Latent class analysis (LCA) has been successfully applied to tasks measuring higher cognitive functioning, suggesting the existence of distinct strategies used in such tasks. With LCA it became possible to classify post hoc. This important step forward in modeling and analyzing cognitive strategies is relevant to the overlapping waves model for…

A fast learning method for large scale and multi-class samples of SVM

NASA Astrophysics Data System (ADS)

Fan, Yu; Guo, Huiming

2017-06-01

A multi-class classification SVM(Support Vector Machine) fast learning method based on binary tree is presented to solve its low learning efficiency when SVM processing large scale multi-class samples. This paper adopts bottom-up method to set up binary tree hierarchy structure, according to achieved hierarchy structure, sub-classifier learns from corresponding samples of each node. During the learning, several class clusters are generated after the first clustering of the training samples. Firstly, central points are extracted from those class clusters which just have one type of samples. For those which have two types of samples, cluster numbers of their positive and negative samples are set respectively according to their mixture degree, secondary clustering undertaken afterwards, after which, central points are extracted from achieved sub-class clusters. By learning from the reduced samples formed by the integration of extracted central points above, sub-classifiers are obtained. Simulation experiment shows that, this fast learning method, which is based on multi-level clustering, can guarantee higher classification accuracy, greatly reduce sample numbers and effectively improve learning efficiency.

Classifying with confidence from incomplete information.

DOE PAGES

Parrish, Nathan; Anderson, Hyrum S.; Gupta, Maya R.; ...

2013-12-01

For this paper, we consider the problem of classifying a test sample given incomplete information. This problem arises naturally when data about a test sample is collected over time, or when costs must be incurred to compute the classification features. For example, in a distributed sensor network only a fraction of the sensors may have reported measurements at a certain time, and additional time, power, and bandwidth is needed to collect the complete data to classify. A practical goal is to assign a class label as soon as enough data is available to make a good decision. We formalize thismore » goal through the notion of reliability—the probability that a label assigned given incomplete data would be the same as the label assigned given the complete data, and we propose a method to classify incomplete data only if some reliability threshold is met. Our approach models the complete data as a random variable whose distribution is dependent on the current incomplete data and the (complete) training data. The method differs from standard imputation strategies in that our focus is on determining the reliability of the classification decision, rather than just the class label. We show that the method provides useful reliability estimates of the correctness of the imputed class labels on a set of experiments on time-series data sets, where the goal is to classify the time-series as early as possible while still guaranteeing that the reliability threshold is met.« less

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.

Simple and Flexible Classification of Gene Expression Microarrays Via Swirls and Ripples | Division of Cancer Prevention

Cancer.gov

By Stuart G. Baker The program requires Mathematica 7.01.0 The key function is Classify [datalist,options] where datalist={data, genename, dataname} data ={matrix for class 0, matrix for class 1}, matrix is gene expression by specimen genename a list of names of genes, dataname ={name of data set, name of class0, name of class1} |

Purely in silico BCS classification: science based quality standards for the world's drugs.

PubMed

Dahan, Arik; Wolk, Omri; Kim, Young Hoon; Ramachandran, Chandrasekharan; Crippen, Gordon M; Takagi, Toshihide; Bermejo, Marival; Amidon, Gordon L

2013-11-04

BCS classification is a vital tool in the development of both generic and innovative drug products. The purpose of this work was to provisionally classify the world's top selling oral drugs according to the BCS, using in silico methods. Three different in silico methods were examined: the well-established group contribution (CLogP) and atom contribution (ALogP) methods, and a new method based solely on the molecular formula and element contribution (KLogP). Metoprolol was used as the benchmark for the low/high permeability class boundary. Solubility was estimated in silico using a thermodynamic equation that relies on the partition coefficient and melting point. The validity of each method was affirmed by comparison to reference data and literature. We then used each method to provisionally classify the orally administered, IR drug products found in the WHO Model list of Essential Medicines, and the top-selling oral drug products in the United States (US), Great Britain (GB), Spain (ES), Israel (IL), Japan (JP), and South Korea (KR). A combined list of 363 drugs was compiled from the various lists, and 257 drugs were classified using the different in silico permeability methods and literature solubility data, as well as BDDCS classification. Lastly, we calculated the solubility values for 185 drugs from the combined set using in silico approach. Permeability classification with the different in silico methods was correct for 69-72.4% of the 29 reference drugs with known human jejunal permeability, and for 84.6-92.9% of the 14 FDA reference drugs in the set. The correlations (r(2)) between experimental log P values of 154 drugs and their CLogP, ALogP and KLogP were 0.97, 0.82 and 0.71, respectively. The different in silico permeability methods produced comparable results: 30-34% of the US, GB, ES and IL top selling drugs were class 1, 27-36.4% were class 2, 22-25.5% were class 3, and 5.46-14% were class 4 drugs, while ∼8% could not be classified. The WHO list included significantly less class 1 and more class 3 drugs in comparison to the countries' lists, probably due to differences in commonly used drugs in developing vs industrial countries. BDDCS classified more drugs as class 1 compared to in silico BCS, likely due to the more lax benchmark for metabolism (70%), in comparison to the strict permeability benchmark (metoprolol). For 185 out of the 363 drugs, in silico solubility values were calculated, and successfully matched the literature solubility data. In conclusion, relatively simple in silico methods can be used to estimate both permeability and solubility. While CLogP produced the best correlation to experimental values, even KLogP, the most simplified in silico method that is based on molecular formula with no knowledge of molecular structure, produced comparable BCS classification to the sophisticated methods. This KLogP, when combined with a mean melting point and estimated dose, can be used to provisionally classify potential drugs from just molecular formula, even before synthesis. 49-59% of the world's top-selling drugs are highly soluble (class 1 and class 3), and are therefore candidates for waivers of in vivo bioequivalence studies. For these drugs, the replacement of expensive human studies with affordable in vitro dissolution tests would ensure their bioequivalence, and encourage the development and availability of generic drug products in both industrial and developing countries.

Mapping wetlands in Nova Scotia with multi-beam RADARSAT-2 Polarimetric SAR, optical satellite imagery, and Lidar data

NASA Astrophysics Data System (ADS)

Jahncke, Raymond; Leblon, Brigitte; Bush, Peter; LaRocque, Armand

2018-06-01

Wetland maps currently in use by the Province of Nova Scotia, namely the Department of Natural Resources (DNR) wetland inventory map and the swamp wetland classes of the DNR forest map, need to be updated. In this study, wetlands were mapped in an area southwest of Halifax, Nova Scotia by classifying a combination of multi-date and multi-beam RADARSAT-2 C-band polarimetric SAR (polSAR) images with spring Lidar, and fall QuickBird optical data using the Random Forests (RF) classifier. The resulting map has five wetland classes (open-water/marsh complex, open bog, open fen, shrub/treed fen/bog, swamp), plus lakes and various upland classes. Its accuracy was assessed using data from 156 GPS wetland sites collected in 2012 and compared to the one obtained with the current wetland map of Nova Scotia. The best overall classification was obtained using a combination of Lidar, RADARSAT-2 HH, HV, VH, VV intensity with polarimetric variables, and QuickBird multispectral (89.2%). The classified image was compared to GPS validation sites to assess the mapping accuracy of the wetlands. It was first done considering a group consisting of all wetland classes including lakes. This showed that only 69.9% of the wetland sites were correctly identified when only the QuickBird classified image was used in the classification. With the addition of variables derived from lidar, the number of correctly identified wetlands increased to 88.5%. The accuracy remained the same with the addition of RADARSAT-2 (88.5%). When we tested the accuracy for identifying wetland classes (e.g. marsh complex vs. open bog) instead of grouped wetlands, the resulting wetland map performed best with either QuickBird and Lidar, or QuickBird, Lidar, and RADARSAT-2 (66%). The Province of Nova Scotia's current wetland inventory and its associated wetland classes (aerial-photo interpreted) were also assessed against the GPS wetland sites. This provincial inventory correctly identified 62.2% of the grouped wetlands and only 18.6% of the wetland classes. The current inventory's poor performance demonstrates the value of incorporating a combination of new data sources into the provincial wetland mapping.

Patient selection for whole brain radiotherapy (WBRT) in a large lung cancer cohort: Impact of a new Dutch guideline on brain metastases.

PubMed

Hendriks, Lizza E L; Troost, Esther G C; Steward, Allan; Bootsma, Gerben P; De Jaeger, Katrien; van den Borne, Ben E E M; Dingemans, Anne-Marie C

2014-07-01

Median survival after diagnosis of brain metastases is, depending on the Recursive Partitioning Analysis (RPA) classes, 7.1 (class I) to 2.3 months (class III). In 2011 the Dutch guideline on brain metastases was revised, advising to withhold whole brain radiotherapy (WBRT) in RPA class III. In this large retrospective study, we evaluated the guideline's use in daily practice. Data of 428 lung cancer patients undergoing WBRT for brain metastases (2004-2012) referred from three Dutch hospitals were retrospectively analyzed. Details on Karnofsky performance score (KPS), age, control of primary tumor, extracranial metastases, histology, and survival after diagnosis of brain metastases were collected. RPA class was determined using the first four items. In total 327 patients had non-small cell lung cancer (NSCLC) and 101 small cell lung cancer (SCLC). For NSCLC, 6.1%, 71.9%, and 16.2% were classified as RPA I, II, and III, respectively, and 5.8% could not be classified. For SCLC this was 8.9%, 66.3%, 14.9%, and 9.9%, respectively. Before the revised guideline was implemented, 11.3-21.3% of WBRT patients were annually classified as RPA III. In the year thereafter, this was 13.0% (p = 0.646). Median survival (95% CI) for NSCLC RPA class I, II, and III was 11.4 (9.9-12.9), 4.0 (3.4-4.7), and 1.7 (1.3-2.0) months, respectively. For SCLC this was 7.9 (4.1-11.7), 4.7 (3.3-6.1), and 1.7 (1.5-1.8) months. Although it is advised to withhold WBRT in RPA class III patients, in daily practice 11.3-21.3% of WBRT-treated patients were classified as RPA III. The new guideline did not result in a decrease. Reasons for referral of RPA III patients despite a low KPS were not found. Despite WBRT, survival of RPA III patients remains poor and this poor outcome should be stressed in practice guidelines. Therefore, better awareness amongst physicians would prevent some patients from being treated unnecessarily.

A Machine Learning and Cross-Validation Approach for the Discrimination of Vegetation Physiognomic Types Using Satellite Based Multispectral and Multitemporal Data.

PubMed

Sharma, Ram C; Hara, Keitarou; Hirayama, Hidetake

2017-01-01

This paper presents the performance and evaluation of a number of machine learning classifiers for the discrimination between the vegetation physiognomic classes using the satellite based time-series of the surface reflectance data. Discrimination of six vegetation physiognomic classes, Evergreen Coniferous Forest, Evergreen Broadleaf Forest, Deciduous Coniferous Forest, Deciduous Broadleaf Forest, Shrubs, and Herbs, was dealt with in the research. Rich-feature data were prepared from time-series of the satellite data for the discrimination and cross-validation of the vegetation physiognomic types using machine learning approach. A set of machine learning experiments comprised of a number of supervised classifiers with different model parameters was conducted to assess how the discrimination of vegetation physiognomic classes varies with classifiers, input features, and ground truth data size. The performance of each experiment was evaluated by using the 10-fold cross-validation method. Experiment using the Random Forests classifier provided highest overall accuracy (0.81) and kappa coefficient (0.78). However, accuracy metrics did not vary much with experiments. Accuracy metrics were found to be very sensitive to input features and size of ground truth data. The results obtained in the research are expected to be useful for improving the vegetation physiognomic mapping in Japan.

Comparison of four approaches to a rock facies classification problem

USGS Publications Warehouse

Dubois, M.K.; Bohling, Geoffrey C.; Chakrabarti, S.

2007-01-01

In this study, seven classifiers based on four different approaches were tested in a rock facies classification problem: classical parametric methods using Bayes' rule, and non-parametric methods using fuzzy logic, k-nearest neighbor, and feed forward-back propagating artificial neural network. Determining the most effective classifier for geologic facies prediction in wells without cores in the Panoma gas field, in Southwest Kansas, was the objective. Study data include 3600 samples with known rock facies class (from core) with each sample having either four or five measured properties (wire-line log curves), and two derived geologic properties (geologic constraining variables). The sample set was divided into two subsets, one for training and one for testing the ability of the trained classifier to correctly assign classes. Artificial neural networks clearly outperformed all other classifiers and are effective tools for this particular classification problem. Classical parametric models were inadequate due to the nature of the predictor variables (high dimensional and not linearly correlated), and feature space of the classes (overlapping). The other non-parametric methods tested, k-nearest neighbor and fuzzy logic, would need considerable improvement to match the neural network effectiveness, but further work, possibly combining certain aspects of the three non-parametric methods, may be justified. ?? 2006 Elsevier Ltd. All rights reserved.

Comparing the performance of meta-classifiers—a case study on selected imbalanced data sets relevant for prediction of liver toxicity

NASA Astrophysics Data System (ADS)

Jain, Sankalp; Kotsampasakou, Eleni; Ecker, Gerhard F.

2018-05-01

Cheminformatics datasets used in classification problems, especially those related to biological or physicochemical properties, are often imbalanced. This presents a major challenge in development of in silico prediction models, as the traditional machine learning algorithms are known to work best on balanced datasets. The class imbalance introduces a bias in the performance of these algorithms due to their preference towards the majority class. Here, we present a comparison of the performance of seven different meta-classifiers for their ability to handle imbalanced datasets, whereby Random Forest is used as base-classifier. Four different datasets that are directly (cholestasis) or indirectly (via inhibition of organic anion transporting polypeptide 1B1 and 1B3) related to liver toxicity were chosen for this purpose. The imbalance ratio in these datasets ranges between 4:1 and 20:1 for negative and positive classes, respectively. Three different sets of molecular descriptors for model development were used, and their performance was assessed in 10-fold cross-validation and on an independent validation set. Stratified bagging, MetaCost and CostSensitiveClassifier were found to be the best performing among all the methods. While MetaCost and CostSensitiveClassifier provided better sensitivity values, Stratified Bagging resulted in high balanced accuracies.

Comparing the performance of meta-classifiers—a case study on selected imbalanced data sets relevant for prediction of liver toxicity

NASA Astrophysics Data System (ADS)

Jain, Sankalp; Kotsampasakou, Eleni; Ecker, Gerhard F.

2018-04-01

Cheminformatics datasets used in classification problems, especially those related to biological or physicochemical properties, are often imbalanced. This presents a major challenge in development of in silico prediction models, as the traditional machine learning algorithms are known to work best on balanced datasets. The class imbalance introduces a bias in the performance of these algorithms due to their preference towards the majority class. Here, we present a comparison of the performance of seven different meta-classifiers for their ability to handle imbalanced datasets, whereby Random Forest is used as base-classifier. Four different datasets that are directly (cholestasis) or indirectly (via inhibition of organic anion transporting polypeptide 1B1 and 1B3) related to liver toxicity were chosen for this purpose. The imbalance ratio in these datasets ranges between 4:1 and 20:1 for negative and positive classes, respectively. Three different sets of molecular descriptors for model development were used, and their performance was assessed in 10-fold cross-validation and on an independent validation set. Stratified bagging, MetaCost and CostSensitiveClassifier were found to be the best performing among all the methods. While MetaCost and CostSensitiveClassifier provided better sensitivity values, Stratified Bagging resulted in high balanced accuracies.

Crossed Module Bundle Gerbes; Classification, String Group and Differential Geometry

NASA Astrophysics Data System (ADS)

Jurčo, Branislav

We discuss nonabelian bundle gerbes and their differential geometry using simplicial methods. Associated to any crossed module there is a simplicial group NC, the nerve of the 1-category defined by the crossed module and its geometric realization |NC|. Equivalence classes of principal bundles with structure group |NC| are shown to be one-to-one with stable equivalence classes of what we call crossed module gerbes bundle gerbes. We can also associate to a crossed module a 2-category C'. Then there are two equivalent ways how to view classifying spaces of NC-bundles and hence of |NC|-bundles and crossed module bundle gerbes. We can either apply the W-construction to NC or take the nerve of the 2-category C'. We discuss the string group and string structures from this point of view. Also a simplicial principal bundle can be equipped with a simplicial connection and a B-field. It is shown how in the case of a simplicial principal NC-bundle these simplicial objects give the bundle gerbe connection and the bundle gerbe B-field.

Remote sensing of submerged aquatic vegetation in lower Chesapeake Bay - A comparison of Landsat MSS to TM imagery

NASA Technical Reports Server (NTRS)

Ackleson, S. G.; Klemas, V.

1987-01-01

Landsat MSS and TM imagery, obtained simultaneously over Guinea Marsh, VA, as analyzed and compares for its ability to detect submerged aquatic vegetation (SAV). An unsupervised clustering algorithm was applied to each image, where the input classification parameters are defined as functions of apparent sensor noise. Class confidence and accuracy were computed for all water areas by comparing the classified images, pixel-by-pixel, to rasterized SAV distributions derived from color aerial photography. To illustrate the effect of water depth on classification error, areas of depth greater than 1.9 m were masked, and class confidence and accuracy recalculated. A single-scattering radiative-transfer model is used to illustrate how percent canopy cover and water depth affect the volume reflectance from a water column containing SAV. For a submerged canopy that is morphologically and optically similar to Zostera marina inhabiting Lower Chesapeake Bay, dense canopies may be isolated by masking optically deep water. For less dense canopies, the effect of increasing water depth is to increase the apparent percent crown cover, which may result in classification error.

A hybrid three-class brain-computer interface system utilizing SSSEPs and transient ERPs

NASA Astrophysics Data System (ADS)

Breitwieser, Christian; Pokorny, Christoph; Müller-Putz, Gernot R.

2016-12-01

Objective. This paper investigates the fusion of steady-state somatosensory evoked potentials (SSSEPs) and transient event-related potentials (tERPs), evoked through tactile simulation on the left and right-hand fingertips, in a three-class EEG based hybrid brain-computer interface. It was hypothesized, that fusing the input signals leads to higher classification rates than classifying tERP and SSSEP individually. Approach. Fourteen subjects participated in the studies, consisting of a screening paradigm to determine person dependent resonance-like frequencies and a subsequent online paradigm. The whole setup of the BCI system was based on open interfaces, following suggestions for a common implementation platform. During the online experiment, subjects were instructed to focus their attention on the stimulated fingertips as indicated by a visual cue. The recorded data were classified during runtime using a multi-class shrinkage LDA classifier and the outputs were fused together applying a posterior probability based fusion. Data were further analyzed offline, involving a combined classification of SSSEP and tERP features as a second fusion principle. The final results were tested for statistical significance applying a repeated measures ANOVA. Main results. A significant classification increase was achieved when fusing the results with a combined classification compared to performing an individual classification. Furthermore, the SSSEP classifier was significantly better in detecting a non-control state, whereas the tERP classifier was significantly better in detecting control states. Subjects who had a higher relative band power increase during the screening session also achieved significantly higher classification results than subjects with lower relative band power increase. Significance. It could be shown that utilizing SSSEP and tERP for hBCIs increases the classification accuracy and also that tERP and SSSEP are not classifying control- and non-control states with the same level of accuracy.

Semantic modeling of the structural and process entities during plastic deformation of crystals and rocks

NASA Astrophysics Data System (ADS)

Babaie, Hassan; Davarpanah, Armita

2016-04-01

We are semantically modeling the structural and dynamic process components of the plastic deformation of minerals and rocks in the Plastic Deformation Ontology (PDO). Applying the Ontology of Physics in Biology, the PDO classifies the spatial entities that participate in the diverse processes of plastic deformation into the Physical_Plastic_Deformation_Entity and Nonphysical_Plastic_Deformation_Entity classes. The Material_Physical_Plastic_Deformation_Entity class includes things such as microstructures, lattice defects, atoms, liquid, and grain boundaries, and the Immaterial_Physical_Plastic_Deformation_Entity class includes vacancies in crystals and voids along mineral grain boundaries. The objects under the many subclasses of these classes (e.g., crystal, lattice defect, layering) have spatial parts that are related to each other through taxonomic (e.g., Line_Defect isA Lattice_Defect), structural (mereological, e.g., Twin_Plane partOf Twin), spatial-topological (e.g., Vacancy adjacentTo Atom, Fluid locatedAlong Grain_Boundary), and domain specific (e.g., displaces, Fluid crystallizes Dissolved_Ion, Void existsAlong Grain_Boundary) relationships. The dynamic aspect of the plastic deformation is modeled under the dynamical Process_Entity class that subsumes classes such as Recrystallization and Pressure_Solution that define the flow of energy amongst the physical entities. The values of the dynamical state properties of the physical entities (e.g., Chemical_Potential, Temperature, Particle_Velocity) change while they take part in the deformational processes such as Diffusion and Dislocation_Glide. The process entities have temporal parts (phases) that are related to each other through temporal relations such as precedes, isSubprocessOf, and overlaps. The properties of the physical entities, defined under the Physical_Property class, change as they participate in the plastic deformational processes. The properties are categorized into dynamical, constitutive, spatial, temporal, statistical, and thermodynamical. The dynamical properties, categorized under the Dynamical_Rate_Property and Dynamical_State_Property classes, subsume different classes of properties (e.g., Fluid_Flow_Rate, Temperature, Chemical_Potential, Displacement, Electrical_Charge) based on the physical domain (e.g., fluid, heat, chemical, solid, electrical). The properties are related to the objects under the Physical_Entity class through diverse object type (e.g., physicalPropertyOf) and data type (e.g., Fluid_Pressure unit 'MPa') properties. The changes of the dynamical properties of the physical entities, described by the empirical laws (equations) modeled by experimental structural geologists, are modeled through the Physical_Property_Dependency class that subsumes the more specialized constitutive, kinetic, and thermodynamic expressions of the relationships among the dynamic properties. Annotation based on the PDO will make it possible to integrate and reuse experimental plastic deformation data, knowledge, and simulation models, and conduct semantic-based search of the source data originating from different rock testing laboratories.

Subject-specific and pose-oriented facial features for face recognition across poses.

PubMed

Lee, Ping-Han; Hsu, Gee-Sern; Wang, Yun-Wen; Hung, Yi-Ping

2012-10-01

Most face recognition scenarios assume that frontal faces or mug shots are available for enrollment to the database, faces of other poses are collected in the probe set. Given a face from the probe set, one needs to determine whether a match in the database exists. This is under the assumption that in forensic applications, most suspects have their mug shots available in the database, and face recognition aims at recognizing the suspects when their faces of various poses are captured by a surveillance camera. This paper considers a different scenario: given a face with multiple poses available, which may or may not include a mug shot, develop a method to recognize the face with poses different from those captured. That is, given two disjoint sets of poses of a face, one for enrollment and the other for recognition, this paper reports a method best for handling such cases. The proposed method includes feature extraction and classification. For feature extraction, we first cluster the poses of each subject's face in the enrollment set into a few pose classes and then decompose the appearance of the face in each pose class using Embedded Hidden Markov Model, which allows us to define a set of subject-specific and pose-priented (SSPO) facial components for each subject. For classification, an Adaboost weighting scheme is used to fuse the component classifiers with SSPO component features. The proposed method is proven to outperform other approaches, including a component-based classifier with local facial features cropped manually, in an extensive performance evaluation study.

The Saudi Guidelines for the Diagnosis and Management of COPD

PubMed Central

Khan, Javed H.; Lababidi, Hani M. S.; Al-Moamary, Mohamed S.; Zeitouni, Mohammed O.; AL-Jahdali, Hamdan H.; Al-Amoudi, Omar S.; Wali, Siraj O.; Idrees, Majdy M.; Al-Shimemri, Abdullah A.; Al Ghobain, Mohammed O.; Alorainy, Hassan S.; Al-Hajjaj, Mohamed S.

2014-01-01

The Saudi Thoracic Society (STS) launched the Saudi Initiative for Chronic Airway Diseases (SICAD) to develop a guideline for the diagnosis and management of chronic obstructive pulmonary disease (COPD). This guideline is primarily aimed for internists and general practitioners. Though there is scanty epidemiological data related to COPD, the SICAD panel believes that COPD prevalence is increasing in Saudi Arabia due to increasing prevalence of tobacco smoking among men and women. To overcome the issue of underutilization of spirometry for diagnosing COPD, handheld spirometry is recommended to screen individuals at risk for COPD. A unique feature about this guideline is the simplified practical approach to classify COPD into three classes based on the symptoms as per COPD Assessment Test (CAT) and the risk of exacerbations and hospitalization. Those patients with low risk of exacerbation (<2 in the past year) can be classified as either Class I when they have less symptoms (CAT < 10) or Class II when they have more symptoms (CAT ≥ 10). High-risk COPD patients, as manifested with ≥2 exacerbation or hospitalization in the past year irrespective of the baseline symptoms, are classified as Class III. Class I and II patients require bronchodilators for symptom relief, while Class III patients are recommended to use medications that reduce the risks of exacerbations. The guideline recommends screening for co-morbidities and suggests a comprehensive management approach including pulmonary rehabilitation for those with a CAT score ≥10. The article also discusses the diagnosis and management of acute exacerbations in COPD. PMID:24791168

Determining Effects of Non-synonymous SNPs on Protein-Protein Interactions using Supervised and Semi-supervised Learning

PubMed Central

Zhao, Nan; Han, Jing Ginger; Shyu, Chi-Ren; Korkin, Dmitry

2014-01-01

Single nucleotide polymorphisms (SNPs) are among the most common types of genetic variation in complex genetic disorders. A growing number of studies link the functional role of SNPs with the networks and pathways mediated by the disease-associated genes. For example, many non-synonymous missense SNPs (nsSNPs) have been found near or inside the protein-protein interaction (PPI) interfaces. Determining whether such nsSNP will disrupt or preserve a PPI is a challenging task to address, both experimentally and computationally. Here, we present this task as three related classification problems, and develop a new computational method, called the SNP-IN tool (non-synonymous SNP INteraction effect predictor). Our method predicts the effects of nsSNPs on PPIs, given the interaction's structure. It leverages supervised and semi-supervised feature-based classifiers, including our new Random Forest self-learning protocol. The classifiers are trained based on a dataset of comprehensive mutagenesis studies for 151 PPI complexes, with experimentally determined binding affinities of the mutant and wild-type interactions. Three classification problems were considered: (1) a 2-class problem (strengthening/weakening PPI mutations), (2) another 2-class problem (mutations that disrupt/preserve a PPI), and (3) a 3-class classification (detrimental/neutral/beneficial mutation effects). In total, 11 different supervised and semi-supervised classifiers were trained and assessed resulting in a promising performance, with the weighted f-measure ranging from 0.87 for Problem 1 to 0.70 for the most challenging Problem 3. By integrating prediction results of the 2-class classifiers into the 3-class classifier, we further improved its performance for Problem 3. To demonstrate the utility of SNP-IN tool, it was applied to study the nsSNP-induced rewiring of two disease-centered networks. The accurate and balanced performance of SNP-IN tool makes it readily available to study the rewiring of large-scale protein-protein interaction networks, and can be useful for functional annotation of disease-associated SNPs. SNIP-IN tool is freely accessible as a web-server at http://korkinlab.org/snpintool/. PMID:24784581

Taxonomy of asteroids. [according to polarimetric, spectrophotometric, radiometric, and UBV photometric data

NASA Technical Reports Server (NTRS)

Bowell, E.; Chapman, C. R.; Gradie, J. C.; Zellner, B.; Morrison, D.

1978-01-01

A taxonomic system for asteroids is discussed which is based on seven directly observable parameters from polarimetry, spectrophotometry, radiometry, and UBV photometry. The classification scheme is entirely empirical and independent of specific mineralogical interpretations. Five broad classes (designated C, S, M, E, and R), as well as an 'unclassifiable' designation, are defined on the basis of observational data for 523 asteroids. Computer-generated type classifications and derived diameters are given for the 523 asteroids, and the application of the classification procedure is illustrated. Of the 523 asteroids classified, 190 are identified as C objects, 141 as S type, 13 as type M, three as type E, three as type R, 55 as unclassifiable, and 118 as ambiguous. The present taxonomic system is compared with several other asteroid classification systems.

Proposal of a Classification System for the Assessment and Treatment of Prominent Ear Deformity.

PubMed

Lee, Youngdae; Kim, Young Seok; Lee, Won Jai; Rha, Dong Kyun; Kim, Jiye

2018-06-01

Prominent ear is the most common external ear deformity. To comprehensively treat prominent ear deformity, adequate comprehension of its pathophysiology is crucial. In this article, we analyze cases of prominent ear and suggest a simple classification system and treatment algorithm according to pathophysiology. We retrospectively reviewed a total of 205 Northeast Asian patients' clinical data who underwent an operation for prominent ear deformity. Follow-up assessments were conducted 3, 6, and 12 months after surgery. Prominent ear deformities were classified by diagnostic checkpoints. Class I (simple prominent ear) includes prominent ear that developed with the absence of the antihelix without conchal hypertrophy. Class II (mixed-type prominent ear) is defined as having not only a flat antihelix, but also conchal excess. Class III (conchal-type prominent ear) has an enlarged conchal bowl with a well-developed antihelix. Among the three types of prominent ear, class I was most frequent (162 patients, 81.6%). Class II was observed in 28 patients (13.6%) and class III in 10 patients (4.8%). We used the scaphomastoid suture method for correction of antihelical effacement, the anterior approach conchal resection for correction of conchal hypertrophy, and Bauer's squid incision for lobule prominence. The complication rate was 9.2% including early hematoma, hypersensitivity, and suture extrusion. Unfavorable results occurred in 4% including partial recurrence, overcorrection, and undercorrection. To reduce unfavorable results and avoid recurrence, we propose the use of a classification and treatment algorithm in preoperative evaluation of prominent ear. This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

46 CFR 110.10-1 - Incorporation by reference.

Code of Federal Regulations, 2014 CFR

2014-10-01

... Classing Mobile Offshore Drilling Units, Part 4 Machinery and Systems, 2001 (“ABS MODU Rules”), IBR... Hazardous (Classified) Locations: Type of Protection—Encapsulation “m”, approved July 31, 2009 (“ANSI/ISA... Practice for Classification of Locations for Electrical Installations at Petroleum Facilities Classified as...

Transcriptional and electrophysiological maturation of neocortical fastspiking GABAergic interneurons

PubMed Central

Okaty, Benjamin W; Miller, Mark N; Sugino, Ken; Hempel, Chris M; Nelson, Sacha B

2009-01-01

Fast-spiking (FS) interneurons are important elements of neocortical circuitry that constitute the primary source of synaptic inhibition in adult cortex and impart temporal organization on ongoing cortical activity. The highly specialized intrinsic membrane and firing properties that allow cortical FS interneurons to perform these functions are due to equally specialized gene expression, which is ultimately coordinated by cell-type-specific transcriptional regulation. While embryonic transcriptional events govern the initial steps of cell-type specification in most cortical interneurons, including FS cells, the electrophysiological properties that distinguish adult cortical cell types emerge relatively late in postnatal development, and the transcriptional events that drive this maturational process are not known. To address this, we used mouse whole-genome microarrays and whole-cell patch clamp to characterize the transcriptional and electrophysiological maturation of cortical FS interneurons between postnatal day 7 (P7) and P40. We found that the intrinsic and synaptic physiology of FS cells undergoes profound regulation over the first four postnatal weeks, and that these changes are correlated with largely monotonic but bidirectional transcriptional regulation of thousands of genes belonging to multiple functional classes. Using our microarray screen as a guide, we discovered that upregulation of 2-pore K+ leak channels between P10 and P25 contributes to one of the major differences between the intrinsic membrane properties of immature and adult FS cells, and found a number of other candidate genes that likely confer cell-type specificity on mature FS cells. PMID:19474331

Effect of clebopride, antidopaminergic gastrointestinal prokinetics, on cardiac repolarization.

PubMed

Kim, Ki-Suk; Shin, Won-Ho; Park, Sang-joon; Kim, Eun-Joo

2007-01-01

The inhibition of the potassium current I(Kr) and QT prolongation has been known to be associated with drug-induced torsades de pointes arrhythmias (TdP) and sudden cardiac death. In this study, the authors investigated the cardiac electrophysiological effects of clebopride, a class of antidopaminergic gastrointestinal prokinetic, that has been reported to prolong the QT interval by using the conventional microelectrode recording techniques in isolated rabbit Purkinje fiber and whole-cell patch clamp techniques in human ether-à-go-go-related gene (hERG)-stably transfected Chinese hamster ovarian (CHO) cells. Clebopride at 10 microM significantly decreased the Vmax of phase 0 depolarization (p < .05) and significantly prolonged the action potential duration at 90% repolarization (APD90) (p < .01), whereas the action potential duration at 50% repolarization (APD50) was not prolonged. For hERG potassium channel currents, the IC50 value was 0.62 +/- 0.30 microM. Clebopride was found to have no effect on sodium channel currents. When these results were compared with Cmax (1.02 nM) of clinical dosage (1 mg, [p.o.]), it can be suggested that clebopride is safe at the clinical dosage of 1 mg from the electrophysiological aspect. These findings indicate that clebopride, an antidopaminergic gastrointestinal prokinetic drug, may provide a sufficient "safety factor" in terms of the electrophysiological threshold concentration. But, in a supratherapeutic concentration that might possibly be encountered during overdose or impaired metabolism, clebopride may have torsadogenic potency.

Joint deconvolution and classification with applications to passive acoustic underwater multipath.

PubMed

Anderson, Hyrum S; Gupta, Maya R

2008-11-01

This paper addresses the problem of classifying signals that have been corrupted by noise and unknown linear time-invariant (LTI) filtering such as multipath, given labeled uncorrupted training signals. A maximum a posteriori approach to the deconvolution and classification is considered, which produces estimates of the desired signal, the unknown channel, and the class label. For cases in which only a class label is needed, the classification accuracy can be improved by not committing to an estimate of the channel or signal. A variant of the quadratic discriminant analysis (QDA) classifier is proposed that probabilistically accounts for the unknown LTI filtering, and which avoids deconvolution. The proposed QDA classifier can work either directly on the signal or on features whose transformation by LTI filtering can be analyzed; as an example a classifier for subband-power features is derived. Results on simulated data and real Bowhead whale vocalizations show that jointly considering deconvolution with classification can dramatically improve classification performance over traditional methods over a range of signal-to-noise ratios.

Linear Classifier with Reject Option for the Detection of Vocal Fold Paralysis and Vocal Fold Edema

NASA Astrophysics Data System (ADS)

Kotropoulos, Constantine; Arce, Gonzalo R.

2009-12-01

Two distinct two-class pattern recognition problems are studied, namely, the detection of male subjects who are diagnosed with vocal fold paralysis against male subjects who are diagnosed as normal and the detection of female subjects who are suffering from vocal fold edema against female subjects who do not suffer from any voice pathology. To do so, utterances of the sustained vowel "ah" are employed from the Massachusetts Eye and Ear Infirmary database of disordered speech. Linear prediction coefficients extracted from the aforementioned utterances are used as features. The receiver operating characteristic curve of the linear classifier, that stems from the Bayes classifier when Gaussian class conditional probability density functions with equal covariance matrices are assumed, is derived. The optimal operating point of the linear classifier is specified with and without reject option. First results using utterances of the "rainbow passage" are also reported for completeness. The reject option is shown to yield statistically significant improvements in the accuracy of detecting the voice pathologies under study.

29 CFR 1910.307 - Hazardous (classified) locations.

Code of Federal Regulations, 2013 CFR

2013-07-01

...; conductor insulation, flexible cords, sealing and drainage, transformers, capacitors, switches, circuit... following are acceptable protection techniques for electric and electronic equipment in hazardous...) Nonincendive circuit. This protection technique is permitted for equipment in Class I, Division 2; Class II...

29 CFR 1910.307 - Hazardous (classified) locations.

Code of Federal Regulations, 2012 CFR

2012-07-01

...; conductor insulation, flexible cords, sealing and drainage, transformers, capacitors, switches, circuit... following are acceptable protection techniques for electric and electronic equipment in hazardous...) Nonincendive circuit. This protection technique is permitted for equipment in Class I, Division 2; Class II...

29 CFR 1910.307 - Hazardous (classified) locations.

Code of Federal Regulations, 2014 CFR

2014-07-01

...; conductor insulation, flexible cords, sealing and drainage, transformers, capacitors, switches, circuit... following are acceptable protection techniques for electric and electronic equipment in hazardous...) Nonincendive circuit. This protection technique is permitted for equipment in Class I, Division 2; Class II...

A simple plug-in bagging ensemble based on threshold-moving for classifying binary and multiclass imbalanced data.

PubMed

Collell, Guillem; Prelec, Drazen; Patil, Kaustubh R

2018-01-31

Class imbalance presents a major hurdle in the application of classification methods. A commonly taken approach is to learn ensembles of classifiers using rebalanced data. Examples include bootstrap averaging (bagging) combined with either undersampling or oversampling of the minority class examples. However, rebalancing methods entail asymmetric changes to the examples of different classes, which in turn can introduce their own biases. Furthermore, these methods often require specifying the performance measure of interest a priori, i.e., before learning. An alternative is to employ the threshold moving technique, which applies a threshold to the continuous output of a model, offering the possibility to adapt to a performance measure a posteriori , i.e., a plug-in method. Surprisingly, little attention has been paid to this combination of a bagging ensemble and threshold-moving. In this paper, we study this combination and demonstrate its competitiveness. Contrary to the other resampling methods, we preserve the natural class distribution of the data resulting in well-calibrated posterior probabilities. Additionally, we extend the proposed method to handle multiclass data. We validated our method on binary and multiclass benchmark data sets by using both, decision trees and neural networks as base classifiers. We perform analyses that provide insights into the proposed method.

A SPITZER VIEW OF STAR FORMATION IN THE CYGNUS X NORTH COMPLEX

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

Beerer, I. M.; Koenig, X. P.; Hora, J. L.

2010-09-01

We present new images and photometry of the massive star-forming complex Cygnus X obtained with the Infrared Array Camera (IRAC) and the Multiband Imaging Photometer for Spitzer (MIPS) on board the Spitzer Space Telescope. A combination of IRAC, MIPS, UKIRT Deep Infrared Sky Survey, and Two Micron All Sky Survey data are used to identify and classify young stellar objects (YSOs). Of the 8231 sources detected exhibiting infrared excess in Cygnus X North, 670 are classified as class I and 7249 are classified as class II. Using spectra from the FAST Spectrograph at the Fred L. Whipple Observatory and Hectospecmore » on the MMT, we spectrally typed 536 sources in the Cygnus X complex to identify the massive stars. We find that YSOs tend to be grouped in the neighborhoods of massive B stars (spectral types B0 to B9). We present a minimal spanning tree analysis of clusters in two regions in Cygnus X North. The fraction of infrared excess sources that belong to clusters with {>=}10 members is found to be 50%-70%. Most class II objects lie in dense clusters within blown out H II regions, while class I sources tend to reside in more filamentary structures along the bright-rimmed clouds, indicating possible triggered star formation.« less

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

PubMed

Arif, Muhammad

2012-06-01

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

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.

Heart beats in the cloud: distributed analysis of electrophysiological ‘Big Data’ using cloud computing for epilepsy clinical research

PubMed Central

Sahoo, Satya S; Jayapandian, Catherine; Garg, Gaurav; Kaffashi, Farhad; Chung, Stephanie; Bozorgi, Alireza; Chen, Chien-Hun; Loparo, Kenneth; Lhatoo, Samden D; Zhang, Guo-Qiang

2014-01-01

Objective The rapidly growing volume of multimodal electrophysiological signal data is playing a critical role in patient care and clinical research across multiple disease domains, such as epilepsy and sleep medicine. To facilitate secondary use of these data, there is an urgent need to develop novel algorithms and informatics approaches using new cloud computing technologies as well as ontologies for collaborative multicenter studies. Materials and methods We present the Cloudwave platform, which (a) defines parallelized algorithms for computing cardiac measures using the MapReduce parallel programming framework, (b) supports real-time interaction with large volumes of electrophysiological signals, and (c) features signal visualization and querying functionalities using an ontology-driven web-based interface. Cloudwave is currently used in the multicenter National Institute of Neurological Diseases and Stroke (NINDS)-funded Prevention and Risk Identification of SUDEP (sudden unexplained death in epilepsy) Mortality (PRISM) project to identify risk factors for sudden death in epilepsy. Results Comparative evaluations of Cloudwave with traditional desktop approaches to compute cardiac measures (eg, QRS complexes, RR intervals, and instantaneous heart rate) on epilepsy patient data show one order of magnitude improvement for single-channel ECG data and 20 times improvement for four-channel ECG data. This enables Cloudwave to support real-time user interaction with signal data, which is semantically annotated with a novel epilepsy and seizure ontology. Discussion Data privacy is a critical issue in using cloud infrastructure, and cloud platforms, such as Amazon Web Services, offer features to support Health Insurance Portability and Accountability Act standards. Conclusion The Cloudwave platform is a new approach to leverage of large-scale electrophysiological data for advancing multicenter clinical research. PMID:24326538

Heart beats in the cloud: distributed analysis of electrophysiological 'Big Data' using cloud computing for epilepsy clinical research.

PubMed

Sahoo, Satya S; Jayapandian, Catherine; Garg, Gaurav; Kaffashi, Farhad; Chung, Stephanie; Bozorgi, Alireza; Chen, Chien-Hun; Loparo, Kenneth; Lhatoo, Samden D; Zhang, Guo-Qiang

2014-01-01

The rapidly growing volume of multimodal electrophysiological signal data is playing a critical role in patient care and clinical research across multiple disease domains, such as epilepsy and sleep medicine. To facilitate secondary use of these data, there is an urgent need to develop novel algorithms and informatics approaches using new cloud computing technologies as well as ontologies for collaborative multicenter studies. We present the Cloudwave platform, which (a) defines parallelized algorithms for computing cardiac measures using the MapReduce parallel programming framework, (b) supports real-time interaction with large volumes of electrophysiological signals, and (c) features signal visualization and querying functionalities using an ontology-driven web-based interface. Cloudwave is currently used in the multicenter National Institute of Neurological Diseases and Stroke (NINDS)-funded Prevention and Risk Identification of SUDEP (sudden unexplained death in epilepsy) Mortality (PRISM) project to identify risk factors for sudden death in epilepsy. Comparative evaluations of Cloudwave with traditional desktop approaches to compute cardiac measures (eg, QRS complexes, RR intervals, and instantaneous heart rate) on epilepsy patient data show one order of magnitude improvement for single-channel ECG data and 20 times improvement for four-channel ECG data. This enables Cloudwave to support real-time user interaction with signal data, which is semantically annotated with a novel epilepsy and seizure ontology. Data privacy is a critical issue in using cloud infrastructure, and cloud platforms, such as Amazon Web Services, offer features to support Health Insurance Portability and Accountability Act standards. The Cloudwave platform is a new approach to leverage of large-scale electrophysiological data for advancing multicenter clinical research.

40 CFR 600.315-08 - Classes of comparable automobiles.

Code of Federal Regulations, 2012 CFR

2012-07-01

... Administrator will classify passenger automobiles by car line into one of the following classes based on... as provided in paragraph (a)(3) of this section. (i) Two seaters. A car line shall be classed as “Two Seater” if the majority of the vehicles in that car line have no more than two designated seating...

40 CFR 600.315-08 - Classes of comparable automobiles.

Code of Federal Regulations, 2013 CFR

2013-07-01

... Administrator will classify passenger automobiles by car line into one of the following classes based on... as provided in paragraph (a)(3) of this section. (i) Two seaters. A car line shall be classed as “Two Seater” if the majority of the vehicles in that car line have no more than two designated seating...

40 CFR 600.315-08 - Classes of comparable automobiles.

Code of Federal Regulations, 2014 CFR

2014-07-01

... Administrator will classify passenger automobiles by car line into one of the following classes based on... as provided in paragraph (a)(3) of this section. (i) Two seaters. A car line shall be classed as “Two Seater” if the majority of the vehicles in that car line have no more than two designated seating...

River recreation experience opportunities in two recreation opportunity spectrum (ROS) classes

Treesearch

Duane C. Wollmuth; John H. Schomaker; Lawrence C. Merriam

1985-01-01

The Recreation Opportunity Spectrum (ROS) system is used by the USDA Forest Service and USDI Bureau of Land Management for inventorying, classifying, and managing wildlands for recreation. Different ROS classes from the Colorado and Arkansas Rivers in Colorado were compared, using visitor survey data collected in 1979 and 1981, to see if the different classes offered...

40 CFR 144.80 - What is a Class V injection well?

Code of Federal Regulations, 2013 CFR

2013-07-01

... process; (2) In situ production of uranium or other metals; this category includes only in situ production... described in § 144.6, injection wells are classified as follows: (a) Class I. (1) Wells used by generators...) Class II. Wells which inject fluids: (1) Which are brought to the surface in connection with natural gas...

40 CFR 144.80 - What is a Class V injection well?

Code of Federal Regulations, 2014 CFR

2014-07-01

... process; (2) In situ production of uranium or other metals; this category includes only in situ production... described in § 144.6, injection wells are classified as follows: (a) Class I. (1) Wells used by generators...) Class II. Wells which inject fluids: (1) Which are brought to the surface in connection with natural gas...

40 CFR 144.80 - What is a Class V injection well?

Code of Federal Regulations, 2012 CFR

2012-07-01

... process; (2) In situ production of uranium or other metals; this category includes only in situ production... described in § 144.6, injection wells are classified as follows: (a) Class I. (1) Wells used by generators...) Class II. Wells which inject fluids: (1) Which are brought to the surface in connection with natural gas...

40 CFR 144.80 - What is a Class V injection well?

Code of Federal Regulations, 2011 CFR

2011-07-01

... process; (2) In situ production of uranium or other metals; this category includes only in situ production... described in § 144.6, injection wells are classified as follows: (a) Class I. (1) Wells used by generators...) Class II. Wells which inject fluids: (1) Which are brought to the surface in connection with natural gas...

76 FR 20840 - Medical Devices; General and Plastic Surgery Devices; Classification of the Low Level Laser...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-04-14

... into class II (special controls). The special control(s) that will apply to the device is entitled ``Class II Special Controls Guidance Document: Low Level Laser System for Aesthetic Use.'' The Agency is classifying the device into class II (special controls) in order to provide a reasonable assurance of safety...

Adaptive Bayes classifiers for remotely sensed data

NASA Technical Reports Server (NTRS)

Raulston, H. S.; Pace, M. O.; Gonzalez, R. C.

1975-01-01

An algorithm is developed for a learning, adaptive, statistical pattern classifier for remotely sensed data. The estimation procedure consists of two steps: (1) an optimal stochastic approximation of the parameters of interest, and (2) a projection of the parameters in time and space. The results reported are for Gaussian data in which the mean vector of each class may vary with time or position after the classifier is trained.

TACOA – Taxonomic classification of environmental genomic fragments using a kernelized nearest neighbor approach

PubMed Central

Diaz, Naryttza N; Krause, Lutz; Goesmann, Alexander; Niehaus, Karsten; Nattkemper, Tim W

2009-01-01

Background Metagenomics, or the sequencing and analysis of collective genomes (metagenomes) of microorganisms isolated from an environment, promises direct access to the "unculturable majority". This emerging field offers the potential to lay solid basis on our understanding of the entire living world. However, the taxonomic classification is an essential task in the analysis of metagenomics data sets that it is still far from being solved. We present a novel strategy to predict the taxonomic origin of environmental genomic fragments. The proposed classifier combines the idea of the k-nearest neighbor with strategies from kernel-based learning. Results Our novel strategy was extensively evaluated using the leave-one-out cross validation strategy on fragments of variable length (800 bp – 50 Kbp) from 373 completely sequenced genomes. TACOA is able to classify genomic fragments of length 800 bp and 1 Kbp with high accuracy until rank class. For longer fragments ≥ 3 Kbp accurate predictions are made at even deeper taxonomic ranks (order and genus). Remarkably, TACOA also produces reliable results when the taxonomic origin of a fragment is not represented in the reference set, thus classifying such fragments to its known broader taxonomic class or simply as "unknown". We compared the classification accuracy of TACOA with the latest intrinsic classifier PhyloPythia using 63 recently published complete genomes. For fragments of length 800 bp and 1 Kbp the overall accuracy of TACOA is higher than that obtained by PhyloPythia at all taxonomic ranks. For all fragment lengths, both methods achieved comparable high specificity results up to rank class and low false negative rates are also obtained. Conclusion An accurate multi-class taxonomic classifier was developed for environmental genomic fragments. TACOA can predict with high reliability the taxonomic origin of genomic fragments as short as 800 bp. The proposed method is transparent, fast, accurate and the reference set can be easily updated as newly sequenced genomes become available. Moreover, the method demonstrated to be competitive when compared to the most current classifier PhyloPythia and has the advantage that it can be locally installed and the reference set can be kept up-to-date. PMID:19210774

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

Authentication of bee pollen grains in bright-field microscopy by combining one-class classification techniques and image processing.

PubMed

Chica, Manuel

2012-11-01

A novel method for authenticating pollen grains in bright-field microscopic images is presented in this work. The usage of this new method is clear in many application fields such as bee-keeping sector, where laboratory experts need to identify fraudulent bee pollen samples against local known pollen types. Our system is based on image processing and one-class classification to reject unknown pollen grain objects. The latter classification technique allows us to tackle the major difficulty of the problem, the existence of many possible fraudulent pollen types, and the impossibility of modeling all of them. Different one-class classification paradigms are compared to study the most suitable technique for solving the problem. In addition, feature selection algorithms are applied to reduce the complexity and increase the accuracy of the models. For each local pollen type, a one-class classifier is trained and aggregated into a multiclassifier model. This multiclassification scheme combines the output of all the one-class classifiers in a unique final response. The proposed method is validated by authenticating pollen grains belonging to different Spanish bee pollen types. The overall accuracy of the system on classifying fraudulent microscopic pollen grain objects is 92.3%. The system is able to rapidly reject pollen grains, which belong to nonlocal pollen types, reducing the laboratory work and effort. The number of possible applications of this authentication method in the microscopy research field is unlimited. Copyright © 2012 Wiley Periodicals, Inc.

Classes of Discourse, Acts of Discourse, Writers, and Readers.

ERIC Educational Resources Information Center

Larson, Richard L.

1992-01-01

Argues that a prevalent mistake made by teachers preparing writing curricula and assignments is dividing writing into classes or modes. Suggests alternatives to classifying writing. Envisions writing as a discourse act and assignments as performance of such acts. (HB)

What's at Stake in the Lives of People with Intellectual Disability? Part II: Recommendations for Naming, Defining, Diagnosing, Classifying, and Planning Supports

ERIC Educational Resources Information Center

Luckasson, Ruth; Schalock, Robert L.

2013-01-01

This article focuses on recommendations for naming, defining, diagnosing, classifying, and planning supports for individuals with intellectual disability (ID). The article provides an overview of the essential questions addressed by the respective functions and provides a series of specific recommendations that address the high stakes involved for…

What's at Stake in the Lives of People with Intellectual Disability? Part I: The Power of Naming, Defining, Diagnosing, Classifying, and Planning Supports

ERIC Educational Resources Information Center

Schalock, Robert L.; Luckasson, Ruth

2013-01-01

This article focuses on the power of naming, defining, diagnosing, classifying, and planning supports for people with intellectual disability. The article summarizes current thinking regarding these five functions, states the essential question addressed by the respective function, and provides an overview of the high stakes involved for people…

A collection of homework problems about the application of electricity and magnetism to medicine and biology

NASA Astrophysics Data System (ADS)

Roth, Bradley J.; Hobbie, Russell K.

2014-05-01

This article contains a collection of homework problems to help students learn how concepts from electricity and magnetism can be applied to topics in medicine and biology. The problems are at a level typical of an undergraduate electricity and magnetism class, covering topics such as nerve electrophysiology, transcranial magnetic stimulation, and magnetic resonance imaging. The goal of these problems is to train biology and medical students to use quantitative methods, and also to introduce physics and engineering students to biological phenomena.

The Neurobiology of Opiate Motivation

PubMed Central

Ting-A-Kee, Ryan; van der Kooy, Derek

2012-01-01

Opiates are a highly addictive class of drugs that have been reported to possess both dopamine-dependent and dopamine-independent rewarding properties. The search for how, if at all, these distinct mechanisms of motivation are related is of great interest in drug addiction research. Recent electrophysiological, molecular, and behavioral work has greatly improved our understanding of this process. In particular, the signaling properties of GABAA receptors located on GABA neurons in the ventral tegmental area (VTA) appear to be crucial to understanding the interplay between dopamine-dependent and dopamine-independent mechanisms of opiate motivation. PMID:23028134

Discrimination of malignant lymphomas and leukemia using Radon transform based-higher order spectra

NASA Astrophysics Data System (ADS)

Luo, Yi; Celenk, Mehmet; Bejai, Prashanth

2006-03-01

A new algorithm that can be used to automatically recognize and classify malignant lymphomas and leukemia is proposed in this paper. The algorithm utilizes the morphological watersheds to obtain boundaries of cells from cell images and isolate them from the surrounding background. The areas of cells are extracted from cell images after background subtraction. The Radon transform and higher-order spectra (HOS) analysis are utilized as an image processing tool to generate class feature vectors of different type cells and to extract testing cells' feature vectors. The testing cells' feature vectors are then compared with the known class feature vectors for a possible match by computing the Euclidean distances. The cell in question is classified as belonging to one of the existing cell classes in the least Euclidean distance sense.

An Assessment of Worldview-2 Imagery for the Classification Of a Mixed Deciduous Forest

NASA Astrophysics Data System (ADS)

Carter, Nahid

Remote sensing provides a variety of methods for classifying forest communities and can be a valuable tool for the impact assessment of invasive species. The emerald ash borer (Agrilus planipennis) infestation of ash trees (Fraxinus) in the United States has resulted in the mortality of large stands of ash throughout the Northeast. This study assessed the suitability of multi-temporal Worldview-2 multispectral satellite imagery for classifying a mixed deciduous forest in Upstate New York. Training sites were collected using a Global Positioning System (GPS) receiver, with each training site consisting of a single tree of a corresponding class. Six classes were collected; Ash, Maple, Oak, Beech, Evergreen, and Other. Three different classifications were investigated on four data sets. A six class classification (6C), a two class classification consisting of ash and all other classes combined (2C), and a merging of the ash and maple classes for a five class classification (5C). The four data sets included Worldview-2 multispectral data collection from June 2010 (J-WV2) and September 2010 (S-WV2), a layer stacked data set using J-WV2 and S-WV2 (LS-WV2), and a reduced data set (RD-WV2). RD-WV2 was created using a statistical analysis of the processed and unprocessed imagery. Statistical analysis was used to reduce the dimensionality of the data and identify key bands to create a fourth data set (RD-WV2). Overall accuracy varied considerably depending upon the classification type, but results indicated that ash was confused with maple in a majority of the classifications. Ash was most accurately identified using the 2C classification and RD-WV2 data set (81.48%). A combination of the ash and maple classes yielded an accuracy of 89.41%. Future work should focus on separating the ash and maple classifiers by using data sources such as hyperspectral imagery, LiDAR, or extensive forest surveys.

Non-Mutually Exclusive Deep Neural Network Classifier for Combined Modes of Bearing Fault Diagnosis

PubMed Central

Kim, Jong-Myon

2018-01-01

The simultaneous occurrence of various types of defects in bearings makes their diagnosis more challenging owing to the resultant complexity of the constituent parts of the acoustic emission (AE) signals. To address this issue, a new approach is proposed in this paper for the detection of multiple combined faults in bearings. The proposed methodology uses a deep neural network (DNN) architecture to effectively diagnose the combined defects. The DNN structure is based on the stacked denoising autoencoder non-mutually exclusive classifier (NMEC) method for combined modes. The NMEC-DNN is trained using data for a single fault and it classifies both single faults and multiple combined faults. The results of experiments conducted on AE data collected through an experimental test-bed demonstrate that the DNN achieves good classification performance with a maximum accuracy of 95%. The proposed method is compared with a multi-class classifier based on support vector machines (SVMs). The NMEC-DNN yields better diagnostic performance in comparison to the multi-class classifier based on SVM. The NMEC-DNN reduces the number of necessary data collections and improves the bearing fault diagnosis performance. PMID:29642466

Population Analysis of Disabled Children by Departments in France

NASA Astrophysics Data System (ADS)

Meidatuzzahra, Diah; Kuswanto, Heri; Pech, Nicolas; Etchegaray, Amélie

2017-06-01

In this study, a statistical analysis is performed by model the variations of the disabled about 0-19 years old population among French departments. The aim is to classify the departments according to their profile determinants (socioeconomic and behavioural profiles). The analysis is focused on two types of methods: principal component analysis (PCA) and multiple correspondences factorial analysis (MCA) to review which one is the best methods for interpretation of the correlation between the determinants of disability (independent variable). The PCA is the best method for interpretation of the correlation between the determinants of disability (independent variable). The PCA reduces 14 determinants of disability to 4 axes, keeps 80% of total information, and classifies them into 7 classes. The MCA reduces the determinants to 3 axes, retains only 30% of information, and classifies them into 4 classes.

Spectral sensitivity of a colour changing spider.

PubMed

Defrize, Jérémy; Lazzari, Claudio R; Warrant, Eric J; Casas, Jérôme

2011-04-01

Vision plays a paramount role in some spider families such as the Salticidae, Lycosidae and Thomisidae, as it is involved in prey hunting, orientation or choice of substrate. In the thomisid Misumena vatia, for which the substrate colour affects the body colour, vision seems to mediate morphological colour changes. However, nothing is known about which component of visual signals from the substrate might be perceived, nor whether M. vatia possesses the physiological basis for colour vision. The aim of this study is thus to investigate the vision of this spider species by measuring the spectral sensitivities of the different pairs of eyes using electrophysiological methods. Extra- and intracellular electrophysiological recordings combined with selective adaptation revealed the presence of two classes of photoreceptor cells, one sensitive in the UV region of the spectrum (around 340 nm) and one sensitive in the green (around 520 nm) regions in the four pairs of eyes. We conclude that M. vatia possesses the physiological potential to perceive both chromatic and achromatic components of the environment. Copyright © 2011 Elsevier Ltd. All rights reserved.

Comparison of Hyperspectral and Multispectral Satellites for Forest Alliance Classification in the San Francisco Bay Area

NASA Astrophysics Data System (ADS)

Clark, M. L.

2016-12-01

The goal of this study was to assess multi-temporal, Hyperspectral Infrared Imager (HyspIRI) satellite imagery for improved forest class mapping relative to multispectral satellites. The study area was the western San Francisco Bay Area, California and forest alliances (e.g., forest communities defined by dominant or co-dominant trees) were defined using the U.S. National Vegetation Classification System. Simulated 30-m HyspIRI, Landsat 8 and Sentinel-2 imagery were processed from image data acquired by NASA's AVIRIS airborne sensor in year 2015, with summer and multi-temporal (spring, summer, fall) data analyzed separately. HyspIRI reflectance was used to generate a suite of hyperspectral metrics that targeted key spectral features related to chemical and structural properties. The Random Forests classifier was applied to the simulated images and overall accuracies (OA) were compared to those from real Landsat 8 images. For each image group, broad land cover (e.g., Needle-leaf Trees, Broad-leaf Trees, Annual agriculture, Herbaceous, Built-up) was classified first, followed by a finer-detail forest alliance classification for pixels mapped as closed-canopy forest. There were 5 needle-leaf tree alliances and 16 broad-leaf tree alliances, including 7 Quercus (oak) alliance types. No forest alliance classification exceeded 50% OA, indicating that there was broad spectral similarity among alliances, most of which were not spectrally pure but rather a mix of tree species. In general, needle-leaf (Pine, Redwood, Douglas Fir) alliances had better class accuracies than broad-leaf alliances (Oaks, Madrone, Bay Laurel, Buckeye, etc). Multi-temporal data classifications all had 5-6% greater OA than with comparable summer data. For simulated data, HyspIRI metrics had 4-5% greater OA than Landsat 8 and Sentinel-2 multispectral imagery and 3-4% greater OA than HyspIRI reflectance. Finally, HyspIRI metrics had 8% greater OA than real Landsat 8 imagery. In conclusion, forest alliance classification was found to be a difficult remote sensing application with moderate resolution (30 m) satellite imagery; however, of the data tested, HyspIRI spectral metrics had the best performance relative to multispectral satellites.

From Constraints to Resolution Rules Part II : chains, braids, confluence and T&E

NASA Astrophysics Data System (ADS)

Berthier, Denis

In this Part II, we apply the general theory developed in Part I to a detailed analysis of the Constraint Satisfaction Problem (CSP). We show how specific types of resolution rules can be defined. In particular, we introduce the general notions of a chain and a braid. As in Part I, these notions are illustrated in detail with the Sudoku example - a problem known to be NP-complete and which is therefore typical of a broad class of hard problems. For Sudoku, we also show how far one can go in "approximating" a CSP with a resolution theory and we give an empirical statistical analysis of how the various puzzles, corresponding to different sets of entries, can be classified along a natural scale of complexity. For any CSP, we also prove the confluence property of some Resolution Theories based on braids and we show how it can be used to define different resolution strategies. Finally, we prove that, in any CSP, braids have the same solving capacity as Trial-and-Error (T&E) with no guessing and we comment this result in the Sudoku case.

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...

Feeling Abnormal: Simulation of Deviancy in Abnormal and Exceptionality Courses.

ERIC Educational Resources Information Center

Fernald, Charles D.

1980-01-01

Describes activity in which student in abnormal psychology and psychology of exceptional children classes personally experience being judged abnormal. The experience allows the students to remember relevant research, become sensitized to the feelings of individuals classified as deviant, and use caution in classifying individuals as abnormal.…

Combination of minimum enclosing balls classifier with SVM in coal-rock recognition.

PubMed

Song, QingJun; Jiang, HaiYan; Song, Qinghui; Zhao, XieGuang; Wu, Xiaoxuan

2017-01-01

Top-coal caving technology is a productive and efficient method in modern mechanized coal mining, the study of coal-rock recognition is key to realizing automation in comprehensive mechanized coal mining. In this paper we propose a new discriminant analysis framework for coal-rock recognition. In the framework, a data acquisition model with vibration and acoustic signals is designed and the caving dataset with 10 feature variables and three classes is got. And the perfect combination of feature variables can be automatically decided by using the multi-class F-score (MF-Score) feature selection. In terms of nonlinear mapping in real-world optimization problem, an effective minimum enclosing ball (MEB) algorithm plus Support vector machine (SVM) is proposed for rapid detection of coal-rock in the caving process. In particular, we illustrate how to construct MEB-SVM classifier in coal-rock recognition which exhibit inherently complex distribution data. The proposed method is examined on UCI data sets and the caving dataset, and compared with some new excellent SVM classifiers. We conduct experiments with accuracy and Friedman test for comparison of more classifiers over multiple on the UCI data sets. Experimental results demonstrate that the proposed algorithm has good robustness and generalization ability. The results of experiments on the caving dataset show the better performance which leads to a promising feature selection and multi-class recognition in coal-rock recognition.

Combination of minimum enclosing balls classifier with SVM in coal-rock recognition

PubMed Central

Song, QingJun; Jiang, HaiYan; Song, Qinghui; Zhao, XieGuang; Wu, Xiaoxuan

2017-01-01

Top-coal caving technology is a productive and efficient method in modern mechanized coal mining, the study of coal-rock recognition is key to realizing automation in comprehensive mechanized coal mining. In this paper we propose a new discriminant analysis framework for coal-rock recognition. In the framework, a data acquisition model with vibration and acoustic signals is designed and the caving dataset with 10 feature variables and three classes is got. And the perfect combination of feature variables can be automatically decided by using the multi-class F-score (MF-Score) feature selection. In terms of nonlinear mapping in real-world optimization problem, an effective minimum enclosing ball (MEB) algorithm plus Support vector machine (SVM) is proposed for rapid detection of coal-rock in the caving process. In particular, we illustrate how to construct MEB-SVM classifier in coal-rock recognition which exhibit inherently complex distribution data. The proposed method is examined on UCI data sets and the caving dataset, and compared with some new excellent SVM classifiers. We conduct experiments with accuracy and Friedman test for comparison of more classifiers over multiple on the UCI data sets. Experimental results demonstrate that the proposed algorithm has good robustness and generalization ability. The results of experiments on the caving dataset show the better performance which leads to a promising feature selection and multi-class recognition in coal-rock recognition. PMID:28937987

Estimation of Teacher Practices Based on Text Transcripts of Teacher Speech Using a Support Vector Machine Algorithm

ERIC Educational Resources Information Center

Araya, Roberto; Plana, Francisco; Dartnell, Pablo; Soto-Andrade, Jorge; Luci, Gina; Salinas, Elena; Araya, Marylen

2012-01-01

Teacher practice is normally assessed by observers who watch classes or videos of classes. Here, we analyse an alternative strategy that uses text transcripts and a support vector machine classifier. For each one of the 710 videos of mathematics classes from the 2005 Chilean National Teacher Assessment Programme, a single 4-minute slice was…

Growth classification systems for red fir and white fir in northern California

Treesearch

George T. Ferrell

1983-01-01

Selected crown and bole characteristics were predictor variables in growth classification equations developed for California red fir, Shasta red fir, and white fir in northern California. Individual firs were classified on the basis of percent basal area increment (PCTBAI ) as Class 1 (≤ 1 pct), Class 2 (> 1 pct and ≤ 3 pct), or Class 3 (> 3...

21 CFR 880.5440 - Intravascular administration set.

Code of Federal Regulations, 2012 CFR

2012-04-01

...) Classification. Class II (special controls). The special control for pharmacy compounding systems within this classification is the FDA guidance document entitled “Class II Special Controls Guidance Document: Pharmacy Compounding Systems; Final Guidance for Industry and FDA Reviewers.” Pharmacy compounding systems classified...

21 CFR 880.5440 - Intravascular administration set.

Code of Federal Regulations, 2011 CFR

2011-04-01

...) Classification. Class II (special controls). The special control for pharmacy compounding systems within this classification is the FDA guidance document entitled “Class II Special Controls Guidance Document: Pharmacy Compounding Systems; Final Guidance for Industry and FDA Reviewers.” Pharmacy compounding systems classified...

21 CFR 880.5440 - Intravascular administration set.

Code of Federal Regulations, 2013 CFR

2013-04-01

...) Classification. Class II (special controls). The special control for pharmacy compounding systems within this classification is the FDA guidance document entitled “Class II Special Controls Guidance Document: Pharmacy Compounding Systems; Final Guidance for Industry and FDA Reviewers.” Pharmacy compounding systems classified...

21 CFR 880.5440 - Intravascular administration set.

Code of Federal Regulations, 2014 CFR

2014-04-01

...) Classification. Class II (special controls). The special control for pharmacy compounding systems within this classification is the FDA guidance document entitled “Class II Special Controls Guidance Document: Pharmacy Compounding Systems; Final Guidance for Industry and FDA Reviewers.” Pharmacy compounding systems classified...

76 FR 43689 - Draft Guidance for Industry and Food and Drug Administration Staff; Mobile Medical Applications...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-07-21

... infusion pump stand, which is currently classified as a class I device because it supports the intended use of an infusion pump (class II medical device). A mobile medical app that simply supports the intended...

A new classification system for shoulder instability.

PubMed

Kuhn, John E

2010-04-01

Glenohumeral joint instability is extremely common yet the definition and classification of instability remains unclear. In order to find the best ways to treat instability, the condition must be clearly defined and classified. This is particularly important so that treatment studies can be compared or combined, which can only be done if the patient population under study is the same. The purpose of this paper was to review the problems with historical methods of defining and classifying instability and to introduce the FEDS system of classifying instability, which was developed to have content validity and found to have high interobserver and intraobserver agreement.